Control, test method and device for automatic driving vehicle platooning

Abstract

The application provides a control method and device for automatic driving vehicle formation driving, the method comprising: obtaining current scene characteristics based on a roadside unit and / or a perception device configured by an automatic driving vehicle; matching the current scene characteristics with a preset scene library to obtain target formation control information, wherein the preset scene library stores a mapping relationship between multiple groups of scene characteristics and formation control information, and the target formation control information comprises navigation vehicle control information and control information corresponding to at least one following vehicle; and controlling the navigation vehicle and the following vehicle to drive in formation based on the target formation control information. Through the application, the formation scene of the automatic driving vehicle in multiple scenes such as highways is introduced, the problems of scattered test organization and limited scene coverage in traditional road testing are solved, a multi-source heterogeneous perception fusion dynamic correction can be constructed, the formation cooperative control of the automatic driving vehicle in a high-speed environment is realized, and the problem of perception degradation caused by failure of a single sensor is solved.

Description

Technical Field

[0001] This application relates to the field of autonomous driving technology, and in particular to a control and testing method and apparatus for autonomous vehicle platooning. Background Technology

[0002] With the development of artificial intelligence and modern manufacturing, autonomous driving technology has gradually entered people's daily lives, subtly changing the way people travel. In civilian applications, besides bringing convenience to people's lives, autonomous driving technology can reduce traffic accident rates and improve road efficiency, but it also leads to a surge in energy consumption and frequent traffic accidents. The continuous upgrading and iteration of autonomous driving technology is key to solving these problems. Therefore, autonomous driving technology has become a focus of attention in the automotive industry and academia.

[0003] In autonomous driving technology, vehicle platooning is one of its important applications, but it faces numerous technical challenges. Among these challenges, testing requirements are gradually shifting from closed test tracks to real-world road scenarios. Existing testing platforms are often limited to urban roads or closed parks, lacking comprehensive verification of autonomous vehicle platooning, lane changing, ramp entry and exit, and vehicle-to-infrastructure (V2I) capabilities in complex highway traffic environments. Summary of the Invention

[0004] This application provides a control and testing method and apparatus for autonomous vehicle platooning, which partially or completely solves the above-mentioned technical problems.

[0005] In a first aspect, embodiments of this application provide a control method for autonomous vehicles platooning, comprising: acquiring current scene features based on roadside units (RSUs) and / or perception devices configured on autonomous vehicles; matching the current scene features with a preset scene library to obtain target platooning control information, wherein the preset scene library stores multiple sets of mapping relationships between scene features and platooning control information, and the target platooning control information includes lead vehicle control information and control information corresponding to at least one follower vehicle; and controlling the lead vehicle and follower vehicles to platoon oriented based on the target platooning control information.

[0006] Secondly, embodiments of this application provide a control method for autonomous vehicles to platoon, applied to a following vehicle, including: in response to receiving a platooning instruction sent by a lead vehicle, calculating the driving trajectory from the current state to the target platooning position based on the platooning instruction to generate a reference platooning path; and tracking and dynamically correcting the reference platooning path based on a closed-loop feedback control algorithm combined with obstacle prediction results to achieve platooning.

[0007] Thirdly, embodiments of this application provide a testing method for autonomous vehicles platooning, comprising: acquiring operational data of each autonomous vehicle during platooning based on the aforementioned control method for autonomous vehicles platooning; performing performance index analysis on the operational data to obtain test results for autonomous vehicles platooning.

[0008] Fourthly, embodiments of this application provide a control device for autonomous vehicle platooning, comprising: a scene feature acquisition module, used to acquire current scene features based on a roadside unit (RSU) and / or a perception device configured on the autonomous vehicle; a control information acquisition module, used to match the current scene features with a preset scene library to obtain target platooning control information, wherein the preset scene library stores multiple sets of mapping relationships between scene features and platooning control information, and the target platooning control information includes lead vehicle control information and control information corresponding to at least one follower vehicle; and a platooning driving module, used to control the lead vehicle and follower vehicles to platoon based on the target platooning control information.

