Path planning method and device, vehicle, and storage medium

Abstract

The application discloses a path planning method and device, a vehicle and a storage medium. The method comprises the following steps: acquiring obstacle information, the obstacle information being used for indicating the position and shape of an obstacle in an environment in which a vehicle is located; acquiring vehicle speed information; determining a target region containing the obstacle based on the obstacle information and the vehicle speed information; and determining a planning path based on a two-dimensional Gaussian function and the target region, wherein the planning path does not coincide with the target region. The technical scheme provided in the embodiments of the application can provide accurate driving guidance when the vehicle has an obstacle avoidance requirement, improve the accuracy of path planning in an obstacle avoidance scene, and thus improve driving safety.

Description

Technical Field

[0001] This application relates to the field of autonomous driving technology, and in particular to a speed planning method, apparatus, vehicle, and storage medium. Background Technology

[0002] Automatic obstacle avoidance schemes for vehicles are an important research direction in the field of autonomous driving.

[0003] In related technologies, when a vehicle detects an obstacle, it will automatically detour by: determining a circular area containing the obstacle based on the obstacle's size, shape, and location; transforming the obstacle detour problem into a circular area detour problem; then determining a planned path based on the circular area; and driving along the planned path will allow the vehicle to detour around the obstacle and continue moving forward.

[0004] The path planning solutions provided by related technologies are not accurate enough. Summary of the Invention

[0005] This application proposes a path planning method, apparatus, vehicle, and storage medium.

[0006] In a first aspect, embodiments of this application provide a path planning method, the method comprising: acquiring obstacle information, the obstacle information being used to indicate the position and shape of obstacles in the environment in which the vehicle is located; acquiring vehicle speed information; determining a target area containing obstacles based on the obstacle information and the vehicle speed information; and determining a planned path based on a two-dimensional Gaussian function and the target area, wherein the planned path does not overlap with the target area.

[0007] Secondly, embodiments of this application provide a path planning device, the device comprising: a first acquisition module for acquiring obstacle information, the obstacle information being used to indicate the position and shape of obstacles in the environment in which the vehicle is located; a second acquisition module for acquiring vehicle speed information; a region determination module for determining a target region containing obstacles based on the obstacle information and the vehicle speed information; and a path planning module for determining a planned path based on a two-dimensional Gaussian function and the target region, wherein the planned path does not overlap with the target region.

[0008] Thirdly, embodiments of this application provide a vehicle, including: one or more processors; a memory; and one or more applications, wherein the one or more applications are stored in the memory and configured to be executed by the one or more processors, and the one or more applications are configured to perform the path planning method as described in the first aspect.

[0009] Fourthly, embodiments of this application provide a computer-readable storage medium storing computer program instructions that can be invoked by a processor to execute the path planning method as described in the first aspect.

[0010] Fifthly, embodiments of this application provide a computer program product that, when executed, enables the implementation of the path planning method as described in the first aspect.

[0011] Compared to existing technologies, the path planning method provided in this application determines the target area that the vehicle needs to avoid when an obstacle is detected, based on obstacle information and the vehicle's current speed. Then, path planning is performed based on a two-dimensional Gaussian function and the target area. Since the method of determining the target area fully considers the vehicle speed, the determined target area can adapt to the vehicle's current speed, and the planned path determined based on the target area can also adapt to the vehicle's current speed. This can provide accurate driving guidance when the vehicle has obstacle avoidance needs, improve the accuracy of path planning in obstacle avoidance scenarios, and thus improve driving safety. Attached Figure Description

[0012] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0013] Figure 1 This is a schematic diagram of the implementation environment provided in the embodiments of this application.

[0014] Figure 2 This is a flowchart of a path planning method provided in one embodiment of this application.

[0015] Figure 3 This is a flowchart of a path planning method provided in another embodiment of this application.

[0016] Figure 4 This is a schematic diagram of the obstacles and the obstacle coordinate system provided in the embodiments of this application.

[0017] Figure 5 This is a schematic diagram of the path planning provided in the embodiments of this application.

[0018] Figure 6 This is a structural block diagram of a path planning device provided in one embodiment of this application.

[0019] Figure 7 This is a structural block diagram of a vehicle provided in one embodiment of this application.

[0020] Figure 8 This is a structural block diagram of a computer storage medium provided in one embodiment of this application. Detailed Implementation

[0021] The embodiments of this application are described in detail below. Examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain this application, and should not be construed as limiting this application.

