Driving decision planning method and device based on particle swarm optimization, medium and product

Abstract

The invention relates to the technical field of information, and provides a driving decision planning method and device based on particle swarm optimization, a medium and a product. The driving decision planning method based on particle swarm optimization comprises the steps of obtaining a lane-level path of a current vehicle and data information of an environment vehicle in response to a preset arrival condition; inputting the lane-level path of the current vehicle and the data information of the environment vehicle into a preset particle swarm optimization algorithm to obtain a rough trajectory of the current vehicle; acquiring a final target trajectory based on the rough trajectory; and selectively sending the final target trajectory to a preset terminal based on the rough trajectory. Through the configuration mode, the flexibility of the vehicle facing the local traffic response can be improved on the premise that a large number of data resources are not needed.

Description

Technical Field

[0001] The present application relates to the field of information technology, and in particular to a driving decision planning method, device, medium and product based on particle swarm optimization. Background Art

[0002] Rule-based decision making: The rule-based decision making method relies on a predefined set of rules or logical conditions to determine the behavior of the vehicle. This method usually uses expert knowledge and experience to formulate rules to handle various driving scenarios and emergencies. Design a set of rules based on the driving environment and traffic regulations. For example, keep lane, avoid collisions, obey traffic signals, etc. Monitor the environmental data around the vehicle in real time (such as sensor data, map information, etc.) to determine whether the triggering conditions of a certain rule are met. Once a rule is triggered, the vehicle performs a predefined behavior. For example, if an obstacle is detected ahead, slow down or change lanes.

[0003] Decision-making based on reinforcement learning: The decision-making method based on reinforcement learning finds the best decision-making strategy by letting the agent conduct a large number of attempts and learning in a simulated environment. The agent selects actions based on the state of the environment and learns and optimizes based on the rewards obtained. The agent (such as an autonomous driving system) takes actions in the environment, and the environment returns new states and rewards. Define the state of the agent (such as vehicle position, speed, surrounding vehicle information, etc.) and the actions that can be taken (such as acceleration, deceleration, lane change, etc.). Design a reward function to reward or punish the agent based on its actions. For example, safe driving, energy-saving driving, etc. Use reinforcement learning algorithms (such as Q-learning, deep Q network DQN, etc.) to continuously optimize the strategy so that the agent obtains the maximum cumulative reward in the long run.

[0004] Decision-making based on historical data: The decision-making method based on historical data predicts future driving situations and makes decisions by analyzing and utilizing a large amount of historical driving data. This method usually combines statistical models, machine learning algorithms, and data mining techniques. Collect and store a large amount of historical driving data, including vehicle driving trajectories, traffic conditions, driving behaviors, etc. Extract useful features from historical data, such as traffic flow patterns, accident-prone areas, etc. Use statistical models (such as regression analysis, time series analysis) or machine learning algorithms (such as decision trees, neural networks) to train data and establish a prediction model. Based on real-time environmental data and trained models, predict future driving situations and generate corresponding decisions.

[0005] However, the inventors found that there are at least the following technical problems in the related art:

[0006] The current decision-making algorithms are not flexible enough to respond to local traffic conditions, and the existing decision-making algorithms are also difficult to meet the needs. The traditional rule-based decision-making method is not flexible enough for complex and dynamic traffic environments, and multiple rules need to be designed in various scenarios. The decision-making method based on reinforcement learning requires a large amount of training data and computing resources, and the training process is complicated. Summary of the invention

[0007] One purpose of the present application is to provide a driving decision planning method based on particle swarm optimization, at least to solve the problem of how to improve the flexibility of vehicle response to local traffic without requiring a large amount of data resources.

[0008] To achieve the above objectives, some embodiments of the present application provide the following aspects:

[0009] In a first aspect, some embodiments of the present application further provide a driving decision planning method based on particle swarm optimization, the method comprising:

[0010] In response to reaching a preset condition, obtaining lane-level path information of the current vehicle and data information of surrounding vehicles;

[0011] Inputting the lane-level path of the current vehicle and data information of the surrounding vehicles into a preset particle swarm optimization algorithm to obtain a rough trajectory of the current vehicle;

[0012] Based on the rough trajectory, obtaining a final target trajectory;

[0013] The final target trajectory is selectively sent to a preset terminal based on the rough trajectory.

