Traffic Scenario

Traffic scenario research focuses on creating realistic and controllable simulations of complex driving situations to improve the safety and efficiency of autonomous vehicles (AVs). Current research emphasizes developing accurate trajectory prediction models using architectures like graph neural networks and transformers, often incorporating elements of reinforcement learning and game theory to handle multi-agent interactions and unpredictable events. This work is crucial for validating AV systems, particularly in challenging scenarios like dense urban traffic and safety-critical situations, and for advancing traffic management strategies through adaptive signal control and variable speed limits.