Pre Crash

Pre-crash analysis focuses on understanding the events leading up to collisions to improve safety systems and predict accident severity. Current research employs diverse methods, including Bayesian networks, machine learning algorithms (like random forests and neural networks), and physics-based simulations to model driver behavior, vehicle trajectories, and environmental factors influencing pre-crash dynamics. These models are used to generate representative crash scenarios for validating Advanced Driver-Assistance Systems (ADAS) and autonomous vehicle safety, and to identify critical pre-crash factors contributing to injury severity, ultimately aiming to reduce accidents and improve road safety.