A vehicle fleet cooperative driving control method, device and control equipment

Abstract

The present application relates to a kind of vehicle fleet cooperative driving control method, device and control equipment, belong to vehicle control technical field, wherein, the method includes: based on the driving route information of multiple vehicles determines to be queued vehicle set;Based on the vehicle quantity in to-be-queued vehicle set and the preset platoon scale, the vehicle in to-be-queued vehicle set is divided into at least one platoon and elects out head car;Based on the perception data of all vehicles in each platoon, generate global perception map;Based on the global perception map corresponding to each platoon, the platoon scale of each platoon, the vehicle energy consumption characteristic parameter of each vehicle in each platoon and the road speed limit of target area, determine the following distance and driving speed;Based on the vehicle communication data in each platoon, vehicle sensor data, security authentication data and head car state data, detect the abnormal state to each platoon.The present application improves the safety and efficiency of vehicle fleet cooperative driving, and reduces the energy consumption of vehicle fleet cooperative driving.

Description

Technical Field

[0001] This invention relates to the field of vehicle control technology, and in particular to a method, device and control equipment for convoy cooperative driving control. Background Technology

[0002] Pulleyway technology is of great significance for the efficiency, safety, and environmental protection of highway transportation. By operating in platoons, platooning reduces wind resistance for following vehicles, saving energy. Furthermore, the synchronized operation of vehicles within the platoon improves driving safety and efficiency.

[0003] However, existing platooning technologies generally suffer from limitations in perception boundaries, low following efficiency, delayed risk response, limited platoon size, insufficient anomaly handling capabilities, and poor energy consumption. This results in poor safety, efficiency, and energy consumption performance of existing platooning technologies.

[0004] Therefore, how to improve the safety and efficiency of convoy cooperative driving while reducing energy consumption has become an urgent technical problem to be solved. Summary of the Invention

[0005] In view of this, it is necessary to provide a method, device and control equipment for controlling cooperative vehicle fleets, in order to solve the problems of poor safety, efficiency and energy consumption performance of existing cooperative vehicle fleet technologies.

[0006] To address the aforementioned problems, in a first aspect, the present invention provides a method for controlling convoy cooperative driving, comprising: Obtain the driving route information of multiple vehicles within the target area, and determine the set of vehicles to be platooned based on the driving route information of multiple vehicles; Based on the number of vehicles in the set of vehicles to be formed and the preset formation size, the vehicles in the set of vehicles to be formed are divided into at least one formation, and a lead vehicle is selected from each formation. Acquire perception data of all vehicles in each formation, and generate a global perception map for each formation based on the perception data of all vehicles in each formation. Based on the global perception map corresponding to each formation, the formation size of each formation, the vehicle energy consumption characteristics of each vehicle in each formation, and the road speed limit of the target area, the following distance and driving speed of each vehicle in each formation are determined. Based on vehicle communication data, vehicle sensor data, safety authentication data, and lead vehicle status data within each formation, abnormal status detection is performed on each formation.

[0007] In one possible implementation, determining the set of vehicles to be platooned based on the driving route information of multiple vehicles includes: Based on the driving route information of multiple vehicles, vehicles with a route matching degree greater than or equal to the route matching degree threshold with the preset route are identified, and all vehicles with a route matching degree greater than or equal to the route matching degree threshold with the preset route are identified as the set of vehicles to be platooned.

[0008] In one possible implementation, dividing the vehicles in the set of vehicles to be formed into at least one formation based on the number of vehicles in the set and a preset formation size includes: Using a preset formation size as the maximum formation size, the vehicles in the set of vehicles to be formed are divided into at least one formation.

[0009] In one possible implementation, the election of the lead vehicle from each formation includes: The lead car is elected from each formation based on a distributed consensus election algorithm.

[0010] In one possible implementation, generating a global perception map for each formation based on perception data from all vehicles within each formation includes: The perception data of all vehicles in each formation are processed by coordinate transformation, time synchronization and redundancy filtering. Then, the data of the same type in the perception data of all vehicles in each formation are weighted and fused to generate a global perception map for each formation.

[0011] In one possible implementation, based on the global perception map corresponding to each formation, the formation size of each formation, the vehicle energy consumption characteristics of each vehicle in each formation, and the road speed limit of the target area, the following distance and driving speed of each vehicle in each formation are determined, including: Using the following distance and speed of each vehicle in each platoon as variables, the safety score, traffic efficiency score and energy economy score of each platoon are determined. Based on the driving scenario of each formation, determine the weight matrix corresponding to each formation from the preset weight set; Based on the safety score, traffic efficiency score, and energy consumption economy score of each formation, and the weight matrix corresponding to each formation, the comprehensive decision benefit score function corresponding to each formation is determined. The following distance and speed of each vehicle in each formation are determined with the goal of maximizing the overall decision-making efficiency score for each formation.

[0012] In one possible implementation, the abnormal state detection of each formation based on vehicle communication data, vehicle sensor data, safety authentication data, and lead vehicle status data within each formation includes: Based on the packet loss rate and latency of vehicle communication data within each formation, the validity and continuity of vehicle sensor data, the presence of abnormal behavior data in safety authentication data, and the loss of control status of the lead vehicle determined by the lead vehicle status data, abnormal status detection is performed on each formation.

