Vehicle Behavior

Research on vehicle behavior focuses on understanding and predicting vehicle actions for improved safety and efficiency in transportation systems. Current efforts concentrate on developing accurate models, employing techniques like Bayesian networks for scenario generation, supervised imitation learning for realistic traffic simulation, and neural networks (including decision trees and capsule networks) for trajectory prediction and anomaly detection. These advancements are crucial for enhancing autonomous driving systems, optimizing traffic flow, and improving road safety through better understanding of driver behavior and vehicle interactions.