[0009] Fifthly, embodiments of this application provide a control device for autonomous vehicle platooning, applied to a following vehicle, comprising: a reference platooning path generation module, used to respond to receiving a platooning instruction sent by a lead vehicle, and calculate the driving trajectory from the current state to the target platooning position based on the platooning instruction to generate a reference platooning path; and a correction module, used to track and dynamically correct the reference platooning path based on a closed-loop feedback control algorithm combined with obstacle prediction results to achieve platooning.

[0010] Sixthly, embodiments of this application provide a testing device for autonomous vehicle platooning, comprising: an operation data acquisition module for acquiring operation data of each autonomous vehicle during platooning based on the aforementioned autonomous vehicle platooning control device; and an analysis module for performing performance index analysis on the operation data to obtain test results of autonomous vehicle platooning.

[0011] In a seventh aspect, embodiments of this application provide an electronic device, including a memory, a processor, and a computer program stored in the memory, wherein the processor implements the method described above when executing the computer program.

[0012] Eighthly, embodiments of this application provide a computer program product, including computer instructions, wherein the computer instructions, when executed by a processor, implement any of the above methods.

[0013] Ninthly, embodiments of this application provide a computer-readable storage medium storing a computer program, which, when executed by a processor, implements any of the methods described above.

[0014] Compared with the prior art, this application has the following advantages:

[0015] According to the embodiments of this application, the current scene features are first acquired based on the perception devices configured in the roadside unit (RSU) and / or the autonomous vehicle. Then, the current scene features are matched with a preset scene library to obtain target platooning control information. Subsequently, the lead vehicle and following vehicles are controlled to platoon based on this target platooning control information. On the following vehicle side, in response to receiving the platooning command from the lead vehicle, the vehicle calculates the driving trajectory from the current state to the target platooning position based on the platooning command to generate a reference platooning path. Based on a closed-loop feedback control algorithm combined with obstacle prediction results, the reference platooning path is tracked and dynamically corrected to achieve platooning. This scheme not only introduces platooning disbanding and ramp merging / exiting for autonomous vehicles in various scenarios such as highways, solving the problems of dispersed test organization and limited scene coverage in traditional road testing, but also constructs multi-source heterogeneous perception fusion and dynamic correction to achieve platooning cooperative control of autonomous vehicles in high-speed environments, solving the perception degradation problem caused by the failure of a single sensor.

[0016] The above description is only an overview of the technical solution of this application. In order to better understand the technical means of this application, it can be implemented according to the contents of the specification. In order to make the above and other objects, features and advantages of this application more obvious and understandable, specific embodiments of this application are given below. Attached Figure Description

[0017] In the accompanying drawings, unless otherwise specified, the same reference numerals throughout the various drawings denote the same or similar parts or elements. These drawings are not necessarily drawn to scale. It should be understood that these drawings depict only some embodiments according to this application and should not be construed as limiting the scope of this application.

[0018] Figure 1 A schematic diagram of a scenario for the control method of autonomous vehicle platooning provided in this application;

[0019] Figure 2 This is a flowchart of a control method for autonomous vehicle platooning according to an embodiment of this application;

[0020] Figure 3 This is a flowchart of another embodiment of the control method for autonomous vehicles platooning following other vehicles, according to this application;

[0021] Figure 4 This is a flowchart of a test method for autonomous vehicle platooning according to another embodiment of this application;

[0022] Figure 5 This is a structural block diagram of a control device for autonomous vehicle platooning according to an embodiment of this application;

[0023] Figure 6 This is a structural block diagram of a control device for autonomous vehicle platooning, according to an embodiment of this application;

[0024] Figure 7 This is a structural block diagram of a test device for autonomous vehicle platooning according to another embodiment of this application;

[0025] Figure 8 This is a block diagram of an electronic device used to implement embodiments of this application. Detailed Implementation

[0026] In the following description, only certain exemplary embodiments are briefly described. As those skilled in the art will recognize, the described embodiments can be modified in various ways without departing from the concept or scope of this application. Therefore, the drawings and description are considered to be exemplary in nature and not restrictive.

