Lane changing trajectory planning method and device, electronic equipment and storage medium

Abstract

The embodiment of the invention discloses a lane changing trajectory planning method and device, electronic equipment and a storage medium. The method comprises the following steps: when a lane changing request is received, acquiring an environment perception result and map data; when the lane changing condition is met, multiple initial lane changing tracks of the vehicle are generated; for each initial lane changing track, acquiring a first driving parameter of the vehicle at each moment included in the initial lane changing track data, and for any surrounding vehicle, acquiring a second driving parameter of the surrounding vehicle at each moment and a third driving parameter of a target vehicle corresponding to the surrounding vehicle at each moment from an environment sensing result; based on a preset motion model, determining each predicted trajectory of the surrounding vehicle under the influence of the initial lane changing trajectory; and screening each initial lane changing trajectory based on each corresponding predicted trajectory to obtain screened lane changing trajectories, and determining a final lane changing trajectory in each screened lane changing trajectory. By applying the scheme provided by the embodiment of the invention, the accuracy of lane changing trajectory planning can be improved.

Description

Technical Field

[0001] The present invention relates to the field of autonomous driving technology, and in particular to a lane change trajectory planning method, device, electronic device and storage medium. Background Art

[0002] In autonomous driving technology, when an autonomous driving vehicle is driving on the road, the sensors installed on the vehicle can collect relevant data, such as the camera can capture images of the vehicle's surrounding environment, and lidar (radar) can collect point cloud data around the vehicle. In addition, in the perception link, the perception algorithm can perform target recognition based on the environmental image and point cloud data to obtain target recognition results; in the regulation and control link, the regulation and control algorithm can perform trajectory planning based on the target recognition results to obtain trajectory planning results.

[0003] In some cases, the vehicle needs to change lanes. Specifically, the control algorithm can output the corresponding lane change trajectory based on the current environmental perception results. The known lane change trajectory planning methods only consider the environmental perception results at the current moment. However, in the actual driving process of the autonomous driving vehicle, the future movement trajectories of other vehicles may also have an impact on the lane change trajectory planning of the vehicle. In other words, only considering the environmental perception results at the current moment for the lane change trajectory planning of the vehicle may lead to low accuracy of the lane change trajectory planning results. Therefore, how to improve the accuracy of lane change trajectory planning has become a technical problem that needs to be solved urgently. Summary of the invention

[0004] The present invention provides a lane change trajectory planning method, device, electronic device and storage medium to improve the accuracy of lane change trajectory planning. The specific technical solution is as follows.

[0005] In a first aspect, an embodiment of the present invention provides a lane change trajectory planning method, the method comprising:

[0006] When a lane change request is received, environmental perception results and map data are obtained;

[0007] determining whether a lane change condition is met according to the environment perception result and the map data, and if so, generating a plurality of initial lane change trajectories of the vehicle based on the environment perception result and the map data;

[0008] For each of the initial lane change trajectories, a first driving parameter of the vehicle at each time included in the initial lane change trajectory data is obtained; for any surrounding vehicle located in the current lane and the target lane, a second driving parameter of the surrounding vehicle at each time is obtained from the environmental perception result, as well as a third driving parameter of the surrounding vehicle corresponding to the target vehicle at each time; and according to the first driving parameter, the second driving parameter, and the third driving parameter, based on a preset motion model, each predicted trajectory of the surrounding vehicle under the influence of the initial lane change trajectory is determined;

[0009] Based on the corresponding predicted trajectories, each of the initial lane-changing trajectories is screened to obtain screened lane-changing trajectories, and based on a preset sorting rule, a final lane-changing trajectory is determined from the screened lane-changing trajectories.

[0010] In an embodiment of the present application, after generating multiple initial lane change trajectories for the vehicle based on the environmental perception results and map data, the predicted trajectory of each surrounding vehicle under the influence of the initial lane change trajectory can be determined for each initial lane change trajectory. Furthermore, the initial lane change trajectory can be screened based on each predicted trajectory. That is to say, when determining the trajectory of the vehicle, not only the environmental perception results at the current moment are considered, but also the influence of the future movement trajectories of other vehicles on the lane change trajectory planning of the vehicle is considered. Therefore, the accuracy of the lane change trajectory planning of the vehicle can be improved.

