Vehicle Interaction

Vehicle interaction research focuses on understanding and modeling how vehicles, drivers, and passengers interact in various driving scenarios to improve safety and efficiency. Current research emphasizes developing accurate perception models (often using convolutional neural networks and transformers) for object detection and driver behavior recognition, as well as robust decision-making algorithms (including game theory and reinforcement learning) for autonomous vehicles navigating complex interactions. These advancements are crucial for enhancing the safety and reliability of autonomous driving systems and improving human-machine interfaces within vehicles, ultimately impacting the design and deployment of safer and more efficient transportation systems.