Path planning method and device and computer storage medium

Abstract

The invention provides a path planning method and device and a computer storage medium, and the method comprises the steps: initializing a planning path boundary based on the road width in a navigation map; acquiring a boundary value of each obstacle in the obstacle list; updating the planned path boundary by using the boundary value of each obstacle; and performing path planning on the autonomous vehicle according to the updated planning path boundary. The method combines navigation and map results, senses obstacles, plans a safe and drivable path for the vehicle in a certain space, provides safe path planning for automatic driving, and improves the safety and feasibility of automatic driving.

Description

Technical Field

[0001] This application relates to the field of autonomous driving technology, and particularly to a path planning method, apparatus, and computer storage medium. Background Art

[0002] The urban navigation assisted driving function operates relying on data processing elements within the vehicle system and control modules including various radars, cameras, and GPS navigation. The lidar scans the environment around the vehicle in real time to obtain relative position information such as real-time vehicle speed, lane markings, and the distance to the vehicle ahead, and combines with the GPS navigation function to plan the driving path in real time, achieving efficient and convenient passage from point A to point B. Navigation lane change is a function that combines the map and global navigation, and relies on the output information of both to change lanes in advance to the lane where the subsequent passing intersection is located.

[0003] From the perspective of existing decision-making and planning algorithms, the composition of the basically passable boundary only involves the self-lane and adjacent lanes, and the success rate is relatively low for short-distance and long-span lane change scenarios. Summary of the Invention

[0004] To solve the above technical problems, this application proposes a path planning method, which includes: initializing the planned path boundary based on the road width in the navigation map; obtaining the boundary values of each obstacle in the obstacle list; updating the planned path boundary using the boundary values of each obstacle; and performing path planning for the autonomous driving vehicle according to the updated planned path boundary.

[0005] Among them, the step of updating the planned path boundary using the boundary values of each obstacle includes: in response to a following trajectory planning instruction, determining the static obstacles and dynamic obstacles in the obstacle list; updating the planned path boundary using the left and right boundary values of the static obstacles; and updating the planned path boundary using the predicted trajectory of the dynamic obstacles and the boundary values in all directions.

[0006] Among them, the step of updating the planned path boundary using the left and right boundary values of the static obstacles includes: traversing each boundary point in the planned path boundary; updating the right boundary point using the left boundary value of the static obstacle; and updating the left boundary point using the right boundary value of the static obstacle.

[0007] Among them, after updating the planned path boundary using the left and right boundary values of the static obstacles, the path planning method further includes: in response to the planned path formed by the planned path boundary having an occluded path point, marking the subsequent path points of the occluded path point as unreachable; where the left boundary value of the occluded path point is less than the right boundary value.

[0008] After updating the planned path boundary by using the predicted trajectory of the dynamic obstacle and the boundary values in all directions, the path planning method further includes: obtaining the predicted boundary values of multiple predicted trajectory points of the dynamic obstacle: at each predicted moment, in response to the predicted trajectory point being on the right side of the trajectory path boundary, adjusting the right boundary point of the trajectory path boundary by using the left boundary value of the predicted boundary value; at each predicted moment, in response to the predicted trajectory point being on the left side of the trajectory path boundary, adjusting the left boundary point by using the right boundary value of the predicted boundary value.

[0009] Among them, the path planning of the autonomous vehicle according to the updated planned path boundary includes: in response to a lane change trajectory planning instruction, obtaining the ego-predicted trajectory and the obstacle-predicted trajectory of the autonomous vehicle; obtaining the ego-trajectory points of the ego-predicted trajectory and the obstacle-trajectory points of the obstacle-predicted trajectory at the same moment according to the same time interval; determining the obstacle bounding box of the obstacle-trajectory points based on the boundary values of the obstacle; judging whether there is a collision between the ego-bounding box of the ego-trajectory points and the obstacle-bounding box of the obstacle-trajectory points; if not, performing lane change path planning on the autonomous vehicle according to the updated planned path boundary.

