Motorcade driving control method and device and storage medium

Abstract

The invention relates to the technical field of automatic driving, and discloses a motorcade driving control method and device and a storage medium. The method comprises the following steps: acquiring real-time road surface unevenness characteristic data and vehicle dynamic characteristic data of vehicles in a motorcade; according to the road surface unevenness characteristic data and the vehicle dynamic characteristic data, dynamic driving risk analysis and area division based on a driving risk analysis result are carried out on the road surface on which the vehicle runs currently, so that a current passable area of the vehicle is determined; performing path planning on the vehicle based on the passable area to obtain a plurality of candidate paths; performing driving safety cost analysis on the candidate path, and selecting a corresponding target path from the candidate path according to a safety cost analysis result; and dynamically configuring the running speed of the vehicle according to the real-time vehicle attachment coefficient of the vehicle, so that the vehicle runs along the target path at the configured running speed. According to the embodiment of the invention, the probability of instability of the speed of the vehicles in the motorcade can be reduced, and the driving safety of the motorcade is improved.

Description

Technical Field

[0001] The present application relates to the field of autonomous driving technology, and in particular to a fleet driving control method, device, and storage medium. Background Art

[0002] Currently, research on autonomous driving in multi-vehicle convoys is primarily based on ideal road conditions, such as those with a constant road adhesion coefficient. However, in actual convoy driving, road conditions such as road adhesion coefficient and road roughness may change after some vehicles in the convoy have passed through, causing vehicle speed instability and compromising convoy safety. Summary of the Invention

[0003] The purpose of this application is to provide a fleet driving control method, device and storage medium, aiming to reduce the probability of speed instability of vehicles in the fleet and improve the safety of fleet driving.

[0004] The present invention provides a method for controlling a vehicle fleet, including: Obtain real-time road roughness characteristic data and vehicle dynamic characteristic data of vehicles in the fleet; Performing a dynamic driving risk analysis on the road surface currently being driven by the vehicle based on the road surface roughness characteristic data and the vehicle dynamics characteristic data, and dividing the road surface into regions based on the driving risk analysis results, so as to determine a current passable region for the vehicle; Performing path planning for the vehicle based on the traversable area to obtain a plurality of candidate paths; Performing a driving safety cost analysis on the candidate paths, and selecting a corresponding target path from the candidate paths based on the safety cost analysis result; The driving speed of the vehicle is dynamically configured according to the real-time vehicle adhesion coefficient of the vehicle, so that the vehicle travels along the target path at the configured driving speed.

[0005] In some embodiments, the performing of a dynamic driving risk analysis on the road surface on which the vehicle is currently traveling based on the road surface roughness characteristic data and the vehicle dynamics characteristic data and the region division based on the driving risk analysis results includes: extracting risk feature data from both the road surface roughness feature data and the vehicle dynamics feature data to obtain multi-dimensional risk information data; Determining a current initial traversable area for the vehicle based on the multi-dimensional risk information data; A collision risk analysis and a communication interruption risk analysis are performed on the initial traversable area, and areas with collision risks and communication interruption risks are eliminated to determine the current traversable area of the vehicle.

[0006] In some embodiments, extracting risk feature data from both the road surface roughness feature data and the vehicle dynamics feature data includes: Constructing a road surface roughness risk field for the road surface on which the vehicle is currently traveling based on the road surface roughness characteristic data and the vehicle dynamics characteristic data; extracting pavement risk characteristic data and pavement evolution characteristic data from the pavement roughness risk field; The road surface risk characteristic data, the road surface evolution characteristic data and the driving stability performance characteristic data in the vehicle dynamics characteristic data are spliced together to obtain the multi-dimensional risk information data.

[0007] In some embodiments, performing collision risk analysis and communication interruption risk analysis on the initial traversable area includes: Calculating a global communication safety factor and a global collision safety factor of the vehicle relative to other vehicles based on real-time vehicle distance data between the vehicle and other vehicles, and obtaining a collision risk analysis result and a communication interruption risk analysis result; Areas with collision risks and communication interruption risks are eliminated according to the collision risk analysis result and the communication interruption risk analysis result to determine the current passable area of the vehicle.

[0008] In some embodiments, performing a driving safety cost analysis on the candidate paths and selecting a corresponding target path from the candidate paths based on the safety cost analysis result includes: Performing a driving safety cost analysis on candidate paths of the pilot vehicle, and determining a target path of the pilot vehicle based on the safety cost analysis results of the candidate paths of the pilot vehicle; The path points of the following vehicles are determined according to the target path of the lead vehicle and preset formation keeping parameters to determine the target path of the following vehicles.

[0009] In some embodiments, dynamically configuring the vehicle's travel speed based on the vehicle's real-time vehicle adhesion coefficient includes: Obtaining a front vehicle adhesion coefficient and a bottom vehicle adhesion coefficient of the vehicle; Calculating a rate of change of adhesion coefficients between the vehicles based on a front adhesion coefficient of the rear vehicle and an under-vehicle adhesion coefficient of the front vehicle; The driving speed of the vehicle is configured according to the adhesion coefficient change rate.

