Vehicle longitudinal planning method, computer program product, device and storage medium

Abstract

The invention relates to the technical field of automatic driving, in particular to a vehicle longitudinal planning method, a computer program product, equipment and a storage medium. The method comprises the following steps: performing scene recognition on a surrounding driving environment of an automatic driving vehicle to obtain the complexity of a current scene; determining a matched longitudinal planning mode according to the complexity of the current scene; according to the longitudinal planning mode, obtaining a target longitudinal driving parameter of the autonomous vehicle; and performing longitudinal control on the autonomous vehicle according to the target longitudinal driving parameters. According to the method, the corresponding longitudinal planning mode can be matched in a simple scene and a complex scene, and the fitness of the scene and the flexibility of automatic driving are improved.

Description

Technical Field

[0001] The present application relates to the field of autonomous driving technology, and more specifically, to a vehicle longitudinal planning method, computer program product, device and storage medium. Background Art

[0002] In the autonomous driving system, Longitudinal Speed ​​Planning is one of the core modules of vehicle decision-making and control. Its main goal is to determine the longitudinal speed, acceleration and corresponding braking / acceleration actions of the vehicle according to the surrounding traffic environment (such as the vehicle ahead, pedestrians, traffic signs / signals, intersections, etc.) to achieve safe, comfortable and efficient driving.

[0003] In the prior art, the longitudinal speed of the vehicle is mainly controlled based on the relative distance and relative speed between the vehicle and the vehicle in front. This method results in the vehicle's insufficient adaptability to complex scenarios and insufficient flexibility.

[0004] In view of this, this application is filed. Summary of the invention

[0005] The purpose of this application is to provide a vehicle longitudinal planning method, computer program product, device and storage medium to adapt to driving environments in different scenarios and improve driving flexibility.

[0006] In order to achieve the above objectives, this application adopts the following technical solutions: In a first aspect, the present application provides a vehicle longitudinal planning method, comprising: Perform scene recognition on the vehicle's surrounding driving environment to obtain the complexity of the current scene; Determining a matching longitudinal planning mode according to the complexity of the current scene; According to the longitudinal planning model, obtaining target longitudinal driving parameters of the vehicle; The vehicle is longitudinally controlled according to the target longitudinal driving parameter.

[0007] In a second aspect, the present application provides a computer program product, comprising: The computer program product stores computer instructions, and when the computer instructions are executed by a processor, the steps of the vehicle longitudinal planning method are implemented.

[0008] In a third aspect, the present application provides an electronic device, including: at least one processor, and a memory communicatively coupled to at least one of the processors; The memory stores instructions executable by at least one of the processors, and the instructions are executed by at least one of the processors to enable at least one of the processors to perform a vehicle longitudinal planning method.

[0009] In a fourth aspect, the present application provides a computer-readable storage medium, on which computer instructions are stored, and the computer instructions are used to enable the computer to execute the above-mentioned vehicle longitudinal planning method.

[0010] Compared with the prior art, the beneficial effects of this application are: The present application performs scene recognition on the surrounding driving environment of the autonomous driving vehicle to obtain the complexity of the current scene. The matching longitudinal planning mode can be determined according to the complexity of the current scene, so that the corresponding longitudinal planning mode can be matched in both simple and complex scenes, thereby improving the adaptability of the scene and the flexibility of autonomous driving. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] In order to more clearly illustrate the specific implementation methods of the present application or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0012] Figure 1 is a flow chart of a vehicle longitudinal planning method provided in an embodiment of the present application; Figure 2 It is a structural schematic diagram of the electronic device provided by this application. DETAILED DESCRIPTION

[0013] The following is a description of exemplary embodiments of the present application in conjunction with the accompanying drawings, including various details of the embodiments of the present application to facilitate understanding, which should be considered as merely exemplary. Therefore, it should be recognized by those of ordinary skill in the art that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of the present application. Similarly, for the sake of clarity and conciseness, the description of well-known functions and structures is omitted in the following description.

[0014] As mentioned in the background technology, the vehicles in the prior art have insufficient adaptability to complex scenes and insufficient flexibility. The present application is further described in detail below in conjunction with the embodiments.

