Intelligent automobile obstacle avoidance path planning method and system

Abstract

The invention provides an intelligent automobile obstacle avoidance path planning method and system, and the method comprises the steps: obtaining a 5G-V2X position signal, determining the uncertainty of the 5G-V2X position signal, and determining a minimum safety distance according to the uncertainty of the 5G-V2X position signal; building an obstacle avoidance planning map considering the position uncertainty based on the minimum safety distance; designing a rapid collision detection strategy based on the obstacle avoidance planning map; designing a guiding type sampling strategy deviating from the destination, and fusing the guiding type sampling strategy deviating from the destination, the random sampling strategy and the end point sampling strategy to obtain a planning node; the obstacle avoidance path is determined based on the rapid collision detection strategy and the planning node. The problems of uncertainty interference of 5G-V2X position signals, blind search during obstacle avoidance planning, poor planning result safety and the like can be effectively solved.

Description

Technical Field

[0001] The present invention belongs to the technical field of intelligent energy management and motion control, and specifically relates to a method and system for intelligent vehicle obstacle avoidance path planning. Background Art

[0002] With the continuous advancement of automotive technologies and the maturing of the automotive industry chain, cars have gradually become a common means of transportation in countless households, and global car ownership has experienced explosive growth in the past decade. While this growth in car ownership has improved people's quality of life, it has also brought about numerous social problems. For example, crowded cars have led to a continuous increase in traffic accident rates, increasing the pressure on road traffic safety.

[0003] To alleviate the pressure on road traffic safety and reduce traffic accidents, smart car-related technologies and products are being continuously developed. As a key technology for smart car implementation, obstacle avoidance planning generates a path based on environmental perception information that allows the vehicle to avoid obstacles on the road. This path allows the smart car to maintain a safe distance from obstacles and avoid collisions. Due to environmental factors such as tree shade and fog, environmental perception information contains certain uncertainties, especially in obstacle detection. Furthermore, the blind search nature of traditional planning methods can easily lead to planned paths that fail to avoid obstacles, hindering the rapid operation of smart cars and actually causing further traffic congestion.

[0004] Therefore, how to take into account the uncertainty information of environmental perception and reduce the blind expansion of the planned path so that it is as close as possible to the destination to be reached by the vehicle is an urgent problem to be solved by those skilled in the art. Summary of the Invention

[0005] In order to solve the above technical problems, the present invention provides a method and system for intelligent vehicle obstacle avoidance path planning, which are used to solve the technical problems in the prior art.

[0006] In one aspect, the present invention provides the following technical solution: a method for planning an obstacle avoidance path for an intelligent vehicle, comprising: Obtaining a 5G-V2X position signal and determining an uncertainty of the 5G-V2X position signal, and determining a minimum safety distance based on the uncertainty of the 5G-V2X position signal; Constructing an obstacle avoidance planning map taking into account position uncertainty based on the minimum safety distance; Designing a fast collision detection strategy based on the obstacle avoidance planning map; Designing a destination-biased guided sampling strategy, and fusing the destination-biased guided sampling strategy, a random sampling strategy, and an endpoint sampling strategy to obtain a planning node; An obstacle avoidance path is determined based on the rapid collision detection strategy and the planning node.

[0007] Compared with the existing technology, the beneficial effects of the present invention are as follows: the present invention can convert the uncertainty information of the 5G-V2X sensor into quantitative certain information, and calculate the minimum safe distance that the smart car should maintain from the obstacle based on the safety confidence set by the user, thereby suppressing the uncertainty interference of the position signal. After that, a fast collision detection strategy is designed to quickly detect the validity of the planning node, improve the real-time calculation of the overall algorithm, and thus improve the safety of the planning. Then, a guided sampling strategy biased towards the destination is designed to reduce the blind search of the planning, accelerate the planning algorithm to search for the driving path to the destination, improve the real-time performance of the planning, and further improve the safety of the planning.

[0008] Preferably, the steps of obtaining a 5G-V2X position signal and determining the uncertainty of the 5G-V2X position signal, and determining the minimum safety distance according to the uncertainty of the 5G-V2X position signal include: Acquire a 5G-V2X position signal, wherein the 5G-V2X position signal is normally distributed. , calculate the obstacle position signal in the 5G-V2X position signal Neighborhood Confidence : ; Where, is the obstacle position signal, is the probability, It is the location signal of smart car; According to the confidence Determine the distance judgment formula: ; Where, Set confidence levels for users; Determine the minimum distance that satisfies the distance judgment formula , to obtain the minimum safe distance .

