CN115092182B - Vehicle intention prediction method, system, electronic equipment and medium - Google Patents
Vehicle intention prediction method, system, electronic equipment and medium Download PDFInfo
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W60/00—Drive control systems specially adapted for autonomous road vehicles
- B60W60/001—Planning or execution of driving tasks
- B60W60/0027—Planning or execution of driving tasks using trajectory prediction for other traffic participants
- B60W60/00274—Planning or execution of driving tasks using trajectory prediction for other traffic participants considering possible movement changes
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W2420/00—Indexing codes relating to the type of sensors based on the principle of their operation
- B60W2420/40—Photo, light or radio wave sensitive means, e.g. infrared sensors
- B60W2420/403—Image sensing, e.g. optical camera
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W2420/00—Indexing codes relating to the type of sensors based on the principle of their operation
- B60W2420/40—Photo, light or radio wave sensitive means, e.g. infrared sensors
- B60W2420/408—Radar; Laser, e.g. lidar
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W2520/00—Input parameters relating to overall vehicle dynamics
- B60W2520/10—Longitudinal speed
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W2552/00—Input parameters relating to infrastructure
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W2554/00—Input parameters relating to objects
- B60W2554/40—Dynamic objects, e.g. animals, windblown objects
- B60W2554/404—Characteristics
- B60W2554/4041—Position
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W2554/00—Input parameters relating to objects
- B60W2554/40—Dynamic objects, e.g. animals, windblown objects
- B60W2554/404—Characteristics
- B60W2554/4042—Longitudinal speed
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Abstract
The invention provides a vehicle intention prediction method, a vehicle intention prediction system, electronic equipment and a medium. The vehicle intention prediction method includes: acquiring environment image information and lane change state information of a vehicle to be controlled, wherein the lane change state information comprises a turn signal activation state, vehicle body position information and longitudinal speed information; determining running state information of the target vehicle according to the environment image information, wherein the running state information comprises vehicle body position information and longitudinal speed information; and predicting the driving intention of the target vehicle according to the lane change state information of the vehicle to be controlled and the driving state information of the target vehicle, and generating an intention prediction result. According to the method and the device, the physical motion state and the interaction behavior are combined, the driving intention of the target vehicle is predicted according to the lane change state information of the vehicle to be controlled and the driving state information of the target vehicle, an intention prediction result is generated, and the accuracy of the prediction result is improved.
Description
Technical Field
The application relates to the technical field of automatic driving, in particular to a vehicle intention prediction method, a vehicle intention prediction system, electronic equipment and a vehicle intention prediction medium.
Background
The automatic driving system mainly comprises three modules of environment sensing, decision planning and control execution. With the continuous development of automatic driving technology, the functional requirements of triggering lane changing, automatic lane changing, navigation automatic driving and the like are met, and higher requirements are put forward on decision planning capability of an automatic driving vehicle. In order to improve decision planning capability of the vehicle in a lane change driving scene, the behavior of other target vehicles around the vehicle, especially the behavior of target vehicles behind the vehicle, needs to be predicted. Therefore, the rationality and the advance of decision planning can be ensured, and sufficient processing time is reserved for the control execution part so as to improve driving safety and comfort.
The existing target behavior prediction algorithm mainly comprises the following two main categories: (1) A prediction method based on a physical motion state of a target vehicle or a driving intention prediction method based on data training. However, the former mainly considers whether the front target will execute lane change to enter the own lane, and does not make intention prediction on the backward target; the latter mainly predicts the lateral behaviour of the object and does not predict the longitudinal behaviour of the object. (2) a prediction method based on vehicle interaction behavior. However, the method only depends on the prediction result of the interaction relationship, and often cannot cover the actual behaviors of all types of drivers, only considers the behavior of the theoretical highest benefit between the target vehicle and the vehicle to be controlled, and does not consider other traffic participants, so that the accuracy of the prediction result is lower.
Disclosure of Invention
In view of the above-mentioned drawbacks of the prior art, the present application provides a vehicle intention prediction method, system, electronic device and medium, so as to solve the problem that the existing vehicle intention prediction method mainly considers whether a front target will execute lane change to enter a host lane, and does not perform intention prediction on a backward target. The transverse behaviors of the target are mainly predicted, and the longitudinal behaviors of the target are not predicted; the prediction results only according to the interaction relationship often cannot cover the actual behaviors of drivers in all styles, only the behavior of the theoretical highest income between the target vehicle and the vehicle to be controlled is considered, and other traffic participants are not considered, so that the technical problem of lower accuracy of the prediction results is solved.
