A Vehicular Ad Hoc Network System and Method Based on Deep Reinforcement Learning

Abstract

The present invention discloses a vehicle ad-hoc network system and method based on deep reinforcement learning, which relates to the field of vehicle-to-everything (V2X) communication technology. The system includes a dynamic routing module that uses a deep reinforcement learning algorithm to adjust the relay path in real time and optimize the network load; a relay signal transmission module that constructs a multi-hop relay link by leveraging the network signals of other vehicles when the signal is weak; a multi-mode communication module that intelligently switches between V2X and Wi-Fi 6 communication protocols according to the signal strength and network environment; a positioning cooperation module that fuses the data of UWB and IMU sensors to achieve high-precision vehicle-to-vehicle positioning; a security verification module that uses the national cryptographic SM4 encryption algorithm and blockchain smart contracts to ensure data security and identity credibility; and a communication control module that coordinates data transmission, path selection, and protocol switching. The present invention effectively solves the problems of signal attenuation, protocol switching, and security in the dynamic environment of the vehicle ad-hoc network, and improves the communication ability and system stability of intelligent connected vehicles.

Description

Technical Field

[0001] The present invention relates to the technical field of vehicle networking communication, and particularly to a vehicle ad-hoc network system and method based on deep reinforcement learning. Background Art

[0002] With the rapid development of intelligent connected vehicle (ICV) technology, vehicle-to-everything (V2X) communication has become a key technology for improving traffic safety, optimizing road utilization, and enhancing the performance of intelligent transportation systems (ITS). V2X communication enables information interaction between vehicles and other traffic participants (including other vehicles, infrastructure, pedestrians, etc.) to support applications such as vehicle platooning, autonomous driving, and collision warning. However, existing V2X communication systems still face many technical challenges in actual deployment. Especially in a dynamic network environment, how to ensure efficient data transmission, stable connection, and security remains the focus of current research.

[0003] Although traditional V2X communication systems have improved the information interaction ability between vehicles to a certain extent, there are still multiple problems. First, the dynamic change of the network topology leads to unstable routing. Due to the mobility of vehicles, traditional static or semi-dynamic routing protocols are difficult to quickly adapt to topology changes, easily causing communication link breaks and affecting the reliability of data transmission. Second, the efficiency of multi-hop transmission is low and network congestion is serious. When the single-hop communication signal attenuates, data needs to be transmitted through multiple relay nodes, but the existing relay mechanisms lack intelligent optimization, which may lead to too many hops, increasing latency and causing network congestion. In addition, the existing heterogeneous network switching methods are not intelligent enough. Although V2X systems and communication technologies such as Wi-Fi 6 are complementary, they still rely on fixed thresholds during protocol switching and are difficult to adapt to complex network environments, resulting in a decline in communication quality. At the same time, the high-precision positioning ability of existing V2X systems is limited. Traditional GPS positioning is vulnerable to interference in complex environments such as high-rise building clusters and tunnels, and it is difficult to provide high-precision relative positioning information, affecting the effects of vehicle platooning and cooperative perception. Finally, due to the openness of V2X communication, there are risks in data security and credibility of the system. Malicious nodes may interfere with communication through identity forgery or man-in-the-middle attacks, and existing authentication and encryption mechanisms are difficult to provide sufficient security while ensuring low latency. Summary of the Invention

[0004] In view of the above problems, the present invention proposes a vehicle ad-hoc network system and method based on deep reinforcement learning. By optimizing network topology management, intelligent relay transmission, protocol dynamic switching, high-precision positioning fusion, and blockchain security verification through deep reinforcement learning, a more efficient, secure, and stable vehicle ad-hoc network system is realized, further enhancing the communication ability of intelligent connected vehicles and the overall operation efficiency of the traffic system.

[0005] To achieve the above object, the present invention is realized through the following technical solutions:

[0006] A vehicle ad-hoc network system based on deep reinforcement learning, comprising:

[0007] A dynamic routing module, configured to collect vehicle density, signal strength, and network load data in real time through a deep reinforcement learning algorithm, select relay nodes according to the data, and adaptively adjust multi-hop paths, where the upper limit of the number of hops for path adjustment is dynamically optimized according to the network load condition;

[0008] A relay signal transmission module, configured to build a multi-hop relay link through the path selection result provided by the dynamic routing module when the signal strength is lower than a preset threshold, and borrow the network signals of other vehicles through wireless propagation;

[0009] A multi-mode communication module, configured to intelligently switch between the V2X communication protocol and the Wi-Fi 6 communication protocol according to the network environment and signal strength where the vehicle is located;

[0010] A positioning cooperation module, configured to fuse the positioning data between vehicles, including generating high-precision relative positioning information between vehicles by using UWB technology and IMU sensor data, and performing positioning cooperation based on this information;

[0011] A security verification module, configured to protect the security of the data transmitted in the system, encrypt the data by using an encryption algorithm, verify the identity of the relay nodes, and use the blockchain smart contract mechanism to ensure the effectiveness and non-tamperability of the identity verification;

[0012] A communication control module, configured to coordinate data transmission, path selection, and communication protocols in the vehicle ad-hoc network system based on deep reinforcement learning.

[0013] As a preferred solution of the present invention, the dynamic routing module includes:

[0014] A signal acquisition unit, configured to collect vehicle density, signal strength RSSI, and network load data in real time, with a sampling period of 100 milliseconds, where the RSSI threshold range is adjusted according to the vehicle density. When the vehicle density is high, the RSSI threshold range is set in a low range; when the vehicle density is low, the RSSI threshold range is set in a high range;

[0015] A policy decision unit, adopting the deep Q-network DQN algorithm, with input parameters including the RSSI threshold range and the bandwidth occupancy rate threshold range, and outputting the relay node selection and hop count allocation policy according to the input parameters;

[0016] The instruction execution unit constructs a relay link according to the relay node selection and hop count allocation strategy output by the policy decision unit, and adjusts the hop count upper limit according to the network load rate. When the network load rate exceeds the preset value for three consecutive sampling periods, the hop count upper limit is the preset value; when the network load rate does not exceed the preset value for three consecutive sampling periods, the hop count upper limit is another preset value.

