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Driving SMARTS
Amir Rasouli · Matthew Taylor · Iuliia Kotseruba · Tianpei Yang · Randolph Goebel · Soheil Mohamad Alizadeh Shabestary · Montgomery Alban · Florian Shkurti · Liam Paull

Tue Dec 06 05:00 AM -- 07:00 AM (PST) @ Virtual
Event URL: https://smarts-project.github.io/ »

Driving SMARTS is a regular competition designed to tackle problems caused by the distribution shift in dynamic interaction contexts that are prevalent in real-world autonomous driving (AD). The proposed competition supports methodologically diverse solutions, such as reinforcement learning (RL) and offline learning methods, trained on a combination of naturalistic AD data and open-source simulation platform SMARTS. The two-track structure allows focusing on different aspects of the distribution shift. Track 1 is open to any method and will give ML researchers with different backgrounds an opportunity to solve a real-world autonomous driving challenge. Track 2 is designed for strictly offline learning methods. Therefore, direct comparisons can be made between different methods with the aim to identify new promising research directions. The proposed setup consists of 1) realistic traffic generated using real-world data and micro simulators to ensure fidelity of the scenarios, 2) framework accommodating diverse methods for solving the problem, and 3) a baseline method. As such it provides a unique opportunity for the principled investigation into various aspects of autonomous vehicle deployment.

Author Information

Amir Rasouli (Huawei)

I have received my Ph.D. in computer science from York University, Canada. My research areas are computer vision and robotics, in particular, autonomous driving systems with a focus on pedestrian behavior understanding and prediction.

Matthew Taylor (U. of Alberta)
Iuliia Kotseruba (York University)
Iuliia Kotseruba

I am a PhD student supervised by Prof. John K. Tsotsos and member of the Lab for Active and Attentive Vision at York University. I received my BSc degree in Artificial Intelligence from University of Toronto and MSc degree in Computer Science from York University. I study human visual attention with the goal of integrating attention and vision with other cognitive abilities in AI systems. My current research topics are understanding driver-pedestrian interaction and predicting driver attention for designing assistive and autonomous driving technology.

Tianpei Yang (University of Alberta)
Randolph Goebel (University of Alberta, Alberta Machine Intelligence Institute)

R.G. (Randy) Goebel is currently professor and chair in the Department of Computing Science at the University of Alberta He received the B.Sc. (Computer Science), M.Sc. (Computing Science), and Ph.D. (Computer Science) from the Universities of Regina, Alberta, and British Columbia, respectively. Professor Goebel's research is focused on the theory and application of intelligent systems. His theoretical work on abduction, hypothetical reasoning and belief revision is internationally well know, and his recent application of practical belief revision and constraint programming to scheduling, layout, and web mining is now having industrial impact. He is one of the founders of the Alberta Ingenuity Centre for Machine Learning (AICML), and is now working on applications of machine learning to various problems, including web visualization and scheduling. Randy has previously held faculty appointments at the University of Waterloo and the University of Tokyo, and is actively involved in academic and industrial collaborative research projects in Canada, Australia, Malaysia, Europe and Japan.

Soheil Mohamad Alizadeh Shabestary (Huawei Technologies Canada)
Montgomery Alban (Huawei)
Florian Shkurti (University of Toronto)
Liam Paull (University of Montreal)

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