Single-lane passing safety analysis method based on vehicle networking

Abstract

The application discloses a single-lane meeting safety analysis method based on Internet of Vehicles, and relates to the technical field of intelligent transportation, which comprises collecting original data and transmitting the original data to a roadside edge computing unit; the original data is divided into a forward driving vehicle data group and a reverse driving vehicle data group according to driving directions by using the roadside edge computing unit, a trajectory prediction model is established according to an improved Kalman filtering algorithm, the driving trajectories and longitudinal distance values of two vehicles driving in opposite directions are predicted, and a minimum safe meeting distance threshold is calculated; the longitudinal distance value and the minimum safe meeting distance threshold are judged, a meeting risk coefficient is sent to an Internet of Vehicles road test board; the meeting risk coefficient is received by the Internet of Vehicles road test board and converted into an audible and visual early warning signal, and meeting risk early warning information is sent to vehicles by using the roadside edge computing unit. The application improves meeting safety by data collection, dynamic risk assessment and diversified early warning mechanisms.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of intelligent transportation, in particular to a single-lane passing safety analysis method based on vehicle networking. BACKGROUND

[0002] Traditional single-lane passing safety measures mainly rely on static management means such as road markings, markings, and sight distance requirements, or use simple vehicle detectors to obtain traffic flow parameters. However, these traditional methods have many limitations, such as incomplete information acquisition, poor real-time performance, and single early warning mechanism. In addition, existing vehicle safety warning systems mostly rely on sensors mounted on individual vehicles for obstacle detection and trajectory prediction, but in terms of sensing range, data accuracy, and communication timeliness, it is difficult to meet the strict requirements for safety in single-lane passing scenarios.

[0003] In recent years, with the development of edge computing technology, its combination with vehicle networking systems has become a new approach to solving single-lane passing safety problems. However, existing edge computing architectures still face a series of challenges when dealing with high-dynamic traffic scenarios. First, existing vehicle trajectory prediction algorithms have low accuracy in handling sudden speed changes, sharp turns, and other complex conditions, making it difficult to meet safety requirements. Second, the existing safety passing distance threshold is too simple and does not fully consider the influence of road geometric features, vehicle type differences, road conditions, and other factors. Third, the passing risk assessment method lacks systematicness and dynamic adaptability, making it difficult to achieve accurate hierarchical warning and personalized avoidance strategies. In addition, the latency problem in data transmission and processing of existing systems further restricts the real-time and effectiveness of warning information.

[0004] To address the above problems, the present application proposes a single-lane passing safety analysis method based on vehicle networking. This method collects vehicle data by deploying vehicle networking road test boards and conducts real-time analysis and processing with roadside edge computing units, establishes an improved trajectory prediction model and dynamic risk assessment mechanism, aiming to realize intelligent management and proactive warning of single-lane passing safety. SUMMARY

[0005] In view of the problems of incomplete information acquisition, poor real-time performance, and single early warning mechanism in existing single-lane passing safety measures, the present application is proposed.

[0006] Therefore, the problem to be solved by the present application is how to combine vehicle networking and edge computing technology to improve safety in single-lane passing scenarios, achieve accurate trajectory prediction, dynamic risk assessment, and real-time warning, and effectively solve the limitations of traditional methods in complex conditions.

[0007] To solve the above technical problems, the present application provides the following technical solutions:

[0008] In a first aspect, embodiments of the present invention provide a single-lane oncoming traffic safety analysis method based on vehicle-to-everything (V2X) technology. The method includes: collecting raw data of vehicles traveling in a single lane using a V2X roadside testing board and transmitting the raw data to a roadside edge computing unit; using the roadside edge computing unit to divide the raw data into forward-moving vehicle data groups and reverse-moving vehicle data groups according to the driving direction, and establishing a trajectory prediction model based on an improved Kalman filter algorithm; predicting the driving trajectories and longitudinal distance values ​​of two vehicles traveling in opposite directions based on the trajectory prediction model, and calculating a minimum safe oncoming traffic distance threshold for the two vehicles; judging the longitudinal distance value and the minimum safe oncoming traffic distance threshold using the roadside edge computing unit; when the longitudinal distance value is less than the minimum safe oncoming traffic distance threshold, calculating a oncoming traffic risk coefficient based on the driving speed, and sending the oncoming traffic risk coefficient to the V2X roadside testing board; receiving the oncoming traffic risk coefficient through the V2X roadside testing board and converting it into an audible and visual warning signal, and sending oncoming traffic risk warning information to the vehicles using the roadside edge computing unit.

