Enhancing End-to-End Autonomous Driving Systems Through Synchronized Human Behavior Data
Fuente:
arXiv
Gespeichert in:
| Hauptverfasser: | , , , , , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2024
|
| Schlagworte: | |
| Online-Zugang: | |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866917754271956992 |
|---|---|
| author | Duan, Yiqun Zhuang, Zhuoli Zhou, Jinzhao Chang, Yu-Cheng Wang, Yu-Kai Lin, Chin-Teng |
| author_facet | Duan, Yiqun Zhuang, Zhuoli Zhou, Jinzhao Chang, Yu-Cheng Wang, Yu-Kai Lin, Chin-Teng |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_10908 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Enhancing End-to-End Autonomous Driving Systems Through Synchronized Human Behavior Data Duan, Yiqun Zhuang, Zhuoli Zhou, Jinzhao Chang, Yu-Cheng Wang, Yu-Kai Lin, Chin-Teng Robotics Human-Computer Interaction 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. |
| title | Enhancing End-to-End Autonomous Driving Systems Through Synchronized Human Behavior Data |
| topic | Robotics Human-Computer Interaction |
| url | https://arxiv.org/abs/2408.10908 |