[0010] In some embodiments, configuring the vehicle's driving speed based on the adhesion coefficient change rate includes: When the adhesion coefficient change rate is not less than a preset adhesion coefficient threshold change rate, calculating a speed configuration amount of the following vehicle according to the adhesion coefficient change rate; Performing a weighted sum operation on the speed configuration amounts of each following vehicle to obtain a total speed configuration amount; Calculating a speed configuration amount of the pilot vehicle according to the total speed configuration amount; The driving speed of the following vehicle is dynamically configured according to the speed configuration amount of the following vehicle, and the driving speed of the leading vehicle is dynamically configured according to the speed configuration amount of the leading vehicle.

[0011] In some embodiments, configuring the vehicle's driving speed based on the adhesion coefficient change rate further includes: generating a reference acceleration of the following vehicle based on the driving speed of the following vehicle before and after deployment; The reference acceleration of the following vehicle is compensated according to the distance error data between the following vehicle and the preceding vehicle to obtain the actual acceleration of the following vehicle.

[0012] An embodiment of the present application further provides an electronic device, comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the above method when executing the computer program.

[0013] An embodiment of the present application further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and the computer program implements the above method when executed by a processor.

[0014] The beneficial effects of the present application are as follows: based on real-time road surface roughness characteristic data and vehicle dynamics characteristic data of vehicles in the fleet, a dynamic driving risk analysis is performed on the road surface on which the vehicle is currently traveling, thereby determining the vehicle's current passable area based on the driving risk analysis results; after generating candidate paths based on the vehicle's current passable area and determining the corresponding target path through driving safety cost analysis, the vehicle's driving speed is dynamically configured according to the vehicle's real-time vehicle adhesion coefficient, and the vehicle is controlled to travel along the target path at the configured driving speed, thereby realizing automatic control of the fleet's driving. Since the vehicle's current passable area is determined by dynamic driving risk analysis based on real-time road surface roughness characteristic data and vehicle dynamics characteristic data, and the dynamic changes of road surface roughness characteristic data and vehicle dynamics characteristic data are taken into consideration, it adapts to the dynamic planning of the passable area for each vehicle in the fleet, and improves the rationality of the driving path planning of each vehicle in the fleet. After determining the target path of each vehicle, the vehicle's driving speed is dynamically configured according to the vehicle's real-time vehicle adhesion coefficient to ensure that the driving speed of each vehicle adapts to the changes in the vehicle adhesion coefficient, avoid the situation where the vehicle driving is unstable due to sudden changes in the adhesion coefficient, ensure the movement stability of the fleet under complex road conditions, and improve the safety of the fleet's driving. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1This is a diagram of the application environment of the fleet driving control method provided in an embodiment of the present application.

[0016] Figure 2 This is an optional flowchart of the fleet driving control method provided in an embodiment of the present application.

[0017] Figure 3 It is a flowchart of the specific method of step S202 provided in an embodiment of the present application.

[0018] Figure 4 It is a flowchart of the specific method of step S204 provided in an embodiment of the present application.

[0019] Figure 5 It is a flowchart of the specific method of step S205 provided in an embodiment of the present application.

[0020] Figure 6 This is a schematic diagram of the hardware structure of the electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0021] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0022] It should be noted that although the device schematics illustrate functional module divisions and the flowcharts illustrate logical sequences, in certain circumstances, the steps illustrated may be performed in a sequence that differs from the module divisions in the device or the sequence in the flowcharts. Terms such as "first" and "second" in the specification, claims, and drawings are used to distinguish similar items and are not intended to describe a specific sequence or precedence.

[0023] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this application pertains. The terms used herein are for the purpose of describing the embodiments of this application only and are not intended to limit this application.

[0024] The fleet driving control method provided in the embodiments of the present application can be executed by a computer device, which can be a terminal device or a server. Terminal devices include, but are not limited to, mobile phones, computers, intelligent voice interaction devices, smart home appliances, vehicle-mounted terminals, aircraft, etc. The server can be a standalone physical server, a server cluster composed of multiple physical servers, a distributed system, or a cloud server.

[0025] In addition, the information, data, and signals involved in the embodiments of this application are authorized by the relevant objects or fully authorized by all parties, and the collection, use, and processing of relevant data comply with the relevant laws, regulations, and standards of relevant countries and regions.

[0026] To facilitate understanding of the fleet driving control method provided in the embodiments of the present application, the following uses an example in which the execution subject of the fleet driving control method is an on-board terminal of a pilot vehicle in a fleet to exemplify the application scenario of the fleet driving control method.

[0027] Figure 1 This is an application environment diagram of the fleet driving control method provided in the embodiment of the present application. Figure 1 The convoy driving control method is applied to a convoy driving control system. The convoy driving control system includes a first vehicle-mounted terminal 110 and multiple second vehicle-mounted terminals 120. The first vehicle-mounted terminal 110 and the second vehicle-mounted terminals 120 are connected via a network. The first vehicle-mounted terminal 110 is the vehicle-mounted terminal of the lead vehicle in the convoy, and the second vehicle-mounted terminal 120 is the vehicle-mounted terminal of the following vehicle in the convoy. The second vehicle-mounted terminal 120 is configured to transmit real-time road surface roughness characteristic data and vehicle dynamic characteristic data of the following vehicles to the first vehicle-mounted terminal 110. The first vehicle-mounted terminal 110 is used to obtain real-time road surface roughness characteristic data and vehicle dynamics characteristic data of vehicles in the fleet, and perform dynamic driving risk analysis on the road surface on which the vehicle is currently traveling based on the road surface roughness characteristic data and vehicle dynamics characteristic data, and divide the area based on the driving risk analysis results to determine the vehicle's current passable area, and plan the vehicle path based on the passable area to obtain several candidate paths, and perform driving safety cost analysis on the candidate paths. According to the safety cost analysis results, a corresponding target path is selected from the candidate paths, and the real-time driving speed of the vehicle is dynamically configured according to the real-time vehicle adhesion coefficient of the vehicle, so that the vehicle travels along the target path at the real-time driving speed.