[0009] Preferably, the step of constructing an obstacle avoidance planning map taking into account position uncertainty based on the minimum safety distance includes: Plan the starting point based on the user-set starting point and planned end point Draw a two-dimensional map of the boundary; Aligning the X-axis and Y-axis in the two-dimensional plane map with the X-axis and Y-axis defined by the positioning system; According to the 5G-V2X position signal, the boundaries of obstacles are moved outward to the minimum safe distance , to obtain the confidence obstacle; The confident obstacles are projected onto the two-dimensional plane map to obtain an obstacle avoidance planning map.

[0010] Preferably, in the step of designing a rapid collision detection strategy based on the obstacle avoidance planning map, the rapid collision detection strategy is: Calculate any node in the obstacle avoidance planning map The sum of the interior angles of the triangle constructed with all sides of the trusted obstacle , determine the interior angle sum Is it greater than 360°? , if the sum of the interior angles Greater than 360°- , then the decision node There is no collision with the trust obstacle, if the inner angle and No more than 360°- , then the decision node Collision with a trusted obstacle where Set a security threshold for the user. If the node If there is no collision with any trusted obstacles, then the node Security, if the node If a node collides with a trusted obstacle, Unsafe, where the interior angle and for: ; Where, is the confidence barrier The first corner point on the edge, is the total number of edges of the confident obstacles, It is a pointer Arrive The Euclidean length of It is a pointer Arrive The Euclidean length of It is a pointer Arrive The Euclidean length of It is a pointer Arrive The Euclidean length of It is a pointer Arrive The Euclidean length of .

[0011] Preferably, in the step of designing a destination-biased guided sampling strategy, the destination-biased guided sampling strategy includes: For the transition nodes in the obstacle avoidance planning map and transition nodes Closest node , construct a biased planning endpoint Energy Field , based on the energy field Determine the initial planning node : ; Where, 、 Represent points Pointing Point ,point Pointing Point vector.

[0012] Preferably, the step of fusing the destination-biased guided sampling strategy, the random sampling strategy, and the endpoint sampling strategy to obtain a planning node includes: Generate a random number in the range [0,1] in each planned sampling ,when When , a random sampling strategy is used to generate planning nodes ;when When , a random sampling strategy is first used to generate transition nodes , and then adopt a guided sampling strategy biased towards the destination to generate planning nodes ;when When the planning end point is directly As a planning node : ; Where, They are the maximum and minimum boundaries of the X-axis of the obstacle avoidance planning map, They are the maximum and minimum boundaries of the Y axis of the obstacle avoidance planning map, 、 Represent points Pointing Point ,point Pointing Point vector, is the transition node, For The nearest node, They are respectively a first set sampling constant and a second set sampling constant.

[0013] Preferably, the step of determining an obstacle avoidance path based on the rapid collision detection strategy and the planning node includes: Determine the planning starting point based on the fast collision detection strategy and planned end point Is it safe? If you plan the starting point and planned end point If it is unsafe, stop planning and set the planning flag to flag Marked as 0, end planning and return an empty path, if the planning starting point and planned end point If it is safe, set the current sampling times to , and set the maximum number of sampling times and plan the starting point As the root node, store the planned planning tree T middle; If the current sampling times And planning flag flag If is 0, the current sampling times is , from the planning tree T Search and plan nodes using the method of minimum Euclidean distance Nearest Node ,right Line segments are spaced at set intervals Perform equidistant scattering to obtain discrete node sets , ,in, is the number of nodes in the discrete node set, is the ceiling function, for Euclidean length of a line segment; If the current sampling times Planning sign flag If it is 0, the planning ends and returns an empty path. And planning flag flag If it is not 0, the safe status of all nodes in the discrete node set is identified by the fast collision detection strategy. If there are unsafe nodes in the discrete node set, sampling is continued. If there are no unsafe nodes in the discrete node set, the planning node is calculated. and planned end point The Euclidean distance between , if the Euclidean distance , then the planning node Save plan tree T In the mark node Planning nodes The parent node and continue to start sampling, if the Euclidean distance , then the planning end point Save plan tree T Mark the planning node Planning nodes The parent node of the planning tree is backtracked. T Find the link planning starting point and planned end point Path Path, To obtain the obstacle avoidance path.

