Unmanned container truck multi-vehicle formation simulation method and system based on OBU protocol

Abstract

The invention relates to an OBU protocol-based unmanned container truck multi-vehicle formation simulation method and system, and the method comprises the steps: Q1, collecting the data information of the chassis state of a vehicle, the positioning data information of the vehicle, and the data information of the driving track of the vehicle in real time in a process that an OBU protocol-based simulation unmanned container truck executes a port task; and Q2, on the basis of the data information of the chassis states of the vehicles, the positioning data information of the vehicles and the data information of the driving tracks of the vehicles, constructing a coexistence non-uniform Gaussian variation pre-formation model of the multiple vehicles, and performing pre-formation on the vehicles to obtain the data information of the pre-formed vehicles. According to the method, the formation strategy and the control algorithm are verified and optimized through methods such as analogue simulation and data analysis so as to improve the stability and safety of formation driving, and the formation driving requirements of different scales and scenes can be met.

Description

Technical Field

[0001] The present invention relates to the technical field of unmanned container trucks, and in particular to an unmanned container truck multi-vehicle formation simulation method and system based on an OBU protocol. Background Art

[0002] After the port's unmanned container truck autonomous driving program is released, a large number of tests are needed to check the safety of unmanned container truck driving and operation. The test needs to cover all driving sections and operation scenarios of unmanned container trucks in the port. The actual vehicle test of unmanned container trucks is inefficient and has low coverage. The coordinated formation driving and following functions of more than three unmanned container trucks are restricted by port safety factors and cannot be tested in real vehicles.

[0003] In the prior art, a Chinese patent (application number: 202120779439.4, publication number: CN214896242U) discloses a VTD-based multi-unmanned mining truck simulation test system, including a VTD intelligent scene simulation module, a dynamic model module, and a controller module, wherein the VTD intelligent scene simulation module is connected to the dynamic model module and the controller module, and the dynamic model module is connected to the controller module; the controller module includes several mining truck simulation computers, and an unmanned driving logic simulation module; the VTD intelligent scene simulation module includes a scene simulation module connected to the dynamic model module for sending road information, and a sensor simulation module connected to the mining truck simulation computer for feedback. However, this solution mainly simulates the mining truck operating environment in the VTD simulation software, which is used to simulate the real environment and transmit the simulated sensor data to the unmanned mining truck controller; this solution mainly simulates the unmanned driving operating environment of a single vehicle, and cannot simulate the data interaction of multiple vehicles, and cannot reflect the scene of unmanned driving of multiple vehicles in the port. Summary of the invention

[0004] In view of the above shortcomings of the prior art, the present invention provides a method and system for simulating a multi-vehicle platoon of unmanned container trucks based on the OBU protocol, which not only verifies and optimizes the platoon strategy and control algorithm through simulation and data analysis methods to improve the stability and safety of platoon driving, but also can meet the needs of platoon driving of different scales and scenarios.

[0005] In order to achieve the above-mentioned purpose and other related purposes, the technical solution provided by the present invention is as follows:

[0006] A method for simulating a multi-vehicle formation of unmanned container trucks based on an OBU protocol, the method comprising:

[0007] Q1. In the process of the simulated unmanned container truck based on the OBU protocol performing port tasks, real-time data information of the vehicle's chassis status, vehicle positioning data information and vehicle driving trajectory data information are collected;

[0008] Q2. Based on the data information of the chassis status of the vehicle, the positioning data information of the vehicle and the data information of the driving trajectory of the vehicle, a multi-vehicle coexistence non-uniform Gaussian mutation pre-formation model is constructed to pre-form the vehicles and obtain the data information of the pre-formed vehicles;

[0009] Q3. Based on the data information of the pre-formed vehicles, collect data information of the priority of the port tasks of the vehicles, and use the crazy adaptive Archimedean optimization algorithm to optimize the pre-formed vehicles to obtain the optimized vehicle formation data information;

[0010] Q4. Based on the optimized vehicle formation data information, a feasibility function F of the vehicle formation is established, the feasibility of the operation of the vehicles after the formation is evaluated, and the estimated operation data information of the vehicle formation is obtained for feedback and fine-tuning of the vehicle formation.