[0009] As a preferred embodiment of the single-lane oncoming traffic safety analysis method based on vehicle-to-everything (V2X) of the present invention, the oncoming traffic risk warning information includes deceleration magnitude and oncoming traffic avoidance strategy; the raw data includes position coordinates, driving speed, and driving direction; the conversion method of the audible and visual warning signal is as follows: the oncoming traffic risk coefficient is converted into a graded warning signal according to a preset risk level classification rule, and the audible and visual warning device is triggered, wherein the audible and visual warning device includes a variable information sign and an audio alarm; based on the oncoming traffic risk coefficient, the driving speed, and the longitudinal distance value, the suggested deceleration magnitude is calculated by the roadside edge calculation unit, and an oncoming traffic avoidance strategy is formulated according to the lane width and road surface conditions; the deceleration magnitude and the oncoming traffic avoidance strategy are packaged into oncoming traffic risk warning information, and the oncoming traffic risk warning information is sent to the vehicle terminals of the two vehicles traveling in opposite directions through the V2X communication protocol, wherein the vehicle terminals, after receiving the oncoming traffic risk warning information, issue a warning prompt to the driver through the in-vehicle display screen and voice broadcast system.

[0010] As a preferred embodiment of the single-lane oncoming traffic safety analysis method based on vehicle-to-everything (V2X) of the present invention, the method for calculating the oncoming traffic risk coefficient is as follows: A dynamic evaluation period is set for the roadside edge computing unit, and the longitudinal distance value and the minimum safe oncoming traffic distance threshold are compared in real time; when the longitudinal distance value is detected to be less than the minimum safe oncoming traffic distance threshold, the driving speeds of the two vehicles traveling in opposite directions are extracted by the roadside edge computing unit to establish an oncoming traffic risk calculation model; when the longitudinal distance value is detected to be greater than or equal to the minimum safe oncoming traffic distance threshold, the roadside edge computing unit continues to monitor the trend of longitudinal distance value changes between the two vehicles; if a safe distance is maintained for a continuous preset sampling period, the oncoming traffic risk monitoring state is deactivated; the relative speed of the two vehicles, the difference between the longitudinal distance value and the minimum safe oncoming traffic distance threshold are used as inputs to the oncoming traffic risk calculation model to calculate the oncoming traffic risk coefficient; the oncoming traffic risk coefficient is sent to the V2X road test board of the corresponding road segment via the V2X communication protocol, where the V2X road test board converts the oncoming traffic risk coefficient into a warning signal.

[0011] As a preferred embodiment of the single-lane oncoming traffic safety analysis method based on vehicle-to-everything (V2X) as described in this invention, the method for calculating the minimum safe oncoming traffic distance threshold is as follows: Based on the prediction results of the trajectory prediction model, vehicles in the forward-moving vehicle data group and the reverse-moving vehicle data group are paired for oncoming traffic using the roadside edge computing unit; according to the pairing results, the position coordinates and driving speeds of each pair of oncoming vehicles are extracted; the position coordinates are projected onto the same coordinate system to calculate the straight-line distance between the center points of the two vehicles, and combined with the road direction, this straight-line distance is projected onto the road centerline direction to obtain the longitudinal distance value between the two vehicles; based on the longitudinal distance value, the minimum safe oncoming traffic distance threshold between the two vehicles is calculated using the roadside edge computing unit based on the vehicle type characteristics, driving speed, road curvature, and road surface conditions, as shown in the following specific formula:

[0012]

[0013] Among them, D safe Let v1 be the current speed of the first vehicle, v2 be the current speed of the second vehicle, a1 be the maximum deceleration of the first vehicle, a2 be the maximum deceleration of the second vehicle, γ be the road condition coefficient, θ be the road curvature angle, and R be the minimum safe passing distance threshold. c Let ξ be the road curvature radius, η(ξ) be the vehicle characteristic function, ξ be the vehicle characteristic parameter, k be the sensitivity coefficient, μ be the standard vehicle parameter, and ξ0 be the vehicle baseline value.

[0014] As a preferred embodiment of the single-lane oncoming traffic safety analysis method based on vehicle-to-everything (V2X) as described in this invention, the method for establishing the trajectory prediction model is as follows: The original data is classified and processed according to the driving direction; the original data with the driving direction towards the V2X road test board is divided into a forward-driving vehicle data group; simultaneously, the original data with the driving direction away from the V2X road test board is divided into a reverse-driving vehicle data group; vehicle motion state matrices are established for the forward-driving vehicle data group and the reverse-driving vehicle data group respectively; an improved Kalman filter algorithm is used to smooth the vehicle motion state matrices to establish the trajectory prediction model, wherein the improved Kalman filter algorithm introduces an adaptive noise covariance matrix.

[0015] As a preferred embodiment of the single-lane oncoming traffic safety analysis method based on vehicle-to-everything (V2X) as described in this invention, the specific formula of the trajectory prediction model is as follows:

[0016]

[0017] Where P(t) is the predicted position at time t, and α i Here, β is the adaptive weighting coefficient, and v is the velocity sensitivity parameter. i Let μ be the current speed of the i-th vehicle, μ be the speed baseline value, and n be the number of sampling points. Let x be the position of the i-th vehicle. i The rate of change is the instantaneous velocity, λ is the time decay factor, τ is the integral variable, and R(t) is the residual correction term.

