Track planning method in lane changing process of automatic driving vehicle, storage medium and equipment

Abstract

The invention relates to a trajectory planning method, a storage medium and equipment in a lane changing process of an automatic driving vehicle. The invention relates to the trajectory planning method, the storage medium and the equipment in the lane changing process of the automatic driving vehicle. The objective of the invention is to solve the problems of low lane changing feasibility judgment accuracy and poor lane changing necessity judgment rule universality in the lane changing process of the existing automatic driving vehicle. The method comprises the following steps: deciding the longitudinal displacement of the automatic driving vehicle; a game rule of a game participant is established, an optimal decision of the automatic driving vehicle meeting constraint conditions is calculated according to a reverse induction method, and the transverse displacement of the automatic driving vehicle at the next moment is determined; determining a track sequence point of the automatic driving vehicle at the next moment; and setting total time of lane changing, obtaining total revenue of the automatic driving vehicle, selecting a lane corresponding to the maximum value of the total revenue as a target lane, and executing an optimal decision by the automatic driving vehicle every time to realize a final lane changing track of the automatic driving vehicle. The method is applied to the field of trajectory planning.

Description

Technical Field

[0001] The present invention relates to a trajectory planning method, storage medium and device during a lane changing process of an autonomous driving vehicle. Background Art

[0002] At present, the global unmanned driving technology field is developing in a positive direction. Relying on advanced sensor technology, communication technology, intelligent decision-making and intelligent control technology, unmanned vehicles are gradually upgraded in terms of active safety and intelligent decision-making. Among them, trajectory planning has achieved the leap from state perception to intelligent control of unmanned vehicles.

[0003] Trajectory planning focuses on the real-time transformation of vehicle status, while taking into account vehicle dynamics, obstacle constraints, and risk assessment of driving operations. At present, domestic and foreign scholars have done a lot of research on trajectory planning for lane changes of autonomous vehicles, but most of them are trajectory planning methods established in scenarios such as vehicle obstacle avoidance or forced lane changes such as on and off ramps. The specific technical details involve maximum utility theory, risk field theory, quadratic programming, and machine learning. Among them, the rule-based model becomes increasingly difficult to design the migration conditions of the driving scenario when the traffic scenario is complex, and the model becomes more complex; while the method based on machine learning can only guarantee optimality in a statistical sense, with poor intrinsic explanatory power and low universality.

[0004] In fact, when an autonomous vehicle is driving on a structured highway, in addition to forced lane changes, the autonomous vehicle will change lanes to improve the driving environment. In this decision-making process, it will compare the benefits before and after the lane change and make the decision that maximizes its own benefits. To this end, we propose a trajectory planning method for autonomous vehicles in the lane change process with a simple structure and strong generalization ability by improving the Stackelberg game model and supplementing it with the IDM model. Summary of the invention

[0005] The purpose of the present invention is to solve the problems of low accuracy in judging the feasibility of lane changing and poor universality of the rules for judging the necessity of lane changing during the lane changing process of existing autonomous driving vehicles, and proposes a trajectory planning method, storage medium and device for the lane changing process of autonomous driving vehicles.

[0006] A trajectory planning method for an autonomous driving vehicle during lane change is as follows:

[0007] Step 1: Obtain the self-motion information of the autonomous driving vehicle SV and the geometric dimensions of the autonomous driving vehicle SV;

[0008] Acquire geometric dimensions of vehicles Z around the autonomous driving vehicle and motion state information of vehicles Z around the autonomous driving vehicle;

[0009] Step 2: Based on the self-driving vehicle's own motion information in step 1 and the geometric dimensions and motion state information of vehicles around the self-driving vehicle, make a decision on the longitudinal displacement of the self-driving vehicle;

[0010] Step 3: Establish the game rules for the game participants. The autonomous driving vehicle is the leader and needs to make decisions first. The vehicles behind it on both sides of the lane are followers and they respond based on the leader's decision.

[0011] Based on the response made by the Follower according to the Leader's decision, the optimal decision of the autonomous driving vehicle that meets the constraints is calculated according to the reverse induction method, and the lateral displacement of the autonomous driving vehicle at the next moment is determined based on the optimal decision;

[0012] Step 4: Determine the trajectory sequence points of the autonomous driving vehicle at the next moment according to the lateral displacement and the longitudinal displacement;

[0013] Step 5: Set the total lane change time to L seconds, repeat steps 1 to 4 L / Δt times, and obtain the corresponding total revenue U of the autonomous driving vehicle total , select the total revenue U of the autonomous driving vehicle total The lane corresponding to the maximum value is the target lane. The autonomous driving vehicle executes the optimal decision each time to achieve the final lane changing trajectory of the autonomous driving vehicle.

