Automatic driving truck formation control method and device, and automatic driving truck formation

Abstract

The invention discloses a control method and device for an automatic driving truck formation and the automatic driving truck formation. The method comprises the steps of obtaining vehicle image data of an automatic driving truck formation when it is detected that the automatic driving truck formation is driven in; the vehicle image data is processed, formation information of the automatic driving truck formation is obtained, and the formation information at least comprises the following information of the automatic driving truck formation: the number of vehicles, the formation form and the type of loaded articles; according to the formation information, a guiding strategy of the automatic driving truck formation is generated, the guiding strategy is used for guiding various operations of the automatic driving trucks in a collecting and distributing area, and the collecting and distributing area is a transfer area of the automatic driving truck formation; and controlling the automatic driving truck formation to act according to the guiding strategy. The technical problems of low formation efficiency and unreasonable resource allocation caused by the fact that the existing highway port cannot carry out effective information identification and action guidance on the automatic driving truck formation are solved.

Description

Technical Field

[0001] The present invention relates to the field of autonomous driving technology, and in particular to a control method and device for an autonomous driving truck platoon, and an autonomous driving truck platoon. Background Art

[0002] Autonomous truck platooning refers to a convoy of trucks on the road, led by a lead truck and followed by several trucks traveling at a fixed distance and synchronized speed. This model can reduce wind resistance, save fuel consumption, improve road utilization, and enhance driving safety through cooperative communication between vehicles. Vehicles in the platoon usually use V2V (Vehicle-to-Vehicle) communication technology to ensure information sharing and coordinated actions between vehicles. Although autonomous truck platooning brings many potential benefits, the current design and operation model of highway ports has failed to fully utilize these advantages. Existing highway port facilities are not fully adapted to the emerging transportation form of autonomous truck platooning, especially in the efficient handling of platoons. There are obvious deficiencies. This is specifically manifested in the following aspects:

[0003] 1) Lack of support for platoon coordination: The layout and facilities of traditional highway ports mainly serve trucks operated by a single driver, without considering the overall operational needs of the platoon. Therefore, platoon vehicles often need to disband before entering the highway port and re-form after completing tasks such as loading and unloading, charging, or maintenance. This process is time-consuming and reduces the efficiency of platoon transportation.

[0004] 2) High demand for manual intervention: In existing highway ports, vehicle guidance, parking allocation, charging, and maintenance often rely on manual operation and management, which not only increases operating costs but also may cause safety risks. This is especially true when facing self-driving truck platoons, where the inaccuracy and inefficiency of manual operation are more prominent.

[0005] 3) Static scheduling: Traditional highway port scheduling systems are usually based on fixed rules or pre-made plans and cannot respond to dynamic changes within the port in real time, such as the real-time location of vehicles, task requirements, charging status, etc. This leads to waste of resources and delays in task processing.

[0006] 4) Poor compatibility: Existing highway port facilities were not designed with full consideration of future technological developments, such as automatic charging, remote diagnosis and maintenance, and intelligent trailer systems. Therefore, it is difficult to seamlessly integrate with emerging intelligent equipment, limiting the modernization and efficiency improvement of highway ports.

[0007] Therefore, with the popularization of autonomous truck platoons, existing highway ports urgently need to improve their hardware facilities and software management to achieve more efficient, safer and smarter logistics and transportation services. Summary of the Invention

[0008] The embodiments of the present invention provide a control method and device for an autonomous driving truck platoon, and an autonomous driving truck platoon, to at least solve the technical problem that existing highway ports are unable to effectively identify information and provide action guidance for autonomous driving truck platoons, resulting in low platoon efficiency and unreasonable resource allocation.

[0009] According to one aspect of an embodiment of the present invention, a control method for an autonomous driving truck formation is provided, comprising: upon detecting the entry of an autonomous driving truck formation, acquiring vehicle image data of the autonomous driving truck formation; processing the vehicle image data to obtain formation information of the autonomous driving truck formation, wherein the formation information includes at least the following information of the autonomous driving truck formation: the number of vehicles, the formation form, and the type of loaded items; generating a guidance strategy for the autonomous driving truck formation based on the formation information, wherein the guidance strategy is used to guide various operations of the autonomous driving trucks in a gathering and distribution area, wherein the gathering and distribution area is a transfer area for the autonomous driving truck formation, and the gathering and distribution area is provided with the following areas: a platoon vehicle driving road, a platoon vehicle parking space, a platoon vehicle charging area, and a platoon vehicle maintenance area, wherein the various operations include at least: parking, charging, and maintenance; and controlling the autonomous driving truck formation to act according to the guidance strategy.

[0010] Optionally, detecting the entry of a convoy of self-driving trucks includes at least one of the following: determining that the self-driving truck convoy has entered when the self-driving truck convoy exists in image data, wherein the image data is data collected by an image acquisition component arranged at the entrance of the gathering and distribution area; determining that the self-driving truck convoy has entered when a detection signal detects the self-driving truck convoy, wherein the detection signal is a signal detected by a signal detector arranged at the entrance of the gathering and distribution area.

[0011] Optionally, the vehicle image data is processed to obtain the formation information of the autonomous driving truck formation, including: processing the vehicle image data through an image recognition model to obtain the formation information corresponding to the vehicle image data, wherein the image recognition model is a model trained by machine learning using multiple groups of first training data, and each of the multiple groups of first training data includes: sample vehicle image data and sample formation information corresponding to the sample vehicle image data.

[0012] Optionally, a guidance strategy for the autonomous driving truck formation is generated based on the formation information, including: processing the formation information through a guidance strategy generation model to obtain the guidance strategy corresponding to the formation information, wherein the guidance strategy generation model is a model trained by machine learning using multiple sets of second training data, and each of the multiple sets of second training data includes: sample formation information and a sample guidance strategy corresponding to the formation information.

[0013] Optionally, generating a guidance strategy for the autonomous driving truck formation based on the formation information includes: obtaining first vehicle-related information of the autonomous driving truck formation, and generating a driving guidance strategy for the autonomous driving truck formation based on the vehicle-related information, wherein the first vehicle-related information includes at least one of the following: vehicle current status, task priority, and traffic congestion information; obtaining parking space information of each parking space in the distribution area and second vehicle-related information of the autonomous driving truck formation, and generating a parking guidance strategy based on the parking space information and the second vehicle-related information, wherein the parking space information includes at least: physical coordinates of the parking space, adapted vehicle type, and functional area attributes, The second vehicle-related information includes: current task type, vehicle size and estimated stop time; obtain charging pile information of each charging area in the distribution area and third vehicle-related information of the autonomous driving truck formation, and generate a charging guidance strategy based on the charging pile information and the third vehicle-related information, wherein the charging pile information includes at least: charging power and docking method, and the third vehicle-related information includes: battery state of charge, battery temperature, and expected power consumption for the next task; obtain third vehicle-related information of the autonomous driving truck formation, and generate a maintenance guidance strategy based on the third vehicle-related information, wherein the third vehicle-related information includes: vehicle operating status and fault code.

[0014] Optionally, controlling the autonomous driving truck formation to act in accordance with the guidance strategy includes: when the autonomous driving truck formation is in the entering state, triggering the identification system at the entrance of the distribution area to perform identity recognition on the autonomous driving truck formation, and after the autonomous driving truck formation is identified, controlling the lifting rod at the entrance to remain in the raised state until it is determined that the last truck in the autonomous driving truck formation has passed the entrance.

[0015] Optionally, controlling the autonomous driving truck formation to act in accordance with the guidance strategy includes: when determining that the autonomous driving truck formation as a whole has passed the entrance of the distribution area, controlling the autonomous driving truck formation to travel along the target formation vehicle driving road to the target formation vehicle parking space, wherein the target formation vehicle driving road is the road indicated by the guidance strategy, and the target formation vehicle parking space is the parking space indicated by the guidance strategy.

[0016] Optionally, controlling the autonomous driving truck formation to act in accordance with the guidance strategy includes: when determining that the autonomous driving truck formation needs to be charged, determining the charging order of each vehicle in the autonomous driving truck formation and the target formation vehicle charging area based on the battery state of charge, battery temperature and expected power consumption of the next mission of each vehicle in the autonomous driving truck formation; controlling the autonomous driving truck formation to travel to the target formation vehicle charging area, and performing charging operations in accordance with the charging order.

[0017] According to another aspect of an embodiment of the present invention, a control device for an autonomous driving truck formation is provided, comprising: an acquisition unit for acquiring vehicle image data of the autonomous driving truck formation when a autonomous driving truck formation is detected entering; a processing unit for processing the vehicle image data to obtain formation information of the autonomous driving truck formation, wherein the formation information includes at least the following information of the autonomous driving truck formation: the number of vehicles, the formation form, and the type of loaded items; a generation unit for generating a guidance strategy for the autonomous driving truck formation based on the formation information, wherein the guidance strategy is used to guide various operations of the autonomous driving trucks in a gathering and distribution area, wherein the gathering and distribution area is a transfer area for the autonomous driving truck formation, and the gathering and distribution area is provided with the following areas: a formation vehicle driving road, a formation vehicle parking space, a formation vehicle charging area, and a formation vehicle maintenance area, and the various operations include at least: parking, charging, and maintenance; a control unit for controlling the autonomous driving truck formation to act according to the guidance strategy.