[0018] As a preferred embodiment of the single-lane oncoming traffic safety analysis method based on vehicle-to-everything (V2X) as described in this invention, the method for acquiring the raw data is as follows: A V2X roadside board scans vehicles traveling in a single lane to collect vehicle data; based on the vehicle data, the position coordinates of the vehicles traveling in the single lane are obtained according to the principle of laser ranging, and the speed of the vehicles traveling in the single lane is calculated based on the changes in the position coordinates at adjacent times; the travel direction of the vehicles traveling in the single lane is determined by comparing the trend of changes in the position coordinates at adjacent times; the position coordinates, the speed, and the direction of travel are used as raw data by the V2X roadside board, and the raw data is transmitted to a roadside edge computing unit via a V2X communication protocol, wherein the roadside edge computing unit receives the raw data transmitted by several V2X roadside boards.

[0019] Secondly, embodiments of the present invention provide a single-lane oncoming traffic safety analysis system based on the Internet of Vehicles (IoV), comprising: a data acquisition module for acquiring raw data of vehicles traveling in a single lane through an IoV roadside test board and transmitting the raw data to a roadside edge computing unit; a model building module for using the roadside edge computing unit to classify the raw data into a forward-moving vehicle data group and a reverse-moving vehicle data group according to the driving direction, and establishing a trajectory prediction model based on an improved Kalman filter algorithm; a prediction module for predicting the driving trajectory and longitudinal distance value of two vehicles traveling in opposite directions based on the trajectory prediction model, and calculating a minimum safe oncoming traffic distance threshold for the two vehicles; a judgment module for judging the longitudinal distance value and the minimum safe oncoming traffic distance threshold through the roadside edge computing unit, and when the longitudinal distance value is less than the minimum safe oncoming traffic distance threshold, calculating a oncoming traffic risk coefficient based on the driving speed and sending the oncoming traffic risk coefficient to the IoV roadside test board; and an oncoming traffic risk warning module for receiving the oncoming traffic risk coefficient through the IoV roadside test board and converting it into an audible and visual warning signal, and sending oncoming traffic risk warning information to vehicles using the roadside edge computing unit.

[0020] Thirdly, embodiments of the present invention provide a computer device, including a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program instructions are executed by the processor, they implement the steps of the single-lane oncoming traffic safety analysis method based on vehicle networking as described in the first aspect of the present invention.

[0021] Fourthly, embodiments of the present invention provide a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program instructions are executed by a processor, they implement the steps of the single-lane oncoming traffic safety analysis method based on vehicle networking as described in the first aspect of the present invention.

[0022] The beneficial effects of this invention are as follows: Vehicle data is collected via a vehicle-to-everything (V2X) road test board, solving the delay and error problems inherent in traditional manual monitoring and achieving real-time and accurate data collection; an improved Kalman filter algorithm is used to classify and predict vehicle data, and an adaptive noise covariance matrix is ​​introduced to improve prediction accuracy; a dynamic minimum safe passing distance threshold calculation model is established by combining multi-dimensional parameters such as vehicle type characteristics, speed, and road conditions, enabling a proactive assessment of passing risks; a dynamic risk assessment mechanism is established by comparing longitudinal distance values ​​with the safety threshold in real time, ensuring the system's timely detection of potential hazards; and clear response plans are provided to drivers through multi-dimensional audio-visual warnings and avoidance strategy guidance. Attached Figure Description

[0023] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. Wherein:

[0024] Figure 1 This is a flowchart of the single-lane oncoming traffic safety analysis method based on vehicle-to-everything (V2X) in Example 1. Detailed Implementation

[0025] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0026] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0027] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0028] Example 1

[0029] Reference Figure 1 This is the first embodiment of the present invention, which provides a single-lane oncoming traffic safety analysis method based on vehicle-to-everything (V2X) communication, including:

[0030] S1: Collect raw data of vehicles traveling in a single lane through the vehicle-to-everything (V2X) roadside test board, and transmit the raw data to the roadside edge computing unit.

[0031] Specifically, the raw data is obtained by scanning vehicles traveling in a single lane using a vehicle-to-everything (V2X) road test board to collect vehicle data.

[0032] It should be noted that vehicle-to-everything (V2X) road test boards are deployed every 50-100 meters along the single lane. These V2X road test boards are installed on one side of the road using poles, at a height of 4-6 meters above the ground. They are equipped with high-precision millimeter-wave radar sensors and high-definition vision sensors, and collect vehicle data through dual-sensor fusion. The signal coverage of each V2X road test board extends 100 meters in front of and behind it, with a 20-30 meter overlap in the signal coverage of adjacent road test boards.

[0033] Furthermore, based on vehicle data, the position coordinates of vehicles traveling in a single lane are obtained according to the principle of laser ranging. At the same time, the speed of vehicles traveling in a single lane is calculated based on the changes in position coordinates at adjacent time points. The direction of travel of vehicles traveling in a single lane is determined by comparing the changing trends of position coordinates at adjacent time points.

[0034] Furthermore, the vehicle-to-everything (V2X) roadside test board uses the location coordinates, driving speed, and driving direction as raw data, and transmits the raw data to the roadside edge computing unit in real time through the V2X communication protocol. The roadside edge computing unit receives raw data transmitted from several V2X roadside test boards; the raw data includes location coordinates, driving speed, and driving direction.