[0014] In a second aspect, some embodiments of the present application further provide an electronic device, comprising: one or more processors; and a memory storing computer program instructions, wherein the computer program instructions, when executed, cause the processor to perform the steps of the method described above.

[0015] In a third aspect, some embodiments of the present application further provide a computer-readable medium having computer program instructions stored thereon, wherein the computer program instructions can be executed by a processor to implement the method as described above.

[0016] In a fourth aspect, some embodiments of the present application further provide a computer program product, comprising a computer program / instruction, which implements the steps of the method described above when executed by a processor.

[0017] Compared with the related art, in the solution provided by the embodiment of the present application, in response to reaching the preset conditions, the lane-level path of the current vehicle and the data information of the surrounding vehicles are obtained; the lane-level path of the current vehicle and the data information of the surrounding vehicles are input into the preset particle swarm optimization algorithm to obtain the rough trajectory of the current vehicle; based on the rough trajectory, the final target trajectory is obtained; based on the rough trajectory, the final target trajectory is selectively sent to a preset terminal. Through the above configuration, the present application uses the preset particle swarm optimization algorithm to find the local optimal solution for the lane-level path of the current vehicle and the data information of the surrounding vehicles. Compared with the decision-making method of historical data, it does not require a large amount of data resources, and the rough trajectory is calculated through real-time data. It is highly flexible and can cope with complex and dynamic traffic environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] One or more embodiments are exemplarily described by pictures in the corresponding drawings, and these exemplified descriptions do not constitute limitations on the embodiments. Elements with the same reference numerals in the drawings represent similar elements, and unless otherwise stated, the figures in the drawings do not constitute proportional limitations.

[0019] Figure 1 is a flowchart of the main steps of a driving decision planning method based on particle swarm optimization according to an embodiment of the present application;

[0020] Figure 2 is a schematic diagram of a lane-level path according to an implementation of an embodiment of the present application;

[0021] Figure 3 is a schematic diagram of a lane centerline according to an implementation of an embodiment of the present application;

[0022] Figure 4 is a schematic diagram of a driving decision planning algorithm for executing particle swarm optimization according to an implementation of an embodiment of the present application;

[0023] Figure 5 It is a schematic diagram of the final effect of an implementation method according to an embodiment of the present application;

[0024] Figure 6 It is a schematic diagram of the final effect of an implementation method according to an embodiment of the present application;

[0025] Figure 7 It is a schematic diagram of an exemplary structure of an electronic device according to the present application. DETAILED DESCRIPTION

[0026] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0027] See attached Figure 1 , Figure 1 FIG. 1 is a flow chart of the main steps of a driving decision planning method based on particle swarm optimization according to an embodiment of the present application. Figure 1 As shown, the driving decision planning method based on particle swarm optimization in the embodiment of the present application mainly includes the following steps S101 to S104.

[0028] Step S101: In response to reaching a preset condition, the lane-level path of the current vehicle and data information of the surrounding vehicles are obtained.

[0029] In this embodiment, when the preset conditions are met, the lane-level path of the current vehicle and the data information of the surrounding vehicles (future surrounding vehicle behaviors) are obtained.

[0030] In one embodiment, the method for obtaining the lane-level path of the current vehicle is the patent publication number CN115077547A "A lane-level vehicle driving path planning method, device and equipment"

[0031] Step S102: inputting the lane-level path of the current vehicle and data information of the surrounding vehicles into a preset particle swarm optimization algorithm to obtain a rough trajectory of the current vehicle.

[0032] In this embodiment, the lane-level path of the current vehicle and the data information of the surrounding vehicles are input into a preset particle swarm optimization algorithm to obtain a rough trajectory of the current vehicle, wherein the rough trajectory has a larger time granularity. For example, the time granularity of a normal trajectory is 0.1s, and the time granularity of a rough trajectory is 1s, that is, the time interval between each two adjacent points is 1s.

[0033] In one embodiment, the preset condition includes at least one of the following conditions:

[0034] Lane level path update of the current vehicle;

[0035] The time interval from the last call of the particle swarm optimization algorithm is greater than a first preset time interval (such as 1s);

[0036] The current vehicle deviates from the rough trajectory output last time;

[0037] The path or speed planning of the current vehicle fails.