[0014] In a second aspect, the present invention provides the following technical solution: an intelligent vehicle obstacle avoidance path planning system, the system comprising: a distance module, configured to obtain a 5G-V2X position signal and determine an uncertainty of the 5G-V2X position signal, and determine a minimum safety distance based on the uncertainty of the 5G-V2X position signal; A map module, configured to construct an obstacle avoidance planning map taking into account position uncertainty based on the minimum safety distance; A strategy module, configured to design a fast collision detection strategy based on the obstacle avoidance planning map; A node module is used to design a guided sampling strategy biased towards the destination, and fuse the guided sampling strategy biased towards the destination, the random sampling strategy and the endpoint sampling strategy to obtain a planning node; A path module is used to determine an obstacle avoidance path based on the fast collision detection strategy and the planning node.

[0015] In a third aspect, the present invention provides the following technical solution: a computer comprising a memory, a processor, and a computer program stored in the memory and executable on the processor; when the processor executes the computer program, the intelligent vehicle obstacle avoidance path planning method as described above is implemented.

[0016] In a fourth aspect, the present invention provides the following technical solution: a storage medium storing a computer program, which, when executed by a processor, implements the above-mentioned intelligent vehicle obstacle avoidance path planning method. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0018] Figure 1 This is a flow chart of the intelligent vehicle obstacle avoidance path planning method provided in the first embodiment of the present invention; Figure 2Schematic diagram of the obstacle avoidance path planned by the traditional method; Figure 3 A schematic diagram of an obstacle avoidance path planned by the method of the present invention; Figure 4 This is a structural block diagram of the intelligent vehicle obstacle avoidance path planning system provided in the second embodiment of the present invention; Figure 5 A schematic diagram of the hardware structure of a computer provided in another embodiment of the present invention.

[0019] The embodiments of the present invention will be further described below with reference to the accompanying drawings. DETAILED DESCRIPTION

[0020] The following describes embodiments of the present invention in detail, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the embodiments of the present invention, and should not be construed as limiting the present invention.

[0021] Example 1 In the first embodiment of the present invention, Figure 1 As shown, a method for intelligent vehicle obstacle avoidance path planning includes: S1. Obtain a 5G-V2X position signal and determine the uncertainty of the 5G-V2X position signal, and determine a minimum safety distance based on the uncertainty of the 5G-V2X position signal; Wherein, the step S1 includes: S11. Acquire a 5G-V2X position signal, where the acquired 5G-V2X position signal obeys a normal distribution. , calculate the obstacle position signal in the 5G-V2X position signal Neighborhood Confidence : ; Where, is the obstacle position signal, is the probability, It is the location signal of smart car; Specifically, for the right side of the above equation, it is Obey the standard normal distribution probability.

[0022] S12, according to the confidence Determine the distance judgment formula: ; Where, Set a confidence level for the user.

[0023] S13, determine the minimum distance that satisfies the distance judgment formula , to obtain the minimum safe distance .

[0024] S2. Constructing an obstacle avoidance planning map taking into account position uncertainty based on the minimum safety distance; Wherein, the step S2 includes: S21. Planning the starting point based on the starting point set by the user and planned end point Draw a two-dimensional flat map of the boundary.

[0025] S22, aligning the X-axis and Y-axis in the two-dimensional plane map with the X-axis and Y-axis defined by the positioning system; Specifically, the positioning system here is the Beidou satellite positioning system.

[0026] S23: Move the boundaries of obstacles outward by the minimum safe distance based on the 5G-V2X position signal , to get the confidence obstacle.

[0027] S24: Project the trusted obstacle onto the two-dimensional plane map to obtain an obstacle avoidance planning map.

[0028] S3. Designing a rapid collision detection strategy based on the obstacle avoidance planning map; Among them, the fast collision detection strategy is: Calculate any node in the obstacle avoidance planning map The sum of the interior angles of the triangle constructed with all sides of the trusted obstacle , determine the interior angle sum Is it greater than 360°? , if the sum of the interior angles Greater than 360°- , then the decision node There is no collision with the trust obstacle, if the inner angle and No more than 360°- , then the decision node Collision with a trusted obstacle where Set a security threshold for the user. If the node If there is no collision with any trusted obstacles, then the node Security, if the node If a node collides with a trusted obstacle, Unsafe, where the interior angle and for: ; Where, is the confidence barrier The first corner point on the edge, is the total number of edges of the confident obstacles, It is a pointer Arrive The Euclidean length of It is a pointer Arrive The Euclidean length of It is a pointer Arrive The Euclidean length of It is a pointer Arrive The Euclidean length of It is a pointer Arrive The Euclidean length of .