[0014] A computer storage medium, wherein at least one instruction is stored in the storage medium, and the at least one instruction is loaded and executed by a processor to implement a trajectory planning method for an autonomous driving vehicle during a lane change process.

[0015] A trajectory planning device for an autonomous driving vehicle during a lane change process, the device comprising a processor and a memory, the memory storing at least one instruction, the at least one instruction being loaded and executed by the processor to implement a trajectory planning method for an autonomous driving vehicle during a lane change process.

[0016] The beneficial effects of the present invention are:

[0017] The present invention proposes a trajectory planning method for an autonomous driving vehicle during a lane changing process. The present invention improves the Stackelberg game model in economics, uses the advanced sensor technology of the autonomous driving vehicle to perceive the state information of the surrounding vehicles, and considers the potential reactions of the surrounding vehicles to the decision of the autonomous driving vehicle, so that the autonomous driving vehicle can adjust the driving strategy in time, determine the best time to change lanes, and output the lateral displacement that takes into account both safety and efficiency. At the same time, the IDM model is used to plan the longitudinal displacement for the autonomous driving vehicle, and finally the trajectory planning of the autonomous driving vehicle during the lane changing process is realized, which improves the accuracy of the lane changing feasibility judgment during the lane changing process of the existing autonomous driving vehicle, enhances the universality of the lane changing necessity judgment rules during the lane changing process of the existing autonomous driving vehicle, and opens up a new path for the trajectory planning of autonomous driving vehicles in the future. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 This is a flow chart of a trajectory planning method during a lane changing process of an autonomous driving vehicle described in the present invention.

[0019] Figure 2 It is a schematic diagram of a vehicle trajectory during a lane changing process of an autonomous driving vehicle according to the present invention. DETAILED DESCRIPTION

[0020] Specific implementation method 1: In this implementation method, a trajectory planning method for an autonomous driving vehicle during lane change is specifically as follows:

[0021] Step 1: Obtain the self-motion information of the autonomous driving vehicle SV and the geometric dimensions of the autonomous driving vehicle SV;

[0022] Acquire geometric dimensions of vehicles Z around the autonomous driving vehicle and motion state information of vehicles Z around the autonomous driving vehicle;

[0023] Step 2: Based on the self-driving vehicle's own motion information in step 1 and the geometric dimensions and motion state information of vehicles around the self-driving vehicle, make a decision on the longitudinal displacement of the self-driving vehicle;

[0024] Step 3: Establish the game rules for the game participants. The autonomous driving vehicle is the leader and needs to make the decision first. The vehicles behind it on both sides of the lane are followers, and the followers respond according to the leader's decision (step 21 to step 24).

[0025] Based on the response made by the Follower according to the Leader's decision, the optimal decision of the autonomous driving vehicle that meets the constraints is calculated according to the reverse induction method, and the lateral displacement of the autonomous driving vehicle at the next moment is determined based on the optimal decision;

[0026] Step 4: Determine the trajectory sequence points of the autonomous driving vehicle at the next moment according to the lateral displacement and the longitudinal displacement;

[0027] Step 5: Set the total lane change time to L seconds, repeat steps 1 to 4 L / Δt times, and obtain the corresponding total revenue U of the autonomous driving vehicle total , select the total revenue U of the autonomous driving vehicle total The lane corresponding to the maximum value is the target lane. The autonomous driving vehicle executes the optimal decision each time to achieve the final lane changing trajectory of the autonomous driving vehicle.

[0028] Specific implementation method 2: This implementation method is different from specific implementation method 1 in that: in step 1, the self-motion information of the autonomous driving vehicle SV and the geometric dimensions of the autonomous driving vehicle SV are obtained;

[0029] Acquire geometric dimensions of vehicles Z around the autonomous driving vehicle and motion state information of vehicles Z around the autonomous driving vehicle;

[0030] The specific process is:

[0031] The autonomous driving vehicle obtains the autonomous driving vehicle SV's own motion information and the autonomous driving vehicle SV's geometric dimensions through the on-board computing unit;

[0032] The self-driving vehicle SV’s own motion information includes the speed v SV , acceleration acc SV ;

[0033] The geometric dimensions of the autonomous vehicle SV include the vehicle length l SV 、Vehicle width w SV ;

[0034] The autonomous driving vehicle obtains geometric dimensions of vehicles Z around the autonomous driving vehicle and motion state information of vehicles Z around the autonomous driving vehicle through an on-board detection unit;

[0035] The geometric dimensions of the vehicles Z surrounding the autonomous vehicle include the vehicle length l Z 、Vehicle width w Z ;