[0035] S2: The roadside edge calculation unit is used to divide the original data into forward-moving vehicle data groups and reverse-moving vehicle data groups according to the driving direction, and a trajectory prediction model is established based on the improved Kalman filter algorithm.

[0036] Specifically, the trajectory prediction model is established by classifying the raw data according to the driving direction. The raw data with the driving direction towards the vehicle network road test board is divided into a forward driving vehicle data group; at the same time, the raw data with the driving direction away from the vehicle network road test board is divided into a reverse driving vehicle data group.

[0037] Furthermore, vehicle motion state matrices are established for the forward-moving vehicle data group and the reverse-moving vehicle data group respectively; an improved Kalman filter algorithm is used to smooth the vehicle motion state matrices and establish a trajectory prediction model.

[0038] It should be noted that the row vectors of the vehicle motion state matrix represent the sequence of sampling time points, and the column vectors represent the vehicle's motion state parameters; the improved Kalman filter algorithm introduces an adaptive noise covariance matrix; the trajectory prediction model predicts the subsequent motion trajectories of vehicles in the forward-moving vehicle data group and the reverse-moving vehicle data group, respectively.

[0039] Furthermore, the specific formula for the trajectory prediction model is as follows:

[0040]

[0041] Where P(t) is the predicted position at time t, and α i Here, β is the adaptive weighting coefficient, and v is the velocity sensitivity parameter. i Let μ be the current speed of the i-th vehicle, μ be the speed baseline value, and n be the number of sampling points. Let x be the position of the i-th vehicle. i The rate of change is the instantaneous velocity, λ is the time decay factor, τ is the integral variable, and R(t) is the residual correction term.

[0042] S3: Based on the trajectory prediction model, predict the driving trajectory and longitudinal distance value of two vehicles traveling in opposite directions, and calculate the minimum safe passing distance threshold between the two vehicles.

[0043] Specifically, the minimum safe passing distance threshold is calculated by pairing vehicles in the forward-moving vehicle data group and the reverse-moving vehicle data group with each other based on the prediction results of the trajectory prediction model and the roadside edge calculation unit.

[0044] It should be noted that if the position coordinates of a vehicle in the forward-moving vehicle data group are within 100 meters ahead of a vehicle in the reverse-moving vehicle data group, this vehicle will be paired for oncoming traffic. If the minimum longitudinal distance between the predicted trajectories of two vehicles within the next 2 seconds is less than 50 meters, these two vehicles will be marked as potential oncoming traffic targets. If the speeds of both vehicles are greater than 30 km / h and their relative speeds are greater than 50 km / h, these two vehicles will be included in the priority monitoring list. If the predicted trajectories of two vehicles indicate that they will meet on the same road segment, these two vehicles will be identified as having an oncoming traffic pairing relationship. If a vehicle meets the pairing conditions with multiple reverse vehicles simultaneously, the vehicle with the smallest longitudinal distance value will be selected as its oncoming traffic pairing target. If the angle between the driving directions of two vehicles is between 150° and 210°, they are considered to meet the angle requirements for oncoming traffic. If both vehicles are on a road curve with a curve radius of less than 200 meters, the priority of their oncoming traffic pairing will be increased.

[0045] Furthermore, based on the pairing results of the oncoming vehicles, the position coordinates and driving speeds of each pair of vehicles traveling in opposite directions are extracted; the position coordinates are projected onto the same coordinate system to calculate the straight-line distance between the center points of the two vehicles, and combined with the road direction, this straight-line distance is projected onto the direction of the road centerline to obtain the longitudinal distance value between the two vehicles.

[0046] Furthermore, based on the longitudinal spacing value, the roadside edge calculation unit calculates the minimum safe passing distance threshold for the two vehicles by considering their vehicle type characteristics, driving speed, road curvature, and road surface conditions. The specific formula is as follows:

[0047]

[0048] Among them, D safe Let v1 be the current speed of the first vehicle, v2 be the current speed of the second vehicle, a1 be the maximum deceleration of the first vehicle, a2 be the maximum deceleration of the second vehicle, γ be the road condition coefficient, θ be the road curvature angle, and R be the minimum safe passing distance threshold. cLet ξ be the road curvature radius, η(ξ) be the vehicle characteristic function, ξ be the vehicle characteristic parameter, k be the sensitivity coefficient, μ be the standard vehicle parameter, and ξ0 be the vehicle baseline value.

[0049] S4: The roadside edge calculation unit determines the longitudinal spacing value and the minimum safe passing distance threshold. When the longitudinal spacing value is less than the minimum safe passing distance threshold, the passing risk coefficient is calculated based on the driving speed, and the passing risk coefficient is sent to the vehicle network road test board.

[0050] Specifically, the method for calculating the meeting risk coefficient is to set the dynamic evaluation cycle of the roadside edge calculation unit and compare the longitudinal spacing value with the minimum safe meeting distance threshold in real time.

