Hello all, We are happy to announce the *1st Workshop on Safe Learning for Autonomous Driving* (SL4AD), co-located with the International Conference on Machine Learning (ICML 2022), to be held in Baltimore and online.
*Workshop website:* https://learn-to-race.org/workshop-sl4ad-icml2022 All papers related to autonomous driving and safe learning are welcome (4-page extended abstracts or 8-page full papers; page count does not include references or appendices). As the goal is to aggregate all efforts in relevant areas, dual submission is allowed: feel free to submit work-in-progress, work under review, latest results, or work already accepted/published elsewhere. *Start a submission:* https://cmt3.research.microsoft.com/SL4AD2022 We also feature Learn-to-Race <https://learn-to-race.org/>, an exciting and *new AI Challenge* in high-speed autonomous racing. Here, the goal is to evaluate the joint safety, performance, and generalization capabilities of perception and control algorithms, as they operate simulated *Formula*-style racing vehicles at their physical limits! Prize information and more, here: https://www.aicrowd.com/challenges/learn-to-race-autonomous-racing-virtual-challenge *Important dates* (all deadlines are in Eastern Daylight Time (EDT), UTC -4, New York): - Paper submissions due: 20 May 2022 - Author notification: 6 June 2022 - Workshop: 22 July 2022 (tentative) Everyone is welcome to attend, in-person and/or online. If you are interested, you can subscribe to our *mailing list* for updates, here: https://lnkd.in/eBHUfFn *Organizers:* - Jonathan Francis; CMU + Bosch Research - Hitesh Arora; Amazon - Bingqing Chen; CMU + Bosch Research - Xinshuo Weng; CMU + NVIDIA Research - Siddha Ganju; NVIDIA - Daniel Omeiza; Oxford - Jean Oh; CMU - Eric Nyberg; CMU - Sylvia L. Herbert; UCSD - Li Erran Li; Amazon
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