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arXiv · 2408.10908

Enhancing End-to-End Autonomous Driving Systems Through Synchronized Human Behavior Data

Yiqun Duan·Zhuoli Zhuang·Jinzhao Zhou·Yu-Cheng Chang·Yu-Kai Wang·Chin-Teng Lin
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Abstract

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.

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Yiqun Duan, Zhuoli Zhuang, Jinzhao Zhou, Yu-Cheng Chang, Yu-Kai Wang, Chin-Teng Lin. 2024-08-20. Enhancing End-to-End Autonomous Driving Systems Through Synchronized Human Behavior Data. https://arxiv.org/abs/2408.10908

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