[0027] To facilitate understanding of the technical solutions of the embodiments of this application, the relevant technologies of the embodiments of this application are described below. The following relevant technologies are optional solutions and can be combined with the technical solutions of the embodiments of this application in any way, and all of them fall within the protection scope of the embodiments of this application.

[0028] To more clearly illustrate the technical solutions of the embodiments in this specification, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are merely some examples or embodiments of this specification. For those skilled in the art, these drawings can be applied to other similar scenarios without creative effort. Unless obvious from the context or otherwise stated, the same reference numerals in the drawings represent the same structures or operations.

[0029] It should be understood that the terms “system,” “device,” “unit,” and / or “module” used herein are one method of distinguishing different components, elements, parts, sections, or assemblies at different levels. However, if other words can achieve the same purpose, they may be replaced by other expressions.

[0030] As indicated in this specification and claims, unless the context clearly indicates otherwise, the words "a," "an," "an," and / or "the" do not specifically refer to the singular and may also include the plural. Generally speaking, the terms "comprising" and "including" only indicate the inclusion of expressly identified steps and elements, which do not constitute an exclusive list, and the method or apparatus may also include other steps or elements.

[0031] Flowcharts are used in this specification to illustrate the operations performed by the system according to embodiments of this specification. It should be understood that the preceding or following operations are not necessarily performed in exact order. Instead, the steps can be processed in reverse order or simultaneously. Furthermore, other operations can be added to these processes, or one or more steps can be removed from them.

[0032] In the field of autonomous driving, existing control and testing methods are often limited to urban roads or closed parks, lacking comprehensive verification of autonomous vehicle platooning, lane changing, ramp entry and exit, and vehicle-to-infrastructure (V2I) capabilities in complex traffic environments such as highways (high-speed, low-traffic environments). Secondly, existing traditional scenario libraries only support basic single-vehicle scenarios, such as following and lane changing, lacking dedicated configuration items for highway platooning and coordination scenarios, and cannot dynamically generate complex interaction logic such as ramp merging and long-distance platooning loops. Furthermore, traditional platooning relies on fixed vehicle spacing control, with platooning time exceeding 12 seconds and lacking support for dynamic role switching.

[0033] In view of the above problems, this application provides a control and testing method for autonomous vehicles platooning to solve all or part of the above technical problems.

[0034] The technical solutions involved in the embodiments disclosed in this application will be described below in conjunction with the scenarios in which they are applied.

[0035] Figure 1 This is a schematic diagram illustrating an application scenario for implementing the method of the embodiments of this application.

[0036] like Figure 1As shown, within the dedicated autonomous driving lane, platooned vehicles can calculate a safe following distance within a speed range of 80-120 km / h based on the speed and distance of the vehicle ahead, as well as the weight, speed, and braking relationship of their own vehicles. This enables them to automatically adjust their cruising speed and coordinate speed adjustments on curves. When a background vehicle is detected cutting in, the lead vehicle slows down and the following vehicle adjusts synchronously. If an entry into a ramp is detected, the current scene features obtained by the Roadside Unit (RSU) are matched with a preset scene library to obtain target platooning control information. Based on this information, the lead vehicle and follower vehicles are controlled to platoon, decelerating in advance to complete merging and exiting operations. In response to a platooning command received from the lead vehicle, the driving trajectory from the current state to the target platooning position is calculated to generate a reference platooning path. Based on a closed-loop feedback control algorithm combined with obstacle prediction results, the reference platooning path is tracked and dynamically corrected to achieve platooning. The operating data of each autonomous vehicle during platooning is obtained using the control method for autonomous vehicle platooning based on this application and the control method applied to the follower vehicles. Performance index analysis is performed on the operating data to obtain test results for autonomous vehicle platooning.

[0037] In this embodiment of the application, the lead vehicle and the follower vehicle may include cars, freight trucks, buses, emergency rescue vehicles, agricultural machinery vehicles, etc.

