A method and system for controlling communication safety of car navigation

Abstract

The application relates to the technical field of automobile navigation safety communication, in particular to a method and system for controlling automobile navigation communication safety. The method comprises the following steps: collecting multi-modal data of a vehicle to obtain a vehicle-mounted multi-modal data set; performing Kalman filtering based on the vehicle-mounted multi-modal data set to obtain vehicle pose reference data; extracting elliptic curve parameters of the vehicle pose reference data to obtain vehicle pose elliptic curve parameters; generating a lightweight communication safety key based on the vehicle pose elliptic curve parameters; obtaining an original map incremental data packet; performing safety level layering on the original map incremental data packet to obtain a layered map data set; and performing layered decryption on the layered map data set according to the lightweight communication safety key to obtain verified vehicle navigation map data. The application can guarantee the safety of navigation data and significantly improve the real-time performance, adaptability and reliability of the navigation system.

Description

Technical Field

[0001] This invention relates to the field of automotive navigation safety communication technology, and in particular to a method and system for automotive navigation communication safety control. Background Technology

[0002] Early navigation systems relied primarily on pre-installed static map data, which had a low update frequency and struggled to meet the real-time navigation needs of complex road conditions. In recent years, high-precision navigation systems have gradually become one of the core functions of intelligent connected vehicles. These systems rely on cloud-based interaction with real-time dynamic map data, obtaining the latest road condition information and map updates through high-frequency communication between the vehicle and cloud servers to achieve accurate route planning and real-time navigation.

[0003] In specific scenarios, such as during OTA (Over-The-Air) incremental updates of navigation maps in dense urban elevated road networks, vehicles need to receive differential map patches via cellular networks and fuse them with onboard sensor data for calibration. While this real-time dynamic update mechanism improves navigation accuracy and timeliness, it also introduces new technical challenges. Existing security solutions generally employ two-way certificate authentication and data signature mechanisms to ensure the integrity and authenticity of map data. However, these traditional security mechanisms overlook a crucial contradiction: there is an irreconcilable conflict between the timeliness requirements of map updates and the verification delays caused by multi-layered encryption.

[0004] When a vehicle enters a multi-ramp intersection at 80 km / h, a 0.5-second delay in the cloud-based emergency lane change alert due to decryption at each level could easily cause the system to miss the optimal lane-changing opportunity. This operational lag caused by the safety mechanism itself can easily lead to accidents in complex road conditions. Therefore, how to ensure navigation data security while meeting real-time requirements has become a pressing technical problem to be solved in the field of intelligent connected vehicle navigation. Summary of the Invention

[0005] Therefore, it is necessary for the present invention to provide a method and system for safety control of automotive navigation and communication, in order to solve at least one of the above-mentioned technical problems.

[0006] To achieve the above objectives, a method for safety control of automotive navigation communication includes the following steps:

[0007] Step S1: Obtain vehicle pose reference data;

[0008] Step S2: Extract elliptic curve parameters from the vehicle pose reference data to obtain the vehicle pose elliptic curve parameters; generate a lightweight communication security key based on the vehicle pose elliptic curve parameters.

[0009] Step S3: Obtain the original map incremental data packet; perform security level layering on the original map incremental data packet to obtain a layered map dataset; perform layered decryption on the layered map dataset according to the lightweight communication security key to obtain the verified vehicle navigation map data;

[0010] Step S4: Perform vehicle driving map adaptation on the verification vehicle navigation map data to obtain adaptive map security data;

[0011] Step S5: Determine the vehicle's degradation protection threshold and obtain the communication degradation trigger signal; extract local cached data based on the communication degradation trigger signal to obtain local backup map data; acquire map information from adjacent vehicles to obtain collaborative backup map data; perform fault-tolerant fusion of adaptive map security data based on the local backup map data and collaborative backup map data to obtain the final navigation safety control data.

[0012] This invention acquires vehicle pose reference data and uses elliptic curve cryptography to generate lightweight communication security keys. This significantly reduces the complexity and resource consumption of key generation and management while ensuring data security, avoiding the verification delay issues associated with traditional multi-layered encryption mechanisms. By layering security levels in the original map incremental data packets and prioritizing and decrypting them according to vehicle path demand predictions, critical navigation data is ensured to be processed and used quickly and preferentially. This meets the real-time requirements of vehicles in complex road conditions, such as rapid response to emergency lane change prompts in multi-ramp intersection areas. Adapting the navigation map data to the vehicle's driving map through driving risk assessment further enhances the adaptability and security of the navigation system, enabling it to flexibly adjust navigation strategies based on real-time road conditions and vehicle status. Finally, fault-tolerant fusion combining locally cached data and collaboratively backed-up map data enhances the reliability and fault tolerance of the navigation system. Even under communication degradation or network instability, the stable operation of the navigation system is ensured, guaranteeing vehicle driving safety. In summary, this invention significantly improves the real-time performance, adaptability, and reliability of navigation systems while ensuring the security of navigation data, effectively resolving key contradictions in existing technologies, and providing strong support for safe navigation of intelligent connected vehicles.

[0013] Preferably, the present invention also provides an application in a vehicle navigation communication safety control system for executing the vehicle navigation communication safety control method described above, the vehicle navigation communication safety control system comprising:

[0014] The data acquisition module is used to acquire vehicle position and orientation reference data;

[0015] The key generation module is used to extract elliptic curve parameters from vehicle pose reference data to obtain vehicle pose elliptic curve parameters; and to generate a lightweight communication security key based on the vehicle pose elliptic curve parameters.

[0016] The map processing module is used to acquire the original map incremental data packet; to perform security level layering on the original map incremental data packet to obtain a layered map dataset; and to perform layered decryption of the layered map dataset according to the lightweight communication security key to obtain the verified vehicle navigation map data.

[0017] The risk assessment and adjustment module is used to adapt the vehicle navigation map data to the vehicle driving map to obtain adaptive map safety data.

[0018] The fault-tolerant fusion module is used to determine the degradation protection threshold of the vehicle and obtain the communication degradation trigger signal; based on the communication degradation trigger signal, it extracts local cached data to obtain local backup map data; it acquires map information of adjacent vehicles to obtain collaborative backup map data; and it performs fault-tolerant fusion of adaptive map security data based on the local backup map data and collaborative backup map data to obtain the final navigation safety control data.

[0019] This invention accurately acquires vehicle pose reference data through a data acquisition module, providing reliable foundational data support for subsequent safety control. A key generation module utilizes elliptic curve cryptography to generate lightweight communication security keys, ensuring data transmission security while significantly reducing key management complexity and resource consumption, thus improving system efficiency. The map processing module, by layering and prioritizing map data based on security levels and combining this with vehicle route demand prediction, enables rapid decryption and processing of critical navigation data, effectively meeting the real-time requirements of vehicles in complex road conditions. The risk assessment and adjustment module dynamically adjusts the security strategy of navigation map data based on the vehicle's real-time driving risk level, further enhancing the system's adaptability and flexibility, ensuring optimal navigation strategy under different risk scenarios. The fault-tolerant fusion module significantly improves the system's fault tolerance and reliability through fault-tolerant fusion of locally cached data and collaboratively backed-up map data, ensuring stable operation of the navigation system even under communication degradation or network instability. In summary, this system, through the synergistic effect of its modules, achieves a dynamic balance between the security, real-time performance, and reliability of navigation data, providing a comprehensive and efficient solution for safe navigation in intelligent connected vehicles and effectively resolving key contradictions in existing technologies. Attached Figure Description

[0020] Other features, objects, and advantages of the present invention will become more apparent from the following detailed description taken in conjunction with the accompanying drawings:

[0021] Figure 1 A flowchart illustrating the steps of an embodiment of a vehicle navigation communication safety control method is shown.

[0022] Figure 2 A detailed flowchart of step S28 of one embodiment is shown.

[0023] Figure 3 A detailed flowchart of step S4 of one embodiment is shown. Detailed Implementation

[0024] The technical method of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0025] Furthermore, the accompanying drawings are merely illustrative of the invention and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and therefore repeated descriptions of them will be omitted. Some block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities can be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor methods and / or microcontroller methods.

[0026] It should be understood that although the terms "first," "second," etc., may be used herein to describe various units, these units should not be limited by these terms. These terms are used merely to distinguish one unit from another. For example, without departing from the scope of the exemplary embodiments, a first unit may be referred to as a second unit, and similarly, a second unit may be referred to as a first unit. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0027] To achieve the above objectives, please refer to Figures 1 to 3 This invention provides a method for safety control of automotive navigation communication, comprising the following steps:

[0028] Step S1: Obtain vehicle pose reference data;

[0029] Step S2: Extract elliptic curve parameters from the vehicle pose reference data to obtain the vehicle pose elliptic curve parameters; generate a lightweight communication security key based on the vehicle pose elliptic curve parameters.

[0030] Step S3: Obtain the original map incremental data packet; perform security level layering on the original map incremental data packet to obtain a layered map dataset; perform layered decryption on the layered map dataset according to the lightweight communication security key to obtain the verified vehicle navigation map data;

[0031] Step S4: Perform vehicle driving map adaptation on the verification vehicle navigation map data to obtain adaptive map security data;

[0032] Step S5: Determine the vehicle's degradation protection threshold and obtain the communication degradation trigger signal; extract local cached data based on the communication degradation trigger signal to obtain local backup map data; acquire map information from adjacent vehicles to obtain collaborative backup map data; perform fault-tolerant fusion of adaptive map security data based on the local backup map data and collaborative backup map data to obtain the final navigation safety control data.

