AI-Based Models for Predicting and Preventing Car Accidents in Self-Driving Cars

This study aims to explore the potential of autonomous vehicles to reduce accidents by applying artificial intelligence models. Through machine learning and artificial intelligence, vehicles can detect potential risks and make decisions to prevent accidents. Specifically, this study focuses on three types of artificial intelligence models—VGGNet, ResNet, and CNN, and evaluates their effectiveness in predicting car accidents based on visual data. These models process input images from the car's vision system to predict the likelihood of an accident, which helps the driver take necessary measures in time to avoid the accident. The findings of this study could provide valuable insights into the integration of AI-driven systems in autonomous vehicles, ultimately contributing to safer driving environments and reducing the number of traffic-related accidents. Through the predictive capabilities of artificial intelligence models, road accidents can be effectively reduced and overall traffic safety can be improved in the future.