To solve the above-mentioned technical problem, in a first aspect, the present invention provides a vehicle intention prediction method including:
Acquiring environment image information and lane change state information of a vehicle to be controlled, wherein the lane change state information comprises a turn signal activation state, vehicle body position information and longitudinal speed information;
Determining driving state information of a target vehicle according to the environment image information, wherein the target vehicle comprises a forward vehicle, a parallel vehicle and a backward vehicle in a driving direction, and the driving state information comprises vehicle body position information and longitudinal speed information;
and predicting the driving intention of the target vehicle according to the lane change state information of the vehicle to be controlled and the driving state information of the target vehicle, and generating an intention prediction result.
In an exemplary embodiment of the present application, determining driving state information of a target vehicle includes:
Calculating the distance between the vehicle to be controlled and the potential target vehicle according to the environment image information, and determining the target vehicle based on the distance between the vehicle to be controlled and the potential target vehicle;
And collecting running state information of the target vehicle.
In an exemplary embodiment of the present application, predicting a traveling intention of a target vehicle includes:
According to the vehicle body position information and the longitudinal speed information of the vehicle to be controlled, the vehicle body position information and the longitudinal speed information of the target vehicle, the longitudinal distance between the target vehicle and the vehicle to be controlled, the longitudinal speed ratio between the target vehicle and the vehicle to be controlled and the collision time between the target vehicle and the vehicle to be controlled are calculated;
An intent prediction result is generated based on a longitudinal distance between the target vehicle and the vehicle to be controlled, a longitudinal speed ratio between the target vehicle and the vehicle to be controlled, and a collision time between the target vehicle and the vehicle to be controlled.
In an exemplary embodiment of the present application, generating an intention prediction result based on a longitudinal distance, a longitudinal speed ratio, a collision time includes:
Judging the driving behavior of the target vehicle in the current state based on the longitudinal distance, the longitudinal speed ratio and the collision time;
Acquiring starting time information of a target vehicle entering a current driving behavior and current time information of the target vehicle, and calculating the number of periods of the target vehicle in the current driving behavior based on the starting time information of the target vehicle entering the current driving behavior, the current time information of the target vehicle and preset period time information;
Acquiring starting time information of a vehicle to be controlled entering a current turn light activation state and current time information of a target vehicle, and calculating the total cycle number of the target vehicle entering the current turn light activation state in the vehicle to be controlled based on the starting time information, the current time information and preset cycle time information;
Calculating the ratio of the cycle number of the target vehicle in the current driving behavior to the total cycle number of the target vehicle in the current turn signal activation state of the vehicle to be controlled based on the cycle number of the target vehicle in the current driving behavior and the total cycle number of the target vehicle in the current turn signal activation state of the vehicle to be controlled;
Comparing the ratio of the cycle number of the current driving behavior of the target vehicle to the total cycle number of the target vehicle in the state that the vehicle to be controlled enters the current turn signal activation state with a preset threshold value to obtain a comparison result;
confirming the driving intention of the target vehicle in the current turn light activation state of the vehicle to be controlled based on the comparison result;
And generating an intention prediction result based on the driving intention of the target vehicle in the current turn signal activation state of the vehicle to be controlled.
In a second aspect, the present invention provides a vehicle control method including:
Acquiring environment image information and lane change state information of a vehicle to be controlled, wherein the lane change state information comprises a turn signal activation state, vehicle body position information and longitudinal speed information;
determining driving state information of a target vehicle according to the environment image information, wherein the target vehicle comprises a forward vehicle, a parallel vehicle and a backward vehicle in the driving direction, and the driving state information comprises vehicle body position information and longitudinal speed information;
predicting the driving intention of the target vehicle according to the lane change state information of the vehicle to be controlled and the driving state information of the target vehicle, and generating an intention prediction result;
And generating a control instruction of the vehicle to be controlled based on the intention prediction result so as to confirm whether the vehicle to be controlled dodges the target vehicle or not, and executing corresponding control action based on the control instruction.
In a third aspect, the present invention provides a vehicle intention prediction system including:
The system comprises an acquisition module, a control module and a control module, wherein the acquisition module is used for acquiring environment image information and lane change state information of a vehicle to be controlled, and the lane change state information comprises a turn signal activation state, vehicle body position information and longitudinal speed information;
The processing module is used for determining running state information of a target vehicle according to the environment image information, wherein the target vehicle comprises a forward vehicle, a parallel vehicle and a backward vehicle in the running direction, and the running state information comprises vehicle body position information and longitudinal speed information;
the intention prediction module predicts the running intention of the target vehicle according to the lane change state information of the vehicle to be controlled and the running state information of the target vehicle, and generates an intention prediction result.