[0033] In this embodiment, the navigation and communication security control scheme for intelligent connected vehicles acquires vehicle pose reference data through the vehicle's sensor system (such as GPS, IMU, LiDAR, and cameras). Specifically, a high-precision GPS receiver is used to acquire the vehicle's real-time position coordinates, the IMU records the vehicle's motion state, and LiDAR and cameras perceive the vehicle's surrounding environment. This data is integrated into the vehicle's central processing unit (such as a Linux-based embedded system) to form vehicle pose reference data. Elliptic curve parameters are extracted from the vehicle pose reference data using Python and the ecdsa library. By calling functions in the ecdsa library, the vehicle pose data is mapped onto an elliptic curve, obtaining the vehicle pose elliptic curve parameters. Based on these parameters, the os.urandom function generates random seed data, and a lightweight communication security key is generated using the elliptic curve algorithm. The vehicle's onboard communication unit receives raw map incremental data packets from the cloud. These data packets are parsed and classified using Python and the pandas library, and layered according to security requirements to obtain a layered map dataset. Combining vehicle path demand prediction (predicting future driving paths through vehicle motion models and sensor data), the `pandas` library is used to prioritize the layered map dataset, and lightweight communication security keys are used for layered decryption to obtain verified navigation map data. Real-time environmental data around the vehicle is collected using the vehicle's environmental perception sensors (such as cameras and millimeter-wave radar), and combined with the vehicle's motion state data, the driving risk is assessed using Python and the scikit-learn library to obtain a real-time risk level. Based on the risk level, the security strategy for the navigation map data is adjusted, such as encryption algorithm strength and data transmission priority, to obtain adaptive map security data. When a communication link quality degradation is detected, the vehicle's communication management system (such as a 4G / 5G module) determines the degradation protection threshold and generates a communication degradation trigger signal. Based on this signal, backup map data is extracted from the vehicle's local cache system, and map information from neighboring vehicles is obtained through the vehicle's V2V communication unit (such as DSRC or 5G-V2X module) to obtain collaborative backup map data. Finally, the pandas library is used to perform consistency checks and fusion processing on the local backup map data and the collaborative backup map data to obtain the final navigation safety control data.

[0034] Preferably, step S1 includes the following steps:

[0035] Step S11: Obtain the vehicle-mounted multimodal dataset, which includes raw GPS location data, raw IMU motion data, raw LiDAR point cloud data, and raw visual radar data;

[0036] Of particular importance, step S11 also includes the following steps:

[0037] Data is collected from the vehicle's GPS to obtain raw GPS location data;

[0038] Data is acquired from the vehicle-mounted IMU inertial measurement unit to obtain raw IMU motion data;

[0039] The vehicle-mounted lidar sensor is scanned to collect data, resulting in raw lidar point cloud data.

[0040] Image data is acquired from the vehicle's forward-facing camera to obtain raw visual image data;

[0041] The vehicle-mounted millimeter-wave radar collects target detection data to obtain raw radar target data;

[0042] The raw visual image data and the raw radar target data are synchronized in time to obtain the raw visual radar data;

[0043] The raw GPS location data, raw IMU motion data, raw LiDAR point cloud data, and raw visual radar data are denoted as the vehicle-mounted multimodal dataset.

[0044] Step S12: Initialize the Kalman filter state vector of the vehicle multimodal dataset to obtain the filter initial state data;

[0045] Step S13: Perform Kalman filtering prediction on the original GPS position data based on the filter's initial state data to obtain GPS filtered prediction data;

[0046] Step S14: Use the original IMU motion data to update the GPS filtered prediction data using Kalman filtering to obtain vehicle pose fusion data;

[0047] Step S15: Perform multi-sensor Kalman filter correction on the vehicle pose fusion data based on the original LiDAR point cloud data and the original visual radar data to obtain the vehicle real-time state parameter table.

[0048] Step S16: Evaluate the confidence level of each sensor data in the vehicle multimodal dataset based on the vehicle real-time status parameter table to obtain sensor confidence level evaluation data;

[0049] Step S17: Adjust the weights of the vehicle real-time state parameter table based on the sensor confidence evaluation data to obtain the vehicle pose reference data.

[0050] In this embodiment, the navigation and communication safety control scheme for intelligent connected vehicles utilizes multiple sensors for data acquisition. Specifically, a high-precision onboard GPS receiver is used to acquire raw GPS location data at a frequency of 10Hz, including longitude, latitude, and altitude. Simultaneously, the vehicle's inertial measurement unit (IMU) records raw IMU motion data at a frequency of 100Hz, covering parameters such as acceleration and angular velocity. An onboard LiDAR sensor scans at a rotation speed of 10 revolutions per second to acquire raw LiDAR point cloud data, used to perceive the three-dimensional structure of the vehicle's surrounding environment. The vehicle's forward-facing camera acquires raw visual image data at a frame rate of 30fps, used to identify visual information such as road signs and lane lines. Millimeter-wave radar detects the distance, speed, and angle information of targets around the vehicle at a frequency of 20Hz, obtaining raw radar target data. A timestamp alignment method is used to synchronize the visual image data and radar target data. The vehicle's central processing unit (CPU) calibrates the timestamps of the two sets of data to ensure temporal consistency, thereby obtaining the raw visual radar data. The raw GPS location data, raw IMU motion data, raw LiDAR point cloud data, and raw visual radar data collected above are integrated to form an in-vehicle multimodal dataset. Matlab software is used as the tool, and its built-in Kalman filter toolbox is utilized to process the in-vehicle multimodal dataset. Based on the characteristics of the in-vehicle multimodal dataset, a Kalman filter state vector is defined, including the vehicle's position, velocity, and attitude parameters. Based on the vehicle's motion model and the sensor's measurement model, the initial state data of the Kalman filter is initialized. Specifically, assuming the vehicle's initial position is the first coordinate point acquired by GPS, the initial velocity is zero, and the initial attitude is horizontal, the filter initialization process is completed by setting the initial state vector and covariance matrix of the Kalman filter. The position information in the filter's initial state data includes the vehicle's initial latitude, longitude, and altitude; the velocity information is a zero vector; and the attitude information includes the initial estimates of parameters such as the vehicle's initial pitch angle, yaw angle, and roll angle. This initial state data will serve as the starting point for Kalman filter prediction. Using the Kalman filter module in Matlab, this paper performs Kalman filtering prediction on raw GPS position data based on the vehicle's motion model. According to the vehicle's equation of motion, combined with its historical pose information and current motion state, the next position of the vehicle is predicted. Specifically, assuming the vehicle is traveling at a constant speed in a straight line on a level road, its motion model can be simplified to a simple linear equation: the next position equals the current position plus the product of velocity and time interval. In Matlab, by setting parameters such as the Kalman filter's system matrix and noise covariance matrix, the GPS filtered prediction data is calculated based on the raw GPS position data and the vehicle's motion model.GPS-filtered prediction data includes the predicted position coordinates of the vehicle at the next moment. This prediction data serves as a reference value in the Kalman filter update process and is used for subsequent fusion calculations with IMU motion data. The Kalman filter update operation is performed using Matlab software. The GPS-filtered prediction data is used as input, combined with the original IMU motion data, and the vehicle pose is fused using the Kalman filter update equation. Specifically, based on the acceleration and angular velocity data measured by the IMU, the displacement and attitude changes of the vehicle between two consecutive sampling moments are calculated. These changes are fused with the GPS-filtered prediction data. By adjusting the gain matrix of the Kalman filter, the fused vehicle pose data contains both the high-precision position information from GPS and the high-frequency attitude and velocity change information from the IMU. The final vehicle pose fusion data includes parameters such as the vehicle's real-time position, velocity, and attitude, which more accurately reflect the vehicle's motion state during operation. In the vehicle's navigation and communication safety control scheme, Matlab software is used to perform multi-sensor Kalman filter correction on the vehicle pose fusion data. Specifically, raw LiDAR point cloud data and raw visual radar data are used as auxiliary sensor data and jointly corrected with vehicle pose fusion data. A 3D point cloud map of the vehicle's surrounding environment is constructed using LiDAR point cloud data, while visual radar data is used to identify features such as road signs and lane lines. These environmental features are compared with the position and attitude information in the vehicle pose fusion data. The vehicle pose fusion data is corrected by adjusting the observation matrix and noise covariance matrix of the Kalman filter. For example, if the LiDAR detects an obstacle in front of the vehicle, but this information is not reflected in the vehicle pose fusion data, the vehicle pose estimation can be adjusted to be closer to the actual state. The resulting real-time vehicle state parameter table contains the vehicle's precise position, speed, attitude, and surrounding environmental features. In the vehicle's navigation, communication, and safety control scheme, Matlab software is used to evaluate the confidence level of each sensor data in the real-time vehicle state parameter table. Specifically, the confidence level of each sensor data is calculated based on the performance indicators and current operating status of each sensor. For example, for GPS data, the confidence level is calculated using a pre-defined confidence assessment model based on parameters such as signal strength, number of satellites, and positioning accuracy. For LiDAR data, the confidence level is assessed based on parameters such as point cloud density, reflection intensity, and measurement distance. For visual radar data, the confidence level is determined based on parameters such as image clarity, target recognition accuracy, and time synchronization accuracy. The confidence levels of each sensor's data are quantitatively evaluated using data processing and analysis functions in Matlab, ultimately yielding sensor confidence assessment data. In the vehicle's navigation and communication safety control scheme, the vehicle's real-time status parameter table is weighted and adjusted based on the sensor confidence assessment data.In practice, Matlab software is used to dynamically allocate the weights of each sensor data point. For example, if the confidence level of GPS data is high while that of visual radar data is low, the weight of GPS data is appropriately increased and the weight of visual radar data is decreased in the vehicle's real-time status parameter table. The specific method for weight adjustment is to perform a weighted average of the data in the vehicle's real-time status parameter table based on the proportional relationship of their confidence levels. For example, assuming the confidence level of GPS data is 0.8, IMU data is 0.6, LiDAR data is 0.7, and visual radar data is 0.5, then when calculating the vehicle's final pose reference data, the sensor data is weighted and fused according to these confidence level proportions. The resulting vehicle pose reference data is a comprehensive pose information after weight adjustment, which can more accurately reflect the vehicle's actual driving state.