[0029] S4. Designing a guided sampling strategy biased towards the destination, and fusing the guided sampling strategy biased towards the destination, the random sampling strategy, and the endpoint sampling strategy to obtain a planning node; The destination-biased guided sampling strategy includes: For the transition nodes in the obstacle avoidance planning map and transition nodes Closest node , construct a biased planning endpoint Energy Field , based on the energy field Determine the initial planning node : ; Where, 、 Represent points Pointing Point ,point Pointing Point vector.

[0030] Specifically, the step S4 is as follows: Generate a random number in the range [0,1] in each planned sampling ,when When , a random sampling strategy is used to generate planning nodes ;when When , a random sampling strategy is first used to generate transition nodes , and then adopt a guided sampling strategy biased towards the destination to generate planning nodes ;when When the planning end point is directly As a planning node : ; Where, They are the maximum and minimum boundaries of the X-axis of the obstacle avoidance planning map, They are the maximum and minimum boundaries of the Y axis of the obstacle avoidance planning map, 、 Represent points Pointing Point ,point Pointing Point vector, is the transition node, For The nearest node, They are respectively the first set sampling constant and the second set sampling constant; Specifically, the above process is a multimodal sampling fusion process, and different node determination strategies are selected according to the range of random numbers.

[0031] S5. Determine an obstacle avoidance path based on the fast collision detection strategy and the planning node.

[0032] Wherein, the step S5 includes: S51: Determine the planning starting point based on the rapid collision detection strategy and planned end point Is it safe? If you plan the starting point and planned end point If it is unsafe, stop planning and set the planning flag to flag Marked as 0, end planning and return an empty path, if the planning starting point and planned end point If it is safe, set the current sampling times to , and set the maximum number of sampling times and plan the starting point As the root node, store the planned planning tree T middle; S52, if the current sampling number And planning flag flag If is 0, the current sampling times is , from the planning tree T Search and plan nodes using the method of minimum Euclidean distance Nearest Node ,right Line segments are spaced at set intervals Perform equidistant scattering to obtain discrete node sets , ,in, is the number of nodes in the discrete node set, is the ceiling function, for Euclidean length of a line segment; S53, if the current sampling times Planning sign flag If it is 0, the planning ends and returns an empty path. And planning flag flag If it is not 0, the safe status of all nodes in the discrete node set is identified by the fast collision detection strategy. If there are unsafe nodes in the discrete node set, sampling is continued. If there are no unsafe nodes in the discrete node set, the planning node is calculated. and planned end point The Euclidean distance between , if the Euclidean distance , then the planning node Save plan tree T In the mark node Planning nodes The parent node and continue to start sampling, if the Euclidean distance , then the planning end point Save plan tree T Mark the planning node Planning nodes The parent node of the planning tree is backtracked. T Find the link planning starting point and planned end point Path Path, To obtain the obstacle avoidance path.

[0033] Specifically, to further verify the effect of this application, the following experimental case is provided. A 3-lane 3-obstacle obstacle avoidance scenario is set up. Figure 2 and Figure 3 As shown, Figure 2 Obstacle avoidance paths planned by traditional methods, Figure 3 The obstacle avoidance path planned by the method of the present invention is Figure 2 It can be seen that there are many blind search nodes in the planning tree of the traditional method. Figure 3 It can be seen that the nodes in the planning tree of the method of the present invention are all biased towards the target point, and there are fewer nodes, which shows that the method of the present invention can significantly reduce the blind search during obstacle avoidance planning; at the same time, Figure 2 and Figure 3It can be seen that the obstacle avoidance path planned by the method of the present invention is significantly shorter than that of the traditional method. The data shows that it is about 5 meters shorter and has fewer detours. The traditional method has a problem of continuously changing lanes from the slow lane to the middle lane, then continuously changing lanes to the fast lane, and then changing lanes back to the middle lane. This shows that the planning results of the method of the present invention are safer than those of the traditional method.