[0022] To enable those skilled in the art to better understand the solutions of this application, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0023] Please see Figure 1 This diagram illustrates an implementation environment provided in one embodiment of the present application. The implementation environment includes a vehicle 100, which refers to a means of transportation driven or towed by a power unit for the purpose of carrying people or transporting goods, including but not limited to cars, suburban utility vehicles (SUVs), multi-purpose vehicles (MPVs), etc.

[0024] Vehicle 100 includes a path planning module, which performs path planning. Path planning refers to finding a collision-free path from a starting state to a target state in an environment with obstacles, according to certain evaluation criteria. In this embodiment, the path planning module determines the target area that the vehicle needs to avoid based on the detected obstacle information (including position, shape, etc.) and vehicle speed information. Then, it performs path planning based on the target area and a two-dimensional Gaussian function. Since the method of determining the target area fully considers the vehicle speed factor, the determined target area can adapt to the current speed of vehicle 100. Path planning based on the target area allows the determined planned path to also adapt to the current vehicle speed, providing accurate driving guidance when the vehicle has obstacle avoidance requirements, improving the accuracy of path planning in obstacle avoidance scenarios, and thus improving driving safety. The algorithm used by the vehicle for path planning based on the target area and the two-dimensional Gaussian function can be the A* (A* Star) algorithm.

[0025] In some embodiments, the vehicle 100 further includes a detection module for detecting information about obstacles around the vehicle 100 (such as shape, speed, and distance from the vehicle 100), including other vehicles, pedestrians, roadblocks, etc., traveling in front of the vehicle 100. The detection module includes, but is not limited to, millimeter-wave radar, lidar, ultrasonic radar, binocular cameras, monocular cameras, etc. In some embodiments, the vehicle 100 further includes a positioning module for locating the current position of the vehicle 100. The positioning module may be a Global Positioning System (GPS) module. In some embodiments, the vehicle 100 also includes various sensors for collecting various parameters of the vehicle 100 during operation. These sensors include, but are not limited to, speed sensors, acceleration sensors, wheel speed sensors, temperature sensors, etc.

[0026] Please refer to Figure 2 The diagram illustrates a flowchart of a path planning method provided in one embodiment of this application. The method includes the following steps S201-S204.

[0027] Step S201: Obtain obstacle information.

[0028] Obstacle information is used to indicate the position and shape of obstacles in the environment in which the vehicle is traveling. These obstacles are located in front of the vehicle and obstruct its movement. In some embodiments, the obstacle information includes the position information of the obstacle's center and the position information of at least two vertices of the obstacle. The position information of the obstacle's center and the position information of the at least two vertices can be used to indicate the location of the obstacle; the position information of the at least two vertices can be used to indicate the shape of the obstacle.

[0029] Optionally, the vehicle is equipped with a detection component to acquire obstacle information. This detection component includes, but is not limited to, lidar, millimeter-wave radar, monocular cameras, binocular cameras, etc. Taking lidar as an example, the specific process for the vehicle to acquire obstacle information via lidar is as follows: The lidar emits a laser beam. After the laser beam illuminates the obstacle, it generates an echo signal. The lidar receives the echo signal and then calculates point cloud data corresponding to each laser beam based on the difference between the emission time of the laser beam and the reception time of the echo signal. The point cloud data corresponding to each laser beam includes the three-dimensional coordinates of the target point detected by the laser beam. By analyzing and processing the point cloud data detected by the lidar, the vehicle can determine the location information of the obstacle's center and the location information of at least two vertices of the obstacle.

[0030] In some embodiments, when a vehicle detects an obstacle and determines that the automatic obstacle avoidance function is enabled, it acquires obstacle information and performs subsequent path planning steps. The automatic obstacle avoidance function refers to the function of avoiding obstacles through path planning upon detection. The driver can set the automatic obstacle avoidance function to be enabled on the vehicle's control panel. Specifically, the vehicle's control panel includes an automatic obstacle avoidance function control. Upon receiving a first trigger signal for the automatic obstacle avoidance function control, the automatic obstacle avoidance function is set to enabled, and a stored automatic obstacle avoidance function flag is set to a predetermined value for later retrieval. In this embodiment, after detecting an obstacle, the vehicle checks whether the value of the automatic obstacle avoidance function flag is the predetermined value. If the value of the automatic obstacle avoidance function flag is the predetermined value, the obstacle information is acquired, and subsequent path planning steps are performed.