[0010] Among them, judging whether there is a collision between the ego-bounding box of the ego-trajectory points and the obstacle-bounding box of the obstacle-trajectory points includes: obtaining the distance value between the ego-trajectory points and the obstacle-trajectory points; judging whether the distance value is less than a preset threshold; if so, judging whether there is a separating axis between the ego-bounding box of the ego-trajectory points and the obstacle-bounding box of the obstacle-trajectory points; if so, determining that there is no collision between the ego-bounding box of the ego-trajectory points and the obstacle-bounding box of the obstacle-trajectory points.

[0011] Among them, the lane change path planning of the autonomous vehicle according to the updated planned path boundary includes: obtaining the predicted position and predicted speed of the autonomous vehicle according to a preset sampling acceleration; determining the interested obstacles based on the predicted position and predicted speed; performing collision detection on the interested obstacles to obtain the interval of the interested obstacles; in response to the interval meeting the lane change condition of the autonomous vehicle, sending a navigation lane change action instruction to the autonomous vehicle.

[0012] After obtaining the interval of the obstacle of interest, the path planning method further includes: in response to the interval satisfying the side-attachment condition of the autonomous vehicle, sending a navigation side-attachment action instruction to the autonomous vehicle; detecting that the predicted trajectory of the obstacle of interest does not satisfy the lane-changing condition of the autonomous vehicle within a duration, maintaining the lane-riding state of the autonomous vehicle until the obstacle of interest passes or the changed predicted trajectory satisfies the lane-changing condition of the autonomous vehicle, and sending a navigation lane-changing action instruction to the autonomous vehicle.

[0013] To solve the above technical problems, the present application provides a path planning device, which includes a memory and a processor coupled to the memory; wherein, the memory is used to store program data, and the processor is used to execute the program data to implement the above path planning method.

[0014] To solve the above technical problems, the present application provides a computer storage medium, which is used to store program data, and when the program data is executed by a computer, it is used to implement the above path planning method.

[0015] Different from the prior art, the beneficial effect of the present application lies in that: the path planning device initializes the planning path boundary based on the road width in the navigation map; obtains the boundary values of each obstacle in the obstacle list; uses the boundary values of each obstacle to update the planning path boundary; and performs path planning for the autonomous vehicle according to the updated planning path boundary. The present application combines navigation and map results, senses obstacles, and plans a safe drivable path for the vehicle within a certain space, providing safe path planning for autonomous driving, thereby improving the safety and feasibility of autonomous driving. Description of the Drawings

[0016] To more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are only some embodiments of the present invention, and those of ordinary skill in the art can also obtain other drawings without creative efforts based on these drawings.

[0017] Figure 1 It is a flowchart of the first embodiment of the path planning method provided by the present application;

[0018] Figure 2 It is in the path planning method provided by the present application Figure 1 It is a flowchart of the sub-steps of step S13;

[0019] Figure 3 It is a schematic diagram of path boundary update provided by the present application;

[0020] Figure 4 It is a schematic flowchart of the second embodiment of the path planning method provided by the present application;

[0021] Figure 5 It is a flowchart of collision detection in the path planning method provided by the present application;

[0022] Figure 6 It is a schematic diagram of collision detection in the path planning method provided by the present application;

[0023] Figure 7 It is a schematic diagram of the first embodiment of the separating axis theorem provided by the present application;

[0024] Figure 8 It is a schematic diagram of the second embodiment of the separating axis theorem provided by the present application;

[0025] Figure 9 It is a schematic diagram of the third embodiment of the separating axis theorem provided by the present application;

[0026] Figure 10 It is a schematic diagram of the fourth embodiment of the separating axis theorem provided by the present application;

[0027] Figure 11 It is a schematic diagram of the fifth embodiment of the separating axis theorem provided by the present application;

[0028] Figure 12 It is a schematic structural diagram of an embodiment of the path planning device provided by the present application;

[0029] Figure 13 It is a schematic diagram of the computer storage medium provided by the present application. Detailed implementation manners

[0030] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0031] Among them, the path planning method of the present application is applied to a path planning device. Among them, the path planning device of the present application can be a server or a system in which the server and the local terminal cooperate with each other. Correspondingly, each part included in the path planning device, such as each unit, sub-unit, module, and sub-module, can be all arranged in the server or can be respectively arranged in the server and the local terminal.