[0018] Optionally, the acquisition unit includes at least one of the following: a first determination module, used to determine that the self-driving truck formation has entered when the self-driving truck formation exists in the image data, wherein the image data is data collected by an image acquisition component arranged at the entrance of the distribution area; a second determination module, used to determine that the self-driving truck formation has entered when a detection signal detects the self-driving truck formation, wherein the detection signal is a signal detected by a signal detector arranged at the entrance of the distribution area.

[0019] Optionally, the processing unit includes: a first processing module, used to process the vehicle image data through an image recognition model to obtain the formation information corresponding to the vehicle image data, wherein the image recognition model is a model obtained by machine learning training using multiple sets of first training data, and each of the multiple sets of first training data includes: sample vehicle image data and sample formation information corresponding to the sample vehicle image data.

[0020] Optionally, a guidance strategy for the autonomous driving truck formation is generated based on the formation information, including: a second processing module, used to process the formation information through a guidance strategy generation model to obtain the guidance strategy corresponding to the formation information, wherein the guidance strategy generation model is a model obtained by machine learning training using multiple groups of second training data, and each group of the multiple groups of second training data includes: sample formation information and a sample guidance strategy corresponding to the formation information.

[0021] Optionally, the generation unit includes: a first acquisition module for acquiring first vehicle-related information of the autonomous driving truck formation, and generating a driving guidance strategy for the autonomous driving truck formation based on the vehicle-related information, wherein the first vehicle-related information includes at least one of the following: vehicle current status, task priority, and traffic congestion information; a second acquisition module for acquiring parking space information of each parking space in the distribution area and second vehicle-related information of the autonomous driving truck formation, and generating a parking guidance strategy based on the parking space information and the second vehicle-related information, wherein the parking space information includes at least: physical coordinates of the parking space, compatible vehicle type, and functional area attributes, and the second vehicle-related information includes : current task type, vehicle size and estimated stop time; a third acquisition module, used to obtain the charging pile information of each charging area in the distribution area and the third vehicle-related information of the autonomous driving truck fleet, and generate a charging guidance strategy based on the charging pile information and the third vehicle-related information, wherein the charging pile information includes at least: charging power, docking method, and the third vehicle-related information includes: battery state of charge, battery temperature, and expected power consumption for the next task; a fourth acquisition module, used to obtain the third vehicle-related information of the autonomous driving truck fleet, and generate a maintenance guidance strategy based on the third vehicle-related information, wherein the third vehicle-related information includes: vehicle operating status and fault code.

[0022] Optionally, the control unit includes: a first control module, configured to trigger the identification system at the entrance of the distribution area to identify the autonomous driving truck formation when the autonomous driving truck formation is in the entering state, and after the autonomous driving truck formation is identified, control the lifting rod at the entrance to keep the rod in the raised state until it is determined that the last truck in the autonomous driving truck formation has passed the entrance.

[0023] Optionally, the control unit includes: a second control module, which is used to control the autonomous driving truck formation to travel to the target formation vehicle parking space along the target formation vehicle driving road when it is determined that the autonomous driving truck formation has passed the entrance of the distribution area as a whole, wherein the target formation vehicle driving road is the road indicated by the guidance strategy, and the target formation vehicle parking space is the parking space indicated by the guidance strategy.

[0024] Optionally, the control unit includes: a third determination module, used to determine the charging order and target platoon vehicle charging area of ​​each vehicle in the autonomous driving truck platoon based on the battery state of charge, battery temperature and expected power consumption of the next mission of each vehicle in the autonomous driving truck platoon when it is determined that the autonomous driving truck platoon needs to be charged; a third control module, used to control the autonomous driving truck platoon to travel to the target platoon vehicle charging area and perform charging operations according to the charging order.

[0025] According to another aspect of an embodiment of the present invention, an autonomous driving truck platoon is provided, characterized in that the autonomous driving truck platoon uses any one of the control methods for the autonomous driving truck platoon described above.

[0026] According to another aspect of an embodiment of the present invention, a computer-readable storage medium is further provided, wherein the computer-readable storage medium includes a stored program, wherein the program executes any one of the above-mentioned control methods for the autonomous driving truck formation.

[0027] According to another aspect of an embodiment of the present invention, a processor is further provided, wherein the processor is used to run a program, wherein the program, when running, executes any one of the above-mentioned control methods for the autonomous driving truck formation.

[0028] According to another aspect of an embodiment of the present invention, a computer program product is provided, comprising computer instructions, which, when executed by a processor, execute any one of the above-described methods for controlling an autonomous driving truck platoon.

[0029] In an embodiment of the present invention, upon detecting the entry of a platoon of autonomous trucks, vehicle image data of the platoon is acquired; the vehicle image data is processed to obtain platoon information of the autonomous trucks, wherein the platoon information includes at least the following information about the autonomous trucks: the number of vehicles, the platoon form, and the type of items loaded; a guidance strategy for the autonomous trucks is generated based on the platoon information, wherein the guidance strategy is used to guide various operations of the autonomous trucks in a distribution area, which is a transit area for the autonomous trucks and includes the following areas: a platoon vehicle driving road, a platoon vehicle parking space, a platoon vehicle charging area, and a platoon vehicle maintenance area, wherein various operations include at least parking, charging, and maintenance; and the autonomous trucks are controlled to operate according to the guidance strategy. The technical solution provided by the present invention achieves rapid identification and intelligent scheduling of autonomous truck platoons, significantly improving the operational efficiency of highway ports, while reducing platoon waiting time and energy consumption, enhancing the intelligence level and automated operation capabilities of highway ports, and reducing safety hazards, thereby solving the technical problem that existing highway ports are unable to effectively identify and guide the actions of autonomous truck platoons, resulting in low platoon efficiency and unreasonable resource allocation. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of this application. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0031] Figure 1 This is a hardware structure block diagram of a mobile terminal for a control method of an autonomous driving truck platoon according to an embodiment of the present invention;

[0032] Figure 2 is a flowchart of a control method for an autonomous driving truck platoon according to an embodiment of the present invention;

[0033] Figure 3 is a schematic diagram of a highway port according to an embodiment of the present invention;

[0034] Figure 4 2 is a schematic diagram of a control device for an autonomous driving truck platoon according to an embodiment of the present invention. DETAILED DESCRIPTION

[0035] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of the present invention.

[0036] It should be noted that the terms "first", "second", etc. in the description and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the numbers used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0037] As described in the background, existing highway ports are unable to effectively identify and guide the actions of autonomous truck platoons, resulting in low platooning efficiency and irrational resource allocation. Embodiments of the present invention provide a control method and apparatus for autonomous truck platoons, an autonomous truck platoon, a computer-readable storage medium, a processor, and a computer program product.

[0038] The technical solutions in the embodiments of the present invention will be described clearly and completely below with reference to the accompanying drawings in the embodiments of the present invention.

[0039] The method embodiments provided in the embodiments of the present invention can be executed in a mobile terminal, a computer terminal or a similar computing device. Taking running on a mobile terminal as an example, Figure 1 This is a hardware structure block diagram of a mobile terminal for a control method of an autonomous driving truck platoon according to an embodiment of the present invention. Figure 1 As shown, the mobile terminal may include one or more ( Figure 1 Only one is shown) a processor 102 (the processor 102 may include but is not limited to a microprocessor MCU or a programmable logic device FPGA and other processing devices) and a memory 104 for storing data, wherein the mobile terminal may also include a transmission device 106 and an input and output device 108 for communication functions. It will be understood by those skilled in the art that Figure 1 The structure shown is only for illustration and does not limit the structure of the mobile terminal. Figure 1 More or fewer components than shown, or with Figure 1 Different configurations shown.

[0040] Memory 104 can be used to store computer programs, such as software programs and modules for application software, such as the computer program corresponding to the control method for an autonomous truck platoon in an embodiment of the present invention. Processor 102 executes the computer program stored in memory 104 to execute various functional applications and data processing, thereby implementing the aforementioned method. Memory 104 may include high-speed random access memory (RAM) and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, memory 104 may further include memory remotely located relative to processor 102, which can be connected to the mobile terminal via a network. Examples of such networks include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof. Transmission device 106 is used to receive or transmit data via a network. Specific examples of such networks may include a wireless network provided by the mobile terminal's telecommunications provider. In one example, transmission device 106 includes a network interface controller (NIC), which can be connected to other network devices via a base station to enable communication with the Internet. In one example, the transmission device 106 may be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0041] Example 1

[0042] According to an embodiment of the present invention, a method embodiment of a control method for an autonomous driving truck platoon is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0043] Figure 2 FIG. 1 is a flow chart of a control method for an autonomous driving truck platoon according to an embodiment of the present invention. Figure 2 As shown, the control method of the autonomous driving truck platoon includes the following steps:

[0044] Step S202: When a platoon of autonomous driving trucks is detected entering, vehicle image data of the platoon of autonomous driving trucks is obtained.