In a fourth aspect, the present invention provides a vehicle control system, where the vehicle control system includes the vehicle intent prediction system as described above, a planning module, and an execution module, where the planning module generates a control instruction of a vehicle to be controlled based on the intent prediction result to confirm whether the vehicle to be controlled is avoiding a target vehicle, and the execution module executes a corresponding control action based on the control instruction.
In another aspect, the present invention also provides an electronic device, including:
One or more processors;
and a storage means for storing one or more programs that, when executed by the one or more processors, cause the electronic device to implement the vehicle intent prediction method as described above.
In an exemplary embodiment of the application, the one or more programs, when executed by the one or more processors, cause the electronic device to implement the vehicle control method as described above.
In yet another aspect, the present invention also provides a computer-readable medium having stored thereon computer-readable instructions that, when executed by a processor of a computer, cause the computer to perform the vehicle intention prediction method as described above or the vehicle control method as described above.
As described above, the vehicle intention prediction method, system, electronic device and medium of the present invention have the following beneficial effects:
(1) According to the vehicle intention prediction method, physical movement information and vehicle interaction behavior are combined, and the running intention of the target vehicle is predicted according to the lane change state information of the vehicle to be controlled and the running state information of the target vehicle, so that an intention prediction result is generated, and the accuracy of the prediction result is improved; meanwhile, the running intentions of the front vehicle, the parallel vehicle and the rear vehicle are comprehensively considered, and the accuracy of the prediction result is further improved.
(2) According to the method, the control instruction of the vehicle to be controlled is generated based on the intention prediction result so as to confirm whether the vehicle to be controlled dodges the target vehicle, help the vehicle to be controlled plan in advance and execute the rollback action, avoid collision with other vehicles, ensure the driving safety, avoid emergency rollback and improve the driving comfort.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the application as claimed.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the application and together with the description, serve to explain the principles of the application. It is evident that the drawings in the following description are only some embodiments of the present application and that other drawings may be obtained from these drawings without inventive effort for a person of ordinary skill in the art. In the drawings:
fig. 1 is a schematic view of environmental image information of a vehicle to be controlled, which is shown in an exemplary embodiment of the present application;
FIG. 2 is a flowchart of a method of predicting vehicle intent in accordance with an exemplary embodiment of the present application;
FIG. 3 is a flow chart of step S220 in an exemplary embodiment of the embodiment of FIG. 2;
FIG. 4 is a flow chart of step S230 in an exemplary embodiment of the embodiment of FIG. 2;
FIG. 5 is a flow chart of step S420 in the embodiment of FIG. 4 in an exemplary embodiment;
FIG. 6 is a flowchart of a vehicle control method according to an exemplary embodiment of the present application;
FIG. 7 is a block diagram of a vehicle intent prediction system shown in accordance with an exemplary embodiment of the present application;
FIG. 8 is a schematic diagram of a vehicle control system according to an exemplary embodiment of the present application;
fig. 9 is a schematic structural view of an electronic device according to an exemplary embodiment of the present application;
Detailed Description
Further advantages and effects of the present invention will become readily apparent to those skilled in the art from the disclosure herein, by referring to the accompanying drawings and the preferred embodiments. The invention may be practiced or carried out in other embodiments that depart from the specific details, and the details of the present description may be modified or varied from the spirit and scope of the present invention. It should be understood that the preferred embodiments are presented by way of illustration only and not by way of limitation.
It should be noted that the illustrations provided in the following embodiments merely illustrate the basic concept of the present invention by way of illustration, and only the components related to the present invention are shown in the drawings and are not drawn according to the number, shape and size of the components in actual implementation, and the form, number and proportion of the components in actual implementation may be arbitrarily changed, and the layout of the components may be more complicated.
In the following description, numerous details are set forth in order to provide a more thorough explanation of embodiments of the present invention, it will be apparent, however, to one skilled in the art that embodiments of the present invention may be practiced without these specific details, in other embodiments, well-known structures and devices are shown in block diagram form, rather than in detail, in order to avoid obscuring the embodiments of the present invention.
Fig. 2 is a flowchart of a vehicle intention prediction method according to an exemplary embodiment of the present application, where the vehicle intention prediction method is used to predict whether a forward vehicle, a parallel vehicle, and a backward target vehicle of an adjacent lane of a vehicle to be controlled will avoid aiming at a lane change behavior of the vehicle to be controlled, help the vehicle to be controlled plan in advance, perform a rollback action, avoid collision with other vehicles, ensure driving safety, and avoid emergency rollback to improve driving comfort.
