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CN114792148A - Method and device for predicting motion trail - Google Patents

Method and device for predicting motion trail Download PDF

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CN114792148A
CN114792148A CN202110099754.7A CN202110099754A CN114792148A CN 114792148 A CN114792148 A CN 114792148A CN 202110099754 A CN202110099754 A CN 202110099754A CN 114792148 A CN114792148 A CN 114792148A
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蒋竺希
张驰
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Momenta Suzhou Technology Co Ltd
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Abstract

The embodiment of the invention discloses a method and a device for predicting a motion trail, wherein the method comprises the following steps: obtaining historical track and motion attribute information of each traffic participating object corresponding to the target object and corresponding current map information; determining the track prediction characteristics corresponding to each traffic participant by using the characteristic extraction layer of the target track prediction model and the initial characteristics corresponding to each traffic participant; determining hidden random variable multi-modal probability distribution corresponding to each traffic participant by utilizing a feature extraction layer of a target track prediction model and track prediction features corresponding to each traffic participant; and determining the multi-modal predicted track corresponding to each traffic participating object by utilizing the characteristic regression layer of the target track prediction model, the track prediction characteristics corresponding to each traffic participating object and the hidden random variable multi-modal probability distribution corresponding to each traffic participating object so as to reduce the limitation of motion track prediction and better adapt to a more complex automatic driving scene.

Description

一种运动轨迹的预测方法及装置A kind of motion trajectory prediction method and device

技术领域technical field

本发明涉及轨迹预测技术领域,具体而言,涉及一种运动轨迹的预测方法及装置。The present invention relates to the technical field of trajectory prediction, and in particular, to a method and device for predicting a motion trajectory.

背景技术Background technique

在自动驾驶领域中,自动驾驶车辆在行驶过程中,需要参考其周围的交通参与对象的未来运动轨迹,来进行自车的行驶轨迹规划,以保证自车以及交通参与对象的安全。相应的,自动驾驶车辆能够准确及时地预测出交通参与对象未来运动轨迹至关重要。In the field of autonomous driving, when an autonomous vehicle is driving, it needs to refer to the future motion trajectories of the surrounding traffic participants to plan the driving trajectory of the self-driving vehicle to ensure the safety of the self-driving vehicle and the traffic participants. Correspondingly, it is very important for autonomous vehicles to accurately and timely predict the future motion trajectories of traffic participants.

考虑到交通参与对象未来行为具有明显的不确定性,即交通参与对象的未来运动轨迹具有明显的不确定性,相关技术中,一般以混合高斯分布来建模交通参与对象的未来运动轨迹的概率分布;并人工设计未来运动轨迹的模态类别以及分类规则,采用分类加回归的方式训练得到神经网络模型,以通过神经网络模型和各交通参与对象的历史运动轨迹及所对应的其他动态信息和静态信息,来预测得到各交通参与对象的各模态类别对应的未来运行轨迹以及未来运行轨迹对应的概率。其中,交通参与对象对应的其他动态信息可以包括除该交通参与对象外的其他交通参与对象和自动驾驶车辆的历史轨迹,静态信息可以包括自动驾驶车辆对应的当前地图信息。Considering that the future behavior of traffic participants has obvious uncertainty, that is, the future motion trajectories of traffic participants have obvious uncertainties. In related technologies, the probability of future motion trajectories of traffic participants is generally modeled by a mixed Gaussian distribution. distribution; and artificially design the modal categories and classification rules of future motion trajectories, and use the method of classification and regression to train the neural network model, so as to pass the neural network model and the historical motion trajectories of each traffic participant object and other corresponding dynamic information and information. The static information is used to predict the future running trajectories corresponding to each modal category of each traffic participant object and the corresponding probability of the future running trajectories. The other dynamic information corresponding to the traffic participant object may include other traffic participant objects and the historical track of the autonomous vehicle except the traffic participant object, and the static information may include current map information corresponding to the autonomous vehicle.

上述过程中,需要人工设计神经网络模型所预测的未来运动轨迹的模态的类别数以及分类规则,这在一定程度上使得所预测的未来运动轨迹存在局限性,难以适应比较复杂的自动驾驶场景。In the above process, it is necessary to manually design the number of modal categories and classification rules of the future motion trajectory predicted by the neural network model, which makes the predicted future motion trajectory limited to a certain extent, and it is difficult to adapt to more complex autonomous driving scenarios. .

发明内容SUMMARY OF THE INVENTION

本发明提供了一种运动轨迹的预测方法及装置,以实现降低运动轨迹预测的局限性,以更好的适应比较复杂的自动驾驶场景。具体的技术方案如下:The present invention provides a motion trajectory prediction method and device, so as to reduce the limitation of motion trajectory prediction and better adapt to more complex automatic driving scenarios. The specific technical solutions are as follows:

第一方面,本发明实施例提供了一种运动轨迹的预测方法,所述方法包括:In a first aspect, an embodiment of the present invention provides a method for predicting a motion trajectory, the method comprising:

获得目标对象对应的各交通参与对象的历史轨迹和运动属性信息以及对应的当前地图信息;Obtain the historical trajectory and motion attribute information of each traffic participating object corresponding to the target object and the corresponding current map information;

利用目标轨迹预测模型的特征提取层以及各交通参与对象对应的初始特征,确定各交通参与对象对应的轨迹预测特征,其中,交通参与对象对应的初始特征包括:交通参与对象的历史轨迹和运动属性信息,及其对应的其他交通参与对象和目标对象的历史轨迹和运动属性信息以及所述当前地图信息;Using the feature extraction layer of the target trajectory prediction model and the initial features corresponding to each traffic participant object, determine the trajectory prediction feature corresponding to each traffic participant object, wherein the initial feature corresponding to the traffic participant object includes: the historical trajectory and motion attributes of the traffic participant object information, and the historical trajectory and motion attribute information of other corresponding traffic participating objects and target objects, as well as the current map information;

利用目标轨迹预测模型的特征提取层以及各交通参与对象对应的轨迹预测特征,确定各交通参与对象对应的隐随机变量多模态概率分布,其中,隐随机变量表征各交通参与对象的行为随机性;Using the feature extraction layer of the target trajectory prediction model and the trajectory prediction features corresponding to each traffic participant, the multimodal probability distribution of the hidden random variables corresponding to each traffic participant is determined. The hidden random variable represents the behavior randomness of each traffic participant. ;

利用所述目标轨迹预测模型的特征回归层、各交通参与对象对应的轨迹预测特征以及各交通参与对象对应的隐随机变量多模态概率分布,确定各交通参与对象对应的多模态预测轨迹。Using the feature regression layer of the target trajectory prediction model, the trajectory prediction features corresponding to each traffic participant object, and the multimodal probability distribution of hidden random variables corresponding to each traffic participant object, the multimodal prediction trajectory corresponding to each traffic participant object is determined.

可选的,交通参与对象对应的初始特征为基于时间顺序排列的特征,其包括交通参与对象对应的多个历史时刻的特征;Optionally, the initial features corresponding to the traffic participating objects are features arranged in chronological order, which include features of multiple historical moments corresponding to the traffic participating objects;

所述利用目标轨迹预测模型的特征提取层以及各交通参与对象对应的初始特征,确定各交通参与对象对应的轨迹预测特征的步骤,包括:The step of determining the trajectory prediction feature corresponding to each traffic participant object by utilizing the feature extraction layer of the target trajectory prediction model and the corresponding initial features of each traffic participant object includes:

针对每一交通参与对象,利用目标轨迹预测模型的特征提取层,对该交通参与对象对应的初始特征循环多次执行如下步骤A-C,确定出该交通参与对象对应的中间预测特征;For each traffic participant object, using the feature extraction layer of the target trajectory prediction model, the following steps A-C are performed repeatedly for the initial feature corresponding to the traffic participant object, and the intermediate prediction feature corresponding to the traffic participant object is determined;

针对每一交通参与对象,基于图神经网络将该交通参与对象对应的中间预测特征中各静态对象对应的中间预测特征进行融合,确定出该交通参与对象对应的轨迹预测特征,其中,所述静态对象包括所述当前地图信息中的各静态对象;For each traffic participant object, the intermediate prediction features corresponding to each static object in the intermediate prediction features corresponding to the traffic participant object are fused based on the graph neural network, and the trajectory prediction feature corresponding to the traffic participant object is determined, wherein the static The object includes each static object in the current map information;

步骤A:从特征维度对该交通参与对象对应的待处理特征进行非线性映射,得到该交通参与对象对应的映射特征,其中,所述待处理特征为该交通参与对象对应的初始特征或前一次迭代生成的该交通参与对象对应的中间预测特征;Step A: Perform nonlinear mapping on the to-be-processed feature corresponding to the traffic participant object from the feature dimension to obtain the map feature corresponding to the traffic participant object, wherein the to-be-processed feature is the initial feature or the previous time corresponding to the traffic participant object The iteratively generated intermediate prediction feature corresponding to the traffic participant object;

步骤B:从时间维度对所述映射特征进行特征聚合操作,得到该交通参与对象对应的聚合特征;Step B: performing a feature aggregation operation on the mapping feature from the time dimension to obtain the aggregation feature corresponding to the traffic participant object;

步骤C:将所述聚合特征与所述待处理特征中各历史时刻的特征进行融合。Step C: Fusion of the aggregated feature and the feature of each historical moment in the feature to be processed.

可选的,所述利用目标轨迹预测模型的特征提取层以及各交通参与对象对应的轨迹预测特征,确定各交通参与对象对应的隐随机变量多模态概率分布的步骤,包括:Optionally, the step of using the feature extraction layer of the target trajectory prediction model and the trajectory prediction features corresponding to each traffic participant object to determine the multimodal probability distribution of the hidden random variables corresponding to each traffic participant object includes:

针对每一交通参与对象,利用目标轨迹预测模型的特征提取层以及该交通参与对象对应的轨迹预测特征,确定该交通参与对象对应的隐随机变量单模态概率分布;For each traffic participant object, use the feature extraction layer of the target trajectory prediction model and the trajectory prediction feature corresponding to the traffic participant object to determine the hidden random variable unimodal probability distribution corresponding to the traffic participant object;

针对每一交通参与对象,利用规范化流映射算法以及该交通参与对象对应的隐随机变量单模态概率分布,得到该交通参与对象对应的隐随机变量多模态概率分布。For each traffic participant, using the normalized flow mapping algorithm and the unimodal probability distribution of the hidden random variable corresponding to the traffic participant, the multimodal probability distribution of the hidden random variable corresponding to the traffic participant is obtained.

可选的,在所述利用目标轨迹预测模型的特征提取层以及各交通参与对象对应的初始特征,确定各交通参与对象对应的轨迹预测特征的步骤之前,所述方法还包括:Optionally, before the step of determining the trajectory prediction feature corresponding to each traffic participant object using the feature extraction layer of the target trajectory prediction model and the initial feature corresponding to each traffic participant object, the method further includes:

训练得到目标轨迹预测模型的过程,其中,所述过程,包括:The process of obtaining a target trajectory prediction model by training, wherein the process includes:

获得初始轨迹预测模型;Obtain the initial trajectory prediction model;

获得各样本交通对象对应的样本训练信息以及各样本交通对象对应的样本未来轨迹,其中,样本交通对象对应的样本训练信息包括:该样本交通对象的样本历史轨迹和样本运动属性信息、其对应的样本动态对象的样本历史轨迹、样本运动属性信息和样本静态对象信息;Obtain the sample training information corresponding to each sample traffic object and the sample future trajectory corresponding to each sample traffic object, wherein the sample training information corresponding to the sample traffic object includes: the sample historical trajectory and sample motion attribute information of the sample traffic object, its corresponding Sample historical trajectory, sample motion attribute information and sample static object information of sample dynamic objects;

针对每一样本交通对象,利用所述初始轨迹预测模型的特征提取层以及该样本交通对象对应的初始样本特征,确定该样本交通对象对应的样本预测特征,其中,该样本交通对象对应的初始样本特征包括:样本交通对象样本历史轨迹和样本运动属性信息,其对应的样本动态对象的样本历史轨迹、样本运动属性信息和样本静态对象信息;For each sample traffic object, use the feature extraction layer of the initial trajectory prediction model and the initial sample feature corresponding to the sample traffic object to determine the sample prediction feature corresponding to the sample traffic object, wherein the initial sample corresponding to the sample traffic object The features include: sample historical trajectories and sample motion attribute information of sample traffic objects, sample historical trajectories, sample motion attribute information and sample static object information of corresponding sample dynamic objects;

针对每一样本交通对象,利用所述初始轨迹预测模型的特征提取层以及该样本交通对象对应的样本预测特征,确定该样本交通对象对应的隐随机变量多模态概率分布;For each sample traffic object, use the feature extraction layer of the initial trajectory prediction model and the sample prediction feature corresponding to the sample traffic object to determine the multimodal probability distribution of the latent random variable corresponding to the sample traffic object;

针对每一样本交通对象,利用所述初始轨迹预测模型的特征回归层、该样本交通对象对应的样本预测特征以及该样本交通对象对应的隐随机变量多模态概率分布,确定该样本交通对象对应的多模态预测轨迹;For each sample traffic object, use the feature regression layer of the initial trajectory prediction model, the sample prediction feature corresponding to the sample traffic object, and the multimodal probability distribution of the latent random variables corresponding to the sample traffic object to determine the corresponding sample traffic object. The multimodal prediction trajectory of ;

针对每一样本交通对象,利用预设变分算法对该样本交通对象对应的样本未来轨迹进行处理,得到该样本交通对象对应的隐随机变量变分概率分布;For each sample traffic object, use a preset variational algorithm to process the sample future trajectory corresponding to the sample traffic object, and obtain the variational probability distribution of the latent random variable corresponding to the sample traffic object;

针对每一样本交通对象,利用该样本交通对象对应的隐随机变量多模态概率分布以及该样本交通对象对应的隐随机变量变分概率分布,确定该样本交通对象对应的隐随机变量KL散度值;For each sample traffic object, use the multimodal probability distribution of the hidden random variable corresponding to the sample traffic object and the variational probability distribution of the hidden random variable corresponding to the sample traffic object to determine the KL divergence of the hidden random variable corresponding to the sample traffic object value;

针对每一样本交通对象,利用该样本交通对象对应的多模态预测轨迹、隐随机变量变分概率分布以及该样本交通对象对应的样本未来轨迹,确定该样本交通对象对应的轨迹重构损失值;For each sample traffic object, use the multimodal predicted trajectory corresponding to the sample traffic object, the variational probability distribution of latent random variables and the sample future trajectory corresponding to the sample traffic object to determine the trajectory reconstruction loss value corresponding to the sample traffic object ;

针对每一样本交通对象,利用该样本交通对象对应的隐随机变量KL散度值、该样本交通对象对应的轨迹重构损失值,构建最大化似然函数的变分下界;判断所构建的最大化似然函数的变分下界是否达到最大化;For each sample traffic object, the KL divergence value of the hidden random variable corresponding to the sample traffic object and the trajectory reconstruction loss value corresponding to the sample traffic object are used to construct the variational lower bound of the maximum likelihood function; Whether the variational lower bound of the likelihood function is maximized;

若所构建的最大化似然函数的变分下界未达到最大化,则调整所述初始轨迹预测模型的特征提取层与特征回归层的模型参数,并返回所述针对每一样本交通对象,利用所述初始轨迹预测模型的特征提取层以及该样本交通对象对应的初始样本特征,确定该样本交通对象对应的样本预测特征的步骤;If the variation lower bound of the constructed maximum likelihood function does not reach the maximum, adjust the model parameters of the feature extraction layer and feature regression layer of the initial trajectory prediction model, and return the traffic object for each sample, using The feature extraction layer of the initial trajectory prediction model and the initial sample feature corresponding to the sample traffic object, and the step of determining the sample prediction feature corresponding to the sample traffic object;

若所构建的最大化似然函数的变分下界达到最大化,确定所述初始轨迹预测模型收敛,得到包含特征提取层与特征回归层的所述目标轨迹预测模型。If the constructed variational lower bound of the maximized likelihood function is maximized, it is determined that the initial trajectory prediction model converges, and the target trajectory prediction model including the feature extraction layer and the feature regression layer is obtained.