[0017] As a preferred solution of the present invention, the policy decision unit constructs an adaptive path optimization model based on an improved deep Q-network (DQN) algorithm to solve the problems of fixed path selection, insufficient dynamic adjustment ability, and high computational complexity in the prior art, optimize channel resource allocation and transmission paths, and adopts the following reward function:

[0018] ;

[0019] Where: : The channel quality indication value between vehicles based on V2X communication, dynamically weighing the communication quality of relay node selection;

[0020] : The minimum channel quality threshold adjusted adaptively to ensure network stability in different scenarios; : The current channel load, dynamically optimizing bandwidth resource allocation through reinforcement learning to reduce channel congestion; : The maximum available bandwidth of the channel, adaptively adjusting the load balancing strategy in a complex vehicle-mounted network environment; : The change in relative speed between vehicles, using a non-linear exponential function to adjust the path stability weight to reduce communication breaks caused by high-speed movement; : The average speed of the vehicle group to optimize the stability of data transmission and make path selection more intelligent; : The maximum communication reachable distance, adaptively adjusting in combination with the channel state to improve the network coverage; : The communication delay of the current link, adopting a delay optimization strategy to reduce the cumulative delay of multi-hop transmission; : The hop count of the data packet, automatically optimizing the hop count through a reinforcement learning method to reduce the cumulative packet loss rate; 、 、 、 、 : The adaptive optimization coefficient for adjusting the weights of each parameter, enabling the network to dynamically adjust the optimization strategy in different vehicle-mounted environments.

[0021] Based on the reward function, the policy decision-making unit dynamically optimizes path selection using reinforcement learning, enabling the system to perceive the communication environment in real time, intelligently adjust channel load allocation, and optimize relay node selection and the number of hops to reduce latency, improve link stability, and enhance data throughput. Compared with existing path optimization methods based on static thresholds or fixed optimization strategies, the adaptive path optimization model of the present invention can dynamically optimize the communication path according to different vehicle network conditions, reduce computational complexity while ensuring network stability, is applicable to the vehicle edge computing environment, and improves the real-time performance and computational efficiency of the system.

[0022] As a preferred embodiment of the present invention, the relay signal transmission module includes:

[0023] A signal strength detection unit for real-time detecting the signal strength of the current vehicle, and triggering the relay signal transmission function when the signal strength is lower than a preset signal strength threshold;

[0024] A relay path selection unit for selecting a suitable relay node to construct a relay link according to the path selection result provided by the dynamic routing module;

[0025] A wireless propagation unit for borrowing the network signal of other vehicles through wireless propagation to implement a multi-hop relay link, and transmitting the network signal back to the current vehicle or other vehicles in need of network resources through the relay link.

[0026] As a preferred embodiment of the present invention, the multi-mode communication module includes:

[0027] A protocol switching unit for selecting one of the V2X communication protocol and the Wi-Fi 6 communication protocol for switching according to the network environment and available signal strength. The working frequency band of the V2X communication protocol is 5.9 GHz, the working frequency band of the Wi-Fi 6 communication protocol is 5 GHz or 6 GHz, and the frequency band interval between the two is not less than 200 MHz;

[0028] A signal strength monitoring unit for real-time monitoring the current signal strength, and switching to other communication protocols for data transmission when the signal strength is lower than a preset threshold;

[0029] A protocol adaptation strategy unit for selecting a communication protocol for data transmission according to the distance between the vehicle and other vehicles, bandwidth requirements, and signal stability.

[0030] As a preferred embodiment of the present invention, the positioning cooperation module includes:

[0031] A positioning information receiving unit for receiving positioning information from other vehicles and transmitting the received positioning data to the positioning processing unit;

[0032] A positioning processing unit, which is used to fuse the positioning data from the UWB technology and the IMU sensor to generate high-precision relative positioning information between vehicles. The working frequency band of the UWB technology is from 6.5 GHz to 7.5 GHz;

[0033] A collaborative positioning unit, which is used to perform positioning collaboration according to the relative positioning information between vehicles to ensure high-precision positioning results in complex environments.

[0034] As a preferred solution of the present invention, the security verification module includes:

[0035] A data encryption unit, which is used to encrypt the data transmitted in the network by using the national cryptographic SM4 algorithm to ensure the confidentiality and integrity of the data during the transmission process;

[0036] An identity verification unit, which is used to verify the identity of the relay node through the blockchain smart contract to ensure the credibility of the relay node and prevent malicious nodes from interfering with the system;

[0037] A secure communication unit, which is used to ensure the security of the communication link during data transmission and authenticate both communication parties by using digital certificates to prevent the data from being tampered with during the transmission process.

[0038] As a preferred solution of the present invention, the blockchain smart contract includes:

[0039] A contract creation unit, which is used to generate a smart contract and sign the contract content by using public key encryption technology to ensure the legality and immutability of the contract;

[0040] A contract execution unit, which is used to execute the verification logic in the smart contract and judge the legality of the relay node according to the identity information of the relay node and the preset verification rules;

[0041] A contract recording unit, which is used to record the execution results of each smart contract on the blockchain, including the verification records and verification results of the relay node, to ensure that all operations are traceable and immutable.

[0042] A vehicle ad-hoc network method based on deep reinforcement learning includes the following steps:

[0043] Step 1: Real-time collect vehicle density, signal strength RSSI, and network load data;

[0044] Step 2: Select relay nodes through the deep reinforcement learning algorithm;

[0045] Step 3: Adjust the multi-hop path according to the network load situation and optimize the hop count limit of the path;

[0046] Step 4: When the signal strength is lower than the preset threshold, select a path through the relay signal transmission module and construct a multi-hop relay link, and borrow the network signals of other vehicles through wireless propagation;

[0047] Step 5: Switch the V2X communication protocol and the Wi-Fi 6 communication protocol according to the network environment and signal strength. The working frequency band of the V2X communication protocol is 5.9 GHz, the working frequency band of the Wi-Fi 6 communication protocol is 5 GHz or 6 GHz, and the frequency band interval is greater than or equal to 200 MHz;

[0048] Step 6: Fuse the data from the UWB technology and the IMU sensor to generate the relative positioning information between vehicles, and perform positioning coordination based on this information;

[0049] Step 7: Encrypt the data transmitted over the network, verify the identity of the relay node, and use the blockchain smart contract mechanism to ensure the effectiveness and immutability of the identity verification;

[0050] Step 8: Coordinate data transmission, path selection, and communication protocols to ensure the normal operation of the vehicle ad-hoc network system based on deep reinforcement learning.