[0034] The intelligent vehicle obstacle avoidance path planning method provided in the first embodiment of the present invention can convert the uncertainty information of the 5G-V2X sensor into quantitative certain information, and calculate the minimum safe distance that the intelligent vehicle should maintain from the obstacle based on the safety confidence set by the user, thereby suppressing the uncertainty interference of the position signal. Then, a fast collision detection strategy is designed to quickly detect the validity of the planning node, improve the real-time calculation performance of the overall algorithm, and thus improve the safety of the planning. Then, a guided sampling strategy biased towards the destination is designed to reduce blind searches in the planning, accelerate the planning algorithm to search for the driving path to the destination, improve the real-time performance of the planning, and further improve the safety of the planning.

[0035] Example 2 like Figure 4 As shown, in a second embodiment of the present invention, a smart car obstacle avoidance path planning system is provided, the system comprising: Distance module 1, configured to obtain a 5G-V2X position signal and determine an uncertainty of the 5G-V2X position signal, and determine a minimum safety distance based on the uncertainty of the 5G-V2X position signal; Map module 2, used to construct an obstacle avoidance planning map taking into account position uncertainty based on the minimum safety distance; Strategy module 3, for designing a fast collision detection strategy based on the obstacle avoidance planning map; Node module 4 is used to design a guided sampling strategy biased towards the destination, and fuse the guided sampling strategy biased towards the destination, the random sampling strategy and the endpoint sampling strategy to obtain a planning node; A path module 5, configured to determine an obstacle avoidance path based on the fast collision detection strategy and the planning node; The distance module 1 includes: Confidence submodule, used to obtain 5G-V2X position signal, the obtained 5G-V2X position signal obeys normal distribution , calculate the obstacle position signal in the 5G-V2X position signal Neighborhood Confidence : ; Where, is the obstacle position signal, is the probability, It is the location signal of smart car; The judgment submodule is used to judge the Determine the distance judgment formula: ; Where, Set confidence levels for users; The distance submodule is used to determine the minimum distance that satisfies the distance judgment formula. , to obtain the minimum safe distance .

[0036] The map module 2 includes: Map submodule, used to plan the starting point based on the user-set starting point and planned end point Draw a two-dimensional map of the boundary; a coincidence submodule, configured to coincide the X-axis and the Y-axis in the two-dimensional plane map with the X-axis and the Y-axis defined by the positioning system; The translation submodule is used to translate the boundaries of obstacles outward to the minimum safe distance based on the 5G-V2X position signal , to obtain the confidence obstacle; The projection submodule is used to project the confident obstacle into the two-dimensional plane map to obtain an obstacle avoidance planning map.

[0037] The node module 4 is used for: Generate a random number in the range [0,1] in each planned sampling ,when When , a random sampling strategy is used to generate planning nodes ;when When , a random sampling strategy is first used to generate transition nodes , and then adopt a guided sampling strategy biased towards the destination to generate planning nodes ;when When the planning end point is directly As a planning node : ; Where, They are the maximum and minimum boundaries of the X-axis of the obstacle avoidance planning map, They are the maximum and minimum boundaries of the Y axis of the obstacle avoidance planning map, 、 Represent points Pointing Point ,point Pointing Point vector, is the transition node, For The nearest node, They are respectively a first set sampling constant and a second set sampling constant.

[0038] The path module 5 includes: The first planning submodule is used to determine the planning starting point based on the fast collision detection strategy and planned end point Is it safe? If you plan the starting point and planned end point If it is unsafe, stop planning and set the planning flag to flag Marked as 0, end planning and return an empty path, if the planning starting point and planned end point If it is safe, set the current sampling times to , and set the maximum number of sampling times and plan the starting point As the root node, store the planned planning tree T middle; The second planning submodule is used to calculate the current sampling times. And planning flag flag If is 0, the current sampling times is , from the planning tree T Search and plan nodes using the method of minimum Euclidean distance Nearest Node ,right Line segments are spaced at set intervals Perform equidistant scattering to obtain discrete node sets , ,in, is the number of nodes in the discrete node set, is the ceiling function, for Euclidean length of a line segment; The third planning submodule is used to calculate the current sampling times. Planning sign flag If it is 0, the planning ends and returns an empty path. And planning flag flag If it is not 0, the safe status of all nodes in the discrete node set is identified by the fast collision detection strategy. If there are unsafe nodes in the discrete node set, sampling is continued. If there are no unsafe nodes in the discrete node set, the planning node is calculated. and planned end point The Euclidean distance between , if the Euclidean distance , then the planning node Save plan tree T In the mark node Planning nodes The parent node and continue to start sampling, if the Euclidean distance , then the planning end point Save plan tree T Mark the planning node Planning nodes The parent node of the planning tree is backtracked. T Find the link planning starting point and planned end point Path Path, To obtain the obstacle avoidance path.