[0051] Preferably, step S2 includes the following steps:

[0052] Step S21: Extract elliptic curve parameters from the vehicle pose reference data to obtain the vehicle pose elliptic curve parameters;

[0053] Step S22: Initialize the elliptic curve domain parameters according to the vehicle pose elliptic curve parameters to obtain the curve domain initialization data;

[0054] Step S23: Determine the elliptic curve generation origin points based on the curve domain initialization data to obtain the elliptic curve generation origin point data;

[0055] Step S24: Extract vehicle GPS coordinates from vehicle pose reference data to obtain vehicle GPS coordinates; calculate vehicle velocity vector based on vehicle GPS coordinates to obtain vehicle velocity vector data;

[0056] Step S25: Construct an elliptic curve entropy source based on vehicle GPS coordinates and vehicle speed vector data to obtain spatiotemporally unique entropy source data;

[0057] Step S26: Obtain the system time millisecond-level timestamp for the vehicle to obtain the vehicle millisecond-level timestamp data;

[0058] Step S27: Generate a random number seed based on the spatiotemporal unique entropy source data and the vehicle millisecond-level timestamp data to obtain elliptic curve random seed data;

[0059] Step S28: Generate a key based on the elliptic curve random seed data to obtain a lightweight communication security key.

[0060] In this embodiment, Python is used in conjunction with an open-source elliptic curve cryptography library (such as the ECDSA library) to extract elliptic curve parameters from vehicle pose reference data. Specifically, the vehicle pose reference data includes the vehicle's real-time position, velocity, and attitude information. By calling the elliptic curve parameter extraction function in the ECDSA library, the vehicle's GPS coordinates, velocity vector, and attitude angles are input to extract the elliptic curve parameters related to the vehicle's pose. For example, the elliptic curve secp256k1 is selected as the working curve, and its parameters (such as coefficients of the elliptic curve equation, base points, etc.) are predefined in the ECDSA library. By calling the library function, the vehicle pose reference data is mapped onto the elliptic curve to obtain the vehicle pose elliptic curve parameters. Python is then used in conjunction with the ECDSA library to initialize the elliptic curve domain parameters of the vehicle pose elliptic curve. Specifically, based on the vehicle pose elliptic curve parameters, the elliptic curve domain parameter initialization function in the ECDSA library is called. This function generates curve domain initialization data based on the elliptic curve parameters (such as coefficients of the curve equation, base points, etc.). For example, for the elliptic curve secp256k1, its curve equation is... The base point is a predefined point. By calling library functions, the vehicle pose elliptic curve parameters are combined with the predefined parameters of the curve to generate curve domain initialization data. This data includes the finite field parameters of the elliptic curve and the coordinates of the base point. The generation point of the elliptic curve is determined using Python in conjunction with the ecdsa library. Specifically, based on the curve domain initialization data, the generation point determination function in the ecdsa library is called. This function calculates the generation point of the elliptic curve based on the finite field parameters and the coordinates of the base point. For example, for the elliptic curve secp256k1, its generation point is a predefined point. By calling the library function and combining it with the curve domain initialization data, the coordinates of the generation point are verified and determined. The generation point is a key parameter in elliptic curve cryptography, used for subsequent key generation and encryption operations, ultimately yielding the elliptic curve generation point data. The vehicle's GPS coordinates and velocity vector data are obtained using Python in conjunction with the vehicle's sensor data interface. Specifically, the vehicle's real-time GPS coordinates, including longitude, latitude, and altitude, are obtained through the vehicle's GPS receiver module. Simultaneously, the vehicle's IMU (Inertial Measurement Unit) is used to acquire the vehicle's velocity vector data, including the magnitude and direction of the velocity. For example, the vehicle's GPS receiver module outputs GPS coordinate data at a frequency of 1Hz, and the IMU outputs velocity vector data at a frequency of 10Hz. A Python script calls the vehicle's sensor data interface to read this data and store it as part of the vehicle's pose reference data. An elliptic curve entropy source is constructed using Python in conjunction with the ecdsa library and the vehicle's GPS coordinates and velocity vector data. Specifically, based on the vehicle's GPS coordinates and velocity vector data, the entropy source construction function in the ecdsa library is called. This function takes the vehicle's spatiotemporal information (such as GPS coordinates and velocity vectors) as input and generates spatiotemporally unique entropy source data through a series of hash operations and mathematical transformations. For example, the vehicle's GPS coordinates and velocity vector data are concatenated into a string, and then hashed using the SHA-256 hash algorithm to obtain a fixed-length hash value. This hash value is spatiotemporally unique and can serve as the basic data for the elliptic curve entropy source. Millisecond-level timestamp data of the vehicle is obtained using Python in conjunction with the vehicle's system time interface. In practice, the current timestamp of the vehicle system is obtained by calling the `time()` function in Python's `time` module, with millisecond precision. For example, the `time.time()` function returns a floating-point representation of the current time in seconds, with the decimal part representing milliseconds. By calling this function, the vehicle's millisecond-level timestamp data is obtained and stored as a floating-point number. An elliptic curve random seed is generated using Python, combining the ECDSA library, the vehicle's spatiotemporal unique entropy source data, and the millisecond-level timestamp data.In practice, the spatiotemporally unique entropy source data is combined with millisecond-level timestamp data, and elliptic curve random seed data is generated using the random number seed generation function in the ECDSA library. For example, the spatiotemporally unique entropy source data (such as hash values) is concatenated with millisecond-level timestamp data, and a random seed is generated through a series of mathematical transformations (such as modular arithmetic). For detailed implementation of step S28, please refer to the sub-steps of step S28.

[0061] Preferably, step S28 includes the following steps:

[0062] Step S281: Generate an elliptic curve private key based on the elliptic curve random seed data to obtain elliptic curve temporary private key data;

[0063] Step S282: Calculate the elliptic curve public key based on the elliptic curve temporary private key data and the elliptic curve generator point data to obtain the elliptic curve temporary public key data;

[0064] Step S283: Configure the parameters of the preset digital signature algorithm according to the elliptic curve temporary private key data to obtain the elliptic curve signature algorithm configuration data;

[0065] Step S284: Use the elliptic curve temporary public key data to set the parameters of the preset key verification algorithm to obtain the elliptic curve verification algorithm configuration data;

[0066] Step S285: Set the key pair validity period based on the elliptic curve signature algorithm configuration data and the elliptic curve verification algorithm configuration data to obtain key lifecycle data; set the key update trigger conditions based on the key lifecycle data to obtain key update trigger data;

[0067] Step S286: Encapsulate the key pair based on the elliptic curve temporary private key data, the elliptic curve temporary public key data, and the key update trigger data to obtain a lightweight communication security key.

[0068] In this embodiment, Python is used in conjunction with the open-source elliptic curve cryptography library ECDSA to generate the elliptic curve private key. Specifically, the private key generation function in the ECDSA library is called, taking the elliptic curve random seed data as input. This function generates a random private key value within a finite field of the elliptic curve based on the random seed data. For example, choosing the elliptic curve secp256k1, its private key is a random integer between 1 and the order n of the curve. The elliptic curve temporary private key data is generated by calling the `ecdsa.SigningKey.from_string()` function with the random seed data as input. The elliptic curve public key is then calculated using Python in conjunction with the ECDSA library. Specifically, based on the elliptic curve temporary private key data and the elliptic curve generation metapoint data, the public key calculation function in the ECDSA library is called. This function multiplies the private key by the generation metapoint according to the mathematical properties of the elliptic curve to obtain the elliptic curve temporary public key data. For example, the public key is generated by calling the `ecdsa.VerifyingKey.from_public_point()` function with the result of multiplying the private key by the generation metapoint as input. The generated public key contains the coordinates (x and y) of a point, representing the public key's position on an elliptic curve. The default digital signature algorithm is configured using Python and the ECDSA library. Specifically, based on the elliptic curve temporary private key data, the signature algorithm configuration function in the ECDSA library is called. This function configures the relevant parameters of the signature algorithm according to the private key parameters (such as elliptic curve type and private key value). For example, selecting the Elliptic Curve Digital Signature Algorithm (ECDSA), the signature algorithm is configured by calling the `ecdsa.SigningKey.sign()` function, taking the private key and the message to be signed (such as the vehicle's GPS coordinates and timestamp) as input. After configuration, the elliptic curve signature algorithm configuration data is obtained, including the signature algorithm's parameter settings and the signature value. The default key verification algorithm is also configured using Python and the ECDSA library. Specifically, based on the elliptic curve temporary public key data, the key verification algorithm configuration function in the ECDSA library is called. This function sets the relevant parameters of the key verification algorithm according to the public key parameters (such as elliptic curve type and public key coordinates). For example, the verification algorithm is set by calling the `ecdsa.VerifyingKey.verify()` function with the public key and signature value as input. The validity period of the key pair is then set using Python in conjunction with the `ecdsa` library. Specifically, based on the elliptic curve signature algorithm configuration data and the elliptic curve verification algorithm configuration data, the key lifecycle management function in the `ecdsa` library is called. This function sets the validity period of the key pair according to the configuration data of the signature and verification algorithms.For example, setting the key pair's validity period to one hour, the `ecdsa.SigningKey.get_verifying_key()` function is called, taking the signature algorithm configuration data and verification algorithm configuration data as input to generate key lifecycle data. Simultaneously, key update trigger conditions are set based on the key lifecycle data; for example, a key update operation is triggered when the key is nearing its expiration date. The Python language, combined with the `ecdsa` library, is used to encapsulate the elliptic curve temporary private key data, elliptic curve temporary public key data, and key update trigger data. Specifically, the key encapsulation function in the `ecdsa` library is called to combine the private key, public key, and key update trigger data into a lightweight communication security key. For example, by calling the `ecdsa.SigningKey.to_pem()` function, the private key and public key data are encapsulated into a PEM format key pair, and the key update trigger data is stored as additional information in the key pair's metadata.