[0011] Furthermore, the feasibility function F of the vehicle formation is,

[0012]

[0013] Among them, x is the optimized vehicle formation data information, and α1, α2 and α3 are the feasibility estimation factors of the vehicle formation.

[0014] Furthermore, the constraint function f of the feasibility estimation factors α1, α2 and α3 of the vehicle formation is,

[0015]

[0016] The value range of the constraint function f is (1,2).

[0017] Furthermore, the method further comprises:

[0018] Q5. Based on the estimated operation value data information of the vehicle formation, a preset threshold is set. If the estimated operation value of the vehicle formation is less than the preset threshold, the operation of the vehicle formation remains unchanged. If the estimated operation value of the vehicle formation is greater than the preset threshold, return to step Q2 until the estimated operation value of the vehicle formation is less than the preset threshold.

[0019] Furthermore, in step Q2, the construction of a multi-vehicle coexistence non-uniform Gaussian variation pre-formation model to pre-form the vehicles includes:

[0020] Q21. Construct a multi-vehicle coexistence sequence function W based on the vehicle chassis status data information, vehicle positioning data information and vehicle driving trajectory data information,

[0021]

[0022] Among them, y1 is the data information of the chassis state of the vehicle, y2 is the positioning data information of the vehicle, y3 is the data information of the driving trajectory of the vehicle, β1, β2 and β3 are the coexistence sequence decision factors of the vehicle, and the coexistence sequence of multiple vehicles is characterized to obtain the data information of the coexistence sequence of multiple vehicles;

[0023] Q22. Based on the data information of the coexistence sequence of the multiple vehicles, establish a non-uniform Gaussian variation function R of the pre-formation of the vehicle,

[0024]

[0025] Among them, z is the data information of the coexistence sequence of multiple vehicles, δ1, δ2 and δ3 are the weight factors of the pre-formation of multiple vehicles;

[0026] Q23. Based on the non-uniform Gaussian variation function R of the pre-formation of the vehicles, the vehicles are pre-formed to obtain data information of the vehicles after the pre-formation.

[0027] Furthermore, the weight factors δ1, δ2 and δ3 of the multi-vehicle formation are,

[0028]

[0029] Wherein, z is the data information of the coexistence sequence of multiple vehicles.

[0030] Furthermore, the constraints of the coexistence sequence decision factors β1, β2 and β3 of the vehicle are:

[0031]

[0032] Furthermore, in step Q3, the optimization of the pre-formed vehicles using the crazy adaptive Archimedean optimization algorithm includes:

[0033] Q31. Based on the data information of the priority of the port mission of the vehicle and the data information of the vehicle after the pre-formation, establish the Tent mapping function U of the vehicle formation,

[0034]

[0035] Among them, r1 is the data information of the priority of the vehicle's port task, r2 is the data information of the vehicle after pre-formation, γ1, γ2 and γ3 are the correction parameters of the vehicle formation, and the Tent mapping sequence matrix of the vehicle formation is characterized to obtain the data information of the Tent mapping sequence matrix of the vehicle formation;

[0036] Q32. Based on the data information of the Tent mapping sequence matrix of the vehicle formation, the Archimedes population is initialized, various parameters of the population and the maximum number of iterations are determined, and the data information of the initialized Archimedes population is obtained;

[0037] Q33. Based on the initialized Archimedes population data information, establish the fitness function P of the population individuals,

[0038]

[0039] Among them, a is the population data information of Archimedes after initialization, η1, η2 and η3 are the fitness adjustment factors of the population individuals, and the fitness values ​​of the population individuals are calculated to obtain the fitness value data information of the population individuals;

[0040] Q34. Based on the fitness value data information of the individuals in the population, establish an optimization function G of the population,

[0041]

[0042] Among them, h is the fitness value data information of the population individuals, λ1, λ2 and λ3 are adaptive learning factors, and the pre-formed vehicles are optimized to obtain the optimized vehicle formation data information.