[0039] In other embodiments of the present invention, the embodiments of the present invention provide the following technical solutions: a computer, comprising a memory 102, a processor 101, and a computer program stored on the memory 102 and executable on the processor 101; the processor 101 implements the above-described intelligent vehicle obstacle avoidance path planning method when executing the computer program.

[0040] Specifically, the processor 101 may include a central processing unit (CPU), or an application specific integrated circuit (ASIC), or may be configured as one or more integrated circuits for implementing the embodiments of the present invention.

[0041] Memory 102 may include a large-capacity memory for data or instructions. By way of example, and not limitation, memory 102 may include a hard disk drive (HDD), a floppy disk drive, a solid-state drive (SSD), flash memory, an optical disk, a magneto-optical disk, a magnetic tape, or a Universal Serial Bus (USB) drive, or a combination of two or more of these. Where appropriate, memory 102 may include removable or non-removable (or fixed) media. Where appropriate, memory 102 may be internal or external to the data processing device. In certain embodiments, memory 102 is non-volatile memory. In certain embodiments, memory 102 includes read-only memory (ROM) and random access memory (RAM). Where appropriate, the ROM may be a mask-programmed ROM, a programmable ROM (PROM), an erasable PROM (EPROM), an electrically erasable PROM (EEPROM), an electrically alterable ROM (EAROM) or a flash memory (FLASH), or a combination of two or more of these. Under appropriate circumstances, the RAM can be a static random access memory (SRAM) or a dynamic random access memory (DRAM), where the DRAM can be a fast page mode dynamic random access memory (FPMDRAM), an extended data output dynamic random access memory (EDODRAM), a synchronous dynamic random access memory (SDRAM), etc.

[0042] The memory 102 may be used to store or cache various data files that need to be processed and / or used for communication, as well as possible computer program instructions executed by the processor 101 .

[0043] The processor 101 implements the above-mentioned intelligent vehicle obstacle avoidance path planning method by reading and executing computer program instructions stored in the memory 102.

[0044] In some embodiments, the computer may further include a communication interface 103 and a bus 100. Figure 5 As shown, the processor 101 , the memory 102 , and the communication interface 103 are connected via a bus 100 and communicate with each other.

[0045] The communication interface 103 is used to implement communication between the various modules, devices, units, and / or equipment in the embodiments of the present invention. The communication interface 103 can also implement data communication with other components such as external devices, image / data acquisition equipment, databases, external storage, and image / data processing workstations.

[0046] Bus 100 includes hardware, software, or both, and couples components of a computer device to each other. Bus 100 includes, but is not limited to, at least one of the following: a data bus, an address bus, a control bus, an expansion bus, and a local bus. By way of example and not limitation, bus 100 may include an Accelerated Graphics Port (AGP) or other graphics bus, an Extended Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), a Hyper Transport (HT) interconnect, an Industry Standard Architecture (ISA) bus, an InfiniBand interconnect, a Low Pin Count (LPC) bus, a memory bus, a Micro Channel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a Serial Advanced Technology Attachment (SATA) bus, a Video Electronics Standards Association Local Bus (VLB) bus, or other suitable buses, or a combination of two or more of these. Bus 100 may include one or more buses, where appropriate. Although embodiments of the present invention describe and illustrate a particular bus, the present invention contemplates any suitable bus or interconnect.

[0047] The computer can execute the intelligent vehicle obstacle avoidance path planning method of the present invention based on the acquired intelligent vehicle obstacle avoidance path planning system, thereby realizing intelligent vehicle obstacle avoidance path planning.

[0048] In some further embodiments of the present invention, in combination with the above-mentioned intelligent vehicle obstacle avoidance path planning method, the embodiments of the present invention provide the following technical solutions: a storage medium having a computer program stored thereon, and the computer program implements the above-mentioned intelligent vehicle obstacle avoidance path planning method when executed by a processor.