[0013] In one possible implementation, determining the loss of control state of the lead vehicle includes: The lead vehicle is determined to be out of control if at least one of the following conditions is met: The mutation rate of the control commands for the lead vehicle exceeds the mutation rate threshold. The deviation of the lead vehicle's driving status exceeds the deviation threshold; The number of consecutive heartbeat signals received by the lead vehicle is less than the threshold for the number of heartbeat signal receptions. Among them, the control command mutation rate of the lead vehicle is determined based on the control command at the current moment and the control command at the previous moment, and the driving state deviation of the lead vehicle is determined based on the driving state at the current moment and the driving state at the current moment predicted based on the driving state at the previous moment.

[0014] On the other hand, the present invention also provides a fleet cooperative driving control device, comprising: The acquisition module is used to acquire the driving route information of multiple vehicles within the target area, and determine the set of vehicles to be platooned based on the driving route information of multiple vehicles. The platooning module is used to divide the vehicles in the set of vehicles to be platooned into at least one platoon based on the number of vehicles in the set and the preset platoon size, and to elect a lead vehicle from each platoon. The generation module is used to acquire the perception data of all vehicles in each formation and generate a global perception map for each formation based on the perception data of all vehicles in each formation. The determination module is used to determine the following distance and speed of each vehicle in each formation based on the global perception map corresponding to each formation, the formation size of each formation, the vehicle energy consumption characteristic parameters of each vehicle in each formation, and the road speed limit of the target area. The anomaly detection module is used to detect anomalies in each formation based on vehicle communication data, vehicle sensor data, safety authentication data, and lead vehicle status data.

[0015] Secondly, the present invention also provides a control device, including electronic equipment. The processor, coupled to the memory, is used to execute the program stored in the memory to implement the steps in the vehicle cooperative driving control method described in any of the above implementations.

[0016] The beneficial effects of this invention are as follows: The convoy cooperative driving control method, device, and control equipment provided by this invention first avoid excessively large convoy sizes by vehicle formation and lead vehicle election, ensuring driving efficiency. Then, based on the perception data of all vehicles in each convoy, a global perception map corresponding to each convoy is generated. Combining the convoy size, the vehicle energy consumption characteristic parameters of each vehicle in each convoy, and the road speed limit of the target area, the following distance and driving speed of each vehicle in each convoy are determined, realizing the control decision of convoy vehicles and ensuring the energy consumption and driving safety of convoy driving. Finally, through vehicle communication data, vehicle sensor data, safety authentication data, and lead vehicle status data in each convoy, abnormal status detection is performed on each convoy, further improving driving safety. This invention improves the safety and efficiency of convoy cooperative driving and reduces the energy consumption of convoy cooperative driving. Attached Figure Description

[0017] Figure 1 A schematic flowchart of an embodiment of the vehicle cooperative driving control method provided by the present invention; Figure 2 A schematic flowchart of an embodiment of the vehicle cooperative driving control process provided by the present invention; Figure 3 A schematic diagram of an embodiment of the vehicle cooperative driving control device provided by the present invention; Figure 4 A schematic diagram of an embodiment of the control device provided by the present invention. Detailed Implementation

[0018] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0019] In the description of the embodiments of the present invention, unless otherwise stated, "multiple" means two or more. "And / or" describes the relationship between related objects, indicating that there can be three relationships. For example, A and / or B can represent three situations: A exists alone, A and B exist simultaneously, and B exists alone.

[0020] The terms "first," "second," etc., used in the embodiments of this invention are for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of technical features indicated. Therefore, a technical feature defined with "first" or "second" may explicitly or implicitly include at least one of that feature.

[0021] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of the invention. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0022] This invention provides a method, device, and control equipment for controlling coordinated driving of a fleet, which will be described below.

[0023] Figure 1 This is a schematic flowchart of an embodiment of the vehicle cooperative driving control method provided by the present invention, as shown below. Figure 1 As shown, the convoy cooperative driving control method includes: S101. Obtain the driving route information of multiple vehicles within the target area, and determine the set of vehicles to be platooned based on the driving route information of multiple vehicles.

[0024] It should be noted that the fleet cooperative driving control method provided by this invention can be applied to vehicle autonomous driving scenarios, especially autonomous driving scenarios involving fleet cooperative driving.

[0025] When performing convoy coordination control, the control device (such as a desktop or portable computer) can first obtain the driving route information of multiple vehicles in the target area (such as a certain road segment), and then determine the set of vehicles to be convoyed based on the driving route information of multiple vehicles, providing a basis for vehicle convoying.

[0026] S102. Based on the number of vehicles in the set of vehicles to be formed and the preset formation size, divide the vehicles in the set of vehicles to be formed into at least one formation, and elect a lead vehicle from each formation.

[0027] It should be noted that after determining the set of vehicles to be platooned, the vehicles in the set can be divided into at least one platoon based on the number of vehicles and the preset platoon size, and a lead vehicle can be elected from each platoon. Limiting the size of the platoon can prevent the platoon from becoming too large, thereby ensuring driving efficiency. The election of the lead vehicle can also improve driving efficiency.

[0028] S103. Obtain the perception data of all vehicles in each formation, and generate a global perception map corresponding to each formation based on the perception data of all vehicles in each formation.