[0038] By setting preset conditions, the real-time and flexibility of the algorithm can be further ensured.

[0039] In one implementation, step S102 may further include steps S1021 to S1024:

[0040] Step S1021: based on the lane-level path of the current vehicle, obtaining particle swarm data; wherein the particle swarm data includes position information and initial velocity information of each particle.

[0041] In this implementation, particle swarm data is randomly generated according to the lane-level path of the current vehicle, wherein the particle swarm data includes position information and initial velocity information of each particle.

[0042] It should also be noted that the particle swarm optimization algorithm needs to output trajectories, so in the future, if Figure 2 As shown, the state sequence (horizontal and vertical positions, heading angles, and speeds) at fixed time intervals represents the potential trajectory T = {p1, p2, ... p n},p i ={s,l,θ,v}. Among them, s represents the distance traveled by the vehicle from the initial point to the current position, l represents the distance the vehicle deviates from the center line, θ represents the heading angle, v represents the speed, and the state change between two adjacent moments is used as the candidate solution for iterative optimization (that is, the position of the particle) x = {u1,u2,…u n},u i ={Δv,Δθ}. The purpose of selecting candidate solutions in this way is to limit u by acceleration and yaw rate. i The range is used as a constraint to exclude infeasible trajectories when generating candidate solutions. Assuming that the vehicle moves with uniform yaw rate and uniform acceleration, the potential trajectory can be obtained according to the initial state of the vehicle and the control quantity sequence.

[0043] Step S1022: calling a preset fitness function to solve each particle to obtain the initial fitness of each particle.

[0044] In this embodiment, a preset fitness function is called to solve each particle to obtain the initial fitness of each particle. The smaller the initial fitness of the particle, the closer it is to the optimal solution, wherein the preset fitness function is constructed based on the data information of the environmental vehicle.

[0045] In one implementation, the preset fitness function is constructed according to step S10221 to step S10222:

[0046] Step S10221: obtaining an initial fitness function;

[0047] Step S10222: Obtaining information on the current vehicle's deviation from the lane centerline, acceleration information, and yaw rate information;

[0048] Step S10223: constructing a penalty item based on the information of the current vehicle deviating from the lane centerline, the acceleration information and the yaw rate information, and constructing a reward item based on the data information of the environment vehicle;

[0049] Step S10224: construct the preset fitness function based on the penalty item, the reward item and the initial fitness function.

[0050] In this embodiment, an initial fitness function is first established. In the fitness function setting, the behavior to be punished is deviating from the center line of the lane (such as Figure 3 As shown, it is expressed by the minimum value of the distance between the vehicle and the center line of the left and right lanes), the acceleration and yaw rate are large, and the behavior to be rewarded (reward item) is the target lane output by selecting the high speed, far from the obstacle, and close to the lane. Among them, the distance to the obstacle is determined by the current vehicle position information and the data information of the environment vehicle.

[0051] In one implementation, the preset fitness function may be the following function:

[0052] fitness=WEIGHT SPEED *(velocity-vMax) 2 +WEIGHT LANEDEPART *min(a,b) 2 +WEIGHT ACC

[0053] *accel 2 +WEIGHT TARGETLANE *dis2targetLane 2 +WEIGHT YAWRATE *yawrate 2

[0054] +WEIGHT DIS2OBS *1 / dis2obs 2

[0055] Among them, the acceleration accel, yaw rate yawrate, speed velocity, maximum speed vMax, distance to obstacles dis2obs, distance to target lane dis2targetLane, and the minimum value of the distance between the vehicle and the center line of the left and right lanes are used to express min(a,b), and fitness fitness. WEIGHT SPEED WEIGHT LANEDEPART WEIGHT ACC WEIGHT YAWRATE WEIGHT DIS2OBS The weights are weights, and those skilled in the art can customize the above weights according to actual usage.