[0032] Further, the above server may be hardware or software. When the server is hardware, it may be implemented as a distributed server cluster composed of multiple servers or as a single server. When the server is software, it may be implemented as multiple software or software modules, such as software or software modules for providing a distributed server, or as a single software or software module, which is not specifically limited herein. In some possible implementation manners, the path planning method of the embodiments of the present application may be implemented by a processor calling computer-readable instructions stored in a memory.

[0033] To solve the above technical problems, the present application proposes a path planning method. In this embodiment, please refer to Figure 1 , Figure 1 which is a schematic flowchart of the first embodiment of the path planning method provided by the present application.

[0034] As Figure 1 shown, the specific steps are as follows:

[0035] Step S11: Initialize the planned path boundary based on the road width in the navigation map.

[0036] The path planning device reads the road width in the navigation map and uses it to initialize the path boundary.

[0037] Step S12: Obtain the boundary values of each obstacle in the obstacle list.

[0038] In an embodiment of the present application, the obstacles include static obstacles and dynamic obstacles, and the boundary values include left boundary values and right boundary values.

[0039] Step S13: Update the planned path boundary by using the boundary values of each obstacle.

[0040] Specifically, the present application proposes steps S131 - S133 as sub-steps of step S13 for updating the planned path boundary. For details, please refer to Figures 2 - 3 , Figure 2 which is a schematic flowchart of the sub-steps of step S13 in the path planning method provided by the present application; as Figure 1 shown, Figure 3 shown, Figure 3 which is a schematic diagram of path boundary update provided by the present application.

[0041] As Figure 2 shown, the specific steps are as follows:

[0042] Step S131: Respond to the follow-up trajectory planning instruction and determine the static obstacles and dynamic obstacles in the obstacle list.

[0043] The path planning device traverses all the obstacles in the obstacle list, determines whether the obstacle type is a static obstacle or a dynamic obstacle through obstacle judgment, extracts the static obstacles and decomposes them into start_s and end_s, calculates the corresponding left and right boundary values, and fills them into a new set, denoted as sorted_static_objects.

[0044] Step S132: Update the planned path boundary using the left and right boundary values of the static obstacle.

[0045] Specifically, the path planning device traverses each boundary point in the planned path boundary; updates the right boundary point using the left boundary value of the static obstacle; updates the left boundary point using the right boundary value of the static obstacle. Traverse each boundary point in the planned path boundary; update the right boundary point using the left boundary value of the static obstacle; update the left boundary point using the right boundary value of the static obstacle.

[0046] The path planning device sorts the obstacles in the set in ascending order according to the size of s. If s is equal, they are sorted according to the rule that start_s comes first. Then it traverses all the points on the road boundary, that is, the points of the planned path boundary path_boundary, and updates the boundary of the planned path boundary path_boundary according to the left and right boundaries of the obstacle at each point. If the left and right boundaries of a certain path_boundary_point cross, that is, the updated value of the right boundary is greater than the left boundary value, or the left boundary value is less than the right boundary, it is considered that there is an obstacle blockage at this point and the vehicle cannot pass, and all path_boundary_points after this point are cut off.

[0047] Step S133: Update the planned path boundary using the predicted trajectory of the dynamic obstacle and the boundary values in all directions.

[0048] The path planning device filters all dynamic obstacles according to the obstacle attributes and fills them into a new set, denoted as dynamic_objects. Traverse the 3s predicted trajectory of the obstacle, generate the bounding_box of the obstacle at each time point, and calculate the boundary values of the four sides of the obstacle.

