Vehicle driving path planning method and device, medium and program product

Abstract

The invention discloses a vehicle driving path planning method, which comprises the steps of acquiring current driving information of a target vehicle at the current moment, and determining vehicle driving error information of the target vehicle at the current moment according to the current driving information and first driving information of the target vehicle on a first driving path, under the condition that the vehicle driving error information meets a preset path splicing condition, second driving information and a splicing path are determined based on the current driving information, a preset constraint condition and a preset cost function through a preset path planning algorithm; and determining splicing position information according to the current driving information and the signal transmission delay time, splicing the first driving path and the splicing path according to the splicing position information to obtain a second driving path, and controlling the target vehicle to drive along the second driving path according to second driving information. According to the embodiment of the invention, the driving continuity in the vehicle driving process can be improved, and the path planning efficiency is improved on the basis of ensuring the safe driving of the vehicle.

Description

Technical Field

[0001] The present invention relates to the field of autonomous driving technology, and in particular to a vehicle driving path planning method, device, medium and program product. Background Art

[0002] With the continuous development of intelligent driving technology, autonomous vehicles are gradually being applied in various industries. During the autonomous driving process, how to achieve automatic obstacle avoidance for the vehicle, as well as smoother path connection and speed regulation during the process of automatic obstacle avoidance and autonomous driving have become urgent issues that need to be addressed.

[0003] Existing technologies typically achieve automatic obstacle avoidance for autonomous vehicles by generating local obstacle avoidance paths in real time based on preset rules and sensor data. For example, these methods utilize sensors like lidar and cameras to perceive the surrounding environment, and then determine the obstacle avoidance direction and path based on simple geometric algorithms or empirical rules. However, this technology suffers from inefficient trajectory planning and poor continuity when the vehicle follows the planned path. Summary of the Invention

[0004] The present invention provides a vehicle driving path planning method, device, medium and program product to solve the problems of low efficiency in vehicle driving trajectory planning and poor vehicle driving continuity in the prior art.

[0005] According to one aspect of the present invention, a vehicle driving path planning method is provided, comprising:

[0006] Obtaining current driving information of a target vehicle at a current moment, and determining vehicle driving error information of the target vehicle at the current moment based on the current driving information and first driving information of the target vehicle on a first driving path, wherein the first driving information and the first driving path are determined by a preset path planning algorithm;

[0007] When the vehicle driving error information satisfies a preset path splicing condition, determining second driving information and a splicing path based on the current driving information, preset constraints, and a preset cost function by using the preset path planning algorithm;

[0008] Determine splicing position information based on the current driving information and the signal transmission delay time, splice the first driving path and the splicing path according to the splicing position information to obtain a second driving path, and control the target vehicle to travel along the second driving path based on the second driving information.

[0009] According to one aspect of the present invention, a vehicle driving path planning device is provided, comprising:

[0010] a driving error determination module, configured to obtain current driving information of a target vehicle at a current moment, and determine vehicle driving error information of the target vehicle at the current moment based on the current driving information and first driving information of the target vehicle on a first driving path, wherein the first driving information and the first driving path are determined by a preset path planning algorithm;

[0011] a splicing path determining module, configured to determine second driving information and a splicing path based on the current driving information, preset constraints, and a preset cost function using the preset path planning algorithm when the vehicle driving error information satisfies a preset path splicing condition;

[0012] a path splicing module, configured to determine splicing position information based on the current driving information and a signal transmission delay time, splice the first driving path and the splicing path according to the splicing position information to obtain a second driving path, and control the target vehicle to travel along the second driving path according to the second driving information.

[0013] According to another aspect of the present invention, an electronic device is provided, comprising:

[0014] at least one processor; and

[0015] a memory communicatively connected to the at least one processor; wherein,

[0016] The memory stores a computer program that can be executed by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the vehicle driving path planning method described in any embodiment of the present invention.

[0017] According to another aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the vehicle driving path planning method described in any embodiment of the present invention when executed.

[0018] According to another aspect of the present invention, a computer program product is provided. The computer program product includes a computer program. When the computer program is executed by a processor, the vehicle driving path planning method according to any embodiment of the present invention is implemented.

[0019] The technical solution of the embodiment of the present invention obtains the current driving information of the target vehicle at the current moment, determines the vehicle driving error information of the target vehicle at the current moment according to the current driving information and the first driving information of the target vehicle on the first driving path, and when the vehicle driving error information meets the preset path splicing condition, determines the second driving information and the splicing path based on the current driving information, the preset constraint condition and the preset cost function through a preset path planning algorithm; determines the splicing position information according to the current driving information and the signal transmission delay time, splices the first driving path and the splicing path according to the splicing position information to obtain the second driving path, and controls the target vehicle to travel along the second driving path according to the second driving information, which can improve the driving continuity of the vehicle during driving and improve the path planning efficiency on the basis of ensuring the safe driving of the vehicle.

[0020] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present invention, nor is it intended to limit the scope of the present invention. Other features of the present invention will become readily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] 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 description of the embodiments. 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 creative work.

