Vehicle lane-changing control method, device, equipment and storage medium

Abstract

The present application relates to the field of vehicle control technology, and discloses a vehicle lane change control method, device, equipment and storage medium, the method comprising: performing lateral and longitudinal risk analysis on the current vehicle to obtain key obstacles; obtaining attribute information of the key obstacles, and generating a reference acceleration curve based on the attribute information; determining a target speed planning trajectory based on the reference acceleration curve; and controlling the vehicle to change lanes according to the target speed planning trajectory. The present application can effectively improve the success rate of lane change.

Description

Technical Field

[0001] The present application relates to the field of vehicle control technology, and in particular to a vehicle lane change control method, device, equipment and storage medium. Background Art

[0002] At present, intelligent driving is developing rapidly. Some OEMs represented by new forces are gradually implementing the intelligent navigation function. This function can allow the vehicle to autonomously choose to change lanes based on the navigation route, which improves driving safety and greatly reduces driver fatigue. The decision-making process of autonomous lane change requires a comprehensive consideration of the road environment, the movement status of surrounding vehicles and the vehicle's own information to obtain the decision intention. This process needs to ensure safety, comfort, and a certain degree of efficiency. Currently, many companies use the DP+QP method for path and speed planning. This method is not good at considering the information of dynamic targets when doing path planning. When planning speed, if a backward risk target is encountered, most of them will adopt the principle of slowing down and giving way. This will appear very unintelligent for some necessary lane change scenarios at on and off ramps or intersections, and may even miss the best time, resulting in a low lane change success rate.

[0003] Therefore, how to effectively improve the lane changing success rate is a problem that needs to be solved urgently.

[0004] The above contents are only used to assist in understanding the technical solution of the present application and do not constitute an admission that the above contents are prior art. Summary of the invention

[0005] The main purpose of this application is to provide a vehicle lane changing control method, device, equipment and storage medium, aiming to solve the technical problem of how to effectively improve the lane changing success rate.

[0006] To achieve the above objectives, the present application proposes a vehicle lane change control method, the method comprising:

[0007] Perform lateral and longitudinal risk analysis on the current vehicle to obtain key obstacles;

[0008] Acquiring attribute information of the key obstacle, and generating a reference acceleration curve based on the attribute information;

[0009] Determining a target speed planning trajectory based on the reference acceleration curve;

[0010] The vehicle is controlled to change lanes according to the target speed planning trajectory.

[0011] In one embodiment, the performing of lateral and longitudinal risk analysis on the current vehicle to obtain key obstacles includes:

[0012] Conduct longitudinal risk analysis on the current vehicle to obtain the longitudinal risk target;

[0013] Perform lateral risk analysis on the current vehicle to obtain lateral risk targets;

[0014] The horizontal risk target and the vertical risk target are regarded as key obstacles.

[0015] In one embodiment, performing longitudinal risk analysis on the current vehicle to obtain a longitudinal risk target includes:

[0016] Get the surrounding vehicle information of the current vehicle;

[0017] Determining whether surrounding vehicles meet lane conditions according to the surrounding vehicle information;

[0018] When surrounding vehicles meet lane conditions, the surrounding vehicles are used as candidate vehicles;

[0019] Determining whether the candidate vehicle meets a distance condition;

[0020] When the candidate vehicle meets the distance condition, the candidate vehicle is taken as a longitudinal risk target.

[0021] In one embodiment, performing lateral risk analysis on the current vehicle to obtain a lateral risk target includes:

[0022] Acquire a first boundary lateral position of the current vehicle and a second boundary lateral position of an adjacent vehicle, wherein the adjacent vehicle is located in an adjacent lane of the current vehicle;

[0023] determining a vehicle lateral distance based on the first boundary lateral position and the second boundary lateral position;

[0024] Determining that the lateral distance of the vehicle reaches a preset lateral safety distance;

[0025] When the lateral distance between the vehicles does not reach the preset lateral safety distance, the adjacent vehicle is taken as a lateral risk target.

[0026] In one embodiment, the acquiring the attribute information of the key obstacle and generating a reference acceleration curve based on the attribute information includes:

[0027] Obtaining attribute information of the key obstacle;

[0028] generating an obstacle prediction trajectory based on the attribute information of the key obstacle;

[0029] determining whether there is a risk of collision based on the predicted trajectory of the obstacle;

[0030] When there is no collision risk, sampling parameters are obtained, and acceleration sampling is performed based on the sampling parameters to obtain a reference acceleration curve.

[0031] In one embodiment, determining the target speed planning trajectory based on the reference acceleration curve includes:

[0032] Calculating based on the reference acceleration curve to obtain a velocity curve and a position curve;

[0033] generating a candidate velocity planning trajectory based on the velocity profile and the position profile;

[0034] Evaluate the candidate speed planning trajectories to obtain evaluation cost values ​​of each candidate speed planning trajectory;

[0035] A target speed planning trajectory is determined based on the evaluation cost values ​​of the candidate speed planning trajectories.