[0036] The motion state information of the vehicle Z around the autonomous driving vehicle includes the speed v Z , acceleration acc Z , the longitudinal relative distance Δy between the autonomous driving vehicle and the surrounding vehicles Z Z (measured from the midpoint of the vehicle's front bumper) and the distance d between the autonomous vehicle and the left edge of the innermost lane in its direction of travel SV,1 , the distance d between the autonomous driving vehicle and the right edge of the outermost lane in its driving direction SV,2 ;

[0037] Among them, Z∈{LV1,LV2,LV3,FV1,FV2}, LV1 represents the vehicle in front of the left lane of the autonomous driving vehicle that is closest to the autonomous driving vehicle; LV2 represents the vehicle in front of the current lane of the autonomous driving vehicle that is closest to the autonomous driving vehicle; LV3 represents the vehicle in front of the right lane of the autonomous driving vehicle that is closest to the autonomous driving vehicle; FV1 represents the vehicle behind the left lane of the autonomous driving vehicle that is closest to the autonomous driving vehicle; FV2 represents the vehicle behind the right lane of the autonomous driving vehicle that is closest to the autonomous driving vehicle.

[0038] The above information will serve as input for the subsequent behavior decisions of the autonomous vehicle.

[0039] The other steps and parameters are the same as those in the first embodiment.

[0040] Specific implementation method three: This implementation method is different from specific implementation methods one or two in that: in step two, the decision on the longitudinal displacement of the autonomous driving vehicle is made based on the autonomous driving vehicle's own motion information and the geometric dimensions and motion state information of the vehicles around the autonomous driving vehicle in step one;

[0041] The specific process is:

[0042] The longitudinal displacement Δy(k) of the autonomous driving vehicle is calculated based on the IDM (Intelligent Driver Model) model; the IDM model is:

[0043]

[0044] v SV (k) = v SV (k-1)+acc SV (k-1)×Δt

[0045]

[0046] Among them, acc SV (k) represents the acceleration of the autonomous driving vehicle at time k, and its initial value is set to 0; a IDM is the maximum acceleration of the autonomous vehicle; v SV (k) is the speed of the autonomous driving vehicle at time k, and its initial value is set to 0; is the speed of LV2 at time k; v des is the expected speed of the autonomous vehicle; δ is the acceleration index, which is 1.86; s(v SV (k),T) is a function; Δv(k) is the relative speed between the autonomous driving vehicle and the preceding vehicle at time k, and T is the expected headway of the autonomous driving vehicle; b IDMis the maximum deceleration of the autonomous driving vehicle, Δt is the unit step size of the trajectory planning of the autonomous driving vehicle, in seconds, and Δy(k) is the longitudinal displacement planned for the autonomous driving vehicle at time k.

[0047] The other steps and parameters are the same as those in the first or second embodiment.

[0048] Specific implementation method 4: This implementation method is different from specific implementation methods 1 to 3 in that: in step 3, the game rules of the game participants are established, the autonomous driving vehicle is the leader, and needs to make the decision first, and the following vehicles in the lanes on both sides are followers, and the followers respond according to the decision of the leader (step 21 to step 24);

[0049] Based on the response made by the Follower according to the Leader's decision, the optimal decision of the autonomous driving vehicle that meets the constraints is calculated according to the reverse induction method, and the lateral displacement of the autonomous driving vehicle at the next moment is determined based on the optimal decision;

[0050] The behavior decision of the autonomous driving vehicle is divided into lateral displacement decision and longitudinal displacement decision. The lateral displacement is determined based on the improved Stackelberg game model, and the longitudinal displacement is determined based on the IDM model.

[0051] According to the improved Stackelberg game model, the autonomous driving vehicle must first traverse the strategy set that has been integrated into the on-board computing unit to obtain its execution strategy for the next moment; secondly, it determines the surrounding vehicles in the driving environment under different execution decisions that will constitute a conflict of interest with it, and the autonomous driving vehicle and these vehicles act as game participants together; finally, according to the Stackelberg game process, for different execution decisions of the autonomous driving vehicle, the autonomous driving vehicle predicts the optimal response of other game participants, and based on this, makes the optimal decision that maximizes its own benefit, and outputs the lateral displacement of the autonomous driving vehicle at the next moment.

[0052] When an autonomous vehicle is driving on a highway, if the vehicle ahead is too slow or the headway is too small, it is difficult to achieve the desired driving speed or headway. Under the condition of ensuring safe driving, the driving environment can be improved by changing lanes. When the autonomous vehicle changes lanes, it will have a conflict of interest with the vehicle behind it in the target lane. The vehicle behind it in the target lane can allow or prevent the autonomous vehicle from changing lanes by changing the acceleration of the vehicle. Therefore, the game participants in the lane change process of the autonomous vehicle are its own vehicle and the vehicles behind it in the lanes on both sides (FV1, FV2). The game participants in the process of the autonomous vehicle changing lanes are N, and the strategy set of the autonomous vehicle is A. SV , the strategy set of other game participants is A FV ;

[0053] The specific process is:

[0054] Step 31: Determine the game participants, which include the autonomous driving vehicle and the vehicles behind it that are closest to the autonomous driving vehicle in the lanes on both sides of the autonomous driving vehicle;

[0055] N∈{SV,FV1,FV2}, SV represents the autonomous vehicle, FV1 represents the vehicle closest to the autonomous vehicle in the left lane, and FV2 represents the vehicle closest to the autonomous vehicle in the right lane.

