An automatic driving vehicle platoon scheduling method and system

Abstract

The application relates to an automatic driving vehicle formation scheduling method and system, which comprises the following steps: acquiring the real-time power of each vehicle, the daily working time of each vehicle and a preset departure energy threshold, and calculating the energy supplement target energy according to the real-time power and the departure energy threshold; calculating the energy supplement time according to the energy supplement target energy; calculating the daily maximum transportation frequency of the vehicle based on the energy supplement time and the daily working time of each vehicle; calculating the daily transportation capacity of the vehicle based on the daily maximum transportation frequency of the vehicle; selecting the vehicle to form a formation according to the real-time power of each vehicle and the preset energy balance constraint; acquiring the transportation task data in real time, and calculating the dynamic emergency degree weight based on the total task time and the remaining task time; calculating the real-time priority of the task based on the dynamic emergency degree weight; and distributing the task to each formation for execution based on the real-time priority and the daily transportation capacity. Compared with the prior art, the application has the advantage of strong formation structure stability.

Description

Technical Field

[0001] This invention relates to the fields of intelligent logistics and autonomous driving technology, and in particular to a method and system for scheduling autonomous vehicle platoons. Background Technology

[0002] The existing autonomous vehicle platooning and scheduling technologies mainly suffer from the following technical solutions and their inherent defects: 1. Energy Management: Ignoring Vehicle Energy Differences. Current technologies for vehicle platooning typically focus only on vehicle location information or task requirements, assuming all vehicles have the same range or battery status and employing a uniform task allocation standard. This approach ignores the real-time energy disparity caused by differences in battery degradation, historical task consumption, and charging start times among different vehicles. The direct consequence is that some vehicles may be forced to withdraw mid-mission or urgently recharge due to depleted battery power, causing the entire platoon to stall and significantly reducing transportation efficiency.

[0003] 2. Scheduling Optimization Level: Most existing scheduling algorithms for single-trip optimization focus on the current single transport task as the optimization objective, calculating the optimal route or vehicle combination only for the current trip, lacking overall planning for the vehicles' working time throughout the day. This single-trip optimization model results in short-sighted scheduling decisions, with vehicles remaining idle or disorganized after completing their current task, failing to form efficient scheduling throughout the day, leading to low utilization of vehicle resources during the day and difficulty in maximizing overall transport capacity.

[0004] 3. Fleet Organization Level: Fixed Fleet Size. Existing technologies typically employ a static fleet management model, meaning that once a fleet is formed, its number and composition remain unchanged throughout the transportation process. This fixed fleet size solution lacks flexibility and cannot be dynamically adjusted based on the actual cargo volume of each trip. When the transportation workload decreases, fixed fleets still need to operate at the original size, resulting in a waste of capacity due to "large fleets carrying small amounts of cargo." When the workload surges, fixed fleets cannot be quickly split to execute multiple tasks in parallel, limiting responsiveness.

[0005] The invention disclosed in CN115907170A presents a method and system for optimizing vehicle scheduling in a coal transportation fleet, considering carbon emissions. The system includes a database module for recording the order status data of each vehicle; a data acquisition module for responding to requests for original orders from demanders; an order splitting module for splitting or merging original orders into system-input orders based on capacity; a data query module for filtering candidate vehicles in the database module based on the coal demand and loading / unloading point information in the system-input orders; a vehicle scheduling module for prioritizing candidate vehicles according to carbon emission factor coefficients and economic factor coefficients; and a data sending module for sending corresponding order requests to the on-board terminal according to the priority ranking. This invention prioritizes candidate vehicles by calculating carbon emission factor coefficients and economic factor coefficients, which can reduce carbon emissions generated by the transportation fleet while meeting capacity requirements. However, this solution fails to overcome the energy discretization problem within the fleet, resulting in poor fleet transportation capacity. Summary of the Invention

[0006] The purpose of this invention is to overcome the shortcomings of the existing technology and provide an autonomous vehicle platooning scheduling method and system.