[0031] In other embodiments, after detecting an obstacle, the vehicle issues an inquiry message to indicate the presence of an obstacle ahead and to inquire whether to activate the automatic obstacle avoidance process. If a confirmation instruction is received in response to the inquiry message, obstacle information is acquired and subsequent path planning steps are executed. In some embodiments, the inquiry message and the confirmation instruction can both be voice messages. In other embodiments, the inquiry message can be a pop-up message displayed on the vehicle's central control screen, and the confirmation instruction can be a voice message or a second trigger signal for a specified control in the pop-up message.

[0032] Step S202: Obtain vehicle speed information.

[0033] Vehicle speed information represents the vehicle's current speed. In some embodiments, the vehicle is equipped with a speed sensor, which collects the aforementioned current speed.

[0034] The execution order of steps S201 and S202 is not limited in this embodiment. The vehicle may execute step S201 first and then step S202; or it may execute step S202 first and then step S201; or it may execute steps S201 and S202 simultaneously.

[0035] Step S203: Based on obstacle information and vehicle speed information, determine the target area containing obstacles.

[0036] The target area is the area that a vehicle needs to avoid while driving. The area of ​​the target area is typically slightly larger than the projected area of ​​the obstacle on the road surface. In this embodiment, when there is only one obstacle, the target area is an elliptical region containing that obstacle; when there are multiple obstacles, the target area is a collection of elliptical regions determined separately for each obstacle. The method for determining the target area will be described in the following embodiments. In this embodiment, the vehicle determines the target area to be avoided based on obstacle information and vehicle speed information, making the determination of the target area adaptable to different vehicle speed information and resulting in a more reasonable target area.

[0037] Step S204: Determine the planning path based on the two-dimensional Gaussian function and the target region.

[0038] The planned path and the target area do not overlap. The two-dimensional Gaussian distribution, also known as the two-dimensional normal distribution, is graphically represented as a bell-shaped curve. The closer a point is to the center, the larger its value; the farther away from the center, the smaller its value. The specific implementation details of path planning will be described in the examples below.

[0039] In this embodiment, path planning is performed based on a two-dimensional Gaussian function and a target area determined based on vehicle speed information. Since the method of determining the target area fully considers the vehicle speed factor, the determined target area can adapt to the current vehicle speed. Path planning based on the above target area can also adapt the determined planned path to the current vehicle speed, providing accurate driving guidance when the vehicle has obstacle avoidance needs, improving the accuracy of path planning in obstacle avoidance scenarios, and thus improving driving safety.

[0040] In summary, the technical solution provided in this application, when an obstacle is detected, determines the target area that the vehicle needs to avoid based on the obstacle information and the vehicle's current speed. Then, path planning is performed based on a two-dimensional Gaussian function and the target area. Since the method of determining the target area fully considers the vehicle speed factor, the determined target area can adapt to the vehicle's current speed, and the planned path determined based on the target area can also adapt to the vehicle's current speed. This can provide accurate driving guidance when the vehicle has obstacle avoidance needs, improve the accuracy of path planning in obstacle avoidance scenarios, and thus improve driving safety.

[0041] Please refer to Figure 3 The diagram illustrates a flowchart of a path planning method provided in one embodiment of this application. The method includes the following steps S301-S308.

[0042] Step S301: Obtain obstacle information.

[0043] Obstacle information is used to indicate the position and shape of obstacles in the environment in which the vehicle is traveling. In this embodiment, the shape of an obstacle is characterized by the first coordinates corresponding to at least two vertices of the obstacle in an obstacle coordinate system. The position of an obstacle is characterized by the second coordinate of the obstacle's center in the obstacle coordinate system. The obstacle coordinate system is a coordinate system with the obstacle center as the origin, a first straight line as the vertical axis, and a second straight line as the horizontal axis. The first straight line is parallel to the lane centerline of the vehicle's lane and passes through the obstacle center. The second straight line is perpendicular to the first straight line, with the foot of the perpendicular at the obstacle center.

[0044] The vehicle can first obtain the coordinates of the obstacle in the lidar coordinate system. Based on the coordinates of the obstacle in the lidar coordinate system, it determines the relative positional relationship (including relative distance, relative azimuth, etc.) between at least two vertices of the obstacle and the obstacle center. Then, based on the above relative positional relationship, it determines the first coordinates corresponding to at least two vertices of the obstacle. The second coordinate corresponding to the obstacle center is (0, 0).