[0056] N represents SV, FV1 or FV2 among the game participants;

[0057] Step 32: Establish the strategy space of the game participants;

[0058] Step 3. Establish constraints to ensure safe driving under different decisions of the autonomous driving vehicle;

[0059] Step 34: Establishing the autonomous driving vehicle profit function based on step 31, step 32 and step 33;

[0060] Step 35: Based on the revenue function of the autonomous driving vehicle, use the reverse induction method to deduce the optimal decision of the autonomous driving vehicle to meet the constraints. Based on optimal decision Determine the lateral displacement Δx of the autonomous driving vehicle at the next moment.

[0061] The other steps and parameters are the same as those in Specific Embodiments 1 to 3.

[0062] Specific implementation method five: This implementation method is different from specific implementation methods one to four in that: in step three two, the strategy space of the game participants is established;

[0063] The strategy space is:

[0064] A SV ={TL,KL,TR}

[0065]

[0066] Among them, A SV is the strategy set of the SV among the game participants, TL means that the autonomous driving vehicle SV will change lanes to the left, KL means that the autonomous driving vehicle SV will keep the current lane, and TR means that the autonomous driving vehicle SV will change lanes to the right;

[0067] is the strategy set of FV1 among the game participants, is the jerk of FV1 at time k, is the strategy set of FV2 among the game participants, is the jerk of FV2 at time k;

[0068] is the acceleration of FV1 car at time k; is the acceleration of FV1 car at time k-1;

[0069] is the acceleration of vehicle FV2 at time k; is the acceleration of FV2 car at time k-1;

[0070] Δt is the step length of trajectory planning for the autonomous driving vehicle; a SV Execution decision for the autonomous vehicle SV.

[0071] The other steps and parameters are the same as those in Specific Embodiments 1 to 4.

[0072] Specific implementation method 6: This implementation method is different from specific implementation methods 1 to 5 in that: in step 33, constraint conditions for ensuring safe driving under different decisions of the autonomous driving vehicle are established;

[0073] The specific process is:

[0074] The safe driving constraints of the autonomous vehicle include constraints for avoiding collisions between the autonomous vehicle and surrounding vehicles and avoiding collisions between the autonomous vehicle and the road shoulder;

[0075] If a SV =TL

[0076]

[0077]

[0078] 0.5×w SV +Δx mean <d SV,1

[0079] in, is the longitudinal relative distance between SV and LV1 at time k, is the speed of LV1 at time k, v SV (k) is the speed of the autonomous driving vehicle SV at time k, Δt represents the trajectory planning step, Δy min is the minimum longitudinal relative distance;

[0080] v max is the maximum speed of the autonomous vehicle, which is 33.33 m / s; represents the longitudinal relative distance between SV and FV1 at time k;

[0081] wSV is the width of the autonomous vehicle, d SV,1 The distance between the autonomous vehicle and the left boundary of the innermost lane in its driving direction (the boundary in the upbound and downbound directions);

[0082] If a SV =KL

[0083]

[0084] in, It represents the longitudinal relative distance between SV and LV2;

[0085] If a SV =TR

[0086]

[0087]

[0088] 0.5×w SV +Δx mean <d SV,2

[0089] Among them, d SV,2 The distance between the autonomous vehicle and the right edge of the outermost lane in its driving direction; is the longitudinal relative distance between SV and LV3 at time k, is the speed of LV3 at time k; Represents the longitudinal relative distance between SV and FV3 at time k, obtained by step 1.

[0090] The other steps and parameters are the same as those in Specific Embodiments 1 to 5.

[0091] Specific implementation method 7: This implementation method is different from any one of specific implementation methods 1 to 6 in that: in step 34, the autonomous driving vehicle benefit function is established based on step 31, step 32 and step 33; the specific process is:

[0092] Constructing a penalty function f for an autonomous vehicle SV And benefit function. The benefit function of the autonomous driving vehicle under various driving conditions after executing different decisions is divided into safety benefit function U safe and the spatial benefit function U spaceThe safety benefit function is represented by the longitudinal relative distance between SV and FV after t seconds, which is used to evaluate the risk of rear-end collision with the vehicle behind the autonomous driving vehicle after completing the lane change. The spatial benefit function is used to characterize the spatial leading benefit that the autonomous driving vehicle can achieve after performing different driving operations. The necessity of executing a certain decision at the current moment can be quantified through the penalty function. The penalty function is characterized by the speed difference between the vehicle's expected speed and the vehicle in front of the target lane and the decisions of other game participants. The smaller the speed difference and jerk, the more the driving conditions can be improved by executing the decision. Under the premise of ensuring safe driving, it becomes more urgent for the vehicle to execute the decision. The final benefit value will be jointly characterized by the single benefit function and the penalty function.