[0038] The technical solution of this application and how it solves the aforementioned technical problems are described in detail below with specific embodiments. The listed specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of this application will be described in detail below with reference to the accompanying drawings.

[0039] like Figure 2 The diagram shown is a flowchart of a control method for autonomous vehicle platooning according to an embodiment of this application, which may include:

[0040] Step S201: Obtain current scene features based on the perception devices configured in the roadside unit (RSU) and / or the autonomous vehicle.

[0041] When autonomous vehicles are in operation, the core objective of acquiring current scene characteristics based on Roadside Units (RSUs) and the vehicles' own perception devices is to build a more comprehensive, reliable, and accurate environmental perception capability. This provides a strong data foundation for the decision-making of autonomous vehicle platooning systems, ultimately improving the safety and efficiency of autonomous driving. The combination of RSUs and onboard perception devices enables autonomous vehicle platooning to not only "see" what's around them but also "foresee" what's ahead; not only to operate independently but also to collaborate and enhance efficiency. This provides crucial perception assurance for achieving safe, efficient, and highly robust autonomous vehicle platooning.

[0042] In one optional embodiment, each scenario includes at least one scenario feature parameter, which describes road information, traffic information, environmental information, and / or dynamic traffic participant information. Specifically, road information describes the basic physical infrastructure and its state for vehicle platooning, including road curvature, gradient, lane width, number of lanes, road type, traffic signs and markings, and road surface adhesion coefficient. Traffic information describes the traffic rules, control equipment, and overall traffic flow state affecting vehicle platooning, including traffic light status, speed limits, stop signs, traffic flow density, average vehicle speed, and driver behavior. Environmental information describes the natural and weather conditions during vehicle platooning operation, which directly affect sensor performance and vehicle handling stability, including precipitation, wind speed, temperature, humidity, horizontal visibility, and lighting conditions. Dynamic traffic participant information describes objects moving or potentially appearing suddenly in the road environment, whose states change over time and pose challenges to the safe operation of the platoon, including, for example, surrounding vehicles, pedestrians, non-motorized vehicles, and temporary road conditions.

[0043] In one optional embodiment, the sensing device includes a multimodal sensor, and the determination of the preset scene library includes: if the scene includes scene feature parameters describing road information, then setting the speed limit information and platoon spacing of different road structures as platoon control information to obtain a first mapping relationship; if the scene includes scene feature parameters describing traffic information and dynamic traffic participant information, then setting the dynamic adjustment strategy of platoon spacing as platoon control information to obtain a second mapping relationship; if the scene includes scene feature parameters describing environmental information, then setting the weight adjustment strategy of the multimodal sensor and / or the vehicle-to-infrastructure (V2I) enhanced positioning strategy as platoon control information to obtain a third mapping relationship.

[0044] Among them, considering that the existing technology cannot cover autonomous vehicles in the complex environment of highways, such as platooning and disbanding, ramp merging and merging, and lane changing, the preset scenario library can be used to simulate ramp scenarios with different road structures, traffic information can be used to simulate high-speed low-traffic environments, dynamic traffic participants can be used for social vehicles, pedestrians, etc., and environmental information can be used to simulate severe weather such as snow, fog, and rain.

[0045] In one alternative embodiment, a background vehicle generation algorithm is used to simulate a high-speed, low-traffic environment (such as the Beijing-Xiong'an Expressway with an average daily traffic flow of less than 5,000 vehicles). When interacting with other vehicles, the platooning spacing is dynamically adjusted to avoid inapplicability to congestion scenarios.

[0046] In one alternative embodiment, when the autonomous vehicle is a truck, the speed limit gradient for the ramp merging scenario is dynamically generated based on the truck dynamics model (mass of 10-30 tons, braking distance increased by 50%), such as 40km / h on the ramp → 80km / h on the main road, and a safety distance threshold of 200 meters.

[0047] Furthermore, when environmental information simulates severe weather such as snow, rain, and fog, the sensor weights are dynamically adjusted based on the environmental information. For example, in a rainstorm scenario, the weight of the millimeter-wave radar is increased to 0.7, solving the perception degradation problem caused by the failure of a single sensor, and keeping the positioning error within 0.5 meters in rainy and foggy weather. For instance, when the rainfall is greater than 25 mm / h, the lidar attenuation model is automatically activated, and when the visibility is less than 50 m in foggy weather, V2I enhanced positioning is triggered.