[0015] Figure 1This is a flow chart of a vehicle longitudinal planning method provided in this embodiment. This embodiment is applicable to situations where longitudinal planning of an autonomous driving vehicle is performed, such as determining the longitudinal speed, acceleration and corresponding braking / acceleration actions of the autonomous driving vehicle to achieve safe, comfortable and efficient driving.

[0016] like Figure 1 As shown, this embodiment provides a vehicle longitudinal planning method, comprising the following steps: S110: Perform scene recognition on the surrounding driving environment of the autonomous driving vehicle to obtain the complexity of the current scene.

[0017] Optionally, based on the perception system installed on the autonomous driving vehicle and / or the vehicle-road-cloud collaborative technology, the surrounding vehicles, obstacles, road structures, and traffic light status in the surrounding driving environment are identified to obtain the current scene. The perception equipment on the vehicle includes but is not limited to radars and cameras. The road structure includes but is not limited to highways, urban roads, ramps, and intersections.

[0018] Optionally, sense the longitudinal position of surrounding vehicles (such as the vehicle in front) , horizontal , longitudinal speed , lateral speed , longitudinal acceleration , lateral acceleration , and the location information of the autonomous driving vehicle; a behavior prediction model based on a constant acceleration model (see the following formula) or based on reinforcement learning (RL) training is used to predict the target vehicle’s future (2~4 seconds) Internal trajectory: ; ; , Indicates the value range of the prediction time domain.

[0019] The behavior prediction model based on reinforcement learning training can be used to predict the trajectory of surrounding vehicles, thereby improving the robustness and safety of longitudinal control. Optionally, the driving status of surrounding vehicles, the driving status of the autonomous driving vehicle, and environmental information are used to construct the state space, and the possible behaviors of surrounding vehicles are defined to construct the action space. The design goal is to make the predicted trajectory close to the actual driving behavior. Common reward items include trajectory similarity, physical feasibility, and interaction rationality.

[0020] Next, based on the lateral speed or acceleration of the surrounding vehicles in the predicted time domain, determine whether the surrounding vehicles are cutting in or out; and identify whether it is an intersection, ramp entrance or exit based on the map or lane structure.

[0021] The scenes in this embodiment include but are not limited to cut-in scenes, cut-out scenes, intersection scenes, on-ramp and off-ramp scenes, following car scenes and cruising scenes. Different scenes have different levels of complexity. For example, following car scenes require attention to the vehicle in front and less attention to the driving conditions of surrounding vehicles, and the complexity is relatively low. Cut-in scenes and cut-out scenes require attention to the direction and speed of surrounding vehicles, as well as lane width and other information, and the complexity is relatively high. Those skilled in the art can assign complexity variables to each scene on their own.

[0022] S120: Determine a matching longitudinal planning mode according to the complexity of the current scene.

[0023] S130. Obtain target longitudinal driving parameters of the automatic driving vehicle according to the longitudinal planning model.

[0024] The longitudinal planning mode is used to determine the target longitudinal driving parameters of the autonomous driving vehicle, such as longitudinal acceleration and longitudinal speed, etc. Optionally, the longitudinal planning mode includes a kinematics-based longitudinal planning mode, an interpolation-based multi-objective speed planning mode, an adaptive PID (proportional-differential-integral) and torque compensation mode, a model predictive control mode, a dynamic programming mode, and a quadratic programming mode, etc.

[0025] Different longitudinal planning modes are suitable for scenes of different complexity. When selecting a matching longitudinal planning mode, you can match it according to the difficulty of solving each longitudinal planning mode and the accuracy of longitudinal control in scenes of different complexity. For example, in the following and cruising scenes with lower complexity, the kinematics-based longitudinal planning mode has lower difficulty of solving and higher accuracy, so the kinematics-based longitudinal planning mode is matched. For example, in the cutting-in and cutting-out scenes with higher complexity, the kinematics-based longitudinal planning mode has low accuracy, while the dynamic planning mode has high accuracy, so the dynamic planning mode is matched.

[0026] S140. Perform longitudinal control on the automatic driving vehicle according to the target longitudinal driving parameters.

