Traffic Environment

Traffic environment research focuses on improving the safety and efficiency of autonomous vehicles and intelligent transportation systems within complex, dynamic settings. Current research emphasizes developing accurate and real-time prediction models for pedestrian and vehicle trajectories, often employing neural networks (like LSTMs and transformers) and incorporating large language models for improved decision-making and path planning. These advancements leverage diverse data sources, including LiDAR, cameras, and V2X communication, to create more robust and adaptable systems, ultimately aiming to enhance safety and efficiency in various traffic scenarios. The resulting improvements in perception, prediction, and control have significant implications for autonomous driving, traffic management, and overall road safety.