可选的,所述利用所述目标轨迹预测模型的特征回归层、各交通参与对象对应的轨迹预测特征以及各交通参与对象对应的隐随机变量多模态概率分布,确定各交通参与对象对应的多模态预测轨迹的步骤,包括:Optionally, the feature regression layer of the target trajectory prediction model, the trajectory prediction feature corresponding to each traffic participant object, and the hidden random variable multimodal probability distribution corresponding to each traffic participant object are used to determine the corresponding traffic participant object. Steps for multimodal prediction of trajectories, including:

针对每一交通参与对象,对交通参与对象对应的隐随机变量多模态概率分布进行采样,得到该交通参与对象对应的多个隐随机变量样本;For each traffic participant object, sampling the multi-modal probability distribution of the hidden random variable corresponding to the traffic participant object, and obtain a plurality of hidden random variable samples corresponding to the traffic participant object;

利用所述目标轨迹预测模型的特征回归层、各交通参与对象对应的轨迹预测特征以及各交通参与对象对应的多个隐随机变量样本,确定各交通参与对象对应的多模态预测轨迹。Using the feature regression layer of the target trajectory prediction model, the trajectory prediction features corresponding to each traffic participant object, and a plurality of latent random variable samples corresponding to each traffic participant object, the multimodal prediction trajectory corresponding to each traffic participant object is determined.

第二方面,本发明实施例提供了一种运动轨迹的预测装置,所述装置包括:In a second aspect, an embodiment of the present invention provides an apparatus for predicting a motion trajectory, the apparatus comprising:

获得模块,被配置为获得目标对象对应的各交通参与对象的历史轨迹和运动属性信息以及对应的当前地图信息;an obtaining module, configured to obtain the historical trajectory and motion attribute information of each traffic participating object corresponding to the target object and the corresponding current map information;

第一确定模块,被配置为利用目标轨迹预测模型的特征提取层以及各交通参与对象对应的初始特征,确定各交通参与对象对应的轨迹预测特征,其中,交通参与对象对应的初始特征包括:交通参与对象的历史轨迹和运动属性信息,及其对应的其他交通参与对象和目标对象的历史轨迹和运动属性信息以及所述当前地图信息;The first determination module is configured to use the feature extraction layer of the target trajectory prediction model and the initial features corresponding to each traffic participant object to determine the trajectory prediction feature corresponding to each traffic participant object, wherein the initial feature corresponding to the traffic participant object includes: traffic The historical trajectory and motion attribute information of the participating objects, and the historical trajectory and motion attribute information of other corresponding traffic participating objects and target objects, and the current map information;

第二确定模块,被配置为利用目标轨迹预测模型的特征提取层以及各交通参与对象对应的轨迹预测特征,确定各交通参与对象对应的隐随机变量多模态概率分布,其中,隐随机变量表征各交通参与对象的行为随机性;The second determination module is configured to use the feature extraction layer of the target trajectory prediction model and the trajectory prediction features corresponding to each traffic participant object to determine the multimodal probability distribution of hidden random variables corresponding to each traffic participant object, wherein the hidden random variables represent The randomness of behavior of each traffic participant;

第三确定模块,被配置为利用所述目标轨迹预测模型的特征回归层、各交通参与对象对应的轨迹预测特征以及各交通参与对象对应的隐随机变量多模态概率分布,确定各交通参与对象对应的多模态预测轨迹。The third determination module is configured to use the feature regression layer of the target trajectory prediction model, the trajectory prediction features corresponding to each traffic participant object, and the multimodal probability distribution of hidden random variables corresponding to each traffic participant object to determine each traffic participant object. Corresponding multimodal prediction trajectory.

可选的,交通参与对象对应的初始特征为基于时间顺序排列的特征,其包括交通参与对象对应的多个历史时刻的特征;Optionally, the initial features corresponding to the traffic participating objects are features arranged in chronological order, which include features of multiple historical moments corresponding to the traffic participating objects;

所述第一确定模块,被具体配置为针对每一交通参与对象,利用目标轨迹预测模型的特征提取层,对该交通参与对象对应的初始特征循环多次执行如下步骤A-C,确定出该交通参与对象对应的中间预测特征;The first determination module is specifically configured to, for each traffic participant object, use the feature extraction layer of the target trajectory prediction model to perform the following steps A-C repeatedly for the initial feature corresponding to the traffic participant object, and determine the traffic participant. The intermediate prediction feature corresponding to the object;

针对每一交通参与对象,基于图神经网络将该交通参与对象对应的中间预测特征中各静态对象对应的中间预测特征进行融合,确定出该交通参与对象对应的轨迹预测特征,其中,所述静态对象包括所述当前地图信息中的各静态对象;For each traffic participant object, the intermediate prediction features corresponding to each static object in the intermediate prediction features corresponding to the traffic participant object are fused based on the graph neural network, and the trajectory prediction feature corresponding to the traffic participant object is determined, wherein the static The object includes each static object in the current map information;

步骤A:从特征维度对该交通参与对象对应的待处理特征进行非线性映射,得到该交通参与对象对应的映射特征,其中,所述待处理特征为该交通参与对象对应的初始特征或前一次迭代生成的该交通参与对象对应的中间预测特征;Step A: Perform nonlinear mapping on the to-be-processed feature corresponding to the traffic participant object from the feature dimension to obtain the map feature corresponding to the traffic participant object, wherein the to-be-processed feature is the initial feature or the previous time corresponding to the traffic participant object The iteratively generated intermediate prediction feature corresponding to the traffic participant object;

步骤B:从时间维度对所述映射特征进行特征聚合操作,得到该交通参与对象对应的聚合特征;Step B: performing a feature aggregation operation on the mapping feature from the time dimension to obtain the aggregation feature corresponding to the traffic participant object;

步骤C:将所述聚合特征与所述待处理特征中各历史时刻的特征进行融合。Step C: Fusion of the aggregated feature and the feature of each historical moment in the feature to be processed.

可选的,所述第二确定模块,被具体配置为针对每一交通参与对象,利用目标轨迹预测模型的特征提取层以及该交通参与对象对应的轨迹预测特征,确定该交通参与对象对应的隐随机变量单模态概率分布;Optionally, the second determining module is specifically configured to, for each traffic participant object, use the feature extraction layer of the target trajectory prediction model and the trajectory prediction feature corresponding to the traffic participant object to determine the hidden traffic participant object corresponding to the traffic participant object. Unimodal probability distribution of random variables;

针对每一交通参与对象,利用规范化流映射算法以及该交通参与对象对应的隐随机变量单模态概率分布,得到该交通参与对象对应的隐随机变量多模态概率分布。For each traffic participant, using the normalized flow mapping algorithm and the unimodal probability distribution of the hidden random variable corresponding to the traffic participant, the multimodal probability distribution of the hidden random variable corresponding to the traffic participant is obtained.

可选的,所述装置还包括:Optionally, the device further includes:

训练模块,被配置为在所述利用目标轨迹预测模型的特征提取层以及各交通参与对象对应的初始特征,确定各交通参与对象对应的轨迹预测特征之前,训练得到目标轨迹预测模型,其中,所述训练模块,被具体配置为The training module is configured to obtain the target trajectory prediction model by training before determining the trajectory prediction feature corresponding to each traffic participant object using the feature extraction layer of the target trajectory prediction model and the initial features corresponding to each traffic participant object, wherein the The training module described above is specifically configured as

获得初始轨迹预测模型;Obtain the initial trajectory prediction model;

获得各样本交通对象对应的样本训练信息以及各样本交通对象对应的样本未来轨迹,其中,样本交通对象对应的样本训练信息包括:该样本交通对象的样本历史轨迹和样本运动属性信息、其对应的样本动态对象的样本历史轨迹、样本运动属性信息和样本静态对象信息;Obtain the sample training information corresponding to each sample traffic object and the sample future trajectory corresponding to each sample traffic object, wherein the sample training information corresponding to the sample traffic object includes: the sample historical trajectory and sample motion attribute information of the sample traffic object, its corresponding Sample historical trajectory, sample motion attribute information and sample static object information of sample dynamic objects;

针对每一样本交通对象,利用所述初始轨迹预测模型的特征提取层以及该样本交通对象对应的初始样本特征,确定该样本交通对象对应的样本预测特征,其中,该样本交通对象对应的初始样本特征包括:样本交通对象样本历史轨迹和样本运动属性信息,其对应的样本动态对象的样本历史轨迹、样本运动属性信息和样本静态对象信息;For each sample traffic object, use the feature extraction layer of the initial trajectory prediction model and the initial sample feature corresponding to the sample traffic object to determine the sample prediction feature corresponding to the sample traffic object, wherein the initial sample corresponding to the sample traffic object The features include: sample historical trajectories and sample motion attribute information of sample traffic objects, sample historical trajectories, sample motion attribute information and sample static object information of corresponding sample dynamic objects;

针对每一样本交通对象,利用所述初始轨迹预测模型的特征提取层以及该样本交通对象对应的样本预测特征,确定该样本交通对象对应的隐随机变量多模态概率分布;For each sample traffic object, use the feature extraction layer of the initial trajectory prediction model and the sample prediction feature corresponding to the sample traffic object to determine the multimodal probability distribution of the latent random variable corresponding to the sample traffic object;

针对每一样本交通对象,利用所述初始轨迹预测模型的特征回归层、该样本交通对象对应的样本预测特征以及该样本交通对象对应的隐随机变量多模态概率分布,确定该样本交通对象对应的多模态预测轨迹;For each sample traffic object, use the feature regression layer of the initial trajectory prediction model, the sample prediction feature corresponding to the sample traffic object, and the multimodal probability distribution of the latent random variables corresponding to the sample traffic object to determine the corresponding sample traffic object. The multimodal prediction trajectory of ;

针对每一样本交通对象,利用预设变分算法对该样本交通对象对应的样本未来轨迹进行处理,得到该样本交通对象对应的隐随机变量变分概率分布;For each sample traffic object, use a preset variational algorithm to process the sample future trajectory corresponding to the sample traffic object, and obtain the variational probability distribution of the latent random variable corresponding to the sample traffic object;

针对每一样本交通对象,利用该样本交通对象对应的隐随机变量多模态概率分布以及该样本交通对象对应的隐随机变量变分概率分布,确定该样本交通对象对应的隐随机变量KL散度值;For each sample traffic object, use the multimodal probability distribution of the hidden random variable corresponding to the sample traffic object and the variational probability distribution of the hidden random variable corresponding to the sample traffic object to determine the KL divergence of the hidden random variable corresponding to the sample traffic object value;

针对每一样本交通对象,利用该样本交通对象对应的多模态预测轨迹、隐随机变量变分概率分布以及该样本交通对象对应的样本未来轨迹,确定该样本交通对象对应的轨迹重构损失值;For each sample traffic object, use the multimodal predicted trajectory corresponding to the sample traffic object, the variational probability distribution of latent random variables and the sample future trajectory corresponding to the sample traffic object to determine the trajectory reconstruction loss value corresponding to the sample traffic object ;

针对每一样本交通对象,利用该样本交通对象对应的隐随机变量KL散度值、该样本交通对象对应的轨迹重构损失值,构建最大化似然函数的变分下界;判断所构建的最大化似然函数的变分下界是否达到最大化;For each sample traffic object, the KL divergence value of the hidden random variable corresponding to the sample traffic object and the trajectory reconstruction loss value corresponding to the sample traffic object are used to construct the variational lower bound of the maximum likelihood function; Whether the variational lower bound of the likelihood function is maximized;

若所构建的最大化似然函数的变分下界未达到最大化,则调整所述初始轨迹预测模型的特征提取层与特征回归层的模型参数,并返回所述针对每一样本交通对象,利用所述初始轨迹预测模型的特征提取层以及该样本交通对象对应的初始样本特征,确定该样本交通对象对应的样本预测特征的步骤;If the variation lower bound of the constructed maximum likelihood function does not reach the maximum, adjust the model parameters of the feature extraction layer and feature regression layer of the initial trajectory prediction model, and return the traffic object for each sample, using The feature extraction layer of the initial trajectory prediction model and the initial sample feature corresponding to the sample traffic object, and the step of determining the sample prediction feature corresponding to the sample traffic object;

若所构建的最大化似然函数的变分下界达到最大化,确定所述初始轨迹预测模型收敛,得到包含特征提取层与特征回归层的所述目标轨迹预测模型。If the constructed variational lower bound of the maximized likelihood function is maximized, it is determined that the initial trajectory prediction model converges, and the target trajectory prediction model including the feature extraction layer and the feature regression layer is obtained.

