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CN114662967A - Unmanned driving collision risk assessment method and system based on dynamic Bayesian network - Google Patents

Unmanned driving collision risk assessment method and system based on dynamic Bayesian network Download PDF

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CN114662967A
CN114662967A CN202210363894.5A CN202210363894A CN114662967A CN 114662967 A CN114662967 A CN 114662967A CN 202210363894 A CN202210363894 A CN 202210363894A CN 114662967 A CN114662967 A CN 114662967A
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黄文成
张寅�
于耀程
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Southwest Jiaotong University
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Abstract

The invention discloses an unmanned driving collision risk assessment method and system based on a dynamic Bayesian network, which are characterized in that a network collision risk assessment model is constructed to obtain a network collision risk by acquiring the current road traffic condition and preprocessing the current road traffic condition to obtain a division performance index and the divided road traffic condition; constructing a vehicle collision risk estimation model, and evaluating vehicle collision risk probability according to the network collision risk; constructing a dynamic Bayesian network model, and carrying out unmanned collision risk assessment on the vehicle collision risk probability in multiple periods; according to the method, the collision risk of the automatic driving vehicle is evaluated by constructing the interactive perception-dynamic Bayesian model and combining the influence of the network-level risk, early warning can be carried out according to the evaluation risk, certain reference is provided for the development of the automatic driving in the aspect of safety, and the problem that the influence of the vehicle collision risk in the process of vehicle driving is not considered, namely the safety is not high enough, is solved.

Description

Unmanned driving collision risk assessment method and system based on dynamic Bayesian network
Technical Field
The invention relates to the technical field of driving collision risk assessment, in particular to an unmanned driving collision risk assessment method and system based on a dynamic Bayesian network.
Background
The automatic driving technology is continuously developed, the safety of the automatic driving technology is concerned by the public, the requirement on the risk assessment technology under the automatic driving environment is higher and higher, the considered aspect of the automatic driving technology is more complex and comprehensive, the collision accident which is one of the road driving accident forms is frequently generated, and the automatic driving technology is also one of the risk conditions which are firstly considered as the road driving safety. Therefore, in order to ensure that the evaluation of the automatic driving collision risk is safer, a driver or a vehicle is prompted to take measures, the possibility of collision threat is reduced, and the factors influencing the probability of the vehicle collision accident are various, at present, a system influencing the probability of the vehicle collision accident is mainly considered to be established from an automatic driving vehicle layer, a kinematics layer and a sensor measurement layer, however, the difference of road traffic environments can also influence the occurrence of the driving collision accident, and the probability of the road environments with different traffic flow attributes of the collision accident is different.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides an unmanned driving collision risk assessment method based on interactive perception-dynamic Bayes, which is used for assessing the collision risk of an automatic driving vehicle by constructing an interactive perception-dynamic Bayes model and combining the influence of network-level risks, can perform early warning according to the assessed risk and provides a certain reference for promoting the development of automatic driving in the aspect of safety.
In order to achieve the purpose of the invention, the invention adopts the technical scheme that:
in one aspect, a method for assessing risk of unmanned collision based on a dynamic Bayesian network comprises the following steps:
s1, collecting the current road traffic condition, and preprocessing the current road traffic condition to obtain a division performance index and the divided road traffic condition;
s2, constructing a network collision risk evaluation model, and obtaining a network collision risk according to the division performance indexes;
s3, constructing a vehicle collision risk estimation model, and evaluating the vehicle collision risk probability according to the network collision risk;
and S4, based on the vehicle collision risk probability of not less than one time period, evaluating through a dynamic Bayesian network model.
Preferably, step S1 is specifically:
collecting current road traffic conditions, classifying the current road traffic conditions to obtain road traffic conditions in a dangerous state, road traffic conditions in a safe state and division performance indexes; wherein, the dividing performance indexes are respectively expressed as:
Figure BDA0003585065770000021
Figure BDA0003585065770000022
Figure BDA0003585065770000023
wherein W is the overall accuracy of classification, D is the accuracy of judgment as a dangerous state, S is the accuracy of judgment as a safe state, and TpzTo correctly determine the number of dangerous states, TaqTo correctly determine the number of safe states, FpzFor the number of misjudged dangerous states, FaqThe number of false positives as a safety state.