[0045] Optionally, the above-mentioned truck platooning is intended to improve the safety, efficiency and economy of road transportation. In a truck platooning, multiple trucks are connected to each other through wireless communication technology (such as V2V, Vehicle-to-Vehicle) to form a "formation" or "convoy". The leading truck is responsible for driving decisions such as acceleration, deceleration, and steering, while the following trucks automatically adjust to maintain a safe distance and driving route from the vehicle in front. The autonomous driving truck platoon here refers to a vehicle formation consisting of a group of autonomous driving trucks.

[0046] In this embodiment of the present invention, a highway port is used as an example for illustration. A highway port is a comprehensive logistics facility dedicated to logistics distribution, transit, and value-added services for road transportation. Similar to the role of ports in maritime transport, it serves the road transportation system, offering services such as cargo storage, loading and unloading, information processing, vehicle repair and maintenance, and driver rest areas. It typically has a considerable amount of parking and office space.

[0047] In this embodiment, when a convoy of self-driving trucks approaches the highway port, a high-resolution camera at the entrance begins to continuously capture vehicle images to obtain the above-mentioned vehicle image data.

[0048] Step S204: Process the vehicle image data to obtain formation information of the autonomous driving truck formation, wherein the formation information includes at least the following information of the autonomous driving truck formation: the number of vehicles, the formation form, and the type of loaded items.

[0049] In this embodiment, after collecting vehicle image data, the system uses advanced computer vision algorithms to analyze the images, identifying the number of vehicles in the formation, their arrangement, and the types of cargo they carry (e.g., refrigerated goods, liquid chemicals, etc.). Based on this information, the intelligent dispatching system generates a specific guidance strategy for the formation, clearly indicating the roads, parking spaces, charging areas, and maintenance areas to which the formation should proceed. The entire formation will autonomously follow this strategy to complete operations such as parking, charging, and maintenance.

[0050] Step S206: Generate a guidance strategy for the autonomous driving truck formation based on the formation information, wherein the guidance strategy is used to guide various operations of the autonomous driving trucks in the distribution area. The distribution area is the transfer area for the autonomous driving truck formation. The distribution area is provided with the following areas: a formation vehicle driving road, a formation vehicle parking space, a formation vehicle charging area, and a formation vehicle maintenance area. Various operations include at least: parking, charging, and maintenance.

[0051] In this embodiment, a guidance strategy for the autonomous truck platoon can be generated based on the platoon information obtained above, including parking locations, charging requirements, maintenance tasks, etc., and the strategy information can be sent to each truck in the platoon through V2V communication between vehicles or V2I communication between vehicles and infrastructure to ensure that each truck can act according to the strategy.

[0052] Figure 3 Schematic diagram of a highway port according to an embodiment of the present invention, Figure 3 As shown, the highway port may include: an intelligent channel supporting the entry and exit of the platoon (i.e., the road for the platoon vehicles), an energy replenishment and maintenance area (i.e., the maintenance area for the platoon vehicles), an intelligent channel supporting the entry and exit of the platoon (i.e., the road for the platoon vehicles), and a platoon parking area (i.e., the parking space for the platoon vehicles).

[0053] The entrance and exit lanes feature direct platooning lanes, allowing 3-5 self-driving trucks, each 20 meters long, to enter or exit in formation without breaking up. License plate recognition systems are also installed at the entrances and exits for automatic identification. Once identification is passed, the barrier remains raised until the last truck in the platoon has passed.

[0054] The aforementioned platoon parking area features a parallel layout with multiple parking spaces capable of accommodating entire platoons (e.g., three vehicles). Clear ground markings are provided in the parking area to assist with precise automated parking. The aforementioned automatic charging and maintenance area features automatic alignment charging stations (with retractable charging arms at the end) to facilitate precise charging of unmanned platoon vehicles. The maintenance area is equipped with automated detection devices (camera cleaners, radar calibration equipment, and communication self-test systems) to quickly perform health checks on the perception system.

[0055] Step S208: Control the autonomous driving truck formation to act according to the guidance strategy.

[0056] As can be seen from the above, in an embodiment of the present invention, when a self-driving truck formation is detected entering, vehicle image data of the self-driving truck formation is obtained; the vehicle image data is processed to obtain formation information of the self-driving truck formation, wherein the formation information at least includes the following information of the self-driving truck formation: the number of vehicles, the formation form, and the type of loaded items; a guidance strategy for the self-driving truck formation is generated based on the formation information, wherein the guidance strategy is used to guide various operations of the self-driving trucks in the distribution area, which is a transit area for the self-driving truck formation. The distribution area is provided with the following areas: a road for the formation vehicles, a parking space for the formation vehicles, a charging area for the formation vehicles, and a maintenance area for the formation vehicles, and various operations include at least: parking, charging, and maintenance; the self-driving truck formation is controlled to act according to the guidance strategy, thereby realizing rapid identification and intelligent scheduling of the self-driving truck formation, significantly improving the operational efficiency of the highway port, while reducing the waiting time and energy consumption of the formation, enhancing the intelligence level and automated operation capability of the highway port, and reducing safety hazards.

[0057] Therefore, the above-mentioned technical solution provided by the embodiment of the present invention solves the technical problem that the existing highway port is unable to effectively identify information and provide action guidance for the self-driving truck formation, resulting in low formation efficiency and unreasonable resource allocation.

[0058] According to the above-mentioned embodiment of the present invention, detecting the entry of a convoy of self-driving trucks may include at least one of the following: determining that a convoy of self-driving trucks has entered when a convoy of self-driving trucks exists in image data, wherein the image data is data collected by an image acquisition component disposed at the entrance of a gathering and distribution area; determining that a convoy of self-driving trucks has entered when a detection signal detects a convoy of self-driving trucks, wherein the detection signal is a signal detected by a signal detector disposed at the entrance of a gathering and distribution area.

[0059] In this embodiment, the entrance to the highway port is equipped with RFID readers and lidar. When a platoon of autonomous trucks enters range, the RFID reader reads the electronic tags on the vehicles, while the lidar detects the vehicle's outline and exact location. Using these two methods, the system can accurately determine whether a platoon has entered. Furthermore, signal detectors at the entrance (such as ultrasonic sensors) monitor approaching vehicles. When a vehicle is detected, the system also determines that the platoon has entered.

[0060] By fusing multiple sensors, the accuracy and robustness of the formation entry judgment are improved, ensuring that the correct action strategy can be made immediately regardless of the external conditions.

[0061] According to the above embodiment of the present invention, vehicle image data is processed to obtain formation information of an autonomous driving truck formation, including: processing the vehicle image data through an image recognition model to obtain formation information corresponding to the vehicle image data, wherein the image recognition model is a model trained by machine learning using multiple groups of first training data, and each of the multiple groups of first training data includes: sample vehicle image data and sample formation information corresponding to the sample vehicle image data.

[0062] In this embodiment, the system uses a deep learning network model to process vehicle image data, and the model learns how to accurately extract formation information through a large amount of training data. The training data includes images annotated with formation information (i.e., first training data), covering a variety of formation combinations and load types. The model is optimized to accurately identify the number of vehicles, formation form, and load type, and then generate formation information. That is, the cloud scheduling system stores a large amount of first training data, including sample vehicle images and their corresponding formation information. The system uses deep learning methods to train the image recognition model so that it can accurately identify formation information from images. In actual operation, the model receives real-time image data, quickly parses and outputs formation information, and provides a basis for subsequent guidance strategy generation.

[0063] The image recognition model using deep learning technology here can significantly improve the recognition accuracy in complex environments, speed up information processing, and provide real-time and accurate guidance information for the formation.

[0064] According to the above embodiment of the present invention, a guidance strategy for a formation of autonomous driving trucks is generated based on the formation information, including: processing the formation information through a guidance strategy generation model to obtain a guidance strategy corresponding to the formation information, wherein the guidance strategy generation model is a model trained by machine learning using multiple groups of second training data, and each of the multiple groups of second training data includes: sample formation information and a sample guidance strategy corresponding to the formation information.

[0065] In this embodiment, the system constructs a decision tree model based on the formation information, and the model generates a guidance strategy based on the number of vehicles in the formation, the formation form, the type of loaded items, the status of the vehicles, and the current mission requirements. For example, if the formation carries perishable refrigerated goods, the system will give priority to guiding the formation to parking spaces and maintenance areas near the door that are equipped with fast loading and unloading and low-temperature maintenance functions. The training of the model uses a second training data set with formation information and the best guidance strategy to ensure the intelligence and optimization effect of strategy generation. That is, the cloud-based scheduling system trains and generates a model for the guidance strategy based on the second training data set, i.e., historical formation information and the best guidance strategy instance. The model can intelligently generate the optimal guidance strategy based on the formation information (such as the number of vehicles, the type of loaded items) and the real-time status of the highway port (such as berth availability, charging facility utilization rate), including driving routes, parking locations, charging arrangements and maintenance services.

[0066] Through the intelligent guidance strategy generation mechanism, this implementation method achieves efficient utilization of resources and rapid response to formation needs, greatly improving the throughput capacity of the highway port and the operational efficiency of the formation, and reducing operating costs.

