May 14, 2024, 4:42 a.m. | Jia Hu (Jason), Mingyue Lei (Jason), Duo Li (Jason), Zhenning Li (Jason), Jaehyun (Jason), So, Haoran Wang

cs.LG updates on

arXiv:2405.07543v1 Announce Type: new
Abstract: Personalization is crucial for the widespread adoption of advanced driver assistance system. To match up with each user's preference, the online evolution capability is a must. However, conventional evolution methods learn from naturalistic driving data, which requires a lot computing power and cannot be applied online. To address this challenge, this paper proposes a lesson learning approach: learning from driver's takeover interventions. By leveraging online takeover data, the driving zone is generated to ensure perceived …

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