Invited Talk: Learning Affordance for Direct Perception in Autonomous Driving (JiaoXiong Xiao, AutoX)

Abstract: Today, there are two major paradigms for vision-based autonomous driving systems: mediated perception approaches that parse an entire scene to make a driving decision, and behavior reflex approaches that directly map an input image to a driving action by a regressor. In this paper, we propose a third paradigm: a direct perception based approach to estimate the affordance for driving. We propose to map an input image to a small number of key perception indicators that directly relate to the affordance of a road/traffic state for driving. Our representation provides a set of compact yet complete descriptions of the scene to enable a simple controller to drive autonomously. Falling in between the two extremes of mediated perception and behavior reflex, we argue that our direct perception representation provides the right level of abstraction. We evaluate our approach in a virtual racing game as well as real world driving and show that our model can work well to drive a car in a very diverse set of virtual and realistic environments.

Bio: Jianxiong Xiao (a.k.a., Professor X) is the Founder and CEO of AutoX, Inc., a high-tech startup currently in stealth mode. Previously, he was an Assistant Professor in the Department of Computer Science at Princeton University and the founding director of the Princeton Computer Vision and Robotics Labs from 2013 to 2016. He received his Ph.D. from the Computer Science and Artificial Intelligence Laboratory (CSAIL) at the Massachusetts Institute of Technology (MIT) in 2013. Before that, he received a BEng. and MPhil. in Computer Science from the Hong Kong University of Science and Technology in 2009. His research focuses on bridging the gap between computer vision and robotics by building extremely robust and dependable computer vision systems for robot perception. In particular, he is a pioneer in the fields of 3D Deep Learning, Autonomous Driving, RGB-D Recognition and Mapping, Big Data, Large-scale Crowdsourcing, and Deep Learning for Robotics. His work has received the Best Student Paper Award at the European Conference on Computer Vision (ECCV) in 2012 and the Google Research Best Papers Award for 2012, and has appeared in the popular press. Jianxiong was awarded the Google U.S./Canada Fellowship in Computer Vision in 2012, the MIT CSW Best Research Award in 2011, and two Google Faculty Awards in 2014 and in 2015 respectively. He co-lead the MIT+Princeton joint team to participate in the Amazon Picking Challenge in 2016, and won the 3rd and 4th place worldwide. More information can be found at: http://www.jianxiongxiao.com.