[0051] Furthermore, when the longitudinal distance value is detected to be less than the minimum safe passing distance threshold, the driving speeds of the two vehicles traveling in opposite directions are extracted by the roadside edge calculation unit to establish a passing risk calculation model; when the longitudinal distance value is detected to be greater than or equal to the minimum safe passing distance threshold, the longitudinal distance value of the two vehicles continues to be monitored by the roadside edge calculation unit; if a safe distance is maintained for a continuous preset sampling period, the passing risk monitoring state is deactivated.

[0052] Furthermore, if the longitudinal distance value is lower than the minimum safe passing distance threshold for the first time, the corresponding vehicle pair will be marked as being in a warning state; if the longitudinal distance value is lower than 80% of the minimum safe passing distance threshold, the passing risk coefficient will be increased by 0.2; if the longitudinal distance value is lower than the minimum safe passing distance threshold for three consecutive sampling periods, continuous monitoring mode will be activated; if the longitudinal distance value is lower than the minimum safe passing distance threshold and the relative speed between the two vehicles is greater than 60 km / h, the passing risk coefficient will be increased by 0.3; if the longitudinal distance value suddenly decreases rapidly and the rate of change exceeds 2 m / s, an emergency warning mechanism will be triggered immediately; if the longitudinal distance value fluctuates within the range of 80%-120% of the minimum safe passing distance threshold, this vehicle pair will be included in the key monitoring list.

[0053] It should be noted that the oncoming vehicle risk calculation model is a comprehensive assessment model built upon the motion states and environmental parameters of two vehicles traveling in opposite directions. This model uses the relative speed of the two vehicles, the ratio of the longitudinal distance to the safety threshold, and the acceleration trend as the main assessment factors. It also incorporates environmental factors such as road curvature and road surface friction coefficient, as well as vehicle characteristic parameters, as correction factors. After normalization, each assessment factor is weighted and calculated to obtain the final risk coefficient.

[0054] Specifically, the relative speed of the two vehicles, the difference between the longitudinal distance value and the minimum safe passing distance threshold are used as inputs to the passing risk calculation model to calculate the passing risk coefficient; the passing risk coefficient is sent to the vehicle network road test board of the corresponding road segment through the vehicle network communication protocol, and the vehicle network road test board converts the passing risk coefficient into a warning signal after receiving the passing risk coefficient.

[0055] S5: Receive the oncoming traffic risk coefficient through the vehicle network road test board and convert it into an audible and visual warning signal, and send the oncoming traffic risk warning information to the vehicle using the roadside edge computing unit.

[0056] Specifically, based on the preset risk level classification rules, the vehicle risk coefficient is converted into a graded warning signal, which triggers the audible and visual warning device. The audible and visual warning device includes variable information signs and an audible alarm. The variable information signs display warning prompts in different colors and frequencies, and the audible alarm emits warning sounds with different tones and rhythms.

[0057] It should be noted that when the risk factor for oncoming traffic is less than 0.3, the roadside board displays a yellow flashing signal at a frequency of 60 flashes per minute; when the risk factor is between 0.3 and 0.7, it displays a rapid orange flashing signal at a frequency of 120 flashes per minute; and when the risk factor is greater than 0.7, it displays a high-frequency red flashing signal at a frequency of 180 flashes per minute. The display screen for the audible and visual warning signals uses a high-brightness LED display module, with a visibility distance of no less than 200 meters during the day and no less than 300 meters at night.

[0058] Furthermore, based on the oncoming traffic risk coefficient, driving speed, and longitudinal spacing value, the recommended deceleration magnitude is calculated through the roadside edge calculation unit, and an oncoming traffic avoidance strategy is formulated according to the lane width and road surface conditions.

[0059] Furthermore, the deceleration magnitude and oncoming traffic avoidance strategy are packaged into oncoming traffic risk warning information, which is then sent to the on-board terminals of the two vehicles traveling in opposite directions via the vehicle-to-everything (V2X) communication protocol.

[0060] It should be noted that the oncoming traffic risk warning information includes the deceleration range and the oncoming traffic avoidance strategy; after receiving the oncoming traffic risk warning information, the vehicle terminal issues a warning prompt to the driver through the in-vehicle display screen and voice broadcast system.

[0061] Furthermore, this embodiment also provides a single-lane oncoming traffic safety analysis system based on vehicle-to-everything (V2X) technology, comprising: a data acquisition module for acquiring raw data of vehicles traveling in a single lane through a V2X roadside test board and transmitting the raw data to a roadside edge computing unit; a model building module for using the roadside edge computing unit to classify the raw data into forward-moving vehicle data groups and reverse-moving vehicle data groups according to the driving direction, and establishing a trajectory prediction model based on an improved Kalman filter algorithm; a prediction module for predicting the driving trajectory and longitudinal distance value of two vehicles traveling in opposite directions based on the trajectory prediction model, and calculating the minimum safe oncoming traffic distance threshold between the two vehicles; a judgment module for judging the longitudinal distance value and the minimum safe oncoming traffic distance threshold through the roadside edge computing unit, and when the longitudinal distance value is less than the minimum safe oncoming traffic distance threshold, calculating a oncoming traffic risk coefficient based on the driving speed and sending the oncoming traffic risk coefficient to the V2X roadside test board; and an oncoming traffic risk warning module for receiving the oncoming traffic risk coefficient through the V2X roadside test board and converting it into an audible and visual warning signal, and sending oncoming traffic risk warning information to vehicles using the roadside edge computing unit.