[0049] Further, the path planning device traverses each point on the path_boundary. If the obstacle is near the ego vehicle (ego_s > start_s && ego_s < end_s), the path_boundary is updated as follows: If the obstacle trajectory point is on the right side of the lane line, the right boundary of the path_boundary is adjusted according to the left boundary of the obstacle; if the obstacle is on the left side of the lane line, the left boundary of the path_boundary is adjusted according to the right boundary of the obstacle; otherwise, it is not updated.

[0050] By the above method, the obstacle information is processed, the obstacles are classified and sorted according to the distance, reducing the processing requirements for obstacles in subsequent steps.

[0051] Step S14: Perform path planning for the autonomous vehicle according to the updated planned path boundary.

[0052] Specifically, the present application proposes an embodiment. For details, please refer to Figures 4 - 6 , Figure 4 which is a schematic flowchart of the second embodiment of the path planning method provided by the present application, Figure 5 which is a flowchart of collision detection in the path planning method provided by the present application, Figure 6 which is a schematic diagram of collision detection in the path planning method provided by the present application.

[0053] As Figure 4 shown, the specific steps are as follows:

[0054] Step S21: In response to a lane change trajectory planning instruction, obtain the ego vehicle prediction trajectory and the obstacle prediction trajectory of the autonomous vehicle.

[0055] Among them, the lane change planning instruction is an instruction generated when the vehicle changes lanes.

[0056] The method for generating the path boundary of the lane change trajectory is the same as the path planning method in any embodiment of the present application. In the embodiment of the present application, when responding to a lane change trajectory planning instruction, the safety of the lane to be changed needs to be checked. Determine whether the surrounding environment is suitable for lane change. If the inspection result is unsafe, a lane change unsafe reminder flag bit is issued.

[0057] Specifically, in response to a lane change trajectory planning instruction, the path planning device accesses the ego vehicle prediction trajectory and the obstacle prediction trajectory output by the upstream prediction module to obtain the ego vehicle prediction trajectory and the obstacle prediction trajectory of the autonomous vehicle.

[0058] Step S22: Obtain the ego vehicle trajectory points of the ego vehicle prediction trajectory and the obstacle trajectory points of the obstacle prediction trajectory at the same moment at the same time interval.

[0059] Specifically, the path planning device takes the current position every dt on the trajectories of the host vehicle and the obstacle vehicle.

[0060] Step S23: Determine the obstacle bounding box of the obstacle trajectory point based on the boundary value of the obstacle.

[0061] As Figure 6 shown, using the method of hierarchical bounding boxes, first calculate the distance of the bounding sphere.

[0062] Step S24: Determine whether the host vehicle bounding box of the host vehicle trajectory point and the obstacle bounding box of the obstacle trajectory point collide.

[0063] In the embodiment of the present application, the method for determining whether the host vehicle bounding box of the host vehicle trajectory point and the obstacle bounding box of the obstacle trajectory point collide is as follows: The path planning device obtains the distance value between the host vehicle trajectory point and the obstacle trajectory point; determines whether the distance value is less than a preset threshold; if so, determines whether there is a separating axis between the host vehicle bounding box of the host vehicle trajectory point and the obstacle bounding box of the obstacle trajectory point; if so, determines that the host vehicle bounding box of the host vehicle trajectory point and the obstacle bounding box of the obstacle trajectory point do not collide.

[0064] Specifically, the path planning device obtains the distance value d between the host vehicle trajectory point and the obstacle trajectory point. If the distance d between the two vehicles is less than the threshold, then there is a collision; otherwise, there is no collision. The calculation method of the distance value d is as follows.

[0065]

[0066] If the distance d between the two vehicles is less than the threshold, determine whether there is a separating axis between the host vehicle bounding box of the host vehicle trajectory point and the obstacle bounding box of the obstacle trajectory point.