[0093] If a SV =TL

[0094]

[0095] If a SV =KL

[0096]

[0097] U safe =1

[0098]

[0099] If a SV =TR

[0100]

[0101] Among them, U total is the total revenue function, f SV is the penalty function, U safe is the safety benefit function, U space is the spatial benefit function; Constraint is True means that the decision of the autonomous driving vehicle meets the safe driving constraint; Constraint is False means that the decision of the autonomous driving vehicle does not meet the safe driving constraint;

[0102] w1, w2, w3 and w4 are control parameters;

[0103] T headway is the headway time;

[0104] represents the longitudinal relative distance between SV and LV1 at time k, obtained by step 1;

[0105] v SV (k) represents the speed of the autonomous driving vehicle SV at time k; represents the speed of FV1 at time k; represents the speed of FV2 at time k;

[0106] v des is the expected speed of the autonomous vehicle, is the speed of LV1 at time k;

[0107] is the jerk of FV1 at time k;

[0108] represents the longitudinal relative distance between SV and FV1 at time k, obtained by step 1;

[0109] represents the longitudinal relative distance between SV and FV1 at time k+1;

[0110] y SV (k+1) and It is an intermediate variable and there is no need to measure the specific value;

[0111] y SV (k) and It represents an intermediate variable, and no specific value needs to be measured;

[0112] acc SV (k) is the acceleration of the SV car at time k, and its initial value is set to 0. is the acceleration of FV1 car at time k, and its initial value is set to 0;

[0113] Δt is the unit step length of the autonomous vehicle trajectory planning;

[0114] represents the longitudinal relative distance between SV and LV2 at time k, obtained by step 1;

[0115] is the speed of LV2 at time k;

[0116] represents the longitudinal relative distance between SV and FV2 at time k, obtained by step 1;

[0117] represents the longitudinal relative distance between SV and FV2 at time k+1;

[0118] represents the longitudinal relative distance between SV and LV3 at time k, obtained by step 1;

[0119] is the speed of LV3 at time k;

[0120] is the jerk of FV2 at time k;

[0121] It represents an intermediate variable, and no specific value needs to be measured;

[0122] represents the speed of FV2 at time k;

[0123] is the acceleration of FV2 car at time k, and its initial value is set to 0;

[0124] is the jerk of FV2 at time k.

[0125] The other steps and parameters are the same as those in Specific Embodiments 1 to 6.

[0126] Specific implementation eight: This implementation differs from one of the specific implementations one to seven in that: in step three or five, based on the benefit function of the autonomous driving vehicle, the optimal decision of the autonomous driving vehicle to satisfy the constraint conditions is inferred according to the reverse induction method. Based on optimal decision Determine the lateral displacement Δx of the autonomous driving vehicle at the next moment;

[0127] The specific process is:

[0128] According to the reverse induction method, the optimal decision of the autonomous driving vehicle to meet the constraints is calculated Determine the lateral displacement Δx of the autonomous vehicle:

[0129]

[0130] in, To optimize the decision making of autonomous vehicles, is the strategy candidate set of FV1 based on SV decision, is the strategy candidate set of FV2 based on SV decision, Decision making for the execution of FV1; Make executive decisions for FV2;

[0131] ξ is the response of FV1 based on the decision of the autonomous driving vehicle;

[0132] ζ is the response of FV2 based on the decision of the autonomous vehicle;

[0133] Δx is the lateral displacement of the autonomous driving vehicle SV at the next moment, Δx mean is the average lateral displacement per unit step of the autonomous driving vehicle SV; It is the optimal decision for the autonomous driving vehicle SV.

[0134] The other steps and parameters are the same as those in Specific Embodiments 1 to 7.

[0135] Specific implementation method nine: This implementation method is a computer storage medium, in which at least one instruction is stored. The at least one instruction is loaded and executed by a processor to implement the trajectory planning method during the lane changing process of an autonomous driving vehicle.

[0136] It should be understood that the instructions include computer program products, software or computerized methods corresponding to any method described in the present invention; the instructions can be used to program a computer system, or other electronic device. Computer storage media may include readable media on which instructions are stored, which may include but are not limited to magnetic storage media, optical storage media; magneto-optical storage media include read-only memory ROM, random access memory RAM, erasable programmable memory (e.g., EPROM and EEPROM) and flash memory layers, or other types of media suitable for storing electronic instructions.