Referring to fig. 2, the vehicle intention prediction method of the present embodiment includes the following steps:
s210, collecting environment image information (shown in fig. 1) and lane change state information of a vehicle to be controlled, wherein the lane change state information comprises a turn signal activation state, vehicle body position information and longitudinal speed information;
Specifically, the environmental image information can be collected by adopting a camera, a millimeter wave radar and the like. The acquisition of the lane change state information can be performed by adopting a camera, a millimeter wave radar and the like. The lane change state of the vehicle is divided into the following three cases: turn signal activation state, lane change start state and continuous lane change state. The activation state of the steering lamp specifically refers to: the turn signal of the vehicle to be controlled is activated by a lever or the turn signal is requested to be activated. The channel change starting state specifically means: when the steering lamp is in an activated state, the distance of the center of the front bumper of the vehicle to be controlled, which deviates from the center line of the lane along the direction of the steering lamp, is more than a preset threshold (such as 0.2 m) and the duration time after the lane change state is started is less than a preset threshold (such as 2 s), or the included angle between the center line of the vehicle to be controlled, which deviates from the center line of the lane along the direction of the steering lamp, and the center line of the lane is more than a preset threshold (such as 0.05 rad) and the duration time after the lane change state is started is less than a preset threshold (such as 2 s). The continuous channel change state specifically means: the duration time after the channel change state is started is more than or equal to a preset threshold value (for example, 2 s).
S220, determining running state information of a target vehicle according to the environment image information, wherein the target vehicle comprises a forward vehicle, a parallel vehicle and a backward vehicle in the running direction, and the running state information comprises vehicle body position information and longitudinal speed information;
And S230, predicting the driving intention of the target vehicle according to the lane change state information of the vehicle to be controlled and the driving state information of the target vehicle, and generating an intention prediction result.
In the related art, the prediction of the intention of the vehicle mainly considers whether the front target can execute lane change to enter the lane, so that the running of the vehicle is interfered, the lateral behaviors of the target are mainly predicted, the behaviors of the rear vehicle are not considered or the prediction result only according to the interaction relation cannot always cover the actual behaviors of all types of drivers, only the behaviors of the theoretical highest benefit between the target vehicle and the vehicle are considered, and other traffic participants are not considered, so that the accuracy of the prediction result is lower. After analyzing the scheme, the inventor finds that the related technology does not consider the longitudinal behavior of the prediction target, and does not consider other traffic participants, so that the accuracy of the prediction result is lower. Therefore, the intention prediction of the target vehicle is carried out by considering the lane change state of the vehicle to be controlled and the interaction behavior of the target vehicle, and the accuracy of the prediction result is improved; and the interactive behaviors of the forward vehicles, the parallel vehicles and the backward vehicles are comprehensively considered, so that the accuracy of the prediction result is further improved.
Referring to fig. 3, fig. 3 is a flowchart of step S220 in an exemplary embodiment in the embodiment shown in fig. 2.
Referring to fig. 3, in one embodiment, the process of determining the driving state information of the target vehicle according to the environmental image information may include steps S310 and S320, which are described in detail as follows:
s310, calculating the distance between the vehicle to be controlled and the potential target vehicle according to the environment image information, and determining the target vehicle based on the distance between the vehicle to be controlled and the potential target vehicle;
The solution of the present embodiment firstly calculates a lateral distance and a longitudinal distance between a vehicle to be controlled and a potential target vehicle according to environmental image information, determines lane information in which the potential vehicle is located and lane information in which the vehicle to be controlled is located based on the lateral distance between the vehicle to be controlled and the potential target vehicle, and confirms the target vehicle according to the lane information in which the potential target vehicle is located, the lane information in which the vehicle to be controlled is located and the longitudinal distance between the vehicle to be controlled and the potential target vehicle.
And S320, collecting running state information of the target vehicle.
Specifically, the driving state information may be collected by using a camera, a millimeter wave radar, or the like.
Referring to fig. 4, fig. 4 is a flowchart of step S230 in an exemplary embodiment in the embodiment shown in fig. 2.
Referring to fig. 4, in one embodiment, according to lane change status information of a vehicle to be controlled and driving status information of a target vehicle, a process of predicting a driving intention of the target vehicle and generating an intention prediction result may include step S410 and step S420, which are described in detail as follows:
s410, calculating the longitudinal distance between the target vehicle and the vehicle to be controlled, the longitudinal speed ratio between the target vehicle and the vehicle to be controlled and the collision time between the target vehicle and the vehicle to be controlled according to the vehicle body position information and the longitudinal speed information of the vehicle to be controlled and the vehicle body position information and the longitudinal speed information of the target vehicle;
And S420, generating an intention prediction result based on the longitudinal distance between the target vehicle and the vehicle to be controlled, the longitudinal speed ratio between the target vehicle and the vehicle to be controlled and the collision time between the target vehicle and the vehicle to be controlled.
Referring to fig. 5, fig. 5 is a flowchart of step S420 in an exemplary embodiment in the embodiment shown in fig. 4.