Preferably, step S2 is specifically:
constructing a network collision risk evaluation model, evaluating the probability of the road section having the traffic condition which is easy to collide in the current road traffic condition according to the division performance indexes, and obtaining the network collision risk, wherein a collision risk calculation formula in the network collision risk evaluation model is represented as follows:
Figure BDA0003585065770000031
Figure BDA0003585065770000032
the TC is a road traffic condition, the values of 0 and 1 correspond to two cases, namely a safe state and a dangerous state, the NIR is a network-level risk condition, the P (NLR ═ danger ") is a probability of the network-level risk condition in the dangerous state, and the P (NLR ═ safe") is a probability of the network-level risk condition in the safe state.
Preferably, step S3 is specifically:
constructing a vehicle collision risk estimation model, wherein the model is expressed as follows:
Figure BDA0003585065770000033
wherein,
Figure BDA0003585065770000034
the collision risk probability of the vehicle under the dangerous condition of the current vehicle at the moment t, N is the total number of the vehicles around the current vehicle, VLRtFor the current vehicle condition at time t,
Figure BDA0003585065770000035
is the total condition of the surrounding vehicles at time (t-1),
Figure BDA0003585065770000036
the total situation of the risk of vehicle movement around the time (t-1),
Figure BDA0003585065770000037
for the network collision risk situation at time t,
Figure BDA0003585065770000038
and
Figure BDA0003585065770000039
parameters for vehicle movement risk, surrounding vehicle risk and network collision risk, respectivelyCounting;
and calculating the probability of the vehicle collision risk by using the network collision risk and combining the vehicle collision risk estimation model.
Preferably, the network collision risk parameter calculation formula in step S3 is expressed as:
Figure BDA0003585065770000041
wherein W is the overall accuracy of classification, D is the accuracy of judgment as a dangerous state, S is the accuracy of judgment as a safe state,
Figure BDA0003585065770000042
a vehicle having no threat to the current vehicle in the surroundings is 0,
Figure BDA0003585065770000043
if the number is more than 0, the vehicles which form threats to the current vehicle exist in the periphery,
Figure BDA0003585065770000044
the network collision risk under the safety condition at the moment t;
Figure BDA0003585065770000045
and the network collision risk of the current vehicle in the dangerous condition at the moment t.
In another aspect, a system for unmanned collision risk assessment based on a dynamic bayesian network comprises:
the road traffic preprocessing module is used for acquiring the current road traffic condition and preprocessing the current road traffic condition to obtain a dividing performance index and the divided road traffic condition;
the network collision risk evaluation module is used for constructing a network collision risk evaluation model and obtaining a network collision risk according to the division performance indexes;
the vehicle collision risk estimation module is used for constructing a vehicle collision risk estimation model and evaluating the vehicle collision risk probability according to the network collision risk;
and the dynamic Bayesian network module is used for constructing a dynamic Bayesian network model and evaluating the vehicle collision risk probability in multiple periods.
The invention has the following beneficial effects:
collecting the current road traffic condition, preprocessing the current road traffic condition to obtain a division performance index and the divided road traffic condition, constructing a network collision risk evaluation model, and obtaining a network collision risk by using the division performance index; constructing a vehicle collision risk estimation model, and evaluating vehicle collision risk probability according to the network collision risk; constructing a dynamic Bayesian network model, and carrying out unmanned collision risk assessment on the vehicle collision risk probability in multiple periods; the collision risk of the automatic driving vehicle can be evaluated by constructing an interactive perception-dynamic Bayesian model and combining the influence of network-level risk, early warning can be carried out according to the evaluation risk, certain reference is provided for promoting the development of the automatic driving in the aspect of safety, and the problem that the influence of the vehicle collision risk is not considered in the estimation of the vehicle collision risk in the vehicle driving process, namely the safety is not high enough is solved.