[0051] As a preferred solution of the present invention, based on the real-time relative position data between vehicles, a weighting factor is used to calculate the priority of candidate relay nodes, and the path selection strategy is adjusted;

[0052] Based on the traffic flow monitoring data, combined with the time series prediction model, optimize the path hop count allocation, and dynamically adjust the maximum hop count limit to adapt to the network load conditions of different traffic flow densities.

[0053] Compared with the prior art, the beneficial effects of the present invention are as follows: The present invention collects vehicle density, signal strength, and network load data in real time through a deep reinforcement learning algorithm, adaptively adjusts multi-hop paths and relay node selection, and improves the stability and reliability of data transmission. Compared with traditional static or semi-dynamic routing protocols, this system is more adaptable to the highly dynamic environment of vehicular networks, ensuring the stability of communication links. When the vehicle signal strength is lower than the preset threshold, the system automatically triggers a multi-hop relay link, supplements communication with the network signals of other vehicles, and avoids network interruption caused by signal attenuation, thereby expanding the communication coverage and improving the overall connection quality. In addition, the system can intelligently switch between V2X and Wi-Fi 6 protocols according to different network environments and signal strengths, optimize bandwidth utilization while reducing latency, and ensure the communication quality of the vehicle network in various application scenarios. In terms of positioning, the system integrates UWB technology and IMU sensor data to achieve high-precision relative positioning between vehicles, and can perform positioning collaboration in complex environments, improving the safety and efficiency of autonomous driving and intelligent transportation systems. At the same time, the present invention adopts the national cryptographic SM4 encryption algorithm and blockchain smart contract technology to ensure the security of data during transmission, and conducts trusted verification of the identities of relay nodes to prevent attacks by malicious nodes, enhancing the security and anti-tampering ability of the system. The present invention optimizes the path selection, communication protocol, signal transmission, and security mechanism of vehicular ad hoc networks through deep reinforcement learning, significantly improving the communication efficiency, reliability, and security of the vehicle network system, and being able to effectively cope with the dynamics and complexity in vehicle network applications, providing more efficient and stable technical support for intelligent connected vehicles. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts. Among them:

[0055] Figure 1 is the system modular structure diagram of the embodiment of the present invention;

[0056] Figure 2 is the method flow chart of the embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0057] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the following will clearly and completely describe the technical solutions of the embodiments of the present invention in conjunction with the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the described embodiments of the present invention fall within the scope of protection of the present invention.

[0058] As Figure 1 shown, this is an embodiment of the present invention, which provides a vehicle ad-hoc network system based on deep reinforcement learning, including:

[0059] (1) Dynamic routing module

[0060] It is used to collect vehicle density, signal strength, and network load data in real time through a deep reinforcement learning algorithm, select relay nodes according to the data, and adaptively adjust multi-hop paths, where the upper limit of the number of hops for path adjustment is dynamically optimized according to the network load situation;

[0061] This embodiment addresses the dynamic routing problem in vehicle-to-everything (V2X) communication. This method can collect vehicle density, signal strength, and network load data in real time, and adaptively select the optimal relay node through a reinforcement learning algorithm to optimize the multi-hop path, so as to reduce communication latency and improve network stability.

[0062] The dynamic routing module of this embodiment mainly includes a signal acquisition unit, a policy decision unit, and an instruction execution unit. Each unit works together to ensure the stability, real-time performance, and efficiency of data transmission.

[0063] The dynamic routing module includes:

[0064] Signal acquisition unit

[0065] The signal acquisition unit is mainly used to obtain key parameters of the vehicle network environment in real time, including:

[0066] Vehicle density detection: Based on the number of wireless communication connections between vehicles, calculate the number of neighboring vehicles per unit time, and judge the dynamics of the network topology. When the density is high, the channel competition is fierce and data conflicts are likely to occur; when the density is low, there may be signal coverage blind spots.

[0067] Signal strength (RSSI) detection:

[0068] When the vehicle density is relatively high, the RSSI threshold range is dynamically adjusted to a lower value to reduce the interference of long-distance communication and improve the stability of short-distance communication;

[0069] When the vehicle density is relatively low, the RSSI threshold range is increased to ensure that effective connections can still be established for long-distance communication.

[0070] Network load monitoring: By measuring the bandwidth occupancy rate, channel utilization rate, and congestion situation, the current network load situation is determined, and sampling is performed every 100 ms to ensure the real-time nature of the data.

[0071] This unit ensures that the dynamic routing module is always optimized based on the latest network environment data, improving the stability of data transmission.

[0072] The policy decision unit adopts the Deep Q-Network (DQN) algorithm. The input parameters include the RSSI threshold range and the bandwidth occupancy rate threshold range. According to the input parameters, it outputs the relay node selection and hop count allocation strategy.

[0073] The instruction execution unit constructs a relay link according to the relay node selection and hop count allocation strategy output by the policy decision unit, and adjusts the hop count upper limit according to the network load rate. When the network load rate exceeds the preset value for three consecutive sampling periods, the hop count upper limit is the preset value; when the network load rate does not exceed the preset value for three consecutive sampling periods, the hop count upper limit is another preset value.

[0074] In one of the embodiments, the policy decision unit constructs an adaptive path optimization model based on the improved Deep Q-Network (DQN) algorithm to optimize the channel resource allocation and transmission path, and adopts the following reward function:

[0075] ;

[0076] Where: : Channel Quality Indicator value between vehicles based on V2X communication; : The lowest available threshold of channel quality; : The current channel load, indicating the bandwidth occupancy situation; : The maximum available bandwidth of the channel; : The change in relative speed between vehicles; : The average speed of the vehicle group; : The maximum communication reachable distance; : The communication delay of the current link; : The hop count of the data packet; 、 、 、 、 : Optimization coefficients used to adjust the weights of each parameter;

[0077] Based on this reward function, the policy decision unit uses the reinforcement learning method to calculate the communication environment parameters in real time, dynamically adjusts the channel load allocation, optimizes the relay node selection and hop count, determines the data transmission path based on the path optimization model, and adjusts the maximum communication distance and delay control strategy according to the channel state.