[0069] Preferably, step S3 includes the following steps:

[0070] Step S31: Receive map data packets from the vehicle-mounted communication unit to obtain the original map incremental data packets; parse the header information of the original map incremental data packets to obtain the map data packet metadata.

[0071] Step S32: Classify and identify map data content based on map data packet metadata to obtain map data type identifiers; use map data type identifiers to classify the security level of the original map incremental data packets to obtain a layered map dataset;

[0072] Step S33: Identify the current driving state of the vehicle based on the vehicle pose reference data to obtain vehicle motion state data;

[0073] Step S34: Based on the vehicle motion state data, perform future trajectory prediction modeling for the vehicle to obtain the vehicle trajectory prediction model;

[0074] Step S35: Use the vehicle trajectory prediction model to perform spatial calculations on the future driving path of the vehicle to obtain the spatial data of the vehicle prediction path;

[0075] Step S36: Match the vehicle's map data requirements with the vehicle's predicted path spatial data to obtain the vehicle's predicted path requirement data.

[0076] Step S37: Decrypt the layered map dataset layer by layer according to the lightweight communication security key and the vehicle prediction path requirement data to obtain the verified vehicle navigation map data.

[0077] In this embodiment, the vehicle's onboard communication unit (e.g., a 4G / 5G module or a V2X communication module) receives map data packets from the cloud or roadside units. These data packets are transmitted using the TCP / IP protocol and contain raw map incremental data. Specifically, the netcat tool under the Linux system listens on a specified network port to receive raw map incremental data packets. After the data packets arrive, the header information of the data packets is parsed using Python and the struct module. For example, the data packet header typically contains fields such as data packet length, version number, timestamp, and data type. These fields are parsed according to a predefined format (e.g., !IHHQ, representing unsigned integer, short integer, short integer, and long long integer) using the struct.unpack() function to obtain the map data packet metadata. The raw map incremental data packets are then classified and identified using Python and a predefined map data type identifier table. Specifically, the predefined type identifier table is searched based on the data type field in the map data packet metadata. For example, the type identifier table defines 0x01 as road information update, 0x02 as traffic sign information update, and 0x03 as high-precision map data update, etc. The raw map incremental data packets are categorized into different map data types by matching data type fields. Security levels are then determined based on the security sensitivity of the data type. For example, high-precision map data involves stricter security requirements and is classified as high-security data; while ordinary road information updates are classified as medium-security data, resulting in a layered map dataset. Vehicle sensor data (such as GPS, IMU, and cameras) combined with vehicle motion models are used to identify the vehicle's current driving state. Specifically, the vehicle's real-time location coordinates (longitude, latitude, and altitude) are obtained through its GPS receiver, acceleration and angular velocity data are obtained through the IMU, and lane lines and traffic signs are identified using cameras. This data is processed using Python and the NumPy library. For example, the vehicle's speed is obtained by calculating the rate of change of GPS coordinates, and whether the vehicle is turning or accelerating is determined by analyzing IMU data. Combining this information, the vehicle's current motion state is identified, such as constant speed, acceleration, deceleration, or turning. Based on the vehicle's current motion state data, Python combined with machine learning libraries (such as scikit-learn) is used to predict and model the vehicle's future trajectory. In practice, the vehicle's current speed, acceleration, and heading angle are used as input features, and a trajectory prediction model is trained using historical trajectory data. For example, a linear regression model or a multinomial regression model can be chosen as the base model for trajectory prediction. The vehicle's motion data is then fitted using scikit-learn's Linear Regression or Polynomial Features module to obtain the vehicle trajectory prediction model.This model can predict a vehicle's driving path over a future period based on its current state. It uses Python in conjunction with the NumPy library to perform spatial calculations on the vehicle's future driving path. Specifically, based on the vehicle trajectory prediction model, it calculates the vehicle's position coordinates over a future period. For example, it predicts the vehicle's position over the next 10 seconds at 1-second intervals. The latitude and longitude coordinates of each time point are calculated using the trajectory prediction model and converted into actual locations in geospatial space. These location points constitute the vehicle's predicted path spatial data, used for subsequent map data matching. For example, if the vehicle's predicted path passes through a specific intersection or overpass, this spatial data will help the system determine the type and scope of map data required for the vehicle's future journey. Python is used in conjunction with Geographic Information System (GIS) tools (such as geopandas) to match the vehicle's predicted path spatial data with map data in an onboard map database. Specifically, the vehicle's predicted path spatial data is spatially compared with map data in an onboard map database. For example, the geopandas library is used to load onboard map data (such as road networks, traffic signs, high-precision maps, etc.), and the vehicle's predicted path is spatially queried against the map data. Using geopandas' overlay or sjoin functions, the required map data type and range for the vehicle prediction path are identified. For example, if the vehicle prediction path traverses an area covered by a high-precision map, the high-precision map data for that area is matched; if traffic signs exist along the path, the relevant traffic sign information is matched, ultimately obtaining the required data for the vehicle prediction path. For detailed implementation of step S37, please refer to the sub-steps of step S37.

[0078] Preferably, step S37 includes the following steps:

[0079] Step S371: Calculate the priority of data packets based on the vehicle predicted route demand data and the hierarchical map dataset to obtain the priority weight of the vehicle map data;

[0080] Step S372: Sort the layered map dataset according to the priority weight of the vehicle map data to obtain the differential verification priority queue, wherein the differential verification priority queue includes key safety layer data, navigation core layer data and auxiliary information layer data;

[0081] Step S373: Use a lightweight communication security key to quickly decrypt the critical security layer data to obtain the vehicle's critical security data;

[0082] Step S374: Use a lightweight communication security key to perform standard decryption of the navigation core layer data to obtain the vehicle navigation core data;

[0083] Step S375: Use a lightweight communication security key to decrypt the auxiliary information layer data in the background to obtain vehicle auxiliary information data;

[0084] Step S376: Verify the integrity of the decryption results based on the vehicle's key safety data, core vehicle navigation data, and vehicle auxiliary information data to obtain the verified map data;

[0085] Step S377: Standardize the map format based on the verified map data to obtain standard navigation map data; verify the data validity of the standard navigation map data to obtain verified vehicle navigation map data.