Referring to fig. 5, in one embodiment, the process of generating the intent prediction result based on the longitudinal distance between the target vehicle and the vehicle to be controlled, the longitudinal speed ratio between the target vehicle and the vehicle to be controlled, and the collision time between the target vehicle and the vehicle to be controlled may include steps S510, S520, S530, S540, S550, S560, and S570, which are described in detail as follows:
S510, judging driving behaviors of the target vehicle in the current state based on the longitudinal distance, the longitudinal speed ratio and the collision time;
Specifically, the driving behavior includes two cases: for example, the distance between the target vehicle and the vehicle to be controlled in the longitudinal direction is continuously shortened, the speed of the target vehicle in the longitudinal direction is continuously greater than the speed of the vehicle to be controlled in the longitudinal direction, and the collision time TTC between the target vehicle and the vehicle to be controlled is continuously increased, so that the driving behavior of the target vehicle is an avoidance-free behavior; otherwise, the driving behavior of the target vehicle is the avoidance behavior.
S520, acquiring starting time information of a target vehicle entering a current driving behavior and current time information of the target vehicle, calculating the cycle number of the target vehicle in the current driving behavior (for example, 0.02s is a cycle) based on the starting time information of the target vehicle entering the current driving behavior, the current time information of the target vehicle and the preset cycle time information, and calculating the cycle number of the target vehicle in the current driving behavior;
Specifically, based on the starting time information of the target vehicle entering the current driving behavior and the current time information of the target vehicle, the time information of the target vehicle entering the current driving behavior (that is, the difference value between the starting time information and the time information of the current time) can be calculated, and based on the time information of the target vehicle entering the current driving behavior and the preset period time information (for example, 0.02s is a period), the period number of the current driving behavior (avoidance behavior or non-avoidance behavior) of the target vehicle can be calculated.
S530, acquiring starting time information of the vehicle to be controlled entering the current turn light activation state and current time information of the target vehicle, and calculating the total cycle number of the target vehicle entering the current turn light activation state in the vehicle to be controlled based on the starting time information, the current time information and the preset cycle time information;
Specifically, based on the starting time information and the current time information, the starting time information of the target vehicle entering the current turn light activation from the vehicle to be controlled and the time information of the current time (i.e. the difference value between the starting time information and the time information of the current time) can be calculated, and based on the time information of the target vehicle entering the current turn light activation state from the vehicle to be controlled and the preset period time information (the preset period is the same as the preset period parameter in the step S520), the total period number of the target vehicle entering the current turn light activation state from the vehicle to be controlled (i.e. the total period number of the vehicle to be controlled entering the current turn light activation state from the vehicle to be controlled) can be calculated, and the total period number of the target vehicle entering the current turn light activation state from the vehicle to be controlled is the same as the value of the total period number of the target vehicle entering the current turn light activation state from the vehicle to be controlled due to the fixed attribute of the time parameter;
S540, calculating the ratio of the cycle number of the current driving behavior of the target vehicle to the total cycle number of the target vehicle entering the current turn signal activation state of the vehicle to be controlled based on the cycle number of the current driving behavior of the target vehicle and the total cycle number of the target vehicle entering the current turn signal activation state of the vehicle to be controlled;
S550, comparing the ratio of the cycle number of the current driving behavior of the target vehicle to the total cycle number of the target vehicle in the state that the vehicle to be controlled enters the current turn signal activation state with a preset threshold (such as 0.8), and obtaining a comparison result;
S560, confirming the driving intention of the target vehicle in the current turn light activation state of the vehicle to be controlled based on the comparison result;
And S570, generating an intention prediction result based on the driving intention of the target vehicle in the current turn light activation state of the vehicle to be controlled.
Specifically, the specific process of generating the intention prediction result based on the driving intention of the target vehicle in the current turn signal activation state of the vehicle to be controlled is obtained through a state machine.
Fig. 6 is a flowchart of a vehicle control method for helping a vehicle to be controlled plan in advance, performing a rollback action, avoiding collision with other vehicles, ensuring driving safety, and avoiding emergency rollback to improve riding comfort according to an exemplary embodiment of the present application.
Referring to fig. 6, the vehicle control method of the present embodiment includes the steps of:
S610, predicting the driving intention of the target vehicle by adopting the method;
And S620, generating a control instruction of the vehicle to be controlled based on the intention prediction result so as to confirm whether the vehicle to be controlled dodges the target vehicle or not, and executing corresponding control actions based on the control instruction.
According to the vehicle control method provided by the embodiment, based on the result of predicting the driving intention of the target vehicle, the control instruction of the vehicle to be controlled is generated to confirm whether the vehicle to be controlled avoids the target vehicle or not, and based on the control instruction, corresponding control actions are executed, so that the vehicle to be controlled can be helped to plan in advance and execute the rollback action, collision with the forward vehicle, the parallel vehicle and the backward vehicle is avoided, driving safety is ensured, emergency rollback is avoided, and driving comfort is improved.