[0067] Specifically, in the embodiment of the present application, according to the separating axis theorem, it is determined whether two rectangles collide. As Figures 7 - 11 shown, Figure 7 is a schematic diagram of the first embodiment of the separating axis theorem provided by the present application; Figure 8 is a schematic diagram of the second embodiment of the separating axis theorem provided by the present application; Figure 9 is a schematic diagram of the third embodiment of the separating axis theorem provided by the present application; Figure 10 is a schematic diagram of the fourth embodiment of the separating axis theorem provided by the present application; Figure 11 is a schematic diagram of the fifth embodiment of the separating axis theorem provided by the present application.

[0068] If an axis can be found such that the projections of two convex shapes on this axis do not overlap, then these two shapes do not intersect. If this axis does not exist and the shapes are convex, then it can be determined that the two shapes intersect.

[0069] That is, if a straight line can be found such that bounding box A is completely on one side of the line and bounding box B is completely on the other side, then the two bounding boxes do not overlap. And this straight line becomes the separating line (referred to as the separating plane in the three-dimensional world), and it must be perpendicular to the separating axis.

[0070] If a certain axis is the separating axis, the projections on this axis satisfy the following relationship:

[0071] Proj(T)>0.5*Proj(A)+0.5*Proj(B)

[0072] |T·L|>|(W A *A x )·L|+|(H A *A y )·L|+|(W B *B x )·L|

[0073] +|(H B *B y )·L|

[0074] If there is a separating axis between the ego vehicle bounding box of the ego vehicle trajectory point and the obstacle bounding box of the obstacle trajectory point, it is determined that the ego vehicle bounding box of the ego vehicle trajectory point and the obstacle bounding box of the obstacle trajectory point do not collide.

[0075] For two rectangles, it is only necessary to calculate whether the projections of the two rectangles on the four sides respectively satisfy this theorem to judge the collision relationship. The specific formula is as follows:

[0076] 1)L=A x

[0077] |T·A x |>|(W A *A x )·A x |+|(H A *A y )·A x |+|(W B *B x )·A x |

[0078] +|(H B *B y )·A x |

[0079] |T·A x |>W A +|(W B *B x )·Ax |+|(H B *B y )·A x |

[0080] 2) L = A y

[0081] |T·A y | > H A +|(W B *B x )·A y |+|(H B *B y )·A y |

[0082] 3) L = B x

[0083] |T·B x | > |(W A *A x )·B x |+|(H A *A y )·B x |+W B

[0084] 4) L = B y

[0085] |T·B y | > |(W A *A x )·B y |+|(H A *A y )·B y |+H B

[0086] Step S25: If not, perform a lane-changing path planning for the autonomous vehicle according to the updated planned path boundary.

[0087] In an embodiment of the present application, based on the quadratic programming algorithm, an optimal path is planned for the boundary.

[0088] Quadratic programming standard form:

[0089]

[0090] subject to l ≤ Ax ≤ u

[0091] The quadratic programming optimization problem is quadratic, and its constraints are linear. x is the variable to be optimized and is an n-dimensional vector. p is the quadratic term coefficient and is a positive definite matrix. q is the linear term coefficient and is an n-dimensional vector. A is an m×n matrix, where A is the linear term coefficient of the constraint function and m is the number of constraint functions. l and u are the lower and upper bounds of the constraint function, respectively.

[0092] Using the horizontal OSQP solver, the CostFunction is given as follows:

[0093]

[0094] Minimize x i value, which is the lateral offset, so that the generated trajectory follows the reference trajectory as much as possible;

[0095] Minimize value, that is, minimize the first derivative of the trajectory. Generally speaking, it is to minimize the speed. The shortest trajectory for linear motion has the least energy consumption;

[0096] Minimize value, that is, minimize the second derivative of the trajectory, which is the acceleration;

[0097] Minimize value, that is, minimize the third derivative of the trajectory, which is the jerk.

[0098] Since quadratic programming is not the protected point of this application, it will not be further introduced.

