A method for platooning autonomous driving vehicles in the slow lane of a circular curve section of a highway

Abstract

This invention relates to a method for platooning autonomous driving vehicles in the slow lane of a curved section of a highway. By acquiring basic attribute information of the autonomous driving vehicle and its interoperating vehicles, preliminary information on the autonomous driving vehicle platooning scheme, highway alignment design and environmental information, and weather information as input information, the method analyzes scenarios based on risk scenario types, categorizing them into autonomous driving vehicle line-of-sight (LOS) failure and interoperating vehicle LOS failure. A virtual testing method is used to obtain the available LOS of the lead vehicle and interoperating vehicles in the analyzed scenarios, constructing a database linking the input information and available LOS. Furthermore, the optimal autonomous driving vehicle platooning scheme is determined based on the principles of LOS safety and speed coordination. This invention comprehensively considers road environmental conditions on curved sections of highways and factors related to the level of autonomous driving, effectively proposing a platooning scheme that meets the safe driving requirements of autonomous driving vehicles, providing a reliable technical means for their actual safe operation.

Description

Technical Field

[0001] This invention belongs to the field of autonomous driving vehicle fleet formation technology, specifically relating to a method for autonomous driving vehicle fleet formation in the slow lane of a highway circular curve section. Background Technology

[0002] With increasingly mature intelligent driving systems and compact fleet structures, autonomous driving fleets are expected to provide effective solutions for improving road traffic efficiency, enhancing passenger comfort, reducing driver difficulty, and decreasing vehicle emissions. Given these benefits, autonomous driving fleets have become one of the key physical deployment forms of intelligent driving technology. Currently, the main application scenarios for autonomous driving fleets are logistics, ports, and fixed-route area connections, with their operating environments mostly closed or semi-closed, less affected by the actual structured road environment or the surrounding traffic. Therefore, to further promote the application of autonomous driving fleets in public road environments, it is necessary to give greater consideration to the impact of actual road conditions on the operation of autonomous driving fleets.

[0003] Currently, autonomous vehicle fleets typically use truck models, deployed in the right-hand slow lane of road sections, with the lead vehicle mostly being a traditionally manually driven vehicle, while the following vehicles are autonomous vehicles. Highways, as the primary road scenario for truck operations, have proven that their curved sections are critical risk elements for vehicle operation. However, few autonomous driving platooning methods consider the linear factors affecting curved sections and lack consideration of the comprehensive impact of the road environment and the driving of surrounding vehicles. Furthermore, existing technical solutions are limited by the current level of autonomous driving, rarely considering combinations of lead and following vehicles with different levels of autonomous driving within the fleet. Summary of the Invention

[0004] The purpose of this invention is to provide a method for platooning autonomous driving vehicles in the slow lane of a curved section of a highway. This method helps to comprehensively consider the road environment conditions and autonomous driving level factors of the curved section of the highway, effectively propose a platooning scheme that meets the safe driving requirements of autonomous driving vehicles, and provide a reliable technical means for their actual safe operation.

[0005] It obtains basic information on the attributes of autonomous driving fleets and interactive vehicles, preliminary information on autonomous driving fleet formation schemes, highway alignment design and environmental information, and weather information as input information. Based on the risk scenario type, it divides the analysis scenarios into autonomous driving fleet line-of-sight failure and interactive vehicle line-of-sight failure. It uses virtual testing methods to obtain the available line-of-sight distances of the lead vehicle and interactive vehicles in the analysis scenarios, builds a database linking the input information and available line-of-sight distances, and further determines the optimal autonomous driving fleet formation scheme based on the principles of line-of-sight safety and speed coordination.