[0036] In one embodiment, the planning of the trajectory according to the target speed to control the vehicle to change lanes includes:

[0037] Determining a guide speed based on the target speed planning trajectory;

[0038] Performing speed optimization on the guiding speed to obtain an optimized speed;

[0039] The vehicle is controlled to change lanes according to the optimized speed.

[0040] In addition, to achieve the above-mentioned purpose, the present application also proposes a vehicle lane changing control device, the vehicle lane changing control device comprising:

[0041] The analysis module is used to perform lateral and longitudinal risk analysis on the current vehicle to obtain key obstacles;

[0042] A generating module, used for acquiring attribute information of the key obstacle and generating a reference acceleration curve based on the attribute information;

[0043] A determination module, configured to determine a target speed planning trajectory based on the reference acceleration curve;

[0044] The control module is used to control the vehicle to change lanes according to the target speed planning trajectory.

[0045] In addition, to achieve the above-mentioned purpose, the present application also proposes a vehicle lane changing control device, which includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program is configured to implement the steps of the vehicle lane changing control method as described above.

[0046] In addition, to achieve the above-mentioned purpose, the present application also proposes a storage medium, which is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, the steps of the vehicle lane change control method described above are implemented.

[0047] In addition, to achieve the above-mentioned purpose, the present application also provides a computer program product, which includes a computer program, and when the computer program is executed by a processor, the steps of the vehicle lane change control method described above are implemented.

[0048] The present application provides a vehicle lane-changing control method, which first performs a lateral and longitudinal risk analysis on the current vehicle to obtain key obstacles, thereby screening risk targets and improving lane-changing efficiency; obtains attribute information of the key obstacles, and generates a reference acceleration curve based on the attribute information to improve the rationality of the reference acceleration curve; determines a target speed planning trajectory based on the reference acceleration curve to improve the accuracy of the speed planning trajectory; controls the vehicle to change lanes according to the target speed planning trajectory, thereby effectively improving the lane-changing success rate.

[0049] In summary, the present application screens out key obstacles by performing lateral and longitudinal risk analysis, generates reference acceleration curves based on the key obstacles and determines the target speed planning trajectory, thereby controlling the vehicle to change lanes. By controlling the vehicle to change lanes according to the optimal speed planning trajectory, the technical defect of low lane changing success rate is overcome, and the lane changing success rate can be effectively improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0051] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0052] Figure 1 A flow chart of the first embodiment of the vehicle lane change control method of the present application;

[0053] Figure 2 An overall flow chart of a vehicle lane changing control method provided in Embodiment 1 of the present application;

[0054] Figure 3 A flow chart of the second embodiment of the vehicle lane changing control method of the present application;

[0055] Figure 4 A schematic diagram of lane condition judgment of a vehicle lane change control method provided in Embodiment 2 of the present application;

[0056] Figure 5A schematic diagram of distance condition judgment of a vehicle lane change control method provided in Embodiment 2 of the present application;

[0057] Figure 6 This is a schematic diagram of the module structure of the vehicle lane change control device according to an embodiment of the present application;

[0058] Figure 7 Schematic diagram of the device structure of the hardware operating environment involved in the vehicle lane changing control method in the embodiment of the present application.

[0059] The purpose, features and advantages of this application will be further described in conjunction with the embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0060] It should be understood that the specific embodiments described herein are only used to explain the technical solutions of the present application and are not used to limit the present application.

[0061] In order to better understand the technical solution of the present application, a detailed description will be given below in conjunction with the accompanying drawings and specific implementation methods.

[0062] The main solution of the embodiment of the present application is: perform lateral and longitudinal risk analysis on the current vehicle to obtain key obstacles; obtain attribute information of the key obstacles and generate a reference acceleration curve based on the attribute information; determine a target speed planning trajectory based on the reference acceleration curve; and control the vehicle to change lanes according to the target speed planning trajectory.

[0063] Currently, many companies use the DP+QP method for path and speed planning. This method is not good at considering dynamic target information when planning paths. When planning speed, if there is a backward risk target, most companies will adopt the principle of slowing down and giving way. This is not smart for some necessary lane change scenarios at on-ramp or intersection, and may even miss the best time, resulting in a low lane change success rate. Therefore, how to effectively improve the lane change success rate is a problem that needs to be solved urgently.

[0064] The present application screens out key obstacles by performing lateral and longitudinal risk analysis, generates reference acceleration curves based on the key obstacles, and determines a target speed planning trajectory, thereby controlling the vehicle to change lanes. By controlling the vehicle to change lanes based on the optimal speed planning trajectory, the technical defect of low lane changing success rate is overcome, and the lane changing success rate can be effectively improved.