Referring to fig. 7, the present embodiment further provides a vehicle intention prediction system 700 for predicting a driving intention of a target vehicle, so that a vehicle to be controlled further performs subsequent planning and control according to a result of the driving intention, avoiding collision with other vehicles, ensuring driving safety, and avoiding emergency rollback to improve driving comfort.
Referring to fig. 7, a vehicle intention prediction system 700 of the present embodiment includes:
The acquisition module 710 is configured to acquire environmental image information and lane change status information of a vehicle to be controlled, where the lane change status information includes a turn signal activation status, vehicle body position information, and longitudinal speed information;
A processing module 720 for determining driving state information of a target vehicle according to the environmental image information, wherein the target vehicle comprises a forward vehicle, a parallel vehicle and a backward vehicle in a driving direction, and the driving state information comprises vehicle body position information and longitudinal speed information;
The intention prediction module 730 predicts the driving intention of the target vehicle based on the lane change status information of the vehicle to be controlled and the driving status information of the target vehicle, and generates an intention prediction result.
In the present embodiment, the vehicle intention prediction system 700 is essentially provided with several modules for performing the vehicle intention prediction method in the above embodiment to achieve the prediction of the traveling intention of the target vehicle.
Referring to fig. 8, the present embodiment further provides a vehicle control system 800, where the vehicle control system 800 includes a vehicle intention prediction system 810 (the specific structure is shown in fig. 7, that is, the vehicle intention prediction system 700 in fig. 7), a planning module 820, and an executing module 830, the planning module 800 generates a control instruction of the vehicle to be controlled based on the intention prediction result of the vehicle intention prediction system 810, so as to confirm whether the vehicle to be controlled dodges the target vehicle, and the executing module 830 executes a corresponding control action based on the control instruction.
In this embodiment, the vehicle control system essentially provides several modules for performing the methods of the embodiments described above to achieve control of the vehicle.
Referring to fig. 9, an embodiment of the application further provides an electronic device 900.
Referring to fig. 9, an electronic device 900 of an embodiment of the present application includes a processor 910 and a memory 920 and a communication bus 930:
A communication bus 930 is used to connect processor 910 and memory 920;
The processor 910 is configured to execute a computer program stored in the memory 920 to implement the vehicle intention prediction method in the above embodiment or the vehicle control method in the above embodiment.
The embodiment of the present invention also provides a computer-readable storage medium having stored thereon computer-readable instructions that, when executed by a processor of a computer, cause the computer to perform the vehicle intention prediction method described above or the vehicle control method in the embodiment described above.
It should be noted that, the computer readable medium shown in the embodiments of the present application may be a computer readable signal medium or a computer readable medium, or any combination of the two. The computer readable medium can be, for example, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples of the computer-readable medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-Only Memory (ROM), an erasable programmable read-Only Memory (Erasable Programmable Read Only Memory, EPROM), a flash Memory, an optical fiber, a portable compact disc read-Only Memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present application, a computer-readable signal medium may comprise a data signal propagated in baseband or as part of a carrier wave, with a computer-readable computer program embodied therein. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A computer readable signal medium may also be any computer readable medium that is not a computer readable medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.
The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present application. Where each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams or flowchart illustration, and combinations of blocks in the block diagrams or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The units involved in the embodiments of the present application may be implemented by software, or may be implemented by hardware, and the described units may also be provided in a processor. Wherein the names of the units do not constitute a limitation of the units themselves in some cases.
Another aspect of the present application also provides a computer-readable medium having stored thereon a computer program which, when executed by a processor, implements the vehicle intention prediction method or the vehicle control method as described above. The computer-readable medium may be included in the electronic device described in the above embodiment or may exist alone without being incorporated in the electronic device.
The above embodiments are merely illustrative of the principles of the present invention and its effectiveness, and are not intended to limit the invention. Modifications and variations may be made to the above-described embodiments by those skilled in the art without departing from the spirit and scope of the invention. It is therefore intended that all equivalent modifications and changes made by those skilled in the art without departing from the spirit and technical spirit of the present invention shall be covered by the appended claims.