[0011] Optionally, the step of determining, according to the first driving parameter, the second driving parameter, and the third driving parameter and based on a preset motion model, each predicted trajectory of the surrounding vehicle under the influence of the initial lane change trajectory comprises:

[0012] According to the first driving parameter, the second driving parameter, and the third driving parameter, determining the acceleration information of the surrounding vehicle based on a longitudinal motion model, and determining the lane change decision of the surrounding vehicle based on a lateral motion model;

[0013] The predicted trajectories of the surrounding vehicles are constructed according to the acceleration information and the lane change decision.

[0014] Optionally, the step of screening each of the initial lane-changing trajectories based on the corresponding predicted trajectories to obtain the screened lane-changing trajectories includes:

[0015] For each of the initial lane change trajectories, determining whether the initial lane change trajectory satisfies the following conditions at the same time: a preset safety constraint, a preset kinematic constraint, a preset traffic regulation restriction, and no intersection with the corresponding predicted trajectories;

[0016] If yes, the initial lane-changing trajectory is used as the lane-changing trajectory after screening.

[0017] Optionally, the step of determining a final lane-changing trajectory from the filtered lane-changing trajectories based on a preset sorting rule includes:

[0018] Calculating a score for each filtered lane change trajectory based on the vehicle speed and acceleration change rate included in each filtered lane change trajectory;

[0019] The selected lane-changing trajectory with the highest score is taken as the final lane-changing trajectory.

[0020] Optionally, the step of calculating the score of each filtered lane change trajectory based on the vehicle speed and acceleration change rate included in each filtered lane change trajectory includes:

[0021] For any lane-changing trajectory after filtering, the score s of the lane-changing trajectory after filtering is calculated according to the following formula:

[0022]

[0023] Where t represents time, T represents the time length of the lane change trajectory after screening, jerk(t) represents the acceleration change rate at time t, jerk upper bound represents the upper bound of the pre-set jerk, and v ref represents the driver set speed, v lon (t) represents the vehicle speed at time t.

[0024] Optionally, the lane changing conditions include: the lane changing space meets preset conditions, the lane line on the target lane is a non-solid line, and the steering wheel angle of the vehicle is greater than a preset threshold.

[0025] Optionally, after screening each of the initial lane-changing trajectories based on the corresponding predicted trajectories to obtain the screened lane-changing trajectories, the method further includes:

[0026] When the lane change trajectory is empty after screening, the vehicle is controlled to enter the lane change waiting state.

[0027] In a second aspect, an embodiment of the present application provides a lane change trajectory planning device, the device comprising:

[0028] A data acquisition module, used to acquire environmental perception results and map data when a lane change request is received;

[0029] a trajectory planning module, configured to determine whether a lane change condition is met according to the environment perception result and the map data, and if so, generate a plurality of initial lane change trajectories of the vehicle based on the environment perception result and the map data;

[0030] a trajectory prediction module, for obtaining, for each of the initial lane change trajectories, a first driving parameter of the vehicle at each time included in the initial lane change trajectory data, and for any surrounding vehicle located in the current lane and the target lane, obtaining, from the environmental perception result, a second driving parameter of the surrounding vehicle at each time, and a third driving parameter of the surrounding vehicle corresponding to the target vehicle at each time, and determining, based on a preset motion model and according to the first driving parameter, the second driving parameter, and the third driving parameter, each predicted trajectory of the surrounding vehicle under the influence of the initial lane change trajectory;

[0031] The trajectory screening module is used to screen each of the initial lane-changing trajectories based on the corresponding predicted trajectories to obtain a filtered lane-changing trajectory, and to determine a final lane-changing trajectory from each of the filtered lane-changing trajectories based on a preset sorting rule.

[0032] Optionally, the trajectory prediction module is specifically used to:

[0033] According to the first driving parameter, the second driving parameter, and the third driving parameter, determining the acceleration information of the surrounding vehicle based on a longitudinal motion model, and determining the lane change decision of the surrounding vehicle based on a lateral motion model;

[0034] The predicted trajectories of the surrounding vehicles are constructed according to the acceleration information and the lane change decision.