[0027] The vehicle controller drives or brakes the autonomous vehicle according to the target longitudinal driving parameter to achieve the target longitudinal driving parameter. For example, if the target longitudinal driving parameter is a longitudinal speed of 50km / h and the current speed is 30km / h, the vehicle is driven to reach an acceleration of 50km / h.

[0028] The present application performs scene recognition on the surrounding driving environment of the autonomous driving vehicle to obtain the complexity of the current scene. The matching longitudinal planning mode can be determined according to the complexity of the current scene, so that the corresponding longitudinal planning mode can be matched in both simple and complex scenes, thereby improving the adaptability of the scene and the flexibility of autonomous driving.

[0029] Optionally, the scenes are divided into a first type of scenes and a second type of scenes, and the complexity of the first type of scenes is less than that of the second type of scenes; if the current scene belongs to the first type of scenes, a first longitudinal planning mode based on kinematics is determined. If the current scene belongs to the second type of scenes, a second longitudinal planning mode is determined; wherein the second longitudinal planning mode includes at least one of a distance-time ST diagram, dynamic programming (DP) and quadratic programming (QP).

[0030] Among them, the first longitudinal planning mode based on kinematics is mainly based on the relative distance, relative speed and other information between the vehicle itself and the vehicle in front, and performs longitudinal control through kinematic formulas and preset logical rules (such as time distance keeping, minimum safety distance constraints, speed limits, etc.).

[0031] In some simple scenarios, it is necessary to adopt the first longitudinal planning mode to obtain the target longitudinal driving parameters of the autonomous driving vehicle. The advantages of the first longitudinal planning mode include: 1) Good stability: Since the formulas and rules are relatively fixed, the overall output is stable and the sensitivity to perceived noise is low; 2) Simple implementation: The algorithm implementation logic is relatively concise and occupies less hardware resources; 3) High reliability: Consistent and safe performance in simple scenarios such as structured roads and cruise control. The disadvantages of the first longitudinal planning mode include: 1) Insufficient adaptability to complex scenarios: In dynamic scenarios such as urban roads, frequent lane changes, and crowded intersections, the response of this mode is often relatively passive, and it is unable to fully utilize the predicted information of the surrounding target trajectories, resulting in low accuracy; 2) Insufficient flexibility: It is impossible to make fast and optimal decision-making plans for sudden scenarios (such as sudden braking and sudden cut-in) in a short period of time.

[0032] In some complex scenarios, it is necessary to adopt a second longitudinal planning mode to obtain the target longitudinal driving parameters of the autonomous driving vehicle, for example, the longitudinal planning mode based on ST graph, dynamic programming and quadratic programming has the following advantages: 1) Strong adaptability to complex scenarios: It can flexibly handle urban intersections, on and off ramps, and frequent entry / exit of target vehicles; 2) Utilize short-term prediction: Use the speed, acceleration and other information of surrounding vehicles to predict trajectories, and make decisions more forward-looking. Its disadvantages include: 1) Dependence on perception accuracy: If the perception estimates the target speed or acceleration inaccurately, it may cause a large difference between the predicted trajectory and the actual trajectory, thereby affecting the planning effect; 2) High solution complexity: In real-time systems, high computing performance needs to be guaranteed. Once the algorithm fails to solve or is too time-consuming in extreme scenarios, it will cause safety or experience problems; 3) The consistency may be weak: Different environmental noises, perception errors or differences in assumptions about future trajectories will cause significant fluctuations in the planning results, resulting in inconsistent vehicle driving experience.

[0033] This embodiment adopts a scenario-driven dual-mode longitudinal planning strategy, and can select different planning algorithms according to the complexity of the scenario, taking into account both the flexibility of complex scenarios and the stability of simple scenarios.

[0034] In some embodiments, the longitudinal planning mode is a first longitudinal planning mode; for a vehicle following scenario, obtaining a target longitudinal driving parameter of the autonomous driving vehicle according to the longitudinal planning mode includes the following steps: The first step is to collect the relative distance between the autonomous driving vehicle and the vehicle in front if the current scenario is a following vehicle.

[0035] Assume that the expected time distance between the autonomous driving vehicle and the preceding vehicle is h, then the expected distance for: ; in, is the speed of the autonomous vehicle.

