VR Technology and the Theoretical Model and Method of Road Network Traffic State Information Accident Prediction and Perception under the Environment of Internet of Vehicles

With the rapid increase in the number of private cars and buses in the country, the living standard has gradually improved, and vehicles as a means of transportation commonly used by people have caused more traffic accidents. The present study mainly discusses the theoretical models and methods of VR technology and road network traffic status information accident prediction perception on the Internet of Vehicles (IoV) environment. Use of VR technology to simulate the experimental environment. The main techniques of VR real-time rendering are the visualization and roaming of large-area spatial data and the simulation of things that currently exist in computer vision simulation methods. Use VR technology to simulate ordinary highways, and use VR technology to simulate six sets of virtual cars. Use the real-time positioning function and data transmission function of the IoV technology to systematically collect driving data in the VR environment and advance collisions in advance to perceive and control the speed of the cars. Experimental data shows that when the time level is 2, the brake response sensitivity in the data is 88.9%, and the brake sensitivity value is relatively high. When the distance level is 1, the car safety factor value is 97.5%, and the data value is the highest. The research results show that when the time level is 2, the brake sensitivity value is relatively high, indicating that the VR technology and the traffic network information environment accident prediction effect on the Internet of Vehicles environment are better. When the distance level is 1, the performance of road network traffic status information accident perception based on VR technology and IoV environment is the best. Through the real-time sharing of the image recognition performance of VR technology and the geographic location of the Internet of Vehicles technology, the real-time prediction and perception of road network traffic status information accidents can minimize the chances of accidents.