[0035] Optionally, the trajectory screening module is specifically used to:

[0036] For each of the initial lane change trajectories, determining whether the initial lane change trajectory satisfies the following conditions at the same time: a preset safety constraint, a preset kinematic constraint, a preset traffic regulation restriction, and no intersection with the corresponding predicted trajectories;

[0037] If yes, the initial lane-changing trajectory is used as the lane-changing trajectory after screening.

[0038] Optionally, the trajectory screening module includes:

[0039] A score calculation submodule, for calculating the score of each filtered lane change trajectory based on the vehicle speed and acceleration change rate included in each filtered lane change trajectory;

[0040] The trajectory determination submodule is used to select the filtered lane change trajectory with the highest score as the final lane change trajectory.

[0041] Optionally, the score calculation submodule is specifically used to:

[0042] For any lane-changing trajectory after filtering, the score s of the lane-changing trajectory after filtering is calculated according to the following formula:

[0043]

[0044] Where t represents time, T represents the time length of the lane change trajectory after screening, jerk(t) represents the acceleration change rate at time t, jerk upper bound represents the upper bound of the pre-set jerk, and v ref represents the driver set speed, v lon (t) represents the vehicle speed at time t.

[0045] Optionally, the lane changing conditions include: the lane changing space meets preset conditions, the lane line on the target lane is a non-solid line, and the steering wheel angle of the vehicle is greater than a preset threshold.

[0046] Optionally, the device further comprises:

[0047] The control module is used to control the vehicle to enter a lane change waiting state when the lane change trajectory is empty after screening.

[0048] In a third aspect, an embodiment of the present invention provides an electronic device, including: a memory and a processor, wherein the memory and the processor are coupled;

[0049] The memory is used to store one or more computer instructions;

[0050] The processor is used to execute the one or more computer instructions to implement the lane change trajectory planning method as described in the first aspect above.

[0051] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium having one or more computer instructions stored thereon, which are executed by a processor to implement the lane change trajectory planning method as described in the first aspect above.

[0052] In a fifth aspect, the present application provides a computer program product, which includes a computer program, and when the computer program is executed by a processor, it implements the lane change trajectory planning method described in the first aspect. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the prior art descriptions. Obviously, the drawings described below are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can also be obtained based on these drawings without creative work.

[0054] Figure 1 A schematic diagram of a flow chart of a lane change trajectory planning method provided by an embodiment of the present invention;

[0055] Figure 2A schematic diagram of the structure of a lane change trajectory planning device provided by an embodiment of the present invention;

[0056] Figure 3 A schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0057] The following will be combined with the accompanying drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0058] It should be noted that the terms "including" and "having" and any variations thereof in the embodiments of the present invention and the accompanying drawings are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device comprising a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products or devices.

[0059] The embodiment of the present invention discloses a lane change trajectory planning method, device, electronic device and storage medium, which can improve the accuracy of lane change trajectory planning. The embodiment of the present invention is described in detail below.

[0060] Figure 1 A flowchart of a lane change trajectory planning method provided by an embodiment of the present invention. The method can be applied to an autonomous driving vehicle, and specifically, can be applied to a controller of an autonomous driving vehicle. The method specifically includes the following steps.

[0061] S110: When a lane change request is received, environmental perception results and map data are obtained.

[0062] During the driving process, the autonomous vehicle may encounter various situations where it needs to change lanes, such as overtaking, driving to the corresponding turning lane before turning, etc. Before changing lanes, the user can issue a left or right lane change request, such as toggling a lane change lever, etc. Alternatively, the autonomous vehicle can plan a route based on the destination pre-set by the user, and determine whether a lane change operation is about to be performed based on the planned route.

[0063] Autonomous vehicles are equipped with various types of sensors, which can collect relevant data based on various types of sensors. For example, cameras can capture images of the vehicle's surroundings, and lidar can collect point cloud data around the vehicle. In addition, in the perception stage, the perception algorithm can perform target recognition based on the environmental image and point cloud data, respectively, and obtain target recognition results, which can also be called environmental perception results. Specifically, the environmental perception results may include relevant information about other vehicles around the vehicle, such as current location, speed, acceleration, and other information.