[0078] The dynamic routing module of this embodiment has the following innovative points:

[0079] Intelligent routing optimization based on reinforcement learning: Compared with traditional static or history-data-based routing methods, the DQN algorithm can adaptively adjust the path to improve data transmission efficiency;

[0080] Real-time signal acquisition and network adaptability: Dynamically adjust the communication strategy based on the RSSI threshold, enabling the system to adapt to network environments with different vehicle densities;

[0081] Adaptive hop count optimization: The reinforcement learning algorithm optimizes the multi-hop path selection strategy to ensure the minimization of end-to-end delay;

[0082] Path prediction mechanism: Combine vehicle movement trajectories and signal strength predictions to adjust the data transmission path in advance and reduce the incidence of link interruptions.

[0083] This embodiment is applicable to the following typical application scenarios:

[0084] Intelligent transportation system: Used for optimizing V2X communication in the vehicle network to improve the efficiency of road information sharing and cooperative control;

[0085] Highway fleet communication: Support intelligent fleet management to ensure low-latency and high-stability information transmission between vehicles;

[0086] Disaster emergency network: When fixed communication facilities are damaged, use vehicle ad-hoc networks for emergency data transmission to ensure uninterrupted communication.

[0087] This embodiment details a dynamic routing optimization method based on deep reinforcement learning, including the collaborative working mechanism of the signal acquisition unit, policy decision-making unit, and instruction execution unit, and ensures that the system can adapt to complex vehicle network environments through reinforcement learning algorithms, adaptive path optimization models, and dynamic hop count adjustments, improving the reliability of data transmission and communication efficiency. The innovation of this invention lies in combining deep reinforcement learning and real-time path optimization technologies, enabling the system to continuously optimize the data transmission path in a dynamic network environment, thereby enhancing the overall performance of vehicle ad-hoc networks.

[0088] (2) Relay signal transmission module

[0089] When the signal strength is lower than the preset threshold, construct a multi-hop relay link through the path selection result provided by the dynamic routing module, and borrow the network signals of other vehicles through wireless propagation;

[0090] In this embodiment, the relay signal transmission module aims to construct a multi-hop relay link when the signal strength is lower than the preset threshold based on the path selection result provided by the dynamic routing module, so as to ensure the continuous transmission of data and the stability of communication. This module consists of a signal strength detection unit, a relay path selection unit, and a wireless propagation unit, ensuring that the system can continue to transmit data by leveraging the network resources of other vehicles in an environment with unstable signals or insufficient coverage.

[0091] Signal strength detection unit

[0092] The signal strength detection unit is responsible for real-time monitoring of the vehicle's network signal strength and evaluating the signal quality. When the vehicle's signal strength is lower than the preset threshold, the relay signal transmission function is triggered. To ensure the accuracy and timeliness of detection, the signal strength detection unit uses a high-frequency (e.g., 100 ms) sampling period to provide real-time feedback on the signal status of the vehicle's environment. This unit determines whether the current signal meets the communication requirements through the RSSI (Received Signal Strength Indicator) value. When the signal strength is insufficient, the system will automatically trigger the relay link construction process. Through precise signal strength monitoring, network transmission interruptions can be effectively avoided, thus ensuring the stable transmission of data.

[0093] Relay path selection unit

[0094] The relay path selection unit selects appropriate relay nodes to construct the relay link based on the path selection result provided by the dynamic routing module. The core of this unit is to optimize path selection by combining dynamic routing algorithms. The dynamic routing module uses a deep reinforcement learning algorithm to adaptively adjust the path hop count based on data such as vehicle density, signal strength, and network load. When the signal strength is lower than the threshold, the relay path selection unit selects the vehicle closest to the current vehicle, with better signal quality and lower load as the relay node according to these real-time environmental parameters. This can effectively avoid the degradation of path quality caused by improper selection of relay nodes, ensuring smooth and low-latency data transmission.

[0095] In addition, the relay path selection unit also needs to consider the selection of multi-hop paths. By calculating the relative positions and signal strengths between vehicles in real time, the hop count of the multi-hop relay path is optimized to minimize transmission delay and maximize transmission bandwidth. This path selection is not only based on the single shortest path or lowest latency, but also comprehensively considers path stability, channel load, and available bandwidth, thus forming an efficient relay link.

[0096] Wireless propagation unit

[0097] The function of the wireless propagation unit is to borrow the network signals of other vehicles through wireless propagation to build a multi-hop relay link when the signal strength is insufficient. In traditional network architectures, communication links often cannot maintain stability when the signal is weak. In this system, the wireless propagation unit uses the network signals of surrounding vehicles and, by building a relay link, forwards the network signals back to the current vehicle or other vehicles in need of network resources.

[0098] The wireless propagation unit selects the most suitable path for data transmission based on the signal quality of multiple candidate relay nodes. When a vehicle receives a signal forwarded by a relay node, the data is gradually transmitted through this link until it reaches the final destination node. To further improve the efficiency of the relay link, the wireless propagation unit can introduce signal enhancement and beamforming technologies to improve the signal quality and transmission stability during multi-hop transmission. In specific implementation, the wireless propagation unit not only needs to ensure the reliable transmission of data but also optimize the multi-path propagation strategy to avoid packet loss caused by signal interference or path loss.

[0099] Through this wireless propagation mechanism, the system can make full use of the wireless communication capabilities between vehicles, expand the signal coverage range, and enhance the connectivity of vehicle ad-hoc networks. In areas with low signal strength, such as busy urban blocks and tunnels, it can ensure continuous network connection between vehicles.

[0100] The relay signal transmission module of this embodiment has the following advantages:

[0101] Flexible dynamic routing adjustment: The dynamic routing module based on deep reinforcement learning can dynamically adjust the relay path according to the real-time network conditions, avoiding network bottlenecks caused by static path planning.

[0102] Optimized multi-hop relay mechanism: By intelligently selecting relay nodes and optimizing the number of path hops, it reduces the transmission delay and ensures the efficient utilization of the network.

[0103] Wireless propagation ability: By borrowing the network signals of other vehicles, the relay link can continuously transmit data in a weak signal environment, greatly improving the communication reliability.

[0104] Adaptive path selection: The relay path selection unit maximizes the efficiency of data transmission by calculating the optimal path in real time, reducing the performance degradation caused by improper path selection.

[0105] The relay signal transmission module in this embodiment can effectively solve the problems of signal attenuation and network load through accurate signal strength monitoring, intelligent relay path selection, and efficient wireless propagation mechanism, improving the stability and reliability of the vehicle ad-hoc network system in complex dynamic environments.