[0065] It should be noted that the execution subject of this embodiment may be a computing service device with data processing, network communication and program running functions, such as a tablet computer, a personal computer, a mobile phone, etc., or an electronic device capable of realizing the above functions, a vehicle lane change control device, etc. The following takes the vehicle lane change control device as an example to illustrate this embodiment and the following embodiments.

[0066] Based on this, the embodiment of the present application provides a vehicle lane change control method, referring to Figure 1 , Figure 1 This is a flow chart of the first embodiment of the vehicle lane changing control method of the present application.

[0067] In this embodiment, the vehicle lane change control method includes steps S10 to S40:

[0068] Step S10, performing lateral and longitudinal risk analysis on the current vehicle to obtain key obstacles.

[0069] It should be noted that by performing lateral and longitudinal risk analysis on the current vehicle, targets that are truly risky to the vehicle's driving, namely key obstacles, are screened out.

[0070] In addition, it should be noted that the lateral and longitudinal risk analysis can adopt a variety of methods, such as target detection and tracking based on sensor data, risk assessment based on traffic rules and road conditions, etc., which are not specifically limited in this embodiment. Through risk analysis, key obstacles that have a major impact on the lane change of the vehicle can be accurately identified, providing an important basis for subsequent trajectory planning and speed control.

[0071] Step S20: acquiring attribute information of the key obstacle, and generating a reference acceleration curve based on the attribute information.

[0072] It should be noted that after determining the key obstacles, it is necessary to further obtain their attribute information, such as position, speed, acceleration, size, etc. Based on this attribute information, a reference acceleration curve can be generated through an algorithm. The reference acceleration curve is calculated based on the motion state of the key obstacles and the motion state of the ego vehicle. It represents how the ego vehicle should adjust the acceleration during the lane change process to ensure safe lane change.

[0073] In a feasible implementation, step S20 may include: obtaining attribute information of the key obstacle; generating an obstacle prediction trajectory based on the attribute information of the key obstacle; determining whether there is a collision risk based on the obstacle prediction trajectory; when there is no collision risk, obtaining sampling parameters, and performing acceleration sampling based on the sampling parameters to obtain a reference acceleration curve.

[0074] It should be noted that the corresponding STboundary is generated based on the screened key obstacles, that is, the range occupied by the obstacle's driving trajectory is generated within the time range of speed planning, that is, the upper and lower limits of the obstacle trajectory within the time range, so as to determine the obstacle prediction trajectory.

[0075] It can be understood that the sampling parameters are configured, including the maximum acceleration (a_max), the maximum velocity (v_max), the acceleration sampling step (a_step) and other parameter assignments. At a given time t_traver, the sampling is evenly divided into three sections [t0-t1], [t1-t2], and [t2-t3] to ensure the sampling coverage. In the first section [t1-t2], m acceleration curves are generated with a sampling point interval of a_step; in the second section [t2-t3], based on the ends of the m acceleration curves, n acceleration curves are sampled with the same acceleration interval a_step; in the third section [t2-t3], based on the ends of the n acceleration curves, q acceleration curves are sampled again with a_step, then the total number of acceleration curves generated by this sampling is m*n*q.

[0076] Step S30, determining a target speed planning trajectory based on the reference acceleration curve.

[0077] It should be noted that the target speed planning trajectory is a speed curve that changes over time. It guides the ego vehicle on how to adjust its speed during the lane change process to ensure the smoothness and safety of the lane change process. When determining the target speed planning trajectory, factors such as the ego vehicle's dynamic characteristics and road conditions also need to be considered.

[0078] In a feasible implementation, step S30 may include: performing calculations based on the reference acceleration curve to obtain a speed curve and a position curve; generating candidate speed planning trajectories based on the speed curve and the position curve; evaluating the candidate speed planning trajectories to obtain evaluation cost values ​​of each candidate speed planning trajectory; and determining a target speed planning trajectory based on the evaluation cost values ​​of each candidate speed planning trajectory.

[0079] It should be noted that the sampled acceleration curve (at) is integrated to sequentially obtain the ego vehicle velocity curve (vt) and position curve (st), thereby generating a series of candidate velocity planning trajectories. These trajectories are based on the reference acceleration curve and take into account the ego vehicle's dynamic limitations and road conditions.

[0080] It is understandable that the candidate trajectories are evaluated, and the evaluation criteria may include the smoothness, safety, and compliance with traffic rules and road conditions of the trajectory, etc., which are not specifically limited in this embodiment. Each candidate trajectory is assigned an evaluation cost value, which reflects the pros and cons of the trajectory under these evaluation criteria. The trajectory with the smallest evaluation cost value is found among all candidate trajectories as the target speed planning trajectory, and is output to the speed planning module.

[0081] Step S40, controlling the vehicle to change lanes according to the target speed planning trajectory.

[0082] It should be noted that the lane change control of the vehicle according to the target speed planning trajectory can be achieved by adjusting the vehicle's throttle, brake, steering and other actuators. By accurately controlling these actuators, the vehicle can change lanes according to the target speed planning trajectory, thereby improving the lane change success rate and ensuring the safety of the lane change process.