[0064] S120: judging whether the lane change condition is met according to the environment perception result and the map data, and if so, generating a plurality of initial lane change trajectories of the vehicle based on the environment perception result and the map data.

[0065] It can be understood that certain conditions are required for the vehicle to change lanes, such as the distance to the front and rear vehicles in the target lane cannot be too close, otherwise dangerous situations such as rear-end collisions may occur. Therefore, in an embodiment of the present application, before performing a lane change operation, the autonomous driving vehicle can first determine whether the lane change conditions are met based on the environmental perception results and map data. The lane change conditions may, for example, include: the lane change space meets the preset conditions, the lane line on the target lane is a non-solid line, and the steering wheel angle of the vehicle is greater than a preset threshold. Specifically, the lane change space meets the preset conditions, for example, including: the distance between the vehicle and the front and rear vehicles in the target lane is greater than the set value, and ttc (Time To Collision) meets the safety threshold.

[0066] When it is determined that the lane change conditions are met, the autonomous driving vehicle can generate multiple initial lane change trajectories based on the environmental perception results and map data. For example, all lane change paths that meet the conditions can be determined based on the map data and the relevant information of the surrounding vehicles, and all of them are used as the initial lane change trajectory of the vehicle. Each initial lane change trajectory can include all the driving parameters of the vehicle at each moment, such as position, speed, acceleration and other information.

[0067] S130: For each initial lane change trajectory, obtain the first driving parameter of the vehicle at each moment included in the initial lane change trajectory data; for any surrounding vehicles located in the current lane and the target lane, obtain the second driving parameter of the surrounding vehicle at each moment from the environmental perception result, as well as the third driving parameter of the surrounding vehicle corresponding to the target vehicle at each moment; and according to the first driving parameter, the second driving parameter, and the third driving parameter, based on a preset motion model, determine the predicted trajectories of the surrounding vehicles under the influence of the initial lane change trajectory.

[0068] It is understandable that when a vehicle is changing lanes, the driving conditions of the vehicle and other surrounding vehicles affect each other. For example, when the vehicle is planning to change lanes at the next moment, but the vehicle in front of it in the target lane suddenly slows down, if the vehicle continues to change lanes, it may cause a traffic accident. Therefore, when the vehicle is planning the lane change path, if it can fully consider the path planning of surrounding vehicles, it can maximize the accuracy of the vehicle's path planning, thereby improving the safety of the driving process.

[0069] In the embodiment of the present application, after determining multiple initial lane change trajectories of the self-driving vehicle, the self-driving vehicle can further determine the planned trajectories of the surrounding vehicles in the current lane and the target lane under the influence of the initial lane change trajectory of the self-driving vehicle, which can be called the predicted trajectory. In other words, it can be determined that when the self-driving vehicle changes lanes according to any initial lane change trajectory at the next moment, the corresponding driving trajectory of other vehicles can be determined. It can be understood that for different initial lane change trajectories of the self-driving vehicle, the corresponding predicted trajectories of the surrounding vehicles are generally different.

[0070] Specifically, the vehicle may determine the predicted trajectories corresponding to the surrounding vehicles for each initial lane change trajectory in turn. For example, the vehicle may obtain the first driving parameter of the vehicle at each time included in the initial lane change trajectory data for each initial lane change trajectory, obtain the second driving parameter of the surrounding vehicle at each time from the environmental perception result for any surrounding vehicle, and the third driving parameter of the surrounding vehicle corresponding to the target vehicle at each time, and determine the predicted trajectories of the surrounding vehicle under the influence of the initial lane change trajectory based on the first driving parameter, the second driving parameter, and the third driving parameter and the preset motion model.

[0071] Among them, the above-mentioned surrounding vehicles may include all vehicles in the current lane and the target lane that are selected according to the preset selection rules with the vehicle as the center and may have an impact on the driving of the vehicle. For example, it can be the two vehicles in front and behind the vehicle in the current lane, and the two vehicles closest to the vehicle in the target lane, that is, a total of four vehicles. The target vehicle corresponding to the surrounding vehicles can be the two vehicles in front and behind any surrounding vehicle in the current lane, and the two vehicles closest to the surrounding vehicle in the adjacent lanes of the current lane, that is, a total of six vehicles. Alternatively, it can be determined according to other rules, which is all possible, and the embodiments of the present application do not make specific limitations on this.