[0099] If it is determined that the self-vehicle bounding box of the self-vehicle trajectory point and the obstacle bounding box of the obstacle trajectory point do not collide, then the autonomous vehicle will change lanes according to the updated planned path boundary, as follows:

[0100] In the embodiment of this application, the path planning device obtains the predicted position and predicted speed of the autonomous vehicle according to a preset sampling acceleration; determines the interested obstacles based on the predicted position and predicted speed; performs collision detection on the interested obstacles to obtain the interval of the interested obstacles; and in response to the interval meeting the lane change condition of the autonomous vehicle, issues a navigation lane change action instruction to the autonomous vehicle.

[0101] The navigation direction and remaining distance can be obtained through the navigation module, and these are used as the trigger intention for navigation lane changes. First, the obstacles are classified according to the lane width obtained from the map and their own bounding boxes, and the obstacles of interest, that is, the obstacles that may affect lane changes in the target lane, are selected. The acceleration of the host vehicle is sampled as a={-1.0, -0.5, 0.0, 0.5, 1.0, 1.5}. For the sampled acceleration, the kinematics is used to predict the position and speed information of the host vehicle within 5 seconds. At the same time, a preliminary collision check is performed on the obstacles of interest obtained in the first step. There is a certain spacing between the selected intervals, that is, between two adjacent obstacles, which meets the condition for the host vehicle to enter.

[0102] Note that if there is only one obstacle, the result of the interval is (infinity, obstacle) or (obstacle, infinity).

[0103] Using the predicted obstacle trajectories, each existing interval is checked, and a cost function is designed to finally obtain an interval that best meets the conditions. The design of the cost function has the following weights: acceleration and deceleration weight w1, interval space length weight w2, remaining drivable distance weight w3, and obstacle speed weight w4.

[0104] Furthermore, using the obstacle information obtained by perception and the host vehicle information obtained through positioning and the chassis, kinematic prediction is performed. Through comparing the positions of the obstacles and the host vehicle at 4 seconds in the future, a further safety check logic is carried out. If the obstacles and the host vehicle can still maintain a certain safety distance after 4 seconds, then it is considered that the interval selected in the current situation meets the lane change requirements, and a navigation lane change action is initiated.

[0105] In an embodiment of the present application, after obtaining the interval of the obstacles of interest, in response to the interval meeting the edge - sticking condition of the autonomous vehicle, a navigation edge - sticking action instruction is issued to the autonomous vehicle; it is detected that the predicted trajectory of the obstacles of interest does not meet the lane change condition of the autonomous vehicle within a duration, and the riding - on - the - line state of the autonomous vehicle is maintained until the obstacles of interest pass or the changed predicted trajectory meets the lane change condition of the autonomous vehicle, and a navigation lane change action instruction is issued to the autonomous vehicle.

[0106] After the gap is selected, in some cases, due to heavy traffic, the length of the interval space does not always meet the safety inspection threshold. At this time, the side-by-side game logic of the host vehicle will be triggered; on the premise that the time to collision is less than 4s but greater than 3s, the decision-making lane-changing state opportunity will jump to the side-by-side state. After reaching the road edge position, it will enter the game state, try to modify the predicted trajectory of the following vehicle, and initiate a lane change; however, if during the lane change process, the obstacle does not show courtesy behavior, the lane-changing state opportunity will jump to the straddling line stage to ensure that there will be no collision with the obstacle when going straight. Wait until the obstacle passes or the obstacle decelerates, and then play the game with the obstacle or the following vehicle again until the lane change is completed. This behavior can ensure no collision between the host vehicle and the obstacle on the premise of improving the lane-changing success rate.

[0107] Through the above method, the acceleration of the host vehicle is sampled, a preliminary collision check is performed with the obstacle set, and the optimal interval is obtained using the cost function. Using the interval, initiate a game or lane-changing behavior, design a collision-free path, and finally complete the navigation lane change. By sampling the acceleration of the host vehicle, combining with the predicted trajectory of the obstacle, select the optimal interval, and then use the interval to play the game between the host vehicle and the obstacle to plan a lane-changing path without collision with the obstacle, complete the navigation lane change, and improve the lane-changing success rate.