[0028] Figure 2 This is a flow chart of a fleet driving control method provided by an embodiment of the present application. Figure 2 In some embodiments, the method includes but is not limited to steps S201 to S205.

[0029] Step S201 , obtaining real-time road surface roughness characteristic data and vehicle dynamics characteristic data of vehicles in the fleet.

[0030] Road surface roughness characteristic data refers to dynamic parameters that reflect the road surface's geometry and friction characteristics. Specifically, this data can be generated using LiDAR point cloud data combined with vibration spectrum data collected by an inertial measurement unit (IMU). It is used to assess the traffic risk level in different areas. In this embodiment, road surface roughness characteristic data includes road surface texture, humidity, material, and temperature characteristics, as well as regional range data for both concave and convex areas, regional characteristics, and regional risk data.

[0031] Vehicle dynamics data includes parameters such as suspension system response characteristics and tire slip. Specifically, onboard sensors can acquire real-time data such as steering torque and longitudinal acceleration to construct a vehicle motion model. The traversable area is delineated based on spatial segmentation of multidimensional risk data. Specifically, a rasterized map combined with a probabilistic risk assessment algorithm can be used to dynamically exclude areas with collision or communication failure risks. In this embodiment, the vehicle dynamics data includes vehicle model code data, ground clearance data, vehicle mass data, tire contact patch data, wheelbase data, and vehicle adhesion coefficient.

[0032] Step S202 : performing a dynamic driving risk analysis on the road surface on which the vehicle is currently traveling based on the road surface roughness characteristic data and the vehicle dynamics characteristic data, and dividing the area based on the driving risk analysis results to determine the current passable area for the vehicle.

[0033] Performing a dynamic driving risk analysis on the road surface on which the vehicle is currently traveling based on road roughness characteristic data and vehicle dynamics characteristic data refers to the process of performing a driving risk analysis on each vehicle in the fleet separately.

[0034] Specifically, after obtaining road surface roughness characteristic data and vehicle dynamics characteristic data, a dynamic driving risk analysis is performed on the road surface currently traveled by each vehicle in the fleet based on the vehicle dynamics characteristic data and the road surface roughness characteristic data of the current road surface and the road surface to be traveled, to determine the driving risk of each vehicle, thereby obtaining corresponding driving risk analysis results. Based on the obtained driving risk analysis results, the current road surface and the road surface to be traveled by each vehicle are then divided into regions, and the regions that meet the driving risk conditions are classified as the current traversable areas of the vehicle. More specifically, risk characteristic data can be extracted from both the road surface roughness characteristic data and the vehicle dynamics characteristic data, and then a coupled analysis is performed on the extracted risk characteristic data to calculate the comprehensive driving risk of each area on the road surface. Based on the comprehensive driving risk, a real-time updated traversable area map is generated to determine the current traversable area of the vehicle.

[0035] Step S203 : planning a path for the vehicle based on the traversable area to obtain a number of candidate paths.

[0036] After determining the current passable area of the vehicle, the corresponding path planning algorithm is used to plan the path of the vehicle within the current passable area to generate several candidate paths. When driving along the candidate paths, the lead vehicle is driven within the passable area to reach the destination of the convoy, and the following vehicle is driven within the passable area to follow the lead vehicle.

[0037] Step S204 : performing a driving safety cost analysis on the candidate paths, and selecting a corresponding target path from the candidate paths based on the safety cost analysis result.

[0038] After obtaining the candidate paths for each vehicle, a driving safety cost analysis is performed on each candidate path of the vehicle to select a candidate path that meets the safety cost conditions as the target path of the vehicle.

[0039] Specifically, the system traverses candidate routes for each vehicle, evaluates the driving safety cost and driving stability performance cost of each candidate route, and selects the candidate route with the lowest cost, calculated by combining the driving safety cost and driving stability performance cost, as the target route for the vehicle. More specifically, each candidate route for the vehicle can be input into a pre-set driving safety cost analysis model to obtain a cost that combines the driving safety cost and driving stability performance cost, and then selects the candidate route with the lowest cost as the target route for the vehicle.

[0040] Step S205 , dynamically configuring the vehicle's travel speed based on the vehicle's real-time vehicle adhesion coefficient, so that the vehicle travels along the target path at the configured travel speed.

[0041] After determining the corresponding target path for each vehicle, each vehicle is controlled to travel along the corresponding target path. During the driving process, the vehicle's driving speed is dynamically configured according to the vehicle's real-time vehicle adhesion coefficient, so that the vehicle travels along the target path at the configured speed to ensure smooth driving of each vehicle.

[0042] Specifically, after determining the target path for each vehicle, each vehicle is controlled to travel along the target path. During travel, the vehicle's vehicle adhesion coefficient and driving speed are acquired in real time. When the vehicle adhesion coefficient meets the speed configuration conditions, the vehicle's driving speed is dynamically configured based on the real-time vehicle adhesion coefficient, causing the vehicle to travel along the target path at the configured driving speed. More specifically, the vehicle adhesion coefficient and driving speed may be input into a preset driving speed configuration model to dynamically configure the vehicle's driving speed based on the real-time vehicle adhesion coefficient, generate a configured driving speed, and control the vehicle to travel along the target path at the configured driving speed.