[0007] The objective of this invention can be achieved through the following technical solutions: A method for scheduling autonomous vehicle platoons includes: The system acquires the real-time battery level of each vehicle, the daily operating time of each vehicle, and a preset starting energy threshold. Based on the real-time battery level and the starting energy threshold, it calculates the target energy for recharging and the recharging time based on the target energy. Based on the refueling time and the daily working time of each vehicle, the maximum number of daily transport trips of the vehicle is calculated; based on the maximum number of daily transport trips of the vehicle, the daily transport capacity of the vehicle and the daily transport capacity of the standard formation are calculated; and vehicles are selected to form a formation according to the real-time battery level of each vehicle and the preset energy balance constraints. Real-time acquisition of transportation task data, including total task time and remaining task time, and calculation of dynamic urgency weight based on the total task time and remaining task time; calculation of real-time priority of tasks based on the dynamic urgency weight; and allocation of tasks to each formation for execution based on the real-time priority and the daily transportation capacity of the standard formation.

[0008] Furthermore, based on the real-time power level and the starting energy threshold, the target energy for replenishment is calculated, and the corresponding calculation formula is as follows: in, To replenish the target energy, The starting energy threshold, The energy corresponding to vehicle v; This is for safety margin.

[0009] Furthermore, the energy replenishment time is calculated based on the target energy level, and the corresponding calculation formula is as follows: in, For the time to replenish energy, Let v be the energy replenishment rate of vehicle v.

[0010] Furthermore, based on the refueling time and the daily working hours of each vehicle, the maximum number of daily transport trips for each vehicle is calculated using the following formula: in, This represents the maximum number of trips the vehicle can make per day. For daily working hours, For daily energy replenishment time, This refers to the loading and unloading time.

[0011] Furthermore, based on the maximum number of daily transport trips of the vehicles, the daily transport capacity of the vehicles and the daily transport capacity of the standard fleet are calculated using the following formulas: in, For the daily transport capacity of a standard formation, For the daily transport capacity of vehicles, For standard formation size, This represents the maximum capacity for a single vehicle per trip.

[0012] Furthermore, the task is divided into the first n transport trips and the last transport trip; when executing the last transport trip, the number of vehicles used for transport is calculated and adjusted based on the remaining cargo volume, and the corresponding calculation formula is: in, This refers to the number of vehicles used for the last transport trip. This refers to the remaining cargo that the fleet needs to transport on its last trip. This represents the maximum capacity for a single vehicle per trip.

[0013] Furthermore, based on the dynamic urgency weight, the real-time priority of the task is calculated, and the corresponding calculation formula is as follows: in, The real-time priority of the task. For dynamic urgency weighting, For the remaining quantity of goods, For total cargo volume, For the remaining time, Where k is the total time, and k is the weighting coefficient. For dynamic weighting coefficients, This is the critical time threshold.

[0014] Furthermore, the specific expression for the energy balance constraint is as follows: in, For the first The energy of a vehicle For the first The energy of a vehicle The maximum energy difference within the formation. The starting energy threshold, For the vehicle's energy.

[0015] Furthermore, before executing the task, the energy of each vehicle in the formation is checked. If the energy of any vehicle is less than the starting energy threshold, the entire formation is recharged.

[0016] The present invention also provides a system for a method of scheduling autonomous vehicle platoons, comprising a memory and a processor, wherein the memory stores a computer program, and the processor invokes the computer program to execute the steps of any of the methods described above.

[0017] Compared with the prior art, the present invention has the following advantages: (1) This invention obtains the real-time battery level of the vehicles and a preset departure energy threshold to accurately calculate the target energy and required refueling time for each vehicle, and uses the refueling time as an important parameter for subsequent scheduling; it realizes unified refueling decision-making for vehicles in the formation, avoids long waiting times for the entire formation due to insufficient battery power of some vehicles, and significantly improves the attendance efficiency and time utilization of the formation. At the same time, by decoupling the calculation of refueling time from daily working time, it provides an accurate time baseline for subsequent multi-trip planning.

[0018] This invention, when forming a platoon, selects vehicles based on their real-time battery level and preset energy balance constraints to ensure that the energy levels of vehicles within the platoon are within a similar range, avoiding excessive energy differences. This overcomes the energy dispersion problem within the platoon caused by traditional random or nearest-neighbor matching, ensuring consistent range during long-distance missions and preventing the platoon from breaking up mid-journey or requiring frequent refueling due to excessive battery differences. This constraint guarantees the structural stability of the platoon from the outset and reduces the complexity of platoon management.

