Complex Driving

Complex driving scenarios, characterized by dense traffic, adverse weather, and unpredictable human behavior, pose significant challenges for autonomous vehicle development. Current research focuses on improving perception and decision-making through advanced models like vision-language models (VLMs), incorporating risk assessment and driving priors into reinforcement learning frameworks, and developing efficient data processing techniques such as coreset selection to handle large datasets. These advancements aim to enhance the safety and reliability of autonomous vehicles by enabling them to navigate and react appropriately in complex and dynamic environments, ultimately contributing to safer and more efficient transportation systems.

Papers