[0043] Furthermore, the constraint function g of the adaptive learning factors λ1, λ2 and λ3 is,

[0044] The value range of g is (2,3).

[0045] In order to achieve the above-mentioned purpose and other related purposes, the present invention also provides an unmanned container truck multi-vehicle formation simulation system based on the OBU protocol, including a computer device, which is programmed or configured to execute any one of the steps of the unmanned container truck multi-vehicle formation simulation method based on the OBU protocol.

[0046] The present invention has the following positive effects:

[0047] 1. The present invention constructs a multi-vehicle coexistence non-uniform Gaussian variation pre-formation model to pre-form the vehicles, and optimizes the pre-formed vehicles by combining the crazy adaptive Archimedean optimization algorithm. Not only does it use the OBU protocol to achieve short-range wireless communication between vehicles, but also verify and optimize the formation strategy and control algorithm through simulation and data analysis methods to improve the stability and safety of formation driving. In addition, the platform also has the advantages of low cost, easy expansion and easy deployment, and can meet the needs of formation driving of different scales and scenarios.

[0048] 2. The present invention establishes a feasibility function F of the vehicle formation and evaluates the feasibility of the operation of the vehicles after the formation. It not only efficiently analyzes the operation data of multiple vehicles to ensure the accuracy and completeness of the data, but also further accurately optimizes the operation of the vehicle formation and improves the efficiency of the operation of the unmanned container truck. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 It is a schematic diagram of the method flow of the present invention;

[0050] Figure 2 A schematic diagram of a process of constructing a multi-vehicle coexistence non-uniform Gaussian variation pre-formation model according to the present invention;

[0051] Figure 3 It is a flow chart of the crazy adaptive Archimedean optimization algorithm of the present invention;

[0052] Figure 4 Schematic diagram of the system architecture of the present invention. DETAILED DESCRIPTION

[0053] The following is a description of exemplary embodiments of the present disclosure in conjunction with the accompanying drawings, including various details of the embodiments of the present disclosure to facilitate understanding, which should be considered as merely exemplary. Therefore, it should be recognized by those of ordinary skill in the art that various changes and modifications may be made to the embodiments described herein without departing from the scope and spirit of the present disclosure. Similarly, for the sake of clarity and conciseness, descriptions of well-known functions and structures are omitted in the following description.

[0054] Example 1: Figure 1 As shown, a method for simulating a multi-vehicle formation of unmanned container trucks based on the OBU protocol, the method comprising:

[0055] Q1. In the process of the simulated unmanned container truck based on the OBU protocol performing port tasks, real-time data information of the vehicle's chassis status, vehicle positioning data information and vehicle driving trajectory data information are collected;

[0056] Q2. Based on the data information of the chassis status of the vehicle, the positioning data information of the vehicle and the data information of the driving trajectory of the vehicle, a multi-vehicle coexistence non-uniform Gaussian mutation pre-formation model is constructed to pre-form the vehicles and obtain the data information of the pre-formed vehicles;

[0057] Q3. Based on the data information of the pre-formed vehicles, collect data information of the priority of the port tasks of the vehicles, and use the crazy adaptive Archimedean optimization algorithm to optimize the pre-formed vehicles to obtain the optimized vehicle formation data information;

[0058] Q4. Based on the optimized vehicle formation data information, a feasibility function F of the vehicle formation is established, the feasibility of the operation of the vehicles after the formation is evaluated, and the estimated operation data information of the vehicle formation is obtained for feedback and fine-tuning of the vehicle formation.

[0059] In this embodiment, the feasibility function F of the vehicle formation is,

[0060]

[0061] Among them, x is the optimized vehicle formation data information, and α1, α2 and α3 are the feasibility estimation factors of the vehicle formation.