[0045] Reference Figure 4 This diagram illustrates the obstacle and its coordinate system as described in the embodiments of this application. The origin of the obstacle coordinate system is the center of the obstacle, the vertical axis is parallel to the center line of the lane and passes through the center of the obstacle, and the horizontal axis is perpendicular to the vertical axis. The obstacle includes four vertices, namely vertex A, vertex B, vertex C, and vertex D.

[0046] Step S302: Obtain vehicle speed information.

[0047] Step S303: Determine the minimum length of the elliptical region based on the first coordinates corresponding to at least two vertices of the obstacle.

[0048] In some embodiments, the vehicle determines the minimum length of the elliptical region by: projecting at least two vertices of the obstacle onto a reference heading to obtain projection points corresponding to the at least two vertices respectively; determining the distance between the projection points corresponding to the at least two vertices based on the first coordinates corresponding to the at least two vertices respectively; and determining the maximum value among the distances between the projection points corresponding to the at least two vertices as the minimum length of the elliptical region.

[0049] The reference heading is parallel to the center line of the lane in which the vehicle is traveling, which is also the vertical axis of the obstacle coordinate system.

[0050] The distance between the projection points corresponding to at least two vertices includes the distance between any two projection points. Please refer again. Figure 4The projection point corresponding to vertex A is A1, the projection point corresponding to vertex B is B1, the projection point corresponding to vertex C is C1, and the projection point corresponding to vertex D is D1. The distance between the projection points corresponding to at least two vertices includes the distance between projection points A1 and B1, the distance between projection points A1 and C1, the distance between projection points A1 and D1, the distance between projection points B1 and C1, the distance between projection points B1 and D1, and the distance between projection points C1 and D1.

[0051] The method for calculating the distance between the projected points corresponding to at least two vertices based on their respective first coordinates is as follows: Obtain the ordinate values ​​of the first coordinates corresponding to any two vertices. If the two ordinate values ​​have opposite signs, the distance between the projected points corresponding to the two vertices is the sum of the absolute values ​​of the two ordinate values; if the two ordinate values ​​have the same sign, the distance between the projected points corresponding to the two vertices is the difference between the absolute values ​​of the two ordinate values. For example, if the first coordinates of vertex A are (x1, y1), the first coordinates of vertex B are (x2, -y2), and the first coordinates of vertex C are (x3, y3), then the distance between projected points A1 and C1 is (y1 - y3).

[0052] Step S304: Based on the minimum length of the elliptical region and the vehicle speed information, determine the first length information of the major axis of the elliptical region.

[0053] The length information is positively correlated with the vehicle speed information. That is, the higher the vehicle's current speed, the larger the major axis of the elliptical region; the lower the vehicle's current speed, the smaller the major axis of the elliptical region. When the vehicle's current speed is high, the vehicle needs to avoid obstacles at a greater distance to prevent collisions caused by insufficient time to avoid obstacles at high speeds.

[0054] In some embodiments, a vehicle may determine the first length information of the major axis of an elliptical region by: obtaining a predetermined ratio and a predetermined extension length; determining the product between the vehicle speed information and the predetermined ratio as a first intermediate value; determining the sum of the first intermediate value and the predetermined extension length as a second intermediate value; and determining the sum of the second intermediate value and the minimum length of the elliptical region as the first length information of the major axis.

[0055] Both the predetermined ratio and the predetermined extension length are greater than zero. The predetermined ratio is a coefficient specifying the vehicle speed information in the calculation formula, which can be actually set by technicians for different operating conditions and different vehicle models. In this embodiment, the predetermined ratio is set to 1. The specified calculation formula is the formula for calculating the first length information of the major axis of the elliptical region. The predetermined extension length can also be actually set by technicians for different operating conditions and different vehicle models. In this embodiment, the predetermined extension length is set to 1 meter.

[0056] Optionally, the first length information of the major axis of the elliptical region is calculated using the following formula:

[0057] long_axis_half=(ego_speed*length_ratio+min_add_length)+min_length.

[0058] Here, `long_axis_half` is the first length information of the major axis of the elliptical region, `ego_speed` is the current vehicle speed, `length_ratio` is the predetermined ratio, and `min_add_length` is the predetermined extension length. `min_length` refers to the minimum length of the elliptical region.