[0056] It should also be noted that the accuracy of calculating fitness can be improved by adding penalty items and reward items. In addition, in the fitness function setting, it is necessary to perform feasibility checks on potential trajectories (if not feasible, the fitness is extremely low), including collision checks, checks on being in the drivable area (the car is on the road), vehicle dynamics checks (lateral acceleration), and speed limit checks (finding the speed limit of the vehicle position).

[0057] Step S1023: Based on the initial fitness of each particle, an individual optimal solution and a group optimal solution are obtained.

[0058] In this implementation, the minimum fitness among the initial fitness of each particle is found, and the solution corresponding to the minimum fitness is the individual optimal solution.

[0059] Step S1024: updating and iterating the individual optimal solution and the group optimal solution. When a preset condition is met, stopping the updating and iteration and outputting the latest updated individual optimal solution and the group optimal solution as the final solution, wherein the final solution is the rough trajectory of the current vehicle.

[0060] In this implementation, the preset condition includes one of the following conditions:

[0061] When the preset number of iterations is reached, those skilled in the art can customize the preset number of iterations according to actual usage;

[0062] The individual optimal solution and the group optimal solution converge, that is, the fitness change in several consecutive iterations is less than a preset iteration threshold. Those skilled in the art can customize the preset iteration threshold according to actual usage.

[0063] In one implementation, the individual optimal solution and the group optimal solution may be updated and iterated according to the following steps S10241 to S10242:

[0064] Step S10241: updating the position information of each particle based on the first preset function, and updating the initial velocity information of each particle based on the second preset function.

[0065] In this implementation, the second preset function is v i (t+1)=ωv i (t)+c1r1(p i -x i )+c2r2(gx i ) where v i is the initial velocity information, ω, c1, r1, c2, r2 are all configuration quantities, t is time, p i is the best position for the individual, g is the best position for the whole, x i is the particle position information

[0066] ; The first preset function is x i (t+1)=Median(x i,min ,x i (t)+v i (t+1),x i,max ): Update the particle position according to the new velocity and limit it.

[0067] Step S10242: calling the preset fitness function to solve the updated position information of each particle and the updated initial velocity information of each particle, and then updating the individual optimal solution and the group optimal solution.

[0068] In this implementation, the updated position information of each particle and the updated initial velocity information of each particle are solved according to a preset fitness function, thereby updating the individual optimal solution and the group optimal solution.

[0069] Step S103: Based on the rough trajectory, obtain the final target trajectory.

[0070] In this embodiment, the rough trajectory is input into a preset algorithm to obtain the final target trajectory.

[0071] In one implementation, the preset algorithm may be a path algorithm or a speed planning algorithm based on quadratic programming.

[0072] Step S104: selectively sending the final target trajectory to a preset terminal based on the rough trajectory.

[0073] In one embodiment, the method further includes steps S1041 to S1043:

[0074] Step S1041: Determine whether the rough trajectory meets the first preset requirement, if so, execute step S1042, if not, execute step S1043.

[0075] Step S1042: sending the final target trajectory to the preset terminal;

[0076] Step S1043: Send the emergency brake control information to the preset terminal.

[0077] In this implementation, it is determined whether the rough trajectory meets the first preset requirement, that is, whether the rough trajectory is successfully decided. If so, the final target trajectory is sent to the preset terminal. If not, the emergency braking control information is sent to the preset terminal (vehicle end). If otherwise, it indicates that the decision fails (such as the current vehicle cannot avoid colliding with other vehicles). At this time, the emergency braking control information is sent to the preset terminal to reduce the loss caused by the collision.

[0078] Based on the above steps S101-S104, in response to reaching the preset conditions, the lane-level path of the current vehicle and the data information of the surrounding vehicles are obtained; the lane-level path of the current vehicle and the data information of the surrounding vehicles are input into the preset particle swarm optimization algorithm to obtain the rough trajectory of the current vehicle; based on the rough trajectory, the final target trajectory is obtained; based on the rough trajectory, the final target trajectory is selectively sent to the preset terminal. Through the above configuration, the present application uses the preset particle swarm optimization algorithm to find the local optimal solution for the lane-level path of the current vehicle and the data information of the surrounding vehicles. Compared with the decision-making method of historical data, it does not require a large amount of data resources. The rough trajectory is calculated through real-time data. It is highly flexible and can cope with complex and dynamic traffic environments.