[0067] According to the above embodiment of the present invention, generating a guidance strategy for an autonomous truck platoon based on platoon information may include: obtaining information related to a first vehicle in the autonomous truck platoon, and generating a driving guidance strategy for the autonomous truck platoon based on the vehicle-related information, wherein the first vehicle-related information includes at least one of the following: a current vehicle status, a task priority, and traffic congestion information; obtaining parking space information for each parking space in a distribution area and information related to a second vehicle in the autonomous truck platoon, and generating a parking guidance strategy based on the parking space information and the second vehicle-related information, wherein the parking space information includes at least the physical coordinates of the parking space, the compatible vehicle type, and the functional area attributes, and the second vehicle-related information includes the current task type, vehicle size, and estimated parking duration; obtaining charging pile information for each charging area in the distribution area and information related to a third vehicle in the autonomous truck platoon, and generating a charging guidance strategy based on the charging pile information and the third vehicle-related information, wherein the charging pile information includes at least the charging power and the docking mode, and the third vehicle-related information includes the battery state of charge, the battery temperature, and the expected power consumption for the next task; obtaining information related to the third vehicle in the autonomous truck platoon, and generating a maintenance guidance strategy based on the third vehicle-related information, wherein the third vehicle-related information includes the vehicle operating status and fault codes.

[0068] In this embodiment, the cloud-based scheduling system integrates road condition monitoring, vehicle status monitoring, and task scheduling functions. The system obtains real-time vehicle-related information, such as the vehicle's current status (whether maintenance is required), task priority (whether cargo requires urgent loading and unloading), and traffic congestion information (whether the roads within the highway port are congested). Based on this information, the system uses heuristic algorithms (such as genetic algorithms and simulated annealing algorithms) and constrained optimization techniques to generate the optimal driving guidance strategy, including driving routes and speed recommendations for the formation; parking guidance strategies, allocating parking spaces based on vehicle size and docking requirements; charging guidance strategies, arranging charging sequences and charging areas based on vehicle SOC (remaining battery capacity) and charging requirements; and maintenance guidance strategies, planning maintenance tasks and workstations based on vehicle health and maintenance requirements.

[0069] By integrating multiple vehicle information and highway port status data, dynamic resource allocation and task priority management are achieved, which improves the highway port's operating efficiency and the operational safety of the fleet, while reducing unnecessary waiting time and energy consumption.

[0070] According to the above embodiment of the present invention, controlling the autonomous driving truck formation to act according to the guidance strategy may include: when the autonomous driving truck formation is in the driving-in state, triggering the identification system at the entrance of the distribution area to identify the autonomous driving truck formation, and after the autonomous driving truck formation is identified, controlling the lifting bar at the entrance to remain in the lifting state until it is determined that the last truck in the autonomous driving truck formation has passed the entrance.

[0071] In this embodiment, when a convoy approaches the highway port entrance, an automatic identification system (e.g., an RFID reader or image recognition system) at the entrance automatically collects vehicle information and completes identity verification. Once the convoy is verified, the entrance's automatic gate (e.g., a lifting gate) remains open until all vehicles in the convoy have safely passed. This process requires no human intervention, ensuring the continuity of the convoy and improving entry and exit efficiency.

[0072] In this embodiment of the present invention, a cloud-based intelligent dispatching platform is introduced to enable unified management of all convoy vehicles within the highway port, including route planning, berth allocation, energy replenishment scheduling, and maintenance task scheduling. Based on a digital twin model, the cloud-based system creates a port road network diagram, including main lanes, parking area access lanes, charging guide lanes, and maintenance guide lanes.

[0073] Here, the system receives the current status of each fleet (position, remaining power, scheduled tasks, vehicle speed, etc.), combines task priority and traffic congestion, and uses the Dijkstra or A* algorithm to calculate the shortest / optimal path.

[0074] Specifically, in order to realize the path calculation of the autonomous truck platoon within the highway port, the following steps can be taken: 1) Data collection and synchronization: Collecting platoon status information: The system collects information such as the current GPS location, remaining battery power, scheduling task list, and vehicle speed of each truck in real time. Formation information synchronization: Through V2V communication technology, all trucks in the platoon can receive status information from the lead truck, so that the system can uniformly grasp the detailed status of the entire platoon. 2) Port area map and resource status update: Establishing a digital map: Using digital twin technology, a virtual map of the highway port is established, including the location and attributes of all roads, parking spaces, charging areas, maintenance areas, and other facilities. Resource status monitoring: Monitor the usage of all parking spaces in the port area, the utilization rate of charging facilities, the occupancy status of maintenance stations, and the traffic conditions of the roads. 3) Determining task priorities: Analyzing task requirements: Based on the platoon's scheduling tasks, the system analyzes which trucks or platoon tasks are most urgent, such as urgent delivery or low battery status. Prioritization: Assign a priority to each truck or platoon based on the urgency of the task, vehicle status, and resource requirements. 4) Path Planning Algorithm Selection: Select an appropriate path planning algorithm based on current conditions and requirements. The Dijkstra algorithm is suitable for finding the shortest path between two points, while the A* algorithm, which incorporates heuristic search, is more suitable for finding the optimal path when considering multiple factors. 5) Path Planning and Calculation: Construct a Graph Model: Convert the map of the highway port into a graph model, with nodes representing parking spaces, charging areas, maintenance areas, intersections, etc., and edges representing roads. Edge weights are dynamically adjusted based on factors such as road congestion, distance, and time. Calculation Algorithm Application: Use the Dijkstra or A* algorithm to calculate the shortest or optimal path from the current location to each destination (such as a parking space or charging area). This calculation takes into account resource usage, the integrity of the platoon, and the priority of the task. 6) Dynamic Adjustment and Obstacle Avoidance: Real-time Monitoring: The system continuously monitors traffic conditions within the port area and immediately recalculates the path if any sudden congestion or obstacles are detected. Obstacle avoidance and detour: Utilizing the heuristic characteristics of the A* algorithm, the system can predict potential congestion points and plan detour routes to avoid obstruction of the formation. 7) Issue path instructions: Generate a list of path points: The calculated path is converted into a series of path points (Waypoints), including directions, turning points, etc. Issue instructions: The path point list and speed recommendations are sent to the leading truck of the formation through wireless communication technology, and the leading truck then conveys the information to other members of the formation through V2V communication. 8) Execution and monitoring: Formation execution: The autonomous driving truck formation executes according to the received path instructions while maintaining a safe distance from other vehicles. Real-time monitoring and feedback: The system continuously monitors the execution of the formation and collects feedback data, such as actual driving time, energy consumption, etc., for the next step of optimization and adjustment.9) Result Evaluation and Algorithm Optimization: Result Evaluation: The effectiveness of path planning is evaluated by comparing actual travel time and energy consumption before and after path planning. Algorithm Optimization: If the actual driving conditions of certain platoons or vehicles differ significantly from the planned conditions, or if congestion on certain road sections exceeds expectations, the system adjusts the edge weights in the graph model based on this information and optimizes the algorithm parameters, providing a more accurate basis for the next path planning. This precise path planning reduces inefficient platoon travel within the port, shortens the total time required to complete a task, and improves the overall efficiency of logistics transportation. The optimal path not only minimizes distance but also minimizes energy consumption, such as by avoiding frequent starts and stops, effectively reducing fuel or electricity consumption. Furthermore, dynamic obstacle avoidance and real-time monitoring mechanisms effectively prevent potential traffic risks and ensure the safety of platoon members throughout the entire journey. Through the above steps, intelligent path planning for autonomous truck platoons within the highway port is achieved, improving the efficiency and safety of logistics transportation while also reducing energy consumption, providing strong support for the intelligent management of the highway port.

[0075] The dispatching system also monitors the movement of other vehicles within the port area in real time, anticipating route conflicts and dynamically adjusting routes to avoid cross-interference. Furthermore, after generating a route, it transmits waypoints and speed recommendations via the cellular network for the vehicle control system to execute.

[0076] The automated identification and gate control system here reduces the waiting time for the fleet to pass through the entrance, improves the efficiency of entering and leaving the port, and reduces the error rate and risk of safety accidents in manual operations.

[0077] According to the above embodiment of the present invention, controlling the autonomous driving truck formation to act according to the guidance strategy includes: when it is determined that the autonomous driving truck formation as a whole passes through the entrance of the distribution area, controlling the autonomous driving truck formation to travel along the target formation vehicle driving road to the target formation vehicle parking space, wherein the target formation vehicle driving road is the road indicated by the guidance strategy, and the target formation vehicle parking space is the parking space indicated by the guidance strategy.

[0078] In this embodiment, after the platoon vehicles pass through the entrance, the cloud-based dispatching system uses a generated guidance strategy to guide the platoon along a designated route to the target parking space through V2V or V2I communication. The allocation of target parking spaces takes into account vehicle size, platoon formation, and parking requirements, ensuring that vehicles can park quickly and accurately while avoiding traffic congestion or waiting caused by improper parking space allocation.

[0079] Each berth within the port area is pre-configured with physical coordinates, compatible vehicle types, and functional area attributes (loading / unloading / charging / maintenance). The cloud-based system selects berths based on the vehicle's current mission type (pending loading / unloading, charging, maintenance, or standby), vehicle size, and estimated docking time. Then, based on the principles of minimizing path cost and maximizing berth resource utilization, an allocation algorithm (such as the Hungarian algorithm or a heuristic algorithm) is used to match the optimal berth.

