Year: 2025
Type: preprint
Abstract: The intelligent driving cockpit, an important part of intelligent driving, needs to match different users' comfort, interaction, and safety needs. This paper aims to build a Super-Aligned and GEneralist DRiving agent, SAGE DeeR. Sage Deer achieves three highlights: (1) Super alignment: It achieves different reactions according to different people's preferences and biases. (2) Generalist: It can understand the multi-view and multi-mode inputs to reason the user's physiological indicators, facial emotions, hand movements, body movements, driving scenarios, and behavioral decisions. (3) Self-Eliciting: It can elicit implicit thought chains in the language space to further increase generalist and super-aligned abilities. Besides, we collected multiple data sets and built a large-scale benchmark. This benchmark measures the deer's perceptual decision-making ability and the super alignment's accuracy.
Source: arXiv (Cornell University)
Authors: Lu, Hao, Tang, Jiaqi, Wang, Jiyao, LU, Yunfan, Cao, Xu, Hu, Qingyong, Wang, Yin, Zhang, Yuting, Xie, Tianxin, Zhang, Yunpeng, Chen, Yong, Gao, Jiayu., Huang, Bin, He, Dengbo, Deng, Shuiguang, Chen, Hao, Chen, Ying-Cong
Language: English
Subfield: Social Psychology
Field: Psychology
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W4415199813
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