A Comprehensive Survey on Driving Compliance Assessment Methodologies for Autonomous Vehicles

Autonomous vehicles (AVs) have emerged as a focal point in contemporary technological research, promising significant enhancements in safety and efficiency for transportation systems and heralding a fundamental transformation in future mobility. Despite notable advancements in autonomous driving technology, the full-scale mass production and integration of AVs into transportation networks face substantial challenges, with driving compliance assessment (DCA) of driving behavior being a critical barrier. Compliance assessment encompasses multiple dimensions: from meeting basic safety standards and adhering to traffic regulations to implementing advanced driving protocols. This article systematically elaborates and compares methodologies at various levels of DCA, highlighting the importance and complexity of compliance evaluations in the development of AVs. The survey reveals deficiencies in the existing compliance assessment frameworks for AVs, particularly highlighting their lack of systematic and adaptive capabilities to effectively respond to new scenarios, demands, and technologies. It proposes potential research directions to address these gaps. Through a comprehensive examination of these methodologies, the article aims to provide theoretical and evidence-based guidance for the safe integration of AVs into both current and future transportation systems.

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Publication Details

Journal
IEEE Intelligent Transportation Systems Magazine
Published
2025-03-19
DOI
https://doi.org/10.1109/mits.2025.3548869
Citations
3
Primary Topic
Autonomous Vehicle Technology and Safety
Type
article
Field-Weighted Citation Impact
2.05
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article

A Comprehensive Survey on Driving Compliance Assessment Methodologies for Autonomous Vehicles

Zhengmin Jiang, Leong Hou U, Shunran Zhang, Huiyun Li et al.
3 citations
IEEE Intelligent Transportation Systems Magazine
Autonomous Vehicle Technology and Safety
2.05
article

A Comprehensive Survey on Driving Compliance Assessment Methodologies for Autonomous Vehicles

Zhengmin Jiang, Leong Hou U, Shunran Zhang, Huiyun Li, Yunjie Han, Ming Sang
article en
3 citations

Abstract

Autonomous vehicles (AVs) have emerged as a focal point in contemporary technological research, promising significant enhancements in safety and efficiency for transportation systems and heralding a fundamental transformation in future mobility. Despite notable advancements in autonomous driving technology, the full-scale mass production and integration of AVs into transportation networks face substantial challenges, with driving compliance assessment (DCA) of driving behavior being a critical barrier. Compliance assessment encompasses multiple dimensions: from meeting basic safety standards and adhering to traffic regulations to implementing advanced driving protocols. This article systematically elaborates and compares methodologies at various levels of DCA, highlighting the importance and complexity of compliance evaluations in the development of AVs. The survey reveals deficiencies in the existing compliance assessment frameworks for AVs, particularly highlighting their lack of systematic and adaptive capabilities to effectively respond to new scenarios, demands, and technologies. It proposes potential research directions to address these gaps. Through a comprehensive examination of these methodologies, the article aims to provide theoretical and evidence-based guidance for the safe integration of AVs into both current and future transportation systems.

IEEE Intelligent Transportation Systems MagazineVol. 17(4)
Chinese Academy of Sciences (CN), University of Macau (MO), Shenzhen Institutes of Advanced Technology (CN), City University of Macau (MO)
Openalex Percentile: Top 15%
Autonomous Vehicle Technology and Safety
2.05
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A Comprehensive Survey on Driving Compliance Assessment Methodologies for Autonomous Vehicles — Zhengmin Jiang, Leong Hou U, et al. · IEEE Intelligent Transportation Systems Magazine (2025) | TGRS Research Map | TGRS