[0022] Figure 1 This is a flow chart of a vehicle driving path planning method provided according to the first embodiment of the present invention;

[0023] Figure 2 is a flowchart of another vehicle driving path planning method provided according to the second embodiment of the present invention;

[0024] Figure 3 1. A schematic diagram of a spliced ​​vehicle trajectory according to an embodiment of the present invention;

[0025] Figure 4 This is a schematic structural diagram of a vehicle driving path planning device provided according to a third embodiment of the present invention;

[0026] Figure 5 It is a structural diagram of an electronic device for implementing the vehicle driving path planning method according to an embodiment of the present invention. DETAILED DESCRIPTION

[0027] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of the present invention.

[0028] It should be noted that the terms "first", "second", etc. in the description and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the numbers used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0029] Example 1

[0030] Figure 1 A flowchart of a vehicle driving path planning method is provided for the first embodiment of the present invention. This embodiment is applicable to the case of planning and splicing vehicle driving paths. The method can be executed by a vehicle driving path planning device. The vehicle driving path planning device can be implemented in the form of hardware and / or software. The vehicle driving path planning device can be configured in an electronic device. Figure 1 As shown, the method may include:

[0031] S110. Obtain current driving information of the target vehicle at the current moment, and determine vehicle driving error information of the target vehicle at the current moment based on the current driving information and first driving information of the target vehicle on a first driving path, wherein the first driving information and the first driving path are determined by a preset path planning algorithm.

[0032] The target vehicle may be an intelligent driving or autonomous driving vehicle that requires driving state monitoring and error analysis. For example, an unmanned express delivery vehicle, an unmanned taxi, or a family car or commercial vehicle with intelligent driving functions. The current driving information may be the driving state data of the target vehicle at the current moment. For example, at least one of the location information (such as longitude and latitude coordinates) of the vehicle at the current moment, the current driving speed, the current vehicle driving acceleration, the current vehicle driving direction, and the current vehicle steering angle. The current driving information can be used to characterize the driving state of the vehicle at the current moment.

[0033] The first driving path may be the target vehicle's route planned by a preset path planning algorithm. The first driving path is an expected ideal driving trajectory. By following the first driving path, the target vehicle can reach a destination or complete a task, such as delivering a package from point A to point B or transporting a passenger from point A to point B. The first driving information may be the expected or planned driving status data of the target vehicle along the first driving path. The first driving information may include information such as the expected speed, acceleration, and driving direction of the target vehicle at various points along the first driving path at different times. The first driving information may be driving information of the target vehicle generated by the preset path planning algorithm based on the first driving path and a preset driving strategy (such as speed limit, obstacle avoidance strategy, etc.). The preset path planning algorithm may be a pre-set algorithm for calculating and determining the target vehicle's driving path. For example, the preset path planning algorithm may calculate an optimal or feasible driving path for the vehicle from its starting point to its destination based on map data, traffic regulations, vehicle performance parameters (such as maximum speed, acceleration limit, etc.), destination information, and possible real-time traffic conditions (such as road conditions and obstacle information).

[0034] The vehicle driving error information at the current moment may be the vehicle driving error at the current moment calculated by comparing the current driving information of the target vehicle with the first driving information. The vehicle driving error information may include position error (such as the deviation between the vehicle's current position and the corresponding position point on the first driving path), speed error (such as the deviation between the vehicle's current speed and the expected speed), direction error (such as the deviation between the vehicle's current driving direction and the expected direction), etc. The vehicle driving error information at the current moment can be used to represent the difference between the vehicle's actual driving state and the expected driving state, and can provide data basis for vehicle driving performance and subsequent control (such as path correction, speed adjustment, or continuing to drive according to plan, etc.).

[0035] Specifically, the current driving information of the target vehicle at the current moment is obtained, and the vehicle driving error information of the target vehicle at the current moment is determined based on the current driving information and the first driving information of the target vehicle on the first driving path. This can be done by obtaining the current driving information of the target vehicle at the current moment, and determining the vehicle driving error information of the target vehicle at the current moment based on the current driving information and the first driving information of the target vehicle on the first driving path determined by a preset path planning algorithm. By determining the vehicle driving error information of the target vehicle at the current moment, a data basis can be provided for the subsequent driving performance and subsequent control of the target vehicle, thereby avoiding safety accidents caused by large vehicle driving error information of the vehicle at the current moment, and avoiding resource loss caused by frequent calculation of the target vehicle's driving path when the vehicle's vehicle driving error information at the current moment is small. This can reduce the loss of vehicle computing resources while improving vehicle driving safety.

[0036] S120 : When the vehicle driving error information satisfies a preset path splicing condition, determine second driving information and a splicing path using a preset path planning algorithm based on the current driving information, preset constraints, and a preset cost function.