[0059] Step S305: Based on the first length information of the major axis and the first coordinates corresponding to at least two vertices of the obstacle, determine the second length information of the minor axis of the elliptical region.

[0060] In some embodiments, the vehicle may determine the second length information of the minor axis of the elliptical region in the following manner: based on the first length information of the major axis, the ellipse formula, and the first coordinates corresponding to at least two vertices, determine the second length information of the minor axis corresponding to each vertex; and determine the maximum value among the second length information of the minor axis corresponding to at least two vertices as the second length information of the minor axis of the elliptical region.

[0061] The formula for an ellipse can be expressed by the following formula:

[0062] Where x is the x-coordinate of the first coordinate system corresponding to the vertex, y is the y-coordinate of the first coordinate system corresponding to the vertex, a is the first length information of the major axis of the elliptical region, and b is the second length information of the minor axis of the elliptical region.

[0063] The vehicle substitutes the first coordinates corresponding to each vertex and the first length information of the major axis of the elliptical region into the above ellipse formula to solve for the second length information of the minor axis corresponding to each vertex. Then, the maximum value of the second length information of the minor axis corresponding to each vertex is determined as the second length information of the minor axis of the elliptical region.

[0064] Step S306: Based on the first length information, the second length information, and the second coordinates corresponding to the center of the obstacle, determine the target area containing the obstacle.

[0065] Given that the center, major axis length, and minor axis length of the elliptical region are all determined, the vehicle can uniquely identify the elliptical region containing the obstacle.

[0066] It should be noted that when there are multiple obstacles, the vehicle can perform the above steps S303-S306 for each obstacle to obtain the target area corresponding to each obstacle.

[0067] Step S307: For each sampling point in the reference path, determine the cost of the sampling point using a two-dimensional Gaussian function and the target region.

[0068] There are typically multiple reference paths. In some embodiments, the vehicle calculates the cost of each sampling point as follows: based on the target region and a two-dimensional Gaussian function, a target calculation formula is determined, and the maximum variance corresponding to the target calculation formula is determined; the coordinates of the sampling point in the obstacle coordinate system are obtained; based on the coordinates of the sampling point in the obstacle coordinate system and the target calculation formula, the variance of the sampling point is determined; based on the variance and the maximum variance of the sampling point, the cost of the sampling point is determined.

[0069] The specific formula for calculating the two-dimensional Gaussian function is as follows:

[0070]

[0071] Where σ1 is the major axis radius of the elliptical region, which is half of the first length information. σ2 is the minor axis radius of the elliptical region, which is half of the second length information. μ1 is the x-coordinate value of the center coordinate of the elliptical region, which is 0. μ2 is the y-coordinate value of the center coordinate of the elliptical region, which is also zero. exp() is an exponential function with the natural constant e as its base. Where f(x,y) is the cost of the sampling point, x is the x-coordinate value of the sampling point in the obstacle coordinate system, and y is the y-coordinate value of the sampling point in the obstacle coordinate system.

[0072] Given the first and second length information, σ1 and σ2 can be calculated. Substituting these values ​​into the formula for the two-dimensional Gaussian function, the target calculation formula can be determined. Taking a first length of 6 meters and a second length of 2 meters as an example, with σ1 = 3 and σ2 = 1, the specific target calculation formula is as follows:

[0073]

[0074]

[0075] When the vehicle has x = 0 and y = 0, the calculated f(x,y) is determined to be the maximum variance.

[0076] The vehicle stores the coordinates of the sampling points in geodetic coordinates. By converting these coordinates, the coordinates of the sampling points in the obstacle coordinate system can be determined. Substituting these obstacle coordinates into the target calculation formula yields the variance of the sampling points. Finally, the vehicle determines the cost of each sampling point as the ratio between its variance and the maximum variance. Furthermore, sampling points outside the target area are directly assigned a value of 0.

[0077] It should be noted that when there are multiple obstacles, the vehicle can calculate the cost value of the sampling point in the target area corresponding to each obstacle according to step S307, and then determine the maximum value of the sampling point in the elliptical area corresponding to each obstacle as the cost value of the sampling point.

[0078] It should be noted that the cost of sampling points can take into account not only the relative relationship between the sampling point and the target area, but also other factors, such as the vertical distance from the lane centerline, vehicle steering parameters, etc. This application embodiment does not limit this.

[0079] Step S308: Determine the planned path based on the cost value of each sampling point.

