人間の行動データ同期によるエンドツーエンド自動運転システムの強化
Enhancing End-to-End Autonomous Driving Systems Through Synchronized Human Behavior Data
同一の運転シナリオで人間と機械の運転データ（視線・脳波）を同期収集し、人間の注意を機械の注意誘導に用いることで自動運転性能が向上することをCarlaシミュレーションで示した。
著者: Yiqun Duan, Zhuoli Zhuang, Jinzhao Zhou, Yu-Cheng Chang, Yu-Kai Wang, Chin-Teng Lin
分類: cs.RO, cs.HC
原文アブストラクト
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.
