Collision Risk

Collision risk assessment is a crucial area of research aiming to improve safety in various domains, from autonomous vehicles and robotics to air and space traffic management. Current research focuses on developing robust and generalizable methods for predicting collision probabilities, often employing machine learning techniques like Bayesian methods, neural networks (including convolutional and recurrent architectures), and Markov decision processes, alongside more traditional approaches like time-to-collision calculations. These advancements are vital for enhancing safety systems in autonomous vehicles, improving traffic flow, and mitigating risks in shared spaces, ultimately leading to safer and more efficient operations across multiple sectors.