[0048] In one optional embodiment, the control method for autonomous vehicles platooning further includes acquiring multi-source data output from multimodal sensors; and performing spatiotemporal alignment of the multi-source data based on Precision Time Protocol (PTP) and Simultaneous Localization and Mapping (SLAM) assisted calibration.

[0049] In high-speed, low-traffic formation scenarios, even microsecond-level time deviations can lead to centimeter-level positioning errors, affecting the accuracy of formation maintenance and collaborative decision-making. To eliminate errors caused by asynchronous timestamps in sensor data acquisition, the Precision Clock Protocol (PTP) is employed. This involves separately marking the lead vehicle and following vehicles, using the lead vehicle's or its onboard master clock as the PTP master clock, and the other following vehicles and their internal sensor receivers as slave clocks. Through an onboard Ethernet switch supporting the PTP protocol, all slave clocks continuously adjust their time to synchronize with the master clock. This ensures that LiDAR point clouds, camera image frames, and other data are stamped with a unified and precise timestamp, providing a solid time reference for subsequent data fusion.

[0050] Since initial calibration alone can cause parameter drift due to vehicle vibration, temperature changes, etc., online correction is required. Therefore, in order to determine the relative position and attitude (extrinsic parameters) between different sensors and to transform all sensing data into a unified vehicle coordinate system to eliminate motion blur and coordinate offset in high-speed scenes, simultaneous localization and mapping (SLAM) is used for assisted calibration.

[0051] It is understandable that by using the PTP precision clock protocol to achieve high-precision time synchronization and combining it with SLAM-assisted online calibration and optimization technology to achieve spatial alignment, the spatiotemporal consistency problem in multi-sensor fusion is solved, enabling the autonomous vehicle platooning control method in this embodiment to have high precision, high reliability and high adaptability in high-speed scenarios.

[0052] Step S202: Match the current scene features with the preset scene library to obtain target formation control information. The preset scene library stores multiple sets of mapping relationships between scene features and formation control information. The target formation control information includes the navigator vehicle control information and the control information corresponding to at least one following vehicle.

[0053] Step S203: Control the lead vehicle and follower vehicles to travel in formation based on the target formation control information.

[0054] Through the above steps S201~S203, the road test unit can be used to perceive and predict social vehicles and dynamically adjust the formation strategy, so that the autonomous vehicle formation can achieve complex formation behaviors (such as ramp merging and merging, lane changing, etc.) in multiple scenarios (such as high-speed low-traffic scenarios). It can not only adapt to ordinary cars, but also cover the formation driving of autonomous truck formations by adding truck dynamics models.

[0055] Figure 3 This is a flowchart of another embodiment of the control method for autonomous vehicles platooning following other vehicles, according to this application.

[0056] like Figure 3 As shown, it includes:

[0057] Step S301: In response to receiving the platooning instruction sent by the navigator vehicle, calculate the driving trajectory from the current state to the target platooning position based on the platooning instruction to generate a reference platooning path.

[0058] The platooning instructions issued by the lead vehicle include information such as the target platoon formation, the desired distance between vehicles, the target speed, and the target position coordinates assigned to the following vehicles.

[0059] In one feasible embodiment, when the lead vehicle and the following truck are in the parking space, considering the long braking time, large turning radius and wide body of the truck, the reference platooning path needs to be optimized according to the truck dynamics model.

[0060] Step S302: Based on the closed-loop feedback control algorithm and obstacle prediction results, the reference entry path is tracked and dynamically corrected to achieve platooning.

[0061] To ensure that following vehicles can safely, quickly, and smoothly integrate into the platoon from free-roaming mode according to the lead vehicle's instructions, a trajectory that both reaches the target location and avoids obstacles needs to be generated, and precise execution must be ensured through real-time feedback control. Therefore, this embodiment employs a closed-loop feedback control algorithm combined with obstacle prediction results to track and dynamically correct collision risks on the reference entry path.