[0006] To achieve the above objectives, the technical solution of this invention is: a method for platooning an autonomous driving vehicle fleet in the slow lane of a curved section of a highway. This method involves acquiring basic attribute information of the autonomous driving fleet and the interacting vehicles, preliminary information on the autonomous driving fleet platooning scheme, highway alignment design and environmental information, and weather information as input information. Based on the risk scenario type, the analysis scenarios are divided into autonomous driving fleet line-of-sight (LOS) failure analysis scenarios and interacting vehicle LOS failure analysis scenarios. A virtual testing method is used to obtain the available LOS of the lead vehicle and the interacting vehicles in the analysis scenarios. A database linking the input information and the available LOS is constructed. Furthermore, the optimal autonomous driving fleet platooning scheme is determined based on the principles of LOS safety and speed coordination. The specific implementation steps are as follows:

[0007] Step S1: Obtain basic information on the attributes of the autonomous driving fleet and interactive vehicles, preliminary information on the autonomous driving fleet formation plan, road alignment design and environmental information, and weather environmental information as input information;

[0008] The basic information of the autonomous driving fleet attributes includes at least: vehicle function type and size, lead vehicle and follow vehicle system type, on-board perception sensor configuration and deployment scheme, perception function information, and minimum safe distance between vehicles or between vehicles and obstacles in the autonomous driving fleet. Among them, the lead vehicle system type includes driving automation level 0 to 5, and the follow vehicle system type includes driving automation level 3 to 5.

[0009] The basic information of the interactive vehicle attributes includes at least: vehicle function type and size, vehicle system type, configuration and deployment scheme of on-board perception sensors, perception function information, and minimum safe distance between the vehicle and obstacles. Among them, the vehicle system type includes levels 0 to 5 of driving automation.

[0010] The preliminary information for the autonomous driving fleet formation scheme includes at least: the speed range of the navigating vehicle, the range of the number of following vehicles, the range of the following vehicle's following distance, and the range of the following vehicle's following speed.

[0011] The highway alignment design and environmental information shall include at least: design speed, radius of circular curve, length of circular curve, number of lanes, lane width, width of hard shoulder, width of unpaved shoulder, superelevation, lateral clearance, and types of roadside and in-road obstacles;

[0012] The weather environment information includes at least: weather type and its corresponding weather attribute information, wherein when the weather type is rainy, the corresponding weather attribute information includes at least rainfall level and rainfall intensity; when the weather type is snowy, the corresponding weather attribute information includes at least snowfall level and rainfall intensity; when the weather type is foggy, the corresponding weather attribute information includes at least fog intensity level and visibility.

[0013] The configuration and deployment scheme of the onboard perception sensors in the basic information of the autonomous driving fleet and interactive vehicles shall include at least the following: perception sensor type, detection distance, horizontal field of view, vertical field of view, horizontal angular resolution, vertical angular resolution, and installation location.

[0014] The perception function information in the basic attribute information of the autonomous driving fleet and interactive vehicles includes at least: static and dynamic obstacle perception algorithms;

[0015] Step S2: Based on the risk scenario type, the analysis scenarios are divided into autonomous driving fleet line-of-sight failure analysis scenario and interactive vehicle line-of-sight failure analysis scenario;

[0016] Step S3: Use virtual testing methods to obtain the view distances of the lead vehicle and the interactive vehicle in the analysis scenario, and build a database linking input information and view distances.

[0017] Step S4: Determine the optimal autonomous driving fleet formation scheme based on the principles of line-of-sight safety and speed coordination.

[0018] In one embodiment of the present invention, in step S2,

[0019] The risk factors in the autonomous driving fleet line-of-sight failure analysis scenario include: the autonomous driving fleet leader vehicle experiences line-of-sight failure due to a stationary obstacle in front of its lane under lateral clearance conditions, which leads to a rear-end collision with the following vehicles in the autonomous driving fleet.

[0020] The risk factors in the interactive vehicle line-of-sight failure analysis scenario include: when the interactive vehicle is traveling in the slow lane of a highway curve section under the condition that there is a stationary obstacle in front of the vehicle's lane, the line-of-sight failure occurs.

