Driving Video Retrieval for Complex Queries with Structured Grounding
Researchers have developed STRIVE-D, a new framework designed to improve video retrieval for complex queries in autonomous driving scenarios. This system addresses limitations of existing methods by incorporating data calibration to adapt rule-based retrieval and fuse it with vision-language and keyword signals. STRIVE-D has demonstrated significant improvements, achieving up to an 84% relative increase in top-1 accuracy on driving benchmarks, including new event data from DrivingDojo. AI
IMPACT Enhances autonomous driving safety validation and data curation by improving the ability to retrieve specific driving events.