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1st ICML 2022 Workshop on Safe Learning for Autonomous Driving (SL4AD)
Jonathan Francis · Bingqing Chen · Hitesh Arora · Xinshuo Weng · Siddha Ganju · Daniel Omeiza · Jean Oh · Erran Li · Sylvia Herbert · Eric Nyberg · Eric Nyberg

Fri Jul 22 05:50 AM -- 03:15 PM (PDT) @ Room 301 - 303
Event URL: https://learn-to-race.org/workshop-sl4ad-icml2022/ »

We propose the 1st ICML Workshop on Safe Learning for Autonomous Driving (SL4AD), as a venue for researchers in artificial intelligence to discuss research problems on autonomous driving, with a specific focus on safe learning. While there have been significant advances in vehicle autonomy (e.g., perception, trajectory forecasting, planning and control, etc.), it is of paramount importance for autonomous systems to adhere to safety specifications, as any safety infraction in urban and highway driving, or high-speed racing, could lead to catastrophic failures. We envision the workshop to bring together regulators, researchers, and industry practitioners from different AI subfields, to work towards safer and more robust autonomous technology. This workshop aims to: (i) highlight open questions about safety issues, when autonomous agents must operate in uncertain and dynamically-complex real-world environments; (ii) bring together researchers and industrial practitioners in autonomous driving with control theoreticians in safety analysis, dependability, and verification; (iii) provide a strong AI benchmark, where the joint evaluation of safety, performance, and generalisation capabilities of AD perception and control algorithms is systematically performed; (iv) provide a forum for discussion among researchers, industrial practitioners, and regulators on the core challenges, promising solution strategies, fundamental limitations, and regulatory realities involved in deploying safety-critical autonomous systems; (v) define new algorithms that handle increasingly complex real-world scenarios---where vehicles must: drive at their physical limits, where any infraction could lead to catastrophic failure, make sub-second decisions in fast-changing environments, and remain robust to distribution shifts, novel road features, and other obstacles, to facilitate cross-domain generalisation.

Author Information

Jonathan Francis (Bosch Research Pittsburgh; Carnegie Mellon University)
Bingqing Chen (Carnegie Mellon University)
Hitesh Arora (Amazon)
Xinshuo Weng (Carnegie Mellon University)

Xinshuo Weng is a Ph.D. student (2018-) at Robotics Institute of Carnegie Mellon University (CMU) advised by Kris Kitani. She received a master's degree (2016-17) at CMU, where she worked with Yaser Sheikh and Kris Kitani. Prior to CMU, she worked at Facebook Reality Lab as a research engineer to help build “Photorealistic Telepresence”. Her bachelor was received from Wuhan University. Her research interest lies in 3D computer vision and Graph Neural Networks for autonomous systems. She has developed 3D multi-object tracking systems such as AB3DMOT that received >1,000 stars on GitHub. Also, she is leading a few autonomous driving workshops at major conferences such as NeurIPS 2020, IJCAI 2021, ICCV 2021, and IROS 2021. She was awarded a Qualcomm Innovation Fellowship for 2020 and a Facebook Fellowship Finalist for 2021.

Siddha Ganju (Nvidia)
Daniel Omeiza (University of Oxford)
Jean Oh (Carnegie Mellon University)
Erran Li (Amazon.com)
Sylvia Herbert (UC San Diego)
Eric Nyberg (CMU)
Eric Nyberg (CMU)

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