[0037] The path splicing condition may be a condition used to determine whether the first driving path and the first driving information need to be adjusted and spliced ​​during vehicle travel. For example, when the vehicle's driving error information (such as position deviation, speed deviation, etc.) reaches a certain threshold, or when the deviation caused by the vehicle avoiding an obstacle in front of the vehicle exceeds a preset threshold, the vehicle's driving error information is determined to meet the preset path splicing condition. Preset constraints may refer to restrictions that must be met during the path splicing process, such as the vehicle's maximum speed, minimum turning radius, and obstacle distance to avoid collision when splicing two routes. By setting preset constraints, it is possible to ensure that the vehicle meets requirements such as vehicle performance, traffic regulations, and driving safety during driving. The preset cost function may be a mathematical function used to evaluate the cost (quality) of paths and driving information. The preset cost function may generally consider multiple factors, such as path length, travel time, energy consumption, and comfort. By assigning a weight to each factor and calculating the cost of the path, the quality of different paths and different driving information can be determined, and the path with the lowest cost can be selected as the splicing path.

[0038] The second driving information may be the expected driving state data of the target vehicle on the spliced ​​path, determined by a preset path planning algorithm based on current driving information, preset constraints, and a preset cost function, when the vehicle's driving error information satisfies preset path splicing conditions. The second driving information may include the vehicle's expected position points on the spliced ​​path at different times, as well as driving information such as the expected speed, acceleration, and driving direction at the expected positions. The spliced ​​path may be the spliced ​​path calculated by the preset path planning algorithm based on current driving information, preset constraints, and a preset cost function, when the vehicle's driving error information satisfies the path splicing conditions. The spliced ​​path may be a partial path following the original path that can be spliced ​​with the original path, or a new path that can be spliced ​​with the original path.

[0039] Specifically, when the vehicle's driving error information satisfies preset path splicing conditions, a preset path planning algorithm is used to determine the second driving information and the spliced ​​path based on the current driving information, preset constraints, and a preset cost function. This may be the case where the vehicle's driving error information satisfies the preset path splicing conditions. The preset path planning algorithm determines the spliced ​​path, along with the vehicle's expected locations at different times on the spliced ​​path, and information such as the expected speed, acceleration, and driving direction at the expected locations, based on the current driving information (such as the vehicle's current position, speed, and direction), preset constraints (such as the vehicle's maximum speed, minimum turning radius, and collision avoidance obstacle distance), and a preset cost function (considering multiple factors such as path length, driving time, energy consumption, and comfort and assigning weights). By determining the second driving information and the spliced ​​path based on the current driving information, preset constraints, and a preset cost function, when the vehicle's driving error information satisfies the preset path splicing conditions, driving safety and vehicle driving efficiency can be improved.

[0040] S130: Determine splicing position information based on current driving information and signal transmission delay time, splice the first driving path and the splicing path according to the splicing position information to obtain a second driving path, and control the target vehicle to travel along the second driving path according to the second driving information.

[0041] Among them, the signal transmission delay time can be the time required for the vehicle or related sensors to send driving information (such as current position, speed, etc.) to the control system during the vehicle's driving process, and then make a decision (such as determining the splicing path) after processing. The splicing position information can be the position coordinate information connecting the first driving path and the splicing path, which can be determined based on the current driving information and the signal transmission delay time. By calculating the splicing position information, it can be ensured that the target vehicle can smoothly switch from the first driving path to the splicing path when it reaches the position, thereby improving the continuity and safety of the vehicle's driving. The second driving path can be the target vehicle driving path formed by splicing the first driving path and the splicing path together according to the splicing position information after determining the splicing position information. The second driving path can be the actual route traveled by the target vehicle at a subsequent time. The second driving path may include driving information such as the expected position, speed and direction of the vehicle at different time points.

[0042] Specifically, by determining the splicing position information based on the current driving information and the signal transmission delay time, the first driving path and the splicing path are spliced ​​according to the splicing position information to obtain the second driving path. This can be achieved by determining the location points or location coordinates of the first driving path and the splicing path when the paths are spliced ​​based on the current driving information and the signal transmission delay time, and then splicing the first driving path and the splicing path according to the specific location points when the first driving path and the splicing path are spliced ​​to obtain the second driving path of the target vehicle. Furthermore, the target vehicle is controlled to travel along the second driving path according to the second driving information. This can improve the driving continuity of the vehicle during driving and avoid the problem of discontinuous path transition after the vehicle splices the path.

[0043] The technical solution of the embodiment of the present invention obtains the current driving information of the target vehicle at the current moment, determines the vehicle driving error information of the target vehicle at the current moment according to the current driving information and the first driving information of the target vehicle on the first driving path, and when the vehicle driving error information meets the preset path splicing condition, determines the second driving information and the splicing path based on the current driving information, the preset constraint condition and the preset cost function through a preset path planning algorithm; determines the splicing position information according to the current driving information and the signal transmission delay time, splices the first driving path and the splicing path according to the splicing position information to obtain the second driving path, and controls the target vehicle to travel along the second driving path according to the second driving information, which can improve the driving continuity of the vehicle during driving and improve the path planning efficiency on the basis of ensuring the safe driving of the vehicle.