[0043] The fleet driving control method provided in the embodiments of the present application performs a dynamic driving risk analysis on the road surface on which the vehicles are currently traveling based on real-time road surface roughness characteristic data and vehicle dynamics characteristic data of the vehicles in the fleet, thereby determining the vehicle's current passable area based on the driving risk analysis results. After generating candidate paths based on the vehicle's current passable area and determining the corresponding target path through driving safety cost analysis, the vehicle's driving speed is dynamically configured based on the vehicle's real-time vehicle adhesion coefficient, and the vehicle is controlled to travel along the target path at the configured driving speed, thereby realizing automatic control of the fleet's driving. Since the vehicle's current passable area is determined by dynamic driving risk analysis based on real-time road surface roughness characteristic data and vehicle dynamics characteristic data, and the dynamic changes of road surface roughness characteristic data and vehicle dynamics characteristic data are taken into consideration, it adapts to the dynamic planning of the passable area for each vehicle in the fleet, and improves the rationality of the driving path planning of each vehicle in the fleet. After determining the target path of each vehicle, the vehicle's driving speed is dynamically configured according to the vehicle's real-time vehicle adhesion coefficient to ensure that the driving speed of each vehicle adapts to the changes in the vehicle adhesion coefficient, avoid the situation where the vehicle driving is unstable due to sudden changes in the adhesion coefficient, ensure the movement stability of the fleet under complex road conditions, and improve the safety of the fleet's driving.

[0044] Figure 3 This is a flowchart of the specific method of step S202 provided in the embodiment of the present application. Figure 3 In some embodiments, the method includes but is not limited to steps S301 to S303.

[0045] Step S301 : extract risk feature data from both road surface roughness feature data and vehicle dynamics feature data to obtain multi-dimensional risk information data.

[0046] Multidimensional risk information data refers to a comprehensive evaluation data set formed by integrating multiple dimensional indicators from both road surface roughness characteristic data and vehicle dynamics characteristic data. Specifically, this can be achieved by using a data fusion algorithm to correlate and analyze the road surface roughness risk field, driving stability performance characteristics, and road surface evolution characteristics, in order to comprehensively reflect potential safety hazards in the driving environment.

[0047] In some embodiments, step S301 specifically includes: constructing a road surface roughness risk field for the road surface on which the vehicle is currently traveling based on road surface roughness characteristic data and vehicle dynamics characteristic data; extracting road surface risk characteristic data and road surface evolution characteristic data from the road surface roughness risk field; and splicing driving stability performance characteristic data from the road surface risk characteristic data, road surface evolution characteristic data, and vehicle dynamics characteristic data to obtain multidimensional risk information data.

[0048] The expression of road roughness risk field is: , , , , Where, is the road roughness characteristic risk data, is the risk data of general uneven road surface characteristics, are the coordinates of discrete points on the road surface, Stable performance risk factor, Speed risk factor, For reference ground clearance data, is the minimum ground clearance data of the vehicle, is the speed sensitivity coefficient, is the vehicle's speed, is the center coordinate of the risk feature, is the characteristic risk peak coefficient, 、 、 and They are the characteristic risk random distribution factors, respectively representing the characteristic risk along Axis positive direction, Negative axis direction, Axis positive direction and The distribution range in the negative direction of the axis, is the random decline coefficient of characteristic risk.

[0049] After constructing the road surface roughness risk field, the road surface risk characteristic data and road surface evolution characteristic data in the road surface roughness risk field are extracted, and then the road surface risk characteristic data, road surface evolution characteristic data and driving stability characteristic data in the vehicle dynamics characteristic data are spliced to obtain multi-dimensional risk information data. The road surface risk characteristic data includes characteristic risk peak coefficient , risk peak , risk characteristic center coordinates , Feature Range and the characteristic risk random decline coefficient The driving stability performance characteristic data in the vehicle dynamics characteristic data includes the vehicle state matrix , including vehicle model code, ground clearance , vehicle quality , tire contact area , wheelbase and peak adhesion coefficient The road surface evolution characteristic data includes the number of vehicles passing through each road surface, vehicle model and vehicle load, and evolution trend coefficient. , geological softening factor and wear correction .

[0050] Step S302: determining the current initial passable area of the vehicle based on the multi-dimensional risk information data.

[0051] The initial drivable area refers to the vehicle drivable range delineated based on preliminary risk screening. It can be achieved through a spatial grid division method combined with a risk probability distribution model, and is used to provide a basic area for subsequent refined risk analysis.

[0052] Determining the vehicle's initial traversable area is accomplished by analyzing multidimensional risk information data for each area of the road surface that each vehicle in the fleet will travel on. Specifically, a road surface roughness risk field is constructed using road surface roughness characteristic data and vehicle dynamics characteristic data to extract the corresponding multidimensional risk information data. This multidimensional risk information data is then analyzed using a spatial gridding method combined with a risk probability distribution model to determine the risk index for each area of the road surface that the vehicle will travel on. Areas whose risk index meets the pre-set risk criteria are selected as the vehicle's initial traversable area.

[0053] Step S303 : performing collision risk analysis and communication interruption risk analysis on the initial passable area, eliminating areas with collision risk and communication interruption risk, so as to determine the current passable area for the vehicle.