[0080] Specifically, for intelligent management of berth allocation, the following are specific steps to implement it based on a cloud system:

[0081] 1) Data preparation and berth information initialization:

[0082] Berth information initialization: In the cloud system, an information entry is created for each berth in the port area, including the physical coordinates of the berth (latitude and longitude), a list of applicable vehicle models, the functional attributes of the berth (such as whether it is suitable for loading and unloading, charging, maintenance, etc.), and the current status of the berth (free, occupied, reserved).

[0083] Vehicle information update: The system receives and updates each truck’s current mission type (waiting for loading and unloading, waiting for charging, waiting for maintenance, or on standby), vehicle size, and estimated stop time in real time.

[0084] 2) Berth screening:

[0085] Task type matching: Based on the vehicle's current task type and the functional attributes of the berth, a list of berths suitable for the task type is filtered out.

[0086] Size compatibility check: For the selected berths, check whether they are compatible with the vehicle size, that is, whether the length, width and height of the berth can accommodate the current vehicle.

[0087] Dock duration estimation: Using historical data and machine learning models, the docking time required for a vehicle to complete the current task is estimated, and berths that can meet the docking needs are further selected.

[0088] 3) Berth allocation algorithm selection and application:

[0089] Hungarian Algorithm: If the parking allocation problem can be viewed as a cost matrix matching problem (i.e., matching multiple vehicles with multiple parking spaces, minimizing the overall cost), the Hungarian algorithm can be used for optimization. This algorithm can find a perfect matching with minimal cost.

[0090] Heuristic algorithms: When the berth allocation problem is more complex, involving dynamic resource allocation and multi-objective optimization, heuristic algorithms (such as genetic algorithms and simulated annealing) can be used to find a near-optimal solution. Heuristic algorithms are more efficient when handling large-scale problems. Even if they cannot guarantee a globally optimal solution, they can quickly find a satisfactory solution.

[0091] 4) Optimal berth matching:

[0092] Path cost calculation: The system calculates the path cost from the vehicle's current location to each potential berth, including factors such as driving distance, estimated driving time, and road congestion.

[0093] Resource utilization considerations: Evaluate the resource utilization of each berth, i.e. berth usage frequency, idle time, etc., to ensure maximum resource utilization.

[0094] Comprehensive evaluation: Combined with path cost and resource utilization, the selected allocation algorithm is used to match the optimal berth for each truck or the entire fleet to ensure overall system efficiency and rationality of resource allocation.

[0095] 5) Distribution and execution of allocation results:

[0096] Generate allocation instructions: The system generates allocation instructions for each selected berth, including the physical coordinates of the berth, berth type, estimated docking time, etc.

[0097] Instruction issuance: The allocation instructions are sent to the leading truck in the formation through wireless communication technology (such as cellular network, satellite communication), and the leading truck then conveys the instructions to other trucks in the formation through V2V communication.

[0098] Parking execution: The platoon automatically drives to the designated parking space according to the assigned instructions and performs autonomous parking operations.

[0099] 6) Status update and scheduling optimization:

[0100] Berth status update: When a truck arrives and occupies a berth, the system updates the berth status to "occupied" and starts counting the parking time.

[0101] Continuous monitoring and reallocation: The cloud system continuously monitors the parking status and vehicle task completion. Once a vehicle leaves, the system updates the parking status to "free", re-evaluates the parking resources, and allocates parking spaces to subsequent vehicles to ensure the efficient flow of parking resources.

[0102] 7) Learning and Optimization:

[0103] Historical data accumulation: The system collects the results of each berth allocation, including allocation efficiency, resource utilization, vehicle satisfaction and other data.

[0104] Algorithm optimization: Use machine learning technology to analyze historical data, optimize the parameters of the berth allocation algorithm, and improve the accuracy and efficiency of the allocation strategy.

[0105] Real-time feedback learning: The system dynamically adjusts allocation strategies based on real-time feedback on vehicle task completion and berth usage to cope with the ever-changing logistics needs within the port area.

[0106] Through the above methods, 1) berth allocation efficiency is improved: through intelligent algorithm matching, the time for vehicles to find berths is reduced, and the logistics turnover speed within the port is accelerated. 2) Resource utilization is optimized: a reasonable berth allocation strategy ensures the efficient use of berth resources, avoids idle and excessive use of resources, and improves overall operational efficiency. 3) Traffic congestion is reduced: optimal berth matching reduces the ineffective driving of vehicles in the port, reduces the probability of traffic congestion, and improves road traffic capacity. 4) Enhanced system adaptability: real-time status updates and feedback learning mechanisms enable the system to adapt to changes quickly, and maintain good allocation effects even during peak hours or when berth resources are tight. 5) Improved operational safety: Accurate berth information and dynamic scheduling instructions reduce human errors and improve the operational safety of vehicles and personnel in the port area.

[0107] Through the above steps, the cloud system realizes the intelligent management of berths, which not only improves the utilization rate of berth resources and the operating efficiency of vehicles in the port, but also effectively reduces logistics costs and provides solid technical support for the intelligent operation of the highway port.

[0108] It should be noted that the berth status is updated in real time and supports switching between three states: "occupied / reserved / idle" to avoid scheduling overlap.

[0109] Here, precise parking and route planning of convoy vehicles are achieved, which reduces traffic congestion in the port, improves operational efficiency, ensures vehicle safety, and enhances the intelligent management level of the highway port.

[0110] According to the above embodiment of the present invention, controlling the autonomous driving truck formation to act according to the guidance strategy includes: when it is determined that the autonomous driving truck formation needs to be charged, determining the charging order of each vehicle in the autonomous driving truck formation and the target formation vehicle charging area according to the battery charge state, battery temperature and expected power consumption of the next mission of each vehicle in the autonomous driving truck formation; controlling the autonomous driving truck formation to travel to the target formation vehicle charging area, and performing charging operations according to the charging order.

[0111] In this embodiment, the cloud-based scheduling system monitors the battery status of each vehicle in the formation in real time, especially the SOC (State of Charge). When it is detected that the SOC of the vehicle is lower than a certain threshold (for example, 30%), the system will automatically dispatch the vehicle to the charging area. The allocation of charging areas takes into account factors such as the type of charging pile (such as fast charging, ordinary charging), the telescopic ability of the charging arm, and the idleness of the charging area. After the vehicle arrives at the charging area, it automatically aligns and starts charging according to the pre-calculated charging sequence. After charging is completed, the vehicle returns or goes to the next mission area according to the system instructions.

[0112] The dispatching system here continuously monitors key parameters such as each vehicle's SOC (State of Charge), battery temperature, and expected energy consumption for the next task. Then, based on vehicle priority, SOC threshold, and shift rhythm, it sets the charging priority queue and arranges the charging order. It matches the charging pile capabilities (power type, whether it is an automatic docking pile, etc.), performs position and time allocation, and ensures the rational use of charging pile resources. The system can also implement energy load balancing strategies based on peak and valley electricity prices in the port area to reduce overall electricity costs.

[0113] Specifically, this can be achieved through the following steps:

[0114] 1) Data collection and real-time monitoring:

[0115] State of Charge (SOC) Monitoring: Each autonomous truck is equipped with a battery management system (BMS), which continuously monitors the truck's SOC (State of Charge) and uploads this data in real time to a cloud-based dispatch system. The BMS also monitors battery temperature to ensure it remains within a safe operating range.

[0116] Energy Consumption Prediction: The cloud-based system uses machine learning models to predict the energy consumption required for each vehicle to perform its next mission based on historical data and the current mission. For example, if the next mission involves driving a long distance or loading or unloading a heavy load, the system predicts higher energy requirements accordingly.

[0117] 2) Charging demand priority setting:

[0118] Task priority assessment: The scheduling system assesses the charging demand priority of each vehicle based on the vehicle's scheduled tasks, task type (such as emergency delivery, priority maintenance) and task deadline.

[0119] SOC threshold determination: The system sets an SOC threshold (e.g. 30%). When the vehicle's SOC is lower than this threshold, it is automatically moved to the charging priority queue and given priority for charging.

[0120] Dynamic queue management: The charging priority queue will be dynamically updated according to the actual SOC changes of the vehicle and the real-time adjustment of the task priority, ensuring that the vehicles that need charging the most can receive timely service.

[0121] 3) Charging resource matching and scheduling:

[0122] Charging pile capability identification: The cloud system stores detailed information about all charging piles, including the power type (such as fast charging, slow charging), whether automatic docking is supported, and the current working status (idle, in use).

[0123] Resource matching: Based on the vehicle's battery type and charging requirements, the system will match the appropriate charging pile. For example, for vehicles with extremely low SOC and urgent need for rapid energy replenishment, high-power fast-charging charging piles will be given priority.

[0124] Time allocation: The dispatching system analyzes historical energy consumption data and predicts future electricity demand in the port area to rationally plan charging time periods to avoid competition for charging pile resources during peak hours. At the same time, it takes into account the minimization of charging costs, such as charging during off-peak electricity price periods.