[0083] It is understandable that after controlling the vehicle to change lanes according to the target speed planning trajectory, it may also include: real-time monitoring and adjustment of the lane changing process, and evaluation of the vehicle status after the lane changing is completed.

[0084] In a feasible implementation, step S40 may include: determining a guide speed based on the target speed planning trajectory; optimizing the guide speed to obtain an optimized speed; and controlling the vehicle to change lanes according to the optimized speed.

[0085] It should be noted that the guide speed is the reference speed during the lane change process of the ego vehicle. It plans the trajectory based on the target speed, but may need further adjustment and optimization to adapt to the actual road conditions and vehicle dynamic performance. The purpose of speed optimization of the guide speed is to enable the ego vehicle to better adapt to various uncertain factors during the actual lane change process, such as changes in road conditions, interference from other vehicles, etc. In the process of speed optimization, some constraints may be taken into account, such as the maximum acceleration, maximum deceleration, maximum speed, etc. of the vehicle.

[0086] It can be understood that after speed optimization, an optimized speed is obtained. This optimized speed is the speed that the vehicle needs to follow during the actual lane change process. Then, according to this optimized speed, the vehicle is controlled to change lanes by adjusting the vehicle's throttle, brake, steering and other actuators.

[0087] It is worth noting that during the lane change process, the vehicle status needs to be monitored in real time to ensure the safety and smooth progress of the lane change process. If the vehicle status is found to be inconsistent with expectations during the actual lane change process, or if other emergencies occur, the lane change strategy needs to be adjusted in time, or even the lane change process needs to be interrupted to ensure the safety of the vehicle and passengers. After the lane change is completed, the vehicle status needs to be evaluated to determine whether the lane change process was successful and whether the vehicle is in a safe and stable state. If the evaluation results are not good, some remedial measures may need to be taken, such as adjusting the vehicle position, replanning the speed trajectory, etc.

[0088] like Figure 2 As shown, Figure 2It is the overall flow chart of vehicle lane changing. It judges whether the ego vehicle has cross-lane behavior. If so, the longitudinal distance of all rear dynamic targets is judged to be less than the longitudinal safety distance. If so, the lane condition is judged to judge whether the target vehicle is already in the target lane or has the intention to change lanes to the target lane. If so, a collision analysis is performed based on the target vehicle's predicted trajectory and the ego vehicle's path to determine whether there is a path collision risk. If there is a collision risk, a speed decision is made for the obstacle, and a comprehensive consideration is given to whether to slow down and wait or to accelerate to squeeze in. If there is no lane crossing behavior, the longitudinal distance of the front vehicle in the adjacent lane is judged to be less than the longitudinal safety distance. If so, the lateral distance of the front vehicle in the adjacent lane is judged to be less than the lateral safety distance. If so, a collision analysis is performed based on the target vehicle's predicted trajectory and the ego vehicle's path to determine whether there is a path collision risk. If so, a speed decision is made for the obstacle, and a comprehensive consideration is given to whether to slow down and wait or to accelerate to squeeze in.

[0089] The present embodiment provides a vehicle lane-changing control method. The present embodiment first performs a lateral and longitudinal risk analysis on the current vehicle to obtain key obstacles, thereby screening risk targets and improving lane-changing efficiency; obtains attribute information of the key obstacles, and generates a reference acceleration curve based on the attribute information to improve the rationality of the reference acceleration curve; determines a target speed planning trajectory based on the reference acceleration curve to improve the accuracy of the speed planning trajectory; controls the vehicle to change lanes according to the target speed planning trajectory, thereby effectively improving the lane-changing success rate.

[0090] In summary, this embodiment screens out key obstacles by performing lateral and longitudinal risk analysis, generates reference acceleration curves based on the key obstacles, and determines a target speed planning trajectory, thereby controlling the vehicle to change lanes. By controlling the vehicle to change lanes based on the optimal speed planning trajectory, the technical defect of low lane changing success rate is overcome, and the lane changing success rate can be effectively improved.

[0091] Based on the first embodiment of the present application, in the second embodiment of the present application, the same or similar contents as those in the above-mentioned embodiment 1 can refer to the above introduction, and will not be repeated later. Figure 3 , the step S10 further includes steps S101-S103:

[0092] Step S101, performing longitudinal risk analysis on the current vehicle to obtain a longitudinal risk target.

[0093] It should be noted that the longitudinal risk analysis of the current vehicle mainly evaluates the safety of the current vehicle in the longitudinal direction by considering lane conditions and distance conditions. Lane conditions may include lane width, lane type (such as straight lane, turning lane, etc.), obstacles on the lane, etc., which are not specifically limited in this embodiment. Distance conditions mainly involve the distance between the current vehicle and the vehicle or obstacle ahead, and whether this distance is safe enough.