[0044] Example 2

[0045] Figure 2This is a flowchart of another vehicle driving path planning method provided by the second embodiment of the present invention. Based on the above embodiment, this embodiment adds that before obtaining the current driving information of the target vehicle at the current moment, the task requirement information of the target vehicle and the road topology information corresponding to the task requirement information are obtained, and the initial driving path is determined according to the task requirement information and the road topology information; the road attribute information corresponding to the task requirement information and the vehicle parameter information of the target vehicle are obtained, and global constraints and global cost functions are constructed according to the road attribute information and the vehicle parameter information, wherein the global constraints include maximum curvature constraints, maximum acceleration constraints and maximum acceleration constraints, and the global cost function includes path length cost function, smoothness cost function and obstacle distance cost function; the first driving information and the first driving path of the target vehicle are determined based on the global constraints and the global cost function through a preset path planning algorithm. And the contents of the above embodiment are further refined. As Figure 2 As shown, the method may include:

[0046] S210 , obtaining mission requirement information of the target vehicle and road topology information corresponding to the mission requirement information, and determining an initial driving path according to the mission requirement information and the road topology information.

[0047] Mission requirement information may include information related to the mission or objective that the target vehicle needs to complete. For example, mission requirement information may include mission starting point, mission endpoint, mission waypoints, expected arrival time, speed limit, cargo capacity requirements, and obstacle avoidance requirements. Road topology information may include information about the connectivity and attributes between roads in the road network corresponding to the mission requirement information. Road topology information may include the coordinates of the starting and ending points of the road, the length, width, road type, speed limit information for sections, traffic flow, and information about road construction or closures. The initial driving path may be a preliminary driving route from the departure point to the destination, calculated using a preset path planning algorithm based on the mission requirement information and road topology information.

[0048] Specifically, obtaining the target vehicle's mission requirement information and the road topology information corresponding to the mission requirement information, and determining the initial driving path based on the mission requirement information and the road topology information, can involve obtaining the target vehicle's mission requirement information and the road topology information corresponding to the mission requirement information, and then determining an initial path within the road topology that can complete the mission requirement and meet road topology conditions based on the mission requirement information and the road topology information. Determining the initial driving path based on the mission requirement information and the road topology information provides a foundation for subsequent path optimization, adjustment, or splicing.

[0049] S220. Obtain road attribute information and vehicle parameter information of the target vehicle corresponding to the task requirement information, and construct global constraints and a global cost function based on the road attribute information and the vehicle parameter information, wherein the global constraints include a maximum curvature constraint, a maximum acceleration constraint, and a maximum jerk constraint, and the global cost function includes a path length cost function, a smoothness cost function, and an obstacle distance cost function.

[0050] Road attribute information can be attribute information used to characterize the road corresponding to the task requirement information. Examples include road geometry (e.g., curvature, slope), road type, road surface conditions, speed limit information, traffic signs, and road markings. Vehicle parameter information can be parameter information used to characterize vehicle performance. Examples include vehicle size, mass, power performance (e.g., maximum acceleration, maximum deceleration), braking performance, steering performance, and chassis parameters.

[0051] Among them, global constraints and global cost functions, among which, the global constraints include maximum curvature constraints, maximum acceleration constraints and maximum jerk constraints, and the global cost function includes path length cost function, smoothness cost function and obstacle distance cost function.

[0052] Among them, the maximum curvature constraint condition can be that the curvature of any point on the vehicle's driving path must not exceed the value of a preset maximum path curvature. By setting the maximum curvature constraint condition, it can be avoided that excessive curvature causes the vehicle to be unable to turn smoothly or causes safety hazards. The maximum acceleration constraint condition can be that the absolute value of the acceleration during vehicle driving must not exceed a preset maximum value. By setting the maximum acceleration constraint condition, the safety of vehicle driving can be protected, and accidents caused by excessive acceleration to the vehicle and poor driving experience caused by excessive acceleration can be avoided. The maximum acceleration constraint condition can be that the absolute value of the rate of change of acceleration during vehicle driving must not exceed the maximum value of a preset value.

[0053] The path length cost function can be a weighted value of the path length, used to measure the length of the path. This can be used to minimize the driving path. The smoothness cost function can be a weighted value of the path smoothness, used to measure the curvature and rate of change of the path. The obstacle distance cost function can be a weighted value of the distance between the vehicle and the obstacle, used to measure the proximity of the vehicle to the obstacle during driving.

[0054] Specifically, global constraints and a global cost function are constructed based on road attribute information and vehicle parameter information. This can be done by determining thresholds for maximum curvature, maximum acceleration, and maximum jerk based on factors such as road geometry and type from the road attribute information and steering and power performance from the vehicle parameter information. These constraints are then used to establish maximum curvature constraints, maximum acceleration constraints, and maximum jerk constraints to ensure vehicle safety and stability. Weighted coefficients for path length, path smoothness, and vehicle-obstacle distance are set based on road length and traffic conditions from the road attribute information, as well as vehicle parameter information for driving efficiency and comfort. These weighted coefficients are then used to construct path length, smoothness, and obstacle distance cost functions, providing a foundation for path planning.

[0055] S230: Determine first driving information and a first driving path of the target vehicle based on global constraints and a global cost function using a preset path planning algorithm.