[0072] That is to say, in the embodiment of the present application, the predicted trajectory of the surrounding vehicle can be determined based on the driving parameters of the vehicle itself, the driving parameters of any surrounding vehicle itself, and the driving parameters of the target vehicle corresponding to the surrounding vehicle.

[0073] In one implementation, the vehicle can, for any surrounding vehicle, first determine the acceleration information of the surrounding vehicle based on the longitudinal motion model according to the first driving parameter, the second driving parameter, and the third driving parameter, and determine the lane change decision of the surrounding vehicle based on the lateral motion model; and then construct each predicted trajectory of the surrounding vehicle according to the acceleration information and the lane change decision.

[0074] The longitudinal motion model may include, for example, IDM (Intelligent Driver Model). This model can perform longitudinal modeling on surrounding vehicles to obtain corresponding acceleration information. The lateral motion model may include MOBIL (Minimizing Overall Braking Induced by Lane changes, i.e., a model that determines when to change lanes based on minimizing the number of braking times when changing lanes) to obtain corresponding lane change decisions, i.e., output decisions on whether surrounding vehicles should change lanes.

[0075] S140: Based on the corresponding predicted trajectories, each initial lane change trajectory is screened to obtain a screened lane change trajectory, and based on a preset sorting rule, a final lane change trajectory is determined from each screened lane change trajectory.

[0076] Determine the predicted trajectories of the surrounding vehicles, and each predicted trajectory will in turn impose certain constraints on the initial trajectory. Therefore, in an embodiment of the present application, each initial lane change trajectory can be screened based on the corresponding predicted trajectories. For example, for each initial lane change trajectory, it can be determined whether the initial lane change trajectory satisfies the following conditions at the same time: preset safety constraints, preset kinematic constraints, preset traffic regulations restrictions, and no intersections with the corresponding predicted trajectories; if so, the initial lane change trajectory is used as the filtered lane change trajectory.

[0077] There may be multiple lane change trajectories after screening. In this case, the multiple lane change trajectories after screening can be sorted to determine a final lane change trajectory. For example, the scores of the lane change trajectories after screening can be calculated based on the vehicle speed and acceleration change rate included in each lane change trajectory after screening; and the lane change trajectory after screening with the highest score is used as the final lane change trajectory.

[0078] It is understandable that the vehicle speed and acceleration rate of change may directly affect the riding experience. For example, if the acceleration rate of change is too large, that is, there will be a rapid acceleration or deceleration process, which will lead to a poor riding experience. Therefore, by sorting the filtered lane change trajectories based on speed and acceleration rate of change, the final lane change trajectory with better user experience can be selected.

[0079] Specifically, for any lane-changing trajectory after filtering, the score s of the lane-changing trajectory after filtering can be calculated according to the following formula:

[0080]

[0081] Where t represents time, T represents the time length of the lane change trajectory after screening, jerk(t) represents the acceleration change rate at time t, jerk upper bound represents the upper bound of the pre-set jerk, and v ref represents the driver set speed, v lon (t) represents the vehicle speed at time t.

[0082] It is understandable that there may be a situation where the lane change trajectory is empty after screening, that is, in the current situation, there is no lane change trajectory that meets the conditions. In this case, the vehicle can be controlled to enter the lane change waiting state to ensure the safety of the vehicle. After entering the lane change waiting state, the lane change trajectory can be planned again after a preset time until the vehicle successfully changes lanes.

[0083] In an embodiment of the present application, after generating multiple initial lane change trajectories for the vehicle based on the environmental perception results and map data, the predicted trajectory of each surrounding vehicle under the influence of the initial lane change trajectory can be determined for each initial lane change trajectory. Furthermore, the initial lane change trajectory can be screened based on each predicted trajectory. That is to say, when determining the trajectory of the vehicle, not only the environmental perception results at the current moment are considered, but also the influence of the future movement trajectories of other vehicles on the lane change trajectory planning of the vehicle is considered. Therefore, the accuracy of the lane change trajectory planning of the vehicle can be improved.