[0029] It should be noted that after the vehicle formation is completed, the perception data of all vehicles in each formation can be obtained, and a global perception map corresponding to each formation can be generated based on the perception data of all vehicles in each formation, thereby providing a basis for subsequent control decisions of vehicles in the formation.

[0030] S104. Based on the global perception map corresponding to each formation, the formation size of each formation, the vehicle energy consumption characteristic parameters of each vehicle in each formation, and the road speed limit of the target area, determine the following distance and driving speed of each vehicle in each formation.

[0031] It should be noted that after determining the global perception map corresponding to each formation, the following distance and driving speed of each vehicle in each formation can be determined based on the global perception map corresponding to each formation, the formation size of each formation, the vehicle energy consumption characteristic parameters of each vehicle in each formation, and the road speed limit of the target area. This enables the control decision of the formation vehicles and ensures the energy consumption and driving safety of the formation.

[0032] S105. Based on the vehicle communication data, vehicle sensor data, safety authentication data and lead vehicle status data within each formation, perform abnormal status detection on each formation.

[0033] It should be noted that, in order to further improve driving safety, abnormal status detection can be performed on each formation based on vehicle communication data, vehicle sensor data, safety authentication data, and lead vehicle status data during convoy driving.

[0034] In summary, the convoy cooperative driving control method provided by this invention first avoids excessively large convoy sizes by vehicle formation and lead vehicle election, ensuring driving efficiency. Then, it generates a global perception map corresponding to each convoy based on the perception data of all vehicles in each convoy. Combining the convoy size, the vehicle energy consumption characteristics of each vehicle in each convoy, and the road speed limit of the target area, it determines the following distance and driving speed of each vehicle in each convoy, realizing the control decision of the convoy vehicles and ensuring the energy consumption and driving safety of convoy driving. Finally, it detects abnormal states of each convoy by using vehicle communication data, vehicle sensor data, safety authentication data, and lead vehicle status data, further improving driving safety. This invention improves the safety and efficiency of convoy cooperative driving and reduces the energy consumption of convoy cooperative driving.

[0035] In some embodiments of the present invention, determining the set of vehicles to be platooned based on the driving route information of multiple vehicles includes: Based on the driving route information of multiple vehicles, vehicles with a route matching degree greater than or equal to the route matching degree threshold with the preset route are identified, and all vehicles with a route matching degree greater than or equal to the route matching degree threshold with the preset route are identified as the set of vehicles to be platooned.

[0036] It should be noted that when determining the set of vehicles to be platooned, vehicles with a route matching degree greater than or equal to a preset route can be identified based on the travel route information of multiple vehicles. All vehicles with a route matching degree greater than or equal to the preset route threshold are then included in the set of vehicles to be platooned. The preset route can be determined based on the travel route information of multiple vehicles, for example, based on the road with the highest traffic frequency.

[0037] In some embodiments of the present invention, dividing the vehicles in the set of vehicles to be formed into at least one formation based on the number of vehicles in the set and a preset formation size includes: Using a preset formation size as the maximum formation size, the vehicles in the set of vehicles to be formed are divided into at least one formation.

[0038] It should be noted that when dividing vehicles into formations, a preset formation size can be used as the maximum formation size, and the vehicles in the set to be formed into at least one formation.

[0039] In some embodiments of the present invention, the election of the lead vehicle from each formation includes: The lead car is elected from each formation based on a distributed consensus election algorithm.

[0040] It should be noted that when electing the lead car, a distributed consensus election algorithm can be used to elect the lead car from each formation.

[0041] In some embodiments of the present invention, generating a global perception map corresponding to each formation based on the perception data of all vehicles in each formation includes: The perception data of all vehicles in each formation are processed by coordinate transformation, time synchronization and redundancy filtering. Then, the data of the same type in the perception data of all vehicles in each formation are weighted and fused to generate a global perception map for each formation.

[0042] It should be noted that when generating a global perception map for each formation based on the perception data of all vehicles in each formation, coordinate transformation, time synchronization, and redundancy filtering can be performed on the perception data of all vehicles in each formation. Then, the data of the same data type in the processed perception data of all vehicles in each formation are weighted and fused to generate a global perception map for each formation.

[0043] In some embodiments of the present invention, based on the global perception map corresponding to each formation, the formation size of each formation, the vehicle energy consumption characteristic parameters of each vehicle in each formation, and the road speed limit of the target area, the following distance and driving speed of each vehicle in each formation are determined, including: Using the following distance and speed of each vehicle in each platoon as variables, the safety score, traffic efficiency score and energy economy score of each platoon are determined. Based on the driving scenario of each formation, determine the weight matrix corresponding to each formation from the preset weight set; Based on the safety score, traffic efficiency score, and energy consumption economy score of each formation, and the weight matrix corresponding to each formation, the comprehensive decision benefit score function corresponding to each formation is determined. The following distance and speed of each vehicle in each formation are determined with the goal of maximizing the overall decision-making efficiency score for each formation.

[0044] It should be noted that when determining the following distance and speed of each vehicle within each platoon, these parameters can be used as variables to determine the safety score, traffic efficiency score, and energy economy score for each platoon. Then, based on the driving scenario of each platoon, a weight matrix is ​​determined from a preset weight set. Using the safety score, traffic efficiency score, and energy economy score of each platoon, along with its corresponding weight matrix, a comprehensive decision-making benefit score function is determined for each platoon. Finally, with the goal of maximizing the comprehensive decision-making benefit score for each platoon, the following distance and speed of each vehicle within each platoon are determined.