[0062] This embodiment also provides a computer device applicable to the single-lane oncoming traffic safety analysis method based on vehicle-to-everything (V2X) communication, including a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement the single-lane oncoming traffic safety analysis method based on V2X communication proposed in the above embodiment.

[0063] The computer device can be a terminal, comprising a processor, memory, communication interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, carrier networks, NFC (Near Field Communication), or other technologies. The display screen can be an LCD screen or an e-ink screen. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad on the computer device's casing, or an external keyboard, touchpad, or mouse.

[0064] This embodiment also provides a storage medium storing a computer program. When executed by a processor, the program performs the following steps: collecting raw data of vehicles traveling in a single lane via a vehicle-to-everything (V2X) roadside testing board and transmitting the raw data to a roadside edge computing unit; using the roadside edge computing unit to divide the raw data into forward-moving vehicle data groups and reverse-moving vehicle data groups according to the driving direction, and establishing a trajectory prediction model based on an improved Kalman filter algorithm; predicting the driving trajectories and longitudinal distance values ​​of two vehicles traveling in opposite directions based on the trajectory prediction model, and calculating the minimum safe passing distance threshold between the two vehicles; judging the longitudinal distance value and the minimum safe passing distance threshold using the roadside edge computing unit, and calculating a passing risk coefficient based on the driving speed when the longitudinal distance value is less than the minimum safe passing distance threshold, and sending the passing risk coefficient to the V2X roadside testing board; receiving the passing risk coefficient through the V2X roadside testing board and converting it into an audible and visual warning signal, and sending passing risk warning information to the vehicles using the roadside edge computing unit.

[0065] In summary, this invention collects vehicle data through a vehicle-to-everything (V2X) road test board, solving the delay and error problems of traditional manual monitoring and achieving real-time and accurate data collection. It classifies and predicts vehicle data using an improved Kalman filter algorithm, introducing an adaptive noise covariance matrix to enhance prediction accuracy. By combining multi-dimensional parameters such as vehicle type characteristics, speed, and road conditions, a dynamic minimum safe passing distance threshold calculation model is established, enabling proactive assessment of passing risks. A dynamic risk assessment mechanism is established by comparing longitudinal distance values ​​with the safe threshold in real time, ensuring timely detection of potential hazards. Finally, multi-dimensional audio-visual warnings and avoidance strategy guidance provide drivers with clear response plans.

[0066] Example 2

[0067] Referring to Table 1, which is the second embodiment of the present invention, this embodiment provides a single-lane oncoming traffic safety analysis method based on vehicle networking. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculation and simulation experiments.

[0068] Specifically, the test was conducted on a section of Provincial Highway S203 from K45+000 to K48+000. This section is a typical mountainous, two-way single-lane road, 3 kilometers long and 6.5 meters wide, with a design speed of 40 kilometers per hour. The route features numerous continuous S-shaped curves, with a minimum radius of 125 meters. The test was conducted under both sunny and rainy conditions, lasting for 7 days. A vehicle-to-everything (V2X) road test board was installed every 75 meters along the test section, for a total of 40 boards, all installed at a uniform height of 4.8 meters. Each road test board is equipped with a 77GHz millimeter-wave radar and an 8-megapixel high-definition camera, employing a dual-sensor data fusion algorithm to achieve precise vehicle positioning with an accuracy better than ±0.1 meters. The road test boards communicate in real-time with the roadside edge computing unit via a 5G network, with a data transmission latency of less than 10 milliseconds. The test vehicles included 10 small passenger cars, 10 medium-sized passenger cars, and 10 large trucks, all equipped with onboard terminal equipment.

[0069] Furthermore, an improved Kalman filter algorithm is used to predict vehicle trajectories, with a prediction time window set to 3 seconds and a sampling frequency of 20Hz. The system employs an adaptive dynamic evaluation mechanism; when a potential oncoming traffic risk is detected, the evaluation cycle is automatically shortened from 1 second to 0.2 seconds. At curves, the system considers road curvature and sight distance factors, appropriately increasing the safety threshold. For different weather conditions, the system automatically adjusts the road surface condition coefficient, setting it to 0.2 for sunny days and 0.4 for rainy days. When an oncoming traffic risk is detected, the system issues a warning via an audible and visual warning device, while simultaneously pushing personalized avoidance suggestions to the vehicle terminal.

[0070] Furthermore, as shown in Table 1, in terms of prediction accuracy, the system's trajectory prediction error for different vehicle types under clear weather conditions is controlled within 0.21 meters, a 58% improvement compared to the 0.5-meter error of traditional prediction methods. In particular, the prediction accuracy for small passenger vehicles reaches 0.15 meters, thanks to the adaptive noise covariance matrix introduced by the improved Kalman filter algorithm. The system response latency is generally maintained within 15 milliseconds, even under rainy conditions, the response latency for large trucks is only 15 milliseconds, far exceeding the industry average of 30 milliseconds. Regarding risk identification rate, the system achieves a maximum identification rate of 95.8% under clear weather conditions, and even under rainy conditions, the identification rate for large trucks remains above 90.6%, significantly higher than the existing system average of 80%. Simultaneously, the false alarm rate is effectively controlled, only 2.1%-2.5% under clear weather conditions, and slightly improved under rainy conditions but still controlled within 3.8%, indicating high system reliability.