[0056] Specifically, the first driving information and the first driving path of the target vehicle determined by the preset path planning algorithm based on the global constraints and the global cost function may be the initial driving route of the target vehicle determined by the preset path planning algorithm based on the global constraints and the global cost function, i.e., the first driving path. The driving information of the target vehicle generated by the preset path planning algorithm based on the first driving path, the global constraints, and the global cost function, i.e., the first driving information.

[0057] S240. Obtain current driving information of the target vehicle at the current moment, and determine vehicle driving error information of the target vehicle at the current moment based on the current driving information and first driving information of the target vehicle on the first driving path, wherein the first driving information and the first driving path are determined by a preset path planning algorithm.

[0058] Among them, the current driving information may include current speed information, current acceleration information and current position information; the first driving information includes first target position information and first target speed information; and the vehicle driving error information includes position error information and speed error information.

[0059] The current speed information may be the current speed of the target vehicle, which may be measured in real time by the vehicle's speed sensor. The current acceleration information may be the current acceleration of the target vehicle. The current location information may be the current geographic location of the target vehicle or the coordinates corresponding to the geographic location.

[0060] The first target position information indicates the position that the target vehicle should reach on the first driving path or at a path point at the current moment. The first target speed information indicates the speed that the target vehicle should reach on the first driving path or at a path point at the current moment. The position error information may be a position error value calculated based on the current position information and the first target position information. The speed error information may be a speed error value calculated based on the current speed information and the first target speed information.

[0061] Specifically, the vehicle driving error information of the target vehicle at the current moment is determined based on the current driving information and the first driving information of the target vehicle on the first driving path. The current position information and current speed information of the target vehicle at the current moment are extracted, and the first target position information and first target speed information corresponding to the current moment or the path point closest to the current moment on the first driving path are obtained at the same time. The position error value is obtained by calculating the spatial distance deviation between the current position information and the first target position information, and the speed error value is obtained by calculating the numerical difference between the current speed information and the first target speed information.

[0062] Optionally, when the vehicle driving error information meets the preset path splicing conditions, determining the splicing position information corresponding to the starting point of the splicing path based on the current driving information and the signal transmission delay time may include: when the position error information is greater than a first position error threshold and less than a second position error threshold or the speed error information is greater than a first speed error threshold and less than a second speed error threshold, determining the splicing position information based on the current speed information, current acceleration information, current position information and the signal transmission delay time.

[0063] The first position error threshold may be a preset lower limit of the position error of the vehicle's driving position; the second position error threshold may be a preset upper limit of the position error of the vehicle's driving position, and the first position error threshold is smaller than the second position error threshold. The first speed error threshold may be a preset lower limit of the speed error of the vehicle's driving speed; the second speed error threshold may be a preset upper limit of the speed error of the vehicle's driving speed, and the first speed error threshold is smaller than the second speed error threshold.

[0064] Optionally, determining the splicing position information based on the current speed information, current acceleration information, current position information and signal transmission delay time may include: determining the delay path length based on the current speed information, current acceleration information and signal transmission delay time, and determining the splicing position information based on the delay path length and the current position information.

[0065] The delayed path length may be the length of the path that the vehicle travels during the signal transmission delay time when the vehicle continues to travel according to the current speed information and the current acceleration information when there is a delay in signal transmission.

[0066] Specifically, the delay path length is determined based on current speed information, current acceleration information, and signal transmission delay time, and the splicing position information is determined based on the delay path length and current position information. This can be done by determining the length of the path traveled by the vehicle within the signal transmission delay time, i.e., the delay path length, based on the target vehicle's current speed information, current acceleration information, and signal transmission delay time. Furthermore, the calculated delay path length is combined with the current position information, and the position corresponding to the delay path length is extended along the vehicle's current travel direction, starting from the current position. This position serves as the starting point of the spliced ​​path, i.e., the splicing position information. This ensures that the subsequently spliced ​​path matches the vehicle's actual driving conditions. When the vehicle passes the splicing position, the splicing process will not cause significant speed or steering fluctuations, further improving driving continuity.

[0067] Optionally, when the position error information is less than a first position error threshold and the speed error information is less than a first speed error threshold, the target vehicle is controlled to continue traveling along the first driving path according to the first driving information.

[0068] Optionally, when the position error information is less than a first position error threshold and the speed error information is less than a first speed error threshold, the vehicle's speed can be dynamically optimized and adjusted using a speed optimization cost function, and the target vehicle's travel can be controlled using the dynamically optimized and adjusted vehicle speed. The speed optimization cost function can include a position error cost, a speed error cost, an acceleration cost, and a jerk cost. Specifically, the position error cost is the sum of squared deviations between the vehicle's actual longitudinal position and the dynamically planned longitudinal position. Using the dynamically planned path as a reference can accelerate optimization convergence. The speed error cost: Since the first-order derivative of the longitudinal position with respect to time is the vehicle's longitudinal velocity, the planned speed should be as close as possible to the desired speed. The acceleration cost: Considering occupant comfort, the acceleration at each point during planning should be as small as possible. The jerk cost: To further enhance vehicle comfort, the rate of change of acceleration is limited to a minimum. Dynamically optimizing the vehicle's speed using the position error cost, speed error cost, acceleration cost, and jerk cost can improve vehicle safety and enhance the user experience.