[0045] In some embodiments of the present invention, the abnormal state detection of each formation based on vehicle communication data, vehicle sensor data, safety authentication data, and lead vehicle status data within each formation includes: Based on the packet loss rate and latency of vehicle communication data within each formation, the validity and continuity of vehicle sensor data, the presence of abnormal behavior data in safety authentication data, and the loss of control status of the lead vehicle determined by the lead vehicle status data, abnormal status detection is performed on each formation.

[0046] It should be noted that when performing abnormal state detection on each formation, the abnormal state detection can be performed by considering the packet loss rate and latency of vehicle communication data within each formation, the validity and continuity of vehicle sensor data, the presence of abnormal behavior data in safety authentication data, and the loss of control status of the lead vehicle determined by the lead vehicle status data.

[0047] In some embodiments of the present invention, determining the loss of control state of the lead vehicle includes: The lead vehicle is determined to be out of control if at least one of the following conditions is met: The mutation rate of the control commands for the lead vehicle exceeds the mutation rate threshold. The deviation of the lead vehicle's driving status exceeds the deviation threshold; The number of consecutive heartbeat signals received by the lead vehicle is less than the threshold for the number of heartbeat signal receptions. Among them, the control command mutation rate of the lead vehicle is determined based on the control command at the current moment and the control command at the previous moment, and the driving state deviation of the lead vehicle is determined based on the driving state at the current moment and the driving state at the current moment predicted based on the driving state at the previous moment.

[0048] It should be noted that: when determining whether the lead vehicle is out of control, it can be determined that the lead vehicle is out of control if at least one of the following conditions is met.

[0049] 1) The mutation rate of the control commands of the lead vehicle is greater than the mutation rate threshold; 2) The deviation of the lead vehicle's driving status exceeds the deviation threshold; 3) The number of consecutive heartbeat signals received by the lead vehicle is less than the threshold number of heartbeat signals received.

[0050] The control command mutation rate of the lead car can be determined based on the control command at the current moment and the control command at the previous moment. The deviation of the lead car's driving state can be determined based on the driving state at the current moment and the driving state at the current moment predicted by the driving state at the previous moment.

[0051] The following is a detailed description of the fleet cooperative driving control process provided by the present invention.

[0052] This invention addresses pain points in convoy driving scenarios, such as large blind spots for following vehicles, high redundancy in following safety distances, delayed response to sudden risks, limited convoy size, insufficient robustness in abnormal scenarios, high convoy energy consumption, and the risk of lead vehicle loss of control. Building upon the existing automatic grouping and sensor sharing and fusion architecture, it expands the support capability for large-scale convoys, improves the multi-dimensional abnormal scenario handling mechanism (including special handling for lead vehicle loss of control), and incorporates an overall convoy energy consumption optimization strategy. This achieves a triple improvement in the efficiency, driving safety, and energy consumption performance of ultra-large-scale convoys, adapting to the convoy driving needs of various scenarios such as trunk logistics, public transportation, and special vehicle convoys.

[0053] This invention is based on vehicle-road cooperative and multi-agent cooperative control theory. It realizes real-time interaction of perception data, control commands and status information of vehicles in the platoon through 5G-V2X direct communication technology; it adopts distributed master-slave scheduling logic to break through the limitation of single platoon size and support cooperative driving of multiple sub-platoons; it designs a multi-dimensional anomaly detection and hierarchical processing mechanism to cover all-dimensional anomaly scenarios such as communication, sensors, safety, and loss of control of the lead vehicle; and it constructs a multi-objective optimization decision model that integrates safety, efficiency and energy consumption. Under the premise of ensuring driving safety, it dynamically adjusts the following distance and driving speed to achieve the optimal overall comprehensive benefits of the platoon.

[0054] The core logic of this invention is divided into four levels: 1. Perception Extension Layer: By sharing multi-source sensor data from the preceding vehicle in real time and combining it with the sensor data from the following vehicle, a global perception map covering the entire formation is generated, eliminating the perception blind spots of individual vehicles and expanding the overall perception range.

[0055] The communication architecture adopts 5G-V2X PC5 direct communication as the main link, supporting short-distance high-reliability transmission with latency ≤T1 and coverage range N1; it adopts 5G Uu cellular communication as the backup link, supporting wide-area coverage transmission with latency ≤T2; data transmission is encrypted using national cryptographic algorithms, and each data packet is accompanied by an identity authentication signature to prevent data tampering and malicious access.

[0056] Multi-source data fusion is achieved by using a spatiotemporal registration algorithm to realize coordinate transformation and time synchronization of data from different vehicle sensors, with a time synchronization accuracy of ≤T7; an adaptive weighted fusion algorithm is used to fuse multi-source data, with a redundant data filtering ratio of ≥E5% and a fusion processing delay of ≤T3.

[0057] 2. Formation Management System: It adopts a distributed master-slave scheduling architecture to support dynamic formation of ultra-large-scale fleets, splitting and merging of sub-fleets, and adapting to the formation scale requirements of different scenarios; at the same time, it sets up real-time monitoring of the lead vehicle status and emergency handling logic for lead vehicle failures to ensure formation safety in abnormal lead vehicle scenarios.