[0021] In one embodiment of the present invention, the specific implementation process of step S3 is as follows:

[0022] Step S31: Using the acquired basic information on the attributes of the autonomous driving fleet and interactive vehicles, the preliminary information on the autonomous driving fleet formation scheme, the road alignment design and environmental information, and the weather environment information as input parameter sets, virtual analysis scenarios are automatically built in batches in the software environment using virtual testing methods.

[0023] Step S32: Conduct virtual tests based on the constructed autonomous driving fleet line-of-sight failure analysis scenario and interactive vehicle line-of-sight failure analysis scenario, respectively. The specific process is as follows:

[0024] Step S321: For the line-of-sight failure analysis scenario of autonomous driving fleet, output the farthest driving path distance between the lead vehicle and the stationary obstacle in front of the autonomous vehicle's lane that can be detected, which can be used as the line-of-sight distance for the lead vehicle.

[0025] Step S322: For the scenario of failure of line of sight for interactive vehicles, output the farthest driving path distance between the interactive vehicle located in all lanes except the slow lane on the same side and the stationary obstacle in front of the detectable lane of the vehicle, which is the line of sight that the interactive vehicle can obtain.

[0026] Step S33: Connect the input parameter set of the software with the corresponding output of the navigation vehicle or interactive vehicle to obtain the line of sight, and build a database of obtainable line of sight.

[0027] In one embodiment of the present invention, the specific implementation process of step S4 is as follows:

[0028] Step S41: Based on the safety principle of visibility distance, determine the obtainable visibility distance (S) of the lead vehicle, including a safety margin. a − Δ safe ) and demand line of sight S r They are equal, where Δ safe The minimum safe distance between vehicles in an autonomous driving fleet or between a vehicle and an obstacle; calculate the speed V of the lead vehicle. L ;

[0029] Furthermore, calculate the speed V of the nth following vehicle. F_n , n = 1, 2, …, n ∈ N+;

[0030] Based on this, the accessible line-of-sight distance (S) of vehicles interacting in lane j (excluding the slow lane) on the same side, including the safety margin, is defined. a_I_j − Δ safe ) and demand line of sight S r_I_j For equal values, j = 1, 2, …, j ∈ N+, where the larger j is, the closer the lane is to the center line of the road. Calculate the speed V of the inter-vehicle traffic. I_j ;

[0031] Step 42: For the virtual test input parameter set, select parameters that are greater than V respectively. L V F_n The calculated value of the navigator speed V L_s Following vehicle speed V F_n_s At the same time, select the corresponding number of following vehicles and the following distance between them;

[0032] Step 43: Based on the principle of speed coordination, take the V values ​​from all lanes on the same side except for the slow lane. I_j Minimum value V I_min Determine the speed difference threshold Δ based on user needs. V When (V I_min – Δ V ) ≤ |V L_s | ≤ (V I_min + Δ V When selecting V, please choose V.L_s For the optimal speed of the navigator V L_o When (V I_min – Δ V ) ≤ |V F_n_s | ≤ (V I_min + Δ V When selecting V, please choose V. F_n_s The optimal following speed V F_n_o Meanwhile, the corresponding number of following vehicles and the following distance between following vehicles are the optimal number of following vehicles and the following distance between following vehicles. Finally, the optimal autonomous driving fleet formation scheme is obtained by linking the basic information of the autonomous driving fleet and the interactive vehicle attributes, road design and environmental information, and weather environmental information.

[0033] Compared with the prior art, the present invention has the following beneficial effects:

[0034] (1) Considering the combined influence of basic information on the attributes of autonomous driving fleets and interactive vehicles, road alignment design and environmental information, and weather environment information, the optimal autonomous driving fleet formation scheme is obtained, which makes the calculation results of the technical solution disclosed in this invention more realistic and effective;

[0035] (2) It is compatible with existing autonomous driving virtual testing technology, avoiding the drawback that theoretical calculations may lead to calculation results that are too ideal than the actual situation. At the same time, it can save costs and ensure test safety more than on-site testing.