可选的,所述第二确定模块,被具体配置为针对每一交通参与对象,对交通参与对象对应的隐随机变量多模态概率分布进行采样,得到该交通参与对象对应的多个隐随机变量样本;Optionally, the second determination module is specifically configured to, for each traffic participant object, sample the multimodal probability distribution of the hidden random variable corresponding to the traffic participant object, and obtain a plurality of hidden random variables corresponding to the traffic participant object. variable sample;

利用所述目标轨迹预测模型的特征回归层、各交通参与对象对应的轨迹预测特征以及各交通参与对象对应的多个隐随机变量样本,确定各交通参与对象对应的多模态预测轨迹。Using the feature regression layer of the target trajectory prediction model, the trajectory prediction features corresponding to each traffic participant object, and a plurality of latent random variable samples corresponding to each traffic participant object, the multimodal prediction trajectory corresponding to each traffic participant object is determined.

由上述内容可知,本发明实施例提供的一种运动轨迹的预测方法及装置,获得目标对象对应的各交通参与对象的历史轨迹和运动属性信息以及对应的当前地图信息;利用目标轨迹预测模型的特征提取层以及各交通参与对象对应的初始特征,确定各交通参与对象对应的轨迹预测特征,其中,交通参与对象对应的初始特征包括:交通参与对象的历史轨迹和运动属性信息,及其对应的其他交通参与对象和目标对象的历史轨迹和运动属性信息以及当前地图信息;利用目标轨迹预测模型的特征提取层以及各交通参与对象对应的轨迹预测特征,确定各交通参与对象对应的隐随机变量多模态概率分布,其中,隐随机变量表征各交通参与对象的行为随机性;利用目标轨迹预测模型的特征回归层、各交通参与对象对应的轨迹预测特征以及各交通参与对象对应的隐随机变量多模态概率分布,确定各交通参与对象对应的多模态预测轨迹。It can be seen from the above content that the method and device for predicting a motion trajectory provided by the embodiment of the present invention obtain the historical trajectory and motion attribute information of each traffic participating object corresponding to the target object and the corresponding current map information; The feature extraction layer and the initial features corresponding to each traffic participant object determine the trajectory prediction feature corresponding to each traffic participant object, wherein the initial feature corresponding to the traffic participant object includes: the historical trajectory and motion attribute information of the traffic participant object, and their corresponding The historical trajectory and motion attribute information of other traffic participants and target objects, as well as the current map information; using the feature extraction layer of the target trajectory prediction model and the trajectory prediction features corresponding to each traffic participant object, determine the hidden random variables corresponding to each traffic participant object. The modal probability distribution, in which the hidden random variables represent the behavior randomness of each traffic participant; the feature regression layer of the target trajectory prediction model, the trajectory prediction characteristics corresponding to each traffic participant, and the hidden random variables corresponding to each traffic participant are many The modal probability distribution is used to determine the multi-modal prediction trajectory corresponding to each traffic participant object.

应用本发明实施例,可以利用目标轨迹预测模型中已学习到各交通参与对象的行为随机性的隐随机变量,以及各交通参与对象的历史轨迹和运动属性信息以及其对应的动态对象信息即其对应的其他交通参与对象和目标对象的历史轨迹和运动属性信息,和静态对象信息即当前地图信息,拟合各交通参与对象的隐随机变量多模态概率分布即隐随机变量多模态先验分布,其代表了各交通参与对象与目标对象未来轨迹的多种可能性,进而确定各交通参与对象对应的多模态预测轨迹,以实现对各交通参与对象的多模态预测轨迹的准确确定,且该包括学习到各交通参与对象的行为随机性的隐随机变量的目标轨迹预测模型对该场景具有通用性,不存在算法设计上的瓶颈制约,随着训练得到该目标轨迹预测模型的训练数据的规模扩大,算法对未来轨迹分布建模能力可以不断加强,进而轨迹预测能力也可以随之能不断提升。当然,实施本发明的任一产品或方法并不一定需要同时达到以上所述的所有优点。By applying the embodiments of the present invention, the hidden random variables in the target trajectory prediction model that have learned the randomness of the behavior of each traffic participant, as well as the historical trajectory and motion attribute information of each traffic participant, and the corresponding dynamic object information, namely the Corresponding historical trajectory and motion attribute information of other traffic participants and target objects, and static object information, i.e. current map information, fitting the hidden random variable multimodal probability distribution of each traffic participant object, that is, the hidden random variable multimodal prior distribution, which represents the multiple possibilities of the future trajectories of each traffic participant and the target object, and then determines the multimodal prediction trajectory corresponding to each traffic participant, so as to achieve accurate determination of the multimodal prediction trajectory of each traffic participant , and the target trajectory prediction model including the hidden random variables that learn the behavior randomness of each traffic participant is universal to the scene, and there is no bottleneck restriction in algorithm design, and the training of the target trajectory prediction model is obtained with training. As the scale of data expands, the algorithm's ability to model the future trajectory distribution can be continuously enhanced, and the trajectory prediction ability can also be continuously improved. Of course, it is not necessary for any product or method of the present invention to achieve all of the advantages described above at the same time.

本发明实施例的创新点包括:The innovative points of the embodiments of the present invention include:

1、结合目标轨迹预测模型中已学习到各交通参与对象的行为随机性的隐随机变量以及各交通参与对象的历史轨迹和运动属性信息以及对应的当前地图信息,构建各交通参与对象所对应的隐随机变量多模态概率分布,进而确定各交通参与对象对应的多模态预测轨迹,以实现对各交通参与对象的多模态预测轨迹的准确确定,且该包括学习到各交通参与对象的行为随机性的隐随机变量的目标轨迹预测模型对该场景具有通用性,不存在算法设计上的瓶颈制约,随着训练得到该目标轨迹预测模型的训练数据的规模扩大,算法对未来轨迹分布建模能力可以不断加强,进而轨迹预测能力也可以随之能不断提升。1. Combined with the hidden random variables of the randomness of the behavior of each traffic participant, the historical trajectory and motion attribute information of each traffic participant, and the corresponding current map information, which have been learned in the target trajectory prediction model, construct the corresponding traffic participant object. Multi-modal probability distribution of hidden random variables, and then determine the multi-modal prediction trajectory corresponding to each traffic participant, so as to realize the accurate determination of the multi-modal prediction trajectory of each traffic participant, and this includes learning the trajectories of each traffic participant. The target trajectory prediction model of hidden random variables with random behavior is universal to the scene, and there is no bottleneck restriction in algorithm design. The modeling ability can be continuously strengthened, and then the trajectory prediction ability can also be continuously improved.

2、依次从特征维度和时间维度对交通参与对象对应的待处理特征进行特征处理,实现对待处理特征的不同特征维度的特征和不同时间维度特征的聚合提取,得到聚合特征,进而将聚合特征与初始特征中各历史时刻的特征进行融合,多次重复以上操作,以得到交通参与对象对应的深层抽象的中间预测特征,继而,利用图神经网络将中间预测特征与各动态对象信息和静态对象信息融合,确定出该交通参与对象对应的轨迹预测特征,以为保证后续的未来轨迹预测结果的准确性提供基础。2. Perform feature processing on the features to be processed corresponding to the traffic participants from the feature dimension and the time dimension in turn, realize the aggregation extraction of the features of different feature dimensions and the features of different time dimensions of the features to be processed, obtain the aggregated features, and then combine the aggregated features with The features of each historical moment in the initial features are fused, and the above operations are repeated many times to obtain the deep abstract intermediate prediction features corresponding to the traffic participating objects. Fusion to determine the trajectory prediction feature corresponding to the traffic participant object, so as to provide a basis for ensuring the accuracy of subsequent future trajectory prediction results.

3、通过规范化流映射算法,以及交通参与对象对应的隐随机变量单模态概率分布,以构建出交通参与对象对应的隐随机变量多模态概率分布,为后续的交通参与对象的多模态轨迹的预测提供基础。3. Through the normalized flow mapping algorithm and the unimodal probability distribution of the hidden random variables corresponding to the traffic participating objects, the multimodal probability distribution of the hidden random variables corresponding to the traffic participating objects is constructed, which is the multimodal probability distribution of the subsequent traffic participating objects. The prediction of the trajectory provides the basis.

4、通过各样本交通对象对应的样本历史轨迹和样本运动属性信息、其对应的样本动态对象的样本历史轨迹、样本运动属性信息和静态对象信息以及各样本交通对象对应的样本未来轨迹,训练初始轨迹预测模型,以使得初始轨迹预测模型中的隐随机变量学习得到各样本交通对象的行为的随机性,为后续的交通参与对象的未来轨迹的准确预测提供基础。4. Through the sample historical trajectory and sample motion attribute information corresponding to each sample traffic object, the sample historical trajectory, sample motion attribute information and static object information of the corresponding sample dynamic object, and the sample future trajectory corresponding to each sample traffic object, the training initial Trajectory prediction model, so that the hidden random variables in the initial trajectory prediction model can learn the randomness of the behavior of each sample traffic object, and provide a basis for the accurate prediction of the future trajectory of the subsequent traffic participants.

附图说明Description of drawings

为了更清楚地说明本发明实施例或现有技术中的技术方案,下面将对实施例或现有技术描述中所需要使用的附图作简单介绍。显而易见地,下面描述中的附图仅仅是本发明的一些实施例。对于本领域普通技术人员来讲,在不付出创造性劳动的前提下,还可以根据这些附图获得其他的附图。In order to illustrate the embodiments of the present invention or the technical solutions in the prior art more clearly, the following briefly introduces the accompanying drawings that are required in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the invention. For those of ordinary skill in the art, other drawings can also be obtained from these drawings without creative effort.

图1为本发明实施例提供的运动轨迹的预测方法的一种流程示意图;1 is a schematic flowchart of a method for predicting a motion trajectory according to an embodiment of the present invention;

图2为本发明实施例提供的隐随机变量单模态概率分布映射为隐随机变量多模态概率分布的一种示意图;FIG. 2 is a schematic diagram of a hidden random variable unimodal probability distribution provided by an embodiment of the present invention being mapped to a hidden random variable multimodal probability distribution;

图3为本发明实施例提供的目标轨迹预测模型的训练过程的一种流程示意图;3 is a schematic flowchart of a training process of a target trajectory prediction model provided by an embodiment of the present invention;

图4为本发明实施例提供的运动轨迹的预测装置的一种结构示意图。FIG. 4 is a schematic structural diagram of an apparatus for predicting a motion trajectory according to an embodiment of the present invention.

具体实施方式Detailed ways

下面将结合本发明实施例中的附图,对本发明实施例中的技术方案进行清楚、完整的描述。显然,所描述的实施例仅仅是本发明的一部分实施例,而不是全部的实施例。基于本发明中的实施例,本领域普通技术人员在没有付出创造性劳动前提下所获得的所有其他实施例,都属于本发明保护的范围。The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some, but not all, embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

需要说明的是,本发明实施例及附图中的术语“包括”和“具有”以及它们的任何变形,意图在于覆盖不排他的包含。例如包含的一系列步骤或单元的过程、方法、系统、产品或设备没有限定于已列出的步骤或单元,而是可选地还包括没有列出的步骤或单元,或可选地还包括对于这些过程、方法、产品或设备固有的其他步骤或单元。It should be noted that the terms "comprising" and "having" and any modifications thereof in the embodiments of the present invention and the accompanying drawings are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the steps or units listed, but optionally also includes steps or units not listed, or optionally also includes For other steps or units inherent to these processes, methods, products or devices.

本发明提供了一种运动轨迹的预测方法及装置,以实现降低运动轨迹预测的局限性,以更好的适应比较复杂的自动驾驶场景。下面对本发明实施例进行详细说明。The present invention provides a motion trajectory prediction method and device, so as to reduce the limitation of motion trajectory prediction and better adapt to more complex automatic driving scenarios. The embodiments of the present invention will be described in detail below.

图1为本发明实施例提供的运动轨迹的预测方法的一种流程示意图。该方法可以包括如下步骤:FIG. 1 is a schematic flowchart of a method for predicting a motion trajectory according to an embodiment of the present invention. The method may include the following steps:

S101:获得目标对象对应的各交通参与对象的历史轨迹和运动属性信息以及对应的当前地图信息。S101: Obtain historical trajectory and motion attribute information of each traffic participating object corresponding to the target object and corresponding current map information.

本发明实施例所提供的运动轨迹的预测方法,可以应用于任一具有计算能力的电子设备,该电子设备可以为终端或者服务器。在一种实现中,实现该运动轨迹的预测方法的功能软件可以以单独的客户端软件的形式存在,也可以以目前相关的客户端软件的插件的形式存在,这都是可以的。The motion trajectory prediction method provided in the embodiment of the present invention can be applied to any electronic device with computing capability, and the electronic device can be a terminal or a server. In one implementation, the functional software for implementing the motion trajectory prediction method may exist in the form of a separate client software, or may exist in the form of a plug-in of the currently related client software, which is all possible.

其中,目标对象可以为自动驾驶车辆也可以为智能机器人。目标对象可以通过其设置的传感器获得其对应的各交通参与对象的历史轨迹以及运动属性信息,其中,交通参与对象的运动属性信息可以包括交通参与对象的运动信息和属性信息,其中,交通参与对象的运动信息包括但不限于:交通参与对象的速度以及加速度等信息。交通参与对象的属性信息可以包括但不限于:交通参与对象的类型以及形状和尺寸等。交通参与对象的历史轨迹包括:交通参与对象在当前时刻之前预设时间段内的各历史时刻的位置信息以及姿态信息。The target object may be an autonomous vehicle or an intelligent robot. The target object can obtain the historical trajectory and motion attribute information of each traffic participating object corresponding to it through the sensor set by it, wherein the motion attribute information of the traffic participating object can include the motion information and attribute information of the traffic participating object, wherein the traffic participating object The motion information includes but is not limited to: the speed and acceleration of the traffic participating objects. The attribute information of the traffic participating object may include, but is not limited to, the type, shape and size of the traffic participating object. The historical track of the traffic participant object includes: position information and posture information of the traffic participant object at each historical moment within a preset time period before the current moment.