[0058] Large-scale platoon scheduling is implemented using a distributed consensus election algorithm to elect the lead vehicle. The overall lead vehicle and the lead vehicles of sub-platoons broadcast heartbeat information every T5 cycles. If there is no heartbeat for more than T6 cycles, a re-election is triggered. The size of sub-platoons is dynamically adjusted based on road conditions and platoon requirements and is automatically triggered. The splitting and merging process adopts a non-disruptive switching logic, which does not affect the normal operation of the platoon.

[0059] 3. Anomaly Handling Layer: Real-time monitoring of communication status, sensor operating status, vehicle identity legitimacy, and lead vehicle operating status. Triggering tiered handling strategies for different types of anomalies to ensure driving safety in abnormal scenarios; In particular, a special emergency response is triggered in the case of lead vehicle loss of control, and all vehicles automatically leave the formation control.

[0060] Anomaly detection and handling are implemented by combining threshold judgment and feature recognition. Communication anomalies are judged based on packet loss rate and latency threshold, sensor failures are judged based on data validity and continuity features, security intrusions are judged based on identity authentication and abnormal behavior features, and lead vehicle loss of control is judged based on control command validity and sudden change features in driving state. Anomaly handling adopts a hierarchical response strategy, with different levels of anomalies corresponding to preset handling procedures. The lead vehicle loss of control scenario adopts the highest priority response, with command transmission latency ≤ T8.

[0061] 4. Decision Optimization Layer: Based on global perception data, formation operation status and energy consumption model, a multi-objective optimization decision function is constructed to dynamically adjust following distance and driving speed to achieve comprehensive optimization of safety, efficiency and energy consumption.

[0062] Multi-objective decision-making is implemented by constructing a multi-objective optimization function, with the safety objective weighted at K3, the efficiency objective weighted at K4, and the energy consumption objective weighted at K5. The weights can be dynamically adjusted according to the needs of the scenario. A heuristic optimization algorithm is used to solve for the optimal control parameters, with a decision cycle ≤ T9, ensuring real-time control.

[0063] Combination Figure 2 The specific steps involved in the convoy cooperative driving control process are as follows: 1. Formation Formation Process: During vehicle operation, the vehicle broadcasts its own route, destination, speed, location, and energy consumption characteristics via the V2V module → The large-scale automatic formation management module filters vehicles with a route matching degree ≥ K1 within the surrounding N2 range and initiates formation invitations → After confirmation by the vehicle owner / autonomous driving system, formation registration is completed → When the formation size exceeds M1, it is automatically split into multiple sub-formations, and a sub-formation leader and a general leader are elected → All vehicles synchronize the formation topology and leader vehicle identity information to complete formation.

[0064] 2. Perception Data Processing Flow: The lead vehicle of each sub-formation collects perception data from the front and its own status data at the F1 frequency → synchronizes with all vehicles in the sub-formation via the V2V main link → automatically switches to the backup cellular link for transmission if the main link fails → the receiving vehicle verifies the validity of the data → after verification, performs coordinate transformation, time synchronization, and redundancy filtering → fuses with its own sensor data to generate a global perception map.

[0065] 3. Decision-making and control process: Multi-objective optimization decision-making and control module inputs global perception map, platoon size, road speed limit, and vehicle energy consumption characteristic parameters → calculates the optimal following distance and driving speed based on the multi-objective decision model → outputs control commands to the vehicle chassis actuators → monitors the control effect in real time and dynamically adjusts decision parameters.

[0066] 4. Anomaly Handling Process: The anomaly detection module collects real-time communication, sensor, safety, and lead vehicle status data → compares the data with preset anomaly thresholds to determine the anomaly type and level → if it is a minor anomaly, triggers an audible and visual warning, maintains convoy operation, and continuously monitors the anomaly status → if it is a moderate anomaly, reduces the convoy speed to V1, increases the following distance to D1, and activates the corresponding compensation mechanism → if it is a severe anomaly (including lead vehicle loss of control), immediately triggers a convoy disbandment command, all vehicles disconnect their following control logic, switch to manual operation mode, trigger a takeover warning, and each vehicle resumes independent driving.

[0067] Formation dynamic adjustment process: When a vehicle applies to join / leave the formation → the automatic formation management module verifies the vehicle's identity and route matching degree → after verification, the formation topology is adjusted → if the sub-formation size exceeds M1, a splitting process is triggered; if the sub-formation size is less than M2, a merging process is triggered → after the adjustment is completed, the data sharing logic and decision parameters are updated, and normal formation driving is restored.

[0068] Multi-objective optimization decision-making algorithm: Formula expression: J=K3×S+K4×E+K5×C.

[0069] When D_current≥D_safe, S=1-(D_safe-D_current) / D_safe; Otherwise S=0E=V_current / V_limitC=1-(E_current-E_min) / (E_max-E_min)D_safe=V_current×T_react+D_min.

