Interactive Traffic

Interactive traffic simulation research focuses on accurately modeling complex interactions between autonomous and human-driven vehicles to improve the safety and efficiency of autonomous driving systems. Current efforts leverage machine learning, particularly deep reinforcement learning and graph neural networks, often integrated with large language models for scenario generation and analysis, to predict and plan trajectories in diverse and dynamic traffic situations. This research is crucial for validating autonomous vehicle control systems through rigorous simulation, reducing the need for extensive and potentially risky real-world testing, and ultimately contributing to safer and more efficient transportation systems.