[0137] Specific embodiment 10: This embodiment is a trajectory planning device for an autonomous driving vehicle during lane change, the device includes a processor and a memory. It should be understood that the device includes any device including a processor and a memory described in the present invention, and the device may also include other units and modules that perform display, interaction, processing, control, etc. and other functions through signals or instructions;

[0138] At least one instruction is stored in the memory, and the at least one instruction is loaded and executed by the processor to implement the trajectory planning method during the lane changing process of an autonomous driving vehicle.

[0139] Those skilled in the art will appreciate that at least one instruction stored is a computer program product corresponding to the method or system. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes. The scheme in the embodiment of the present application can be implemented in various computer languages, for example, object-oriented programming language Java and literal scripting language JavaScript, etc.

[0140] The present application is described with reference to the flowcharts and / or block diagrams of the methods, systems, and computer program products according to the embodiments of the present application, and may also be used for corresponding devices. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0141] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0142] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0143] Although the preferred embodiments of the present application have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications falling within the scope of the present application.

[0144] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is also intended to include these modifications and variations.

[0145] The present invention may also have many other embodiments. Without departing from the spirit and essence of the present invention, those skilled in the art may make various corresponding changes and modifications based on the present invention, but these corresponding changes and modifications should all fall within the scope of protection of the claims attached to the present invention.

Claims

1. A trajectory planning method for an autonomous driving vehicle during lane change, characterized in that: The specific process of the method is: Step 1: Obtain the self-motion information of the autonomous driving vehicle SV and the geometric dimensions of the autonomous driving vehicle SV; Acquire geometric dimensions of vehicles Z around the autonomous driving vehicle and motion state information of vehicles Z around the autonomous driving vehicle; Step 2: Based on the self-driving vehicle's own motion information in step 1 and the geometric dimensions and motion state information of vehicles around the self-driving vehicle, a decision on the longitudinal displacement of the self-driving vehicle is made; Step 3: Establish the game rules for the game participants. The autonomous driving vehicle is the leader and needs to make decisions first. The vehicles behind it on both sides of the lane are followers and they respond based on the leader's decision. Based on the response made by the Follower according to the Leader's decision, the optimal decision of the autonomous driving vehicle that meets the constraints is inferred according to the reverse induction method, and the lateral displacement of the autonomous driving vehicle at the next moment is determined based on the optimal decision; Step 4: Determine the trajectory sequence points of the autonomous driving vehicle at the next moment according to the lateral displacement and the longitudinal displacement; Step 5: Set the total lane change time to L seconds, repeat steps 1 to 4 L / Δt times, and obtain the corresponding total revenue U of the autonomous driving vehicle total , select the total revenue U of the autonomous driving vehicle total The lane corresponding to the maximum value is the target lane. Each time the autonomous driving vehicle executes the optimal decision to achieve the final lane changing trajectory of the autonomous driving vehicle.

2. The trajectory planning method for an autonomous driving vehicle during lane change according to claim 1, characterized in that: In the step 1, the self-motion information of the autonomous driving vehicle SV and the geometric dimensions of the autonomous driving vehicle SV are obtained; Acquire geometric dimensions of vehicles Z around the autonomous driving vehicle and motion state information of vehicles Z around the autonomous driving vehicle; The specific process is: The autonomous driving vehicle obtains the autonomous driving vehicle SV's own motion information and the autonomous driving vehicle SV's geometric dimensions through the on-board computing unit; The self-driving vehicle SV’s own motion information includes the speed v SV , acceleration acc SV ; The geometric dimensions of the autonomous vehicle SV include the vehicle length l SV 、Vehicle width w SV ; The autonomous driving vehicle obtains geometric dimensions of vehicles Z around the autonomous driving vehicle and motion state information of vehicles Z around the autonomous driving vehicle through an on-board detection unit; The geometric dimensions of the vehicles Z surrounding the autonomous vehicle include the vehicle length l Z 、Vehicle width w Z ; The motion state information of the vehicle Z around the autonomous driving vehicle includes the speed v Z , acceleration acc Z , the longitudinal relative distance Δy between the autonomous driving vehicle and the surrounding vehicles Z Z , the distance d between the autonomous driving vehicle and the left boundary of the innermost lane in the driving direction SV,1 , the distance d between the autonomous driving vehicle and the right boundary of the outermost lane in the driving direction SV,2 ; Among them, Z∈{LV1,LV2,LV3,FV1,FV2}, LV1 represents the vehicle in front of the left lane of the autonomous driving vehicle that is closest to the autonomous driving vehicle; LV2 represents the vehicle in front of the current lane of the autonomous driving vehicle that is closest to the autonomous driving vehicle; LV3 represents the vehicle in front of the right lane of the autonomous driving vehicle that is closest to the autonomous driving vehicle; FV1 represents the vehicle behind the left lane of the autonomous driving vehicle that is closest to the autonomous driving vehicle; FV2 represents the vehicle behind the right lane of the autonomous driving vehicle that is closest to the autonomous driving vehicle.