[0019] (2) This invention obtains the total time and remaining time of the task in real time, calculates the dynamic urgency weight, and generates the real-time priority of the task accordingly, so that the priority dynamically increases as the task deadline approaches. This avoids the backlog of urgent tasks caused by static priority allocation, enabling the scheduling system to dynamically upgrade the response level as the task deadline approaches, thus enhancing the system's ability to handle sudden urgent tasks. This dynamic mechanism ensures that limited capacity is prioritized for the most urgent tasks, improving overall service quality and task completion rate.

[0020] (3) This invention achieves flexible adjustment of the fleet size by dynamically reducing the number of vehicles in the fleet based on the actual cargo volume during the last transport trip; it solves the problem of wasted transport capacity caused by fixed fleet size when the workload fluctuates, avoids the inefficient transport mode of "large fleet carrying small amount of cargo", and maximizes the utilization rate and energy efficiency of single vehicles while ensuring the completion of the task. This adaptive adjustment mechanism is particularly suitable for actual operation scenarios with large workload fluctuations and has significant economic advantages. Attached Figure Description

[0021] Figure 1 This is a flowchart of a method for scheduling autonomous vehicles in a platoon, as provided in an embodiment of the present invention. Figure 2 This is a flowchart illustrating a method for scheduling autonomous vehicles in a platoon, as provided in an embodiment of the present invention. Figure 3 This is a schematic diagram of vehicle energy balance matching in an autonomous vehicle platooning scheduling method provided in an embodiment of the present invention. Detailed Implementation

[0022] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, 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 some embodiments of the present invention, and not all embodiments. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0023] Therefore, the following detailed description of the embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the invention without inventive effort are within the scope of protection of the invention.

[0024] It should be noted that similar labels and letters in the following figures indicate similar items. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.

[0025] Example 1 like Figure 1 and Figure 2 As shown, this embodiment provides a method for scheduling autonomous vehicles in a platoon, which includes the following steps: S1: Obtain the real-time battery level of each vehicle, the daily working time of each vehicle, and the preset starting energy threshold; calculate the target energy for replenishment based on the real-time battery level and the starting energy threshold; calculate the replenishment time based on the target energy for replenishment. S101: Calculate the target energy for energy replenishment. in, For safety margin; S102: Calculate the replenishment time: in, Let v be the energy replenishment rate of vehicle v.

[0026] Example: Energy of vehicles in formation P1: Vehicle A 85%, Vehicle B 82%, Vehicle C 78%; All vehicles with E≥60% → No need for refueling, depart directly; Energy levels of vehicles in formation P2: D vehicle 92%, E vehicle 88%, F vehicle 58%. F car's energy is 58% < 60% → It needs to be recharged; Target energy: max(0.7, 0.92) = 0.92; Energy replenishment time: Based on car F, (0.92-0.58) / 0.3×30=34 minutes; The entire team replenished their energy for 34 minutes before setting off.

[0027] S103: S2: Calculate the maximum number of daily transport trips for each vehicle based on the refueling time and the daily working time of each vehicle; calculate the daily transport capacity of the vehicle and the daily transport capacity of the standard formation based on the maximum number of daily transport trips for each vehicle; select vehicles to form a formation according to the real-time battery level of each vehicle and the preset energy balance constraints. Conduct vehicle capability assessments and set platooning standards: S201: Calculation of Daily Vehicle Transport Capacity: Considering the vehicle's reusability within a single workday, calculate the maximum daily transport capacity for each vehicle: formula: The variables are explained in Table 1: Table 1 Calculation process: in, Daily working hours (e.g., 480 minutes). Daily energy replenishment time (e.g., 30 minutes), Average transport time per trip (e.g., 45 minutes). Loading and unloading time (e.g., 20 minutes).

[0028] Example: Assuming the logistics park operates for 8 hours (480 minutes), vehicles require 30 minutes of refueling per day, a single trip takes 45 minutes, and loading and unloading takes 20 minutes: S202: Standard Formation Setup and Capability Calculation Let the standard platoon size be K vehicles ( Then, the daily transport capacity of a standard fleet is: Example: When (K = 3), S203: Perform vehicle selection and energy balance matching: S2031: Vehicle Selection Principles like Figure 3 As shown, the core principle of vehicle selection is to choose vehicles with similar energy levels to form a convoy, avoiding waiting for refueling due to energy differences.

[0029] Energy balance constraints: in, This represents the maximum permissible energy difference within the formation (recommended value: 20%).