[0062] In one feasible embodiment, the lead vehicle and the follower vehicle each have at least a lead mode and a follow mode, and the initial mode of the follower vehicle is the follow mode, including: monitoring the status of the lead vehicle; and switching the follower vehicle from the follow mode to the lead mode in response to the triggering condition of the lead vehicle's status meeting the mode switching triggering condition.

[0063] To ensure the continuous and safe operation of the formation in the event of an anomaly in the lead vehicle, a lead vehicle status monitoring mechanism is adopted. When the lead vehicle encounters a problem, it provides system-level redundancy, ensuring that a single point of failure will not cause the entire formation to fail.

[0064] Through the above steps S301~S302, the entry path can be corrected, adapting to various vehicle models, realizing a hierarchical control architecture for lead and follow vehicles, and supporting dynamic role switching.

[0065] Figure 4 This is a flowchart of a test method for autonomous vehicle platooning according to another embodiment of this application.

[0066] like Figure 4 As shown, it includes:

[0067] Step S401: Obtain the operation data of each autonomous vehicle during the platooning process based on the autonomous vehicle platooning control method of this application and the autonomous vehicle platooning control method applied to the following vehicle.

[0068] Step S402: Analyze the performance indicators of the running data to obtain the test results of autonomous vehicles driving in platoons.

[0069] Furthermore, by analyzing the operational data to obtain performance indicators, test results were obtained, including response speed, cooperative driving capability, safety, and comfort.

[0070] In one alternative embodiment, the preset scenario library is optimized based on the test results.

[0071] Traditional pre-built scenario libraries are often based on limited experience and rules. To improve testing efficiency and coverage, testing resources can be focused on scenarios where the system performs poorly or has not been fully tested, avoiding wasting resources on simple, repetitive scenarios and exposing more problems with fewer test mileages. By analyzing performance degradation points, intervention takeover points, and even failure points in the test results, the defects of the current control algorithm or the boundaries of its perception capabilities can be accurately located, thereby generating new scenarios that can continuously challenge these weaknesses. Because the optimization process relies on quantitative analysis of a large amount of objective operational data (such as vehicle distance, speed, acceleration, TTC, etc.) rather than subjective judgment, this makes the test evaluation more accurately reflect the performance of the formation system.

[0072] Corresponding to the application scenarios and methods provided in the embodiments of this application, the embodiments of this application also provide a control device for autonomous vehicle platooning. For example... Figure 5 The diagram shown is a structural block diagram of a control device for autonomous vehicle platooning according to an embodiment of this application. The control device for autonomous vehicle platooning may include:

[0073] Scene feature acquisition module 501 is used to acquire current scene features based on the perception devices configured in the roadside unit (RSU) and / or the autonomous vehicle.

[0074] The control information acquisition module 502 is used to match the current scene features with the preset scene library to obtain the target formation control information. The preset scene library stores multiple sets of mapping relationships between scene features and formation control information. The target formation control information includes the navigator vehicle control information and the control information corresponding to at least one following vehicle.

[0075] The platooning module 503 is used to control the platooning of the lead vehicle and the following vehicles based on the target platooning control information.

[0076] In one optional embodiment, a multi-source data spatiotemporal alignment submodule is further included, which is used to acquire multi-source data output by multimodal sensors; and to perform spatiotemporal alignment of the multi-source data based on Precision Clock Protocol (PTP) and Simultaneous Localization and Mapping (SLAM) assisted calibration.

[0077] Corresponding to the application scenarios and methods provided in the embodiments of this application, the embodiments of this application also provide a control device for autonomous vehicle platooning driving while following other vehicles. For example... Figure 6 The diagram shown is a structural block diagram of a control device for autonomous vehicles following a platoon, according to an embodiment of this application. This control device may include:

[0078] The reference platooning path generation module 601 is used to respond to the platooning instruction sent by the navigator vehicle, calculate the driving trajectory from the current state to the target platooning position based on the platooning instruction, and generate a reference platooning path.