[0036] (3) The determined autonomous driving fleet formation scheme in the slow lane of the highway circular curve section can provide a theoretical basis for the optimization of autonomous driving formation methods, and make up for the fact that the existing technical solutions are only designed for a single autonomous driving level and operating conditions. Attached Figure Description

[0037] Figure 1 This is a flowchart of an autonomous driving vehicle platooning method in the slow lane of a highway circular curve section provided in an embodiment of the present invention;

[0038] Figure 2 This is a schematic diagram of the line-of-sight failure analysis scenario of an autonomous driving fleet according to an embodiment of the present invention. In the diagram: 1 is an obstacle in the road, 2 is the right line-of-sight boundary of the vehicle, 3 is the theoretical farthest obtainable line-of-sight distance, 4 is the navigator vehicle of the autonomous driving fleet, 5 is the following distance of the following vehicle, 6 is the interactive vehicle, 7 is the following vehicle of the autonomous driving fleet, 8 is the center line of the road, 9 is the lane line of the road, 10 is the right edge line of the road, and 11 is the roadside obstacle.

[0039] Figure 3This is a schematic diagram of the interactive vehicle line-of-sight failure analysis scenario divided according to an embodiment of the present invention. In the figure: 1 is an obstacle in the road, 2 is the right line-of-sight boundary of the vehicle, 3 is the theoretical farthest obtainable line-of-sight distance, 4 is the lead vehicle of the autonomous driving fleet, 6 is the interactive vehicle, 7 is the following vehicle of the autonomous driving fleet, 8 is the center line of the road, 9 is the lane line of the road, 10 is the right edge line of the road, and 11 is the roadside obstacle.

[0040] Figure 4 This is a flowchart illustrating the construction of a link input information and an obtainable line-of-sight database according to an embodiment of the present invention.

[0041] Figure 5 This is a flowchart illustrating the process of determining the optimal autonomous driving fleet formation scheme according to an embodiment of the present invention;

[0042] Figure 6 This is a simplified scenario diagram illustrating how the navigator meets the line-of-sight safety principle, as provided in this embodiment of the invention.

[0043] Figure 7 This is a simplified scenario diagram illustrating how the nth following vehicle in an autonomous driving fleet meets the line-of-sight safety principle, as provided in an embodiment of the present invention. In the diagram: L F_n , … , L F_1 Let L represent the lengths of the following vehicles, i.e., the nth, ..., the 1st vehicle. L The length of the lead vehicle;

[0044] Figure 8 This is a simplified scenario diagram illustrating how the interactive vehicle meets the line-of-sight safety principle, as provided in an embodiment of the present invention. Detailed Implementation

[0045] The technical solution of the present invention will now be described in detail with reference to the accompanying drawings.

[0046] To make the features and advantages of this patent application more apparent and understandable, specific examples are provided below for detailed explanation:

[0047] It should be noted that the following detailed descriptions are illustrative and intended to provide further explanation of this application. Unless otherwise specified, all technical and scientific terms used in this specification have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains.

[0048] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the exemplary embodiments according to this application. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Furthermore, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0049] like Figure 1 As shown, this invention proposes a method for platooning autonomous driving vehicles in the slow lane of a circular curve section of a highway, comprising the following steps:

[0050] (1) Obtain basic information on the attributes of autonomous driving fleet and interactive vehicles, preliminary information on autonomous driving fleet formation scheme, highway design and environmental information, and weather environmental information as input information;

[0051] The basic information of the autonomous driving fleet attributes includes at least: vehicle function type and size, lead vehicle and follow vehicle system type, on-board perception sensor configuration and deployment scheme, perception function information, and minimum safe distance between vehicles or between vehicles and obstacles in the autonomous driving fleet. Among them, the lead vehicle system type includes driving automation level 0 to 5, and the follow vehicle system type includes driving automation level 3 to 5.