[0094] It is understandable that in the longitudinal risk analysis, the vehicle may evaluate whether the distance to the vehicle in front is safe based on a preset safety distance threshold or safety time threshold. For example, if the distance between the current vehicle and the vehicle in front is less than the preset safety distance threshold, or at the current vehicle speed, the distance to the vehicle in front is less than the preset safety time threshold (i.e., within the preset safety time, the current vehicle cannot completely stop and avoid collision with the vehicle in front), then it can be considered that there is a risk target in the longitudinal direction.

[0095] In a feasible implementation, step S101 may include: obtaining surrounding vehicle information of the current vehicle; judging whether the surrounding vehicles meet lane conditions based on the surrounding vehicle information; when the surrounding vehicles meet lane conditions, taking the surrounding vehicles as candidate vehicles; judging whether the candidate vehicles meet distance conditions; when the candidate vehicles meet the distance conditions, taking the candidate vehicles as longitudinal risk targets.

[0096] It should be noted that the surrounding vehicle information includes the position information, speed information, lane change intention information, etc. of the surrounding vehicles, and this embodiment does not impose any specific restrictions on this.

[0097] It can be understood that the lane condition means that the risk target must be in the lane change target lane of the ego vehicle or have the intention to change lanes and reach the target lane faster than the ego vehicle.

[0098] like Figure 4 As shown, Figure 4 Figure 1 is a schematic diagram of lane condition judgment. Vehicle E is preparing to change lanes to the left. There are three targets behind it. Target vehicle S2 is already driving in the target lane. Target vehicle S1 has the intention to change lanes and is about to successfully change lanes to the target lane. Therefore, in this scenario, target vehicles S1 and S2 meet the lane conditions. Although target vehicle S3 is the closest to the ego vehicle in the longitudinal distance, it does not meet the lane conditions and is not considered in subsequent risk judgment.

[0099] It is worth noting that the distance condition, i.e. the lane change allowed by the vehicle, needs to meet the following two conditions at the same time: the front vehicle gap FrontGap> the safe front vehicle gap SecureFrontGap and the rear vehicle gap BackGap> the safe rear vehicle gap SecureBackGap, where the front vehicle gap FrontGap = D1 (the distance between the front vehicle and the rear of the front vehicle) - MinGap (the minimum parking gap is 2.5m), and the rear vehicle gap BackGap = D2 (the distance between the rear of the vehicle and the front of the rear vehicle) - MinGap (the minimum parking gap is 2.5m). The safe front vehicle gap and the safe rear vehicle gap are as follows: Formula 1 and Formula 2:

[0100] SecureFrontGap=V_ego*T_ego+D_egobg-D_leaderbg (Formula 1)

[0101] SecureBackGap=V_follow*T_follow+D_followbg-D_egobg (Formula 2)

[0102] In equations 1 and 2, SecureFrontGap is the safe front vehicle gap, SecureBackGap is the safe front vehicle gap, V_ego represents the longitudinal speed of the ego vehicle, V_follow represents the longitudinal speed of the following vehicle, T_follow represents the time headway of the following vehicle (1.5s), and T_ego represents the time headway of the ego vehicle (1.5s). D_egobg, D_follow, and D_leaderbg can be obtained from equations 3 to 5 as follows:

[0103] D_sbg=(V_s*N_s–V_sr*N_s(0.5*(N_s+1)))T_b (Formula 3)

[0104] V_sr=A_max*T_b(Equation 4)

[0105] N_s=V_s / V_sr (round down) (Formula 5)

[0106] In Equations 3 to 5, s can represent ego, follow, and lerder respectively, A_max represents the maximum comfortable deceleration of the vehicle, and T_b represents the operating step length.

[0107] like Figure 5 As shown, Figure 5 Schematic diagram of distance condition judgment, judging whether the front vehicle distance FrontGap between the vehicle E and the front vehicle S1 and the rear vehicle distance BackGap between the vehicle E and the rear vehicle S2 reach the corresponding safety distance.

[0108] It is worth noting that a longitudinal risk target list can be generated based on the longitudinal risk target. The longitudinal risk target list can include all vehicles or obstacles identified as longitudinal risk targets, as well as their related attribute information, such as distance, speed, acceleration, etc. This list can provide important input information for subsequent decision-making and planning.

[0109] Step S102, performing a lateral risk analysis on the current vehicle to obtain a lateral risk target.

[0110] It should be noted that the lateral risk analysis of the current vehicle mainly evaluates the safety of the current vehicle in the lateral direction through the lateral safety distance.

[0111] In a feasible implementation, step S102 may include: obtaining a first boundary lateral position of the current vehicle and a second boundary lateral position of an adjacent vehicle, wherein the adjacent vehicle is located in an adjacent lane of the current vehicle; determining a lateral distance of the vehicle based on the first boundary lateral position and the second boundary lateral position; determining whether the lateral distance of the vehicle reaches a preset lateral safety distance; and when the lateral distance of the vehicle does not reach the preset lateral safety distance, treating the adjacent vehicle as a lateral risk target.