[0070] Wherein: J: Overall score of decision objective, ranging from 0 to 1, which measures the overall performance of the current control strategy; the higher the score, the better the strategy. K3: Weight coefficient of safety objective, ranging from 0 to 1, with a default value of K3_default, which adjusts the priority of safety objective in decision-making. S: Safety objective score, ranging from 0 to 1, which measures the safety level of the current following distance. K4: Weight coefficient of efficiency objective, ranging from 0 to 1, with a default value of K4_default, which adjusts the priority of traffic efficiency objective in decision-making. E: Efficiency objective score, ranging from 0 to 1, which measures the traffic efficiency level at the current driving speed. K5: Weight coefficient of energy consumption objective, ranging from 0 to 1, with a default value of K5_default, satisfying K3+K4+K5=1, which adjusts the priority of energy consumption objective in decision-making. C: Energy consumption objective score, ranging from 0 to 1, which measures the energy consumption performance at the current driving speed. D_safe: Minimum safe following distance at the current speed, in meters; serves as a safety threshold for following distance. D_current: Current actual following distance, in meters. V_current: Current platoon speed, in km / h. V_limit: Current maximum speed limit, in km / h. E_current: Vehicle energy consumption per unit at the current speed, in kWh / km or L / 100km. E_min: Minimum energy consumption per unit in the vehicle's economic operating range, in the same unit as E_current. E_max: Maximum energy consumption per unit in the vehicle's extreme operating range, in the same unit as E_current. T_react: Overall system response time, in seconds, with a value ≤ T11. D_min: Minimum safe stopping distance when stationary, in meters, with a value ≥ N4.

[0071] Application Example: In a platooning scenario for a trunk logistics line, the preset weights are: safety target weight K3=K3_1, efficiency target weight K4=K4_1, and energy consumption target weight K5=K5_1. The current maximum speed limit is V_limit_1, the minimum vehicle response time is T_react_1, the minimum safe following distance is D_min_1, the minimum unit energy consumption is E_min_1, and the maximum unit energy consumption is E_max_1. When the platooning speed is V_current_1, the minimum safe following distance D_safe = V_current_1 × T_react_1 + D_min_1 is calculated first. If the current following distance D_current_1 ≥ D_safe, then the safety target score S = 1 - (D_safe - D_current_1) / D_safe; the efficiency target score E = V_current_1 / V_limit_1; and the optimal overall strategy is obtained by querying the vehicle energy consumption characteristics.

[0072] Lead vehicle loss of control detection algorithm: Formula expression: Fault_head=OR(A>Th_A,B>Th_B,C) <Th_C)。

[0073] A=|Cmd_t-Cmd_t-1| / Cmd_max; B=|State_t-Predict_t| / State_max.

[0074] in: Fault_head: The result of the loss of control of the lead vehicle. It is a boolean type, where 1 indicates that the vehicle is out of control and 0 indicates that it is normal. Its function is to trigger the emergency response for loss of control of the lead vehicle.

[0075] A: The mutation rate of the lead car control command ranges from 0 to 1. Its function is to measure the degree of mutation of the lead car control command. The higher the mutation rate, the greater the probability of an anomaly.

[0076] Th_A: Controls the threshold for instruction mutation rate. The default value is Th_A_default, and the value range is 0-1. Its function is to serve as the threshold for judging abnormal control instructions.

[0077] B: Deviation of lead vehicle driving status, with a value range of 0-1. Its function is to measure the degree of deviation between the actual driving status of the lead vehicle and the predicted status. The higher the deviation, the greater the probability of an anomaly.

[0078] Th_B: Driving status deviation threshold, the default value is Th_B_default, the value range is 0-1, and its function is to serve as the threshold for judging abnormal driving status.

[0079] C: Number of consecutive heartbeat signals received by the lead vehicle, an integer type. Its function is to measure the continuity of communication of the lead vehicle. The fewer the number of times, the greater the probability of communication abnormality.

[0080] Th_C: Threshold for the number of consecutive lost heartbeat signals. The default value is Th_C_default, and the value is ≥1. It is used as the threshold for determining communication interruption.

[0081] Cmd_t: The acceleration / braking control command value output by the lead vehicle at the current moment, with a value range of 0-100%.

[0082] Cmd_t-1: The acceleration / braking control command value output by the lead vehicle at the previous moment.

[0083] Cmd_max: Controls the maximum allowed mutation value of the command. The default value is Cmd_max_default, and the value range is 10%-50%.

[0084] State_t: The actual driving state value (speed, acceleration) of the lead vehicle at the current moment.

[0085] Predict_t: The current driving state value of the lead vehicle based on the state of the previous time step.

[0086] State_max: The maximum permissible deviation value for driving conditions. The default value is State_max_default, and the value range is 10%-30%.

[0087] Application example: In a convoy driving scenario, preset thresholds for control command mutation rate (Th_A = Th_A_1), driving state deviation (Th_B = Th_B_1), and heartbeat loss count (Th_C = Th_C_1) are defined. If, at a certain moment, the braking command of the lead vehicle changes abruptly from 0% to 100% in an adjacent moment, the control command mutation rate (A = |100% - 0%| / Cmd_max_1) exceeds the threshold (Th_A_1), and the lead vehicle is directly deemed out of control, triggering a convoy disbandment response. If the actual speed of the lead vehicle is B_1 lower than the predicted speed, exceeding the driving state deviation threshold (Th_B_1), it is also deemed out of control. If no heartbeat signal is received from the lead vehicle for C_1 consecutive times, which is less than the threshold (Th_C_1), it is also deemed out of control.

[0088] This invention improves the perception capabilities of individual vehicles through deep fusion of sensor sharing, distributed scheduling, and multi-objective optimization, achieving a breakthrough in platoon size and simultaneously enhancing multiple benefits in safety, efficiency, and energy consumption.