该当前时刻可以指电子设备当前要预测轨迹的时刻。上述历史轨迹以及历史时刻中的“历史”是相对于电子设备当前要预测轨迹的时刻,即当前时刻而言的,指当前时刻之前的时间段内生成的轨迹。The current moment may refer to the moment at which the electronic device currently wants to predict the trajectory. The "history" in the above-mentioned historical trajectory and historical moment is relative to the moment at which the electronic device currently predicts the trajectory, that is, the current moment, and refers to the trajectory generated in the time period before the current moment.

电子设备本地或所连接的存储设备可以预先存储于目标对象所在区域的完整地图信息,该当前地图信息可以为该完整地图信息,也可以是基于目标对象在当前时刻的当前位姿信息从完整地图信息中,确定出其当前位姿信息所对应区域范围内的地图信息,这都是可以的。The local or connected storage device of the electronic device can pre-store the complete map information of the area where the target object is located, and the current map information can be the complete map information, or can be obtained from the complete map based on the current pose information of the target object at the current moment. In the information, it is possible to determine the map information within the area corresponding to the current pose information.

目标对象所设置的传感器可以包括但不限于:图像采集设备、轮速传感器、雷达、IMU(Inertial measurement unit,惯性测量单元)、GPS(Global Positioning System,全球定位系统)以及GNSS(Global Navigation Satellite System,全球卫星导航系统/全球导航卫星系统)等。The sensors set by the target object may include but are not limited to: image acquisition equipment, wheel speed sensors, radar, IMU (Inertial measurement unit, inertial measurement unit), GPS (Global Positioning System, global positioning system) and GNSS (Global Navigation Satellite System) , GNSS/GNSS), etc.

在一种情况中,若目标对象为自动驾驶车辆,相应的交通参与对象可以包括但不限于:机动车辆、自行车、三轮车、行人以及动物等对象。交通参与对象为机动车辆的情况下,电子设备还可以获得该机动车辆的车灯信息,例如:转向灯的亮灭情况。In one case, if the target object is an autonomous vehicle, the corresponding traffic participation objects may include, but are not limited to, objects such as motor vehicles, bicycles, tricycles, pedestrians, and animals. In the case where the traffic participant object is a motor vehicle, the electronic device can also obtain the vehicle lamp information of the motor vehicle, for example, the on-off status of the turn signal.

在一种情况中,该目标对象为自动驾驶车辆,该当前地图信息可以包括但不限于:车道线、停车位、行人道、交通指示牌、交通指示箭头、路灯杆等交通标识信息,其中,可以称当前地图信息中所包括的各交通标识信息为静态对象,还可以包括:场景中位置固定的建筑物、植物以及其他物体等静态对象。In one case, the target object is an autonomous vehicle, and the current map information may include, but is not limited to, traffic sign information such as lane lines, parking spaces, sidewalks, traffic signs, traffic sign arrows, street light poles, etc., wherein, Each traffic sign information included in the current map information may be called a static object, and may also include static objects such as buildings, plants, and other objects with fixed positions in the scene.

S102:利用目标轨迹预测模型的特征提取层以及各交通参与对象对应的初始特征,确定各交通参与对象对应的轨迹预测特征。S102: Determine the trajectory prediction feature corresponding to each traffic participant object by using the feature extraction layer of the target trajectory prediction model and the initial feature corresponding to each traffic participant object.

其中,交通参与对象对应的初始特征包括:交通参与对象的历史轨迹和运动属性信息,及其对应的其他交通参与对象和目标对象的历史轨迹和运动属性信息以及当前地图信息。The initial features corresponding to the traffic participating object include: historical trajectory and motion attribute information of the traffic participating object, and the corresponding historical trajectory and motion attribute information of other traffic participating objects and target objects, as well as current map information.

该目标轨迹预测模型为:基于样本交通对象对应的样本历史轨迹及样本运动属性信息、其对应的样本动态对象的样本历史轨迹及样本运动属性信息、静态对象信息以及各样本交通对象对应的样本未来轨迹训练所得模型。该目标轨迹预测模型为神经网络隐变量模型。为了布局清楚,后续针对目标轨迹预测模型的训练过程进行说明。The target trajectory prediction model is based on the sample historical trajectory and sample motion attribute information corresponding to the sample traffic objects, the sample historical trajectory and sample motion attribute information of the corresponding sample dynamic objects, the static object information, and the sample future corresponding to each sample traffic object. Trajectory training resulting model. The target trajectory prediction model is a neural network latent variable model. In order to make the layout clear, the training process of the target trajectory prediction model will be described later.

其中,样本交通对象对应的样本动态对象可以包括:样本交通对象所在场景中其他处于动态的交通对象。样本交通对象对应的静态对象信息可以包括:样本交通对象所在场景对应的地图信息中的各静态对象。The sample dynamic objects corresponding to the sample traffic objects may include: other dynamic traffic objects in the scene where the sample traffic objects are located. The static object information corresponding to the sample traffic object may include: each static object in the map information corresponding to the scene where the sample traffic object is located.

电子设备可以针对每一交通参与对象,将该交通参与对象对应的初始特征输入目标轨迹预测模型的特征提取层,以通过目标轨迹预测模型的特征提取层对该交通参与对象对应的初始特征进行特征提取,确定出该交通参与对象对应的轨迹预测特征,以确定出各交通参与对象对应的轨迹预测特征。For each traffic participant object, the electronic device may input the initial feature corresponding to the traffic participant object into the feature extraction layer of the target trajectory prediction model, so as to characterize the initial feature corresponding to the traffic participant object through the feature extraction layer of the target trajectory prediction model. Extracting and determining the trajectory prediction feature corresponding to the traffic participant object, and determining the trajectory prediction feature corresponding to each traffic participant object.

S103:利用目标轨迹预测模型的特征提取层以及各交通参与对象对应的轨迹预测特征,确定各交通参与对象对应的隐随机变量多模态概率分布。S103: Using the feature extraction layer of the target trajectory prediction model and the trajectory prediction feature corresponding to each traffic participant object, determine the multimodal probability distribution of the latent random variable corresponding to each traffic participant object.

其中,隐随机变量表征各交通参与对象的行为随机性。Among them, the hidden random variables represent the behavior randomness of each traffic participant.

本步骤中,目标轨迹预测模型为设置有隐随机变量的模型,电子设备获得各交通参与对象对应的轨迹预测特征之后,针对每一交通参与对象,利用该交通参与对象对应的轨迹预测特征以及目标轨迹预测模型的特征提取层的隐随机变量,确定该交通参与对象对应的隐随机变量多模态概率分布。以通过隐随机变量表示出交通参与对象的轨迹的随机性以及不确定性。In this step, the target trajectory prediction model is a model with hidden random variables. After the electronic device obtains the trajectory prediction features corresponding to each traffic participant object, for each traffic participant object, the trajectory prediction feature corresponding to the traffic participant object and the target are used. The hidden random variables of the feature extraction layer of the trajectory prediction model are used to determine the multimodal probability distribution of the hidden random variables corresponding to the traffic participants. The randomness and uncertainty of the trajectory of the traffic participants are represented by hidden random variables.

在本发明的一种实现方式中,所述S103,可以包括如下步骤011-012:In an implementation manner of the present invention, the S103 may include the following steps 011-012:

011:针对每一交通参与对象,利用目标轨迹预测模型的特征提取层以及该交通参与对象对应的轨迹预测特征,确定该交通参与对象对应的隐随机变量单模态概率分布。011: For each traffic participant object, use the feature extraction layer of the target trajectory prediction model and the trajectory prediction feature corresponding to the traffic participant object to determine the hidden random variable unimodal probability distribution corresponding to the traffic participant object.

012:针对每一交通参与对象,利用规范化流映射算法以及该交通参与对象对应的隐随机变量单模态概率分布,得到该交通参与对象所对应隐随机变量对应的多模态轨迹分布。012: For each traffic participant object, use the normalized flow mapping algorithm and the unimodal probability distribution of the hidden random variable corresponding to the traffic participant object to obtain the multimodal trajectory distribution corresponding to the hidden random variable corresponding to the traffic participant object.

本实现方式中,电子设备首先利用目标轨迹预测模型的特征提取层以及该交通参与对象对应的轨迹预测特征,确定该交通参与对象对应的隐随机变量单模态概率分布;进而通过规范化流(Normalizing Flow)映射算法,将该交通参与对象对应的隐随机变量单模态概率分布,映射成多模态概率分布,得到该交通参与对象对应的隐随机变量多模态概率分布。In this implementation manner, the electronic device first uses the feature extraction layer of the target trajectory prediction model and the trajectory prediction feature corresponding to the traffic participant object to determine the unimodal probability distribution of the hidden random variable corresponding to the traffic participant object; Flow) mapping algorithm, the hidden random variable unimodal probability distribution corresponding to the traffic participant object is mapped into a multimodal probability distribution, and the hidden random variable multimodal probability distribution corresponding to the traffic participant object is obtained.

在一种情况中,假设该交通参与对象对应的隐随机变量单模态概率分布为多元高斯分布,目标轨迹预测模型的特征提取层可以输出其对应的均值和方差,通过其对应的均值和方差,可以构建出该交通参与对象对应的隐随机变量单模态概率分布。后续的,通过规范化流映射算法,将该交通参与对象对应的隐随机变量单模态概率分布映射成多模态轨迹分布,得到该交通参与对象对应的隐随机变量多模态概率分布,以简化后续的目标轨迹预测模型把轨迹的随机性即隐随机变量的随机性,映射成轨迹空间多模态概率分布的难度,达到更好的多模态未来轨迹建模效果。效果图如图2所示,其中,“单模态分布”表示交通参与对象对应的隐随机变量单模态概率分布,“多模态分布”表示交通参与对象对应的隐随机变量多模态轨迹分布。In one case, assuming that the unimodal probability distribution of the hidden random variable corresponding to the traffic participant object is a multivariate Gaussian distribution, the feature extraction layer of the target trajectory prediction model can output its corresponding mean and variance. , the unimodal probability distribution of the hidden random variable corresponding to the traffic participant object can be constructed. Subsequently, through the normalized flow mapping algorithm, the unimodal probability distribution of the hidden random variable corresponding to the traffic participant object is mapped to the multimodal trajectory distribution, and the multimodal probability distribution of the hidden random variable corresponding to the traffic participant object is obtained to simplify the process. The subsequent target trajectory prediction model maps the randomness of the trajectory, that is, the randomness of the hidden random variables, into the difficulty of the multimodal probability distribution in the trajectory space, so as to achieve a better multimodal future trajectory modeling effect. The effect diagram is shown in Figure 2, in which "single-modal distribution" represents the single-modal probability distribution of the hidden random variables corresponding to the traffic participants, and "multi-modal distribution" represents the multi-modal trajectories of the hidden random variables corresponding to the traffic participants distributed.

S104:利用目标轨迹预测模型的特征回归层、各交通参与对象对应的轨迹预测特征以及各交通参与对象对应的隐随机变量多模态概率分布,确定各交通参与对象对应的多模态预测轨迹。S104: Determine the multimodal predicted trajectory corresponding to each traffic participant by using the feature regression layer of the target trajectory prediction model, the trajectory prediction feature corresponding to each traffic participant object, and the multimodal probability distribution of the hidden random variable corresponding to each traffic participant object.

电子设备确定出各交通参与对象对应的隐随机变量多模态概率分布,针对每一交通参与对象,利用目标轨迹预测模型的特征回归层,将交通参与对象对应的轨迹预测特征以及该交通参与对象对应的隐随机变量多模态概率分布进行融合,以确定出交通参与对象对应的多模态预测轨迹。The electronic device determines the multimodal probability distribution of the hidden random variables corresponding to each traffic participant, and for each traffic participant, the feature regression layer of the target trajectory prediction model is used to predict the trajectory corresponding to the traffic participant object and the traffic participant object. The corresponding hidden random variable multimodal probability distributions are fused to determine the multimodal predicted trajectory corresponding to the traffic participants.

在本发明的一种实现方式中,所述S104,可以包括如下步骤021-022:In an implementation manner of the present invention, the S104 may include the following steps 021-022:

021:针对每一交通参与对象,对交通参与对象对应的隐随机变量多模态概率分布进行采样,得到该交通参与对象对应的多个隐随机变量样本。021: For each traffic participant object, sample the multimodal probability distribution of the latent random variable corresponding to the traffic participant object to obtain a plurality of latent random variable samples corresponding to the traffic participant object.

022:利用目标轨迹预测模型的特征回归层、各交通参与对象对应的轨迹预测特征以及各交通参与对象对应的多个隐随机变量样本,确定各交通参与对象对应的多模态预测轨迹。022: Using the feature regression layer of the target trajectory prediction model, the trajectory prediction features corresponding to each traffic participant object, and a plurality of latent random variable samples corresponding to each traffic participant object, determine the multimodal prediction trajectory corresponding to each traffic participant object.

本实现方式中,电子设备针对每一交通参与对象,对交通参与对象对应的隐随机变量多模态概率分布进行采样,得到该交通参与对象对应的多个隐随机变量样本,进而,利用目标轨迹预测模型的特征回归层,将该交通参与对象对应的轨迹预测特征与该交通参与对象对应的多个隐随机变量样本映射到轨迹空间,即将该交通参与对象对应的轨迹预测特征与该交通参与对象对应的每一隐随机变量样本进行融合,以得到各交通参与对象对应的多模态预测轨迹。In this implementation manner, for each traffic participant object, the electronic device samples the multimodal probability distribution of the hidden random variable corresponding to the traffic participant object, and obtains a plurality of hidden random variable samples corresponding to the traffic participant object, and then uses the target trajectory The feature regression layer of the prediction model maps the trajectory prediction feature corresponding to the traffic participant object and a plurality of latent random variable samples corresponding to the traffic participant object to the trajectory space, that is, the trajectory prediction feature corresponding to the traffic participant object and the traffic participant object. Each corresponding hidden random variable sample is fused to obtain the multi-modal prediction trajectory corresponding to each traffic participant object.