[0112] It should be noted that when the vehicle is keeping lane in its own lane, in order to prevent a collision with a vehicle crossing the line in the adjacent lane, the lateral safety distance from the preceding vehicle in the adjacent lane is mainly considered, as shown in Formula 6:

[0113] Abs(L_ego-L_obs)≥SafeTolerance

[0114] In Equation 6, L_ego is the lateral position of the boundary of the ego vehicle, L_obs is the lateral position of the boundary of the vehicle in the adjacent lane, and SafeTolerance is the lateral safety distance.

[0115] Step S103: taking the horizontal risk target and the vertical risk target as key obstacles.

[0116] It should be noted that in the autonomous driving system, key obstacles refer to vehicles that are truly risky to the self-driving car. By analyzing the lateral risk targets and longitudinal risk targets, it is possible to determine which vehicles are the objects that need to be focused on during the current vehicle driving process. These key obstacles will become an important basis for subsequent decision-making and planning.

[0117] In this embodiment, by performing lateral risk analysis and longitudinal risk analysis on the current vehicle, key obstacles are determined, thereby improving the efficiency and success rate of lane change trajectory planning.

[0118] It should be noted that the above examples are only used to understand the present application and do not constitute a limitation on the vehicle lane changing control method of the present application. More simple transformations based on this technical concept are all within the protection scope of the present application.

[0119] This application also provides a vehicle lane change control device, please refer to Figure 6 , the vehicle lane changing control device comprises:

[0120] The analysis module 10 is used to perform lateral and longitudinal risk analysis on the current vehicle to obtain key obstacles.

[0121] The generating module 20 is used to obtain the attribute information of the key obstacle and generate a reference acceleration curve based on the attribute information.

[0122] The determination module 30 is used to determine a target speed planning trajectory based on the reference acceleration curve.

[0123] The control module 40 is used to control the vehicle to change lanes according to the target speed planning trajectory.

[0124] The present embodiment provides a vehicle lane-changing control device. The present embodiment first performs a lateral and longitudinal risk analysis on the current vehicle to obtain key obstacles, thereby screening risk targets and improving lane-changing efficiency; obtains attribute information of the key obstacles, and generates a reference acceleration curve based on the attribute information to improve the rationality of the reference acceleration curve; determines a target speed planning trajectory based on the reference acceleration curve to improve the accuracy of the speed planning trajectory; controls the vehicle to change lanes according to the target speed planning trajectory, thereby effectively improving the lane-changing success rate.

[0125] In summary, this embodiment screens out key obstacles by performing lateral and longitudinal risk analysis, generates reference acceleration curves based on the key obstacles, and determines a target speed planning trajectory, thereby controlling the vehicle to change lanes. By controlling the vehicle to change lanes based on the optimal speed planning trajectory, the technical defect of low lane changing success rate is overcome, and the lane changing success rate can be effectively improved.

[0126] Optionally, the analysis module 10 is further used to perform a longitudinal risk analysis on the current vehicle to obtain a longitudinal risk target; perform a transverse risk analysis on the current vehicle to obtain a transverse risk target; and use the transverse risk target and the longitudinal risk target as key obstacles.

[0127] Optionally, the analysis module 10 is also used to obtain surrounding vehicle information of the current vehicle; determine whether the surrounding vehicles meet lane conditions based on the surrounding vehicle information; if the surrounding vehicles meet lane conditions, use the surrounding vehicles as candidate vehicles; determine whether the candidate vehicles meet distance conditions; if the candidate vehicles meet the distance conditions, use the candidate vehicles as longitudinal risk targets.

[0128] Optionally, the analysis module 10 is further used to obtain a first boundary lateral position of the current vehicle and a second boundary lateral position of an adjacent vehicle, wherein the adjacent vehicle is located in an adjacent lane of the current vehicle; determine a lateral distance of the vehicle based on the first boundary lateral position and the second boundary lateral position; determine whether the lateral distance of the vehicle reaches a preset lateral safety distance; and when the lateral distance of the vehicle does not reach the preset lateral safety distance, treat the adjacent vehicle as a lateral risk target.

[0129] Optionally, the generation module 20 is also used to obtain attribute information of the key obstacle; generate an obstacle prediction trajectory based on the attribute information of the key obstacle; determine whether there is a collision risk based on the obstacle prediction trajectory; when there is no collision risk, obtain sampling parameters, and perform acceleration sampling based on the sampling parameters to obtain a reference acceleration curve.

[0130] Optionally, the determination module 30 is also used to perform calculations based on the reference acceleration curve to obtain a speed curve and a position curve; generate candidate speed planning trajectories based on the speed curve and the position curve; evaluate the candidate speed planning trajectories to obtain evaluation cost values ​​of each candidate speed planning trajectory; and determine the target speed planning trajectory based on the evaluation cost values ​​of each candidate speed planning trajectory.