[0036] Current relative distance for: ; in, and Represent the positions of the front vehicle and the autonomous driving vehicle respectively; The second step is to determine the speed correction by minimizing the difference between the relative distance and the expected distance.

[0037] Will As the difference between the relative distance and the expected distance (i.e., position error), it is input into a closed-loop controller (such as a proportional-differential-integral controller) to obtain the speed correction , this speed correction can minimize the position error at the current moment and realize the control of the position loop.

[0038] Step 3: Determine the target speed based on the speed correction , see the following formula: ; in, is the speed of the autonomous driving vehicle at the last moment.

[0039] Step 4: Determine the target longitudinal acceleration by minimizing the difference between the current speed and the target speed.

[0040] Compare the current speed with the target speed The difference is input as an error into a closed-loop controller (such as a proportional-derivative-integral controller) to obtain the target longitudinal acceleration. , to realize the control of speed loop.

[0041] In some embodiments, the longitudinal planning mode is a first longitudinal planning mode; for a cruising scenario, obtaining a target longitudinal driving parameter of the autonomous driving vehicle according to the longitudinal planning mode includes the following steps: Step 1: If the current scene is cruising, determine the target speed to be the cruise setting speed .

[0042] The second step is to determine the target longitudinal acceleration by minimizing the difference between the current speed and the target speed.

[0043] Compare the current speed with the target speed The difference is input as an error into a closed-loop controller (such as a proportional-derivative-integral controller) to obtain the target longitudinal acceleration. , to realize the control of speed loop.

[0044] Whether in a cruising scenario or a vehicle-following scenario, the following logical rules must be met when using the first longitudinal planning mode to obtain the target longitudinal acceleration: 1) Safety distance check: If , the emergency brake is triggered immediately; 2) Speed ​​Limit: (Road speed limit or vehicle limit); 3) Acceleration limit: .

[0045] This embodiment adopts a reliable solution of kinematics and logic rules, adopts closed-loop control principles such as position loop and speed loop, and cooperates with safety distance and time interval restrictions to ensure that the vehicle has stable and consistent longitudinal behavior when there are no special emergency scenarios.

[0046] In some embodiments, the longitudinal planning mode is a second longitudinal planning mode; and obtaining a target longitudinal driving parameter of the autonomous driving vehicle according to the longitudinal planning mode comprises the following steps: The first step is to construct a distance-time ST diagram based on the relative distance between the autonomous driving vehicle and surrounding vehicles or obstacles; and determine the feasible area based on the ST diagram.

[0047] The ST diagram is the core tool for longitudinal planning. The horizontal axis is time (T), and the vertical axis is the relative distance between the autonomous vehicle and the vehicle in front (S). It is used to describe the future motion state of the vehicle, as well as for collision detection and feasible speed range calculation. The horizontal axis of the ST diagram is time t, and the vertical axis is the relative distance d between the autonomous vehicle and the surrounding vehicles or obstacles. Within the time range of [0, T], the motion space of the surrounding vehicles or obstacles is predicted and mapped into a series of infeasible regions (Obstacle Region); on the contrary, other regions are feasible regions for the autonomous vehicle.

[0048] The second step is to perform dynamic programming search in the feasible area to obtain candidate paths.

[0049] Dynamic programming is one of the core algorithms for solving the speed planning problem in ST graphs. Its basic idea is to find the global optimal trajectory by constructing a two-dimensional cost table and calculating the cost value for each grid.

[0050] First, the feasible region is discretized into several trajectory points (t i ,d j );in, t i is the i-th moment, d j is the jth distance. Based on the cost function, the cost of each trajectory point is calculated, and a dynamic programming algorithm (such as A* or Dijkstra algorithm) is used to search the cost table to obtain the path with the minimum cost, that is, the rough candidate path.