应用本发明实施例,可以利用目标轨迹预测模型中已学习到各交通参与对象的行为随机性的隐随机变量,以及各交通参与对象的历史轨迹和运动属性信息以及其对应的动态对象信息即其对应的其他交通参与对象和目标对象的历史轨迹和运动属性信息,和静态对象信息即当前地图信息,拟合参与对象的未来轨迹的条件概率分布,即各交通参与对象对应的隐随机变量多模态概率分布,进而确定各交通参与对象对应的多模态预测轨迹,以实现对各交通参与对象的多模态预测轨迹的准确确定,且该包括学习到各交通参与对象的行为随机性的隐随机变量的目标轨迹预测模型对该场景具有通用性,不存在算法设计上的瓶颈制约,随着训练得到该目标轨迹预测模型的训练数据的规模扩大,算法对未来轨迹分布建模能力可以不断加强,进而轨迹预测能力也可以随之能不断提升。By applying the embodiments of the present invention, the hidden random variables that have learned the randomness of the behavior of each traffic participant in the target trajectory prediction model, as well as the historical trajectory and motion attribute information of each traffic participant, and the corresponding dynamic object information, namely the The corresponding historical trajectory and motion attribute information of other traffic participants and target objects, and static object information, that is, the current map information, fit the conditional probability distribution of the future trajectory of the participants, that is, the hidden random variables corresponding to each traffic participant. modal probability distribution, and then determine the multi-modal prediction trajectory corresponding to each traffic participant, so as to realize the accurate determination of the multi-modal prediction trajectory of each traffic participant, and this includes learning the hidden hidden behavior of each traffic participant’s behavior randomness. The target trajectory prediction model of random variables is universal to this scenario, and there is no bottleneck restriction in algorithm design. With the expansion of the training data scale of the target trajectory prediction model obtained through training, the algorithm's ability to model the future trajectory distribution can be continuously strengthened. , and then the trajectory prediction ability can also be continuously improved.

在本发明的另一实施例中,交通参与对象对应的初始特征为基于时间顺序排列的特征,其包括交通参与对象对应的多个历史时刻的特征;In another embodiment of the present invention, the initial features corresponding to the traffic participating objects are features arranged in chronological order, which include features of multiple historical moments corresponding to the traffic participating objects;

所述S102,可以包括如下步骤031-032:The S102 may include the following steps 031-032:

031:针对每一交通参与对象,利用目标轨迹预测模型的特征提取层,对该交通参与对象对应的初始特征循环多次执行如下步骤A-C,确定出该交通参与对象对应的中间预测特征。031: For each traffic participant object, use the feature extraction layer of the target trajectory prediction model to perform the following steps A-C repeatedly for the initial feature corresponding to the traffic participant object to determine the intermediate prediction feature corresponding to the traffic participant object.

032:针对每一交通参与对象,基于图神经网络将该交通参与对象对应的中间预测特征中各静态对象对应的中间预测特征进行融合,确定出该交通参与对象对应的轨迹预测特征。032: For each traffic participant object, fuse the intermediate prediction features corresponding to each static object in the intermediate prediction features corresponding to the traffic participant object based on the graph neural network, and determine the trajectory prediction feature corresponding to the traffic participant object.

其中,静态对象包括当前地图信息中的各静态对象。The static objects include all static objects in the current map information.

步骤A:从特征维度对该交通参与对象对应的待处理特征进行非线性映射,得到该交通参与对象对应的映射特征。Step A: Perform nonlinear mapping on the to-be-processed feature corresponding to the traffic participant object from the feature dimension to obtain the mapping feature corresponding to the traffic participant object.

其中,待处理特征为该交通参与对象对应的初始特征或前一次迭代生成的该交通参与对象对应的中间预测特征。The feature to be processed is the initial feature corresponding to the traffic participant object or the intermediate prediction feature corresponding to the traffic participant object generated in the previous iteration.

步骤B:从时间维度对映射特征进行特征聚合操作,得到该交通参与对象对应的聚合特征。Step B: Perform a feature aggregation operation on the mapping feature from the time dimension to obtain the aggregated feature corresponding to the traffic participant object.

步骤C:将聚合特征与待处理特征中各历史时刻的特征进行融合。Step C: Integrate the aggregated features with the features of each historical moment in the features to be processed.

本实现方式中,交通参与对象对应的初始特征为基于时间顺序排列的特征,其包括交通参与对象对应的多个历史时刻的特征。可以理解的是,每一历史时刻可以对应交通参与对象对应的多个特征,例如:交通参与对象的位置信息、姿态信息如朝向角、速度、形状、类型和大小,交通参与对象对应的其他交通参与对象和目标对象的位置信息、姿态信息如朝向角、速度、形状、类型和大小以及交通参与对象与其对应的当前地图信息中各静态对象的相对位置信息以及类型等信息。各历史时刻对应的特征按各历史时刻信息的先后顺序排列。In this implementation manner, the initial features corresponding to the traffic participating objects are features arranged in chronological order, which include features of multiple historical moments corresponding to the traffic participating objects. It can be understood that each historical moment can correspond to multiple features corresponding to the traffic participating objects, for example: the location information of the traffic participating objects, attitude information such as heading angle, speed, shape, type and size, other traffic corresponding to the traffic participating objects. The position information and attitude information of participating objects and target objects, such as heading angle, speed, shape, type and size, as well as the relative position information and type of each static object in the current map information corresponding to the traffic participating object and its corresponding information. The features corresponding to each historical moment are arranged in the order of the information of each historical moment.

电子设备可以针对每一交通参与对象,首先利用目标轨迹预测模型的特征提取层,从特征维度对该交通参与对象对应的初始特征进行非线性映射,即针对各历史时刻,对该历史时刻对应的特征,从特征维度进行非线性映射,得到该交通参与对象对应的映射特征;进而从时间维度对映射特征进行特征聚合操作,即针对各历史时刻对应的映射特征进行特征聚合操作,得到该交通参与对象对应的聚合特征;将该交通参与对象对应的聚合特征与初始特征中各历史时刻的特征进行融合,将融合所得的特征作为该交通参与对象对应的新的待处理特征,并重新执行针对每一交通参与对象,首先利用目标轨迹预测模型的特征提取层,从特征维度对该交通参与对象对应的初始特征进行非线性映射,得到该交通参与对象对应的映射特征的步骤,直至上述步骤重复执行多次,以得到该交通参与对象对应的包含深层抽象特征的中间预测特征。For each traffic participant object, the electronic device can first use the feature extraction layer of the target trajectory prediction model to non-linearly map the initial feature corresponding to the traffic participant object from the feature dimension, that is, for each historical moment, the feature, perform nonlinear mapping from the feature dimension to obtain the mapping feature corresponding to the traffic participating object; and then perform feature aggregation operation on the mapping feature from the time dimension, that is, perform feature aggregation operation on the mapping features corresponding to each historical moment, and obtain the traffic participation object. The aggregated feature corresponding to the object; the aggregated feature corresponding to the traffic participant object is fused with the features of each historical moment in the initial feature, and the resulting feature is taken as the new feature to be processed corresponding to the traffic participant object, and re-execution is performed for each traffic participant object. For a traffic participant object, first use the feature extraction layer of the target trajectory prediction model to non-linearly map the initial feature corresponding to the traffic participant object from the feature dimension to obtain the mapping feature corresponding to the traffic participant object, until the above steps are repeated. multiple times to obtain the intermediate prediction feature corresponding to the traffic participant object containing the deep abstract feature.

进而,电子设备针对每一交通参与对象,基于图神经网络将该交通参与对象对应的中间预测特征中各静态对象对应的中间预测特征进行融合,确定出该交通参与对象对应的轨迹预测特征。Further, for each traffic participant object, the electronic device fuses the intermediate prediction features corresponding to each static object in the intermediate prediction features corresponding to the traffic participant object based on the graph neural network, and determines the trajectory prediction feature corresponding to the traffic participant object.

在本发明的另一实施例中,在所述S102之前,所述方法还可以包括:In another embodiment of the present invention, before the S102, the method may further include:

训练得到目标轨迹预测模型的过程,其中,如图3所示,所述过程,包括:The process of training to obtain a target trajectory prediction model, wherein, as shown in Figure 3, the process includes:

S301:获得初始轨迹预测模型。S301: Obtain an initial trajectory prediction model.

S302:获得各样本交通对象对应的样本训练信息以及各样本交通对象对应的样本未来轨迹。S302: Obtain sample training information corresponding to each sample traffic object and sample future trajectories corresponding to each sample traffic object.

其中,样本交通对象对应的样本训练信息包括:该样本交通对象的样本历史轨迹和样本运动属性信息、其对应的样本动态对象的样本历史轨迹、样本运动属性信息和样本静态对象信息。该样本运动属性信息可以包括样本交通对象的运动信息和属性信息,其中,样本交通对象的运动信息包括但不限于:样本交通对象的速度以及加速度等信息。样本交通对象的属性信息可以包括但不限于:样本交通对象的类型以及形状和尺寸等。样本交通对象的样本历史轨迹包括:样本交通对象在样本轨迹采集时刻之前的预设时间段内的各历史时刻的位置信息以及姿态信息。样本交通对象的样本未来轨迹为:样本交通对象在样本轨迹采集时刻及之后的第一时间段内的真实的运行轨迹,包括样本轨迹采集时刻及之后的第一时间段内各时刻的样本交通对象的真实的位置信息和姿态信息。The sample training information corresponding to the sample traffic object includes: the sample historical trajectory and sample motion attribute information of the sample traffic object, the sample historical trajectory, sample motion attribute information and sample static object information of the corresponding sample dynamic object. The sample motion attribute information may include motion information and attribute information of the sample traffic object, wherein the motion information of the sample traffic object includes, but is not limited to, information such as speed and acceleration of the sample traffic object. The attribute information of the sample traffic object may include, but is not limited to, the type, shape, and size of the sample traffic object. The sample historical trajectory of the sample traffic object includes: position information and attitude information of the sample traffic object at each historical moment in a preset time period before the sample trajectory collection moment. The sample future trajectories of the sample traffic objects are: the real running trajectories of the sample traffic objects at the sample track collection time and the first time period after that, including the sample traffic objects at the sample track collection time and the first time period after that. The real position information and attitude information.

样本交通对象对应的样本动态对象为样本交通对象所处环境中周围的处于动态的对象,可以包括车辆、行人以及动物等;样本交通对象对应的样本静态对象信息包括该样本交通对象所处环境的地图信息中的各静态对象。The sample dynamic objects corresponding to the sample traffic objects are dynamic objects around the environment where the sample traffic objects are located, which may include vehicles, pedestrians, animals, etc.; the sample static object information corresponding to the sample traffic objects includes information about the environment where the sample traffic objects are located. Each static object in the map information.

在一种情况中,目标对象为自动驾驶车辆,相应的,样本车辆在行驶过程中可以针对其所在环境中的各对象采集相应的信息,某一样本交通对象对应的样本训练信息及样本未来轨迹可以是基于样本车辆通过其所设置的传感器采集的传感器数据确定的。In one case, the target object is an autonomous vehicle, and accordingly, the sample vehicle can collect corresponding information for each object in its environment during the driving process, the sample training information corresponding to a sample traffic object, and the sample future trajectory It may be determined based on sensor data collected by the sample vehicle through the sensors it is provided with.

在一种情况中,样本交通对象为机动车辆的情况下,样本交通对象对应的样本训练信息还可以包括该机动车辆的车灯信息,例如:转向灯的亮灭情况。In one case, in the case where the sample traffic object is a motor vehicle, the sample training information corresponding to the sample traffic object may also include vehicle lamp information of the motor vehicle, for example, the turning lights on and off.

S303:针对每一样本交通对象,利用所述初始轨迹预测模型的特征提取层以及该样本交通对象对应的初始样本特征,确定该样本交通对象对应的样本预测特征。S303: For each sample traffic object, use the feature extraction layer of the initial trajectory prediction model and the initial sample feature corresponding to the sample traffic object to determine the sample prediction feature corresponding to the sample traffic object.

其中,该样本交通对象对应的初始样本特征包括:样本交通对象样本历史轨迹和样本运动属性信息,其对应的样本动态对象的样本历史轨迹、样本运动属性信息和样本静态对象信息。The initial sample features corresponding to the sample traffic object include: sample historical trajectory and sample motion attribute information of the sample traffic object, and sample historical trajectory, sample motion attribute information and sample static object information of the corresponding sample dynamic object.

S304:针对每一样本交通对象,利用初始轨迹预测模型的特征提取层以及该样本交通对象对应的样本预测特征,确定该样本交通对象对应的隐随机变量多模态概率分布。S304: For each sample traffic object, use the feature extraction layer of the initial trajectory prediction model and the sample prediction feature corresponding to the sample traffic object to determine the multimodal probability distribution of the latent random variable corresponding to the sample traffic object.

S305:针对每一样本交通对象,利用初始轨迹预测模型的特征回归层、该样本交通对象对应的样本预测特征以及该样本交通对象对应的隐随机变量多模态概率分布,确定该样本交通对象对应的多模态预测轨迹。S305: For each sample traffic object, use the feature regression layer of the initial trajectory prediction model, the sample prediction feature corresponding to the sample traffic object, and the multimodal probability distribution of the latent random variables corresponding to the sample traffic object to determine the corresponding sample traffic object. The multimodal prediction trajectory of .

S306:针对每一样本交通对象,利用预设变分算法对该样本交通对象对应的样本未来轨迹进行处理,得到该样本交通对象对应的隐随机变量变分概率分布。S306 : For each sample traffic object, use a preset variational algorithm to process the sample future trajectory corresponding to the sample traffic object to obtain a variational probability distribution of a latent random variable corresponding to the sample traffic object.

S307:针对每一样本交通对象,利用该样本交通对象对应的隐随机变量多模态概率分布以及该样本交通对象对应的隐随机变量变分概率分布,确定该样本交通对象对应的隐随机变量KL散度值。S307: For each sample traffic object, use the multimodal probability distribution of the hidden random variable corresponding to the sample traffic object and the variational probability distribution of the hidden random variable corresponding to the sample traffic object to determine the hidden random variable KL corresponding to the sample traffic object Divergence value.

S308:针对每一样本交通对象,利用该样本交通对象对应的多模态预测轨迹以及该样本交通对象对应的样本未来轨迹,确定该样本交通对象对应的轨迹重构损失值。S308: For each sample traffic object, use the multimodal predicted trajectory corresponding to the sample traffic object and the sample future trajectory corresponding to the sample traffic object to determine a trajectory reconstruction loss value corresponding to the sample traffic object.

S309:针对每一样本交通对象,利用该样本交通对象对应的隐随机变量KL散度值、该样本交通对象对应的轨迹重构损失值,构建最大化似然函数的变分下界;并判断所构建的最大化似然函数的变分下界是否达到最大化。S309: For each sample traffic object, use the KL divergence value of the latent random variable corresponding to the sample traffic object and the trajectory reconstruction loss value corresponding to the sample traffic object to construct a variational lower bound of the maximum likelihood function; Whether the variational lower bound of the constructed maximal likelihood function is maximized.

