报告题目：Towards Realistic and Scalable Simulation for Autonomous Driving
报告时间：2021年6月10号 （星期四） 上午10:00-11:30
报告地点：腾讯会议 540 151 044
邀 请 人：谢 晋
报告摘要： Self-driving vehicles could make ground transportation safer, cleaner, and more scalable. Achieving this requires earning public trust by showing self-driving vehicles have demonstrably safe behavior. Computer simulations are a safe and scalable tool for validating and training self-driving autonomy. However, there is a significant realism gap between the simulated environment and the real world – a demonstration of safety in a simulation might not be reliable. In this talk, I will present our recent work on realistic simulation for autonomous driving. I will first demonstrate how we integrate real-world assets, learnable components, and physical models to simulate realistic sensor input, such as LiDARs and cameras. I will then discuss a novel deep generative model to generate high-fidelity and diverse traffic scenarios. Finally, I will give a brief personal outlook on open research topics on building simulation environments for self-driving.
报告专家简介：Shenlong Wang will be joining the UIUC Department of Computer Science as an Assistant Professor in Fall 2021. He is currently visiting Intel Intelligent Systems Lab, working with Dr. Vladlen Koltun. He had been a research scientist at Uber ATG and a Ph.D. student at the University of Toronto under the supervision of Prof. 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. His co-authored work received IROS Best Application Award Finalist in 2020. He was selected as the recipient of the Facebook, Adobe, and Royal Bank of Canada Fellowships in 2017.