Yanqiang Li

METDrive: Multimodal End-to-End Autonomous Driving with Temporal Guidance

Multimodal end-to-end autonomous driving has shown promising advancements in recent work. By embedding more modalities into end-to-end networks, the system's understanding of both static and dynamic aspects of the driving environment is enhanced, thereby improving the safety of autonomous driving. I