S310:若所构建的最大化似然函数的变分下界未达到最大化,则调整初始轨迹预测模型的特征提取层与特征回归层的模型参数,并返回S303。S310: If the variation lower bound of the constructed maximum likelihood function does not reach the maximum, adjust the model parameters of the feature extraction layer and the feature regression layer of the initial trajectory prediction model, and return to S303.

S311:若所构建的最大化似然函数的变分下界达到最大化,确定初始轨迹预测模型收敛,得到包含特征提取层与特征回归层的目标轨迹预测模型。S311 : If the variation lower bound of the constructed maximization likelihood function is maximized, determine that the initial trajectory prediction model converges, and obtain a target trajectory prediction model including a feature extraction layer and a feature regression layer.

为了保证对各交通参与对象的未来轨迹预测的准确性,本发明实施例还包括目标轨迹预测模型的训练过程。相应的,电子设备可以首先获得初始轨迹预测模型,其中,该初始轨迹预测模型可以为神经网络隐变量模型;获得各样本交通对象对应的样本训练信息以及各样本交通对象对应的样本未来轨迹。In order to ensure the accuracy of the future trajectory prediction of each traffic participant object, the embodiment of the present invention further includes a training process of the target trajectory prediction model. Correspondingly, the electronic device can first obtain an initial trajectory prediction model, where the initial trajectory prediction model can be a neural network latent variable model; obtain sample training information corresponding to each sample traffic object and sample future trajectories corresponding to each sample traffic object.

进而,电子设备针对每一样本交通对象,将该样本交通对象对应的初始样本特征输入初始轨迹预测模型的特征提取层,利用初始轨迹预测模型的特征提取层对该样本交通对象对应的初始样本特征进行特征提取融合,确定样本交通对象对应的样本预测特征,其中,初始用初始轨迹预测模型的特征提取层对该样本交通对象对应的初始样本特征进行特征提取融合的过程,可以参见目标轨迹预测模型的特征提取层对交通参与对象的初始特征的特征提取融合的过程,在此不再赘述。Further, for each sample traffic object, the electronic device inputs the initial sample feature corresponding to the sample traffic object into the feature extraction layer of the initial trajectory prediction model, and uses the feature extraction layer of the initial trajectory prediction model to the initial sample feature corresponding to the sample traffic object Perform feature extraction and fusion to determine the sample prediction features corresponding to the sample traffic objects. The process of feature extraction and fusion of the initial sample features corresponding to the sample traffic objects using the feature extraction layer of the initial trajectory prediction model can be found in the target trajectory prediction model. The feature extraction layer of the feature extraction and fusion process of the initial features of the traffic participating objects will not be repeated here.

针对每一样本交通对象,利用初始轨迹预测模型的特征提取层以及该样本交通对象对应的样本预测特征,得到该样本交通对象对应的隐随机变量单模态概率分布,进而通过规范化流映射算法,将样本交通对象对应的隐随机变量单模态概率分布,映射为该样本交通对象对应的隐随机变量多模态概率分布。For each sample traffic object, the feature extraction layer of the initial trajectory prediction model and the sample prediction feature corresponding to the sample traffic object are used to obtain the unimodal probability distribution of the latent random variable corresponding to the sample traffic object, and then through the normalized flow mapping algorithm, The hidden random variable unimodal probability distribution corresponding to the sample traffic object is mapped to the hidden random variable multimodal probability distribution corresponding to the sample traffic object.

针对每一样本交通对象,将该样本交通对象对应的样本预测特征以及该样本交通对象对应的隐随机变量多模态概率分布,输入初始轨迹预测模型的特征回归层,以通过初始轨迹预测模型的特征回归层对该样本交通对象对应的样本预测特征以及该样本交通对象对应的隐随机变量多模态概率分布进行融合,得到该样本交通对象对应的多模态预测轨迹。For each sample traffic object, the sample prediction feature corresponding to the sample traffic object and the multimodal probability distribution of the latent random variable corresponding to the sample traffic object are input into the feature regression layer of the initial trajectory prediction model to pass the initial trajectory prediction model. The feature regression layer fuses the sample prediction feature corresponding to the sample traffic object and the multimodal probability distribution of the latent random variable corresponding to the sample traffic object to obtain the multimodal prediction trajectory corresponding to the sample traffic object.

后续的,为了保证所构建的目标轨迹预测模型的预测结果的准确性,可以利用该样本交通对象对应的多模态预测轨迹以及该样本交通对象对应的样本未来轨迹,构建最大化似然函数的变分下界,通过所构建的最大化似然函数的变分下界,调整初始轨迹预测模型的模型参数,进而,得到最终的目标轨迹预测模型。为了构建最大化似然函数的变分下界,电子设备针对每一样本交通对象,利用预设变分算法对该样本交通对象对应的样本未来轨迹进行处理,得到该样本交通对象对应的隐随机变量变分概率分布,其中,该预设变分算法可以是基于变分贝叶斯原理构建的变分算法。Subsequently, in order to ensure the accuracy of the prediction results of the constructed target trajectory prediction model, the multimodal prediction trajectory corresponding to the sample traffic object and the sample future trajectory corresponding to the sample traffic object can be used to construct a maximum likelihood function. The variational lower bound is used to adjust the model parameters of the initial trajectory prediction model through the constructed variational lower bound of the maximized likelihood function, and then, the final target trajectory prediction model is obtained. In order to construct the variational lower bound of the maximum likelihood function, the electronic device uses a preset variational algorithm to process the sample future trajectory corresponding to the sample traffic object for each sample traffic object, and obtains the hidden random variable corresponding to the sample traffic object Variational probability distribution, wherein the preset variational algorithm may be a variational algorithm constructed based on the principle of variational Bayes.

针对每一样本交通对象,基于KL散度算法,利用该样本交通对象对应的隐随机变量多模态概率分布以及该样本交通对象对应的隐随机变量变分概率分布,确定该样本交通对象对应的隐随机变量KL散度值。并利用该样本交通对象对应的多模态预测轨迹以及该样本交通对象对应的样本未来轨迹,确定该样本交通对象对应的轨迹重构损失值。利用该样本交通对象对应的轨迹重构损失值以及该样本交通对象对应的隐随机变量KL散度值,构建最大化似然函数的变分下界;并计算该最大化似然函数的变分下界对应的函数值,判断所构建的最大化似然函数的变分下界是否达到最大化,即最大化似然函数的变分下界对应的函数值是否达到最大化,若所构建的最大化似然函数的变分下界未达到最大化,则利用预设优化算法,调整初始轨迹预测模型的特征提取层与特征回归层的模型参数,并返回执行S203;若所构建的最大化似然函数的变分下界达到最大化,则确定初始轨迹预测模型收敛,得到包含特征提取层与特征回归层的目标轨迹预测模型。For each sample traffic object, based on the KL divergence algorithm, the multimodal probability distribution of the hidden random variable corresponding to the sample traffic object and the variational probability distribution of the hidden random variable corresponding to the sample traffic object are used to determine the corresponding sample traffic object. Hidden random variable KL divergence value. And using the multi-modal prediction trajectory corresponding to the sample traffic object and the sample future trajectory corresponding to the sample traffic object, the trajectory reconstruction loss value corresponding to the sample traffic object is determined. Using the trajectory reconstruction loss value corresponding to the sample traffic object and the KL divergence value of the hidden random variable corresponding to the sample traffic object, the variational lower bound of the maximal likelihood function is constructed; and the variational lower bound of the maximal likelihood function is calculated. Corresponding function value, determine whether the variational lower bound of the constructed maximal likelihood function is maximized, that is, whether the function value corresponding to the variational lower bound of the maximized likelihood function is maximized, if the constructed maximal likelihood If the variational lower bound of the function is not maximized, use the preset optimization algorithm to adjust the model parameters of the feature extraction layer and the feature regression layer of the initial trajectory prediction model, and return to execute S203; When the lower bound is maximized, the initial trajectory prediction model is determined to converge, and the target trajectory prediction model including the feature extraction layer and the feature regression layer is obtained.

在一种情况中,构建所得的样本交通对象对应多模态预测轨迹所对应的概率分布,利用该概率分布可以构建出该样本交通对象对应的多模态预测轨迹,可以通过如下公式(1)表示:In one case, the probability distribution corresponding to the multimodal predicted trajectory corresponding to the obtained sample traffic object is constructed, and the multimodal predicted trajectory corresponding to the sample traffic object can be constructed by using the probability distribution, and the following formula (1) express:

p(xf|xp,Φ)=∫p(xf|z,xp,Φ)p(z|xp,Φ)dz; (1)p(x f |x p ,Φ)=∫p(x f |z,x p ,Φ)p(z|x p ,Φ)dz; (1)

其中,xp表示该样本交通对象对应的样本历史轨迹,xf表示该样本交通对象对应的多模态预测轨迹;Φ表示该样本交通对象对应的初始样本特征中除该样本交通对象对应的样本历史轨迹外的其他信息;p(xf|xp,Φ)表示样本交通对象对应多模态预测轨迹对应的概率分布;z表示隐随机变量;p(z|xp,Φ)表示该样本交通对象对应的隐随机变量多模态概率分布,为给定样本历史轨迹及初始样本特征中除该样本交通对象对应的样本历史轨迹外的其他信息的情况下隐随机变量z的先验分布,表示了根据样本交通对象的历史轨迹及周围地图即样本静态对象信息以及样本动态对象整体考虑,该样本交通对象未来轨迹的随机性;p(xf|z,xp,Φ)表示该样本交通对象对应的多模态预测轨迹对应的概率分布,即为给定隐随机变量、样本历史轨迹及地图等额外信息的情况下未来轨迹的概率分布,即通过综合考虑所有确定性及随机性信息,输出未来轨迹的预测结果。该建模方式可以通过隐随机变量z表示样本交通对象或交通参与对象的行为随机性,并把这种随机性利用神经网络模型即初始轨迹预测模型或目标轨迹预测模型,映射到原始轨迹数据空间,理论上可以拟合任意的未来轨迹分布,具有很高的通用性和效果。Among them, x p represents the sample historical trajectory corresponding to the sample traffic object, x f represents the multimodal prediction trajectory corresponding to the sample traffic object; Φ represents the sample traffic object corresponding to the sample traffic object except the sample corresponding to the sample traffic object. Other information other than the historical trajectory; p(x f |x p ,Φ) represents the probability distribution corresponding to the multimodal predicted trajectory of the sample traffic object; z represents the hidden random variable; p(z|x p ,Φ) represents the sample The multimodal probability distribution of the hidden random variable corresponding to the traffic object is the prior distribution of the hidden random variable z given the sample historical trajectory and other information in the initial sample characteristics except the sample historical trajectory corresponding to the sample traffic object, It represents the randomness of the future trajectory of the sample traffic object according to the historical trajectory of the sample traffic object and the surrounding map, that is, the sample static object information and the sample dynamic object as a whole; p(x f |z,x p ,Φ) represents the sample traffic object The probability distribution corresponding to the multimodal predicted trajectory corresponding to the object is the probability distribution of the future trajectory given additional information such as hidden random variables, sample historical trajectories and maps, that is, by comprehensively considering all deterministic and random information, Output the predicted results of future trajectories. This modeling method can represent the randomness of the behavior of the sample traffic objects or traffic participants through the hidden random variable z, and map this randomness to the original trajectory data space using the neural network model, namely the initial trajectory prediction model or the target trajectory prediction model. , which can theoretically fit any future trajectory distribution, with high versatility and effect.

相应的,可以通过如下公式表示所构建的最大化似然函数的变分下界;Correspondingly, the variational lower bound of the constructed maximal likelihood function can be expressed by the following formula;

logp(xf|xp,Φ)≥Eq[(xf|z,xp,Φ)]-KL(q(z|xf,xp,Φ)||p(z|xp,Φ));logp(x f |x p ,Φ)≥E q [(x f |z,x p ,Φ)]-KL(q(z|x f ,x p ,Φ)||p(z|x p , Φ));

其中,logp(xf|xp,Φ)表示所构建的最大化似然函数,Eq[(xf|z,xp,Φ)]表示该样本交通对象对应的轨迹重构损失值,KL(q(z|xf,xp,Φ)||p(z|xp,Φ)表示该样本交通对象对应的隐随机变量KL散度值,Eq[(xf|z,xp,Φ)]-KL(q(z|xf,xp,Φ)||p(z|xp,Φ))表示最大化似然函数的变分下界。Among them, logp(x f |x p ,Φ) represents the constructed maximum likelihood function, E q [(x f |z,x p ,Φ)] represents the trajectory reconstruction loss value corresponding to the sample traffic object, KL(q(z|x f ,x p ,Φ)||p(z|x p ,Φ) represents the KL divergence value of the hidden random variable corresponding to the sample traffic object, E q [(x f |z,x p ,Φ)]-KL(q(z|x f ,x p ,Φ)||p(z|x p ,Φ)) represents the variational lower bound of the maximum likelihood function.

本实现方式中,在构建目标轨迹预测模型的过程中,充分考虑了各交通参与对象的历史轨迹、运行属性信息以及周围的静态对象的信息,这些信息呈现高维多源异构的特征,通过初始轨迹预测模型的特征提取层对各特征之间的各方向即特征维度和时间维度的特征提取和融合,实现了对交通参与对象对应的各特征的充分的提取和融合,以用于支撑后续的模型对未来轨迹的预测。In this implementation, in the process of constructing the target trajectory prediction model, the historical trajectory of each traffic participant object, the operation attribute information and the information of the surrounding static objects are fully considered. The feature extraction layer of the initial trajectory prediction model extracts and fuses the features in each direction between the features, that is, the feature dimension and the time dimension, and realizes the sufficient extraction and fusion of the features corresponding to the traffic participants to support the follow-up. The model's prediction of future trajectories.