[0030] S2032: Vehicle Selection Algorithm 1. Available vehicle screening: Select vehicles with energy E≥E_depart (E_depart=60%). 2. Energy Grouping: Group available vehicles according to energy range (e.g., 80-100%, 60-80%). 3. Formation Formation: Prioritize selecting vehicles from the same energy range to form formations; 4. Special cases: If there are not enough vehicles in the same section, they shall be supplemented from the adjacent section, but the energy difference constraint must be met.

[0031] Negative examples and correct practices: Negative example: Vehicle A: 100% energy; Vehicle B: 95% energy; Vehicle C: 30% energy (although ≥ 60%, the difference from other vehicles is too large); Problem: Vehicle C needs to recharge soon, while vehicles A and B have sufficient energy, causing the platoon to wait.

[0032] Correct approach: Option 1: Select A (100%), B (95%), and D (92%) to form a formation (difference ≤ 8%). Option 2: Select C (30%), E (35%), and F (40%) to form a low-energy formation separately, and arrange for energy replenishment first.

[0033] S2033: Calculation Example: Available vehicle energy status: Vehicle 1: 92%, Vehicle 2: 88%, Vehicle 3: 85%, Vehicle 4: 82%; Vehicle 5: 78%, Vehicle 6: 75%, Vehicle 7: 40%, Vehicle 8: 35%.

[0034] Formation formation: 1. High-energy formation: Select vehicles 1, 2, and 3 (energy difference: 92% - 85% = 7% ≤ 20%). 2. Medium-energy formation: Select vehicles 4, 5, and 6 (energy difference: 82% - 75% = 7% ≤ 20%). 3. Low-energy vehicles: Vehicles 7 and 8 will be refueled separately and will not be mixed with other vehicles.

[0035] S204: Perform task allocation and trip planning: S2041: Formation Task Assignment: Daily tasks are assigned to each fleet according to priority, ensuring that the daily transport volume of each fleet does not exceed its capacity limit.

[0036] Allocation formula: For formation Assign task volume Must meet: S204: Conduct dynamic adjustments to train schedules and formation sizes. Core innovation: Dynamically adjust the number of vehicles in the convoy based on the actual cargo volume of each trip.

[0037] Adjustment rules: 1. When the standard convoy performs the first m-1) transport trips, it uses a complete K-car. 2. For the final transport trip, adjust the number of vehicles based on the remaining cargo volume: in, This refers to the remaining cargo that the fleet needs to transport on its last trip.

[0038] Calculation example: Formation P1 Task Allocation: Total Tasks Boxes, standard formation size K = 3; Transportation planning: 1. First 4 trips: Each trip uses 3 vehicles, transporting 6 boxes per trip, for a total of... box 2. Remaining stock: 28 - 24 = 4 boxes; 3. Number of vehicles on the last trip: vehicle; Complete transportation plan: Trips 1-4: 3 cars in a convoy, transporting 6 boxes per trip; 5th trip: 2 cars in convoy, transporting 4 boxes; Total shipments: (24 + 4 = 28) boxes.

[0039] Advantages: One less vehicle is used on the last trip, which can be scheduled for other tasks or maintenance in advance, thus improving vehicle utilization.

[0040] S3: Real-time acquisition of transportation task data, including total task time and remaining task time, and calculation of dynamic urgency weight based on total task time and remaining task time; calculation of real-time priority of tasks based on dynamic urgency weight; allocation of tasks to each formation for execution based on real-time priority and daily transportation capacity of standard formations.

[0041] S301: Dynamic calculation of task priority: For each task Calculate its priority in real time: Variable explanation: S302: Calculation Example: Task T001 parameters: box, Hours (9:00-15:00); Current time is 10:00 AM, 3 boxes have been transported. box, Hour; parameter: , , Hour; calculate: S303: Priority sorting: All tasks are set as follows Tasks are sorted in descending order, with higher priority tasks scheduled first.

[0042] Preferably, perform formation requirement calculation and planning: S401: Formation Requirement Calculation Formula: Calculate the required number of formations based on the total cargo volume and standard formation capacity: Variable explanation: S402: Vehicle Demand Calculation Total number of vehicles required: Calculation example: Scenario: Total daily task volume Boxes, standard formation size , Box / Vehicle / Day; calculate: ; .

[0043] Preferred, Conduct a pre-departure energy check: in, (60% energy is the starting threshold).

[0044] Example 2 This embodiment provides a system for scheduling autonomous vehicle platoons, including a memory and a processor. The memory stores a computer program, and the processor calls the computer program to execute the steps of the method as described in Embodiment 1.