[0054] Collision risk analysis refers to evaluating whether the dynamic distance and relative speed between vehicles in a fleet meet safety thresholds. This can be achieved by combining real-time vehicle distance monitoring and speed prediction algorithms with a safety distance model to identify potential areas where collisions may occur.

[0055] Communication interruption risk analysis refers to determining whether the communication links between vehicles in a fleet are failing due to excessive vehicle distances. This can be achieved by combining real-time vehicle distance monitoring and channel quality assessment with a communication coverage range prediction algorithm to eliminate areas where collaborative control fails due to communication failures.

[0056] In some embodiments, step S303 specifically includes: calculating the global communication safety factor and global collision safety factor of the vehicle relative to other vehicles based on the real-time vehicle distance data between the vehicle and other vehicles, and obtaining collision risk analysis results and communication interruption risk analysis results; based on the collision risk analysis results and communication interruption risk analysis results, eliminating areas with collision risks and communication interruption risks to determine the current passable area of the vehicle.

[0057] The global communication safety factor is a quantitative indicator generated based on the correlation between the real-time distance between vehicles and the communication signal strength. Specifically, it can be calculated using the ratio of the distance between vehicles to a preset communication distance threshold combined with a signal attenuation model. It is used to assess the stability of the inter-vehicle communication link. The global collision safety factor is a dynamic risk indicator generated based on the relative speed and real-time distance between vehicles. Specifically, it can be calculated by combining the difference between the distance between vehicles and the braking distance of the vehicles with the rate of change of relative speed. It is used to predict the probability of collision between vehicles.

[0058] Specifically, when vehicles are platooning, sensors mounted on the vehicle bodies collect real-time three-dimensional position data from adjacent vehicles to calculate longitudinal and lateral safety distances. For communication interruption risk analysis, a communication signal attenuation model is established based on vehicle-to-vehicle distance data. When the distance exceeds a preset communication stability threshold, a communication interruption risk is determined. For collision risk analysis, the safety distance threshold is dynamically adjusted based on current vehicle speed and road adhesion conditions. A collision warning is triggered when the actual distance falls below the dynamic safety threshold. By simultaneously performing these two risk assessments, the system simultaneously eliminates dangerous areas that could lead to communication failure or physical collision. For example, if the distance between the rear and front vehicles decreases to 2 meters, the system automatically marks the area as a collision risk area. If the communication signal strength between the rear and front vehicles drops below -90dBm, the system automatically marks the area as a communication interruption risk area.

[0059] The calculation formula of the global collision safety factor is: , , in, is the global collision safety factor of vehicle i, is the center coordinate of vehicle i, is the local collision safety factor of vehicle i, is the distance data between vehicle i and vehicle k, 、 and are the collision distance boundaries, is the critical collision distance data, To obtain the obstacle avoidance buffer distance data, is the total number of other vehicles.

[0060] The calculation formula of the global communication safety factor is: , , in, is the global communication safety factor of vehicle i, is the local communication safety factor of vehicle i, and are the communication vehicle distance boundaries, and R is the maximum communication radius of vehicle i.

[0061] Based on the above-mentioned calculation formulas for the global collision safety factor and the global communication safety factor, as well as the predefined collision distance boundaries corresponding to areas without collision risk and the communication distance boundaries corresponding to areas without communication interruption risk, a comprehensive analysis is performed on the collision risk and communication interruption risk of each vehicle, thereby eliminating areas with collision risk and communication interruption risk, and using the remaining area in the initial passable area as the vehicle's current passable area.

[0062] Figure 4 This is a flowchart of the specific method of step S204 provided in the embodiment of the present application. Figure 4 In some embodiments, the method includes but is not limited to steps S401 to S402.

[0063] Step S401 : performing a driving safety cost analysis on the candidate paths of the pilot vehicle, and determining a target path of the pilot vehicle based on the safety cost analysis results of the candidate paths of the pilot vehicle.

[0064] Driving safety cost analysis involves calculating the safety factor of a route by quantifying and evaluating potential risk factors along the route. This can be achieved using a multi-dimensional weighted superposition model. For example, the rate of change of road adhesion coefficient, obstacle density, and communication signal strength can be used as evaluation parameters.

[0065] The target path for the pilot vehicle is the candidate path with the lowest risk value after screening through driving safety cost analysis. This can be achieved by using a cost function minimum comparison algorithm, such as a dynamic programming algorithm to traverse the cost calculation results of all candidate paths.

[0066] The expression for the driving safety cost analysis of the candidate paths of the pilot vehicle is: , , , , in, is the total cost index of the candidate path of the pilot vehicle, Safety cost index of the candidate path of the pilot vehicle, is the driving cost index of the candidate path of the pilot vehicle, and are the cost weights, , is the oth path point of the candidate path, o∈[1, ], is the total number of path points of the candidate path, is the safety cost index of the oth path point of the candidate path, is the road surface roughness characteristic risk data of the oth path point of the candidate path, is the minimum ground clearance data of the vehicle, is the curvature of the oth path point of the candidate path.

[0067] Step S402 : determining the path points of the following vehicles based on the target path of the lead vehicle and the preset formation keeping parameters to determine the target path of the following vehicles.

[0068] Formation-keeping parameters are constraints that maintain the spacing and relative positions between vehicles within a platoon. These can be achieved using preset lateral offset thresholds and longitudinal following distance ranges. For example, the lateral offset between the following vehicle and the lead vehicle can be set to no more than 0.5 meters, while the longitudinal spacing can be maintained between 3 and 5 meters.