[0079] In one embodiment, if Figure 6 As shown, the green autonomous driving vehicle using the present application accelerates to overtake the yellow environment vehicle and then changes lanes, avoiding the red faulty vehicle in front.

[0080] In one embodiment, if Figure 7 As shown, the green autonomous driving vehicle using the present application needs to change lanes to the middle straight lane to go straight at the intersection ahead, but there is a slow car blocking the way ahead, so it does not follow the car in front, but slows down to let the surrounding car in the side lane pass, and then changes lanes.

[0081] In one embodiment, see the attached Figure 4 , Figure 4 is a schematic diagram of a driving decision planning algorithm for executing particle swarm optimization according to an implementation of an embodiment of the present application, such as Figure 4 As shown, it can be Figure 4 The logical relationship in the particle swarm optimization driving decision planning algorithm is implemented.

[0082] It should also be noted that the present application adopts the particle swarm optimization algorithm (PSO) for autonomous driving decision planning, which has the following significant advantages: Global search capability: The particle swarm optimization algorithm has a strong global search capability by simulating the collaborative search behavior of the particle group. Compared with the rule-based method, PSO can more effectively avoid falling into the local optimal solution and find the global optimal solution. This is particularly important for complex traffic environments, because in these environments, the local optimal solution is often not enough to ensure driving safety and efficiency. Flexibility and adaptability: The PSO algorithm does not rely on predefined rules and can be adaptively adjusted according to real-time traffic data. Compared with the rule-based method, PSO can show higher flexibility and adaptability when facing unforeseen complex and dynamic traffic environments. At the same time, PSO does not need to rely heavily on training data and computing resources like reinforcement learning methods, and can adapt to new environments more quickly. Multi-objective optimization: The design of the fitness function can comprehensively consider multiple factors, such as speed, acceleration, path feasibility, distance from obstacles, etc., so as to achieve multi-objective optimization. Compared with the traditional single-objective optimization method, PSO can better balance different goals and generate better decision-making solutions. High computational efficiency: PSO has low computational complexity and is suitable for application in embedded systems and real-time decision-making systems. Compared with the large amount of training and reasoning time required by reinforcement learning methods, PSO shows high computational efficiency in practical applications and can meet real-time requirements. Strong robustness: The PSO algorithm has strong robustness through group collaborative search and information sharing. In the face of environmental data noise and uncertainty, it can maintain a high decision quality. Compared with the decision-making method based on historical data, PSO has lower requirements on the accuracy and robustness of the prediction model and can better cope with complex situations in actual driving. Easy to integrate and expand: The PSO algorithm has a simple structure and is easy to understand and implement. Its modular design makes the algorithm easy to integrate into existing autonomous driving systems and can be expanded and optimized according to specific needs. Compared with other complex decision-making methods, the integration and maintenance cost of PSO in practical applications is low. Through these advantages, the autonomous driving decision planning algorithm based on particle swarm optimization in this application can significantly improve the decision-making ability of vehicles in complex traffic environments, enhance driving safety and comfort, and improve traffic efficiency.

[0083] The step division of the above methods is only for the purpose of clear description. When implemented, they can be combined into one step or some steps can be split and decomposed into multiple steps. As long as they include the same logical relationship, they are all within the scope of protection of this patent; adding insignificant modifications to the algorithm or process or introducing insignificant designs without changing the core design of the algorithm and process are all within the scope of protection of this patent.

[0084] In addition, some embodiments of the present application also provide an electronic device. The electronic device may be a digital computer in various forms, such as a laptop computer, a desktop computer, a workbench, a personal digital assistant, a server, a blade server, a mainframe computer, etc. The electronic device may also be a mobile device in various forms, such as a personal digital processing, a cellular phone, a smart phone, a wearable device, and other similar computing devices.

[0085] The electronic device includes: one or more processors; and a memory storing computer program instructions, wherein when the computer program instructions are executed, the processor executes the steps of the method provided in any one or more of the above embodiments. Figure 7 An exemplary structural diagram of the electronic device is disclosed. Figure 7 As shown, the electronic device includes: one or more processors 1101, memory 1102, and 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 the memory to display graphical information of the GUI on an external input / output device (such as a display device coupled to the interface). In some other embodiments, if necessary, multiple processors and / or multiple buses can be used together with multiple memories and multiple memories. Similarly, multiple electronic devices can be connected, and each device provides some necessary operations (for example, as a server array, a group of blade servers, or a multi-processor system). Among them, the components shown in this article, their connections and relationships, and their functions are only used as examples, and are not intended to limit the implementation of the present application described and / or required herein.