[0062] In this embodiment, the constraint function f of the feasibility estimation factors α1, α2 and α3 of the vehicle formation is,

[0063]

[0064] The value range of the constraint function f is (1,2).

[0065] In this embodiment, the method further includes:

[0066] Q5. Based on the estimated operation value data information of the vehicle formation, a preset threshold is set. If the estimated operation value of the vehicle formation is less than the preset threshold, the operation of the vehicle formation remains unchanged. If the estimated operation value of the vehicle formation is greater than the preset threshold, return to step Q2 until the estimated operation value of the vehicle formation is less than the preset threshold.

[0067] In this embodiment, if Figure 2 As shown, in step Q2, the construction of a multi-vehicle coexistence non-uniform Gaussian variation pre-formation model and the pre-formation of vehicles include:

[0068] Q21. Construct a multi-vehicle coexistence sequence function W based on the vehicle chassis status data information, vehicle positioning data information and vehicle driving trajectory data information,

[0069]

[0070] Among them, y1 is the data information of the chassis state of the vehicle, y2 is the positioning data information of the vehicle, y3 is the data information of the driving trajectory of the vehicle, β1, β2 and β3 are the coexistence sequence decision factors of the vehicle, and the coexistence sequence of multiple vehicles is characterized to obtain the data information of the coexistence sequence of multiple vehicles;

[0071] Q22. Based on the data information of the coexistence sequence of the multiple vehicles, establish a non-uniform Gaussian variation function R of the pre-formation of the vehicle,

[0072]

[0073] Among them, z is the data information of the coexistence sequence of multiple vehicles, δ1, δ2 and δ3 are the weight factors of the pre-formation of multiple vehicles;

[0074] Q23. Based on the non-uniform Gaussian variation function R of the pre-formation of the vehicles, the vehicles are pre-formed to obtain data information of the vehicles after the pre-formation.

[0075] In this embodiment, the weight factors δ1, δ2 and δ3 of the multi-vehicle formation are,

[0076] Wherein, z is the data information of the coexistence sequence of multiple vehicles.

[0077] In this embodiment, the constraints of the coexistence sequence decision factors β1, β2 and β3 of the vehicles are:

[0078]

[0079] In this embodiment, if Figure 3 As shown, in step Q3, the optimization of the pre-formed vehicles using the crazy adaptive Archimedean optimization algorithm includes:

[0080] Q31. Based on the data information of the priority of the port mission of the vehicle and the data information of the vehicle after the pre-formation, establish the Tent mapping function U of the vehicle formation,

[0081]

[0082] Among them, r1 is the data information of the priority of the vehicle's port task, r2 is the data information of the vehicle after pre-formation, γ1, γ2 and γ3 are the correction parameters of the vehicle formation, and the Tent mapping sequence matrix of the vehicle formation is characterized to obtain the data information of the Tent mapping sequence matrix of the vehicle formation;

[0083] Q32. Based on the data information of the Tent mapping sequence matrix of the vehicle formation, the Archimedes population is initialized, various parameters of the population and the maximum number of iterations are determined, and the data information of the initialized Archimedes population is obtained;

[0084] Q33. Based on the initialized Archimedes population data information, establish the fitness function P of the population individuals,

[0085]

[0086] Among them, a is the population data information of Archimedes after initialization, η1, η2 and η3 are the fitness adjustment factors of the population individuals, and the fitness values ​​of the population individuals are calculated to obtain the fitness value data information of the population individuals;

[0087] Q34. Based on the fitness value data information of the individuals in the population, establish an optimization function G of the population,

[0088]

[0089] Among them, h is the fitness value data information of the population individuals, λ1, λ2 and λ3 are adaptive learning factors, and the pre-formed vehicles are optimized to obtain the optimized vehicle formation data information.

[0090] In this embodiment, the constraint function g of the adaptive learning factors λ1, λ2 and λ3 is,

[0091]

[0092] The value range of g is (2,3).