[0084] Figure 2 A schematic diagram of the structure of a lane change trajectory planning device provided in an embodiment of the present application, the device comprising:

[0085] A data acquisition module 210, for acquiring environmental perception results and map data when a lane change request is received;

[0086] a trajectory planning module 220, configured to determine whether a lane change condition is met according to the environment perception result and the map data, and if so, to generate a plurality of initial lane change trajectories of the vehicle based on the environment perception result and the map data;

[0087] The trajectory prediction module 230 is used to obtain, for each of the initial lane change trajectories, a first driving parameter of the vehicle at each time included in the initial lane change trajectory data, and for any surrounding vehicle located in the current lane and the target lane, obtain, from the environmental perception result, a second driving parameter of the surrounding vehicle at each time, and a third driving parameter of the surrounding vehicle corresponding to the target vehicle at each time, and determine, based on the first driving parameter, the second driving parameter, and the third driving parameter, each predicted trajectory of the surrounding vehicle under the influence of the initial lane change trajectory based on a preset motion model;

[0088] The trajectory screening module 240 is used to screen each of the initial lane-changing trajectories based on the corresponding predicted trajectories to obtain filtered lane-changing trajectories, and determine a final lane-changing trajectory from each of the filtered lane-changing trajectories based on a preset sorting rule.

[0089] Optionally, the trajectory prediction module 230 is specifically used for:

[0090] According to the first driving parameter, the second driving parameter, and the third driving parameter, determining the acceleration information of the surrounding vehicle based on a longitudinal motion model, and determining the lane change decision of the surrounding vehicle based on a lateral motion model;

[0091] The predicted trajectories of the surrounding vehicles are constructed according to the acceleration information and the lane change decision.

[0092] Optionally, the trajectory screening module 240 is specifically used to:

[0093] For each of the initial lane change trajectories, determining whether the initial lane change trajectory satisfies the following conditions at the same time: a preset safety constraint, a preset kinematic constraint, a preset traffic regulation restriction, and no intersection with the corresponding predicted trajectories;

[0094] If yes, the initial lane-changing trajectory is used as the lane-changing trajectory after screening.

[0095] Optionally, the trajectory screening module 240 includes:

[0096] A score calculation submodule, for calculating the score of each filtered lane change trajectory based on the vehicle speed and acceleration change rate included in each filtered lane change trajectory;

[0097] The trajectory determination submodule is used to select the filtered lane change trajectory with the highest score as the final lane change trajectory.

[0098] Optionally, the score calculation submodule is specifically used to:

[0099] For any lane-changing trajectory after filtering, the score s of the lane-changing trajectory after filtering is calculated according to the following formula:

[0100]

[0101] Where t represents time, T represents the time length of the lane change trajectory after screening, jerk(t) represents the acceleration change rate at time t, jerk upper bound represents the upper bound of the pre-set jerk, and v ref represents the driver set speed, v lon (t) represents the vehicle speed at time t.

[0102] Optionally, the lane changing conditions include: the lane changing space meets preset conditions, the lane line on the target lane is a non-solid line, and the steering wheel angle of the vehicle is greater than a preset threshold.

[0103] Optionally, the device further comprises:

[0104] The control module is used to control the vehicle to enter a lane change waiting state when the lane change trajectory is empty after screening.

[0105] In an embodiment of the present application, after generating multiple initial lane change trajectories for the vehicle based on the environmental perception results and map data, the predicted trajectory of each surrounding vehicle under the influence of the initial lane change trajectory can be determined for each initial lane change trajectory. Furthermore, the initial lane change trajectory can be screened based on each predicted trajectory. That is to say, when determining the trajectory of the vehicle, not only the environmental perception results at the current moment are considered, but also the influence of the future movement trajectories of other vehicles on the lane change trajectory planning of the vehicle is considered. Therefore, the accuracy of the lane change trajectory planning of the vehicle can be improved.

[0106] The above device embodiment corresponds to the method embodiment and has the same technical effect as the method embodiment. For specific description, please refer to the method embodiment. The device embodiment is obtained based on the method embodiment. For specific description, please refer to the method embodiment part, which will not be repeated here.