相应于上述方法实施例,本发明实施例提供了一种运动轨迹的预测装置,如图4所示,所述装置可以包括:Corresponding to the foregoing method embodiments, an embodiment of the present invention provides an apparatus for predicting a motion trajectory. As shown in FIG. 4 , the apparatus may include:

获得模块410,被配置为获得目标对象对应的各交通参与对象的历史轨迹和运动属性信息以及对应的当前地图信息;The obtaining module 410 is configured to obtain the historical trajectory and motion attribute information of each traffic participating object corresponding to the target object and the corresponding current map information;

第一确定模块420,被配置为利用目标轨迹预测模型的特征提取层以及各交通参与对象对应的初始特征,确定各交通参与对象对应的轨迹预测特征,其中,交通参与对象对应的初始特征包括:交通参与对象的历史轨迹和运动属性信息,及其对应的其他交通参与对象和目标对象的历史轨迹和运动属性信息以及所述当前地图信息;The first determination module 420 is configured to utilize the feature extraction layer of the target trajectory prediction model and the initial features corresponding to each traffic participant object to determine the trajectory prediction feature corresponding to each traffic participant object, wherein the corresponding initial features of the traffic participant object include: The historical trajectory and motion attribute information of the traffic participating object, and the historical trajectory and motion attribute information of other corresponding traffic participating objects and target objects, and the current map information;

第二确定模块430,被配置为利用目标轨迹预测模型的特征提取层以及各交通参与对象对应的轨迹预测特征,确定各交通参与对象对应的隐随机变量多模态概率分布,其中,隐随机变量表征各交通参与对象的行为随机性;The second determination module 430 is configured to use the feature extraction layer of the target trajectory prediction model and the trajectory prediction features corresponding to each traffic participant object to determine the multimodal probability distribution of the hidden random variables corresponding to each traffic participant object, wherein the hidden random variable Characterize the randomness of behavior of each traffic participant;

第三确定模块440,被配置为利用所述目标轨迹预测模型的特征回归层、各交通参与对象对应的轨迹预测特征以及各交通参与对象对应的隐随机变量多模态概率分布,确定各交通参与对象对应的多模态预测轨迹。The third determination module 440 is configured to use the feature regression layer of the target trajectory prediction model, the trajectory prediction features corresponding to each traffic participant object, and the multimodal probability distribution of hidden random variables corresponding to each traffic participant object to determine each traffic participant The multimodal predicted trajectory corresponding to the object.

应用本发明实施例,可以利用目标轨迹预测模型中已学习到各交通参与对象的行为随机性的隐随机变量,以及各交通参与对象的历史轨迹和运动属性信息以及其对应的动态对象信息即其对应的其他交通参与对象和目标对象的历史轨迹和运动属性信息,和静态对象信息即当前地图信息,拟合参与对象的未来轨迹的条件概率分布,即各交通参与对象对应的隐随机变量多模态概率分布,进而确定各交通参与对象对应的多模态预测轨迹,以实现对各交通参与对象的多模态预测轨迹的准确确定,且该包括学习到各交通参与对象的行为随机性的隐随机变量的目标轨迹预测模型对该场景具有通用性,不存在算法设计上的瓶颈制约,随着训练得到该目标轨迹预测模型的训练数据的规模扩大,算法对未来轨迹分布建模能力可以不断加强,进而轨迹预测能力也可以随之能不断提升。By applying the embodiments of the present invention, the hidden random variables in the target trajectory prediction model that have learned the randomness of the behavior of each traffic participant, as well as the historical trajectory and motion attribute information of each traffic participant, and the corresponding dynamic object information, namely the The corresponding historical trajectory and motion attribute information of other traffic participating objects and target objects, and static object information, that is, current map information, fit the conditional probability distribution of the future trajectory of the participating objects, that is, the hidden random variables corresponding to each traffic participating object. modal probability distribution, and then determine the multi-modal prediction trajectory corresponding to each traffic participant, so as to realize the accurate determination of the multi-modal prediction trajectory of each traffic participant, and this includes learning the hidden hidden behavior of each traffic participant’s behavior randomness. The target trajectory prediction model of random variables is universal to this scenario, and there is no bottleneck restriction in algorithm design. With the expansion of the training data scale of the target trajectory prediction model obtained through training, the algorithm's ability to model the future trajectory distribution can be continuously strengthened. , and then the trajectory prediction ability can also be continuously improved.

在本发明的另一实施例中,交通参与对象对应的初始特征为基于时间顺序排列的特征,其包括交通参与对象对应的多个历史时刻的特征;In another embodiment of the present invention, the initial features corresponding to the traffic participating objects are features arranged in chronological order, which include features of multiple historical moments corresponding to the traffic participating objects;

所述第一确定模块420,被具体配置为针对每一交通参与对象,利用目标轨迹预测模型的特征提取层,对该交通参与对象对应的初始特征循环多次执行如下步骤A-C,确定出该交通参与对象对应的中间预测特征;The first determining module 420 is specifically configured to, for each traffic participant object, use the feature extraction layer of the target trajectory prediction model to perform the following steps A-C repeatedly for the initial feature corresponding to the traffic participant object to determine the traffic participant object. The intermediate prediction features corresponding to the participating objects;

针对每一交通参与对象,基于图神经网络将该交通参与对象对应的中间预测特征中各静态对象对应的中间预测特征进行融合,确定出该交通参与对象对应的轨迹预测特征,其中,所述静态对象包括所述当前地图信息中的各静态对象;For each traffic participant object, the intermediate prediction features corresponding to each static object in the intermediate prediction features corresponding to the traffic participant object are fused based on the graph neural network, and the trajectory prediction feature corresponding to the traffic participant object is determined, wherein the static The object includes each static object in the current map information;

步骤A:从特征维度对该交通参与对象对应的待处理特征进行非线性映射,得到该交通参与对象对应的映射特征,其中,所述待处理特征为该交通参与对象对应的初始特征或前一次迭代生成的该交通参与对象对应的中间预测特征;Step A: Perform nonlinear mapping on the to-be-processed feature corresponding to the traffic participant object from the feature dimension to obtain the map feature corresponding to the traffic participant object, wherein the to-be-processed feature is the initial feature or the previous time corresponding to the traffic participant object The iteratively generated intermediate prediction feature corresponding to the traffic participant object;

步骤B:从时间维度对所述映射特征进行特征聚合操作,得到该交通参与对象对应的聚合特征;Step B: performing a feature aggregation operation on the mapping feature from the time dimension to obtain the aggregation feature corresponding to the traffic participant object;

步骤C:将所述聚合特征与所述待处理特征中各历史时刻的特征进行融合。Step C: Fusion of the aggregated feature and the feature of each historical moment in the feature to be processed.

在本发明的另一实施例中,所述第二确定模块430,被具体配置为针对每一交通参与对象,利用目标轨迹预测模型的特征提取层以及该交通参与对象对应的轨迹预测特征,确定该交通参与对象对应的隐随机变量单模态概率分布;In another embodiment of the present invention, the second determining module 430 is specifically configured to, for each traffic participant object, use the feature extraction layer of the target trajectory prediction model and the trajectory prediction feature corresponding to the traffic participant object to determine The unimodal probability distribution of the hidden random variable corresponding to the traffic participant object;

针对每一交通参与对象,利用规范化流映射算法以及该交通参与对象对应的隐随机变量单模态概率分布,得到该交通参与对象对应的隐随机变量多模态概率分布。For each traffic participant, using the normalized flow mapping algorithm and the unimodal probability distribution of the hidden random variable corresponding to the traffic participant, the multimodal probability distribution of the hidden random variable corresponding to the traffic participant is obtained.

在本发明的另一实施例中,所述装置还包括:In another embodiment of the present invention, the device further includes:

训练模块(图中未示出),被配置为在所述利用目标轨迹预测模型的特征提取层以及各交通参与对象对应的初始特征,确定各交通参与对象对应的轨迹预测特征之前,训练得到目标轨迹预测模型,其中,所述训练模块,被具体配置为The training module (not shown in the figure) is configured to obtain the target by training before determining the trajectory prediction feature corresponding to each traffic participant object using the feature extraction layer of the target trajectory prediction model and the initial features corresponding to each traffic participant object. A trajectory prediction model, wherein the training module is specifically configured as

获得初始轨迹预测模型;Obtain the initial trajectory prediction model;

获得各样本交通对象对应的样本训练信息以及各样本交通对象对应的样本未来轨迹,其中,样本交通对象对应的样本训练信息包括:该样本交通对象的样本历史轨迹和样本运动属性信息、其对应的样本动态对象的样本历史轨迹、样本运动属性信息和样本静态对象信息;Obtain the sample training information corresponding to each sample traffic object and the sample future trajectory corresponding to each sample traffic object, wherein the sample training information corresponding to the sample traffic object includes: the sample historical trajectory and sample motion attribute information of the sample traffic object, its corresponding Sample historical trajectory, sample motion attribute information and sample static object information of sample dynamic objects;

针对每一样本交通对象,利用所述初始轨迹预测模型的特征提取层以及该样本交通对象对应的初始样本特征,确定该样本交通对象对应的样本预测特征,其中,该样本交通对象对应的初始样本特征包括:样本交通对象样本历史轨迹和样本运动属性信息,其对应的样本动态对象的样本历史轨迹、样本运动属性信息和样本静态对象信息;For each sample traffic object, use the feature extraction layer of the initial trajectory prediction model and the initial sample feature corresponding to the sample traffic object to determine the sample prediction feature corresponding to the sample traffic object, wherein the initial sample corresponding to the sample traffic object The features include: sample historical trajectories and sample motion attribute information of sample traffic objects, sample historical trajectories, sample motion attribute information and sample static object information of corresponding sample dynamic objects;

针对每一样本交通对象,利用所述初始轨迹预测模型的特征提取层以及该样本交通对象对应的样本预测特征,确定该样本交通对象对应的隐随机变量多模态概率分布;For each sample traffic object, use the feature extraction layer of the initial trajectory prediction model and the sample prediction feature corresponding to the sample traffic object to determine the multimodal probability distribution of the latent random variable corresponding to the sample traffic object;

针对每一样本交通对象,利用所述初始轨迹预测模型的特征回归层、该样本交通对象对应的样本预测特征以及该样本交通对象对应的隐随机变量多模态概率分布,确定该样本交通对象对应的多模态预测轨迹;For each sample traffic object, use the feature regression layer of the initial trajectory prediction model, the sample prediction feature corresponding to the sample traffic object, and the multimodal probability distribution of the latent random variables corresponding to the sample traffic object to determine the corresponding sample traffic object. The multimodal prediction trajectory of ;

针对每一样本交通对象,利用预设变分算法对该样本交通对象对应的样本未来轨迹进行变分处理,得到该样本交通对象对应的变分分布概率;For each sample traffic object, use a preset variational algorithm to perform variational processing on the sample future trajectory corresponding to the sample traffic object, and obtain the variational distribution probability corresponding to the sample traffic object;

针对每一样本交通对象,利用该样本交通对象对应的隐随机变量多模态概率分布以及该样本交通对象对应的隐随机变量变分概率分布,确定该样本交通对象对应的隐随机变量KL散度值;For each sample traffic object, use the multimodal probability distribution of the hidden random variable corresponding to the sample traffic object and the variational probability distribution of the hidden random variable corresponding to the sample traffic object to determine the KL divergence of the hidden random variable corresponding to the sample traffic object value;

针对每一样本交通对象,利用该样本交通对象对应的多模态预测轨迹、隐随机变量变分概率分布以及该样本交通对象对应的样本未来轨迹,确定该样本交通对象对应的轨迹重构损失值;For each sample traffic object, use the multimodal predicted trajectory corresponding to the sample traffic object, the variational probability distribution of latent random variables and the sample future trajectory corresponding to the sample traffic object to determine the trajectory reconstruction loss value corresponding to the sample traffic object ;

针对每一样本交通对象,利用该样本交通对象对应的隐随机变量KL散度值、该样本交通对象对应的轨迹重构损失值,构建最大化似然函数的变分下界;判断所构建的最大化似然函数的变分下界是否达到最大化;For each sample traffic object, the KL divergence value of the hidden random variable corresponding to the sample traffic object and the trajectory reconstruction loss value corresponding to the sample traffic object are used to construct the variational lower bound of the maximum likelihood function; Whether the variational lower bound of the likelihood function is maximized;

若所构建的最大化似然函数的变分下界未达到最大化,则调整所述初始轨迹预测模型的特征提取层与特征回归层的模型参数,并返回所述针对每一样本交通对象,利用所述初始轨迹预测模型的特征提取层以及该样本交通对象对应的初始样本特征,确定该样本交通对象对应的样本预测特征的步骤;If the variation lower bound of the constructed maximum likelihood function does not reach the maximum, adjust the model parameters of the feature extraction layer and feature regression layer of the initial trajectory prediction model, and return the traffic object for each sample, using The feature extraction layer of the initial trajectory prediction model and the initial sample feature corresponding to the sample traffic object, and the step of determining the sample prediction feature corresponding to the sample traffic object;

若所构建的最大化似然函数的变分下界达到最大化,确定所述初始轨迹预测模型收敛,得到包含特征提取层与特征回归层的所述目标轨迹预测模型。If the constructed variational lower bound of the maximized likelihood function is maximized, it is determined that the initial trajectory prediction model converges, and the target trajectory prediction model including the feature extraction layer and the feature regression layer is obtained.

在本发明的另一实施例中,所述第二确定模块430,被具体配置为针对每一交通参与对象,对交通参与对象对应的隐随机变量多模态概率分布进行采样,得到该交通参与对象对应的多个隐随机变量样本;In another embodiment of the present invention, the second determining module 430 is specifically configured to, for each traffic participant object, sample the multimodal probability distribution of the hidden random variable corresponding to the traffic participant object to obtain the traffic participant object. Multiple samples of hidden random variables corresponding to the object;

利用所述目标轨迹预测模型的特征回归层、各交通参与对象对应的轨迹预测特征以及各交通参与对象对应的多个隐随机变量样本,确定各交通参与对象对应的多模态预测轨迹。Using the feature regression layer of the target trajectory prediction model, the trajectory prediction features corresponding to each traffic participant object, and a plurality of latent random variable samples corresponding to each traffic participant object, the multimodal prediction trajectory corresponding to each traffic participant object is determined.

上述系统、装置实施例与系统实施例相对应,与该方法实施例具有同样的技术效果,具体说明参见方法实施例。装置实施例是基于方法实施例得到的,具体的说明可以参见方法实施例部分,此处不再赘述。本领域普通技术人员可以理解:附图只是一个实施例的示意图,附图中的模块或流程并不一定是实施本发明所必须的。The foregoing system and device embodiments correspond to the system embodiments, and have the same technical effects as the method embodiments. For specific descriptions, refer to the method embodiments. The apparatus embodiment is obtained based on the method embodiment, and the specific description can refer to the method embodiment section, which will not be repeated here. Those of ordinary skill in the art can understand that the accompanying drawing is only a schematic diagram of an embodiment, and the modules or processes in the accompanying drawing are not necessarily necessary to implement the present invention.