[0071] Table 1 Experimental Data

[0072] Test subject ID Vehicle type Weather condition Prediction accuracy Response delay Risk identification rate False alarm rate Safety improvement rate Vehicle-001 Small passenger car Sunny day 0.15m 8ms 95.8％ 2.1％ 89.5％ Vehicle-002 Medium passenger car Sunny day 0.18m 9ms 94.6％ 2.3％ 88.7％ Vehicle-003 Large truck Sunny day 0.21m 11ms 93.9％ 2.5％ 87.9％ Vehicle-004 Small passenger car Rainy day 0.19m 12ms 92.5％ 3.2％ 86.4％ Vehicle-005 Medium passenger car Rainy day 0.22m 13ms 91.8％ 3.5％ 85.8％ Vehicle-006 Large truck Rainy day 0.25m 15ms 90.6％ 3.8％ 84.6％

[0073] Specifically, the safety improvement rate for different vehicle models under various weather conditions all reached over 84%, with small passenger vehicles achieving a safety improvement rate as high as 89.5% in sunny conditions. This fully demonstrates the significant effectiveness of the system in practical applications. The data also shows that system performance fluctuates with changes in vehicle size and weather conditions, but the fluctuation range remains within an acceptable range, reflecting the system's stability and adaptability.

[0074] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A single-lane oncoming traffic safety analysis method based on vehicle-to-everything (V2X) communication, characterized in that: include, Raw data of vehicles traveling in a single lane is collected by the vehicle-to-everything (V2X) roadside test board and transmitted to the roadside edge computing unit. The roadside edge computing unit is used to divide the raw data into forward-moving vehicle data groups and reverse-moving vehicle data groups according to the driving direction, and a trajectory prediction model is established based on the improved Kalman filter algorithm. Based on the trajectory prediction model, the driving trajectories and longitudinal distance values ​​of two vehicles traveling in opposite directions are predicted, and the minimum safe passing distance threshold between the two vehicles is calculated. The roadside edge calculation unit determines the longitudinal spacing value and the minimum safe passing distance threshold. When the longitudinal spacing value is less than the minimum safe passing distance threshold, the passing risk coefficient is calculated based on the driving speed and the passing risk coefficient is sent to the vehicle network road test board. The vehicle network road test board receives the oncoming traffic risk coefficient and converts it into an audible and visual warning signal, and uses the roadside edge computing unit to send oncoming traffic risk warning information to the vehicle. The method for calculating the minimum safe passing distance threshold is as follows: Based on the prediction results of the trajectory prediction model, the roadside edge computing unit performs oncoming vehicle pairing for vehicles in the forward-moving vehicle data group and the reverse-moving vehicle data group. Based on the pairing results of the oncoming vehicles, the position coordinates and driving speeds of each pair of oncoming vehicles are extracted; Based on the position coordinates, project them onto the same coordinate system to calculate the straight-line distance between the center points of the two vehicles. Combined with the road direction, project this straight-line distance onto the road centerline direction to obtain the longitudinal distance between the two vehicles. Based on the longitudinal spacing value, the roadside edge calculation unit calculates the minimum safe passing distance threshold between the two vehicles by considering their vehicle type characteristics, driving speed, road curvature, and road surface conditions. The specific formula is as follows: ； in, The minimum safe passing distance threshold, The current speed of the first vehicle. The current speed of the second vehicle. The maximum deceleration of the first vehicle. This is the maximum deceleration of the second vehicle. For road surface condition coefficient, For the road curvature angle, The radius of curvature of the road. For vehicle model characteristic functions, These are vehicle characteristic parameters. Here, μ represents the sensitivity coefficient, and μ is a parameter of the standard vehicle model. This is the baseline value for the vehicle model.

2. The single-lane oncoming traffic safety analysis method based on vehicle-to-everything (V2X) as described in claim 1, characterized in that: The oncoming traffic risk warning information includes deceleration magnitude and oncoming traffic avoidance strategy; the raw data includes location coordinates, driving speed, and driving direction; the conversion method of the audible and visual warning signal is as follows: According to the preset risk level classification rules, the vehicle risk coefficient is converted into a graded warning signal and the sound and light warning device is triggered, wherein the sound and light warning device includes a variable information sign and an audio alarm. Based on the meeting risk coefficient, the driving speed, and the longitudinal spacing value, the recommended deceleration magnitude is calculated by the roadside edge calculation unit, and a meeting avoidance strategy is formulated according to the lane width and road conditions. The deceleration magnitude and the oncoming traffic avoidance strategy are packaged into oncoming traffic risk warning information. The oncoming traffic risk warning information is sent to the vehicle terminals of two vehicles traveling in opposite directions through the vehicle network communication protocol. After receiving the oncoming traffic risk warning information, the vehicle terminals issue warning prompts to the drivers through the in-vehicle display screen and voice broadcast system.