[0125] 4) Generation and issuance of charging instructions:

[0126] Route planning: For vehicles determined to need charging, the system plans the optimal driving route from the current parking space to the designated charging area based on the digital map of the port area, ensuring that the vehicle can arrive safely and efficiently.

[0127] Instruction issuance: Using wireless communication technologies (such as 4G / 5G cellular networks or Wi-Fi), the dispatch system sends charging instructions (including charging station location, charging time, charging requirements, etc.) to each truck that needs to be charged. The lead truck then forwards the instructions to other members of the formation through V2V communication.

[0128] 5) Charging process monitoring and feedback:

[0129] Charging status monitoring: The dispatching system continuously monitors the vehicle's charging process, including charging start time, charging rate, SOC update status, etc., to ensure the smooth completion of the charging task.

[0130] Abnormal situation handling: If any abnormality is encountered during the charging process, such as charging pile failure, vehicle charging interruption, etc., the system can respond immediately, re-dispatching the vehicle or calling the backup charging pile to avoid task delays.

[0131] Feedback learning: The system collects data during the charging process, such as charging efficiency, energy consumption, and charging pile utilization, to optimize the charging scheduling algorithm and improve the accuracy and efficiency of future scheduling.

[0132] Through these implementation steps, the dispatch system is able to meticulously manage the charging needs of each autonomous truck, ensuring timely and efficient charging. This optimizes the utilization of charging pile resources, enhancing the highway port's intelligent operations and economic benefits. Furthermore, the system's real-time monitoring and exception handling mechanisms improve the reliability and safety of charging pile management, providing strong support for the operation of autonomous truck platoons.

[0133] In addition, the system implements an energy load balancing strategy based on the peak and valley electricity prices in the port area, which includes the following steps:

[0134] 1) Peak and valley electricity price monitoring and forecasting:

[0135] Real-time electricity price inquiry: The system connects with the electricity price information system of the local power grid operator in real time to obtain the peak and valley electricity price information of the current port area, including the peak electricity price time period and the valley electricity price time period.

[0136] Electricity price prediction model: Using historical electricity price data and machine learning algorithms, such as time series analysis or neural networks, an electricity price prediction model is established to predict the trend of peak and valley electricity prices in the future, providing a basis for formulating charging plans.

[0137] 2) Charging demand analysis:

[0138] Vehicle status monitoring: The system continuously monitors the status of all vehicles in the port area, including SOC (State of Charge), battery temperature, vehicle mission, etc., to determine which vehicles need to be charged immediately and which can be delayed.

[0139] Predicting power consumption: Using machine learning models, the approximate power consumption required for the vehicle to perform future tasks is predicted based on information such as vehicle mission type, distance, and cargo capacity, providing a reference for the formulation of charging plans.

[0140] 3) Intelligent charging scheduling:

[0141] Charging plan generation: Combining peak and valley electricity price forecasts and vehicle charging needs, the system generates a charging plan, giving priority to charging vehicles with low SOC but not in a hurry to perform tasks during low-valley electricity price periods.

[0142] Charging pile resource allocation: The system reasonably allocates charging pile resources according to the charging plan to avoid excessive load on the power grid caused by too many vehicles charging in the same time period, especially during peak electricity price periods.

[0143] Dynamic adjustment mechanism: In response to emergencies (such as temporary tasks, rapid SOC drop, etc.), the system has the ability to adjust the charging plan immediately to ensure that key tasks are not affected while maintaining the optimization of charging costs as much as possible.

[0144] 4) Control and Execution:

[0145] Execute charging plan: Self-driving trucks automatically drive to designated charging stations for charging according to the charging plan sent from the cloud, without the need for human intervention.

[0146] Charging pile response adjustment: After the charging pile receives the signal that the vehicle is about to arrive, it prepares in advance, such as adjusting the charging power to adapt to the vehicle's needs to ensure a smooth charging process.

[0147] 5) Real-time monitoring and feedback:

[0148] Dynamic update of electricity prices: The system continuously monitors changes in electricity prices and dynamically adjusts charging plans based on the latest electricity price information to prevent additional costs caused by sudden fluctuations in electricity prices.

[0149] Charging status feedback: Real-time SOC data and charging pile working status during the charging process are fed back to the cloud system for real-time monitoring of charging progress and status, and adjustment when necessary.

[0150] 6) Learning and Optimization:

[0151] Data analysis: Collect and analyze the result data of each charging dispatch, including charging cost, SOC recovery efficiency, dispatch accuracy, etc., to continuously optimize the electricity price prediction model and charging dispatch algorithm.

[0152] Strategy adjustment: Based on the analysis results, the system can automatically adjust the peak and valley electricity price thresholds, charging priority rules, etc. to better adapt to the charging needs of the port area and the electricity price fluctuations of the power grid.

[0153] Through the above steps, the system can make full use of the difference between peak and valley electricity prices and implement an energy load balancing strategy, which not only effectively reduces charging costs, but also promotes the harmonious operation of the port area and the power grid, demonstrating an advanced application scenario combining smart logistics and smart grids.

[0154] Through intelligent charging scheduling and resource management, this implementation method achieves rapid charging of platoon vehicles, while optimizing the utilization rate of charging piles, reducing energy consumption, and improving the operational continuity of the platoon and the operational efficiency of the highway port.

[0155] In addition, in an embodiment of the present invention, the cloud scheduling platform is connected to the autonomous driving system interface to regularly pull vehicle operating status, fault codes, self-test reports, and sensor health data. Then, based on the urgency of the maintenance task, the required time, and the available maintenance stations, the system performs task sorting + station scheduling to generate a maintenance schedule. At the same time, combined with the operational task plan, maintenance is performed to avoid peak periods or scheduling gaps to ensure both operational efficiency and system reliability. It also supports remote diagnosis and OTA (Over-the-Air) maintenance arrangement confirmation, which can achieve zero on-site intervention for some maintenance tasks.

[0156] Furthermore, in an embodiment of the present invention, for operations such as hooking and unhooking that still require human intervention, the system automatically generates a task list based on dimensions such as trailer requirements, personnel schedules, physical location, and operational skill tags, and sends it to the mobile terminal or operation control terminal. Manual operations can be confirmed through QR code scanning, voice feedback, or terminal confirmation buttons. The task status is automatically fed back to the scheduling system to trigger the next task. Furthermore, the system has an extensible interface that can subsequently connect to hardware such as intelligent trailer systems, automatic unhooking robots, and automatic unloading platforms, gradually achieving fully automated closed-loop operations.

[0157] The above-mentioned technical solution provided by the embodiment of the present invention has the following beneficial effects: 1) The efficiency of the entry and exit of the fleet is improved, and the dedicated import and export channels avoid the disbanding and reorganization process, which greatly improves the passing efficiency and scheduling accuracy; 2) The intelligent scheduling system uniformly manages resources and task scheduling, avoids conflicts in berths, charging, and loading and unloading tasks, and reduces waiting costs; 3) Automatic parking, charging, and remote detection capabilities improve unmanned operation capabilities to meet the requirements of L4 and above autonomous driving; 4) The introduction of an intelligent loading and unloading task allocation mechanism can improve work efficiency through system collaboration even if manual operation is still required; 5) It has good scalability and can be quickly adapted to automated trailers, unmanned forklifts, intelligent warehousing systems, etc. according to future equipment upgrades.

[0158] It should be noted that for the aforementioned method embodiments, for the sake of simplicity, they are all expressed as a series of action combinations, but those skilled in the art should be aware that this application is not limited by the order of the actions described, because according to this application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by this application.

[0159] Through the description of the above implementation methods, those skilled in the art can clearly understand that the method according to the above embodiment can be implemented by means of software plus the necessary general hardware platform, and of course it can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes a number of instructions for enabling a terminal device (which can be a mobile phone, computer, server, or network device, etc.) to execute the methods described in each embodiment of the present application.

[0160] Example 2

[0161] According to an embodiment of the present invention, a control device for an autonomous driving truck platoon is provided for implementing the control method for an autonomous driving truck platoon. Figure 4 FIG. 1 is a schematic diagram of a control device for an autonomous driving truck platoon according to an embodiment of the present invention. Figure 4 As shown, the control device for the autonomous driving truck platoon includes: an acquisition unit 401, a processing unit 403, a generation unit 405, and a control unit 407. The device is described below.

[0162] The acquisition unit 401 is used to acquire vehicle image data of the autonomous driving truck formation when a autonomous driving truck formation is detected entering.

[0163] The processing unit 403 is used to process the vehicle image data to obtain the formation information of the autonomous driving truck formation, wherein the formation information includes at least the following information of the autonomous driving truck formation: the number of vehicles, the formation form, and the type of loaded items.

[0164] A generation unit 405 is used to generate a guidance strategy for the autonomous driving truck formation based on the formation information, wherein the guidance strategy is used to guide various operations of the autonomous driving trucks in the distribution area. The distribution area is a transfer area for the autonomous driving truck formation. The distribution area is provided with the following areas: a formation vehicle driving road, a formation vehicle parking space, a formation vehicle charging area, and a formation vehicle maintenance area. Various operations include at least: parking, charging, and maintenance.

