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pro vyhledávání: '"Notz, Dominik"'
Autor:
Bronstein, Eli, Palatucci, Mark, Notz, Dominik, White, Brandyn, Kuefler, Alex, Lu, Yiren, Paul, Supratik, Nikdel, Payam, Mougin, Paul, Chen, Hongge, Fu, Justin, Abrams, Austin, Shah, Punit, Racah, Evan, Frenkel, Benjamin, Whiteson, Shimon, Anguelov, Dragomir
Publikováno v:
IEEE/RSJ international conference on intelligent robots and systems (IROS) 2022, pages 8652-8659
We demonstrate the first large-scale application of model-based generative adversarial imitation learning (MGAIL) to the task of dense urban self-driving. We augment standard MGAIL using a hierarchical model to enable generalization to arbitrary goal
Externí odkaz:
http://arxiv.org/abs/2210.09539
Collecting realistic driving trajectories is crucial for training machine learning models that imitate human driving behavior. Most of today's autonomous driving datasets contain only a few trajectories per location and are recorded with test vehicle
Externí odkaz:
http://arxiv.org/abs/2004.01288