Enhancing End-to-End Autonomous Driving Systems Through Synchronized Human Behavior Data
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.
Cite
@article{arxiv.2408.10908,
title = {Enhancing End-to-End Autonomous Driving Systems Through Synchronized Human Behavior Data},
author = {Yiqun Duan and Zhuoli Zhuang and Jinzhao Zhou and Yu-Cheng Chang and Yu-Kai Wang and Chin-Teng Lin},
journal= {arXiv preprint arXiv:2408.10908},
year = {2024}
}