3. The trajectory planning method for an autonomous driving vehicle during lane change according to claim 2, characterized in that: In the step 2, based on the self-driving vehicle's own motion information and the geometric dimensions and motion state information of vehicles around the self-driving vehicle in step 1, a decision on the longitudinal displacement of the self-driving vehicle is made; The specific process is: The longitudinal displacement Δy(k) of the autonomous driving vehicle is calculated based on the IDM model; the IDM model is: v SV (k)=v SV (k-1)+acc SV (k-1)×Δt Among them, acc SV (k) represents the acceleration of the autonomous driving vehicle at time k, and its initial value is set to 0; a IDM is the maximum acceleration of the autonomous vehicle; v SV (k) is the speed of the autonomous driving vehicle at time k, and its initial value is set to 0; is the speed of LV2 at time k; v des is the expected speed of the autonomous vehicle; δ is the acceleration index; s(v SV (k),T) is a function; Δv(k) is the relative speed between the autonomous driving vehicle and the preceding vehicle at time k, and T is the expected headway of the autonomous driving vehicle; b IDM is the maximum deceleration of the autonomous driving vehicle, Δt is the unit step size of the trajectory planning of the autonomous driving vehicle, in seconds, and Δy(k) is the longitudinal displacement planned for the autonomous driving vehicle at time k.

4. The trajectory planning method for an autonomous driving vehicle during lane change according to claim 3, characterized in that: In step 3, the game rules of the game participants are established. The autonomous driving vehicle is the leader and needs to make decisions first. The vehicles behind on both sides of the lane are followers and respond according to the leader's decision. Based on the response made by the Follower according to the Leader's decision, the optimal decision of the autonomous driving vehicle that meets the constraints is inferred according to the reverse induction method, and the lateral displacement of the autonomous driving vehicle at the next moment is determined based on the optimal decision; The specific process is: Step 31: Determine the game participants, which include the autonomous driving vehicle and the vehicles behind it that are closest to the autonomous driving vehicle in the lanes on both sides of the autonomous driving vehicle; N∈{SV,FV1,FV2}, SV represents the autonomous vehicle, FV1 represents the vehicle closest to the autonomous vehicle in the left lane, and FV2 represents the vehicle closest to the autonomous vehicle in the right lane. N represents SV, FV1 or FV2 among the game participants; Step 32: Establish the strategy space of the game participants; Step 3. Establish constraints to ensure safe driving under different decisions of the autonomous driving vehicle; Step 34: Establishing the autonomous driving vehicle profit function based on step 31, step 32 and step 33; Step 35: Based on the revenue function of the autonomous driving vehicle, use the reverse induction method to deduce the optimal decision of the autonomous driving vehicle to meet the constraints. Based on optimal decision Determine the lateral displacement Δx of the autonomous driving vehicle at the next moment.

5. The trajectory planning method for an autonomous driving vehicle during lane change according to claim 4, characterized in that: In said step 32, a strategy space of the game participants is established; The strategy space is: A SV ={TL,KL,TR} Among them, A SV is the strategy set of the SV among the game participants, TL means that the autonomous driving vehicle SV will change lanes to the left, KL means that the autonomous driving vehicle SV will keep the current lane, and TR means that the autonomous driving vehicle SV will change lanes to the right; is the strategy set of FV1 among the game participants, is the jerk of FV1 at time k, is the strategy set of FV2 among the game participants, is the jerk of FV2 at time k; is the acceleration of FV1 car at time k; is the acceleration of FV1 car at time k-1; is the acceleration of vehicle FV2 at time k; is the acceleration of FV2 car at time k-1; Δt is the step length of trajectory planning for the autonomous driving vehicle; a SV Execution decision for the autonomous vehicle SV.

