Biography

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Prof. Steven Waslander is a leading authority on autonomous robotics, including self-driving cars and multirotor drones.  He received his B.Sc.E.in 1998 from Queen’s University, his M.S. in 2002 and his Ph.D. in 2007, both from Stanford University in Aeronautics and Astronautics, where as a graduate student he created the Stanford Testbed of Autonomous Rotorcraft for Multi-Agent Control (STARMAC), the world’s most capable multi-vehicle quadrotor platform at the time.  He was recruited to the University of Waterloo from Stanford in 2008, where he founded and directed the Waterloo Autonomous Vehicle Laboratory (WAVELab), extending the state of the art in autonomous robotics through advances in localization and mapping, object detection and tracking, integrated planning and control methods and multi-robot coordination. In 2018, he joined the University of Toronto Institute for Aerospace Studies (UTIAS), and founded the Toronto Robotics and Artificial Intelligence Laboratory (TRAILab).

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Prof. Waslander’s innovations were recognized by the Ontario Centres of Excellence Mind to Market award for the best Industry/Academia collaboration (2012, with Aeryon Labs), best robotics paper, best computer vision paper and best robotics poster awards at the Conference on Robotics and  Vision (2018, 2021, 2018 respectively).  His work on autonomous vehicles has resulted in the Autonomoose, the first autonomous vehicle created at a Canadian University to drive on public roads. His insights into autonomous driving have been featured in the Globe and Mail, Toronto Star, National Post, the Rick Mercer Report, and on national CBC Radio.  He served as the Associate Editor of the IEEE Transactions on Aerospace and Electronic Systems, as the General Chair for the International Autonomous Robot Racing Competition (IARRC 2012-15), as the program chair for the 13th and 14th Conference on Computer and Robot Vision (CRV 2016-17),  as the Competitions Chair for the International Conference on Intelligent Robots and Systems (IROS 2017) and as the General Chair for the Conference on Robots and Vision in 2022.

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Research Interests

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Prof. Waslander's current research focuses on open world perception for autonomous robotics, which involves numerous challenging tasks, such as the identification, localization, tracking and prediction of static and dynamic objects in the environment, the construction of multi-faceted maps for route planning, local path planning and obstacle avoidance, and the localization and state estimation of ego motion.  Our research in these area involves primarily  deep learning approaches, and is seeking new and efficient ways of extracting uncertainty estimates from deep networks to improve sensor fusion and provide a holistic, human-interpretable perceptual representation in real-time on in-vehicle hardware.  These efforts are aided by data collection and public road driving evaluations on the Autonomoose testbed, which resulted in the Canadian Adverse Driving Conditions Dataset (CADC).  This work revealed the myriad challenges with adapting perception methods to adverse conditions and new environments, a major bottleneck in the expansion of self-driving fleets today.  The team’s emphasis is on developing domain generalization and adaptation techniques, including the use and distillation of multi-modal foundation models, that significantly improve perception performance across a wide range of conditions and scenarios.