[0086] In this embodiment, Python is used in conjunction with the pandas library to prioritize vehicle path prediction demand data and layered map datasets. Specifically, the vehicle path prediction demand data (such as key location points on the predicted path, required map type, etc.) is matched with the layered map dataset (including a critical safety layer, a navigation core layer, and an auxiliary information layer). Based on the matching results, a priority weight is assigned to each data packet. For example, critical safety layer data (such as road hazard areas and emergency lane change prompts) is assigned the highest priority weight (e.g., weight 3), navigation core layer data (such as main road information and traffic signals) is assigned a medium priority weight (e.g., weight 2), and auxiliary information layer data (such as nearby points of interest and advertising information) is assigned the lowest priority weight (e.g., weight 1). Using the `apply` function of the pandas library, the priority weight of each data packet is calculated based on its type and relevance to the predicted path, resulting in a vehicle map data priority weight table. Python is then used in conjunction with the pandas and numpy libraries to sort the layered map dataset. Specifically, the layered map dataset is sorted according to the vehicle map data priority weights. The layered map dataset is stored as a pandas DataFrame, containing information such as packet type and priority weight. The DataFrame.sort_values() method is used to sort the packets in descending order according to their priority weight. The sorted queue is the differential verification priority queue, containing critical security layer data, navigation core layer data, and auxiliary information layer data. For example, critical security layer data is at the front of the queue, followed by navigation core layer data, and auxiliary information layer data is at the back. Python is used in conjunction with the cryptography library to quickly decrypt the critical security layer data. Specifically, the critical security layer data is extracted from the differential verification priority queue. A lightweight communication security key is used to call the decryption function in the cryptography library. For example, AES (Advanced Encryption Standard) is selected for fast decryption, and the critical security layer data is decrypted using the cryptography.hazmat.primitives.ciphers module with a key and predefined encryption modes (such as CBC mode). Because critical security layer data has high real-time requirements, the decryption process prioritizes the allocation of computing resources to ensure decryption is completed within a short time, obtaining key vehicle safety data such as road hazard warnings and emergency lane change information. Python is used in conjunction with the cryptography library for standard decryption of the navigation core layer data. Specifically, the navigation core layer data is extracted according to a differential verification priority queue. A lightweight communication security key is also used to call the decryption function in the cryptography library.For example, the AES algorithm is selected for decryption. The `cryptography.hazmat.primitives.ciphers` module uses a key and a predefined encryption mode (such as CBC mode) to decrypt the navigation core layer data. This core layer data contains essential road information and traffic signals required for vehicle navigation. The decryption process follows a standard encryption algorithm. After decryption, the vehicle navigation core data is obtained. The auxiliary information layer data is then decrypted in the background using Python and the `cryptography` library. Specifically, the auxiliary information layer data is extracted based on a differential verification priority queue. A lightweight communication security key is used to call the decryption function in the `cryptography` library. For example, the AES algorithm is selected for decryption. The `cryptography.hazmat.primitives.ciphers` module uses a key and a predefined encryption mode (such as CBC mode) to decrypt the auxiliary information layer data. This auxiliary information layer data contains nearby points of interest and advertising information, and has lower real-time requirements; therefore, the decryption process can be performed in the background without affecting the processing of critical security layer and navigation core layer data. After decryption, the vehicle auxiliary information data is obtained. This paper uses Python with the hashlib library to perform integrity verification on decrypted vehicle safety data, navigation core data, and auxiliary information data. Specifically, for each decrypted data packet, a hash value is calculated using a hash function (such as SHA-256) from the hashlib library. The calculated hash value is then compared with the original hash value carried in the data packet. For example, the hash value of the data packet is calculated using the hashlib.sha256() function and compared with the hash value carried in the packet header. If they match, the data is considered intact and has not been tampered with; if they do not match, the data packet is marked as abnormal. Through this process, integrity verification is performed on all decrypted data packets to obtain verified map data. The paper then uses Python with the geopandas library to perform format standardization and validity verification on the verified map data. Specifically, the geopandas library is first used to load the verified map data into a geospatial data format (such as GeoDataFrame) to ensure that the data conforms to the standard format of Geographic Information System (GIS). The validity of the data is then verified, such as checking whether the geographic coordinates are within a reasonable range and whether the road network is connected. The `is_valid` attribute and `make_valid` method of the geopandas library are used to check and correct the validity of the data. The resulting standard navigation map data will then be used in the vehicle's navigation system.

[0087] Preferably, step S4 includes the following steps:

[0088] Step S41: Real-time data acquisition is performed on the vehicle-mounted environmental perception sensor to obtain raw vehicle environmental perception data; the current road geometric features are identified based on the vehicle pose reference data to obtain current road geometric feature data;

[0089] Step S42: Extract traffic density information based on the verified vehicle navigation map data to obtain the current road traffic density data;

[0090] Step S43: Monitor the vehicle's motion status to obtain vehicle motion status data; conduct a speed risk assessment based on the vehicle motion status data to obtain speed risk assessment data.

[0091] Step S44: Conduct a weather risk assessment based on the raw vehicle environmental perception data to obtain road weather risk assessment data;

[0092] Step S45: Evaluate the road complexity risk factors based on the current road geometric feature data to obtain road complexity evaluation data;

[0093] Step S46: Analyze traffic environment risk factors using current road traffic density data to obtain traffic environment risk data;

[0094] Step S47: Based on vehicle speed risk assessment data, road weather risk assessment data, road complexity assessment data, and traffic environment risk data, conduct a vehicle driving risk assessment to obtain the real-time vehicle driving risk level.

[0095] Step S48: Based on the real-time risk level of vehicle driving, perform vehicle driving map adaptation on the verification vehicle navigation map data to obtain adaptive map safety data.

[0096] In this embodiment, onboard environmental perception sensors (such as cameras, LiDAR, and millimeter-wave radar) are used for real-time data acquisition. Specifically, the vehicle's forward-facing camera acquires road image data at a frame rate of 30fps to identify lane lines, traffic signs, and vehicles ahead. Simultaneously, LiDAR scans the vehicle's surroundings at a rotation speed of 10 revolutions per second to acquire point cloud data for obstacle and road boundary detection. Furthermore, millimeter-wave radar detects the distance, speed, and angle of objects around the vehicle at a frequency of 20Hz. Combining vehicle pose reference data (such as GPS coordinates and attitude information), Python is used with the OpenCV library to process the camera images, identifying the geometric features of the current road, such as lane width, curvature, and slope. OpenCV's edge detection and contour extraction functions are used to analyze road boundaries and lane lines in the images to obtain current road geometric feature data. Python is used with the geopandas library to extract traffic density information from validated navigation map data. Specifically, the navigation map data is loaded into a geospatial data format (such as a Geo Data Frame) and, combined with the vehicle's real-time location information, the road network within a certain range is queried. Traffic density data for the current road is extracted by analyzing traffic flow information (such as vehicle density and average speed) in map data. For example, using the overlay method of geopandas, the vehicle position is spatially overlaid with the traffic flow layer in the map to obtain the traffic density value of the road segment where the vehicle is located. The vehicle's motion state is monitored in real time using an inertial measurement unit (IMU) and a GPS receiver. Specifically, the IMU records the vehicle's acceleration and angular velocity data at a frequency of 100Hz, and the GPS receiver provides the vehicle's real-time position coordinates and speed information at a frequency of 1Hz. This data is processed using Python and the NumPy library to calculate motion state parameters such as instantaneous speed, acceleration, and steering angle. Based on the vehicle's motion state data, a preset speed risk assessment model (such as a model based on speed and acceleration thresholds) is used to assess speed risk. For example, if the vehicle speed exceeds a set speed limit threshold (such as 120km / h) or the acceleration exceeds a safety threshold (such as 3m / s²), it is marked as high-risk, resulting in the final speed risk assessment data. The vehicle's environmental perception sensors (such as cameras and millimeter-wave radar) are used to monitor weather conditions in real time and conduct weather risk assessments. Specifically, a forward-facing camera collects road image data, and image analysis identifies weather features such as raindrops, snow, or fog. Simultaneously, millimeter-wave radar can detect changes in the intensity of radar signal reflection from raindrops or snowflakes, thereby determining weather conditions.Image data is processed using Python and the OpenCV library. Through image grayscale conversion, binarization, and morphological operations, raindrop or fog regions in the images are identified. Based on the identification results and pre-defined weather risk assessment standards (e.g., visibility below 50 meters is marked as high risk), road weather risk assessment data is obtained. Next, Python is used in conjunction with the geopandas library to analyze current road geometric feature data and assess road complexity risk factors. Specifically, road geometric feature data (such as lane width, curvature, and slope) is loaded into a geospatial data format. Geopandas' geometric analysis functions are used to calculate road complexity indices, such as the rate of change of curvature and the rate of change of slope. For example, if the rate of change of road curvature exceeds a certain threshold (e.g., more than 10 degrees per 100 meters), or the slope exceeds a certain threshold (e.g., 15%), the road is considered to have high complexity, resulting in road complexity assessment data. Finally, Python is used in conjunction with the pandas library to analyze current road traffic density data and assess traffic environment risk factors. In practice, traffic density data (such as vehicle density and average vehicle speed) is loaded into a pandas DataFrame format. Using pandas' data analysis capabilities, traffic environment risk indicators, such as traffic congestion levels and vehicle collision risk, are calculated. For example, if vehicle density exceeds a certain threshold (e.g., more than 100 vehicles per kilometer), or average vehicle speed is below a certain threshold (e.g., 20 km / h), the traffic environment risk is considered high, resulting in traffic environment risk data. Python, combined with the pandas library, is used to comprehensively analyze vehicle speed risk assessment data, road weather risk assessment data, road complexity assessment data, and traffic environment risk data to evaluate the real-time risk level of vehicle driving. Specifically, the aforementioned risk assessment data is loaded into a pandas DataFrame format and weighted according to a preset risk weight model. For example, the weight for vehicle speed risk is 0.3, the weight for weather risk is 0.2, the weight for road complexity risk is 0.2, and the weight for traffic environment risk is 0.3. By calculating the weighted risk value, the vehicle's driving risk level is divided into low risk (weighted risk value below 0.3), medium risk (weighted risk value between 0.3 and 0.6), and high risk (weighted risk value above 0.6), ultimately obtaining the real-time driving risk level of the vehicle. For detailed implementation procedures of step S48, please refer to the sub-steps of step S48.

[0097] Of particular importance, step S48 also includes the following steps:

[0098] Step S481: Adjust the safety verification strategy parameters according to the real-time risk level of vehicle driving to obtain communication verification strategy adjustment data;

[0099] Step S482: Configure the encryption algorithm strength based on the communication verification strategy adjustment data to obtain the communication encryption strength configuration data;

[0100] Step S483: Based on the communication verification strategy, adjust the data and communication encryption strength configuration data to reconfigure the verification process and obtain the vehicle communication security control strategy;

[0101] Step S484: Reassess the security level of the verification vehicle navigation map data according to the vehicle communication security control strategy to obtain the security level of the navigation map data;

[0102] Step S485: Adjust the data transmission priority of the vehicle's communication unit based on the security level of the navigation map data to obtain transmission priority adjustment data;

[0103] Step S486: Use vehicle communication security control strategies and transmission priority adjustment data to perform security reconstruction on the verification vehicle navigation map data to obtain vehicle adaptability map security data.