[0080] The vehicle determines the planned path as the reference path that minimizes the sum of the cost values ​​of each sampling point. If there are multiple reference paths, the vehicle calculates the sampled values ​​of each sampling point along each reference path, determines the cost value of that reference path by summing the values ​​of all sampling points along that path, and finally determines the planned path.

[0081] Reference Figure 5 This illustrates a schematic diagram of path planning provided in one embodiment of this application. Figure 5 In the process, there is a static obstacle 51. The vehicle determines the target area 52 containing the obstacle 51 based on the obstacle 51 and its current driving speed, and then determines the planned path 53 based on the target area 52 to bypass the obstacle 51.

[0082] In summary, the technical solution provided in this application, when an obstacle is detected, determines the target area that the vehicle needs to avoid based on the obstacle information and the vehicle's current speed. Then, path planning is performed based on a two-dimensional Gaussian function and the target area. Since the method of determining the target area fully considers the vehicle speed factor, the determined target area can adapt to the vehicle's current speed, and the planned path determined based on the target area can also adapt to the vehicle's current speed. This can provide accurate driving guidance when the vehicle has obstacle avoidance needs, improve the accuracy of path planning in obstacle avoidance scenarios, and thus improve driving safety.

[0083] Please see Figure 6 The diagram shows a structural block diagram of a path planning device provided in an embodiment of this application. The device includes: a first acquisition module 610, a second acquisition module 620, a region determination module 630, and a path planning module 640.

[0084] The first acquisition module 610 is used to acquire obstacle information, which indicates the position and shape of obstacles in the vehicle's environment. The second acquisition module 620 is used to acquire vehicle speed information. The region determination module 630 is used to determine a target region containing obstacles based on the obstacle information and vehicle speed information. The path planning module 640 is used to determine a planned path based on a two-dimensional Gaussian function and the target region, wherein the planned path does not overlap with the target region.

[0085] In some embodiments, the target region is an elliptical region; the shape of the obstacle is characterized by the first coordinates corresponding to at least two vertices of the obstacle in the obstacle coordinate system; the position of the obstacle is characterized by the second coordinate of the obstacle center in the obstacle coordinate system; the region determination module 630 is used to: determine the maximum length of the obstacle based on the first coordinates corresponding to at least two vertices of the obstacle; determine the first length information of the major axis of the elliptical region based on the maximum length of the obstacle and vehicle speed information, wherein the first length information is positively correlated with the vehicle speed information; determine the second length information of the minor axis of the elliptical region based on the first length information of the major axis and the first coordinates corresponding to at least two vertices of the obstacle; and determine the target region containing the obstacle based on the first length information, the second length information, and the second coordinate corresponding to the obstacle center.

[0086] In some embodiments, the region determination module 630 is configured to: obtain a predetermined ratio and a predetermined extension length, both of which are greater than zero; determine the product between the vehicle speed information and the predetermined ratio as a first intermediate value; determine the sum of the first intermediate value and the predetermined extension length as a second intermediate value; and determine the sum of the second intermediate value and the maximum length of the obstacle as the first length information of the major axis.

[0087] In some embodiments, the region determination module 630 is configured to: determine the second length information of the minor axis corresponding to each vertex based on the first length information of the major axis, the ellipse formula, and the first coordinates corresponding to at least two vertices respectively; and determine the maximum value among the second length information of the minor axis corresponding to at least two vertices as the second length information of the minor axis of the elliptical region.

[0088] In some embodiments, the region determination module 630 is configured to: project at least two vertices of the obstacle onto a reference heading to obtain projection points corresponding to the at least two vertices respectively, wherein the reference heading is parallel to the center line of the lane in which the vehicle is traveling; determine the distance between the projection points corresponding to the at least two vertices respectively based on the first coordinates corresponding to the at least two vertices respectively; and determine the maximum value among the distances between the projection points corresponding to the at least two vertices respectively as the maximum length of the obstacle.

[0089] In some embodiments, the path planning module 640 is configured to: determine the cost value of each sampling point in the reference path using a two-dimensional Gaussian function and the target region; and determine the planned path based on the cost value of each sampling point.

[0090] In some embodiments, the path planning module 640 is configured to: determine a target calculation formula based on the target region and a two-dimensional Gaussian function, and determine the maximum variance corresponding to the target calculation formula; obtain the coordinates of the sampling points in the obstacle coordinate system; determine the variance of the sampling points based on the coordinates of the sampling points in the obstacle coordinate system and the target calculation formula; and determine the cost of the sampling points based on the variance and the maximum variance of the sampling points.