[0107] Next, an electronic device provided by an embodiment of the present application is introduced. Figure 3 , Figure 3 A schematic diagram of a structure of a server provided in an embodiment of the present application, the server comprising:

[0108] one or more processors 40;

[0109] The processor 40 is coupled to a storage device 41, and the storage device 41 is used to store one or more programs.

[0110] When the one or more programs are executed by the one or more processors 40, the electronic device implements the following Figure 1 A technical solution for a lane change trajectory planning method.

[0111] The present application also provides a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, the following is achieved: Figure 1 The lane change trajectory planning method.

[0112] The present application provides a computer program product, the computer program product comprising a computer program, the computer program when executed by a processor to achieve the following Figure 1 The lane change trajectory planning method.

[0113] Those skilled in the art can understand that the accompanying drawings are only schematic diagrams of an embodiment, and the modules or processes in the accompanying drawings are not necessarily required to implement the present invention.

[0114] Those skilled in the art can understand that the modules in the device in the embodiment can be distributed in the device in the embodiment according to the description of the embodiment, or can be changed accordingly and located in one or more devices different from the embodiment. The modules in the above embodiment can be combined into one module, or can be further divided into multiple sub-modules.

[0115] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A lane change trajectory planning method, characterized in that: The method comprises: When a lane change request is received, environmental perception results and map data are obtained; determining whether a lane change condition is met according to the environment perception result and the map data, and if so, generating a plurality of initial lane change trajectories of the vehicle based on the environment perception result and the map data; For each of the initial lane change trajectories, a first driving parameter of the vehicle at each time included in the initial lane change trajectory data is obtained; for any surrounding vehicle located in the current lane and the target lane, a second driving parameter of the surrounding vehicle at each time is obtained from the environmental perception result, as well as a third driving parameter of the surrounding vehicle corresponding to the target vehicle at each time; and according to the first driving parameter, the second driving parameter, and the third driving parameter, based on a preset motion model, each predicted trajectory of the surrounding vehicle under the influence of the initial lane change trajectory is determined; Based on the corresponding predicted trajectories, each of the initial lane-changing trajectories is screened to obtain screened lane-changing trajectories, and based on a preset sorting rule, a final lane-changing trajectory is determined from the screened lane-changing trajectories.

2. The method according to claim 1, characterized in that The step of determining the predicted trajectories of the surrounding vehicles under the influence of the initial lane change trajectory based on the first driving parameter, the second driving parameter, and the third driving parameter and a preset motion model comprises: According to the first driving parameter, the second driving parameter, and the third driving parameter, determining the acceleration information of the surrounding vehicle based on the longitudinal motion model, and determining the lane change decision of the surrounding vehicle based on the lateral motion model; The predicted trajectories of the surrounding vehicles are constructed according to the acceleration information and the lane change decision.

3. The method according to claim 1, characterized in that The step of screening each of the initial lane-changing trajectories based on the corresponding predicted trajectories to obtain the screened lane-changing trajectories comprises: For each of the initial lane change trajectories, determining whether the initial lane change trajectory satisfies the following conditions at the same time: a preset safety constraint, a preset kinematic constraint, a preset traffic regulation restriction, and no intersection with the corresponding predicted trajectories; If yes, the initial lane-changing trajectory is used as the lane-changing trajectory after screening.

4. The method according to any one of claims 1 to 3, characterized in that: The step of determining a final lane-changing trajectory from the filtered lane-changing trajectories based on a preset sorting rule comprises: Calculating a score for each filtered lane change trajectory based on the vehicle speed and acceleration change rate included in each filtered lane change trajectory; The selected lane-changing trajectory with the highest score is taken as the final lane-changing trajectory.

5. The method according to claim 4, characterized in that The step of calculating the score of each filtered lane change trajectory based on the vehicle speed and acceleration change rate included in each filtered lane change trajectory includes: For any lane-changing trajectory after filtering, the score s of the lane-changing trajectory after filtering is calculated according to the following formula: Where t represents time, T represents the time length of the lane change trajectory after screening, jerk(t) represents the acceleration change rate at time t, jerk upper bound represents the upper bound of the pre-set jerk, and v ref represents the driver set speed, v lon (t) represents the vehicle speed at time t.