[0106] (3) Multi - mode Communication Module

[0107] It is used to intelligently switch between the V2X communication protocol and the Wi - Fi 6 communication protocol according to the network environment and signal strength where the vehicle is located;

[0108] This embodiment provides a multi - mode communication module that can intelligently switch between the V2X communication protocol and the Wi - Fi 6 communication protocol. This module can intelligently switch between the V2X communication protocol and the Wi - Fi 6 communication protocol according to the network environment and signal strength of the vehicle to ensure the stability and efficiency of communication. The working principle and structure of the multi - mode communication module include a protocol switching unit, a signal strength detection unit, and a protocol adaptation strategy unit, which work together to automatically select the optimal communication protocol according to different network conditions and communication requirements.

[0109] Protocol Switching Unit

[0110] The protocol switching unit is responsible for selecting one of the V2X communication protocol and the Wi - Fi 6 communication protocol for switching according to the real - time network environment and signal strength. The specific working method is as follows:

[0111] V2X Communication Protocol: This protocol works in the 5.9GHz frequency band and is mainly used for efficient communication between vehicle - to - vehicle (V2V), vehicle - to - infrastructure (V2I), and vehicle - to - pedestrian (V2P). This protocol has low latency and high reliability in the vehicle - to - everything network and is especially suitable for real - time, low - latency application scenarios.

[0112] Wi - Fi 6 Communication Protocol: The Wi - Fi 6 protocol works in the 5GHz or 6GHz frequency band, provides higher bandwidth and larger network capacity, and is suitable for data - intensive applications such as high - definition video transmission and real - time map updates.

[0113] The protocol switching unit will select the appropriate communication protocol according to the following factors:

[0114] Signal Strength: When the signal strength of a certain communication protocol is lower than the set threshold, switch to another protocol to ensure communication quality;

[0115] Network Environment: Select the appropriate communication protocol according to the bandwidth requirements and stability of the current network environment to achieve the best data transmission effect;

[0116] Frequency Band Interval: The frequency band interval between V2X and Wi - Fi 6 protocols is not less than 200MHz to avoid interference between communication protocols.

[0117] Signal Strength Detection Unit

[0118] The signal strength detection unit is responsible for real - time monitoring of the current signal strength. When this unit works, it will detect the following information:

[0119] Signal strength evaluation: Real-time evaluation of the signal strength of the current V2X communication protocol and Wi-Fi 6 communication protocol, and calculation of the signal quality (RSSI) of each protocol. When the signal strength of a certain protocol is lower than the set threshold, the system will trigger the protocol switching function.

[0120] Switching trigger condition: When the detected signal strength is lower than the preset threshold, the signal strength detection unit will send a signal switching request to the protocol switching unit to switch to another communication protocol. The signal strength detection unit can respond quickly to ensure timely switching during signal attenuation and avoid communication interruption.

[0121] The design of this unit ensures that the vehicle can maintain a stable communication connection in different environments, whether in high-density urban areas or in areas far from the base station with weak signals.

[0122] Protocol adaptation strategy unit

[0123] The protocol adaptation strategy unit selects the most suitable communication protocol for data transmission based on the following factors:

[0124] Distance between vehicles: When the distance between vehicles is relatively close, the Wi-Fi 6 communication protocol provides higher bandwidth and is suitable for data-intensive applications; when the distance between vehicles is far, the V2X communication protocol can maintain lower latency and higher reliability.

[0125] Bandwidth requirement: If the current communication task has a large bandwidth requirement (such as video streaming, high-definition map update, etc.), the Wi-Fi 6 protocol can provide a higher transmission rate; if the requirement is low, the V2X protocol can ensure real-time performance and low latency.

[0126] Signal stability: If the signal is unstable in the current communication environment, the protocol adaptation strategy unit will select a communication protocol with higher stability to ensure that data transmission does not experience packet loss.

[0127] Through these strategies, the protocol adaptation strategy unit flexibly selects the communication protocol according to the real-time state of the vehicle, network environment, and data requirements, optimizing the efficiency and stability of data transmission.

[0128] The multi-mode communication module in this embodiment has the following advantages:

[0129] Intelligent protocol switching: The system adaptively selects the optimal protocol according to real-time signal strength, network environment, and bandwidth requirements to ensure stable communication quality;

[0130] Improve network efficiency: By selecting the appropriate protocol for data transmission, improve the bandwidth utilization rate and transmission rate of the network;

[0131] Flexibly adapt to changing environments: Whether in densely populated urban areas, suburbs, or environments far from base stations, the system can flexibly respond to different signal strengths and maintain stable communication;

[0132] Optimize resource allocation: Through intelligent handover and bandwidth demand judgment, the system avoids frequent handovers and unnecessary resource waste, improving the overall efficiency of communication.

[0133] In this embodiment, through the collaborative work of the protocol switching unit, signal strength monitoring unit, and protocol adaptation strategy unit, the V2X communication protocol and Wi-Fi 6 communication protocol are intelligently switched, ensuring stable and efficient communication connections for vehicles in different network environments. This technology can enhance the flexibility, stability, and data transmission efficiency of the vehicle networking system, and is an effective solution to meet the requirements of the changing intelligent transportation system.

[0134] (4) Positioning cooperation module

[0135] Used to fuse the positioning data between vehicles, including generating high-precision relative positioning information between vehicles using UWB technology and IMU sensor data, and performing positioning cooperation based on this information;

[0136] This embodiment provides a data fusion technology based on ultra-wideband (UWB) technology and inertial measurement unit (IMU) sensors, which is used to achieve high-precision relative positioning between vehicles and ensure the accurate positioning of intelligent connected vehicles (ICVs) in complex environments. This module mainly includes a positioning information receiving unit, a positioning processing unit, and a cooperative positioning unit, which work together to achieve efficient positioning cooperation.

[0137] The positioning cooperation module includes:

[0138] A positioning information receiving unit, which is used to receive the positioning information from other vehicles and transfer the received positioning data to the positioning processing unit;

[0139] In this embodiment, the main function of the positioning information receiving unit is to receive the positioning information from other vehicles, including:

[0140] UWB ranging data: Provides high-precision absolute distance information;

[0141] IMU sensor data: Provides the acceleration and angular velocity information of the vehicle, which is used for position estimation within a short period of time;

[0142] GNSS data (optional): Provides additional positioning data when the signal is good.

[0143] This unit ensures that accurate positioning information can be shared between vehicles, providing a reliable input for subsequent data fusion.