[0051] Optionally, when constructing the cost function, this embodiment incorporates indicators such as safety distance, collision cost, vehicle speed smoothness and ride comfort into the cost function. For safety distance, the ST graph is constructed to detect whether the candidate path collides with obstacles and surrounding vehicles. If there is a risk of collision, the corresponding penalty term M is increased. For collision cost, the distance between the autonomous driving vehicle and obstacles and surrounding vehicles is included in the cost function. The smaller the distance, the greater the penalty D. For vehicle speed smoothness, acceleration is used as an indicator to measure vehicle speed smoothness. For ride comfort, jerk is used as an indicator to measure ride comfort. Based on the above analysis, the cost function J is constructed as follows: ; in, a i is the acceleration at the ith moment, j i is the acceleration at the i-th moment, is the i-th duration, M i is the penalty term for the collision risk at the i-th moment, D i is the penalty term of the collision cost at the i-th moment. is the weight coefficient.

[0052] The third step is to optimize the candidate paths based on quadratic programming to obtain the target longitudinal acceleration curve.

[0053] Although dynamic programming can provide a rough optimal solution, the candidate paths generated are usually not smooth enough. In order to obtain a smoother control curve, it is necessary to optimize the results of dynamic programming through quadratic programming. The goal of quadratic programming is to fit the piecewise linear trajectory into a smooth nonlinear curve, thereby improving the continuity and comfort of the trajectory.

[0054] Use the candidate paths found by dynamic programming as the initial solution or constraint region. Construct a quadratic programming problem, such as minimizing acceleration, satisfying speed and acceleration upper and lower limits, and safety distance constraints: ; in, and is the weight coefficient, is the acceleration at time t, is the acceleration at time t, is the speed at time t, and are the minimum and maximum speed respectively. and are the minimum and maximum values ​​of acceleration, is the distance between the autonomous driving vehicle and surrounding vehicles or obstacles at time t, For a safe distance.

[0055] Solve the target longitudinal acceleration curve , and then get the optimal speed curve .

[0056] This embodiment is based on the optimization planning of short-term target prediction. In complex scenarios, it uses the perceived driving information of surrounding vehicles and obstacles to construct an ST graph and cost function for dynamic planning and secondary planning, which can control the autonomous driving vehicle to quickly converge to the optimal trajectory.

[0057] In some schemes, if the target longitudinal driving parameters cannot be obtained according to the second longitudinal planning mode, for example, the solution takes too long, the solution fails, there is no response, or the solver of the second longitudinal planning mode crashes, then the system falls back to the first longitudinal planning mode, and according to the first longitudinal planning mode, the target longitudinal driving parameters of the autonomous driving vehicle are obtained to ensure that the autonomous driving vehicle will not encounter a dangerous situation where there is no speed instruction or no acceleration instruction.

[0058] In some embodiments, longitudinally controlling the autonomous driving vehicle according to the target longitudinal driving parameter includes: when the longitudinal planning mode is switched or the target longitudinal driving parameter is updated, smoothing the target longitudinal driving parameter at the current moment based on the target longitudinal driving parameter at the historical moment. This embodiment avoids impact or shaking of the vehicle by smoothing or buffering the target longitudinal driving parameter, such as speed or acceleration.

[0059] This embodiment does not limit the smoothing method, such as an exponential filtering algorithm or an S-curve smoothing algorithm. The following formula gives a feasible smoothing example: ; in, is the weight, t is the time, is the acceleration after smoothing, is the target acceleration, is the acceleration at the previous moment.

[0060] Based on the above embodiments, the present application has the following technical effects: 1. Adapt to multiple scenarios: The longitudinal planning mode is switched through scene recognition. The first longitudinal planning mode based on kinematics is used in simple scenarios such as highways / following vehicles to ensure stability and consistency. The second longitudinal planning mode is used in complex scenarios such as urban roads, intersections, cut-ins, and cut-outs to quickly respond to dynamic changes in the scene.

[0061] 2. Ensure safety and experience: When the longitudinal planning mode fails or the solution fails, it can fall back to the first longitudinal planning mode based on kinematics in real time to avoid loss of control or generation of dangerous instructions.

[0062] 3. Reduce reliance on perception uncertainty: For simple scenarios, reduce over-reliance on target acceleration accuracy; only enable high-accuracy predictions in complex scenarios that require short-term predictions, so that the vehicle remains stable most of the time.

[0063] 4. Flexible expansion: Based on the design of scene recognition and longitudinal planning mode, more environmental information (such as traffic light recognition, pedestrian detection, etc.) can be gradually introduced to perform safe and efficient longitudinal planning under more complex working conditions.