Drawings
FIG. 1 is a flowchart illustrating steps of a method for evaluating a collision risk of an unmanned vehicle based on a dynamic Bayesian network according to the present invention;
fig. 2 is a schematic structural diagram of a dynamic bayesian network model according to an embodiment of the present invention.
Detailed Description
The following description of the embodiments of the present invention is provided to facilitate the understanding of the present invention by those skilled in the art, but it should be understood that the present invention is not limited to the scope of the embodiments, and it will be apparent to those skilled in the art that various changes may be made without departing from the spirit and scope of the invention as defined and defined in the appended claims, and all matters produced by the invention using the inventive concept are protected.
In one aspect, as shown in fig. 1, an embodiment of the present invention provides a method for assessing risk of unmanned collision based on a dynamic bayesian network, including the following steps:
s1, collecting the current road traffic condition, and preprocessing the current road traffic condition to obtain a division performance index and the divided road traffic condition;
in the embodiment of the invention, the road traffic condition can be divided into two conditions of easy collision and safety, which are obtained by a machine learning classifier, the common method generally comprises k neighbor, neural network, support vector machine and the like, a proper road traffic condition evaluation index is selected, certain traffic data is used to divide the traffic condition into two types through a machine learning classification process, namely: road traffic conditions in dangerous conditions and road traffic conditions in safe conditions.
Preferably, step S1 is specifically:
collecting current road traffic conditions, classifying the current road traffic conditions to obtain road traffic conditions in a dangerous state, road traffic conditions in a safe state and division performance indexes; wherein, the dividing performance indexes are respectively expressed as:
Figure BDA0003585065770000061
Figure BDA0003585065770000062
Figure BDA0003585065770000063
wherein W is the overall accuracy of classification, D is the accuracy of judgment as a dangerous state, S is the accuracy of judgment as a safe state, and T ispzTo correctly determine the number of dangerous states, TaqTo correctly judge the number of safe states, FpzFor the number of misjudged dangerous states, FaqThe number of false positives as a safety state.
S2, constructing a network collision risk evaluation model, and obtaining a network collision risk according to the division performance indexes;
preferably, step S2 is specifically:
constructing a network collision risk evaluation model, and evaluating the probability of the traffic condition which is easy to collide in the road section in the current road traffic condition according to the division performance indexes to obtain the network collision risk, wherein a collision risk calculation formula in the network collision risk evaluation model is represented as follows:
Figure BDA0003585065770000071
Figure BDA0003585065770000072
the TC is a road traffic condition, the values of 0 and 1 correspond to two cases, namely a safe state and a dangerous state, the NIR is a network-level risk condition, the P (NLR ═ danger ") is a probability of the network-level risk condition in the dangerous state, and the P (NLR ═ safe") is a probability of the network-level risk condition in the safe state.
In the embodiment of the invention, when the TC is 1, it indicates that the road traffic condition classification result is easy to collide, the probability of collision at the network level is the median of the overall accuracy and the accuracy of judging danger, and when the TC is 0, it indicates that the current road traffic condition classification result is safe;
the network-level collision risk can be evaluated and the collision risk condition of the road traffic can be mastered by classifying the road traffic network into two categories of safety and danger and representing the collision risk of the road network by using the division performance indexes, namely the collision risk is known from the road network layer in the driving process and is used as a part of collision risk estimation, so that the comprehensive and comprehensive driving collision risk evaluation is facilitated.
