Current vision-language models struggle with safety-critical reasoning in autonomous driving; this benchmark provides a standardized way to measure whether they can understand incident context and predict avoidability.
AUTOPILOT-VQA is a benchmark dataset for evaluating vision-language models on safety-critical dashcam understanding. It uses structured questions about real-world driving incidents to test whether AI systems can reliably reason about weather, traffic, road conditions, and accident scenarios—moving beyond simple object recognition to temporally grounded, safety-aware reasoning.