Integrating Machine Learning for Real-Time Intelligent Collision Avoidance

During foggy seasons, accidents are a major problem and can result in serious injury or even death. To solve this problem, we are developing an intelligent vehicle system that predicts accidents in foggy conditions using machine learning. This system warns drivers in time and helps to avoid potential dangers. By analyzing factors such as vehicle speed, reaction time, line of sight, traffic flow and communication, we train the system to accurately detect and respond to threats. Our goal is to reduce accidents in foggy conditions and improve overall road safety. Traditional methods of detecting accidents on foggy roads are ineffective and increase the risks for drivers and passengers. To address this challenge, we propose a hybrid model that combines vehicle cameras and advanced machine learning techniques. By analyzing camera images and taking into account historical data about weather, traffic and other factors, our model accurately predicts future road conditions. Real experiments have proven the effectiveness of our approach. By adopting this hybrid model, we can significantly reduce accidents caused by poor visibility in foggy conditions, making our roads safer for smart cities