[0089] To better implement the fleet cooperative driving control method in the embodiments of the present invention, based on the fleet cooperative driving control method, correspondingly, as follows: Figure 3 As shown, this embodiment of the invention also provides a fleet cooperative driving control device, the fleet cooperative driving control device 300 including: The acquisition module 301 is used to acquire the driving route information of multiple vehicles in the target area, and determine the set of vehicles to be platooned based on the driving route information of multiple vehicles. The platooning module 302 is used to divide the vehicles in the vehicle set to be platooned into at least one platoon based on the number of vehicles in the vehicle set to be platooned and the preset platooning size, and to elect a lead vehicle from each platoon. The generation module 303 is used to acquire the perception data of all vehicles in each formation and generate a global perception map corresponding to each formation based on the perception data of all vehicles in each formation. The determination module 304 is used to determine the following distance and driving speed of each vehicle in each formation based on the global perception map corresponding to each formation, the formation size of each formation, the vehicle energy consumption characteristic parameters of each vehicle in each formation, and the road speed limit of the target area. The anomaly detection module 305 is used to detect anomalies in each formation based on vehicle communication data, vehicle sensor data, safety authentication data, and lead vehicle status data within each formation.

[0090] The fleet cooperative driving control device 300 provided in the above embodiments can realize the technical solutions described in the above fleet cooperative driving control method embodiments. The specific implementation principles of each module or unit can be found in the corresponding content in the above fleet cooperative driving control method embodiments, and will not be repeated here.

[0091] like Figure 4 As shown, the present invention also provides a control device 400. The control device 400 includes a processor 401, a memory 402, a display 403, and a data acquisition device 404. Figure 4 Only some components of the control device 400 are shown, but it should be understood that it is not required to implement all of the components shown, and more or fewer components may be implemented instead.

[0092] In some embodiments, processor 401 may be a central processing unit (CPU), microprocessor, or other data processing chip, used to run program code stored in memory 402 or process data, such as the fleet cooperative driving control method of the present invention.

[0093] In some embodiments, processor 401 may be a single server or a group of servers. The server group may be centralized or distributed. In some embodiments, processor 401 may be local or remote. In some embodiments, processor 401 may be implemented on a cloud platform. In one embodiment, the cloud platform may include a private cloud, public cloud, hybrid cloud, community cloud, distributed cloud, internal cloud, multi-cloud, etc., or any combination thereof.

[0094] In some embodiments, memory 402 may be an internal storage unit of control device 400, such as a hard disk or memory of control device 400. In other embodiments, memory 402 may also be an external storage device of control device 400, such as a plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, etc., provided on control device 400.

[0095] Furthermore, the memory 402 may include both internal storage units of the control device 400 and external storage devices. The memory 402 is used to store application software and various types of data installed on the control device 400.

[0096] In some embodiments, display 403 may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, or an organic light-emitting diode (OLED) touchscreen. Display 403 is used to display information from control device 400 and to display a visual user interface. Components 401-403 of control device 400 communicate with each other via a system bus.

[0097] In some embodiments, the data acquisition device 404 may be a chip or integrated circuit with data transmission and reception functions.

[0098] In one embodiment, when processor 401 executes the fleet cooperative driving control program in memory 402, the following steps can be implemented: Obtain the driving route information of multiple vehicles within the target area, and determine the set of vehicles to be platooned based on the driving route information of multiple vehicles; Based on the number of vehicles in the set of vehicles to be formed and the preset formation size, the vehicles in the set of vehicles to be formed are divided into at least one formation, and a lead vehicle is selected from each formation. Acquire perception data of all vehicles in each formation, and generate a global perception map for each formation based on the perception data of all vehicles in each formation. Based on the global perception map corresponding to each formation, the formation size of each formation, the vehicle energy consumption characteristics of each vehicle in each formation, and the road speed limit of the target area, the following distance and driving speed of each vehicle in each formation are determined. Based on vehicle communication data, vehicle sensor data, safety authentication data, and lead vehicle status data within each formation, abnormal status detection is performed on each formation.

[0099] It should be understood that when the processor 401 executes the fleet cooperative driving control program in the memory 402, in addition to the functions mentioned above, it can also perform other functions, as can be found in the description of the corresponding method embodiments above.

[0100] Furthermore, this embodiment of the invention does not specifically limit the type of control device 400 mentioned. Control device 400 can be a mobile phone, tablet computer, personal digital assistant (PDA), wearable device, laptop computer, or other portable electronic device. Exemplary embodiments of portable electronic devices include, but are not limited to, portable electronic devices running iOS, Android, Microsoft, or other operating systems. The aforementioned portable electronic devices can also be other portable electronic devices, such as laptop computers with touch-sensitive surfaces (e.g., touch panels). It should also be understood that in some other embodiments of the invention, control device 400 may not be a portable electronic device, but rather a desktop computer with a touch-sensitive surface (e.g., a touch panel).