[0091] In summary, the technical solution provided in this application, when an obstacle is detected, determines the target area that the vehicle needs to avoid based on the obstacle information and the vehicle's current speed. Then, path planning is performed based on a two-dimensional Gaussian function and the target area. Since the method of determining the target area fully considers the vehicle speed factor, the determined target area can adapt to the vehicle's current speed, and the planned path determined based on the target area can also adapt to the vehicle's current speed. This can provide accurate driving guidance when the vehicle has obstacle avoidance needs, improve the accuracy of path planning in obstacle avoidance scenarios, and thus improve driving safety.

[0092] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of the above-described device and module can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0093] In the several embodiments provided in this application, the coupling between modules can be electrical, mechanical, or other forms of coupling.

[0094] Furthermore, the functional modules in the various embodiments of this application can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module. The integrated modules described above can be implemented in hardware or as software functional modules.

[0095] Please see Figure 7 The illustration shows that an embodiment of this application also provides a vehicle 700, which includes one or more processors 710, a memory 720, and one or more application programs. The one or more application programs are stored in the memory and configured to be executed by the one or more processors, and are configured to perform the methods described in the above embodiments.

[0096] The processor 710 may include one or more processing cores. The processor 710 connects to various parts of the entire battery management system using various interfaces and lines, and performs various functions and processes data of the battery management system by running or executing instructions, programs, code sets, or instruction sets stored in the memory 720, and by calling data stored in the memory 720. Optionally, the processor 710 may be implemented using at least one hardware form of Digital Signal Processing (DSP), Field-Programmable Gate Array (FPGA), or Programmable Logic Array (PLA). The processor 710 may integrate one or a combination of several of the following: Central Processing Unit (CPU), Graphics Processing Unit (GPU), and modem. The CPU primarily handles the operating system, user interface, and applications; the GPU is responsible for rendering and drawing the displayed content; and the modem handles wireless communication. It is understood that the modem may also not be integrated into the processor 710 and may be implemented separately through a communication chip.

[0097] The memory 720 may include random access memory (RAM) or read-only memory (ROM). The memory 720 can be used to store instructions, programs, code, code sets, or instruction sets. The memory 720 may include a program storage area and a data storage area. The program storage area may store instructions for implementing an operating system, instructions for implementing at least one function (e.g., touch functionality, sound playback functionality, image playback functionality, etc.), and instructions for implementing the various method embodiments described above. The data storage area may also store data created during the use of the electronic device (e.g., phonebook, audio / video data, chat log data, etc.).

[0098] Please see Figure 8 The present application also provides a computer-readable storage medium 800, which stores computer program instructions 810 that can be invoked by a processor to perform the methods described in the above embodiments.

[0099] The computer-readable storage medium 800 may be, for example, flash memory, electrically erasable programmable read-only memory (EEPROM), electrically programmable read-only memory (EPROM), hard disk, or read-only memory (ROM). Optionally, the computer-readable storage medium includes non-transitory computer-readable storage medium. The computer-readable storage medium 800 has storage space for computer program instructions 810 that perform any of the method steps described above. These computer program instructions 810 may be read from or written to one or more computer program products.

[0100] The above are merely preferred embodiments of this application and are not intended to limit this application in any way. Although this application has disclosed preferred embodiments as above, it is not intended to limit this application. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the technical solution of this application. Any simple modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of this application without departing from the scope of the technical solution of this application shall still fall within the scope of the technical solution of this application.

Claims

1. A path planning method, characterized in that, The method includes: Obstacle information is acquired, which is used to indicate the location and shape of obstacles in the environment in which the vehicle is located; Obtain the vehicle speed information; Based on the obstacle information and the vehicle speed information, a target area containing the obstacle is determined; The planned path is determined based on a two-dimensional Gaussian function and the target region, wherein the planned path does not coincide with the target region; wherein the target region is an elliptical region; the shape of the obstacle is characterized by the first coordinates corresponding to at least two vertices of the obstacle in the obstacle coordinate system; the position of the obstacle is characterized by the second coordinate of the obstacle center in the obstacle coordinate system. The step of determining the target area containing the obstacle based on the obstacle information and the vehicle speed information includes: The minimum length of the elliptical region is determined based on the first coordinates corresponding to at least two vertices of the obstacle. Based on the minimum length of the elliptical region and the vehicle speed information, the first length information of the major axis of the elliptical region is determined, and the first length information is positively correlated with the vehicle speed information; Based on the first length information of the major axis and the first coordinates corresponding to at least two vertices of the obstacle, the second length information of the minor axis of the elliptical region is determined. Based on the first length information, the second length information, and the second coordinates corresponding to the center of the obstacle, the target area containing the obstacle is determined.

