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Learn-to-Race Challenge 2022: Benchmarking Safe Learning and Cross-domain Generalisation in Autonomous Racing. (arXiv:2205.02953v1 [cs.RO])
May 9, 2022, 1:11 a.m. | Jonathan Francis, Bingqing Chen, Siddha Ganju, Sidharth Kathpal, Jyotish Poonganam, Ayush Shivani, Sahika Genc, Ivan Zhukov, Max Kumskoy, Anirudh Koul
cs.LG updates on arXiv.org arxiv.org
We present the results of our autonomous racing virtual challenge, based on
the newly-released Learn-to-Race (L2R) simulation framework, which seeks to
encourage interdisciplinary research in autonomous driving and to help advance
the state of the art on a realistic benchmark. Analogous to racing being used
to test cutting-edge vehicles, we envision autonomous racing to serve as a
particularly challenging proving ground for autonomous agents as: (i) they need
to make sub-second, safety-critical decisions in a complex, fast-changing
environment; and (ii) …
arxiv autonomous benchmarking challenge learning race racing
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