[0045] This embodiment also provides a complete daily scheduling example of an autonomous vehicle platooning scheduling method, including: 1. Scene Setup: Logistics Park: Warehouses: W1-W8; Autonomous vehicles: 20; Standard formation size: K = 3 vehicles; Working hours: 8:00-18:00 (10 hours) The daily task list is shown in Table 2 below: Table 2 2. Vehicle energy status (8:00) is shown in Table 3 below: Table 3 3. Scheduling and execution process: 3.1: Formation Requirements Calculation: 3.2: Vehicle Selection and Formation: Available vehicle screening: Exclude vehicles with less than 60% energy; Energy Balance Formation Setup: 1. Formation P1: V01 (92%), V02 (88%), V03 (85%) → Difference 7% ✓; 2. Formation P2: V07 (95%), V08 (91%), V09 (89%) → Difference 6% ✓; 3. Formation P3: V04 (82%), V05 (78%), V06 (75%) → Difference 7% ✓.

[0046] 3.3: Task Allocation and Transportation Planning: Task assignment: P1: T001 (15 boxes) + T004 (8 boxes) = 23 boxes; P2: T003(20 boxes) = 20 boxes; P3: T002 (12 boxes) + T005 (10 boxes) + T006 (18 boxes) = 40 boxes.

[0047] Note: V10 (45%) was not selected to be mixed with other high-energy vehicles to avoid waiting for refueling.

[0048] Check: P3 task volume 40 boxes > daily formation capacity of 36 boxes; Adjustment: Transfer 4 boxes from T006 to P1, final result: P1: 27 boxes, P2: 20 boxes, P3: 36 boxes.

[0049] Trip planning: P1: 27 boxes → The first 4 trips with 3 trucks transported 24 boxes, and the 5th trip with 2 trucks transported 3 boxes; P2: 20 boxes → 18 boxes were transported in 3 trucks on the first 3 trips, and 2 boxes were transported in 1 truck on the 4th trip; P3: 36 boxes → 6 trips, all transported by 3 vehicles, 6 boxes per trip.

[0050] 3.4: Energy replenishment arrangements: First energy replenishment (12:00): Energy levels of each formation: P1 approximately 70%, P2 approximately 75%, P3 approximately 65%. All vehicles have an energy level >60% → No recharging required, continue transporting; Second energy replenishment (15:00): The P3 vehicle's energy level has dropped to approximately 45%, requiring recharging. The energy level is replenished to 80% in about 35 minutes.

[0051] 3.5: Dynamic Adjustment (Actual Implementation): P1 last transport (16:30): Remaining stock: 3 boxes; Adjustment: Use 2 vehicles (instead of 3) for transportation; One of the released vehicles can perform other minor tasks or be maintained ahead of schedule.

[0052] 4. Scheduling effect: Problems with traditional methods: 1. Mixing low-energy and high-energy vehicles in formation may lead to frequent refueling delays in the platoon; 2. All trips used 3 vehicles → the last trip, transporting 3 boxes, also used 3 vehicles, wasting transport capacity; 3. Ignoring energy balance → asynchronous energy replenishment time within the formation; Advantages of this invention: 1. Grouping vehicles with similar energy levels → synchronized refueling, reducing waiting time; 2. Dynamically adjust the number of vehicles on the last trip → improve vehicle utilization; 3. Multi-trip planning based on daily capacity → Maximizing vehicle reuse.

[0053] The key technical features of this invention are: 1. Energy balance matching mechanism: When forming a platoon, the energy level of the vehicles is taken into account to ensure that the energy difference between vehicles in the platoon does not exceed the threshold and avoid refueling waiting time due to energy differences.

[0054] 2. Dynamic fleet size adjustment: Based on the actual cargo volume of each trip, the number of vehicles in the fleet is dynamically reduced during the last trip to improve vehicle utilization efficiency.

[0055] 3. Optimization of daily vehicle reuse: Plan multiple trips based on the daily transportation capacity of vehicles to achieve efficient utilization of vehicle resources; dynamically adjust the fleet size according to the workload to optimize resource allocation.

[0056] 4. Collaborative Refueling Decision: Vehicles within the platoon receive unified refueling, reducing waiting time caused by individual refueling; enabling collaborative refueling of vehicles within the platoon, improving operational efficiency.