[0131] Optionally, the control module 40 is further configured to determine a guide speed based on the target speed planning trajectory; optimize the guide speed to obtain an optimized speed; and control the vehicle to change lanes according to the optimized speed.

[0132] The vehicle lane change control device provided by the present application adopts the vehicle lane change control method in the above embodiment, which can solve the technical problem of vehicle lane change control. Compared with the prior art, the beneficial effects of the vehicle lane change control device provided by the present application are the same as the beneficial effects of the vehicle lane change control method provided by the above embodiment, and other technical features in the vehicle lane change control device are the same as the features disclosed in the above embodiment method, which will not be repeated here.

[0133] The present application provides a vehicle lane changing control device, which includes: at least one processor; and a memory that is communicatively connected to the at least one processor; wherein the memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the vehicle lane changing control method in the above-mentioned embodiment one.

[0134] Reference below Figure 7, which shows a schematic diagram of the structure of a vehicle lane change control device suitable for implementing the embodiment of the present application. The vehicle lane change control device in the embodiment of the present application may include but is not limited to mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Descriptions), PMPs (Portable Media Players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 7 The vehicle lane change control device shown is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0135] like Figure 7 As shown, the vehicle lane change control device may include a processing device 1001 (e.g., a central processing unit, a graphics processor, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM: Read Only Memory) 1002 or a program loaded from a storage device 1003 to a random access memory (RAM: Random Access Memory) 1004. In RAM1004, various programs and data required for the operation of the vehicle lane change control device are also stored. The processing device 1001, ROM1002, and RAM1004 are connected to each other through a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Generally, the following systems can be connected to the I / O interface 1006: an input device 1007 including, for example, a touch screen, a touch pad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, etc.; an output device 1008 including, for example, a liquid crystal display (LCD: Liquid Crystal Display), a speaker, a vibrator, etc.; a storage device 1003 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 1009. The communication device 1009 can allow the vehicle lane change control device to communicate with other devices wirelessly or by wire to exchange data. Although the figure shows a vehicle lane change control device with various systems, it should be understood that it is not required to implement or have all the systems shown. More or fewer systems can be implemented or provided instead.

[0136] In particular, according to the embodiments disclosed in the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, the embodiments disclosed in the present application include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication device, or installed from a storage device 1003, or installed from a ROM 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the method of the embodiment disclosed in the present application are executed.

[0137] The vehicle lane change control device provided by the present application adopts the vehicle lane change control method in the above embodiment, which can solve the technical problem of vehicle lane change control. Compared with the prior art, the beneficial effects of the vehicle lane change control device provided by the present application are the same as the beneficial effects of the vehicle lane change control method provided by the above embodiment, and the other technical features in the vehicle lane change control device are the same as the features disclosed in the method of the previous embodiment, which will not be repeated here.

[0138] It should be understood that the various parts disclosed in this application can be implemented by hardware, software, firmware or a combination thereof. In the description of the above embodiments, specific features, structures, materials or characteristics can be combined in any one or more embodiments or examples in a suitable manner.

[0139] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0140] The present application provides a computer-readable storage medium having computer-readable program instructions (ie, computer programs) stored thereon, and the computer-readable program instructions are used to execute the vehicle lane changing control method in the above-mentioned embodiment.

[0141] The computer-readable storage medium provided in the present application may be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, systems or devices, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, system or device. The program code contained on the computer-readable storage medium may be transmitted using any appropriate medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination of the above.

[0142] The computer-readable storage medium may be included in the vehicle lane-changing control device; or may exist independently without being assembled into the vehicle lane-changing control device.

[0143] The computer-readable storage medium carries one or more programs. When the one or more programs are executed by the vehicle lane change control device, the vehicle lane change control device: performs lateral and longitudinal risk analysis on the current vehicle to obtain key obstacles; obtains attribute information of the key obstacles and generates a reference acceleration curve based on the attribute information; determines a target speed planning trajectory based on the reference acceleration curve; and controls the vehicle to change lanes according to the target speed planning trajectory.

[0144] Computer program code for performing the operations of the present application may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0145] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present application. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0146] The modules involved in the embodiments described in this application may be implemented by software or hardware, wherein the name of the module does not constitute a limitation on the unit itself in some cases.

[0147] The readable storage medium provided in the present application is a computer-readable storage medium, which stores computer-readable program instructions (i.e., computer programs) for executing the above-mentioned vehicle lane change control method, and can solve the technical problem of vehicle lane change control. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in the present application are the same as the beneficial effects of the vehicle lane change control method provided in the above-mentioned embodiment, and will not be repeated here.

[0148] The present application also provides a computer program product, including a computer program, which implements the steps of the vehicle lane change control method as described above when executed by a processor.