[0079] The correction module 602 is used to track and dynamically correct the reference entry path based on the closed-loop feedback control algorithm and obstacle prediction results, so as to achieve platooning.

[0080] Corresponding to the application scenarios and methods provided in the embodiments of this application, the embodiments of this application also provide a testing device for platooning autonomous vehicles. For example... Figure 7 The diagram shown is a structural block diagram of a test device for autonomous vehicle platooning according to this application. The test device for autonomous vehicle platooning may include:

[0081] The operation data acquisition module 701 is used to acquire the operation data of each autonomous vehicle during the formation driving process based on the above-mentioned autonomous vehicle formation driving control device.

[0082] Analysis module 702 is used to perform performance index analysis on the operating data and obtain test results of autonomous vehicle platooning.

[0083] The functions of each module in each device in the embodiments of this application can be found in the corresponding description in the above method, and they have corresponding beneficial effects, which will not be repeated here.

[0084] Figure 8 This is a block diagram of an electronic device used to implement embodiments of this application. For example... Figure 8 As shown, the electronic device includes a memory 810 and a processor 820. The memory 810 stores a computer program that can run on the processor 820. When the processor 820 executes the computer program, it implements the method described in the above embodiments. The number of memories 810 and processors 820 can be one or more.

[0085] The electronic device also includes:

[0086] The communication interface 830 is used to communicate with external devices and exchange and transmit data.

[0087] If the memory 810, processor 820, and communication interface 830 are implemented independently, they can be interconnected via a bus to communicate with each other. This bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. This bus can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 8 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0088] Optionally, in a specific implementation, if the memory 810, processor 820, and communication interface 830 are integrated on a single chip, then the memory 810, processor 820, and communication interface 830 can communicate with each other through an internal interface.

[0089] This application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the method provided in this application.

[0090] This application also provides a chip including a processor for calling and executing instructions stored in a memory, causing a communication device with the chip installed to perform the method provided in this application.

[0091] This application also provides a chip, including: an input interface, an output interface, a processor, and a memory. The input interface, output interface, processor, and memory are connected through an internal connection path. The processor is used to execute code in the memory. When the code is executed, the processor is used to execute the method provided in the application embodiment.

[0092] It should be understood that the aforementioned processor can be a Central Processing Unit (CPU), or other general-purpose processors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field-Programmable Gate Arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. General-purpose processors can be microprocessors or any conventional processor. It is worth noting that the processor can be a processor supporting Advanced Reduced Instruction Set Machines (ARM) architecture.

[0093] Further, optionally, the aforementioned memory may include read-only memory and random access memory. The memory may be volatile memory or non-volatile memory, or may include both. Non-volatile memory may include read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. Volatile memory may include random access memory (RAM), which serves as an external cache. By way of example, but not limitation, many forms of RAM are available. Examples include Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDR SDRAM), Enhanced Synchronous DRAM (ESDRAM), Sync Link DRAM (SLDRAM), and Direct Rambus RAM (DR RAM).

[0094] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented, in whole or in part, as a computer program product. A computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions according to this application are generated. 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 transferred from one computer-readable storage medium to another.

[0095] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of those different embodiments or examples.

[0096] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "a plurality of" means two or more, unless otherwise explicitly specified.

[0097] Any process or method described in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more executable instructions for implementing a particular logical function or process. Furthermore, the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functionality involved.

[0098] The logic and / or steps described in the flowchart or otherwise herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus or device (such as a computer-based system, a processor-included system or other system that can fetch and execute instructions from, an instruction execution system, apparatus or device).

[0099] It should be understood that various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. All or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware, the program being stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiments.

[0100] Furthermore, the functional units in the various embodiments of this application can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. This storage medium can be a read-only memory, a disk, or an optical disk, etc.

