Abstract: Self-driving vehicles will bring us safer, cleaner, and more convenient transportation. To make this dream come true, we need our autonomous system to perceive, plan, and execute effectively in unstructured environments and have verified safety. While machine learning has significantly enhanced autonomous capabilities, we are still missing key ingredients to achieve the desired goal.

In this talk, I will present our approach towards trustworthy autonomous driving from two perspectives: designing robust autonomy systems and verifying their safety with realistic simulation. I will first present how we combine learning methods with model-based knowledge to develop a robust, end-to-end learnable self-driving autonomy stack. I will then demonstrate how we integrate real-world assets, learnable components, and physical models to build a highly realistic and scalable simulation environment to train and validate autonomy. Finally, I will give a brief personal outlook on open research topics towards realistically solving self-driving.

Bio: Shenlong Wang is an Assistant Professor at the UIUC Department of Computer Science. He received his Ph.D. degree from the University of Toronto and was a research scientist at Uber ATG, working with Raquel Urtasun. Shenlong's research interests span the spectrum from computer vision, robotics, and machine learning. His recent work involves developing robust algorithms for self-driving and making autonomous vehicles more reliable and scalable. His research has resulted in over 40 papers at top conferences, including over 15 oral and spotlight presentations. Shenlong's co-authored work received the 2020 IROS Best Application Award Finalist and the 2021 CVPR Best Paper Candidate. He was selected as the recipient of the Facebook, Adobe, and Royal Bank of Canada Fellowships.