[0104] In this embodiment, the security verification strategy parameters are adjusted based on the real-time risk level of vehicle operation. Specifically, Python is used in conjunction with the pandas library to process the risk level data. For example, when the real-time risk level of vehicle operation is high, the number of signature verifications in the communication verification strategy is increased from 2 to 3 to enhance data reliability verification. Simultaneously, the data packet integrity verification algorithm is upgraded from SHA-256 to SHA-384 to improve security. The `apply` function of the pandas library dynamically adjusts these parameters according to the risk level, generating communication verification strategy adjustment data. Encryption algorithm strength is configured based on the communication verification strategy adjustment data. Specifically, Python is used in conjunction with the cryptography library to adjust the encryption algorithm strength. For example, based on the communication verification strategy adjustment data, if the risk level is high, the symmetric encryption algorithm is upgraded from AES-128 to AES-256, and the asymmetric encryption algorithm is upgraded from RSA-2048 to RSA-4096. Using the Cipher module and the `generate_private_key` method of the cryptography library, a new encryption key and certificate are generated based on the adjusted parameters, resulting in communication encryption strength configuration data. The verification process is reconfigured based on the communication verification strategy, adjusting the data and encryption strength configuration data. Specifically, Python is used in conjunction with the cryptography and pandas libraries to adjust the verification process. For example, based on the data generated in steps S481 and S482, the signature verification step in the verification process is changed from single-step verification to double-step verification, i.e., first verifying the digital signature of the data packet, then verifying the integrity of the data packet. Simultaneously, the encryption and decryption process of the data packet is updated according to the adjustment of the encryption strength to ensure that the data can be correctly decrypted under the new encryption algorithm. The adjusted verification strategy and encryption strength configuration data are merged using the merge function of the pandas library to generate the vehicle communication security control strategy. The security level of the verified navigation map data is reassessed based on the vehicle communication security control strategy. Specifically, Python is used in conjunction with the pandas library to classify the navigation map data. For example, based on the risk level and encryption strength in the communication security control strategy, the navigation map data is divided into high security level, medium security level, and low security level. Data in high-risk scenarios is marked as high security level; data in ordinary scenarios is marked as medium security level. The `groupby` function in the pandas library is used to group navigation map data according to data type and risk level, generating a navigation map data security level table. Based on the navigation map data security level, the data transmission priority of vehicle communication units is adjusted. In practice, Python is used in conjunction with the pandas library and the vehicle's communication management system to adjust the data transmission priority.For example, for high-security-level data, the transmission priority is raised from normal to high priority to ensure that this data is transmitted first; for low-security-level data, the transmission priority is lowered from high to normal priority. The `apply` function of the pandas library dynamically adjusts the transmission priority of each data entry based on the navigation map data security level table, generating transmission priority adjustment data. The verified navigation map data is then reconstructed using vehicle communication security control strategies and the transmission priority adjustment data. Specifically, Python is used in conjunction with the pandas and cryptography libraries to process the navigation map data. For example, the navigation map data is re-encrypted and re-signed according to the encryption algorithm and verification process in the communication security control strategy. Simultaneously, the transmission order of data packets is adjusted according to the transmission priority adjustment data to ensure that high-priority data is transmitted first. The `sort_values` function of the pandas library sorts the data packets according to transmission priority, and the `encrypt` and `sign` methods of the cryptography library are used to encrypt and sign the data, ultimately generating vehicle-adaptive map security data.

[0105] Preferably, step S5 includes the following steps:

[0106] Step S51: Monitor the network connection status of the vehicle communication unit to obtain raw data on the communication link status;

[0107] Step S52: Measure the network bandwidth in real time based on the original communication link status data to obtain vehicle network bandwidth measurement data;

[0108] Step S53: Perform network latency statistics based on the original communication link status data to obtain vehicle network latency statistics.

[0109] Step S54: Based on the vehicle network bandwidth measurement data and vehicle network latency statistics, conduct a communication quality assessment to obtain vehicle network connection quality data;

[0110] Step S55: Dynamically determine the degradation protection threshold based on the vehicle network connection quality data, and obtain the degradation protection threshold determination result data;

[0111] Step S56: Based on the degradation protection threshold judgment result data, perform communication degradation condition logic judgment to obtain the communication degradation trigger signal;

[0112] Step S57: Extract local cached data based on the communication degradation trigger signal to obtain local backup map data; acquire map information of adjacent vehicles to obtain collaborative backup map data; perform fault-tolerant fusion of adaptive map security data based on local backup map data and collaborative backup map data to obtain final navigation safety control data.

[0113] In this embodiment, the vehicle's onboard communication unit (such as a 4G / 5G module or V2X communication module) is used to monitor network connection status. Specifically, the `ifconfig` or `ip` command under the Linux system is used to obtain network interface connection status information, including IP address, subnet mask, gateway, etc. Simultaneously, the `netstat` command is used to monitor the real-time status of the network connection, including the connection port number, protocol type, and connection status (e.g., established, listening). The output of these commands is collected and stored as raw communication link status data. The vehicle's network bandwidth is measured in real-time using Python in conjunction with the `speedtest-cli` tool. `speedtest-cli` is an open-source command-line tool that can measure network download and upload speeds by connecting to test servers around the world. Specifically, after installing the `speedtest-cli` tool, it is invoked using Python's `subprocess` module to obtain real-time network bandwidth data. For example, running the command `speedtest-cli --simple` outputs results in a simple format containing download and upload speeds. Finally, the vehicle's network latency is statistically analyzed using Python in conjunction with the `ping` command. In practice, the `ping` command is invoked using Python's `subprocess` module to send ICMP requests to a specified target server (such as a navigation server or roadside unit) and record the round-trip time (RTT). For example, running the command `ping -c 10 <target IP>` sends 10 ICMP requests and records the RTT for each request. By analyzing this RTT data, statistics such as average latency, maximum latency, and minimum latency are calculated to obtain vehicle network latency statistics. The `pandas` library in conjunction with Python is used to evaluate the communication quality of the vehicle's network bandwidth measurement data and network latency statistics. Specifically, the network bandwidth measurement data and network latency statistics are loaded into a `pandas` DataFrame and analyzed according to a predefined communication quality evaluation model. For example, the communication quality evaluation index is defined as follows: when the network bandwidth is below a certain threshold (e.g., 10Mbps) and the latency is above a certain threshold (e.g., 100ms), the communication quality evaluation result is "low"; when the network bandwidth is above a certain threshold (e.g., 50Mbps) and the latency is below a certain threshold (e.g., 50ms), the communication quality evaluation result is "high". By utilizing pandas' conditional filtering capabilities, data is categorized according to these rules to generate vehicle network connectivity quality data. Python, combined with the pandas library, is then used to dynamically determine the degradation protection threshold for this data. Specifically, the degradation protection threshold is dynamically adjusted based on the communication quality assessment results.For example, when the communication quality assessment result is "low" multiple times (e.g., 3 times), the degradation protection threshold is lowered, making it easier for the system to trigger the communication degradation protection mechanism; when the communication quality assessment result is "high," the degradation protection threshold is raised to reduce unnecessary communication degradation. Using pandas' shift and rolling functions, time series analysis is performed on the communication quality assessment results to generate degradation protection threshold judgment result data. Python, combined with pandas and numpy libraries, is used to perform communication degradation condition logic judgment on the degradation protection threshold judgment result data. Specifically, based on the degradation protection threshold judgment result data and the preset communication degradation condition logic, it is determined whether to trigger communication degradation. For example, when the communication quality is lower than the degradation protection threshold multiple times (e.g., 3 times), a communication degradation signal is triggered. Using pandas' conditional filtering function and numpy's logical operation function, logical judgment is performed on the data to generate a communication degradation trigger signal. For detailed implementation of step S57, please refer to the sub-steps of step S57.

[0114] Preferably, step S57 includes the following steps:

[0115] Step S571: Activate the vehicle's local cache system using the communication degradation trigger signal to obtain the vehicle cache system activation data;

[0116] Step S572: Extract real-time cache data based on vehicle cache system activation data to obtain real-time cache map data for vehicles; perform regional cache data space query based on vehicle pose reference data to obtain regional cache map data for vehicles;

[0117] Step S573: Perform emergency cache data matching and filtering based on the adaptability map safety data to obtain vehicle emergency cache map data;

[0118] Step S574: Sort the cached data according to the vehicle real-time cached map data, vehicle area cached map data and vehicle emergency cached map data to obtain local backup map data;

[0119] Step S575: Search for neighboring vehicles on the vehicle-mounted V2V communication unit to obtain a list of neighboring vehicles; evaluate the vehicle communication capability based on the list of neighboring vehicles to obtain vehicle communication capability data.

[0120] Step S576: Send map data requests to neighboring vehicles based on vehicle communication capability data and obtain vehicle data request responses; verify the reception of map data from neighboring vehicles based on vehicle data request responses to obtain collaborative backup map data.

