1. Enhancing End-to-End Autonomous Driving Systems Through Synchronized Human Behavior Data
- Author
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Duan, Yiqun, Zhuang, Zhuoli, Zhou, Jinzhao, Chang, Yu-Cheng, Wang, Yu-Kai, and Lin, Chin-Teng
- Subjects
Computer Science - Robotics ,Computer Science - Human-Computer Interaction - 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.
- Published
- 2024