Research on improving the performance of intelligent driving safety early warning system by using mathematical theory

Vehicle collision is one of the main causes of traffic accidents. To address this problem, we have developed an innovative risk assessment and early warning system. The system uses deep learning technology to process data from multiple sensors, and enables accurate risk assessment and timely warning through an attention-enhanced neural network model. Comprehensive simulations of different traffic scenarios show that the system maintains high accuracy and low false alarm rate, with an average alarm time of 3.0 seconds. It shows strong performance even under challenging conditions such as rain and low visibility. This result provides an effective solution for improving road safety and demonstrates its potential for widespread use in a variety of transport environments.