[0108] To implement the path planning method of the above embodiment, the present application also provides a path planning device. For details, please refer to Figure 12 , Figure 12 is a schematic structural diagram of an embodiment of the path planning device provided by the present application.

[0109] As Figure 12 shown, the path planning device 600 of this embodiment includes a processor 61, a memory 62, an input / output device 63, and a bus 64.

[0110] The processor 61, the memory 62, and the input / output device 63 are respectively connected to the bus 64. The memory 62 stores a computer program, and the processor 61 is configured to execute the computer program to implement the path planning method of the above embodiment.

[0111] In this embodiment, the processor 61 may also be referred to as a CPU (Central Processing Unit). The processor 61 may be an integrated circuit chip with signal processing capabilities. The processor 61 may also be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components. The processor 61 may also be a GPU (Graphics Processing Unit), also known as a display core, a visual processor, a display chip, which is a microprocessor dedicated to image processing on computers, workstations, game consoles, and some mobile devices (such as tablets, smartphones, etc.). The purpose of the GPU is to convert and drive the display information required by the computer system and provide a line scan signal to the display to control the correct display of the display, which is an important component connecting the display and the computer motherboard. The graphics card, as an important part of the computer host, undertakes the task of outputting and displaying graphics. The general-purpose processor may be a microprocessor or the processor 61 may also be any conventional processor, etc.

[0112] The present application also provides a computer storage medium, such as Figure 13 shown Figure 13 is a schematic diagram of the computer storage medium provided by the present application. The computer storage medium 700 is used to store a computer program 71. When the computer program 71 is executed by a processor, it is used to implement the method described in the embodiment of the path planning method of the present application.

[0113] The method involved in the embodiment of the path planning method of the present application, when implemented and existing in the form of a software functional unit and sold or used as an independent product, can be stored in a device, such as a computer-readable storage medium. Based on such an understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions to enable a computer device (which may be a personal computer, a server, or a network device, etc.) or a processor to execute all or part of the steps of the methods described in various embodiments of the present invention. The foregoing storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), magnetic disks, or optical discs that can store program codes.

[0114] In several embodiments provided by the present application, it should be understood that the disclosed methods and apparatuses can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative. For example, the division of modules or units is only a logical function division. In actual implementation, there may be other division methods. For example, units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections to each other can be through some interfaces. The indirect couplings or communication connections of devices or units can be in electrical, mechanical or other forms.

[0115] The units described as separate components may or may not be physically separated. The components displayed as units may or may not be physical units, that is, they can be located in one place or distributed to network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0116] In addition, in each embodiment of the present application, the functional units can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above integrated units can be implemented in the form of hardware or in the form of software functional units.

[0117] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art, or all or part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) or a processor to execute all or part of the steps of the methods in each embodiment of the present application.

[0118] The above is only the embodiment of the present invention, and does not limit the patent scope of the present invention. All equivalent structural or equivalent process transformations made by using the content of the specification and drawings of the present invention, or directly or indirectly applied in other related technical fields, are equally included in the patent protection scope of the present invention.

Claims

1. A path planning method, characterized in that: The path planning method comprises: Initialize the planning path boundary based on the road width in the navigation map; Get the boundary value of each obstacle in the obstacle list; Updating the planned path boundary using the boundary values ​​of the respective obstacles; The path of the autonomous driving vehicle is planned according to the updated planned path boundary.

2. The path planning method according to claim 1, characterized in that: The updating of the planned path boundary by using the boundary values ​​of the respective obstacles includes: In response to the follow-up trajectory planning instruction, determining static obstacles and dynamic obstacles in the obstacle list; Updating the planned path boundary using the left boundary value and the right boundary value of the static obstacle; The planned path boundary is updated using the predicted trajectory of the dynamic obstacle and boundary values ​​in all directions.

3. The path planning method according to claim 2, characterized in that: The updating of the planned path boundary by using the left boundary value and the right boundary value of the static obstacle includes: Traversing each boundary point in the planned path boundary; Updating the right boundary point using the left boundary value of the static obstacle; The left boundary point is updated using the right boundary value of the static obstacle.