[0069] After obtaining the target path for the lead vehicle, a formation target point is set. Based on the preset formation maintenance parameters, a path point for the following vehicle is generated within the current traversable area of the following vehicle to determine the target path for the following vehicle. This is achieved by constructing a formation control potential field and gravity to provide gravity for the following vehicle, pulling it to the path point. The expressions for the formation control potential field and gravity are: , , in, is the gravitational potential field between the following vehicle and the formation target point, is the gravitational potential field constant generated by the path point of the following vehicle on the following vehicle, is the Euclidean distance between the following car’s current position and the following car’s path point, To control the potential field gravity for the formation, To obtain the derivative of the formation control potential field.

[0070] Figure 5 This is a flowchart of the specific method of step S205 provided in the embodiment of the present application. Figure 5 In some embodiments, the method includes but is not limited to steps S501 to S503.

[0071] Step S501: Obtain the front adhesion coefficient and the bottom adhesion coefficient of the vehicle.

[0072] In step S502 , the adhesion coefficient change rate between the vehicles is calculated based on the front adhesion coefficient of the rear vehicle and the under-vehicle adhesion coefficient of the front vehicle.

[0073] Step S503: configuring the vehicle's driving speed according to the adhesion coefficient change rate.

[0074] The front adhesion coefficient refers to the friction coefficient of the contact area between the front wheels of the vehicle and the road surface. Specifically, it can be obtained by collecting the contact force data between the tire and the ground in real time through the force sensor installed on the front wheel and combining it with the wheel slip rate model to calculate it. It is used to characterize the adhesion ability of the front wheels of the vehicle to the road surface during driving. The under-vehicle adhesion coefficient refers to the road friction coefficient of the corresponding area below the vehicle chassis. Specifically, it can be estimated by scanning the road texture information through the lidar or visual sensor arranged at the bottom of the vehicle and combining it with a pre-trained road friction coefficient prediction model. It is used to reflect the real-time adhesion status of the road surface behind the vehicle. The adhesion coefficient change rate refers to the difference in adhesion coefficient of the road area before and after the vehicle passes. Specifically, it can be calculated by using the time series data of the adhesion coefficients of adjacent vehicles through differential operations and sliding window averaging. It is used to quantify the impact of dynamic changes in the road adhesion status on the overall stability of the fleet.

[0075] After obtaining the front and underbody adhesion coefficients of each vehicle, they are constructed into corresponding matrices, resulting in the front and underbody adhesion matrices. The inter-vehicle adhesion coefficient change rate is then calculated based on the front and underbody adhesion matrices. When there is a significant difference between the front and underbody adhesion coefficients of the trailing vehicle and the preceding vehicle, the vehicle's speed is adjusted based on the adhesion coefficient change rate to mitigate potential risks in the platoon's longitudinal dynamics. For example, if the leading vehicle's underbody adhesion coefficient drops sharply due to driving through a flooded area, while the front wheels of the following vehicle remain on dry road, the adhesion coefficient change rate will increase dramatically. At this point, the system determines whether to trigger the speed adjustment mechanism based on the preset adhesion coefficient change rate threshold. By weighting the speed settings of each following vehicle and inferring the speed limit of the lead vehicle, the system ultimately achieves coordinated speed control of the entire platoon.

[0076] In some embodiments, step S503 specifically includes: when the adhesion coefficient change rate is not less than a preset adhesion coefficient threshold change rate, calculating the speed configuration amount of the following vehicle based on the adhesion coefficient change rate; performing a weighted sum operation on the speed configuration amounts of each following vehicle to obtain a total speed configuration amount; calculating the speed configuration amount of the leading vehicle based on the total speed configuration amount; dynamically configuring the driving speed of the following vehicle based on the speed configuration amount of the following vehicle, and dynamically configuring the driving speed of the leading vehicle based on the speed configuration amount of the leading vehicle.

[0077] The calculation formula for the speed configuration of the following vehicle is: , in, is the speed configuration of the following vehicle, is the global scale factor, is the maximum permissible speed of the convoy, is the total number of vehicles in the fleet, is the maximum expected adhesion coefficient difference, is a symbolic vector, is the weight coefficient, is the adhesion coefficient threshold change rate.

[0078] After obtaining the speed configuration of the following vehicle, the following vehicle is controlled to adjust from its current speed to the speed after adding the speed configuration of the following vehicle, and the configured speed of the following vehicle is obtained. In order to smoothly configure the speed of the following vehicle, the acceleration of the following vehicle is limited. The calculation formula is: , , in, is the maximum acceleration of the following vehicle, To configure the speed of the following vehicle, To configure the speed of the vehicle following you, is the maximum permissible acceleration of the team, Configure duration for speed.

[0079] After configuring the speed of the following vehicle, the speed of the following vehicle is dynamically configured according to the speed configuration of the following vehicle, and the speed of the leading vehicle is dynamically configured according to the speed configuration of the leading vehicle. The calculation formula for configuring the driving speed of the leading vehicle is: , in, To configure the driving speed of the pilot car, To configure the driving speed of the front pilot car, is the normalized weight factor. When the adhesion coefficient change rate is not less than the preset adhesion coefficient threshold change rate, When the adhesion coefficient change rate is less than the preset adhesion coefficient threshold change rate .

