Advancing Explainable Autonomous Vehicle Systems
A Comprehensive Review and Research Roadmap
Bibliographic Data
| ID | 22190824 |
|---|---|
| Authors | Sule Tekkesinoglu (0000-0002-1232-346X, University of Oxford), Azra Habibovic (0000-0002-0885-9560, Scania (Sweden)), Lars Kunze (0000-0001-5302-1938, University of the West of England) |
| Year | 2025 |
| Volume | 14 |
| Issue | 3 |
| Pages | 1-46 |
| Publication date | 2025-06-30 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | ACM Transactions on Human-Robot Interaction (JOURNAL) |
| Journal identifiers | ISSN: 2573-9522 • E-ISSN: 2573-9522 |
| Publisher | Association for Computing Machinery (ACM) (PUBLISHER) |
| DOI | 10.1145/3714478 |
| OpenAlex | W4406697187 |
| Language | EN |
| References cited | 143 |
Given the uncertainty surrounding how existing explainability methods for autonomous vehicles (AVs) meet the diverse needs of stakeholders, a thorough investigation is imperative to determine the contexts requiring explanations and suitable interaction strategies. A comprehensive review becomes crucial to assess the alignment of current approaches with varied interests and expectations within the AV ecosystem. This study presents a review to discuss the complexities associated with explanation generation and presentation to facilitate the development of more effective and inclusive explainable AV systems. Our investigation led to categorising existing literature into three primary topics: explanatory tasks, explanatory information and explanatory information communication. Drawing upon our insights, we have proposed a comprehensive roadmap for future research centred on (i) knowing the interlocutor, (ii) generating timely explanations, (ii) communicating human-friendly explanations and (iv) continuous learning. Our roadmap is underpinned by principles of responsible research and innovation, emphasising the significance of diverse explanation requirements. To effectively tackle the challenges associated with implementing explainable AV systems, we have delineated various research directions, including the development of privacy-preserving data integration, ethical frameworks, real-time analytics, human-centric interaction design and enhanced cross-disciplinary collaborations. By exploring these research directions, the study aims to guide the development and deployment of explainable AVs, informed by a holistic understanding of user needs, technological advancements, regulatory compliance and ethical considerations, thereby ensuring safer and more trustworthy autonomous driving experiences
Aeronautics · Systems engineering · Autonomous Vehicle Technology and Safety · Computer Science · Engineering · Explainable Artificial Intelligence (XAI · Human-Automation Interaction and Safety
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Investigating the Importance of Trust on Adopting an Autonomous Vehicle
Trust in Automation
Definitions and Conceptual Dimensions of Responsible Research and Innovation
Humans and Automation
A typology of reviews
An Integrative Model of Organizational Trust
Manipulating music to communicate automation reliability in conditionally automated driving
What drives the acceptance of autonomous driving? An investigation of acceptance factors from an end-user's perspective
The effects of trait anxiety and the big five personality traits on self-driving car acceptance
Validity of a Brief Locus of Control Scale for Survey Research
Improving Passenger Experience and Trust in Automated Vehicles Through User-Adaptive HMIs
Development and validation of a propensity to trust scale
The mind in the machine
Communicating Intent of Automated Vehicles to Pedestrians
| Citation velocity | historical |
|---|---|
| Highly cited | No |