[0093] Embodiment 2: Based on the unmanned container truck multi-vehicle formation simulation method based on the OBU protocol in Embodiment 1, the present invention is further illustrated and described below.

[0094] like Figure 1 As shown, a method for simulating a multi-vehicle formation of unmanned container trucks based on the OBU protocol, the method comprising:

[0095] Q1. In the process of the simulated unmanned container truck based on the OBU protocol performing port tasks, real-time data information of the vehicle's chassis status, vehicle positioning data information and vehicle driving trajectory data information are collected;

[0096] Q2. Based on the data information of the chassis status of the vehicle, the positioning data information of the vehicle and the data information of the driving trajectory of the vehicle, a multi-vehicle coexistence non-uniform Gaussian mutation pre-formation model is constructed to pre-form the vehicles and obtain the data information of the pre-formed vehicles;

[0097] Q3. Based on the data information of the pre-formed vehicles, collect data information of the priority of the port tasks of the vehicles, and use the crazy adaptive Archimedean optimization algorithm to optimize the pre-formed vehicles to obtain the optimized vehicle formation data information;

[0098] Q4. Based on the optimized vehicle formation data information, a feasibility function F of the vehicle formation is established, the feasibility of the operation of the vehicles after the formation is evaluated, and the estimated operation data information of the vehicle formation is obtained for feedback and fine-tuning of the vehicle formation.

[0099] In this embodiment, the present invention provides an unmanned container truck multi-vehicle formation simulation system based on the OBU protocol, including a computer device, which is programmed or configured to execute any one of the steps of the unmanned container truck multi-vehicle formation simulation method based on the OBU protocol.

[0100] In this embodiment, if Figure 4 As shown in the figure, the unmanned truck multi-vehicle formation simulation platform mainly consists of the following modules:

[0101] 1) Dynamics module: The self-developed dynamics model is used to perform 1:1 data calibration with the real data of the unmanned container truck. By receiving the control information sent by the unmanned container truck controller, the status data and position data of the unmanned container truck are calculated in real time, and sent to the scenario software VTD. After being parsed through the RDB data protocol, the status of the unmanned container truck in the simulation software is adjusted in real time, and the sensor information is updated. At the same time, the calculated parameters are sent to the unmanned driving controller;

[0102] 2) Scenario simulation module: Use VTD scenario simulation software to develop complex traffic scene systems for autonomous driving system testing. Combined simulation with self-developed vehicle dynamics software achieves closed loop through RDB communication, integrating vehicle dynamics model with traffic scenarios to form a closed loop of the entire complex traffic environment.

[0103] 3) Real-time hardware and model management software Veristand, based on a graphical programming interface, seamlessly links with the real-time simulation system to implement real-time system configuration, hardware IO resource management, bus communication signal configuration, simulation model management, human-computer interaction interface configuration, real-time simulation operation control and status monitoring;

[0104] 4) Real-time simulation system, built based on NI's PXI Express technology, including chassis, real-time processor and various boards, used for real-time data interaction with the host computer test software, online parameter adjustment and monitoring, output the signals required by the controller through various IO boards, and collect all signals sent by the controller, real-time operation of Simulink simulation model, Simulink is a block diagram environment for multi-domain simulation and model-based design;

[0105] 5) OBU controller: The real-time simulation system uses model calculation to broadcast the chassis status data, positioning data and trajectory data of the vehicle through the OBU protocol. At the same time, the OBU controller receives the OBU data sent by other vehicles, parses it and sends it to the driverless controller of the vehicle through ROS. The steps for obtaining the data of the surrounding driverless trucks are as follows:

[0106] The first step is to build a 1:1 static port scene in the VTD scene simulation software, including containers, bridge cranes, etc.; build a multi-vehicle formation scene in VTD to simulate the real port environment;

[0107] The second step is to configure the self-developed dynamic model parameters in the Windows host computer, calculate the unmanned container truck control data in real time, and then send the parameters to the real-time simulation system for operation; the vehicle chassis data running in the real-time simulation system is sent to the unmanned container truck controller through the bus, hard wire and Ethernet channel.