[0049] Those skilled in the art will appreciate that the logic and / or steps represented in the flowcharts or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, apparatus, or device (e.g., a computer-based system, a system including a processor, or other system that can fetch and execute instructions from an instruction execution system, apparatus, or device), or in conjunction with such instruction execution system, apparatus, or device. For purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by an instruction execution system, apparatus, or device, or in conjunction with such instruction execution system, apparatus, or device.

[0050] More specific examples (a non-exhaustive list) of computer-readable media include the following: an electrical connection with one or more wires (electronic devices), a portable computer disk cartridge (magnetic devices), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disc read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering, or processing in another suitable manner as necessary, and then stored in a computer memory.

[0051] It should be understood that various components of the present invention may be implemented using hardware, software, firmware, or a combination thereof. In the aforementioned embodiments, multiple steps or methods may be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented using hardware, as in another embodiment, any one or a combination of the following technologies known in the art may be used: a discrete logic circuit having logic gate circuits for implementing logic functions on data signals, an application-specific integrated circuit having suitable combinational logic gate circuits, a programmable gate array (PGA), a field-programmable gate array (FPGA), etc.

[0052] The technical features of the above-mentioned embodiments can be combined arbitrarily. In order to make the description concise, not all possible combinations of the technical features in the above-mentioned embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0053] The above-described embodiments merely illustrate several implementations of the present invention, and while their descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the patent. It should be noted that a person skilled in the art would be able to make numerous variations and improvements without departing from the spirit of the present invention, all of which fall within the scope of protection of the present invention. Therefore, the scope of protection of the patent for this invention shall be determined by the appended claims.

Claims

1. A method for intelligent vehicle obstacle avoidance path planning, characterized in that: include: Obtaining a 5G-V2X position signal and determining an uncertainty of the 5G-V2X position signal, and determining a minimum safety distance based on the uncertainty of the 5G-V2X position signal; Constructing an obstacle avoidance planning map taking into account position uncertainty based on the minimum safety distance; Designing a fast collision detection strategy based on the obstacle avoidance planning map; Designing a destination-biased guided sampling strategy, and fusing the destination-biased guided sampling strategy, a random sampling strategy, and an endpoint sampling strategy to obtain a planning node; An obstacle avoidance path is determined based on the rapid collision detection strategy and the planning node.

2. The intelligent vehicle obstacle avoidance path planning method according to claim 1, characterized in that: The steps of obtaining a 5G-V2X position signal and determining the uncertainty of the 5G-V2X position signal, and determining a minimum safety distance based on the uncertainty of the 5G-V2X position signal include: Acquire a 5G-V2X position signal, wherein the 5G-V2X position signal is normally distributed. , calculate the obstacle position signal in the 5G-V2X position signal Neighborhood Confidence : ； Where, is the obstacle position signal, is the probability, It is the location signal of smart car; According to the confidence Determine the distance judgment formula: ； Where, Set confidence levels for users; Determine the minimum distance that satisfies the distance judgment formula , to obtain the minimum safe distance .

3. The intelligent vehicle obstacle avoidance path planning method according to claim 1, characterized in that: The step of constructing an obstacle avoidance planning map considering position uncertainty based on the minimum safety distance includes: Plan the starting point based on the user-set starting point and planned end point Draw a two-dimensional map of the boundary; Aligning the X-axis and Y-axis in the two-dimensional plane map with the X-axis and Y-axis defined by the positioning system; According to the 5G-V2X position signal, the boundaries of obstacles are moved outward to the minimum safe distance , to obtain the confidence obstacle; The confident obstacles are projected onto the two-dimensional plane map to obtain an obstacle avoidance planning map.

4. The intelligent vehicle obstacle avoidance path planning method according to claim 1, characterized in that: In the step of designing a fast collision detection strategy based on the obstacle avoidance planning map, the fast collision detection strategy is: Calculate any node in the obstacle avoidance planning map The sum of the interior angles of the triangle constructed with all sides of the trusted obstacle , determine the interior angle sum Is it greater than 360°? , if the sum of the interior angles Greater than 360°- , then the decision node There is no collision with the trust obstacle, if the inner angle and No more than 360°- , then the decision node Collision with a trusted obstacle where Set a security threshold for the user. If the node If there is no collision with any trusted obstacles, then the node Security, if the node If a node collides with a trusted obstacle, Unsafe, where the interior angle and for: ； Where, is the confidence barrier The first corner point on the edge, is the total number of edges of the confident obstacles, It is a pointer Arrive The Euclidean length of It is a pointer Arrive The Euclidean length of It is a pointer Arrive The Euclidean length of It is a pointer Arrive The Euclidean length of It is a pointer Arrive The Euclidean length of .

