Assistant Driving Safety Early Warning System Based on Internet of Vehicles

Abstract

Due to the complexity of the domestic road environment, the real-time detection and anti-collision warning of various categories of objects on the road are the key points to be solved. The safety assistant driving system based on machine vision is a good solution to avoid danger. Through the use of deep learning algorithm, it can detect the driving, pedestrians, non-motor vehicles and other real-time and anti-collision warning in the road environment. At the same time, it can detect whether drivers have bad driving behaviors such as fatigue driving, driving distraction and timely warning, so as to ensure the safety of the driving environment. This paper mainly studies the auxiliary driving safety early warning system based on the Internet of vehicles. According to the deep learning algorithm, this paper studies the target detection algorithm and collision early warning, analyzes and compares the principles of the related algorithms in machine vision ranging, and completes the camera calibration and the camera parameter acquisition required by visual distance measurement, through stereo matching and depth map mapping, target recognition and range detection are completed. In order to study the accuracy of the relative distance detection estimation algorithm of the camera, this paper selects 6 sample pictures in the special data training center of the road, and uses the distance detection and estimation algorithm to carry out the distance detection experiment, and calculates the distance of the detected object. The experimental results show that the accuracy of distance detection decreases with the increase of distance, the relationship between them is negative.