[0108] The third step is to parse the RDB interface data in VTD, send the vehicle chassis data to the real-time simulation system through UDP / TCP transmission, and send the obstacle perception data simulated in the VTD scene to the unmanned truck controller through ROS to realize obstacle recognition and avoidance functions;

[0109] In the fourth step, the OBU controller receives the vehicle CAN data simulated by the real-time simulation system, encapsulates the chassis data, positioning data, and trajectory data of the vehicle through the OBU protocol, and then broadcasts it; at the same time, the OBU controller receives the data sent by the OBU controllers of other unmanned container trucks, parses it, and sends it to the unmanned driving controller of the vehicle through Ethernet for multi-vehicle formation algorithm control. This step is the key to achieving multi-vehicle collaborative control, and real-time communication and data sharing between vehicles are achieved through the OBU protocol.

[0110] In the fifth step, the unmanned driving controller of the vehicle receives data from the real-time simulation system, VTD simulation software and OBU controller to control the multi-vehicle platoon. The controller sends the calculated dynamic data to the self-developed dynamic model, adjusts the operating status of the unmanned container truck in real time, and updates the sensor information of the unmanned container truck in VTD to complete the data closed-loop simulation.

[0111] In this embodiment, the present invention provides a computer-readable storage medium, on which is stored a computer program programmed or configured to execute any one of the unmanned container truck multi-vehicle formation simulation methods based on the OBU protocol.

[0112] Any reference to memory, storage, database or other media used in the embodiments provided in the present application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM) and memory bus dynamic RAM (RDRAM).

[0113] In summary, the present invention not only verifies and optimizes the formation strategy and control algorithm through simulation and data analysis methods to improve the stability and safety of formation driving, but also can meet the needs of formation driving in different scales and scenarios.

[0114] The above specific implementations do not constitute a limitation on the protection scope of the present disclosure. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present disclosure shall be included in the protection scope of the present disclosure.

Claims

1. A method for simulating multi-vehicle formation of unmanned container trucks based on OBU protocol, characterized in that: The method comprises: Q1. In the process of the simulated unmanned container truck based on the OBU protocol performing port tasks, real-time data information of the vehicle's chassis status, vehicle positioning data information and vehicle driving trajectory data information are collected; Q2. Based on the data information of the chassis status of the vehicle, the positioning data information of the vehicle and the data information of the driving trajectory of the vehicle, a multi-vehicle coexistence non-uniform Gaussian mutation pre-formation model is constructed to pre-form the vehicles and obtain the data information of the pre-formed vehicles; Q3. Based on the data information of the pre-formed vehicles, collect data information of the priority of the port tasks of the vehicles, and use the crazy adaptive Archimedean optimization algorithm to optimize the pre-formed vehicles to obtain the optimized vehicle formation data information; Q4. Based on the optimized vehicle formation data information, a feasibility function F of the vehicle formation is established, the feasibility of the operation of the vehicles after the formation is evaluated, and the estimated operation data information of the vehicle formation is obtained for feedback and fine-tuning of the vehicle formation.

2. The unmanned container truck multi-vehicle formation simulation method based on the OBU protocol according to claim 1 is characterized in that: The feasibility function F of the vehicle formation is, Among them, x is the optimized vehicle formation data information, and α1, α2 and α3 are the feasibility estimation factors of the vehicle formation.

3. The unmanned container truck multi-vehicle formation simulation method based on the OBU protocol according to claim 2 is characterized in that: The constraint function f of the feasibility estimation factors α1, α2 and α3 of the vehicle formation is, The value range of the constraint function f is (1,2).

4. The OBU protocol-based unmanned truck multi-vehicle platoon simulation method according to claim 1 is characterized in that: The method further comprises: Q5. Based on the estimated operation value data information of the vehicle formation, a preset threshold is set. If the estimated operation value of the vehicle formation is less than the preset threshold, the operation of the vehicle formation remains unchanged. If the estimated operation value of the vehicle formation is greater than the preset threshold, return to step Q2 until the estimated operation value of the vehicle formation is less than the preset threshold.