[0080] In some embodiments, step S503 further includes: generating a reference acceleration of the following vehicle based on the driving speed of the following vehicle before and after configuration; and compensating the reference acceleration of the following vehicle according to the distance error data between the following vehicle and the preceding vehicle to obtain the actual acceleration of the following vehicle.

[0081] First, the state space model is performed for each vehicle in the fleet to obtain the longitudinal dynamic state space model for each vehicle: , in, is the state vector of vehicle i, is the position of vehicle i, is the speed of vehicle i, is the process noise, is the observation noise.

[0082] Based on feedforward-feedback speed synchronization, the speed controller of vehicle i adopts a two-layer structure: 1. Feedforward control, the speed of the following vehicle is controlled according to the configuration transmitted by the pilot vehicle Generate a baseline acceleration; 2. Based on the real-time distance error between vehicle i and vehicle i-1 , the acceleration is compensated by the PID controller:

[0083] in, is the actual acceleration of the following vehicle, is the scale parameter, is the differential parameter.

[0084] Figure 6 FIG. 1 is a block diagram of an electronic device according to an exemplary embodiment. Figure 6 hereinafter, an electronic device 600 according to this embodiment of the present disclosure is described. Figure 6 The electronic device 600 shown is merely an example and should not limit the functions and scope of use of the embodiments of the present disclosure.

[0085] like Figure 6 As shown, electronic device 600 is implemented as a general-purpose computing device. Components of electronic device 600 may include, but are not limited to, at least one processing unit 610, at least one storage unit 620, a bus 630 connecting various system components (including storage unit 620 and processing unit 610), a display unit 640, and the like.

[0086] The storage unit stores program codes, which can be executed by the processing unit 610 , so that the processing unit 610 executes the steps described in the above method of this specification according to various exemplary embodiments of the present disclosure.

[0087] The storage unit 620 may include a readable medium in the form of a volatile storage unit, such as a random access memory unit (RAM) 6201 and / or a cache memory unit 6202 , and may further include a read-only memory unit (ROM) 6203 .

[0088] The storage unit 620 may also include a program / utility 6204 having a set (at least one) of program modules 6205, such program modules 6205 including but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include an implementation of a network environment.

[0089] Bus 630 may represent one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus architectures.

[0090] The electronic device 600 can also communicate with one or more external devices 600' (e.g., a keyboard, a pointing device, a Bluetooth device, etc.), one or more devices that enable a user to interact with the electronic device 600, and / or any device that enables the electronic device 600 to communicate with one or more other computing devices (e.g., a router, a modem, etc.). Such communication can occur via an input / output (I / O) interface 650. Furthermore, the electronic device 600 can communicate with one or more networks (e.g., a local area network (LAN), a wide area network (WAN), and / or a public network such as the Internet) via a network adapter 660. The network adapter 660 can communicate with other modules of the electronic device 600 via the bus 630. It should be understood that, although not shown in the figures, other hardware and / or software modules can be used in conjunction with the electronic device 600, including but not limited to microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0091] An embodiment of the present application further provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the above method is implemented.

[0092] The fleet driving control method, device, and storage medium provided in the embodiments of the present application perform a dynamic driving risk analysis of the road surface on which the vehicle is currently traveling based on real-time road surface roughness characteristic data and vehicle dynamics characteristic data of the vehicles in the fleet, thereby determining the vehicle's current passable area based on the driving risk analysis results. After generating candidate paths based on the vehicle's current passable area and determining the corresponding target path through driving safety cost analysis, the vehicle's driving speed is dynamically configured based on the vehicle's real-time vehicle adhesion coefficient, and the vehicle is controlled to travel along the target path at the configured driving speed, thereby realizing automatic control of the fleet's driving. Since the vehicle's current passable area is determined by dynamic driving risk analysis based on real-time road surface roughness characteristic data and vehicle dynamics characteristic data, and the dynamic changes of road surface roughness characteristic data and vehicle dynamics characteristic data are taken into consideration, it adapts to the dynamic planning of the passable area for each vehicle in the fleet, and improves the rationality of the driving path planning of each vehicle in the fleet. After determining the target path of each vehicle, the vehicle's driving speed is dynamically configured according to the vehicle's real-time vehicle adhesion coefficient to ensure that the driving speed of each vehicle adapts to the changes in the vehicle adhesion coefficient, avoid the situation where the vehicle driving is unstable due to sudden changes in the adhesion coefficient, ensure the movement stability of the fleet under complex road conditions, and improve the safety of the fleet's driving.

[0093] Through the description of the above embodiments, it is easy for those skilled in the art to understand that the example embodiments described herein can be implemented by software or by combining software with necessary hardware. Therefore, the technical solution according to the embodiments of the present disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which can be a personal computer, a server, or a network device, etc.) to execute the above-mentioned method according to the embodiments of the present disclosure.

[0094] The program product may employ any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. The readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or component, or any combination thereof. More specific examples (a non-exhaustive list) of readable storage media include: an electrical connection with one or more wires, a portable 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 thereof.

[0095] Computer-readable storage media may include a data signal propagated in baseband or as part of a carrier wave, which carries readable program code. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. The readable storage medium may also be any readable medium other than a readable storage medium, which may send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device. The program code contained on the readable storage medium may be transmitted using any appropriate medium, including but not limited to wireless, wired, optical cable, RF, etc., or any suitable combination thereof.