6. The trajectory planning method for an autonomous driving vehicle during lane change according to claim 5, characterized in that: In the step 33, constraints are established to ensure safe driving under different decisions of the autonomous driving vehicle; The specific process is: The safe driving constraints of the autonomous vehicle include constraints for avoiding collisions between the autonomous vehicle and surrounding vehicles and avoiding collisions between the autonomous vehicle and the road shoulder; young a SV =TL Δy LV1 (k)+(v LV1 (k)-v max )×Δt>Δy min Δy FV1 (k)+(v SV (k)-v max )×Δt>Δy min 0.5×w SV +Δx mean <d SV,1 in, is the longitudinal relative distance between SV and LV1 at time k, is the speed of LV1 at time k, v SV (k) is the speed of the autonomous driving vehicle SV at time k, Δt represents the trajectory planning step, Δy min is the minimum longitudinal relative distance; v max is the maximum speed of the autonomous vehicle, which is 33.33 m / s; represents the longitudinal relative distance between SV and FV1 at time k; w SV is the width of the autonomous vehicle, d SV,1 is the distance between the autonomous driving vehicle and the left boundary of the innermost lane in the driving direction; girl SV =KL in, It represents the longitudinal relative distance between SV and LV2; If a SV =TR 0.5×w SV +Δx mean <d SV,2 Among them, d SV,2 is the distance between the autonomous driving vehicle and the right boundary of the outermost lane in the driving direction; is the longitudinal relative distance between SV and LV3 at time k, is the speed of LV3 at time k; Represents the longitudinal relative distance between SV and FV3 at time k.

7. The trajectory planning method for an autonomous driving vehicle during lane change according to claim 6, characterized in that: In step 34, the autonomous driving vehicle profit function is established based on step 31, step 32 and step 33; the specific process is: young a SV =TL girl SV =KL U safe ＝1 If a SV =TR Among them, U total is the total revenue function, f SV is the penalty function, U safe is the safety benefit function, U space is the spatial benefit function; Constraint is True means that the decision of the autonomous driving vehicle meets the safe driving constraint; Constraint is False means that the decision of the autonomous driving vehicle does not meet the safe driving constraint; w1, w2, w3 and w4 are control parameters; T headway is the headway time; represents the longitudinal relative distance between SV and LV1 at time k; v SV (k) represents the speed of the autonomous driving vehicle SV at time k; represents the speed of FV1 at time k; represents the speed of FV2 at time k; v des is the expected speed of the autonomous vehicle, is the speed of LV1 at time k; is the jerk of FV1 at time k; represents the longitudinal relative distance between SV and FV1 at time k; represents the longitudinal relative distance between SV and FV1 at time k+1; y SV (k+1) and It is an intermediate variable and there is no need to measure the specific value; y SV (k) and It represents an intermediate variable, and no specific value needs to be measured; acc SV (k) is the acceleration of the SV car at time k, and its initial value is set to 0. is the acceleration of FV1 car at time k, and its initial value is set to 0; Δt is the unit step length of the autonomous vehicle trajectory planning; represents the longitudinal relative distance between SV and LV2 at time k; is the speed of LV2 at time k; represents the longitudinal relative distance between SV and FV2 at time k; represents the longitudinal relative distance between SV and FV2 at time k+1; represents the longitudinal relative distance between SV and LV3 at time k; is the speed of LV3 at time k; is the jerk of FV2 at time k; It represents an intermediate variable, and no specific value needs to be measured; represents the speed of FV2 at time k; is the acceleration of FV2 car at time k, and its initial value is set to 0; is the jerk of FV2 at time k.

8. The trajectory planning method for an autonomous driving vehicle during lane change according to claim 7, characterized in that: In the steps 3 and 5, based on the revenue function of the autonomous driving vehicle and according to the reverse induction method, the optimal decision of the autonomous driving vehicle to meet the constraints is calculated. Based on optimal decision Determine the lateral displacement Δx of the autonomous driving vehicle at the next moment; The specific process is: According to the reverse induction method, the optimal decision of the autonomous driving vehicle to meet the constraints is calculated Determine the lateral displacement Δx of the autonomous vehicle: in, To optimize the decision making of autonomous vehicles, is the strategy candidate set of FV1 based on SV decision, is the strategy candidate set of FV2 based on SV decision, Decision making for the execution of FV1; Make executive decisions for FV2; ξ is the response of FV1 based on the decision of the autonomous driving vehicle; ζ is the response of FV2 based on the decision of the autonomous vehicle; Δx is the lateral displacement of the autonomous driving vehicle SV at the next moment, Δx mean is the average lateral displacement per unit step of the autonomous driving vehicle SV; It is the optimal decision for the autonomous driving vehicle SV.

9. A computer storage medium, characterized in that: The storage medium stores at least one instruction, and the at least one instruction is loaded and executed by the processor to implement a trajectory planning method for an autonomous driving vehicle during a lane change process as described in any one of items 1 to 8.

10. A trajectory planning device for an autonomous driving vehicle during lane change, characterized in that: The device includes a processor and a memory, wherein the memory stores at least one instruction, and the at least one instruction is loaded and executed by the processor to implement a trajectory planning method during a lane change process of an autonomous driving vehicle as described in any one of claims 1 to 8.

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