التفاصيل البيبلوغرافية
| العنوان: |
Enhancing End-to-End Autonomous Driving Systems Through Synchronized Human Behavior Data |
| المؤلفون: |
Duan, Yiqun, Zhuang, Zhuoli, Zhou, Jinzhao, Chang, Yu-Cheng, Wang, Yu-Kai, Lin, Chin-Teng |
| سنة النشر: |
2024 |
| المجموعة: |
Computer Science |
| مصطلحات موضوعية: |
Computer Science - Robotics, Computer Science - Human-Computer Interaction |
| الوصف: |
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. |
| نوع الوثيقة: |
Working Paper |
| URL الوصول: |
http://arxiv.org/abs/2408.10908 |
| رقم الانضمام: |
edsarx.2408.10908 |
| قاعدة البيانات: |
arXiv |