[0121] Step S577: Perform a data consistency check based on the local backup map data and the collaborative backup map data to obtain the map data consistency verification result;

[0122] Step S578: Based on the map data consistency verification results, perform map fusion on the local backup map data and the collaborative backup map data to obtain vehicle fault-tolerant fused map data;

[0123] Step S579: Perform fault-tolerant fusion based on vehicle fault-tolerant fusion map data and adaptive map safety data to obtain the final navigation safety control data.

[0124] In this embodiment, when the communication degradation trigger signal is activated, the vehicle's local caching system is used for data extraction. Specifically, the vehicle's onboard computer system (such as a Linux-based embedded system) receives the communication degradation trigger signal. When the signal is detected, the vehicle's local caching system is activated by calling its API interface (such as an SQLite database interface). For example, the sqlite3 library is used to connect to the local cache database, and a SELECT statement is executed to query the cache system's status information, obtaining the vehicle's cache system activation data. This data includes information such as the availability of the cache system, the size and type of cached data, etc. Real-time cached data is extracted based on the vehicle's cache system activation data. Specifically, Python is used in conjunction with the sqlite3 library to extract real-time cached map data from the local cache database. For example, based on the cache data type and size recorded in the cache system activation data, an SQL query statement is executed, such as `SELECT *FROM cache WHERE type='map' AND timestamp > NOW() - INTERVAL 1 HOUR`, to extract the cached map data from the most recent hour. Simultaneously, based on vehicle position reference data (such as GPS coordinates and driving direction), the geopandas library is used to perform spatial queries on the cached data to extract cached map data for the area near the vehicle's current location. For example, the geopandas sjoin function is used to perform spatial overlay analysis of the vehicle's location and cached map data to obtain the vehicle's regional cached map data. Emergency cached data is then matched and filtered based on adaptive map safety data. Specifically, Python combined with the pandas library is used to analyze the adaptive map safety data. For example, based on the risk level and data type in the adaptive map safety data, emergency cached data related to the current vehicle's driving status is filtered. If the vehicle is in a high-risk area (such as a construction zone or an accident-prone area), cached map data related to these areas is prioritized. The pandas query function is used to execute a query statement like df.query('risk_level == "high" and data_type == "map"') to obtain the vehicle's emergency cached map data. The cached data is then prioritized based on the vehicle's real-time cached map data, the vehicle's regional cached map data, and the vehicle's emergency cached map data. In practice, Python is used in conjunction with the pandas library to prioritize these cached data. For example, priority rules are defined based on the urgency, timeliness, and relevance to the vehicle's current location.Emergency cached map data is assigned the highest priority (e.g., priority value 3), regional cached map data is assigned a medium priority (e.g., priority value 2), and real-time cached map data is assigned the lowest priority (e.g., priority value 1). The pandas `sort_values` function sorts the cached data according to priority to obtain local backup map data. Neighboring vehicles are searched using the vehicle's V2V communication unit. Specifically, the vehicle's V2V communication module (e.g., DSRC or 5G-V2X module) sends a broadcast signal to search for nearby vehicles. For example, a Hello message is sent via the V2V communication module, containing the vehicle's ID, location, and communication capability information. Upon receiving responses from other vehicles, a list of neighboring vehicles is recorded using Python and the pandas library, including vehicle ID, distance, and communication protocol type. Based on this list, the communication capability of each neighboring vehicle is evaluated, for example, by analyzing response time, signal strength, and communication protocol version. The evaluation results are stored as vehicle communication capability data. Map data requests are then sent to neighboring vehicles based on this vehicle communication capability data. In practice, Python is used in conjunction with the API interface of the V2V communication module to send map data requests to neighboring vehicles with strong communication capabilities. For example, a neighboring vehicle with high signal strength and short response time is selected, and a request message containing the type and range of the required map data is sent. After receiving the map data returned by the neighboring vehicle, the hashlib library is used to verify the integrity of the data, such as calculating the SHA-256 hash value of the data and comparing it with the expected value. After successful verification, the received map data is marked as collaborative backup map data. Data consistency is checked based on the local backup map data and the collaborative backup map data. In practice, Python is used in conjunction with the pandas library to compare the two sets of data. For example, the local backup map data and the collaborative backup map data are loaded into a pandas DataFrame, and the data is merged using the merge function, comparing the data version number, update time, and content consistency. If data inconsistency is found, it is marked as abnormal data. Through the above operations, the map data consistency verification result is obtained. Based on the map data consistency verification result, the local backup map data and the collaborative backup map data are merged. In practice, Python is used in conjunction with the geopandas library to merge the data. For example, for data that passes consistency verification, geopandas' overlay function is used for spatial fusion, merging local backup map data and collaborative backup map data into a complete map dataset. For inconsistent data, selective fusion is performed based on data priority and reliability, such as prioritizing the retention of local backup data, ultimately resulting in vehicle-tolerant fused map data.Fault-tolerant fusion is performed based on vehicle-compatible fused map data and adaptive map safety data. Specifically, Python is used in conjunction with the pandas and geopandas libraries for comprehensive data processing. For example, the fault-tolerant fused map data is compared and fused with the adaptive map safety data. Based on the risk level and navigation requirements in the safety data, the route planning and safety prompts in the fused map data are adjusted. For instance, if the adaptive map safety data marks a road segment as a high-risk area, the route planning in the fused map data is adjusted to avoid that area. The final navigation safety control data obtained is then used in the vehicle's navigation system.

[0125] Preferably, the present invention also provides an application in a vehicle navigation communication safety control system for executing the vehicle navigation communication safety control method described above, the vehicle navigation communication safety control system comprising:

[0126] The data acquisition module is used to acquire vehicle position and orientation reference data;

[0127] The key generation module is used to extract elliptic curve parameters from vehicle pose reference data to obtain vehicle pose elliptic curve parameters; and to generate a lightweight communication security key based on the vehicle pose elliptic curve parameters.

[0128] The map processing module is used to acquire the original map incremental data packet; to perform security level layering on the original map incremental data packet to obtain a layered map dataset; and to perform layered decryption of the layered map dataset according to the lightweight communication security key to obtain the verified vehicle navigation map data.

[0129] The risk assessment and adjustment module is used to adapt the vehicle navigation map data to the vehicle driving map to obtain adaptive map safety data.

[0130] The fault-tolerant fusion module is used to determine the degradation protection threshold of the vehicle and obtain the communication degradation trigger signal; based on the communication degradation trigger signal, it extracts local cached data to obtain local backup map data; it acquires map information of adjacent vehicles to obtain collaborative backup map data; and it performs fault-tolerant fusion of adaptive map security data based on the local backup map data and collaborative backup map data to obtain the final navigation safety control data.

[0131] Therefore, the embodiments should be considered as exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of the equivalents of the application are intended to be included within the invention.

[0132] The above description is merely a specific embodiment of the present invention, enabling those skilled in the art to understand or implement the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the present invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features of the invention herein.

Claims

1. A method for safety control of automotive navigation communication, characterized in that, Includes the following steps: Step S1: Collect multimodal data from the vehicle to obtain an in-vehicle multimodal dataset; perform Kalman filtering based on the in-vehicle multimodal dataset to obtain vehicle pose reference data; Step S2: Extract elliptic curve parameters from the vehicle pose reference data to obtain the vehicle pose elliptic curve parameters; Generate lightweight communication security keys based on vehicle pose elliptic curve parameters; Step S3: Obtain the original map incremental data package; The original map incremental data packet is layered by security level to obtain a layered map dataset; Layered decryption of the layered map dataset is performed using a lightweight communication security key to obtain verified vehicle navigation map data; Step S4: Perform vehicle driving map adaptation on the verification vehicle navigation map data to obtain adaptive map security data; Step S5: Determine the vehicle's degradation protection threshold and obtain the communication degradation trigger signal; extract local cached data based on the communication degradation trigger signal to obtain local backup map data; Map information of adjacent vehicles is acquired to obtain collaborative backup map data; The adaptive map security data is fault-tolerantly fused based on local backup map data and collaborative backup map data to obtain the final navigation safety control data.

2. The method for safety control of automotive navigation communication according to claim 1, characterized in that, Step S1 includes the following steps: Step S11: Obtain the vehicle-mounted multimodal dataset, which includes raw GPS location data, raw IMU motion data, raw LiDAR point cloud data, and raw visual radar data; Step S12: Initialize the Kalman filter state vector of the vehicle multimodal dataset to obtain the filter initial state data; Step S13: Perform Kalman filtering prediction on the original GPS position data based on the filter's initial state data to obtain GPS filtered prediction data; Step S14: Use the original IMU motion data to update the GPS filtered prediction data using Kalman filtering to obtain vehicle pose fusion data; Step S15: Perform multi-sensor Kalman filter correction on the vehicle pose fusion data based on the original LiDAR point cloud data and the original visual radar data to obtain the vehicle real-time state parameter table. Step S16: Evaluate the confidence level of each sensor data in the vehicle multimodal dataset based on the vehicle real-time status parameter table to obtain sensor confidence level evaluation data; Step S17: Adjust the weights of the vehicle real-time state parameter table based on the sensor confidence evaluation data to obtain the vehicle pose reference data.