[0052] The basic information of the interactive vehicle attributes includes at least: vehicle function type and size, vehicle system type, configuration and deployment scheme of on-board perception sensors, perception function information, and minimum safe distance between the vehicle and obstacles. Among them, the vehicle system type includes levels 0 to 5 of driving automation.

[0053] The preliminary information for the autonomous driving fleet formation scheme includes at least: the speed range of the navigating vehicle, the range of the number of following vehicles, the range of the following vehicle's following distance, and the range of the following vehicle's following speed.

[0054] The highway alignment design and environmental information shall include at least: design speed, radius of circular curve, length of circular curve, number of lanes, lane width, width of hard shoulder, width of unpaved shoulder, superelevation, lateral clearance, and types of roadside and in-road obstacles;

[0055] The weather environment information includes at least: weather type and its corresponding weather attribute information, wherein when the weather type is rainy, the corresponding weather attribute information includes at least rainfall level and rainfall intensity; when the weather type is snowy, the corresponding weather attribute information includes at least snowfall level and rainfall intensity; when the weather type is foggy, the corresponding weather attribute information includes at least fog intensity level and visibility.

[0056] The configuration and deployment scheme of the onboard perception sensors in the basic information of the autonomous driving fleet and interactive vehicles shall include at least the following: perception sensor type, detection distance, horizontal field of view, vertical field of view, horizontal angular resolution, vertical angular resolution, and installation location.

[0057] The perception function information in the basic attribute information of the autonomous driving fleet and interactive vehicles includes at least: static and dynamic obstacle perception algorithms.

[0058] Among them, basic information on the attributes of autonomous driving fleets and interactive vehicles can be obtained through on-site collection or by collecting existing research results and product information; preliminary information on autonomous driving fleet formation schemes can be obtained through user-defined parameter ranges or by referring to existing research results; highway design and environmental information can be obtained through on-site collection or by providing relevant information from road design departments; and weather and environmental information can be obtained through on-site collection or by statistical analysis of publicly available weather information data.

[0059] (2) Based on the risk scenario type, the scenarios are divided into autonomous driving fleet line-of-sight failure and interactive vehicle line-of-sight failure;

[0060] The risk factors in the autonomous driving fleet line-of-sight failure analysis scenario include: the lead vehicle in the autonomous driving fleet experiences line-of-sight failure due to a stationary obstacle in front of its lane under lateral clearance conditions, leading to a rear-end collision with the following vehicles in the autonomous driving fleet. A scenario illustration is shown below. Figure 2 As shown, 1 represents roadside obstacles, 2 represents the right-side line of sight of the vehicle, 3 represents the theoretical maximum obtainable line of sight, 4 represents the lead vehicle of the autonomous driving fleet, 5 represents the following distance of the following vehicle, 6 represents the interactive vehicle, 7 represents the following vehicle of the autonomous driving fleet, 8 represents the center line of the highway, 9 represents the lane lines of the highway, 10 represents the right side line of the highway, and 11 represents roadside obstacles.

[0061] The risk factors in the interactive vehicle line-of-sight failure analysis scenario include: when the interactive vehicle is traveling in the slow lane of a highway curve section in an autonomous driving fleet, the presence of a stationary obstacle in front of the vehicle's lane causes line-of-sight failure. A scenario illustration is shown below. Figure 3 As shown, 1 represents roadside obstacles, 2 represents the right-side line of sight of the vehicle, 3 represents the theoretical maximum obtainable line of sight, 4 represents the lead vehicle of the autonomous driving fleet, 6 represents the interactive vehicle, 7 represents the following vehicle of the autonomous driving fleet, 8 represents the center line of the highway, 9 represents the lane lines of the highway, 10 represents the right-side edge line of the highway, and 11 represents roadside obstacles.

