Application and Optimization of Automobile Collision Early Warning Algorithm Based on Computer Vision and Graph Convolutional Networks

This paper aims to improve road traffic safety through the car collision early warning algorithm based on GCNs (Graph Convolutional Networks). Firstly, this paper expounds the importance of the current automobile collision warning system and the challenges it faces, especially the perception and decision-making problems in complex traffic environment. Aiming at these problems, this paper proposes a novel GCNs model to realize more accurate and efficient collision warning. In terms of methods, this paper introduces the design principle of GCNs and its application in collision warning in detail. By building a simulation experimental platform, a variety of traffic scenes are simulated, and corresponding data sets are prepared for model training and testing. During the experiment, the parameters of the algorithm are carefully tuned to ensure its performance in practical application. In terms of experimental results, this paper makes a comprehensive performance evaluation of the proposed algorithm. Compared with the existing methods, the superiority of GCNs in collision early warning task is verified. The specific results show that the algorithm has achieved significant improvement in key indicators such as accuracy, real-time performance and stability.