[0144] A positioning processing unit, which is used to fuse the positioning data from the UWB technology and the IMU sensor to generate high-precision relative positioning information between vehicles. The working frequency band of the UWB technology is from 6.5 GHz to 7.5 GHz;

[0145] In this embodiment, the positioning processing unit fuses UWB and IMU data and generates high-precision relative positioning information through the **Extended Kalman Filter (EKF)** algorithm. The specific working method is as follows:

[0146] UWB data: It provides centimeter-level accuracy and is suitable for a stable signal environment;

[0147] IMU data: When the UWB signal is blocked, it provides relative motion estimation in the short term.

[0148] This unit can dynamically weight and fuse data, optimize the positioning accuracy, and correct errors in real time.

[0149] A collaborative positioning unit, which is used to perform positioning collaboration according to the relative positioning information between vehicles to ensure high-precision positioning results in complex environments.

[0150] In this embodiment, the collaborative positioning unit performs positioning collaboration based on the relative positioning information between vehicles to ensure high-precision positioning in complex environments. The main functions include:

[0151] Fleet formation collaboration: Ensure that the vehicles within the fleet maintain an accurate distance, improving the driving efficiency;

[0152] Positioning in complex environments: In tunnels or urban high-density areas, use UWB and IMU data to maintain high-precision positioning;

[0153] Emergency avoidance: According to the real-time positioning information, quickly predict and avoid collision risks to ensure driving safety.

[0154] This unit ensures that the vehicle can maintain stable positioning even when the GPS signal is blocked.

[0155] This embodiment fuses UWB and IMU data to provide high-precision and low-latency relative positioning between vehicles, ensuring efficient and reliable positioning collaboration in complex environments, and improving the driving safety and autonomous driving ability of intelligent connected vehicles.

[0156] (5) Security verification module

[0157] It is used to protect the security of the data transmitted in the system, encrypts the data using an encryption algorithm, verifies the identity of the relay node, and uses the blockchain smart contract mechanism to ensure the effectiveness and immutability of the identity verification;

[0158] This embodiment is used to protect the security of data in a vehicular ad hoc network system, preventing malicious attacks and identity forgery. This module mainly includes a data encryption unit, an identity authentication unit, and a secure communication unit, ensuring communication security through end-to-end encryption and distributed authentication.

[0159] The security verification module includes:

[0160] A data encryption unit, which is used to encrypt the data transmitted in the network using the national cryptographic SM4 algorithm to ensure the confidentiality and integrity of the data during transmission;

[0161] The data encryption unit uses the national cryptographic SM4 symmetric encryption algorithm to encrypt the data in the vehicle networking to ensure the confidentiality and integrity of the data. The specific encryption methods include:

[0162] Data transmission encryption: All data packets for communication between vehicles are encrypted using SM4 before being sent, and the receiving party decrypts them using the corresponding key to prevent data from being stolen by a man-in-the-middle (MITM) attack.

[0163] Dynamic key update mechanism: Regularly or triggered by security events to replace the key, preventing security risks caused by key leakage.

[0164] Integrity check: Combined with hash check (SHA-256) to ensure that the data has not been tampered with and improve data integrity.

[0165] An identity authentication unit, which is used to verify the identity of the relay node through a blockchain smart contract to ensure the credibility of the relay node and prevent malicious nodes from interfering with the system;

[0166] In this embodiment, the identity authentication unit verifies the identity of the relay node through a blockchain smart contract to prevent malicious nodes from interfering with system communication. Its main functions include:

[0167] Distributed identity authentication: All legitimate relay nodes register unique identity information on the blockchain to prevent identity forgery.

[0168] Real-time verification mechanism: Before data transmission, the system verifies the identity of the relay node through the smart contract and rejects unauthorized nodes from participating in communication.

[0169] Tamper resistance: Due to the decentralized characteristics of the blockchain, the identity authentication records cannot be tampered with, improving the security and credibility of the system.

[0170] A secure communication unit, which is used to ensure the security of the communication link during data transmission and authenticate both communication parties using digital certificates to prevent the data from being tampered with during transmission.

[0171] In this embodiment, the secure communication unit is used to ensure the security of the communication link and prevent data from being tampered with or forged during transmission. The specific measures include:

[0172] Digital certificate authentication: The system adopts PKI (Public Key Infrastructure) to authenticate the identities of both communication parties and prevent man-in-the-middle attacks.

[0173] Secure handshake mechanism: Before establishing a communication connection, the vehicle needs to complete a secure handshake based on the TLS 1.3 protocol to ensure the legality and security of the connection.

[0174] Data traceability: Each data interaction generates a unique security identifier, which is stored on the blockchain to facilitate tracing the data source.

[0175] Among them, the blockchain smart contract includes:

[0176] Contract creation unit: Used to generate a smart contract and sign the contract content through public key encryption technology to ensure the legality and immutability of the contract.

[0177] Contract execution unit: Used to execute the verification logic in the smart contract and judge the legality of the relay node according to the identity information of the relay node and the preset verification rules.

[0178] Contract recording unit: Used to record the execution results of each smart contract on the blockchain, including the verification records and results of the relay node, to ensure that all operations are traceable and immutable.

[0179] This embodiment realizes end-to-end data protection, distributed identity authentication, and tamper-proof communication through SM4 encryption + blockchain smart contract, effectively preventing data leakage, identity forgery, and network attacks, and improving the security and credibility of the vehicle ad hoc network system.

[0180] (6) Communication control module

[0181] It is used to coordinate data transmission, path selection, and communication protocols in a vehicle ad hoc network system based on deep reinforcement learning.

[0182] The working principle of the communication control module is as follows:

[0183] Deep reinforcement learning model:

[0184] Input: The module obtains real-time information such as vehicle density, signal strength, and network load through the signal acquisition unit and transmits this information as input to the deep reinforcement learning algorithm.

[0185] Output: The deep reinforcement learning algorithm outputs the optimal path selection and protocol switching strategy according to the state of the current network environment.

[0186] Data Transmission Coordination:

[0187] The system adjusts the transmission order of data packets according to the current network load situation, ensuring that high-priority data packets can be transmitted first and avoiding congestion or delay.

[0188] Path Selection Coordination:

[0189] When dynamically selecting the transmission path, the communication control module dynamically calculates the optimal path based on the deep reinforcement learning algorithm, considering factors such as signal strength, bandwidth, and delay. According to the real-time network status and the preset maximum number of hops, the number of hops and transmission nodes of the path are flexibly adjusted.