Claims (8)
1. A vehicle intention prediction method, characterized by comprising:
Acquiring environment image information and lane change state information of a vehicle to be controlled, wherein the lane change state information comprises a turn signal activation state, vehicle body position information and longitudinal speed information;
determining driving state information of a target vehicle according to the environment image information, wherein the target vehicle comprises a forward vehicle, a parallel vehicle and a backward vehicle in the driving direction, and the driving state information comprises vehicle body position information and longitudinal speed information;
According to the vehicle body position information and the longitudinal speed information of the vehicle to be controlled, the vehicle body position information and the longitudinal speed information of the target vehicle, the longitudinal distance between the target vehicle and the vehicle to be controlled, the longitudinal speed ratio between the target vehicle and the vehicle to be controlled and the collision time between the target vehicle and the vehicle to be controlled are calculated;
Judging the driving behavior of the target vehicle in the current state based on the longitudinal distance, the longitudinal speed ratio and the collision time;
Acquiring starting time information of a target vehicle entering a current driving behavior and current time information of the target vehicle, and calculating the number of periods of the target vehicle in the current driving behavior based on the starting time information of the target vehicle entering the current driving behavior, the current time information of the target vehicle and preset period time information;
Acquiring starting time information of a vehicle to be controlled entering a current turn light activation state and current time information of a target vehicle, and calculating the total cycle number of the target vehicle entering the current turn light activation state in the vehicle to be controlled based on the starting time information, the current time information and preset cycle time information;
Calculating the ratio of the cycle number of the target vehicle in the current driving behavior to the total cycle number of the target vehicle in the current turn signal activation state of the vehicle to be controlled based on the cycle number of the target vehicle in the current driving behavior and the total cycle number of the target vehicle in the current turn signal activation state of the vehicle to be controlled;
Comparing the ratio of the cycle number of the current driving behavior of the target vehicle to the total cycle number of the target vehicle in the state that the vehicle to be controlled enters the current turn signal activation state with a preset threshold value to obtain a comparison result;
confirming the driving intention of the target vehicle in the current turn light activation state of the vehicle to be controlled based on the comparison result;
And generating an intention prediction result based on the driving intention of the target vehicle in the current turn signal activation state of the vehicle to be controlled.
2. The vehicle intention prediction method according to claim 1, characterized in that determining the running state information of the target vehicle includes:
Calculating the distance between the vehicle to be controlled and the potential target vehicle according to the environment image information, and determining the target vehicle based on the distance between the vehicle to be controlled and the potential target vehicle;
And collecting running state information of the target vehicle.
3. A vehicle control method characterized by comprising:
Acquiring environment image information and lane change state information of a vehicle to be controlled, wherein the lane change state information comprises a turn signal activation state, vehicle body position information and longitudinal speed information;
determining driving state information of a target vehicle according to the environment image information, wherein the target vehicle comprises a forward vehicle, a parallel vehicle and a backward vehicle in the driving direction, and the driving state information comprises vehicle body position information and longitudinal speed information;
According to the vehicle body position information and the longitudinal speed information of the vehicle to be controlled, the vehicle body position information and the longitudinal speed information of the target vehicle, the longitudinal distance between the target vehicle and the vehicle to be controlled, the longitudinal speed ratio between the target vehicle and the vehicle to be controlled and the collision time between the target vehicle and the vehicle to be controlled are calculated;
Judging the driving behavior of the target vehicle in the current state based on the longitudinal distance, the longitudinal speed ratio and the collision time;
Acquiring starting time information of a target vehicle entering a current driving behavior and current time information of the target vehicle, and calculating the number of periods of the target vehicle in the current driving behavior based on the starting time information of the target vehicle entering the current driving behavior, the current time information of the target vehicle and preset period time information;
Acquiring starting time information of a vehicle to be controlled entering a current turn light activation state and current time information of a target vehicle, and calculating the total cycle number of the target vehicle entering the current turn light activation state in the vehicle to be controlled based on the starting time information, the current time information and preset cycle time information;
Calculating the ratio of the cycle number of the target vehicle in the current driving behavior to the total cycle number of the target vehicle in the current turn signal activation state of the vehicle to be controlled based on the cycle number of the target vehicle in the current driving behavior and the total cycle number of the target vehicle in the current turn signal activation state of the vehicle to be controlled;
Comparing the ratio of the cycle number of the current driving behavior of the target vehicle to the total cycle number of the target vehicle in the state that the vehicle to be controlled enters the current turn signal activation state with a preset threshold value to obtain a comparison result;
confirming the driving intention of the target vehicle in the current turn light activation state of the vehicle to be controlled based on the comparison result;
generating an intention prediction result based on the driving intention of the target vehicle in the current turn signal activation state of the vehicle to be controlled;
And generating a control instruction of the vehicle to be controlled based on the intention prediction result so as to confirm whether the vehicle to be controlled dodges the target vehicle or not, and executing corresponding control action based on the control instruction.
