Safety Critical Scenario

Safety-critical scenario generation focuses on creating realistic simulations of rare, hazardous events for testing autonomous systems, particularly in autonomous driving and robotics. Current research emphasizes generating diverse and controllable scenarios using methods like reinforcement learning, game theory, and generative models (e.g., variational autoencoders, neural radiance fields), often incorporating driver behavior modeling and risk assessment. This work is crucial for improving the safety and reliability of autonomous systems by enabling rigorous testing in situations rarely encountered in real-world data, ultimately leading to safer deployment of these technologies.