[0165] The control unit 407 is used to control the autonomous driving truck formation to act according to the guidance strategy.

[0166] It should be noted here that the above-mentioned acquisition unit 401, processing unit 403, generation unit 405 and control unit 407 correspond to steps S202 to S208 in the above-mentioned embodiment. The four units and the corresponding steps implement the same instances and application scenarios, but are not limited to the contents disclosed in the above-mentioned embodiment.

[0167] As can be seen from the above, in the scheme described in the above embodiments of the present invention, an acquisition unit can be used to acquire vehicle image data of the autonomous driving truck formation when a platoon of autonomous driving trucks is detected entering; then, a processing unit is used to process the vehicle image data to obtain formation information of the autonomous driving truck formation, wherein the formation information includes at least the following information of the autonomous driving truck formation: the number of vehicles, the formation form, and the type of loaded items; then, a generation unit is used to generate a guidance strategy for the autonomous driving truck formation based on the formation information, wherein the guidance strategy is used to guide various operations of the autonomous driving trucks in the distribution area, which is a transfer area for the autonomous driving truck formation. The distribution area is provided with the following areas: a platoon vehicle driving road, a platoon vehicle parking space, a platoon vehicle charging area, and a platoon vehicle maintenance area, and various operations include at least: parking, charging, and maintenance; and a control unit is used to control the autonomous driving truck formation to act according to the guidance strategy, thereby achieving rapid identification and intelligent scheduling of the autonomous driving truck formation, significantly improving the operational efficiency of the highway port, while reducing the waiting time and energy consumption of the platoon, enhancing the intelligence level and automated operation capability of the highway port, and reducing safety hazards.

[0168] Therefore, the above-mentioned technical solution provided by the embodiment of the present invention solves the technical problem that the existing highway port is unable to effectively identify information and provide action guidance for the self-driving truck formation, resulting in low formation efficiency and unreasonable resource allocation.

[0169] Optionally, the acquisition unit includes at least one of the following: a first determination module for determining that a convoy of autonomous driving trucks has entered when a convoy of autonomous driving trucks exists in the image data, wherein the image data is data collected by an image acquisition component arranged at the entrance of the distribution area; a second determination module for determining that a convoy of autonomous driving trucks has entered when a detection signal detects a convoy of autonomous driving trucks, wherein the detection signal is a signal detected by a signal detector arranged at the entrance of the distribution area.

[0170] Optionally, the processing unit includes: a first processing module, used to process the vehicle image data through an image recognition model to obtain formation information corresponding to the vehicle image data, wherein the image recognition model is a model obtained by machine learning training using multiple sets of first training data, and each of the multiple sets of first training data includes: sample vehicle image data and sample formation information corresponding to the sample vehicle image data.

[0171] Optionally, a guidance strategy for an autonomous driving truck formation is generated based on the formation information, including: a second processing module, configured to process the formation information through a guidance strategy generation model to obtain a guidance strategy corresponding to the formation information, wherein the guidance strategy generation model is a model obtained through machine learning training using multiple sets of second training data, and each of the multiple sets of second training data includes: sample formation information and a sample guidance strategy corresponding to the formation information.

[0172] Optionally, the generation unit includes: a first acquisition module for acquiring first vehicle-related information of the autonomous driving truck formation, and generating a driving guidance strategy for the autonomous driving truck formation based on the vehicle-related information, wherein the first vehicle-related information includes at least one of the following: the current state of the vehicle, task priority, and traffic congestion information; a second acquisition module for acquiring parking space information of each parking space in the distribution area and second vehicle-related information of the autonomous driving truck formation, and generating a parking guidance strategy based on the parking space information and the second vehicle-related information, wherein the parking space information includes at least: the physical coordinates of the parking space, the applicable vehicle type, and the functional area attributes, and the second vehicle-related information includes: when The system includes the following information: the previous mission type, vehicle size and estimated stop time; a third acquisition module is used to obtain the charging pile information of each charging area in the distribution area and the related information of the third vehicle in the autonomous driving truck fleet, and generate a charging guidance strategy based on the charging pile information and the related information of the third vehicle, wherein the charging pile information includes at least: charging power and docking method, and the related information of the third vehicle includes: battery state of charge, battery temperature, and expected power consumption for the next mission; a fourth acquisition module is used to obtain the related information of the third vehicle in the autonomous driving truck fleet, and generate a maintenance guidance strategy based on the related information of the third vehicle, wherein the related information of the third vehicle includes: vehicle operating status and fault code.

[0173] Optionally, the control unit includes: a first control module, used to trigger the identification system at the entrance of the distribution area to identify the autonomous driving truck formation when the autonomous driving truck formation is in the entering state, and after the autonomous driving truck formation is identified, control the lifting bar at the entrance to remain in the raised state until it is determined that the last truck in the autonomous driving truck formation has passed the entrance.

[0174] Optionally, the control unit includes: a second control module, which is used to control the autonomous driving truck formation to travel along the target formation vehicle driving road to the target formation vehicle parking space when it is determined that the autonomous driving truck formation has passed the entrance of the distribution area as a whole, wherein the target formation vehicle driving road is the road indicated by the guidance strategy, and the target formation vehicle parking space is the parking space indicated by the guidance strategy.

[0175] Optionally, the control unit includes: a third determination module, used to determine the charging order of each vehicle in the autonomous driving truck formation and the target formation vehicle charging area based on the battery charge state, battery temperature and expected power consumption of each vehicle in the autonomous driving truck formation when it is determined that the autonomous driving truck formation needs to be charged; a third control module, used to control the autonomous driving truck formation to travel to the target formation vehicle charging area and perform charging operations according to the charging order.

[0176] According to another aspect of an embodiment of the present invention, an autonomous driving truck platoon is provided, characterized in that the autonomous driving truck platoon uses any of the above-mentioned control methods for the autonomous driving truck platoon.

[0177] According to another aspect of an embodiment of the present invention, a processor is further provided, which is used to run a program, wherein when the program is running, any one of the above-mentioned control methods for an autonomous driving truck formation is executed.

[0178] According to another aspect of an embodiment of the present invention, a computer program product is provided, comprising computer instructions, which, when executed by a processor, execute any one of the above-mentioned methods for controlling an autonomous driving truck platoon.

[0179] According to another aspect of an embodiment of the present invention, a computer-readable storage medium is provided, which includes a stored program, wherein the program executes any one of the above-mentioned control methods for an autonomous driving truck formation.

[0180] Optionally, in this embodiment, the computer-readable storage medium may be located in any one of the computer terminals in a computer terminal group in a computer network, or in any one of the communication devices in a communication device group.

[0181] Optionally, in this embodiment, the computer-readable storage medium is configured to store program code for executing the following steps: upon detecting the entry of a platoon of autonomous driving trucks, obtaining vehicle image data of the platoon; processing the vehicle image data to obtain formation information of the autonomous driving trucks, wherein the formation information includes at least the following information of the autonomous driving trucks: the number of vehicles, the formation form, and the type of loaded items; generating a guidance strategy for the autonomous driving trucks based on the formation information, wherein the guidance strategy is used to guide various operations of the autonomous driving trucks in the distribution area, which is a transit area for the autonomous driving trucks, and the distribution area is provided with the following areas: platoon vehicle driving roads, platoon vehicle parking spaces, platoon vehicle charging areas, and platoon vehicle maintenance areas, and various operations include at least: parking, charging, and maintenance; controlling the autonomous driving trucks to act according to the guidance strategy.

[0182] Optionally, in this embodiment, the computer-readable storage medium is configured to store program code for executing the following steps: when there is a self-driving truck convoy in the image data, determining that a self-driving truck convoy has entered, wherein the image data is data collected by an image acquisition component arranged at the entrance of the distribution area; when the detection signal detects the self-driving truck convoy, determining that there is a self-driving truck convoy has entered, wherein the detection signal is a signal detected by a signal detector arranged at the entrance of the distribution area.

[0183] Optionally, in this embodiment, the computer-readable storage medium is configured to store program code for executing the following steps: processing the vehicle image data through an image recognition model to obtain formation information corresponding to the vehicle image data, wherein the image recognition model is a model obtained by machine learning training using multiple sets of first training data, and each of the multiple sets of first training data includes: sample vehicle image data and sample formation information corresponding to the sample vehicle image data.

[0184] Optionally, in this embodiment, the computer-readable storage medium is configured to store program code for executing the following steps: processing the formation information through a guidance strategy generation model to obtain a guidance strategy corresponding to the formation information, wherein the guidance strategy generation model is a model obtained by machine learning training using multiple sets of second training data, and each of the multiple sets of second training data includes: sample formation information and a sample guidance strategy corresponding to the formation information.