[0057] 5. Dynamic priority calculation: The priority of tasks is dynamically adjusted based on their urgency and remaining time.

[0058] The innovative points of this invention are shown in Table 4 below: Table 4 The preferred embodiments of the present invention have been described in detail above. It should be understood that those skilled in the art can make numerous modifications and variations based on the concept of the present invention without creative effort. Therefore, all technical solutions that can be obtained by those skilled in the art based on the concept of the present invention through logical analysis, reasoning, or limited experimentation on the basis of existing technology should be within the scope of protection defined by the claims.

Claims

1. A method for scheduling autonomous vehicle platoons, characterized in that, include: The system acquires the real-time battery level of each vehicle, the daily operating time of each vehicle, and a preset starting energy threshold. Based on the real-time battery level and the starting energy threshold, it calculates the target energy for recharging and the recharging time based on the target energy. Based on the refueling time and the daily working time of each vehicle, the maximum number of daily transport trips of the vehicle is calculated; based on the maximum number of daily transport trips of the vehicle, the daily transport capacity of the vehicle and the daily transport capacity of the standard formation are calculated; and vehicles are selected to form a formation according to the real-time battery level of each vehicle and the preset energy balance constraints. Real-time acquisition of transportation task data, including total task time and remaining task time, and calculation of dynamic urgency weight based on the total task time and remaining task time; calculation of real-time priority of tasks based on the dynamic urgency weight; and allocation of tasks to each formation for execution based on the real-time priority and the daily transportation capacity of the standard formation.

2. The autonomous vehicle platooning scheduling method according to claim 1, characterized in that, Based on the real-time power level and the starting energy threshold, the target energy for replenishment is calculated using the following formula: in, To replenish the target energy, The starting energy threshold, The energy corresponding to vehicle v; This is for safety margin.

3. The autonomous vehicle platooning scheduling method according to claim 2, characterized in that, The energy replenishment time is calculated based on the target energy level, and the corresponding calculation formula is as follows: in, For the time to replenish energy, Let v be the energy replenishment rate of vehicle v.

4. The autonomous vehicle platooning scheduling method according to claim 3, characterized in that, Based on the refueling time and the daily working hours of each vehicle, the maximum number of daily transport trips for each vehicle is calculated using the following formula: in, This represents the maximum number of trips the vehicle can make per day. For daily working hours, For daily energy replenishment time, This refers to the loading and unloading time.

5. The autonomous vehicle platooning scheduling method according to claim 4, characterized in that, Based on the maximum number of daily transport trips of the vehicles, the daily transport capacity of the vehicles and the daily transport capacity of the standard fleet are calculated using the following formulas: in, For the daily transport capacity of a standard formation, For the daily transport capacity of vehicles, For standard formation size, This represents the maximum capacity for a single vehicle per trip.

6. The autonomous vehicle platooning scheduling method according to claim 1, characterized in that, The task is divided into the first n transport trips and the last transport trip; when executing the last transport trip, the number of vehicles used for transport is calculated and adjusted based on the remaining cargo volume, and the corresponding calculation formula is as follows: in, This refers to the number of vehicles used for the last transport trip. This refers to the remaining cargo that the fleet needs to transport on its last trip. This represents the maximum capacity for a single vehicle per trip.

7. The autonomous vehicle platooning scheduling method according to claim 1, characterized in that, Based on the dynamic urgency weight, the real-time priority of the task is calculated using the following formula: in, The real-time priority of the task. For dynamic urgency weighting, For the remaining quantity of goods, For total cargo volume, For the remaining time, Where k is the total time, and k is the weighting coefficient. For dynamic weighting coefficients, This is the critical time threshold.

8. The autonomous vehicle platooning scheduling method according to claim 1, characterized in that, The specific expression for the energy balance constraint is as follows: in, For the first The energy of a vehicle For the first The energy of a vehicle The maximum energy difference within the formation. The starting energy threshold, For the vehicle's energy.

9. The autonomous vehicle platooning scheduling method according to claim 1, characterized in that, Before performing the task, check the energy of each vehicle in the formation. If the energy of any vehicle is less than the starting energy threshold, then replenish the energy of the entire formation.

10. A system for scheduling autonomous vehicle platoons, characterized in that, It includes a memory and a processor, the memory storing a computer program, the processor invoking the computer program to perform the steps of the method as described in any one of claims 1 to 9.

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