[0190] Protocol Switching Coordination:

[0191] In the selection of communication protocols, the communication control module makes an intelligent switch according to the current signal quality, bandwidth requirements, and network environment (such as the adaptability between V2X and Wi-Fi 6). If the signal quality is good and the bandwidth requirement is large, the Wi-Fi 6 protocol is preferentially selected; if low-latency communication is required and the bandwidth requirement is low, the V2X protocol will be preferentially selected.

[0192] In this embodiment, through the deep reinforcement learning algorithm, intelligent communication management, dynamic path selection, and adaptive protocol switching are realized. By coordinating data transmission, path selection, and communication protocol switching, the communication control module optimizes the data transmission efficiency and network resource utilization of the vehicle networking system, ensuring the efficient and stable operation of the vehicle ad hoc network in complex environments.

[0193] As Figure 2 shown, this is another embodiment of the present invention, which provides a vehicle ad hoc network method based on deep reinforcement learning, including the following steps:

[0194] Step 1: Real-time collect vehicle density, signal strength RSSI, and network load data;

[0195] Step 2: Select relay nodes through the deep reinforcement learning algorithm;

[0196] Step 3: Adjust the multi-hop path according to the network load situation and optimize the upper limit of the number of hops of the path;

[0197] Step 4: When the signal strength is lower than the preset threshold, select a path through the relay signal transmission module and construct a multi-hop relay link, borrowing the network signals of other vehicles through wireless propagation;

[0198] Step 5: Switch between the V2X communication protocol and the Wi-Fi 6 communication protocol according to the network environment and signal strength, where the working frequency band of the V2X communication protocol is 5.9 GHz, the working frequency band of the Wi-Fi 6 communication protocol is 5 GHz or 6 GHz, and the frequency band interval is greater than or equal to 200 MHz;

[0199] Step 6: Integrate the data from UWB technology and IMU sensors to generate relative vehicle positioning information, and perform positioning collaboration based on this information;

[0200] Step 7: Encrypt the data transmitted over the network, verify the identity of relay nodes, and use the blockchain smart contract mechanism to ensure the effectiveness and immutability of identity verification;

[0201] Step 8: Coordinate data transmission, path selection, and communication protocols to ensure the normal operation of the vehicle ad-hoc network system based on deep reinforcement learning.

[0202] Furthermore, based on the real-time relative position data between vehicles, calculate the priority of candidate relay nodes using a weighting factor and adjust the path selection strategy;

[0203] Based on traffic flow monitoring data, optimize the path hop count allocation by combining with a time series prediction model and dynamically adjust the maximum hop count limit to adapt to network load conditions with different traffic flow densities.

[0204] In summary, the present invention provides a vehicle ad-hoc network system and method based on deep reinforcement learning. Through key technologies such as intelligent routing optimization, relay signal transmission, multi-mode communication protocol switching, high-precision positioning collaboration, and blockchain security verification, efficient, stable, and secure data transmission in the vehicle networking environment is achieved. Compared with the prior art, the present invention can adaptively adjust the routing strategy, optimize the communication path in a complex dynamic environment, improve the stability of data transmission, and enhance signal coverage through an intelligent relay mechanism to avoid communication interruption. In addition, the multi-mode communication protocol switching technology is adopted to ensure that the system can adaptively adjust the communication mode in different scenarios, optimize the bandwidth utilization rate, reduce latency, and increase data throughput. At the same time, by integrating UWB and IMU sensor data, high-precision relative vehicle positioning is realized, enhancing the collaborative perception ability of intelligent connected vehicles. Through the blockchain smart contract and national cryptography SM4 encryption technology, data leakage, forgery, and malicious attacks are effectively prevented, ensuring the security and reliability of vehicle networking communication.

[0205] In conclusion, the present invention is not only applicable to intelligent transportation systems, autonomous driving fleet management, and highway network communication, but also can be widely applied to emergency communication, autonomous unmanned systems, and smart city infrastructure, providing strong technical support for the development of future intelligent connected vehicles, and having broad market application prospects and industrialization value.

[0206] In the description of this specification, the description with reference to terms such as "one embodiment", "some embodiments", "example", "specific example", or "some examples" means that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present application. Moreover, the specific features, structures, materials, or characteristics described can be combined in a suitable manner in any one or more embodiments or examples. In addition, without contradiction, those skilled in the art can combine and combine the different embodiments or examples described in this specification and the features of different embodiments or examples.

[0207] Any process or method description shown in the flowchart or described in other ways herein can be understood to represent a module, segment, or part of code including one or more executable instructions for implementing a specific logical function or process. And the scope of the preferred embodiments of the present application includes additional implementations, where the functions can be executed in a substantially simultaneous manner or in the reverse order according to the functions involved, rather than in the order shown or discussed.

[0208] As mentioned above, the above are only the specific embodiments of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present application can easily think of various changes or substitutions, and these should all be covered by the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.

Claims

1. A vehicle self-organizing network system based on deep reinforcement learning, characterized in that: include: A dynamic routing module is used to collect vehicle density, signal strength and network load data in real time through a deep reinforcement learning algorithm, select relay nodes based on the data and adaptively adjust multi-hop paths, where the upper limit of the number of hops for path adjustment is dynamically optimized based on the network load; The relay signal transmission module is used to build a multi-hop relay link through the path selection result provided by the dynamic routing module when the signal strength is lower than the preset threshold, and borrow the network signal of other vehicles through wireless transmission; A multi-mode communication module, which is used to intelligently switch between the V2X communication protocol and the Wi-Fi 6 communication protocol according to the network environment and signal strength of the vehicle; Positioning coordination module, used to integrate positioning data between vehicles, including using UWB technology and IMU sensor data to generate high-precision relative positioning information between vehicles and perform positioning coordination; The security verification module is used to protect the security of data transmitted in the system. It uses encryption algorithms to encrypt data, verify the identity of relay nodes, and use blockchain smart contract mechanisms to ensure the validity and immutability of identity verification. A communication control module for coordinating data transmission, path selection, and communication protocols in a deep reinforcement learning-based vehicle ad hoc network system; The strategy decision unit adopts the deep Q network DQN algorithm. The input parameters include RSSI threshold range and bandwidth occupancy threshold range. The relay node selection and hop allocation strategy are output according to the input parameters.