S3, constructing a vehicle collision risk estimation model, and evaluating the vehicle collision risk probability according to the network collision risk;
preferably, step S3 is specifically:
constructing a vehicle collision risk estimation model, wherein the model is expressed as:
Figure BDA0003585065770000073
wherein,
Figure BDA0003585065770000074
the probability of the collision risk of the vehicle under the dangerous condition of the current vehicle at the moment t, namely the probability of the current vehicle under the dangerous condition at the moment t under the condition of grasping the total condition of the vehicles around the moment (t-1), the total condition of the motion risks of the vehicles around the moment (t-1) and the network collision risk condition at the moment t, wherein N is the total number of the vehicles around the current vehicle, VLRtFor the current vehicle condition at time t,
Figure BDA0003585065770000081
is the total condition of the surrounding vehicles at time (t-1),
Figure BDA0003585065770000082
the total situation of the risk of vehicle movement around the time (t-1),
Figure BDA0003585065770000083
for the network collision risk situation at time t,
Figure BDA0003585065770000084
and
Figure BDA0003585065770000085
parameters of vehicle motion risk, surrounding vehicle risk and network collision risk respectively;
and calculating the probability of the vehicle collision risk by using the network collision risk and combining the vehicle collision risk estimation model.
Preferably, the network collision risk parameter in step S3
Figure BDA0003585065770000086
The calculation formula is shown as:
Figure BDA0003585065770000087
wherein W is the overall accuracy of classification, D is the accuracy of judgment as a dangerous state, S is the accuracy of judgment as a safe state,
Figure BDA0003585065770000088
a vehicle having no threat to the current vehicle in the surroundings is 0,
Figure BDA0003585065770000089
if the sum is greater than 0, the vehicles which form threats to the current vehicle exist in the periphery,
Figure BDA00035850657700000810
the network collision risk under the safety condition at the moment t;
Figure BDA00035850657700000811
network collision risk of the current vehicle under a dangerous condition at the moment t;
in the embodiments of the present invention, wherein
Figure BDA00035850657700000812
There are four calculation formulas respectively corresponding to four cases that the network collision risk condition is a combination of danger and safety, and whether surrounding vehicles constitute threats,
Figure BDA00035850657700000813
a value of 0 indicates that there is no surrounding vehicle threatening the current vehicle,
Figure BDA00035850657700000814
when the vehicle number is more than 0, the vehicle which threatens the current vehicle exists around the vehicle; risk of vehicle movement
Figure BDA00035850657700000815
The current vehicle senses the speed of the nth vehicle around by using a sensor and the distance between the nth vehicle and the current vehicle to obtain the collision time of the nth vehicle, and the collision time is judgedJudging whether the collision time is less than the critical collision time or not to judge whether the vehicle threatens the current vehicle or not and correspondingly obtaining
Figure BDA0003585065770000091
Taking the value of (A); surrounding vehicle risk
Figure BDA0003585065770000092
The method mainly comprises the steps that according to data interaction between a current vehicle and an nth vehicle around, whether dangerous driving conditions exist in the vehicle around is mastered, and then the value of the vehicle around is obtained; parameters of network collision risk
Figure BDA0003585065770000093
The method is mainly obtained by calculating the division performance index of the road traffic network condition.
In the embodiment of the invention, the vehicle motion risk parameter
Figure BDA0003585065770000094
Is characterized by:
Figure BDA0003585065770000095
wherein TTC is time to collision, TTCCCritical collision time, when the collision time is less than the critical collision time, a dangerous condition, in which case fKTaking 1; when the collision time is not less than the critical collision time, a safe condition is established, in which fKTake 0.
In the embodiment of the invention, the surrounding vehicle risk parameter
Figure BDA0003585065770000096
Expressed as:
Figure BDA0003585065770000097
wherein, when dangerous driving action appears in vehicle around, be dangerous state, promptly:
Figure BDA0003585065770000098
when the dangerous driving behavior does not occur to the surrounding vehicles, the safe state is that:
Figure BDA0003585065770000099
in the embodiment of the invention, through a data interaction and sensor perception mode and combining with the evaluation index of step S2 on the network-level risk, the collision risk situation faced by the current vehicle is comprehensively estimated from three aspects of the surrounding vehicle-level collision risk, the vehicle motion risk and the network-level collision risk, the method for estimating the vehicle collision risk comprehensively considers the road traffic network condition, the surrounding vehicle driving state and the vehicle motion situation, compared with the method for estimating the collision risk only considering a single factor and the like, when the current vehicle perceives the surrounding vehicle-level collision risk and the vehicle motion risk to be only at a lower level but the road traffic condition is worse, the probability result obtained by estimating the collision risk of the current vehicle is at a relatively higher level due to the consideration of the network-level collision risk, so as to remind the driver to keep alert, the driving process is safer.