本领域普通技术人员可以理解:实施例中的装置中的模块可以按照实施例描述分布于实施例的装置中,也可以进行相应变化位于不同于本实施例的一个或多个装置中。上述实施例的模块可以合并为一个模块,也可以进一步拆分成多个子模块。Those skilled in the art may understand that: the modules in the apparatus in the embodiment may be distributed in the apparatus in the embodiment according to the description of the embodiment, and may also be located in one or more apparatuses different from this embodiment with corresponding changes. The modules in the foregoing embodiments may be combined into one module, or may be further split into multiple sub-modules.

最后应说明的是:以上实施例仅用以说明本发明的技术方案,而非对其限制;尽管参照前述实施例对本发明进行了详细的说明,本领域的普通技术人员应当理解:其依然可以对前述实施例所记载的技术方案进行修改,或者对其中部分技术特征进行等同替换;而这些修改或者替换,并不使相应技术方案的本质脱离本发明实施例技术方案的精神和范围。Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, but not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that it can still be The technical solutions described in the foregoing embodiments are modified, or some technical features thereof are equivalently replaced; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions in the embodiments of the present invention.

Claims (10)

1. A method for predicting a motion trajectory, the method comprising:
obtaining historical track and motion attribute information of each traffic participating object corresponding to the target object and corresponding current map information;
determining the track prediction characteristics corresponding to the traffic participation objects by utilizing the characteristic extraction layer of the target track prediction model and the initial characteristics corresponding to the traffic participation objects, wherein the initial characteristics corresponding to the traffic participation objects comprise: historical track and motion attribute information of the traffic participating objects, historical track and motion attribute information of other corresponding traffic participating objects and target objects, and the current map information;
determining hidden random variable multi-mode probability distribution corresponding to each traffic participant by using a feature extraction layer of a target track prediction model and track prediction features corresponding to each traffic participant, wherein the hidden random variables represent behavior randomness of each traffic participant;
and determining the multi-modal predicted track corresponding to each traffic participating object by utilizing the characteristic regression layer of the target track prediction model, the track prediction characteristics corresponding to each traffic participating object and the hidden random variable multi-modal probability distribution corresponding to each traffic participating object.
2. The method of claim 1, wherein the initial features corresponding to the traffic participant are features arranged based on time sequence, and the initial features comprise features of a plurality of historical moments corresponding to the traffic participant;
the step of determining the track prediction characteristics corresponding to the traffic participation objects by using the characteristic extraction layer of the target track prediction model and the initial characteristics corresponding to the traffic participation objects comprises the following steps:
aiming at each traffic participant, circularly executing the following steps A-C for a plurality of times on the initial characteristics corresponding to the traffic participant by using the characteristic extraction layer of the target track prediction model, and determining the intermediate prediction characteristics corresponding to the traffic participant;
for each traffic participating object, fusing intermediate prediction features corresponding to each static object in the intermediate prediction features corresponding to the traffic participating object based on a graph neural network, and determining track prediction features corresponding to the traffic participating object, wherein the static objects comprise each static object in the current map information;
step A: carrying out nonlinear mapping on the to-be-processed features corresponding to the traffic participating objects from feature dimensions to obtain mapping features corresponding to the traffic participating objects, wherein the to-be-processed features are initial features corresponding to the traffic participating objects or intermediate prediction features corresponding to the traffic participating objects generated in previous iteration;
and B: performing feature aggregation operation on the mapping features from a time dimension to obtain aggregation features corresponding to the traffic participation objects;
and C: and fusing the aggregation characteristics with the characteristics of the to-be-processed characteristics at each historical moment.
3. The method according to claim 1, wherein the step of determining the hidden random variable multi-modal probability distribution corresponding to each traffic participant by using the feature extraction layer of the target trajectory prediction model and the trajectory prediction features corresponding to each traffic participant comprises:
aiming at each traffic participant, determining hidden random variable monomodal probability distribution corresponding to the traffic participant by using a feature extraction layer of a target track prediction model and track prediction features corresponding to the traffic participant;
aiming at each traffic participant, a normalized stream mapping algorithm and hidden random variable monomodal probability distribution corresponding to the traffic participant are utilized to obtain hidden random variable multimodal probability distribution corresponding to the traffic participant.
4. The method according to any one of claims 1 to 3, wherein before the step of determining the trajectory prediction feature corresponding to each traffic participant by using the feature extraction layer of the target trajectory prediction model and the initial feature corresponding to each traffic participant, the method further comprises:
training a process of obtaining a target trajectory prediction model, wherein the process comprises:
obtaining an initial trajectory prediction model;
obtaining sample training information corresponding to each sample traffic object and a sample future track corresponding to each sample traffic object, wherein the sample training information corresponding to the sample traffic object comprises: sample historical track and sample motion attribute information of the sample traffic object, and sample historical track, sample motion attribute information and sample static object information of a corresponding sample dynamic object;
for each sample traffic object, determining a sample prediction feature corresponding to the sample traffic object by using a feature extraction layer of the initial trajectory prediction model and an initial sample feature corresponding to the sample traffic object, wherein the initial sample feature corresponding to the sample traffic object comprises: sample traffic object sample historical track and sample motion attribute information, and corresponding sample historical track, sample motion attribute information and sample static object information of a sample dynamic object;
aiming at each sample traffic object, determining hidden random variable multi-modal probability distribution corresponding to the sample traffic object by utilizing a feature extraction layer of the initial track prediction model and sample prediction features corresponding to the sample traffic object;
aiming at each sample traffic object, determining a multi-modal predicted track corresponding to the sample traffic object by utilizing a characteristic regression layer of the initial track prediction model, sample predicted characteristics corresponding to the sample traffic object and hidden random variable multi-modal probability distribution corresponding to the sample traffic object;
processing a sample future track corresponding to each sample traffic object by using a preset variation algorithm to obtain hidden random variable variation probability distribution corresponding to the sample traffic object;
aiming at each sample traffic object, determining a KL divergence value of a hidden random variable corresponding to the sample traffic object by utilizing the multi-modal probability distribution of the hidden random variable corresponding to the sample traffic object and the variation probability distribution of the hidden random variable corresponding to the sample traffic object;
for each sample traffic object, determining a track reconstruction loss value corresponding to the sample traffic object by utilizing a multi-modal predicted track corresponding to the sample traffic object and a sample future track corresponding to the sample traffic object;
aiming at each sample traffic object, constructing a variation lower bound of a maximum likelihood function by utilizing a KL divergence value of an implicit random variable corresponding to the sample traffic object and a track reconstruction loss value corresponding to the sample traffic object; judging whether the variation lower bound of the constructed maximum likelihood function reaches the maximum;
if the variation lower bound of the constructed maximum likelihood function does not reach the maximum, adjusting model parameters of a feature extraction layer and a feature regression layer of the initial track prediction model, returning to the step of determining sample prediction features corresponding to the sample traffic objects by using the feature extraction layer of the initial track prediction model and the initial sample features corresponding to the sample traffic objects;
and if the variation lower bound of the constructed maximum likelihood function reaches the maximum, determining the convergence of the initial trajectory prediction model to obtain the target trajectory prediction model comprising a feature extraction layer and a feature regression layer.
5. The method according to any one of claims 1 to 4, wherein the step of determining the multi-modal predicted trajectory corresponding to each traffic participant by using the feature regression layer of the target trajectory prediction model, the trajectory prediction feature corresponding to each traffic participant and the hidden random variable multi-modal probability distribution corresponding to each traffic participant comprises:
sampling hidden random variable multi-mode probability distribution corresponding to the traffic participation object aiming at each traffic participation object to obtain a plurality of hidden random variable samples corresponding to the traffic participation object;
and determining a multi-modal predicted track corresponding to each traffic participant by using the characteristic regression layer of the target track prediction model, the track prediction characteristics corresponding to each traffic participant and a plurality of hidden random variable samples corresponding to each traffic participant.
6. An apparatus for predicting a motion trajectory, the apparatus comprising:
the obtaining module is configured to obtain historical track and motion attribute information of each traffic participation object corresponding to the target object and corresponding current map information;
the first determining module is configured to determine a track prediction feature corresponding to each traffic participant by using the feature extraction layer of the target track prediction model and the initial feature corresponding to each traffic participant, wherein the initial feature corresponding to the traffic participant comprises: historical track and motion attribute information of the traffic participating objects, historical track and motion attribute information of other corresponding traffic participating objects and target objects, and the current map information;
the second determining module is configured to determine multi-modal probability distribution of hidden random variables corresponding to the traffic participating objects by using the feature extraction layer of the target track prediction model and the track prediction features corresponding to the traffic participating objects, wherein the hidden random variables represent behavior randomness of the traffic participating objects;
and the third determining module is configured to determine a multi-modal predicted track corresponding to each traffic participating object by using the characteristic regression layer of the target track prediction model, the track prediction characteristics corresponding to each traffic participating object and the hidden random variable multi-modal probability distribution corresponding to each traffic participating object.
7. The apparatus of claim 6, wherein the initial feature corresponding to the traffic participant object is a feature arranged based on a time sequence, and comprises a plurality of historical time features corresponding to the traffic participant object;
the first determining module is specifically configured to, for each traffic participant, circularly perform the following steps a-C on the initial feature corresponding to the traffic participant for multiple times by using the feature extraction layer of the target trajectory prediction model, and determine an intermediate prediction feature corresponding to the traffic participant;
for each traffic participating object, fusing the intermediate prediction features corresponding to each static object in the intermediate prediction features corresponding to the traffic participating object based on a graph neural network, and determining the track prediction features corresponding to the traffic participating object, wherein the static objects comprise each static object in the current map information;
step A: carrying out nonlinear mapping on the to-be-processed features corresponding to the traffic participation objects from feature dimensions to obtain mapping features corresponding to the traffic participation objects, wherein the to-be-processed features are initial features corresponding to the traffic participation objects or intermediate prediction features corresponding to the traffic participation objects generated in the previous iteration;
and B, step B: performing feature aggregation operation on the mapping features from the time dimension to obtain aggregation features corresponding to the traffic participation objects;
and C: and fusing the aggregation characteristics with the characteristics of the to-be-processed characteristics at each historical moment.
8. The apparatus of claim 6, wherein the second determining module is specifically configured to determine, for each traffic participant, a hidden random variable monomodal probability distribution corresponding to the traffic participant by using a feature extraction layer of a target trajectory prediction model and a trajectory prediction feature corresponding to the traffic participant;
aiming at each traffic participant, a normalized stream mapping algorithm and hidden random variable monomodal probability distribution corresponding to the traffic participant are utilized to obtain hidden random variable multimodal probability distribution corresponding to the traffic participant.
9. The apparatus of claim 6, wherein the apparatus further comprises:
a training module configured to train to obtain a target trajectory prediction model before determining a trajectory prediction feature corresponding to each traffic participant by using the feature extraction layer of the target trajectory prediction model and the initial feature corresponding to each traffic participant, wherein the training module is specifically configured to train to obtain the target trajectory prediction model
Obtaining an initial trajectory prediction model;
obtaining sample training information corresponding to each sample traffic object and a sample future track corresponding to each sample traffic object, wherein the sample training information corresponding to the sample traffic object comprises: the sample historical track and the sample motion attribute information of the sample traffic object, and the sample historical track, the sample motion attribute information and the sample static object information of the corresponding sample dynamic object;
for each sample traffic object, determining a sample prediction feature corresponding to the sample traffic object by using a feature extraction layer of the initial trajectory prediction model and an initial sample feature corresponding to the sample traffic object, wherein the initial sample feature corresponding to the sample traffic object comprises: sample traffic object sample historical track and sample motion attribute information, and corresponding sample historical track, sample motion attribute information and sample static object information of a sample dynamic object;
aiming at each sample traffic object, determining hidden random variable multi-modal probability distribution corresponding to the sample traffic object by utilizing a feature extraction layer of the initial track prediction model and sample prediction features corresponding to the sample traffic object;
aiming at each sample traffic object, determining a multi-modal predicted track corresponding to the sample traffic object by utilizing a characteristic regression layer of the initial track prediction model, sample predicted characteristics corresponding to the sample traffic object and hidden random variable multi-modal probability distribution corresponding to the sample traffic object;
processing a sample future track corresponding to each sample traffic object by using a preset variation algorithm to obtain hidden random variable variation probability distribution corresponding to the sample traffic object;
aiming at each sample traffic object, determining a KL divergence value of a hidden random variable corresponding to the sample traffic object by utilizing the multi-modal probability distribution of the hidden random variable corresponding to the sample traffic object and the variation probability distribution of the hidden random variable corresponding to the sample traffic object;
for each sample traffic object, determining a track reconstruction loss value corresponding to the sample traffic object by using a multi-modal predicted track, hidden random variable variation probability distribution and a sample future track corresponding to the sample traffic object;
aiming at each sample traffic object, constructing a variation lower bound of a maximized likelihood function by utilizing a KL divergence value of a hidden random variable corresponding to the sample traffic object and a track reconstruction loss value corresponding to the sample traffic object; judging whether the variation lower bound of the constructed maximum likelihood function reaches the maximum;
if the variation lower bound of the constructed maximum likelihood function does not reach the maximum, adjusting model parameters of a feature extraction layer and a feature regression layer of the initial trajectory prediction model, returning to the step of determining sample prediction features corresponding to the sample traffic objects by using the feature extraction layer of the initial trajectory prediction model and the initial sample features corresponding to the sample traffic objects;
and if the variation lower bound of the constructed maximum likelihood function reaches the maximum, determining the convergence of the initial trajectory prediction model to obtain the target trajectory prediction model comprising a feature extraction layer and a feature regression layer.
10. The apparatus according to any one of claims 6 to 9, wherein the second determining module is specifically configured to, for each traffic participant, sample a hidden random variable multi-modal probability distribution corresponding to the traffic participant to obtain a plurality of hidden random variable samples corresponding to the traffic participant;
and determining a multi-modal predicted track corresponding to each traffic participant by using the characteristic regression layer of the target track prediction model, the track prediction characteristics corresponding to each traffic participant and a plurality of hidden random variable samples corresponding to each traffic participant.
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