[0062] (3) Using virtual testing methods, the obtainable line-of-sight distances of the lead vehicle and the interactive vehicle in the analysis scenario are obtained respectively, and a database linking input information and obtainable line-of-sight distances is constructed; the flowchart of this step is as follows. Figure 4 As shown;

[0063] 1) Using the acquired basic information on the attributes of autonomous driving fleets and interactive vehicles, preliminary information on autonomous driving fleet formation schemes, highway alignment design and environmental information, and weather environmental information as input parameter sets, virtual analysis scenarios are automatically built in batches in a software-in-the-loop environment using virtual testing methods.

[0064] The establishment and subsequent management of the input parameter set can rely on data management software such as SPSS, Origin, Excel, and MATLAB; the virtual testing method based on the software-in-the-loop environment can be realized by joint simulation using software such as CarSim, PreScan, and MATLAB / Simulink. The effectiveness of the independent or joint modeling of the above software has been widely verified in the field.

[0065] 2) Virtual tests were conducted based on the established autonomous driving fleet line-of-sight failure analysis scenario and interactive vehicle line-of-sight failure analysis scenario, respectively. The specific process is as follows:

[0066] ① For the scenario of line-of-sight failure analysis of autonomous driving fleets, output the farthest driving path distance between the lead vehicle and the stationary obstacle in front of the autonomous vehicle's lane that can be detected, which can be used as the line-of-sight distance for the lead vehicle;

[0067] Taking into account the configuration and deployment of onboard perception sensors, perception function information, weather and environmental information, the output distance between the leading vehicle and the stationary obstacle in front of the lane that can be detected is usually shorter than the theoretical maximum line of sight.

[0068] ② For the scenario of failure of line of sight for interactive vehicles, output the farthest travel path distance between the interactive vehicle located in all lanes except the slow lane on the same side and the stationary obstacle in front of the detectable lane of the vehicle, which can be used as the line of sight distance of the interactive vehicle.

[0069] Taking into account the configuration and deployment of onboard perception sensors, perception function information, weather and environmental information, the output of the longest driving path distance between the interactive vehicle located in all lanes on the same side except the slow lane and the stationary obstacle in front of the detectable lane is usually shorter than the theoretical longest line of sight.

[0070] 3) Connect the input parameter set of the linking software with the corresponding output of the navigation vehicle or interactive vehicle to obtain the line of sight, and build a database of obtainable line of sight.

[0071] Among these, a database capable of acquiring line-of-sight can be constructed using data management software that establishes and manages the same set of input parameters as mentioned above; virtual testing methods based on software-in-the-loop environments can rely on CarSim, PreScan, and

[0072] (4) Determine the optimal autonomous driving fleet formation scheme based on the principles of line-of-sight safety and speed coordination; the flowchart for this step is as follows. Figure 5 As shown;

[0073] 1) Based on the principle of visual distance safety, the navigator vehicle's obtainable visual distance (S) including a safety margin is determined. a − Δ safe ) and demand line of sight S rEqual, simplified scenario illustration as follows Figure 6 As shown, where Δ safe The minimum safe distance between vehicles in an autonomous driving fleet or between a vehicle and an obstacle; calculate the speed V of the lead vehicle. L ;

[0074] Furthermore, calculate the speed V of the nth (n = 1, 2, …, n ∈ N+) following vehicle. F_n Simplified scenario illustration as follows Figure 7 As shown, where L F_n , … , L F_1 Let L represent the lengths of the following vehicles, i.e., the nth, ..., the 1st vehicle. L The length of the lead vehicle;

[0075] Based on this, let the accessible sight distance (S) of the vehicles in lane j (j = 1, 2, …, j ∈ N+, excluding the slow lane on the same side) have a safety margin. a_I_j − Δ safe ) and demand line of sight S r_I_j Equal, simplified scenario illustration as follows Figure 8 As shown, calculate the speed V of the interactive vehicle. I_j ;