[0185] Optionally, in this embodiment, the computer-readable storage medium is configured to store program code for executing the following steps: obtaining first vehicle-related information of the autonomous driving truck formation, and generating a driving guidance strategy for the autonomous driving truck formation based on the vehicle-related information, wherein the first vehicle-related information includes at least one of the following: the vehicle's current status, task priority, and traffic congestion information; obtaining parking space information for each parking space in the distribution area and second vehicle-related information of the autonomous driving truck formation, and generating a parking guidance strategy based on the parking space information and the second vehicle-related information, wherein the parking space information includes at least: the physical coordinates of the parking space, the compatible vehicle type, and the functional area attributes, and the second vehicle-related information includes: the current task type, the vehicle size, and the estimated parking time; obtaining charging pile information of each charging area in the distribution area and third vehicle-related information of the autonomous driving truck formation, and generating a charging guidance strategy based on the charging pile information and the third vehicle-related information, wherein the charging pile information includes at least: charging power and docking mode, and the third vehicle-related information includes: battery state of charge, battery temperature, and expected power consumption for the next task; obtaining third vehicle-related information of the autonomous driving truck formation, and generating a maintenance guidance strategy based on the third vehicle-related information, wherein the third vehicle-related information includes: vehicle operating status and fault code.

[0186] Optionally, in this embodiment, the computer-readable storage medium is configured to store program code for executing the following steps: when the autonomous driving truck formation is in the driving-in state, triggering the identification system at the entrance of the distribution area to identify the autonomous driving truck formation, and after the autonomous driving truck formation is identified, controlling the lifting bar at the entrance to remain in the raised state until it is determined that the last truck in the autonomous driving truck formation has passed the entrance.

[0187] Optionally, in this embodiment, the computer-readable storage medium is configured to store program code for executing the following steps: when it is determined that the autonomous driving truck formation has passed the entrance of the distribution area as a whole, controlling the autonomous driving truck formation to travel along the target formation vehicle driving road to the target formation vehicle parking space, wherein the target formation vehicle driving road is the road indicated by the guidance strategy, and the target formation vehicle parking space is the parking space indicated by the guidance strategy.

[0188] Optionally, in this embodiment, the computer-readable storage medium is configured to store program code for executing the following steps: when it is determined that the autonomous driving truck formation needs to be charged, determining the charging order of each vehicle in the autonomous driving truck formation and the target formation vehicle charging area based on the battery state of charge, battery temperature and expected power consumption of the next mission of each vehicle in the autonomous driving truck formation; controlling the autonomous driving truck formation to travel to the target formation vehicle charging area, and performing charging operations in accordance with the charging order.

[0189] The serial numbers of the above embodiments of the present invention are for description only and do not represent the advantages or disadvantages of the embodiments.

[0190] In the above embodiments of the present invention, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0191] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only exemplary. For example, the division of the units can be a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0192] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple units. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0193] In addition, the functional units in the various embodiments of the present invention may be integrated into a single processing unit, each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0194] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, magnetic disk or optical disk, etc. Various media that can store program codes.

[0195] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as within the scope of protection of the present invention.

Claims

1. A control method for an autonomous truck platoon, characterized in that: include: When detecting the entry of a platoon of autonomous driving trucks, obtaining vehicle image data of the platoon of autonomous driving trucks; Processing the vehicle image data to obtain formation information of the autonomous driving truck formation, wherein the formation information includes at least the following information of the autonomous driving truck formation: the number of vehicles, the formation form, and the type of loaded items; Generating a guidance strategy for the autonomous truck formation based on the formation information, wherein the guidance strategy is used to guide various operations of the autonomous trucks in a distribution area, the distribution area being a transit area for the autonomous truck formation, the distribution area being provided with the following areas: a platoon vehicle driving road, a platoon vehicle parking space, a platoon vehicle charging area, and a platoon vehicle maintenance area, the various operations including at least: parking, charging, and maintenance; Control the self-driving truck formation to act according to the guidance strategy.

2. The control method for an autonomous driving truck platoon according to claim 1, characterized in that: The detection of a platoon of self-driving trucks entering the vehicle includes at least one of the following: When the self-driving truck formation exists in the image data, determining that the self-driving truck formation has entered, wherein the image data is data collected by an image collection component provided at the entrance of the distribution area; When the detection signal detects the autonomous driving truck formation, it is determined that the autonomous driving truck formation has entered, wherein the detection signal is a signal detected by a signal detector set at the entrance of the distribution area.

3. The control method for an autonomous driving truck platoon according to claim 1, characterized in that: Processing the vehicle image data to obtain formation information of the autonomous driving truck formation includes: The vehicle image data is processed by an image recognition model to obtain the formation information corresponding to the vehicle image data, wherein the image recognition model is a model trained by machine learning using multiple sets of first training data, and each of the multiple sets of first training data includes: sample vehicle image data and sample formation information corresponding to the sample vehicle image data.

4. The control method for an autonomous driving truck platoon according to claim 1, characterized in that: Generating a guidance strategy for the autonomous driving truck formation according to the formation information includes: The formation information is processed through a guidance strategy generation model to obtain the guidance strategy corresponding to the formation information, wherein the guidance strategy generation model is a model obtained by machine learning training using multiple groups of second training data, and each group of the multiple groups of second training data includes: sample formation information and a sample guidance strategy corresponding to the formation information.

5. The control method for an autonomous driving truck platoon according to claim 1, characterized in that: Generating a guidance strategy for the autonomous driving truck formation according to the formation information includes: Obtaining first vehicle-related information of the autonomous truck platoon, and generating a driving guidance strategy for the autonomous truck platoon based on the vehicle-related information, wherein the first vehicle-related information includes at least one of the following: a current state of the vehicle, a task priority, and traffic congestion information; Obtaining parking space information for each parking space in the distribution area and information related to a second vehicle in the autonomous truck platoon, and generating a parking guidance strategy based on the parking space information and the information related to the second vehicle, wherein the parking space information includes at least: physical coordinates of the parking space, compatible vehicle type, and functional area attributes; and the second vehicle information includes: current task type, vehicle size, and estimated parking duration; Obtaining charging pile information for each charging area within the distribution area and information related to a third vehicle in the autonomous truck convoy, and generating a charging guidance strategy based on the charging pile information and the information related to the third vehicle, wherein the charging pile information includes at least: charging power and docking mode, and the information related to the third vehicle includes: battery state of charge, battery temperature, and expected power consumption for the next mission; Obtain information related to a third vehicle of the autonomous driving truck formation, and generate a maintenance guidance strategy based on the information related to the third vehicle, wherein the information related to the third vehicle includes: vehicle operating status and fault code.

6. The control method for an autonomous driving truck platoon according to claim 1, characterized in that: Controlling the autonomous driving truck formation to act according to the guidance strategy includes: When the autonomous driving truck formation is in the driving-in state, the identification system at the entrance of the distribution area is triggered to identify the autonomous driving truck formation, and after the autonomous driving truck formation is identified, the lifting rod at the entrance is controlled to remain in the lifting state until it is determined that the last truck in the autonomous driving truck formation has passed the entrance.

7. The control method for an autonomous driving truck platoon according to claim 1, characterized in that: Controlling the autonomous driving truck formation to act according to the guidance strategy includes: When it is determined that the autonomous driving truck formation has passed the entrance of the distribution area as a whole, the autonomous driving truck formation is controlled to travel along the target formation vehicle driving road to the target formation vehicle parking space, wherein the target formation vehicle driving road is the road indicated by the guidance strategy, and the target formation vehicle parking space is the parking space indicated by the guidance strategy.

8. The control method for an autonomous driving truck platoon according to claim 1, characterized in that: Controlling the autonomous driving truck formation to act according to the guidance strategy includes: When it is determined that the autonomous truck formation needs to be charged, determining the charging order of each vehicle in the autonomous truck formation and the target formation vehicle charging area according to the battery state of charge, battery temperature, and expected power consumption of the next mission of each vehicle in the autonomous truck formation; The autonomous driving truck formation is controlled to travel to the target formation vehicle charging area and perform charging operations according to the charging sequence.

9. A control device for an autonomous truck platoon, characterized in that: include: an acquisition unit, configured to acquire vehicle image data of the autonomous driving truck formation when detecting the entry of the autonomous driving truck formation; a processing unit, configured to process the vehicle image data to obtain formation information of the autonomous driving truck formation, wherein the formation information includes at least the following information of the autonomous driving truck formation: the number of vehicles, the formation form, and the type of loaded items; a generating unit, configured to generate a guidance strategy for the autonomous truck formation based on the formation information, wherein the guidance strategy is used to guide various operations of the autonomous trucks in a distribution area, the distribution area being a transfer area for the autonomous truck formation, the distribution area being provided with the following areas: a platoon vehicle driving road, a platoon vehicle parking space, a platoon vehicle charging area, and a platoon vehicle maintenance area, the various operations including at least: parking, charging, and maintenance; A control unit is used to control the autonomous driving truck formation to act according to the guidance strategy.

10. An autonomous truck platoon, characterized in that: The autonomous driving truck platoon uses the control method for the autonomous driving truck platoon described in any one of claims 1 to 8.

11. A computer-readable storage medium, characterized in that The computer-readable storage medium includes a stored program, wherein the program executes the control method for the autonomous driving truck platoon according to any one of claims 1 to 8.

12. A processor, characterized in that: The processor is used to run a program, wherein the program, when running, executes the control method for the autonomous driving truck platoon according to any one of claims 1 to 8.

13. A computer program product comprising computer instructions, characterized in that When the computer instructions are executed by the processor, the control method for the autonomous driving truck formation according to any one of claims 1 to 8 is performed.

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