[0069] S250 : When the vehicle driving error information satisfies a preset path splicing condition, determine second driving information and a splicing path based on the current driving information, preset constraints, and a preset cost function using a preset path planning algorithm.

[0070] Among them, the preset constraint conditions can be pre-set based on the global constraint conditions in combination with the path splicing scenario, and are used to constrain the second driving information and the spliced ​​path, or they can be conditions set directly based on the global constraint conditions and are used to constrain the second driving information and the spliced ​​path. The preset constraint conditions may include a maximum curvature constraint condition, a maximum acceleration constraint condition, and a maximum jerk constraint condition. The preset cost function can be pre-set based on the global cost function, and is used to calculate the quality of the second driving information and the spliced ​​path, or it can be a function set directly based on the global cost function and is used to calculate the quality of the second driving information and the spliced ​​path. The preset cost functions include a smoothness cost function and an obstacle distance cost function.

[0071] Optionally, the second driving information and the splicing path are determined by a preset path planning algorithm based on the current driving information, preset constraints and a preset cost function, including: determining the second driving information and the splicing path of the target vehicle by a preset path planning algorithm based on the current driving information, the maximum curvature constraint, the maximum acceleration constraint, the maximum jerk constraint, the smoothness cost function and the obstacle distance cost function.

[0072] Optionally, when the position error information is greater than a second position error threshold or the speed error information is greater than a second speed error threshold, the starting point of the re-planned path is determined based on the current driving information and the signal transmission delay time; the third driving information and the third driving path of the target vehicle are determined based on the current driving information, global constraints and the global cost function through a preset path planning algorithm; when the target vehicle arrives at the starting point of the re-planned path, the target vehicle is controlled to travel according to the third driving path and the third driving information.

[0073] The starting point of the re-planned path may be the starting point of a new path that is re-determined based on the current driving information and signal transmission delay time when the vehicle's driving error information exceeds an acceptable range. The starting point of the re-planned path is the starting position of the vehicle's driving path and driving information after the re-planning begins. The third driving information may be the dynamic driving parameters and status information of the target vehicle after the starting point of the re-planned path, determined by a preset path planning algorithm based on the current driving information, global constraints, and a global cost function. The third driving path may be a driving route from the starting point of the re-planned path to the target end point that is re-planned by a preset path planning algorithm after the vehicle's driving error information exceeds an acceptable range.

[0074] Exemplary, exemplary, Figure 3 A schematic diagram of the vehicle driving trajectory splicing provided by an embodiment of the present invention is shown in FIG. Figure 3As shown, the error between the planned vehicle state and the actual vehicle state can be calculated. If the error is greater than the upper error limit, the route is directly replanned. If the error is within the upper and lower error limits, the driving path is spliced. The current driving time of the vehicle can be set as T, the delay time for the planning information to reach the controller is dt, and the previous frame planning projection point calculated based on the vehicle position information is P0. Based on the vehicle speed and acceleration information, the position of the vehicle after time T+dt can be calculated and found to the previous frame planning projection point P1. When splicing or replanning the path, P1 can be used as the starting point, and its planned trajectory can be spliced ​​with the trajectory segment before P1 to obtain a complete path. The path before P1 is consistent with the previous frame. At point P1, the trajectory position, orientation, and curvature are continuous, which can meet the continuity requirements of the vehicle's driving path and improve the safety of vehicle driving.

[0075] S260: Determine splicing position information based on current driving information and signal transmission delay time, splice the first driving path and the splicing path according to the splicing position information to obtain a second driving path, and control the target vehicle to travel along the second driving path according to the second driving information.

[0076] The technical solution of the embodiment of the present invention obtains the current driving information of the target vehicle at the current moment, determines the vehicle driving error information of the target vehicle at the current moment according to the current driving information and the first driving information of the target vehicle on the first driving path, and when the vehicle driving error information meets the preset path splicing condition, determines the second driving information and the splicing path based on the current driving information, the preset constraint condition and the preset cost function through a preset path planning algorithm; determines the splicing position information according to the current driving information and the signal transmission delay time, splices the first driving path and the splicing path according to the splicing position information to obtain the second driving path, and controls the target vehicle to travel along the second driving path according to the second driving information, which can improve the driving continuity of the vehicle during driving and improve the path planning efficiency on the basis of ensuring the safe driving of the vehicle.

[0077] Example 3

[0078] Figure 4 This is a schematic diagram of the structure of a vehicle driving path planning device provided by the third embodiment of the present invention. Figure 4 As shown, the device includes: a driving error determination module 410 , a splicing path determination module 420 and a path splicing module 430 .

[0079] The driving error determination module 410 is used to obtain the current driving information of the target vehicle at the current moment, and determine the vehicle driving error information of the target vehicle at the current moment based on the current driving information and the first driving information of the target vehicle on the first driving path, wherein the first driving information and the first driving path are determined by a preset path planning algorithm; the splicing path determination module 420 is used to determine the second driving information and the splicing path based on the current driving information, preset constraints and preset cost function through a preset path planning algorithm when the vehicle driving error information meets the preset path splicing conditions; the path splicing module 430 is used to determine the splicing position information based on the current driving information and the signal transmission delay time, splice the first driving path and the splicing path according to the splicing position information to obtain the second driving path, and control the target vehicle to travel along the second driving path with the second driving information.