[0101] The above description is merely an exemplary embodiment of this application, but the scope of protection of this application is not limited thereto. Any person skilled in the art can easily conceive of various variations or substitutions within the technical scope described in this application, and these should all be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A control method for platooning of autonomous vehicles, characterized in that, The method comprises: obtaining current scene features based on roadside units RSUs and / or perception devices configured for the autonomous vehicles; the perception devices comprise multi-modal sensors; each scene comprises at least one scene feature parameter, which is used to describe road information, traffic information, environmental information and / or dynamic traffic participant information; wherein the road information is used to simulate ramp scenes, and the traffic information is used to simulate high-speed low-flow environments; matching the current scene features with a preset scene library to obtain target platoon control information, wherein the preset scene library stores mapping relationships between multiple groups of scene features and platoon control information; if the scene comprises scene feature parameters describing road information, speed limit information and platoon spacing of different road structures are set as the platoon control information to obtain a first mapping relationship; if the scene comprises scene feature parameters describing traffic information and dynamic traffic participant information, a dynamic adjustment strategy of platoon spacing is set as the platoon control information to obtain a second mapping relationship; if the scene comprises scene feature parameters describing environmental information, a weight adjustment strategy of the multi-modal sensors and / or a vehicle-to-infrastructure communication V2I enhanced positioning strategy are set as the platoon control information to obtain a third mapping relationship; the target platoon control information comprises lead vehicle control information and control information corresponding to at least one follower vehicle; controlling the lead vehicle and the follower vehicle to platoon based on the target platoon control information.

2. The method of claim 1, wherein, The method further comprises: obtaining multi-source data output by the multi-modal sensors; performing spatio-temporal alignment on the multi-source data based on a precision time protocol PTP and a simultaneous localization and mapping SLAM assisted calibration.

3. A test method for platooning of autonomous vehicles, characterized in that, The method comprises: obtaining operation data of each autonomous vehicle during platooning based on the method of any one of claims 1 or 2; performing performance index analysis on the operation data to obtain test results of the autonomous vehicle platooning.

4. The method of claim 3, wherein, The method further comprises: optimizing the preset scene library based on the test results.

5. A control device for platooning of autonomous vehicles, characterized in that, The device comprises: a scene feature obtaining module configured to obtain current scene features based on roadside units RSUs and / or perception devices configured for the autonomous vehicles; the perception devices comprise multi-modal sensors; each scene comprises at least one scene feature parameter, which is used to describe road information, traffic information, environmental information and / or dynamic traffic participant information; wherein the road information is used to simulate ramp scenes, and the traffic information is used to simulate high-speed low-flow environments; The control information acquisition module is configured to match the current scene feature with a preset scene library to obtain target platoon control information, wherein the preset scene library stores a mapping relationship between multiple groups of scene features and platoon control information, if the scene includes scene feature parameters describing road information, limit speed information and platoon spacing of different road structures are set as the platoon control information to obtain a first mapping relationship; if the scene includes scene feature parameters describing traffic information and dynamic traffic participant information, a dynamic adjustment strategy of platoon spacing is set as the platoon control information to obtain a second mapping relationship; if the scene includes scene feature parameters describing environmental information, a weight adjustment strategy of the multi-modal sensor and / or a vehicle-to-infrastructure (V2I) enhanced positioning strategy is set as the platoon control information to obtain a third mapping relationship; the target platoon control information includes leader vehicle control information and control information corresponding to at least one follower vehicle; The platoon driving module is configured to control the leader vehicle and the follower vehicle to platoon drive based on the target platoon control information.

6. A test device for automatic driving vehicle platooning, characterized by, The device comprises: An operation data acquisition module is configured to acquire operation data of each autonomous vehicle during platoon driving based on the device of claim 5; An analysis module is configured to perform performance index analysis on the operation data to obtain a test result of the autonomous vehicle platoon driving. 7.An electronic device comprising a memory, a processor, and a computer program stored in the memory, wherein the processor implements the method of any one of claims 1-4 when executing the computer program. 8.A computer program product comprising computer instructions, wherein the computer instructions implement the method of any one of claims 1-4 when executed by a processor. 9.A computer-readable storage medium having a computer program stored therein, wherein the computer program implements the method of any one of claims 1-4 when executed by a processor.

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