3. The method for safety control of automotive navigation communication according to claim 1, characterized in that, Step S2 includes the following steps: Step S21: Extract elliptic curve parameters from the vehicle pose reference data to obtain the vehicle pose elliptic curve parameters; Step S22: Initialize the elliptic curve domain parameters according to the vehicle pose elliptic curve parameters to obtain the curve domain initialization data; Step S23: Determine the elliptic curve generation origin points based on the curve domain initialization data to obtain the elliptic curve generation origin point data; Step S24: Extract vehicle GPS coordinates from vehicle pose reference data to obtain vehicle GPS coordinates; calculate vehicle velocity vector based on vehicle GPS coordinates to obtain vehicle velocity vector data; Step S25: Construct an elliptic curve entropy source based on vehicle GPS coordinates and vehicle speed vector data to obtain spatiotemporally unique entropy source data; Step S26: Obtain the system time millisecond-level timestamp for the vehicle to obtain the vehicle millisecond-level timestamp data; Step S27: Generate a random number seed based on the spatiotemporal unique entropy source data and the vehicle millisecond-level timestamp data to obtain elliptic curve random seed data; Step S28: Generate a key based on the elliptic curve random seed data to obtain a lightweight communication security key.

4. The method for safety control of automotive navigation communication according to claim 3, characterized in that, Step S28 includes the following steps: Step S281: Generate an elliptic curve private key based on the elliptic curve random seed data to obtain elliptic curve temporary private key data; Step S282: Calculate the elliptic curve public key based on the elliptic curve temporary private key data and the elliptic curve generator point data to obtain the elliptic curve temporary public key data; Step S283: Configure the parameters of the preset digital signature algorithm according to the elliptic curve temporary private key data to obtain the elliptic curve signature algorithm configuration data; Step S284: Use the elliptic curve temporary public key data to set the parameters of the preset key verification algorithm to obtain the elliptic curve verification algorithm configuration data; Step S285: Set the key pair validity period based on the elliptic curve signature algorithm configuration data and the elliptic curve verification algorithm configuration data to obtain key lifecycle data; set the key update trigger conditions based on the key lifecycle data to obtain key update trigger data; Step S286: Encapsulate the key pair based on the elliptic curve temporary private key data, the elliptic curve temporary public key data, and the key update trigger data to obtain a lightweight communication security key.

5. The method for safety control of automotive navigation communication according to claim 1, characterized in that, Step S3 includes the following steps: Step S31: Receive map data packets from the vehicle-mounted communication unit to obtain the original map incremental data packets; parse the header information of the original map incremental data packets to obtain the map data packet metadata. Step S32: Classify and identify map data content based on map data packet metadata to obtain map data type identifiers; use map data type identifiers to classify the security level of the original map incremental data packets to obtain a layered map dataset; Step S33: Identify the current driving state of the vehicle based on the vehicle pose reference data to obtain vehicle motion state data; Step S34: Based on the vehicle motion state data, perform future trajectory prediction modeling for the vehicle to obtain the vehicle trajectory prediction model; Step S35: Use the vehicle trajectory prediction model to perform spatial calculations on the future driving path of the vehicle to obtain the spatial data of the vehicle prediction path; Step S36: Match the vehicle's map data requirements with the vehicle's predicted path spatial data to obtain the vehicle's predicted path requirement data. Step S37: Decrypt the layered map dataset layer by layer according to the lightweight communication security key and the vehicle prediction path requirement data to obtain the verified vehicle navigation map data.

6. The method for safety control of automotive navigation communication according to claim 5, characterized in that, Step S37 includes the following steps: Step S371: Calculate the priority of data packets based on the vehicle predicted route demand data and the hierarchical map dataset to obtain the priority weight of the vehicle map data; Step S372: Sort the layered map dataset according to the priority weight of the vehicle map data to obtain the differential verification priority queue, wherein the differential verification priority queue includes key safety layer data, navigation core layer data and auxiliary information layer data; Step S373: Use a lightweight communication security key to quickly decrypt the critical security layer data to obtain the vehicle's critical security data; Step S374: Use the lightweight communication security key to perform standard decryption of the navigation core layer data to obtain the vehicle navigation core data; Step S375: Use a lightweight communication security key to decrypt the auxiliary information layer data in the background to obtain vehicle auxiliary information data; Step S376: Verify the integrity of the decryption results based on the vehicle's key safety data, core vehicle navigation data, and vehicle auxiliary information data to obtain the verified map data; Step S377: Standardize the map format based on the verified map data to obtain standard navigation map data; verify the data validity of the standard navigation map data to obtain verified vehicle navigation map data.

7. The method for safety control of automotive navigation communication according to claim 1, characterized in that, Step S4 includes the following steps: Step S41: Real-time data acquisition is performed on the vehicle-mounted environmental perception sensor to obtain raw vehicle environmental perception data; the current road geometric features are identified based on the vehicle pose reference data to obtain current road geometric feature data; Step S42: Extract traffic density information based on the verified vehicle navigation map data to obtain the current road traffic density data; Step S43: Monitor the vehicle's motion status to obtain vehicle motion status data; conduct a speed risk assessment based on the vehicle motion status data to obtain speed risk assessment data. Step S44: Conduct a weather risk assessment based on the raw vehicle environmental perception data to obtain road weather risk assessment data; Step S45: Evaluate the road complexity risk factors based on the current road geometric feature data to obtain road complexity evaluation data; Step S46: Analyze traffic environment risk factors using current road traffic density data to obtain traffic environment risk data; Step S47: Based on vehicle speed risk assessment data, road weather risk assessment data, road complexity assessment data, and traffic environment risk data, conduct a vehicle driving risk assessment to obtain the real-time vehicle driving risk level. Step S48: Based on the real-time risk level of vehicle driving, perform vehicle driving map adaptation on the verification vehicle navigation map data to obtain adaptive map safety data.

8. The method for safety control of automotive navigation communication according to claim 1, characterized in that, Step S5 includes the following steps: Step S51: Monitor the network connection status of the vehicle communication unit to obtain raw data on the communication link status; Step S52: Measure the network bandwidth in real time based on the original communication link status data to obtain vehicle network bandwidth measurement data; Step S53: Perform network latency statistics based on the original communication link status data to obtain vehicle network latency statistics. Step S54: Based on the vehicle network bandwidth measurement data and vehicle network latency statistics, conduct a communication quality assessment to obtain vehicle network connection quality data; Step S55: Dynamically determine the degradation protection threshold based on the vehicle network connection quality data, and obtain the degradation protection threshold determination result data; Step S56: Based on the degradation protection threshold judgment result data, perform communication degradation condition logic judgment to obtain the communication degradation trigger signal; Step S57: Extract local cached data based on the communication degradation trigger signal to obtain local backup map data; acquire map information of adjacent vehicles to obtain collaborative backup map data; perform fault-tolerant fusion of adaptive map security data based on local backup map data and collaborative backup map data to obtain final navigation safety control data.

9. The method for safety control of automotive navigation communication according to claim 8, characterized in that, Step S57 includes the following steps: Step S571: Activate the vehicle's local cache system using the communication degradation trigger signal to obtain the vehicle cache system activation data; Step S572: Extract real-time cache data based on vehicle cache system activation data to obtain real-time cache map data for vehicles; perform regional cache data space query based on vehicle pose reference data to obtain regional cache map data for vehicles; Step S573: Perform emergency cache data matching and filtering based on the adaptability map safety data to obtain vehicle emergency cache map data; Step S574: Sort the cached data according to the vehicle real-time cached map data, vehicle area cached map data and vehicle emergency cached map data to obtain local backup map data; Step S575: Search for neighboring vehicles on the vehicle-mounted V2V communication unit to obtain a list of neighboring vehicles; evaluate the vehicle communication capability based on the list of neighboring vehicles to obtain vehicle communication capability data. Step S576: Send map data requests to neighboring vehicles based on vehicle communication capability data and obtain vehicle data request responses; verify the reception of map data from neighboring vehicles based on vehicle data request responses to obtain collaborative backup map data. Step S577: Perform a data consistency check based on the local backup map data and the collaborative backup map data to obtain the map data consistency verification result; Step S578: Based on the map data consistency verification results, perform map fusion on the local backup map data and the collaborative backup map data to obtain vehicle fault-tolerant fused map data; Step S579: Perform fault-tolerant fusion based on vehicle fault-tolerant fusion map data and adaptive map safety data to obtain the final navigation safety control data.

10. A safety control system for automotive navigation and communication, characterized in that, For performing the vehicle navigation communication safety control method as described in claim 1, the vehicle navigation communication safety control system includes: The data acquisition module is used to acquire vehicle position and orientation reference data; The key generation module is used to extract elliptic curve parameters from vehicle pose reference data to obtain vehicle pose elliptic curve parameters; and to generate a lightweight communication security key based on the vehicle pose elliptic curve parameters. The map processing module is used to acquire the original map incremental data packet; to perform security level layering on the original map incremental data packet to obtain a layered map dataset; and to perform layered decryption of the layered map dataset according to the lightweight communication security key to obtain the verified vehicle navigation map data. The risk assessment and adjustment module is used to adapt the vehicle navigation map data to the vehicle driving map to obtain adaptive map safety data. The fault-tolerant fusion module is used to determine the degradation protection threshold of the vehicle and obtain the communication degradation trigger signal; based on the communication degradation trigger signal, it extracts local cached data to obtain local backup map data; it acquires map information of adjacent vehicles to obtain collaborative backup map data; and it performs fault-tolerant fusion of adaptive map security data based on the local backup map data and collaborative backup map data to obtain the final navigation safety control data.

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