[0086] The electronic device may further include: an input device 1103 and an output device 1104. The processor 1101, the memory 1102, the input device 1103 and the output device 1104 may be connected via a bus or other means. Figure 7 The example of connecting through bus is taken in the following.

[0087] The input device 1103 can receive input digital or character information, and generate key signal input related to the user settings and function control of the electronic device, such as a touch screen, a keypad, a mouse, a track pad, a touch pad, an indicator bar, one or more mouse buttons, a trackball, a joystick and other input devices. The output device 1104 may include a display device, an auxiliary lighting device (e.g., an LED) and a tactile feedback device (e.g., a vibration motor), etc. The display device may 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 may be a touch screen.

[0088] To provide interaction with a user, the electronic device may be a computer. The computer has: 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 a pointing device (e.g., a mouse or a trackball), through which the user can provide input to the computer. Other types of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0089] In the embodiments of the present application, a computer program / instruction is stored on a computer-readable medium, and when the computer program / instruction is executed by a processor, the steps of the method provided by any one or more of the above embodiments are implemented. The computer-readable medium may be included in the electronic device described in the above embodiments; or it may exist independently without being assembled into the device. The above computer-readable medium carries one or more computer-readable instructions.

[0090] The memory 1102 can be used as a non-transient computer-readable storage medium, which can be used to store non-transient software programs, non-transient computer executable programs and modules. The processor 1101 executes various functional applications and data processing of the server by running the non-transient software programs, instructions and modules stored in the memory 1102, so as to implement the program instructions / modules corresponding to the method provided by any one or more embodiments in the embodiments of the present application.

[0091] The memory 1102 may include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function; the data storage area may store data created according to the use of the electronic device, etc. In addition, the memory 1102 may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some embodiments, the memory 1102 may optionally include a memory remotely arranged relative to the processor 1101, and these remote memories may be connected to the electronic 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.

[0092] It should be noted that the computer-readable medium described in the present application may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: 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 the present application, a computer-readable medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, device or device.

[0093] Computer readable media include permanent and non-permanent, removable and non-removable media, and can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, modules of programs or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, read-only compact disk (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape disk storage or other magnetic storage devices or any other non-transmission medium that can be used to store information that can be accessed by a computing device.

[0094] Computer program code for performing the operations of the present application may 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" or similar programming languages. The program code may 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 cases involving a remote computer, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0095] In the above-described embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. For example, it can be implemented by using an application specific integrated circuit (ASIC), a general-purpose computer or any other similar hardware device. In certain embodiments, the software program of the present application can be executed by a processor to implement the above steps or functions. Similarly, the software program of the present application (including relevant data structures) can be stored in a computer-readable recording medium, for example, a RAM memory, a magnetic or optical drive or a floppy disk and similar devices. In addition, some steps or functions of the present application can be implemented by hardware, for example, as a circuit that cooperates with a processor to perform each step or function.

[0096] The computer program product provided in the embodiment of the present application includes one or more computer programs / instructions, and when the computer program / instructions are executed by the processor, the process or function described in the embodiment of the present application is generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions may be stored in a computer-readable storage medium, or transmitted from a computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions may be transmitted from a website site, a computer, a server or a data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) mode to another website site, computer, server or data center. The computer-readable storage medium may be any available medium that a computer can access or a data storage device such as a server or a data center that includes one or more available media integrations. The available medium may be a magnetic medium, (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid state disk (SSD)), etc.

