Scalable Unified Perception for Autonomous Vehicles: Enhancing Efficiency and Cross-Platform Adaptability
Date: March 14, 2025 @ 3:00-4:00PM | Location: Gates B03 | Speaker: Zhuwen Li | Affiliation: Nuro
Abstract:
In the rapidly advancing realm of autonomous driving, developing and deploying efficient and scalable perception models across vehicle platforms is crucial. This presentation introduces a Unified Perception Model crafted to manage the diverse array of perception tasks essential for autonomous vehicles, such as object detection and occupancy estimation, within a single, integrated framework. While there are many topics around the Unified Perception Model, we will focus on the efficiency and scalability of the model. In particular, you will find answers to the following two questions: 1) How can we leverage temporal information without sacrificing training efficiency and model capacity? 2) How to do large scale cross-platform pretraining and cross-platform deployment? Our unified model aims to streamline operations, reduce complexity, and enhance adaptability across diverse autonomous driving platforms, marking a significant advance in the development of autonomous vehicle technology.
Bio:
Dr. Zhuwen LI is the Tech Lead Manager leading the detection, tracking and geometry teams at Nuro. His research interests include a broad range of Perception problems in autonomous driving and he aims to address them using a machine learning focused approach. More specifically, his major focus is on end-to-end autonomous driving, open set perception, unified perception, 3D object detection, tracking, occupancy, scene flow, and etc. Zhuwen Li received the BE degree in computer science from Tianjin University, in 2008, the master's degree in computer science from Zhejiang University, in 2011, and the PhD degree from the Department of Electrical and Computer Engineering, National University of Singapore, in 2014.