[0076] 2) For the virtual test input parameter set, select parameters that are greater than V respectively. L V F_n The calculated value of the navigator speed V L_s Following vehicle speed V F_n_s At the same time, select the corresponding number of following vehicles and the following distance between them;

[0077] 3) Based on the principle of speed coordination, take the V values ​​from all lanes on the same side except for the slow lane. I_j Minimum value V I_min Determine the speed difference threshold Δ based on user needs. V When (V I_min – Δ V ) ≤ |V L_s | ≤ (V I_min + Δ V When selecting V, please choose V. L_s For the optimal speed of the navigator V L_o When (V I_min – Δ V ) ≤ |V F_n_s | ≤ (V I_min + Δ V When selecting V, please choose V. F_n_s The optimal following speed V F_n_oMeanwhile, the corresponding number of following vehicles and the following distance between following vehicles are the optimal number of following vehicles and the following distance between following vehicles. Finally, the optimal autonomous driving fleet formation scheme is obtained by linking the basic information of the autonomous driving fleet and the interactive vehicle attributes, road design and environmental information, and weather environmental information.

[0078] In summary, this invention presents a method for platooning autonomous driving vehicles in the slow lane of a curved highway section. This method utilizes basic information about the autonomous driving vehicle and its interacting vehicles, preliminary information on the platooning scheme, highway alignment and environmental information, and weather information as input. It rationally categorizes and analyzes scenarios based on risk type, employs virtual testing and data management methods suitable for each type of input to construct an accessible line-of-sight database, and further determines the optimal platooning scheme based on line-of-sight safety and speed coordination principles. This provides a reliable technical means for the actual safe operation of autonomous driving vehicles. The method achieves more realistic and effective calculation results, avoiding the drawback of theoretical calculations leading to overly idealized results compared to reality. It also saves costs and ensures testing safety compared to on-site testing, overcoming the limitations of existing technologies that only design operating conditions for a single level of autonomous driving.

[0079] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0080] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0081] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0082] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0083] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention in any other way. Any person skilled in the art may make changes or modifications to the above-disclosed technical content to create equivalent embodiments. However, any simple modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the protection scope of the present invention.

[0084] This patent application is not limited to the above-described preferred embodiment. Anyone can derive other forms of road traffic node segment driving adaptability evaluation method for autonomous driving based on the teachings of this patent. All equivalent changes and modifications made within the scope of this patent application shall fall within the scope of this patent.