[0097] The flow chart or block diagram in the accompanying drawings shows the possible architecture, function and operation of the equipment, method and computer program product according to various embodiments of the present application. In this regard, each box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flow chart, and the combination of the boxes in the block diagram and / or flow chart can be implemented with a dedicated system for hardware that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0098] The scope of the present application is defined by the appended claims rather than the above description, and it is intended that all changes that fall within the meaning and scope of the equivalent elements of the claims are included in the present application. Any figure mark in the claims should not be regarded as limiting the claims involved. In addition, it is clear that the word "comprising" does not exclude other units or steps, and the singular does not exclude the plural. Multiple units or devices stated in the device claim may also be implemented by one unit or device through software or hardware. The words "first", "second", etc. are only used to distinguish the description, and do not indicate any particular order, nor can they be understood as indicating or implying relative importance.

[0099] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art can easily mention changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims, and the above embodiments should be regarded as exemplary and non-restrictive.

Claims

1. A driving decision planning method based on particle swarm optimization, characterized in that: The method comprises: In response to reaching a preset condition, obtaining lane-level path information of the current vehicle and data information of surrounding vehicles; Inputting the lane-level path of the current vehicle and data information of the surrounding vehicles into a preset particle swarm optimization algorithm to obtain a rough trajectory of the current vehicle; Based on the rough trajectory, obtaining a final target trajectory; The final target trajectory is selectively sent to a preset terminal based on the rough trajectory.

2. The driving decision planning method based on particle swarm optimization according to claim 1, characterized in that: The step of inputting the lane-level path of the current vehicle and data information of the surrounding vehicles into a preset particle swarm optimization algorithm to obtain a rough trajectory of the current vehicle further includes: Based on the lane-level path of the current vehicle, particle swarm data is obtained; wherein the particle swarm data includes position information and initial velocity information of each particle; Calling a preset fitness function to solve each particle to obtain an initial fitness of each particle; wherein the preset fitness function is constructed based on the data information of the environment vehicle; Based on the initial fitness of each particle, obtaining the individual optimal solution and the group optimal solution; The individual optimal solution and the group optimal solution are updated and iterated. When a preset condition is met, the update iteration is stopped and the latest updated individual optimal solution and the group optimal solution are output as the final solution, wherein the final solution is the rough trajectory of the current vehicle.

3. The driving decision planning method based on particle swarm optimization according to claim 2, characterized in that: The method further includes updating and iterating the individual optimal solution and the group optimal solution according to the following steps: updating the position information of each particle based on a first preset function, and updating the initial velocity information of each particle based on a second preset function; The preset fitness function is called to solve the updated position information of each particle and the updated initial velocity information of each particle, thereby updating the individual optimal solution and the group optimal solution.

4. The driving decision planning method based on particle swarm optimization according to claim 2, characterized in that: Also includes: The preset condition includes one of the following conditions: Reach the preset number of iterations; The individual optimal solution and the group optimal solution converge.

5. The driving decision planning method based on particle swarm optimization according to claim 2, characterized in that: The method also includes constructing the preset fitness function according to the following steps: Get the initial fitness function; Obtain information about the current vehicle's deviation from the lane centerline, acceleration information, and yaw rate information; Constructing a penalty item based on the information of the current vehicle deviating from the lane centerline, the acceleration information, and the yaw rate information, and constructing a reward item based on the data information of the environment vehicle; The preset fitness function is constructed based on the penalty term, the reward term and the initial fitness function.

6. The driving decision planning method based on particle swarm optimization according to claim 1, characterized in that: The selectively sending the final target trajectory to a preset terminal based on the rough trajectory includes: Determining whether the rough trajectory meets a first preset requirement; If yes, sending the final target trajectory to the preset terminal; If not, the emergency brake control information is sent to the preset terminal.

7. The driving decision planning method based on particle swarm optimization according to claim 1, characterized in that: Also includes: The preset condition includes at least one of the following conditions: Lane-level path update of the current vehicle; The time interval from the last call of the particle swarm optimization algorithm is greater than a first preset time interval; The current vehicle deviates from the rough trajectory output last time; The path or speed planning of the current vehicle fails.

8. An electronic device, characterized in that: The electronic device comprises: one or more processors; and A memory storing computer program instructions, which, when executed, cause the processor to perform the steps of the method as claimed in any one of claims 1 to 7.

9. A computer readable medium having a computer program / instruction stored thereon, characterized in that: When the computer program / instructions are executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer program product comprising a computer program / instructions, characterized in that When the computer program / instructions are executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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