Research On Vehicle Collision Warning Algorithm Controlled Intersection Based On V2X

Enhancing the safety and augmenting the traffic capacity of intersections has emerged as a significant undertaking in the pursuit of developing a smart city.With the rapid advancement of global positioning technology,wireless communication technology,and sensing technology,Vehicle to Everything(V2X)communication technology plays a pivotal role in ensuring traffic safety.In addition to its potential to boost traffic efficiency,vehicle networking communication technology has become one of the foremost means to tackle traffic congestion.By employing vehicle networking communication technology,real-time communication and information sharing can be achieved amongst vehicles,thereby enabling them to perceive and interact with each other while traversing through intersections,thereby mitigating or minimizing the likelihood of traffic accidents.This research paper sets out to examine vehicle collision conflict at intersections regulated by traffic lights,and utilizes the Internet of Vehicles communication technology to conduct the investigation.The primary objective of this research is as follows:(1)A vehicle collision warning algorithm based on the Collision Point Time Calculation(CPTC)is proposed for intersections controlled by traffic lights.This algorithm initially converts the latitude and longitude coordinates of the vehicles into coordinates in the plane coordinate system.Subsequently,it determines the time of arrival of the two vehicles at the collision point by considering the coordinates and heading angles of both vehicles.Furthermore,the algorithm calculates the real-time time difference between the two vehicles and the collision point to determine if there is any potential danger of collision.The efficacy of the CPTC algorithm is assessed by simulating and validating the algorithm using Pre Scan and Matlab/Simulink.The simulation experiment results demonstrate that the CPTC algorithm can accurately estimate the time when the vehicle reaches the collision point,consequently augmenting the safety of vehicles at the intersection.(2)After analyzing and modeling three different scenarios in which vehicles may collide at intersections,a vehicle collision warning algorithm named the Spatiotemporal Position Prediction Warning Algorithm(SPPWA)is proposed,based on the prediction of vehicle spatiotemporal position.SPPWA begins by converting the latitude and longitude coordinates of the vehicle into coordinates in the plane coordinate system.It then utilizes the Kalman filter to denoise the vehicle coordinate.Next,the vehicle turn signal and heading angle parameters are utilized to establish the vehicle data information filtering principle,thus filtering out data that does not meet the collision conditions,and reducing the occupancy of computing resources.Finally,the vehicle is modeled as a rectangle and the indicators of vehicle collision detection are defined.By predicting the space-time position of the two vehicles before they reach the collision point in real-time,the algorithm determines whether the two vehicles will collide.The algorithm is validated by simulating the Pre Scan system combined with Matlab/Simulink,and the impact of using different filters on the accuracy of the algorithm is evaluated.The experimental results demonstrate that the algorithm has a high accuracy rate in predicting vehicle collisions,with an early warning success rate reaching 92%.(3)The virtual simulation test has many advantages such as high safety,low cost and strong repeatability,however,it cannot fully simulate the actual scene,and the test results may have deviations.While actual vehicle tests have strong practical significance,there are issues such as potential safety hazards,high construction costs,long test cycle time,and difficult test conditions to reproduce.To address the advantages and disadvantages of these two testing methods,an algorithm performance test method based on a combination of virtual and real testing is proposed.The CPTC algorithm is selected as the test object,and the sensory data on the real road is collected through a test vehicle equipped with an On-board Unit(OBU).The collected data is then imported into two separate OBUs,and the equipment is started indoors to test the algorithms once again.The test results demonstrate that this test method can significantly improve the efficiency of algorithm testing while ensuring safety,and can further verify the effectiveness of the algorithm in real scenarios.Furthermore,the simulation experiment results are consistent with the OBU equipment verification test results,which proves the practical applicability of the CPTC algorithm.