[0064] like Figure 2 As shown, this embodiment provides an electronic device, including: at least one processor; and a memory communicatively connected to at least one of the processors; wherein, The memory stores instructions executable by at least one of the processors, and the instructions are executed by at least one of the processors to enable at least one of the processors to perform the above method. At least one processor in the electronic device can perform the above method, and thus has at least the same advantages as the above method.

[0065] Optionally, the electronic device also includes interfaces for connecting various components, including high-speed interfaces and low-speed interfaces. The various components are connected to each other using different buses and can be installed on a common mainboard or installed in other ways as needed. The processor can process instructions executed in the electronic device, including instructions stored in or on a memory to display graphical information of a GUI (Graphical User Interface) on an external input / output device (such as a display device coupled to an interface). In other embodiments, if necessary, multiple processors can be used together with multiple memories, and / or multiple buses can be used together with multiple memories. Similarly, multiple electronic devices can be connected (for example, as a server array, a group of blade servers, or a multi-processor system), and each device provides some necessary operations. Figure 2 A processor 301 is taken as an example.

[0066] The memory 302 is a computer-readable storage medium that can be used to store software programs, computer executable programs and modules, such as program instructions / modules corresponding to the vehicle longitudinal planning method in the embodiment of the present application. The processor 301 executes various functional applications and data processing of the device by running the software programs, instructions and modules stored in the memory 302, that is, realizing the above-mentioned vehicle longitudinal planning method.

[0067] The memory 302 may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system and at least one application required for a function; the data storage area may store data created according to the use of the terminal, etc. In addition, the memory 302 may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one disk storage device, a flash memory device, or other non-volatile solid-state storage device. In some instances, the memory 302 may further include a memory remotely arranged relative to the processor 301, and these remote memories may be connected to the device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0068] The electronic device may further include: an input device 303 and an output device 304. The processor 301, the memory 302, the input device 303 and the output device 304 may be connected via a bus or other means. Figure 2 The example of connecting through bus is taken in the following.

[0069] The input device 303 can receive input digital or character information, and the output device 304 can include a display device, an auxiliary lighting device (e.g., LED), and a tactile feedback device (e.g., a vibration motor), etc. The display device can include, but is not limited to, a liquid crystal display (LCD), a light emitting diode (LED) display, and a plasma display. In some embodiments, the display device can be a touch screen.

[0070] This embodiment provides a computer-readable storage medium, on which computer instructions are stored, and the computer instructions are used to enable the computer to execute the above method. The computer instructions on the computer-readable storage medium are used to enable the computer to execute the above method, and thus have at least the same advantages as the above method.

[0071] The medium in this application can adopt any combination of one or more computer-readable media. The medium can be a computer-readable signal medium or a computer-readable storage medium. The medium can be, for example, but not limited to, a system, device or device of electricity, magnetism, light, electromagnetic, infrared, or semiconductor, or any combination of the above. More specific examples of the medium (non-exhaustive list) include: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this document, the medium can be any tangible medium containing or storing a program, which can be used by an instruction execution system, a device or a device or used in combination with it.

[0072] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, which carry computer-readable program code. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Computer-readable signal media may also be any computer-readable medium other than a computer-readable storage medium, which may send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0073] The program code contained on the computer-readable medium can be transmitted using any appropriate medium, including but not limited to wireless, wire, optical cable, RF (Radio Frequency), etc., or any suitable combination of the above.

[0074] Computer program code for performing the operation of the present application can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages, such as Java, Smalltalk, C++, and conventional procedural programming languages, such as "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computer (e.g., using an Internet service provider to connect through the Internet).

[0075] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented by software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer instructions are loaded and executed on the computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from a website site, computer, server or data center by wired, such as coaxial cable, optical fiber, digital subscriber line (DSL) or wireless, such as infrared, wireless, microwave, etc. to another website site, computer, server or data center. The computer-readable storage medium can be any available medium that can be accessed by a computer, or a data storage device such as a server or data center that includes one or more available media integrated. The available medium can be a magnetic medium (for example, a floppy disk, a hard disk, a tape), an optical medium or a semiconductor medium, etc. It is worth noting that the computer-readable storage medium mentioned in the embodiments of the present application may be a non-volatile storage medium, in other words, a non-transitory storage medium.

