Neuro-Cognitive Reward Modeling for Human-Centered Autonomous Vehicle Control
Recent advancements in computer vision have accelerated the development of autonomous driving. Despite these advancements, training machines to drive in a way that aligns with human expectations remains a significant challenge. Human factors are still essential, as humans possess a sophisticated cognitive system capable of rapidly interpreting scene information and making accurate decisions. Aligning machine with human intent has been explored with Reinforcement Learning with Human Feedback (RLHF). Conventional RLHF methods rely on collecting human preference data by manually ranking generated outputs, which is time-consuming and indirect. In this work, we propose an electroencephalograph...
| Authors | Zhuoli Zhuang, Yu-Cheng Chang, Yu-Kai Wang et al. |
|---|---|
| Primary category | cs.CV |
| Published | 26 March 2026 |
| Last updated at source | 31 March 2026 |
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