The autonomous driving stack divides into two halves: perception and planning. While planning has well-defined outputs — trajectories and control signals — perception must decide how to describe the world.
This talk explores perception as a translator from raw sensor data to semantic information, covering panoptic segmentation, LiDAR vs camera paradigms, and the challenges of defining what the model should output. We also discuss prediction, world models, and why even modern “end-to-end” systems still need intermediate labels. Throughout, we highlight practical insights from production autonomous driving, where the hardest problems are often in problem definition, not model architecture.
University of Illinois at Urbana-Champaign
Mingcheng Chen received his Ph.D. in computer science from the University of Illinois at Urbana-Champaign, where his research focused on computer graphics. After graduation, he joined the self-driving project (Chauffeur) at Google [x], which soon became Waymo. After five and a half years on the Perception Team at Waymo, Mingcheng joined AutoX, which later became Tensor Auto, where he has led Perception at Tensor for over four years. In this talk, Mingcheng will briefly introduce autonomous driving systems and touch on several interesting topics, with a clear emphasis on the perception system.