Enhancing End-to-End Autonomous Driving Systems Through Synchronized Human Behavior Data
<|reference_start|>Enhancing End-to-End Autonomous Driving Systems Through Synchronized Human Behavior Data: This paper presents a pioneering exploration into the integration of fine-grained human supervision within the autonomous driving domain to enhance system performance. The current advances in End-to-End autonomous driving normally are data-driven and rely on given expert trials. However, this reliance limits the systems' generalizability and their ability to earn human trust. Addressing this gap, our research introduces a novel approach by synchronously collecting data from human and machine drivers under identical driving scenarios, focusing on eye-tracking and brainwave data to guide machine perception and decision-making processes. This paper utilizes the Carla simulation to evaluate the impact brought by human behavior guidance. Experimental results show that using human attention to guide machine attention could bring a significant improvement in driving performance. However, guidance by human intention still remains a challenge. This paper pioneers a promising direction and potential for utilizing human behavior guidance to enhance autonomous systems.<|reference_end|>
@article{duan2024enhancing,
    title={Enhancing End-to-End Autonomous Driving Systems Through Synchronized
  Human Behavior Data},
    author={Yiqun Duan, Zhuoli Zhuang, Jinzhao Zhou, Yu-Cheng Chang, Yu-Kai Wang,
  Chin-Teng Lin},
    journal={arXiv preprint arXiv:2408.10908},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.10908},
    primaryClass={cs.RO cs.HC}
}