[0101] The above provides a detailed description of the vehicle cooperative driving control method, device, and control equipment provided by the present invention. Specific examples have been used to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only for the purpose of helping to understand the method and core ideas of the present invention. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of the present invention. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A method for controlling cooperative driving of a fleet, characterized in that, include: Obtain the driving route information of multiple vehicles within the target area, and determine the set of vehicles to be platooned based on the driving route information of multiple vehicles; Based on the number of vehicles in the set of vehicles to be formed and the preset formation size, the vehicles in the set of vehicles to be formed are divided into at least one formation, and a lead vehicle is selected from each formation. Acquire perception data of all vehicles in each formation, and generate a global perception map for each formation based on the perception data of all vehicles in each formation. Based on the global perception map corresponding to each formation, the formation size of each formation, the vehicle energy consumption characteristics of each vehicle in each formation, and the road speed limit of the target area, the following distance and driving speed of each vehicle in each formation are determined. Based on vehicle communication data, vehicle sensor data, safety authentication data, and lead vehicle status data within each formation, abnormal status detection is performed on each formation.

2. The convoy cooperative driving control method according to claim 1, characterized in that, The process of determining the set of vehicles to be platooned based on the driving route information of multiple vehicles includes: Based on the driving route information of multiple vehicles, vehicles with a route matching degree greater than or equal to the route matching degree threshold with the preset route are identified, and all vehicles with a route matching degree greater than or equal to the route matching degree threshold with the preset route are identified as the set of vehicles to be platooned.

3. The convoy cooperative driving control method according to claim 1, characterized in that, The step of dividing the vehicles in the set of vehicles to be formed into at least one formation based on the number of vehicles in the set and the preset formation size includes: Using a preset formation size as the maximum formation size, the vehicles in the set of vehicles to be formed are divided into at least one formation.

4. The convoy cooperative driving control method according to claim 1, characterized in that, The process of electing the lead vehicle from each formation includes: The lead car is elected from each formation based on a distributed consensus election algorithm.

5. The convoy cooperative driving control method according to claim 1, characterized in that, The process of generating a global perception map for each formation based on perception data from all vehicles within each formation includes: The perception data of all vehicles in each formation are processed by coordinate transformation, time synchronization and redundancy filtering. Then, the data of the same type in the perception data of all vehicles in each formation are weighted and fused to generate a global perception map for each formation.

6. The convoy cooperative driving control method according to claim 1, characterized in that, Based on the global perception map corresponding to each formation, the formation size of each formation, the vehicle energy consumption characteristics of each vehicle in each formation, and the road speed limit of the target area, the following distance and driving speed of each vehicle in each formation are determined, including: Using the following distance and speed of each vehicle in each platoon as variables, the safety score, traffic efficiency score and energy economy score of each platoon are determined. Based on the driving scenario of each formation, determine the weight matrix corresponding to each formation from the preset weight set; Based on the safety score, traffic efficiency score, and energy consumption economy score of each formation, and the weight matrix corresponding to each formation, the comprehensive decision benefit score function corresponding to each formation is determined. The following distance and speed of each vehicle in each formation are determined with the goal of maximizing the overall decision-making efficiency score for each formation.

7. The convoy cooperative driving control method according to claim 1, characterized in that, The abnormal status detection for each formation is performed based on vehicle communication data, vehicle sensor data, safety authentication data, and lead vehicle status data within each formation, including: Based on the packet loss rate and latency of vehicle communication data within each formation, the validity and continuity of vehicle sensor data, the presence of abnormal behavior data in safety authentication data, and the loss of control status of the lead vehicle determined by the lead vehicle status data, abnormal status detection is performed on each formation.

8. The convoy cooperative driving control method according to claim 7, characterized in that, The determination of the lead vehicle's out-of-control state includes: The lead vehicle is determined to be out of control if at least one of the following conditions is met: The mutation rate of the control commands for the lead vehicle exceeds the mutation rate threshold. The deviation of the lead vehicle's driving status exceeds the deviation threshold; The number of consecutive heartbeat signals received by the lead vehicle is less than the threshold for the number of heartbeat signal receptions. Among them, the control command mutation rate of the lead vehicle is determined based on the control command at the current moment and the control command at the previous moment, and the driving state deviation of the lead vehicle is determined based on the driving state at the current moment and the driving state at the current moment predicted based on the driving state at the previous moment.

9. A vehicle convoy cooperative driving control device, characterized in that, include: The acquisition module is used to acquire the driving route information of multiple vehicles within the target area, and determine the set of vehicles to be platooned based on the driving route information of multiple vehicles. The platooning module is used to divide the vehicles in the set of vehicles to be platooned into at least one platoon based on the number of vehicles in the set and the preset platoon size, and to elect a lead vehicle from each platoon. The generation module is used to acquire the perception data of all vehicles in each formation and generate a global perception map for each formation based on the perception data of all vehicles in each formation. The determination module is used to determine the following distance and speed of each vehicle in each formation based on the global perception map corresponding to each formation, the formation size of each formation, the vehicle energy consumption characteristic parameters of each vehicle in each formation, and the road speed limit of the target area. The anomaly detection module is used to detect anomalies in each formation based on vehicle communication data, vehicle sensor data, safety authentication data, and lead vehicle status data.

10. A control device, characterized in that, Includes a data acquisition unit, memory, and processor, among which, The data acquisition device is communicatively connected to the processor and is used to acquire the driving route information of multiple vehicles within the target area; The memory is used to store programs; The processor, coupled to the memory, is used to execute the program stored in the memory to implement the steps in the fleet cooperative driving control method according to any one of claims 1 to 7.

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