The vehicle collision warning on urban road based on internet of vehicles data

It is of great significance to provide early warning for vehicle collisions on urban roads. In order to overcome the high missing rate and low warning accuracy and poor efficiency of traditional early warning methods, an urban road vehicle collision early warning method based on Internet of Vehicles data was proposed. The extended Kalman filter is used to estimate the vehicle attitude on urban roads, and the vehicle longitudinal safety distance model is established. Based on the Internet of Vehicles, the position, speed, acceleration and heading information of follow-up vehicles on urban roads and vehicles in front of urban roads are obtained, and the dispersion of vehicle spacing changes is obtained. The dispersion is used as the judgment threshold of urban road vehicle collision warning to realize collision warning. The experimental results show that the average false alarm rate is 1.2%, the maximum false alarm rate is only 1%, and the alarm time is only 19.9s.