[0149] The computer program product provided by the present application can solve the technical problem of vehicle lane change control. Compared with the prior art, the beneficial effects of the computer program product provided by the present application are the same as the beneficial effects of the vehicle lane change control method provided by the above embodiment, which will not be repeated here.

[0150] The above descriptions are only some embodiments of the present application, and are not intended to limit the patent scope of the present application. All equivalent structural changes made using the contents of the present application specification and drawings under the technical concept of the present application, or direct / indirect applications in other related technical fields are included in the patent protection scope of the present application.

Claims

1. A vehicle lane-changing control method, characterized in that: The method comprises: Perform lateral and longitudinal risk analysis on the current vehicle to obtain key obstacles; Obtaining attribute information of the key obstacle; generating an obstacle prediction trajectory based on the attribute information of the key obstacle; determining whether there is a risk of collision based on the predicted trajectory of the obstacle; When there is no collision risk, sampling parameters are obtained, and acceleration sampling is performed based on the sampling parameters to obtain a reference acceleration curve, wherein the sampling parameters at least include a maximum acceleration, a maximum speed, and an acceleration sampling step length; Calculating based on the reference acceleration curve to obtain a velocity curve and a position curve; generating a candidate velocity planning trajectory based on the velocity profile and the position profile; Evaluate the candidate speed planning trajectories to obtain evaluation cost values ​​of each candidate speed planning trajectory; Determine a target speed planning trajectory based on the evaluation cost values ​​of each candidate speed planning trajectory; The vehicle is controlled to change lanes according to the target speed planning trajectory.

2. The method according to claim 1, characterized in that The lateral and longitudinal risk analysis of the current vehicle is performed to obtain key obstacles, including: Conduct longitudinal risk analysis on the current vehicle to obtain the longitudinal risk target; Perform lateral risk analysis on the current vehicle to obtain lateral risk targets; The horizontal risk target and the vertical risk target are regarded as key obstacles.

3. The method according to claim 2, characterized in that The longitudinal risk analysis of the current vehicle is performed to obtain the longitudinal risk target, including: Get the surrounding vehicle information of the current vehicle; Determining whether surrounding vehicles meet lane conditions according to the surrounding vehicle information; When surrounding vehicles meet lane conditions, the surrounding vehicles are used as candidate vehicles; Determining whether the candidate vehicle meets a distance condition; When the candidate vehicle meets the distance condition, the candidate vehicle is taken as a longitudinal risk target.

4. The method according to claim 3, characterized in that The lateral risk analysis of the current vehicle is performed to obtain the lateral risk target, including: Acquire a first boundary lateral position of the current vehicle and a second boundary lateral position of an adjacent vehicle, wherein the adjacent vehicle is located in an adjacent lane of the current vehicle; determining a vehicle lateral distance based on the first boundary lateral position and the second boundary lateral position; Determining that the lateral distance of the vehicle reaches a preset lateral safety distance; When the lateral distance between the vehicles does not reach the preset lateral safety distance, the adjacent vehicle is taken as a lateral risk target.

5. The method according to claim 1, characterized in that The controlling the vehicle to change lanes according to the target speed planned trajectory includes: Determining a guide speed based on the target speed planning trajectory; Performing speed optimization on the guiding speed to obtain an optimized speed; The vehicle is controlled to change lanes according to the optimized speed.

6. A vehicle lane-changing control device, characterized in that: The vehicle lane-changing control device comprises: The analysis module is used to perform lateral and longitudinal risk analysis on the current vehicle to obtain key obstacles; A generation module, used to obtain the attribute information of the key obstacle; generate an obstacle prediction trajectory based on the attribute information of the key obstacle; determine whether there is a collision risk based on the obstacle prediction trajectory; when there is no collision risk, obtain sampling parameters, and perform acceleration sampling based on the sampling parameters to obtain a reference acceleration curve, wherein the sampling parameters at least include a maximum acceleration, a maximum speed, and an acceleration sampling step length; A determination module, configured to calculate based on the reference acceleration curve to obtain a speed curve and a position curve; generate candidate speed planning trajectories based on the speed curve and the position curve; evaluate the candidate speed planning trajectories to obtain evaluation cost values ​​of each candidate speed planning trajectory; and determine a target speed planning trajectory based on the evaluation cost values ​​of each candidate speed planning trajectory; The control module is used to control the vehicle to change lanes according to the target speed planning trajectory.

7. A vehicle lane change control device, characterized in that: The vehicle lane change control device includes: a memory, a processor, and a vehicle lane change control program stored in the memory and executable on the processor, wherein the vehicle lane change control program is configured to implement the vehicle lane change control method according to any one of claims 1 to 5.

8. A storage medium, characterized in that: The storage medium stores a vehicle lane-changing control program, and when the vehicle lane-changing control program is executed by the processor, the vehicle lane-changing control method according to any one of claims 1 to 5 is implemented.

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