[0080] Among them, the current driving information includes current speed information, current acceleration information and current position information; the first driving information includes first target position information and first target speed information; and the vehicle driving error information includes position error information and speed error information.

[0081] Furthermore, the splicing path determination module 420 is specifically used to: when the position error information is greater than the first position error threshold and less than the second position error threshold or the speed error information is greater than the first speed error threshold and less than the second speed error threshold, determine the splicing position information based on the current speed information, the current acceleration information, the current position information and the signal transmission delay time.

[0082] Furthermore, the splicing path determining module 420 is specifically configured to determine the delay path length according to the current speed information, the current acceleration information and the signal transmission delay time, and determine the splicing position information according to the delay path length and the current position information.

[0083] Furthermore, the vehicle driving path planning device also includes: a first driving path planning module, which is used to obtain the task requirement information of the target vehicle and the road topology information corresponding to the task requirement information before obtaining the current driving information of the target vehicle at the current moment, and determine the initial driving path according to the task requirement information and the road topology information; obtain the road attribute information corresponding to the task requirement information and the vehicle parameter information of the target vehicle, and construct global constraints and global cost functions according to the road attribute information and the vehicle parameter information, wherein the global constraints include maximum curvature constraints, maximum acceleration constraints and maximum jerk constraints, and the global cost function includes path length cost function, smoothness cost function and obstacle distance cost function; determine the first driving information and the first driving path of the target vehicle based on the global constraints and the global cost function through a preset path planning algorithm.

[0084] The preset constraints include a maximum curvature constraint, a maximum acceleration constraint, and a maximum jerk constraint, and the preset cost functions include a smoothness cost function and an obstacle distance cost function.

[0085] Furthermore, the splicing path determination module 420 is specifically used to determine the second driving information and splicing path of the target vehicle based on the current driving information, the maximum curvature constraint, the maximum acceleration constraint, the maximum jerk constraint, the smoothness cost function and the obstacle distance cost function through a preset path planning algorithm.

[0086] Furthermore, the vehicle driving path planning device also includes: a third driving path planning module, which is used to determine the starting point of the re-planned path based on the current driving information and the signal transmission delay time when the position error information is greater than the second position error threshold or the speed error information is greater than the second speed error threshold; determine the third driving information and the third driving path of the target vehicle based on the current driving information, global constraints and the global cost function through a preset path planning algorithm, and when the target vehicle arrives at the starting point of the re-planned path, control the target vehicle to travel according to the third driving path and the third driving information.

[0087] Furthermore, the vehicle driving path planning device also includes: a vehicle driving module, which is used to control the target vehicle to continue driving along the first driving path with the first driving information when the position error information is less than the first position error threshold and the speed error information is less than the first speed error threshold.

[0088] The vehicle driving path planning device provided in the embodiment of the present invention can execute the vehicle driving path planning method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0089] Example 4

[0090] Figure 5 A schematic diagram of the structure of an electronic device 10 that can be used to implement an embodiment of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or claimed herein.

[0091] like Figure 5As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., which is communicatively connected to the at least one processor 11. The memory stores a computer program that can be executed by the at least one processor. The processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 into the random access memory (RAM) 13. Various programs and data required for the operation of the electronic device 10 can also be stored in the RAM 13. The processor 11, ROM 12, and RAM 13 are connected to each other via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0092] Multiple components in the electronic device 10 are connected to the I / O interface 15, including an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a magnetic disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices via a computer network such as the Internet and / or various telecommunication networks.

[0093] The processor 11 can be any general-purpose and / or specialized processing component with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various specialized artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The processor 11 executes the various methods and processes described above, such as the vehicle path planning method.

[0094] In some embodiments, the vehicle travel path planning method can be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed on the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the vehicle travel path planning method described above can be performed. Alternatively, in other embodiments, the processor 11 can be configured to execute the vehicle travel path planning method in any other appropriate manner (for example, by means of firmware).

[0095] Various embodiments of the systems and techniques described herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), system-on-chip systems (SOCs), programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include being implemented in one or more computer programs that are executable and / or interpreted on a programmable system that includes at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0096] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when the computer program is executed by the processor, the functions / operations specified in the flowcharts and / or block diagrams are implemented. The computer program may be executed entirely on the machine, partially on the machine, as a stand-alone software package, partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0097] In the context of the present invention, computer-readable storage media can be tangible media that can contain or store a computer program for use with an instruction execution system, device or equipment or used in combination with an instruction execution system, device or equipment. Computer-readable storage media can include but are not limited to electronic, magnetic, optical, electromagnetic, infrared or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, computer-readable storage media can be machine-readable signal media. More specific examples of machine-readable storage media can include electrical connections based on one or more lines, portable computer disks, hard disks, random access memories (RAM), read-only memories (ROM), erasable programmable read-only memories (EPROM or flash memory), optical fibers, portable compact disk read-only memories (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0098] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0099] The systems and techniques described herein can be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0100] A computing system may include clients and servers. The clients and servers are typically remote from each other and typically interact via a communication network. This client-server relationship arises through computer programs running on the respective computers, creating a client-server relationship. The server may be a cloud server, also known as a cloud computing server or cloud host. This server is a hosting product within the cloud computing service ecosystem that addresses the management difficulties and limited scalability of traditional physical hosting and VPS services.