2. The method according to claim 1, characterized in that, The determination of the first length information of the major axis of the elliptical region based on the minimum length of the obstacle and the vehicle speed information includes: Obtain a predetermined ratio and a predetermined extension length, wherein both the predetermined ratio and the predetermined extension length are greater than zero; The product of the vehicle speed information and the predetermined ratio is determined as the first intermediate value; The sum of the first intermediate value and the predetermined extension length is determined as the second intermediate value; The sum of the second intermediate value and the minimum length of the obstacle is determined as the first length information of the major axis.

3. The method according to claim 1, characterized in that, The determination of the second length information of the minor axis of the elliptical region based on the first length information of the major axis and the first coordinates corresponding to at least two vertices of the obstacle includes: Based on the first length information of the major axis, the ellipse formula, and the first coordinates corresponding to at least two vertices, the second length information of the minor axis corresponding to each vertex is determined; The maximum value among the second length information of the minor axis corresponding to at least two of the vertices is determined as the second length information of the minor axis of the elliptical region.

4. The method according to claim 1, characterized in that, Determining the minimum length of the obstacle based on the first coordinates corresponding to at least two vertices of the obstacle includes: At least two vertices of the obstacle are projected onto a reference heading to obtain projection points corresponding to at least two vertices respectively, wherein the reference heading is parallel to the center line of the lane in which the vehicle is traveling; Based on the first coordinates corresponding to at least two vertices, determine the distance between the projection points corresponding to at least two vertices; The maximum value among the distances between the projection points corresponding to at least two of the vertices is determined as the minimum length of the obstacle.

5. The method according to any one of claims 1 to 4, characterized in that, The process of determining the planned path based on the two-dimensional Gaussian function and the target region includes: For each sampling point in the reference path, the cost of the sampling point is determined by the two-dimensional Gaussian function and the target region. The planned path is determined based on the cost value of each sampling point.

6. The method according to claim 5, characterized in that, The step of determining the cost value of each sampling point in the reference path using the two-dimensional Gaussian function and the target region includes: Based on the target region and the two-dimensional Gaussian function, the target calculation formula is determined, and the maximum variance corresponding to the target calculation formula is determined. Obtain the coordinates of the sampling point in the obstacle coordinate system; The variance of the sampling points is determined based on the coordinates of the sampling points in the obstacle coordinate system and the target calculation formula. The cost value of the sampling point is determined based on the variance of the sampling point and the maximum variance.

7. A path planning device, characterized in that, The device includes: The first acquisition module is used to acquire obstacle information, which is used to indicate the position and shape of obstacles in the environment in which the vehicle is located; The second acquisition module is used to acquire the vehicle speed information; The region determination module is used to determine a target region containing the obstacle based on the obstacle information and the vehicle speed information; A path planning module is used to determine a planned path based on a two-dimensional Gaussian function and the target region, wherein the planned path does not overlap with the target region; The target area is an elliptical region; the shape of the obstacle is characterized by the first coordinates of at least two vertices of the obstacle in the obstacle coordinate system; the position of the obstacle is characterized by the second coordinate of the obstacle center in the obstacle coordinate system. The region determination module is specifically used for: The minimum length of the elliptical region is determined based on the first coordinates corresponding to at least two vertices of the obstacle. Based on the minimum length of the elliptical region and the vehicle speed information, the first length information of the major axis of the elliptical region is determined, and the first length information is positively correlated with the vehicle speed information; Based on the first length information of the major axis and the first coordinates corresponding to at least two vertices of the obstacle, the second length information of the minor axis of the elliptical region is determined. Based on the first length information, the second length information, and the second coordinates corresponding to the center of the obstacle, the target area containing the obstacle is determined.

8. A vehicle, characterized in that, include: One or more processors; Memory; One or more applications, wherein the one or more said applications are stored in the memory and configured to be executed by one or more said processors, the one or more said applications being configured to perform the path planning method as described in any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer program instructions that can be invoked by a processor to execute the path planning method as described in any one of claims 1-6.

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