[0076] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps recorded in this application can be executed in parallel, sequentially or in different orders, as long as the expected results of the technical solution disclosed in this application can be achieved, and this document is not limited here.

[0077] The above specific implementations do not constitute a limitation on the protection scope of this application. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of this application should be included in the protection scope of this application.

Claims

1. A vehicle longitudinal planning method, characterized in that: include: Perform scene recognition on the driving environment around the autonomous driving vehicle to obtain the complexity of the current scene; Determining a matching longitudinal planning mode according to the complexity of the current scene; Obtaining target longitudinal driving parameters of the autonomous driving vehicle according to the longitudinal planning model; The automatic driving vehicle is longitudinally controlled according to the target longitudinal driving parameter.

2. The method according to claim 1, characterized in that The complexity of the first type of scenario is less than that of the second type of scenario; According to the complexity of the current scene, a matching longitudinal planning mode is determined, including: If the current scene belongs to the first type of scene, determining a first longitudinal planning mode based on kinematics; If the current scene belongs to the second type of scene, a second longitudinal planning mode is determined; wherein the second longitudinal planning mode includes at least one of a distance-time ST diagram, dynamic programming, and quadratic programming.

3. The method according to claim 2, characterized in that The longitudinal planning mode is a first longitudinal planning mode; According to the longitudinal planning mode, the target longitudinal driving parameters of the automatic driving vehicle are obtained, including: If the current scenario is a following vehicle, the relative distance between the autonomous driving vehicle and the vehicle in front is collected; Determining a speed correction by minimizing the difference between the relative distance and the desired distance; determining a target speed according to the speed correction amount; The target longitudinal acceleration is determined by minimizing the difference between the current speed and the target speed.

4. The method according to claim 2, characterized in that: The longitudinal planning mode is a first longitudinal planning mode; According to the longitudinal planning mode, the target longitudinal driving parameters of the automatic driving vehicle are obtained, including: If the current scene is cruising, the target speed is determined to be the cruise setting speed; The target longitudinal acceleration is determined by minimizing the difference between the current speed and the target speed.

5. The method according to claim 2, characterized in that: The longitudinal planning mode is a second longitudinal planning mode; According to the longitudinal planning mode, the target longitudinal driving parameters of the automatic driving vehicle are obtained, including: Constructing a distance-time ST graph according to the relative distance between the autonomous driving vehicle and surrounding vehicles or obstacles; and determining a feasible area based on the ST graph; Perform dynamic programming search in the feasible area to obtain candidate paths; The candidate paths are optimized based on quadratic programming to obtain a target longitudinal acceleration curve.

6. The method according to claim 2, characterized in that According to the longitudinal planning mode, the target longitudinal driving parameters of the automatic driving vehicle are obtained, including: If the target longitudinal travel parameter cannot be obtained according to the second longitudinal planning mode, switching to the first longitudinal planning mode; According to the first longitudinal planning mode, target longitudinal driving parameters of the automatic driving vehicle are obtained.

7. The method according to any one of claims 1 to 6, characterized in that: The method further comprises: performing longitudinal control on the automatic driving vehicle according to the target longitudinal driving parameter, comprising: When the longitudinal planning mode is switched or the target longitudinal driving parameter is updated, the target longitudinal driving parameter at the current moment is smoothed based on the target longitudinal driving parameter at the historical moment.

8. A computer program product, characterized in that include: The computer program product stores computer instructions, and when the computer instructions are executed by a processor, the steps of the vehicle longitudinal planning method according to any one of claims 1 to 7 are implemented.

9. An electronic device, characterized in that: include: at least one processor, and a memory communicatively coupled to at least one of the processors; The memory stores instructions that can be executed by at least one of the processors, and the instructions are executed by at least one of the processors to enable at least one of the processors to execute the vehicle longitudinal planning method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The medium stores computer instructions, and the computer instructions are used to enable the computer to execute the vehicle longitudinal planning method according to any one of claims 1 to 7.

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