Corner Case

Corner cases in autonomous driving refer to rare, unpredictable, and potentially hazardous situations that challenge the robustness of AI-based systems. Current research focuses on detecting these cases using diverse methods, including integrating end-to-end and modular driving approaches, leveraging vision-language models for anomaly detection, and employing uncertainty-based criteria from instance segmentation models. This work is crucial for improving the safety and reliability of autonomous vehicles by identifying weaknesses in perception and decision-making systems and generating synthetic data to enhance model training and robustness.