And S4, based on the vehicle collision risk probability of not less than one time period, evaluating through a dynamic Bayesian network model.
In the embodiment of the invention, traffic condition data of 30 seconds, 1 minute, 3 minutes and 5 minutes are acquired at intervals of time, and the dynamic Bayesian network model is utilized in the whole driving process to dynamically estimate the vehicle collision risk so as to master the collision risk condition in the current driving process of the vehicle and perform corresponding early warning on the collision risk condition;
the collision risk of the vehicle at the next moment can be estimated by using the current vehicle-level collision risk condition, the motion risk condition and the network-level collision risk condition estimated at the next moment. As shown in fig. 2, the collision risk of the current vehicle at the time t is calculated by using the collision risk of the vehicles around the time t-1, the motion risk of the vehicles around the time t-1 and the network-level risk at the time t as inputs, and using the vehicle collision risk estimation model constructed in step S3; the collision risk of the current vehicle at the time t +1 is obtained by taking the collision risk of the vehicle around the time t, the vehicle motion risk around the time t and the network-level risk at the time t +1 as input and calculating through the vehicle collision risk estimation model constructed in the step S3, wherein the vehicle kinematic risks around the time t-1, the time t and the time t +1 are respectively obtained by the sensor measurement and model calculation at the time t-1, the time t and the time t + 1. After a certain time interval is set, data interaction in the aspects of networks, vehicles, sensors and the like is carried out once every fixed time interval, and therefore the dynamic Bayesian network model can estimate the collision risk of the current vehicle.
An unmanned collision risk assessment system based on a dynamic Bayesian network, comprising:
the road traffic preprocessing module is used for acquiring the current road traffic condition and preprocessing the current road traffic condition to obtain a dividing performance index and the divided road traffic condition;
the network collision risk evaluation module is used for constructing a network collision risk evaluation model and obtaining a network collision risk according to the division performance indexes;
the vehicle collision risk estimation module is used for constructing a vehicle collision risk estimation model and evaluating the vehicle collision risk probability according to the network collision risk;
and the dynamic Bayesian network module is used for constructing a dynamic Bayesian network model and evaluating the vehicle collision risk probability in multiple periods.
The unmanned collision risk assessment system based on the dynamic Bayesian network provided by the embodiment of the invention has all the beneficial effects of the unmanned collision risk assessment method based on the dynamic Bayesian network.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The principle and the implementation mode of the invention are explained by applying specific embodiments in the invention, and the description of the embodiments is only used for helping to understand the method and the core idea of the invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present invention.
It will be appreciated by those of ordinary skill in the art that the embodiments described herein are intended to assist the reader in understanding the principles of the invention and are to be construed as being without limitation to such specifically recited embodiments and examples. Those skilled in the art can make various other specific changes and combinations based on the teachings of the present invention without departing from the spirit of the invention, and these changes and combinations are within the scope of the invention.

Claims (6)

1. The unmanned driving collision risk assessment method based on the dynamic Bayesian network is characterized by comprising the following steps of:
s1, collecting the current road traffic condition, and preprocessing the current road traffic condition to obtain a division performance index and the divided road traffic condition;
s2, constructing a network collision risk evaluation model, and obtaining a network collision risk according to the division performance indexes;
s3, constructing a vehicle collision risk estimation model, and evaluating the vehicle collision risk probability according to the network collision risk;
and S4, based on the vehicle collision risk probability of not less than one time period, evaluating through a dynamic Bayesian network model.