Claims

1. A method for platooning an autonomous driving vehicle fleet in the slow lane of a circular curve section of a highway, characterized in that, The system acquires basic attribute information of the autonomous driving fleet and interactive vehicles, preliminary information on the autonomous driving fleet formation scheme, highway alignment design and environmental information, and weather information as input information. Based on the risk scenario type, the analysis scenarios are divided into autonomous driving fleet line-of-sight (LOS) failure analysis scenarios and interactive vehicle LOS failure analysis scenarios. Virtual testing methods are used to obtain the available LOS of the lead vehicle and interactive vehicles in the analysis scenarios. A database linking the input information and available LOS is constructed. Furthermore, the optimal autonomous driving fleet formation scheme is determined based on the principles of LOS safety and speed coordination. The specific implementation steps are as follows: Step S1: Obtain basic information on the attributes of the autonomous driving fleet and interactive vehicles, preliminary information on the autonomous driving fleet formation plan, road alignment design and environmental information, and weather environmental information as input information; The basic information of the autonomous driving fleet attributes includes at least: vehicle function type and size, lead vehicle and follow vehicle system type, on-board perception sensor configuration and deployment scheme, perception function information, and minimum safe distance between vehicles or between vehicles and obstacles in the autonomous driving fleet. Among them, the lead vehicle system type includes driving automation level 0 to 5, and the follow vehicle system type includes driving automation level 3 to 5. The basic information of the interactive vehicle attributes includes at least: vehicle function type and size, vehicle system type, configuration and deployment scheme of on-board perception sensors, perception function information, and minimum safe distance between the vehicle and obstacles. Among them, the vehicle system type includes levels 0 to 5 of driving automation. The preliminary information for the autonomous driving fleet formation scheme includes at least: the speed range of the navigating vehicle, the range of the number of following vehicles, the range of the following vehicle's following distance, and the range of the following vehicle's following speed. The highway alignment design and environmental information shall include at least: design speed, radius of circular curve, length of circular curve, number of lanes, lane width, width of hard shoulder, width of unpaved shoulder, superelevation, lateral clearance, and types of roadside and in-road obstacles; The weather environment information includes at least: weather type and its corresponding weather attribute information, wherein when the weather type is rainy, the corresponding weather attribute information includes at least rainfall level and rainfall intensity; when the weather type is snowy, the corresponding weather attribute information includes at least snowfall level and snowfall intensity; when the weather type is foggy, the corresponding weather attribute information includes at least fog intensity level and visibility. The configuration and deployment scheme of the onboard perception sensors in the basic information of the autonomous driving fleet and interactive vehicles shall include at least the following: perception sensor type, detection distance, horizontal field of view, vertical field of view, horizontal angular resolution, vertical angular resolution, and installation location. The perception function information in the basic attribute information of the autonomous driving fleet and interactive vehicles includes at least: static and dynamic obstacle perception algorithms; Step S2: Based on the risk scenario type, the analysis scenarios are divided into autonomous driving fleet line-of-sight failure analysis scenario and interactive vehicle line-of-sight failure analysis scenario; Step S3: Use virtual testing methods to obtain the view distances of the lead vehicle and the interactive vehicle in the analysis scenario, and build a database linking input information and view distances. Step S4: Determine the optimal autonomous driving fleet formation scheme based on the principles of line-of-sight safety and speed coordination.

2. The method for platooning an autonomous driving vehicle fleet in the slow lane of a highway circular curve section according to claim 1, characterized in that, In step S2, The risk factors in the autonomous driving fleet line-of-sight failure analysis scenario include: the autonomous driving fleet leader vehicle experiences line-of-sight failure due to a stationary obstacle in front of its lane under lateral clearance conditions, which leads to a rear-end collision with the following vehicles in the autonomous driving fleet. The risk factors in the interactive vehicle line-of-sight failure analysis scenario include: when the interactive vehicle is traveling in the slow lane of a highway curve section under the condition that there is a stationary obstacle in front of the vehicle's lane, the line-of-sight failure occurs.

3. The method for platooning an autonomous driving vehicle fleet in the slow lane of a highway circular curve section according to claim 1, characterized in that, The specific implementation process of step S3 is as follows: Step S31: Using the acquired basic information on the attributes of the autonomous driving fleet and interactive vehicles, the preliminary information on the autonomous driving fleet formation scheme, the road alignment design and environmental information, and the weather environment information as input parameter sets, virtual analysis scenarios are automatically built in batches in the software environment using virtual testing methods. Step S32: Conduct virtual tests based on the constructed autonomous driving fleet line-of-sight failure analysis scenario and interactive vehicle line-of-sight failure analysis scenario, respectively. The specific process is as follows: Step S321: For the line-of-sight failure analysis scenario of autonomous driving fleet, output the farthest driving path distance between the lead vehicle and the stationary obstacle in front of the autonomous vehicle's lane that can be detected, which can be used as the line-of-sight distance for the lead vehicle. Step S322: For the scenario of failure of line of sight for interactive vehicles, output the farthest driving path distance between the interactive vehicle located in all lanes except the slow lane on the same side and the stationary obstacle in front of the detectable lane of the vehicle, which is the line of sight that the interactive vehicle can obtain. Step S33: Connect the input parameter set of the software with the corresponding output of the navigation vehicle or interactive vehicle to obtain the line of sight, and build a database of obtainable line of sight.

Images