4. The path planning method according to claim 3, characterized in that: After the planned path boundary is updated by using the left boundary value and the right boundary value of the static obstacle, the path planning method further includes: In response to the existence of an occluded path point in the planned path formed by the planned path boundary, marking a subsequent path point of the occluded path point as unreachable; Among them, the left boundary value of the occluded path point is smaller than the right boundary value.

5. The path planning method according to claim 2, characterized in that: After the predicted trajectory of the dynamic obstacle and the boundary values ​​in all directions are used to update the planned path boundary, the path planning method further includes: Obtain the predicted boundary values ​​of multiple predicted trajectory points of the dynamic obstacle: At each moment of prediction, in response to the predicted trajectory point being located on the right side of the trajectory path boundary, adjusting the right boundary point of the trajectory path boundary using the left boundary value of the predicted boundary value; At each moment of prediction, in response to the predicted trajectory point being located on the left side of the trajectory path boundary, the left boundary point is adjusted using the right boundary value of the predicted boundary value.

6. The path planning method according to claim 1, characterized in that: The performing path planning for the autonomous driving vehicle according to the updated planned path boundary includes: In response to the lane change trajectory planning instruction, obtaining a predicted self-vehicle trajectory and an predicted obstacle trajectory of the autonomous driving vehicle; Acquire the vehicle trajectory points of the vehicle predicted trajectory and the obstacle trajectory points of the obstacle predicted trajectory at the same time interval; Determine an obstacle bounding box of the obstacle trajectory point based on the boundary value of the obstacle; Determine whether the ego vehicle bounding box of the ego vehicle trajectory point collides with the obstacle bounding box of the obstacle trajectory point; If not, the lane change path is planned for the autonomous driving vehicle according to the updated planned path boundary.

7. The path planning method according to claim 6, characterized in that: The determining whether the ego vehicle bounding box of the ego vehicle trajectory point collides with the obstacle bounding box of the obstacle trajectory point comprises: Obtaining the distance between the vehicle trajectory point and the obstacle trajectory point; Determine whether the distance value is less than a preset threshold; If yes, determine whether there is a separation axis between the ego vehicle bounding box of the ego vehicle trajectory point and the obstacle bounding box of the obstacle trajectory point; If so, it is determined that the ego vehicle bounding box of the ego vehicle trajectory point and the obstacle bounding box of the obstacle trajectory point do not collide.

8. The path planning method according to claim 6, characterized in that: The lane change path planning for the autonomous driving vehicle according to the updated planned path boundary includes: Obtaining a predicted position and a predicted speed of the autonomous driving vehicle according to a preset sampling acceleration; determining an obstacle of interest based on the predicted position and the predicted speed; Performing collision detection on the obstacle of interest to obtain the interval of the obstacle of interest; In response to the interval satisfying the lane changing condition of the autonomous driving vehicle, a navigation lane changing action instruction is initiated to the autonomous driving vehicle.

9. The path planning method according to claim 6, characterized in that: After obtaining the interval of the obstacle of interest, the path planning method further includes: In response to the interval satisfying the edge-keeping condition of the autonomous driving vehicle, initiating a navigation edge-keeping action instruction to the autonomous driving vehicle; Detect that the predicted trajectory of the obstacle of interest does not satisfy the lane changing condition of the autonomous driving vehicle within the duration, maintain the line-riding state of the autonomous driving vehicle until the obstacle of interest passes or the changed predicted trajectory satisfies the lane changing condition of the autonomous driving vehicle, and initiate a navigation lane changing action instruction to the autonomous driving vehicle.

10. A path planning device, characterized in that: The path planning device includes a memory and a processor coupled to the memory; The memory is used to store program data, and the processor is used to execute the program data to implement the path planning method as described in any one of claims 1 to 9.

11. A computer storage medium, characterized in that: The computer storage medium is used to store program data, and when the program data is executed by a computer, it is used to implement the path planning method according to any one of claims 1 to 9.

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