4. A vehicle intention prediction system, characterized by comprising:
The system comprises an acquisition module, a control module and a control module, wherein the acquisition module is used for acquiring environment image information and lane change state information of a vehicle to be controlled, and the lane change state information comprises a turn signal activation state, vehicle body position information and longitudinal speed information;
The processing module is used for determining running state information of a target vehicle according to the environment image information, wherein the target vehicle comprises a forward vehicle, a parallel vehicle and a backward vehicle in the running direction, and the running state information comprises vehicle body position information and longitudinal speed information;
The intention prediction module is used for calculating the longitudinal distance between the target vehicle and the vehicle to be controlled, the longitudinal speed ratio between the target vehicle and the vehicle to be controlled and the collision time between the target vehicle and the vehicle to be controlled according to the vehicle body position information and the longitudinal speed information of the vehicle to be controlled and the vehicle body position information and the longitudinal speed information of the target vehicle; judging the driving behavior of the target vehicle in the current state based on the longitudinal distance, the longitudinal speed ratio and the collision time; acquiring starting time information of a target vehicle entering a current driving behavior and current time information of the target vehicle, and calculating the number of periods of the target vehicle in the current driving behavior based on the starting time information of the target vehicle entering the current driving behavior, the current time information of the target vehicle and preset period time information; acquiring starting time information of a vehicle to be controlled entering a current turn light activation state and current time information of a target vehicle, and calculating the total cycle number of the target vehicle entering the current turn light activation state in the vehicle to be controlled based on the starting time information, the current time information and preset cycle time information; calculating the ratio of the cycle number of the target vehicle in the current driving behavior to the total cycle number of the target vehicle in the current turn signal activation state of the vehicle to be controlled based on the cycle number of the target vehicle in the current driving behavior and the total cycle number of the target vehicle in the current turn signal activation state of the vehicle to be controlled; comparing the ratio of the cycle number of the current driving behavior of the target vehicle to the total cycle number of the target vehicle in the state that the vehicle to be controlled enters the current turn signal activation state with a preset threshold value to obtain a comparison result; confirming the driving intention of the target vehicle in the current turn light activation state of the vehicle to be controlled based on the comparison result; and generating an intention prediction result based on the driving intention of the target vehicle in the current turn signal activation state of the vehicle to be controlled, and generating the intention prediction result.
5. A vehicle control system, comprising the vehicle intention prediction system, the planning module and the execution module according to claim 4, wherein the planning module generates a control instruction of a vehicle to be controlled based on the intention prediction result so as to confirm whether the vehicle to be controlled avoids a target vehicle, and the execution module executes a corresponding control action based on the control instruction.
6. An electronic device, comprising:
One or more processors;
Storage means for storing one or more programs that, when executed by the one or more processors, cause the electronic device to implement the vehicle intent prediction method as claimed in claim 1 or 2.
7. The electronic device of claim 6, comprising:
the one or more programs, when executed by the one or more processors, cause the electronic device to implement the vehicle control method of claim 3.
8. A computer-readable medium having stored thereon computer-readable instructions which, when executed by a processor of a computer, cause the computer to perform the vehicle intention prediction method of claim 1 or 2 or the vehicle control method of claim 3.
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| CN112606838A (en) * | 2020-12-15 | 2021-04-06 | 东风汽车集团有限公司 | Anti-collision control method and device for lane change of vehicle |
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| DE102006027325A1 (en) * | 2006-06-13 | 2007-12-20 | Robert Bosch Gmbh | Lane departure warning with lane change function |
| DE102006043149B4 (en) * | 2006-09-14 | 2024-08-01 | Bayerische Motoren Werke Aktiengesellschaft | Integrated lateral and longitudinal guidance assistant to support the driver when changing lanes |
| JP4366419B2 (en) * | 2007-09-27 | 2009-11-18 | 株式会社日立製作所 | Driving support device |
| KR20180043144A (en) * | 2016-10-19 | 2018-04-27 | 임철수 | Control device and method of lane changing in autonomous driving vehicle |
| KR102463720B1 (en) * | 2017-12-18 | 2022-11-07 | 현대자동차주식회사 | System and Method for creating driving route of vehicle |
| KR20200075915A (en) * | 2018-12-07 | 2020-06-29 | 현대자동차주식회사 | Apparatus and method for controlling running of vehicle |
| CN110001782A (en) * | 2019-04-29 | 2019-07-12 | 重庆长安汽车股份有限公司 | Automatic lane-change method, system and computer readable storage medium |
| CN113479201B (en) * | 2021-08-20 | 2022-08-12 | 燕山大学 | Lane changing scene vehicle risk dynamic evaluation method considering driver reaction capacity |
| CN114537441A (en) * | 2022-03-17 | 2022-05-27 | 福思(杭州)智能科技有限公司 | Vehicle driving intention prediction method, device and system and vehicle |
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