2. The unmanned collision risk assessment method based on dynamic Bayesian network as claimed in claim 1, wherein step S1 specifically comprises:
collecting current road traffic conditions, classifying the current road traffic conditions to obtain road traffic conditions in a dangerous state, road traffic conditions in a safe state and division performance indexes; wherein, the dividing performance indexes are respectively expressed as:
Figure FDA0003585065760000011
Figure FDA0003585065760000012
Figure FDA0003585065760000013
wherein W is the overall accuracy of classification, D is the accuracy of judgment as a dangerous state, S is the accuracy of judgment as a safe state, and T ispzTo correctly determine the number of dangerous states, TaqTo correctly judge the number of safe states, FpzFor the number of misjudged dangerous states, FaqThe number of false positives as a safe state.
3. The unmanned collision risk assessment method based on dynamic Bayesian network as claimed in claim 2, wherein step S2 specifically comprises:
constructing a network collision risk evaluation model, and evaluating the probability of the traffic condition which is easy to collide in the road section in the current road traffic condition according to the division performance indexes to obtain the network collision risk, wherein a collision risk calculation formula in the network collision risk evaluation model is represented as follows:
Figure FDA0003585065760000021
Figure FDA0003585065760000022
the TC is a road traffic condition, the values of 0 and 1 correspond to two cases, namely a safe state and a dangerous state, the NIR is a network-level risk condition, the P (NLR ═ danger ") is a probability of the network-level risk condition in the dangerous state, and the P (NLR ═ safe") is a probability of the network-level risk condition in the safe state.
4. The unmanned collision risk assessment method based on dynamic Bayesian network as claimed in claim 1, wherein step S3 specifically comprises:
constructing a vehicle collision risk estimation model, wherein the model is expressed as:
Figure FDA0003585065760000023
wherein,
Figure FDA0003585065760000024
the collision risk probability of the current vehicle in a dangerous condition at the moment t, N is the total number of the current vehicle capable of sensing surrounding vehicles, VLRtFor the current vehicle condition at time t,
Figure FDA0003585065760000031
is the total condition of the surrounding vehicles at time (t-1),
Figure FDA0003585065760000032
the total situation of the risk of vehicle movement around the time (t-1),
Figure FDA0003585065760000033
for the network collision risk situation at time t,
Figure FDA0003585065760000034
and
Figure FDA0003585065760000035
parameters of vehicle motion risk, surrounding vehicle risk and network collision risk respectively;
and calculating the probability of the vehicle collision risk by using the network collision risk and combining the vehicle collision risk estimation model.
5. The unmanned driving collision risk assessment method based on dynamic Bayesian network as claimed in claim 4, wherein the network collision risk parameter calculation formula in step S3 is represented as:
Figure FDA0003585065760000036
wherein W is the overall accuracy of classification, D is the accuracy of judgment as a dangerous state, S is the accuracy of judgment as a safe state,
Figure FDA0003585065760000037
a vehicle having no threat to the current vehicle in the surroundings is 0,
Figure FDA0003585065760000038
if the sum is greater than 0, the vehicles which form threats to the current vehicle exist in the periphery,
Figure FDA0003585065760000039
the network collision risk under the safety condition at the moment t;
Figure FDA00035850657600000310
and the network collision risk of the current vehicle in the dangerous condition at the moment t.
6. An unmanned collision risk assessment system based on a dynamic Bayesian network, comprising:
the road traffic preprocessing module is used for acquiring the current road traffic condition and preprocessing the current road traffic condition to obtain a dividing performance index and the divided road traffic condition;
the network collision risk evaluation module is used for constructing a network collision risk evaluation model and obtaining a network collision risk according to the division performance indexes;
the vehicle collision risk estimation module is used for constructing a vehicle collision risk estimation model and evaluating the vehicle collision risk probability according to the network collision risk;
and the dynamic Bayesian network module is used for constructing a dynamic Bayesian network model and evaluating the vehicle collision risk probability in multiple periods.
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