[0096] Those skilled in the art will appreciate that the modules described above can be distributed in the device according to the description of the embodiment, or can be modified accordingly to be used in one or more devices that are different from the embodiment. The modules of the above embodiment can be combined into one module or further divided into multiple submodules.

[0097] While the exemplary embodiments of the present disclosure have been specifically illustrated and described above, it should be understood that the present disclosure is not limited to the detailed structures, configurations, or implementations described herein; rather, the present disclosure is intended to encompass various modifications and equivalent configurations within the spirit and scope of the appended claims.

Claims

1. A method for controlling a vehicle fleet, characterized in that: include: Obtain real-time road roughness characteristic data and vehicle dynamic characteristic data of vehicles in the fleet; Performing a dynamic driving risk analysis on the road surface currently being driven by the vehicle based on the road surface roughness characteristic data and the vehicle dynamics characteristic data, and dividing the road surface into regions based on the driving risk analysis results, so as to determine a current passable region for the vehicle; Performing path planning for the vehicle based on the traversable area to obtain a plurality of candidate paths; Performing a driving safety cost analysis on the candidate paths, and selecting a corresponding target path from the candidate paths based on the safety cost analysis result; The driving speed of the vehicle is dynamically configured according to the real-time vehicle adhesion coefficient of the vehicle, so that the vehicle travels along the target path at the configured driving speed.

2. The vehicle fleet driving control method according to claim 1, characterized in that: The performing of a dynamic driving risk analysis on the road surface on which the vehicle is currently traveling based on the road surface roughness characteristic data and the vehicle dynamics characteristic data and the region division based on the driving risk analysis results includes: extracting risk feature data from both the road surface roughness feature data and the vehicle dynamics feature data to obtain multi-dimensional risk information data; Determining a current initial traversable area for the vehicle based on the multi-dimensional risk information data; A collision risk analysis and a communication interruption risk analysis are performed on the initial traversable area, and areas with collision risks and communication interruption risks are eliminated to determine the current traversable area of the vehicle.

3. The vehicle fleet driving control method according to claim 2, characterized in that: The extracting risk feature data from both the road surface roughness feature data and the vehicle dynamics feature data includes: Constructing a road surface roughness risk field for the road surface on which the vehicle is currently traveling based on the road surface roughness characteristic data and the vehicle dynamics characteristic data; extracting pavement risk characteristic data and pavement evolution characteristic data from the pavement roughness risk field; The road surface risk characteristic data, the road surface evolution characteristic data and the driving stability performance characteristic data in the vehicle dynamics characteristic data are spliced together to obtain the multi-dimensional risk information data.

4. The vehicle fleet driving control method according to claim 2, characterized in that: The performing collision risk analysis and communication interruption risk analysis on the initial traversable area includes: Calculating a global communication safety factor and a global collision safety factor of the vehicle relative to other vehicles based on real-time vehicle distance data between the vehicle and other vehicles, and obtaining a collision risk analysis result and a communication interruption risk analysis result; Areas with collision risks and communication interruption risks are eliminated according to the collision risk analysis result and the communication interruption risk analysis result to determine the current passable area of the vehicle.

5. The vehicle fleet driving control method according to claim 1, characterized in that: The performing of a driving safety cost analysis on the candidate paths and selecting a corresponding target path from the candidate paths according to the safety cost analysis result includes: Performing a driving safety cost analysis on candidate paths of the pilot vehicle, and determining a target path of the pilot vehicle based on the safety cost analysis results of the candidate paths of the pilot vehicle; The path points of the following vehicles are determined according to the target path of the lead vehicle and preset formation keeping parameters to determine the target path of the following vehicles.

6. The vehicle fleet driving control method according to claim 1, characterized in that: The dynamically configuring the vehicle's travel speed according to the vehicle's real-time vehicle adhesion coefficient includes: Obtaining a front vehicle adhesion coefficient and a bottom vehicle adhesion coefficient of the vehicle; Calculating a rate of change of adhesion coefficients between the vehicles based on a front adhesion coefficient of the rear vehicle and an under-vehicle adhesion coefficient of the front vehicle; The driving speed of the vehicle is configured according to the adhesion coefficient change rate.

7. The vehicle fleet driving control method according to claim 6, characterized in that: Configuring the vehicle's travel speed according to the adhesion coefficient change rate includes: When the adhesion coefficient change rate is not less than a preset adhesion coefficient threshold change rate, calculating a speed configuration amount of the following vehicle according to the adhesion coefficient change rate; Performing a weighted sum operation on the speed configuration amounts of each following vehicle to obtain a total speed configuration amount; Calculating a speed configuration amount of the pilot vehicle according to the total speed configuration amount; The driving speed of the following vehicle is dynamically configured according to the speed configuration amount of the following vehicle, and the driving speed of the leading vehicle is dynamically configured according to the speed configuration amount of the leading vehicle.

8. The vehicle fleet driving control method according to claim 7, characterized in that: The configuring the vehicle's travel speed according to the adhesion coefficient change rate further includes: generating a reference acceleration of the following vehicle based on the driving speed of the following vehicle before and after deployment; The reference acceleration of the following vehicle is compensated according to the distance error data between the following vehicle and the preceding vehicle to obtain the actual acceleration of the following vehicle.

9. An electronic device, characterized in that: The electronic device includes a memory and a processor, the memory stores a computer program, and the processor implements the method according to any one of claims 1 to 8 when executing the computer program.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 8 is implemented.

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