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.org arxiv.org

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 …

abstract adoption advanced advanced driver assistance advanced driver assistance system arxiv automated capability change computing computing power cs.lg cs.ro data driver driver assistance system driving driving data evolution however learn match personalization personalized power through type

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