[0101] It should be understood that the various forms of the processes shown above can be used to reorder, add, or delete steps. For example, the steps described in the present invention can be performed in parallel, sequentially, or in a different order, as long as the desired results of the technical solution of the present invention can be achieved. This is not limited herein.

[0102] The above specific embodiments do not limit the scope of protection of the present invention. Those skilled in the art will appreciate that various modifications, combinations, sub-combinations, and substitutions may be made based on design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention are intended to be included within the scope of protection of the present invention.

Claims

1. A vehicle driving path planning method, characterized in that: include: Obtaining current driving information of a target vehicle at a current moment, and determining vehicle driving error information of the target vehicle at the current moment based on the current driving information and first driving information of the target vehicle on a first driving path, wherein the first driving information and the first driving path are determined by a preset path planning algorithm; When the vehicle driving error information satisfies a preset path splicing condition, determining second driving information and a splicing path based on the current driving information, preset constraints, and a preset cost function by using the preset path planning algorithm; Determine splicing position information based on the current driving information and the signal transmission delay time, splice the first driving path and the splicing path according to the splicing position information to obtain a second driving path, and control the target vehicle to travel along the second driving path based on the second driving information.

2. The method according to claim 1, characterized in that The current driving information includes current speed information, current acceleration information and current position information; the first driving information includes first target position information and first target speed information; The vehicle driving error information includes position error information and speed error information; When the vehicle driving error information satisfies a preset path splicing condition, determining the splicing position information corresponding to the starting point of the splicing path according to the current driving information and the signal transmission delay time includes: When the position error information is greater than the first position error threshold and less than the second position error threshold or the speed error information is greater than the first speed error threshold and less than the second speed error threshold, the splicing position information is determined based on the current speed information, the current acceleration information, the current position information and the signal transmission delay time.

3. The method according to claim 2, characterized in that The determining of the splicing position information according to the current speed information, the current acceleration information, the current position information, and the signal transmission delay time includes: The delay path length is determined according to the current speed information, the current acceleration information, and the signal transmission delay time, and the splicing position information is determined according to the delay path length and the current position information.

4. The method according to claim 1, wherein Before obtaining the current driving information of the target vehicle at the current moment, the method further includes: Obtaining mission requirement information of a target vehicle and road topology information corresponding to the mission requirement information, and determining an initial driving path based on the mission requirement information and the road topology information; Obtaining road attribute information and vehicle parameter information of a target vehicle corresponding to the task requirement information, and constructing global constraints and a global cost function based on the road attribute information and the vehicle parameter information, wherein the global constraints include a maximum curvature constraint, a maximum acceleration constraint, and a maximum jerk constraint, and the global cost function includes a path length cost function, a smoothness cost function, and an obstacle distance cost function; The first driving information and the first driving path of the target vehicle are determined based on the global constraint conditions and the global cost function through a preset path planning algorithm.

5. The method according to claim 4, characterized in that The preset constraints include a maximum curvature constraint, a maximum acceleration constraint, and a maximum jerk constraint, and the preset cost function includes a smoothness cost function and an obstacle distance cost function; The determining of the second driving information and the splicing path based on the current driving information, the preset constraint conditions and the preset cost function by using a preset path planning algorithm includes: The second driving information and the splicing path of the target vehicle are determined by a preset path planning algorithm based on the current driving information, the maximum curvature constraint, the maximum acceleration constraint, the maximum jerk constraint, the smoothness cost function and the obstacle distance cost function.

6. The method according to claim 4, characterized in that Also includes: When the position error information is greater than a second position error threshold or the speed error information is greater than a second speed error threshold, determining a starting point of the replanned path according to the current driving information and the signal transmission delay time; The third driving information and the third driving path of the target vehicle are determined based on the current driving information, the global constraints and the global cost function through a preset path planning algorithm. When the target vehicle reaches the starting point of the replanned path, the target vehicle is controlled to travel according to the third driving path and the third driving information.

7. The method according to claim 1, characterized in that Also includes: When the position error information is smaller than a first position error threshold and the speed error information is smaller than a first speed error threshold, the target vehicle is controlled to continue traveling along the first driving path according to the first driving information.

8. An electronic device, characterized in that: The electronic device comprises: one or more processors; a storage device for storing one or more programs, When the one or more programs are executed by the one or more processors, the one or more processors implement the vehicle driving path planning method according to any one of claims 1 to 7.

9. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the vehicle driving path planning method according to any one of claims 1 to 7 when executed.

10. A computer program product comprising a computer program / instructions, wherein: When the computer program / instructions are executed by a processor, the vehicle driving path planning method according to any one of claims 1 to 7 is implemented.

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