5. The intelligent vehicle obstacle avoidance path planning method according to claim 1, characterized in that: In the step of designing a destination-biased guided sampling strategy, the destination-biased guided sampling strategy includes: For the transition nodes in the obstacle avoidance planning map and transition nodes Closest node , construct a biased planning endpoint Energy Field , based on the energy field Determine the initial planning node : ； Where, 、 Represent points Pointing Point ,point Pointing Point vector.

6. The intelligent vehicle obstacle avoidance path planning method according to claim 1, characterized in that: The step of fusing the destination-biased guided sampling strategy, the random sampling strategy, and the endpoint sampling strategy to obtain a planning node includes: Generate a random number in the range [0,1] in each planned sampling ,when When , a random sampling strategy is used to generate planning nodes ;when When , a random sampling strategy is first used to generate transition nodes , and then adopt a guided sampling strategy biased towards the destination to generate planning nodes ;when When the planning end point is directly As a planning node : ； Where, They are the maximum and minimum boundaries of the X-axis of the obstacle avoidance planning map, They are the maximum and minimum boundaries of the Y axis of the obstacle avoidance planning map, 、 Represent points Pointing Point ,point Pointing Point vector, is the transition node, For The nearest node, They are respectively a first set sampling constant and a second set sampling constant.

7. The intelligent vehicle obstacle avoidance path planning method according to claim 1, characterized in that: The step of determining an obstacle avoidance path based on the fast collision detection strategy and the planning node includes: Determine the planning starting point based on the fast collision detection strategy and planned end point Is it safe? If you plan the starting point and planned end point If it is unsafe, stop planning and set the planning flag to flag Marked as 0, end planning and return an empty path, if the planning starting point and planned end point If it is safe, set the current sampling times to , and set the maximum number of sampling times and plan the starting point As the root node, store the planned planning tree T middle; If the current sampling times And planning flag flag If is 0, the current sampling times is , from the planning tree T Search and plan nodes using the method of minimum Euclidean distance Nearest Node ,right Line segments are spaced at set intervals Perform equidistant scattering to obtain discrete node sets , ,in, is the number of nodes in the discrete node set, is the ceiling function, for Euclidean length of a line segment; If the current sampling times Planning sign flag If it is 0, the planning ends and returns an empty path. And planning flag flag If it is not 0, the safe status of all nodes in the discrete node set is identified by the fast collision detection strategy. If there are unsafe nodes in the discrete node set, sampling is continued. If there are no unsafe nodes in the discrete node set, the planning node is calculated. and planned end point The Euclidean distance between , if the Euclidean distance , then the planning node Save plan tree T In the mark node Planning nodes The parent node and continue to start sampling, if the Euclidean distance , then the planning end point Save plan tree T Mark the planning node Planning nodes The parent node of the planning tree is backtracked. T Find the link planning starting point and planned end point Path Path, To obtain the obstacle avoidance path.

8. An intelligent vehicle obstacle avoidance path planning system, characterized in that: The system comprises: a distance module, configured to obtain a 5G-V2X position signal and determine an uncertainty of the 5G-V2X position signal, and determine a minimum safety distance based on the uncertainty of the 5G-V2X position signal; A map module, configured to construct an obstacle avoidance planning map taking into account position uncertainty based on the minimum safety distance; A strategy module, configured to design a fast collision detection strategy based on the obstacle avoidance planning map; A node module is used to design a guided sampling strategy biased towards the destination, and fuse the guided sampling strategy biased towards the destination, the random sampling strategy and the endpoint sampling strategy to obtain a planning node; A path module is used to determine an obstacle avoidance path based on the fast collision detection strategy and the planning node.

9. A computer comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the intelligent vehicle obstacle avoidance path planning method according to any one of claims 1 to 7 is implemented.

10. A storage medium, characterized in that: The storage medium stores a computer program, which, when executed by a processor, implements the intelligent vehicle obstacle avoidance path planning method according to any one of claims 1 to 7.

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