Enhancing End-to-End Autonomous Driving Systems Through Synchronized Human Behavior Data

Duan, Y; Zhuang, Z; Zhou, J; Chang, YC; Wang, YK; Lin, CT

Enhancing End-to-End Autonomous Driving Systems Through Synchronized Human Behavior Data

Duan, Y

Zhuang, Z Zhou, J

Chang, YC Wang, YK Lin, CT

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Publisher:: Association for Computing Machinery (ACM)
Publication Type:: Conference Proceeding
Citation:: BCIMM 2024 - Proceedings of the 1st International Workshop on Brain-Computer Interfaces BCI for Multimedia Understanding, Co-Located with: MM 2024, 2024, pp. 1-8
Issue Date:: 2024-10-28

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Field	Value	Language
dc.contributor.author	Duan, Y https://orcid.org/0000-0003-1517-994X
dc.contributor.author	Zhuang, Z
dc.contributor.author	Zhou, J https://orcid.org/0000-0002-6620-604X
dc.contributor.author	Chang, YC
dc.contributor.author	Wang, YK
dc.contributor.author	Lin, CT
dc.date.accessioned	2025-01-13T01:40:18Z
dc.date.available	2025-01-13T01:40:18Z
dc.date.issued	2024-10-28
dc.identifier.citation	BCIMM 2024 - Proceedings of the 1st International Workshop on Brain-Computer Interfaces BCI for Multimedia Understanding, Co-Located with: MM 2024, 2024, pp. 1-8
dc.identifier.uri	http://hdl.handle.net/10453/183302
dc.description.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.
dc.language	en
dc.publisher	Association for Computing Machinery (ACM)
dc.relation.ispartof	BCIMM 2024 - Proceedings of the 1st International Workshop on Brain-Computer Interfaces BCI for Multimedia Understanding, Co-Located with: MM 2024
dc.relation.ispartof	Proceedings of the 1st International Workshop on Brain-Computer Interfaces (BCI) for Multimedia Understanding
dc.relation.isbasedon	10.1145/3688862.3689108
dc.rights	info:eu-repo/semantics/openAccess
dc.title	Enhancing End-to-End Autonomous Driving Systems Through Synchronized Human Behavior Data
dc.type	Conference Proceeding
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
pubs.organisational-group	University of Technology Sydney/UTS Groups
pubs.organisational-group	University of Technology Sydney/UTS Groups/Australian Artificial Intelligence Institute (AAII)
utslib.copyright.status	open_access	*
dc.rights.license	This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0). To view a copy of this license, visit https://creativecommons.org/licenses/by/4.0/
dc.date.updated	2025-01-13T01:40:17Z
pubs.publication-status	Published

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.

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http://hdl.handle.net/10453/183302