2. The vehicle self-organizing network system based on deep reinforcement learning according to claim 1 is characterized in that: The dynamic routing module includes: Signal acquisition unit, used to collect vehicle density, signal strength RSSI and network load data in real time, with a sampling period of 100 milliseconds; The instruction execution unit constructs a relay link according to the relay node selection and hop allocation strategy output by the policy decision unit, and adjusts the hop limit according to the network load rate. When the network load rate exceeds the preset value for three consecutive sampling periods, the hop limit is the preset value.

3. The vehicle self-organizing network system based on deep reinforcement learning according to claim 2 is characterized in that: The strategy decision unit builds an adaptive path optimization model based on the improved deep Q network DQN algorithm, optimizes channel resource allocation and transmission path, and adopts the following reward function: ； in: : Vehicle-to-vehicle channel quality indicator value based on V2X communication; : The lowest available threshold of channel quality; : Current channel load, indicating bandwidth occupancy; : The maximum available bandwidth of the channel; : Relative speed change between vehicles; : The average speed of the vehicle group; : Maximum communication distance; : Communication delay of the current link; : Number of hops of the data packet; , , , , : Optimization coefficient used to adjust the weight of each parameter; Based on the reward function, the strategy decision unit uses the reinforcement learning method to calculate the communication environment parameters in real time, dynamically adjust the channel load distribution, optimize the relay node selection and the number of hops, determine the data transmission path based on the path optimization model, and adjust the communication distance and delay control strategy according to the channel status.

4. The vehicle self-organizing network system based on deep reinforcement learning according to claim 1, characterized in that: The relay signal transmission module comprises: A signal strength detection unit is used to detect the signal strength of the current vehicle in real time. When the signal strength is lower than a preset signal strength threshold, the relay signal transmission function is triggered; The relay path selection unit selects a suitable relay node to build a relay link according to the path selection result provided by the dynamic routing module; The wireless communication unit is used to borrow the network signal of other vehicles through wireless communication to realize a multi-hop relay link, and transmit the network signal back to the current vehicle or other vehicles that need network resources through the relay link.

5. The vehicle self-organizing network system based on deep reinforcement learning according to claim 1, characterized in that: The multimode communication module comprises: A protocol switching unit, configured to select one of a V2X communication protocol and a Wi-Fi 6 communication protocol for switching according to a network environment and available signal strength, wherein the V2X communication protocol operates at a frequency band of 5.9 GHz, the Wi-Fi 6 communication protocol operates at a frequency band of 5 GHz or 6 GHz, and the frequency band interval between the two is not less than 200 MHz; A signal strength monitoring unit, used to monitor the current signal strength in real time, and when the signal strength is lower than a preset threshold, switch to other communication protocols for data transmission; The protocol adaptation strategy unit selects the communication protocol for data transmission according to the distance between the vehicle and other vehicles, bandwidth requirements and signal stability.

6. The vehicle self-organizing network system based on deep reinforcement learning according to claim 1, characterized in that: The positioning coordination module includes: A positioning information receiving unit, used to receive positioning information from other vehicles and transmit the received positioning data to the positioning processing unit; A positioning processing unit, used to fuse positioning data from UWB technology and IMU sensors to generate high-precision relative positioning information between vehicles, wherein the operating frequency band of the UWB technology is 6.5 GHz to 7.5 GHz; The collaborative positioning unit is used to coordinate positioning based on the relative positioning information between vehicles to ensure high-precision positioning results in complex environments.

7. The vehicle self-organizing network system based on deep reinforcement learning according to claim 1, characterized in that: The security verification module comprises: Data encryption unit, used to encrypt data transmitted in the network using the national secret SM4 algorithm to ensure the confidentiality and integrity of data during transmission; The identity verification unit is used to verify the identity of the relay node through the blockchain smart contract, ensure the credibility of the relay node, and prevent malicious nodes from interfering with the system; The secure communication unit is used to ensure the security of the communication link during data transmission and use digital certificates to authenticate both communicating parties to prevent data from being tampered with during transmission.

8. The vehicle self-organizing network system based on deep reinforcement learning according to claim 7, characterized in that: The blockchain smart contract includes: The contract creation unit is used to generate smart contracts and sign the contract content through public key encryption technology to ensure the legality and non-tamperability of the contract; The contract execution unit is used to execute the verification logic in the smart contract and determine the legitimacy of the relay node based on the identity information of the relay node and the preset verification rules; The contract recording unit is used to record the execution results of each smart contract on the blockchain, including the verification records and verification results of the relay nodes, to ensure that all operations are traceable and cannot be tampered with.

9. The vehicle self-organizing network method of the vehicle self-organizing network system based on deep reinforcement learning according to any one of claims 1 to 8, characterized in that: The following steps are involved: Step 1: Collect vehicle density, signal strength RSSI and network load data in real time; Step 2: Select relay nodes through deep reinforcement learning algorithm; Step 3: Adjust the multi-hop path according to the network load and optimize the upper limit of the number of hops of the path; Step 4: When the signal strength is lower than the preset threshold, the relay signal transmission module selects a path and builds a multi-hop relay link to borrow the network signal of other vehicles through wireless transmission; Step 5: Switch between the V2X communication protocol and the Wi-Fi 6 communication protocol according to the network environment and signal strength. The V2X communication protocol operates at a frequency band of 5.9 GHz, the Wi-Fi 6 communication protocol operates at a frequency band of 5 GHz or 6 GHz, and the frequency band interval is greater than or equal to 200 MHz. Step 6: Fusion of data from UWB technology and IMU sensors generates relative positioning information between vehicles, and performs positioning coordination based on this information; Step 7: Encrypt the data transmitted on the network, verify the identity of the relay node, and use the blockchain smart contract mechanism to ensure the validity and immutability of identity authentication; Step 8: Coordinate data transmission, path selection, and communication protocols to ensure the normal operation of the vehicle self-organizing network system based on deep reinforcement learning.

10. The vehicle ad hoc networking method according to claim 9, characterized in that: Further including: Based on the real-time relative position data between vehicles, the priority of candidate relay nodes is calculated using weighted factors, and the path selection strategy is adjusted; Based on traffic flow monitoring data, combined with the time series prediction model, the path hop allocation is optimized, and the maximum hop limit is dynamically adjusted to adapt to network load conditions with different traffic densities.

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