3. The single-lane oncoming traffic safety analysis method based on vehicle-to-everything (V2X) as described in claim 2, characterized in that: The method for calculating the risk factor of the meeting of vehicles is as follows: The dynamic evaluation cycle of the roadside edge calculation unit is set, and the longitudinal spacing value and the minimum safe passing distance threshold are compared in real time; When the longitudinal spacing value is detected to be less than the minimum safe passing distance threshold, the driving speed of each of the two vehicles traveling in opposite directions is extracted by the roadside edge calculation unit to establish a passing risk calculation model; when the longitudinal spacing value is detected to be greater than or equal to the minimum safe passing distance threshold, the longitudinal spacing value of the two vehicles continues to be monitored by the roadside edge calculation unit. If a safe distance is maintained for a continuous preset sampling period, the vehicle meeting risk monitoring status will be deactivated. The relative speed of the two vehicles, the difference between the longitudinal distance value and the minimum safe passing distance threshold are used as inputs to the passing risk calculation model to calculate the passing risk coefficient. The oncoming traffic risk coefficient is sent to the vehicle network road test board of the corresponding road segment through the vehicle network communication protocol, and the vehicle network road test board converts the oncoming traffic risk coefficient into a warning signal after receiving the oncoming traffic risk coefficient.

4. The single-lane oncoming traffic safety analysis method based on vehicle-to-everything (V2X) as described in claim 1, characterized in that: The method for establishing the trajectory prediction model is as follows: The raw data is classified and processed according to the driving direction, and the raw data with the driving direction towards the vehicle network road test board is divided into the forward driving vehicle data group. At the same time, the original data whose driving direction is opposite to that of the vehicle network road test board are divided into the reverse driving vehicle data group; A vehicle motion state matrix is ​​established for the forward-moving vehicle data group and the reverse-moving vehicle data group respectively. An improved Kalman filter algorithm is used to smooth the vehicle motion state matrix and establish a trajectory prediction model. The improved Kalman filter algorithm introduces an adaptive noise covariance matrix.

5. The single-lane oncoming traffic safety analysis method based on vehicle-to-everything (V2X) as described in claim 4, characterized in that: The specific formula for the trajectory prediction model is as follows: ； in, The predicted position at time t. For adaptive weighting coefficients, For speed sensitivity parameters, Let be the current speed of the i-th vehicle. As the speed reference value, The number of sampling points. The position of the i-th vehicle Rate of change is instantaneous velocity. The time decay factor, For integration variables, This is the residual correction term.

6. The single-lane oncoming traffic safety analysis method based on vehicle-to-everything (V2X) as described in claim 4 or 5, characterized in that: The method for obtaining the original data is as follows: Vehicle data is collected by scanning vehicles traveling in a single lane using a vehicle-to-everything (V2X) roadside testing board. Based on the vehicle data, the position coordinates of the vehicle traveling in the single lane are obtained according to the principle of laser ranging, and the speed of the vehicle traveling in the single lane is calculated based on the changes in the position coordinates at adjacent times. The driving direction of the single-lane vehicle is determined by comparing the changing trend of the position coordinates at adjacent times. The vehicle-to-everything (V2X) roadside test board uses the location coordinates, driving speed, and driving direction as raw data, and transmits the raw data to the roadside edge computing unit via the V2X communication protocol. The roadside edge computing unit receives the raw data transmitted by the V2X roadside test board.

7. A vehicle-to-everything (V2X)-based single-lane oncoming traffic safety analysis system, based on the V2X-based single-lane oncoming traffic safety analysis method according to any one of claims 1 to 6, characterized in that: include, The data acquisition module is used to collect raw data of vehicles traveling in a single lane through the vehicle-to-everything (V2X) roadside test board and transmit the raw data to the roadside edge computing unit. The model building module is used to use the roadside edge computing unit to divide the original data into a forward-driving vehicle data group and a reverse-driving vehicle data group according to the driving direction, and to build a trajectory prediction model based on the improved Kalman filter algorithm. The prediction module, based on the trajectory prediction model, predicts the driving trajectory and longitudinal distance value of two vehicles traveling in opposite directions, and calculates the minimum safe passing distance threshold between the two vehicles. The judgment module is used to judge the longitudinal spacing value and the minimum safe passing distance threshold through the roadside edge calculation unit. When the longitudinal spacing value is less than the minimum safe passing distance threshold, the passing risk coefficient is calculated based on the driving speed and the passing risk coefficient is sent to the vehicle network road test board. The oncoming traffic risk warning module is used to receive the oncoming traffic risk coefficient through the vehicle network road test board and convert it into an audible and visual warning signal, and use the roadside edge computing unit to send oncoming traffic risk warning information to vehicles.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, it implements the steps of the single-lane oncoming traffic safety analysis method based on vehicle networking as described in any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, it implements the steps of the single-lane oncoming traffic safety analysis method based on vehicle networking as described in any one of claims 1 to 6.

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