5. The OBU protocol-based unmanned truck multi-vehicle platoon simulation method according to claim 1 is characterized in that: In step Q2, the construction of a multi-vehicle coexistence non-uniform Gaussian variation pre-formation model and the pre-formation of vehicles include: Q21. Construct a multi-vehicle coexistence sequence function W based on the vehicle chassis status data information, vehicle positioning data information and vehicle driving trajectory data information, Among them, y1 is the data information of the chassis state of the vehicle, y2 is the positioning data information of the vehicle, y3 is the data information of the driving trajectory of the vehicle, β1, β2 and β3 are the coexistence sequence decision factors of the vehicle, and the coexistence sequence of multiple vehicles is characterized to obtain the data information of the coexistence sequence of multiple vehicles; Q22. Based on the data information of the coexistence sequence of the multiple vehicles, establish a non-uniform Gaussian variation function R of the pre-formation of the vehicle, Among them, z is the data information of the coexistence sequence of multiple vehicles, δ1, δ2 and δ3 are the weight factors of the pre-formation of multiple vehicles; Q23. Based on the non-uniform Gaussian variation function R of the pre-formation of the vehicles, the vehicles are pre-formed to obtain data information of the vehicles after the pre-formation.

6. The unmanned container truck multi-vehicle formation simulation method based on the OBU protocol according to claim 5 is characterized in that: The weight factors δ1, δ2 and δ3 of the multi-vehicle formation are, Wherein, z is the data information of the coexistence sequence of multiple vehicles.

7. The unmanned container truck multi-vehicle formation simulation method based on the OBU protocol according to claim 5 is characterized in that: The constraints of the coexistence sequence decision factors β1, β2 and β3 of the vehicle are:

8. The OBU protocol-based unmanned truck multi-vehicle platoon simulation method according to claim 1 is characterized in that: In step Q3, the optimization of the pre-formed vehicles using the crazy adaptive Archimedean optimization algorithm includes: Q31. Based on the data information of the priority of the port mission of the vehicle and the data information of the vehicle after the pre-formation, establish the Tent mapping function U of the vehicle formation, Among them, r1 is the data information of the priority of the vehicle's port task, r2 is the data information of the vehicle after pre-formation, γ1, γ2 and γ3 are the correction parameters of the vehicle formation, and the Tent mapping sequence matrix of the vehicle formation is characterized to obtain the data information of the Tent mapping sequence matrix of the vehicle formation; Q32. Based on the data information of the Tent mapping sequence matrix of the vehicle formation, the Archimedes population is initialized, various parameters of the population and the maximum number of iterations are determined, and the data information of the initialized Archimedes population is obtained; Q33. Based on the initialized Archimedes population data information, establish the fitness function P of the population individuals, Among them, a is the population data information of Archimedes after initialization, η1, η2 and η3 are the fitness adjustment factors of the population individuals, and the fitness values ​​of the population individuals are calculated to obtain the fitness value data information of the population individuals; Q34. Based on the fitness value data information of the individuals in the population, establish an optimization function G of the population, Among them, h is the fitness value data information of the population individuals, λ1, λ2 and λ3 are adaptive learning factors, and the pre-formed vehicles are optimized to obtain the optimized vehicle formation data information.

9. The OBU protocol-based unmanned truck multi-vehicle platoon simulation method according to claim 8 is characterized by: The constraint function g of the adaptive learning factors λ1, λ2 and λ3 is, The value range of g is (2,3).

10. An unmanned truck multi-vehicle formation simulation system based on OBU protocol, including computer equipment, characterized in that: The computer device is programmed or configured to execute the steps of the unmanned container truck multi-vehicle platoon simulation method based on the OBU protocol as described in any one of claims 1 to 9.

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