6. The method according to any one of claims 1 to 3, characterized in that: The lane change conditions include: the lane change space meets the preset conditions, the lane line on the target lane is a non-solid line, and the steering wheel angle of the vehicle is greater than a preset threshold.

7. The method according to any one of claims 1 to 3, characterized in that: After screening each of the initial lane-changing trajectories based on the corresponding predicted trajectories to obtain the screened lane-changing trajectories, the method further includes: When the lane change trajectory is empty after screening, the vehicle is controlled to enter the lane change waiting state.

8. A lane change trajectory planning device, characterized in that: The device comprises: A data acquisition module, used to acquire environmental perception results and map data when a lane change request is received; a trajectory planning module, configured to determine whether a lane change condition is met according to the environment perception result and the map data, and if so, generate a plurality of initial lane change trajectories of the vehicle based on the environment perception result and the map data; a trajectory prediction module, for obtaining, for each of the initial lane change trajectories, a first driving parameter of the vehicle at each time included in the initial lane change trajectory data, and for any surrounding vehicle located in the current lane and the target lane, obtaining, from the environmental perception result, a second driving parameter of the surrounding vehicle at each time, and a third driving parameter of the surrounding vehicle corresponding to the target vehicle at each time, and determining, based on a preset motion model and according to the first driving parameter, the second driving parameter, and the third driving parameter, each predicted trajectory of the surrounding vehicle under the influence of the initial lane change trajectory; The trajectory screening module is used to screen each of the initial lane-changing trajectories based on the corresponding predicted trajectories to obtain a filtered lane-changing trajectory, and to determine a final lane-changing trajectory from each of the filtered lane-changing trajectories based on a preset sorting rule.

9. The device according to claim 8, characterized in that The trajectory prediction module is specifically used for: According to the first driving parameter, the second driving parameter, and the third driving parameter, determining the acceleration information of the surrounding vehicle based on the longitudinal motion model, and determining the lane change decision of the surrounding vehicle based on the lateral motion model; The predicted trajectories of the surrounding vehicles are constructed according to the acceleration information and the lane change decision.

10. The device according to claim 8, characterized in that The trajectory screening module is specifically used for: For each of the initial lane change trajectories, determining whether the initial lane change trajectory satisfies the following conditions at the same time: a preset safety constraint, a preset kinematic constraint, a preset traffic regulation restriction, and no intersection with the corresponding predicted trajectories; If yes, the initial lane-changing trajectory is used as the lane-changing trajectory after screening.

11. The device according to any one of claims 8 to 10, characterized in that: The trajectory screening module comprises: A score calculation submodule, for calculating the score of each filtered lane change trajectory based on the vehicle speed and acceleration change rate included in each filtered lane change trajectory; The trajectory determination submodule is used to select the filtered lane change trajectory with the highest score as the final lane change trajectory.

12. The device according to claim 11, characterized in that The score calculation submodule is specifically used for: For any lane-changing trajectory after filtering, the score s of the lane-changing trajectory after filtering is calculated according to the following formula: Where t represents time, T represents the time length of the lane change trajectory after screening, jerk(t) represents the acceleration change rate at time t, jerk upper bound represents the upper bound of the pre-set jerk, and v ref represents the driver set speed, v lon (t) represents the vehicle speed at time t.

13. The device according to any one of claims 8 to 10, characterized in that: The lane change conditions include: the lane change space meets the preset conditions, the lane line on the target lane is a non-solid line, and the steering wheel angle of the vehicle is greater than a preset threshold.

14. The device according to any one of claims 8 to 10, characterized in that: The device also includes: The control module is used to control the vehicle to enter a lane change waiting state when the lane change trajectory is empty after screening.

15. An electronic device, characterized in that: include: A memory and a processor, wherein the memory and the processor are coupled; The memory is used to store one or more computer instructions; The processor is used to execute the one or more computer instructions to implement the lane change trajectory planning method as described in any one of claims 1 to 7.

16. A computer-readable storage medium having one or more computer instructions stored thereon, characterized in that: The instruction is executed by the processor to implement the lane change trajectory planning method as described in any one of claims 1 to 7.

Images