Safety Critical Driving Scenario

Safety-critical driving scenarios (SCDs) represent rare but potentially catastrophic events in autonomous driving, demanding robust system responses. Current research focuses on generating diverse and realistic SCD datasets through methods like trajectory optimization and augmentation, often incorporating vision-language models and reinforcement learning to improve both data generation and agent training. These efforts aim to enhance the safety and reliability of autonomous vehicles and advanced driver-assistance systems by improving their ability to handle unexpected or hazardous situations, ultimately contributing to safer roads. The development of novel metrics for assessing SCD criticality and driver intervention behavior further supports this goal.