Advancing Explainable Autonomous Vehicle Systems: A Comprehensive Review and Research Roadmap

TL;DR

This paper reviews current explainability methods for autonomous vehicles, categorizes existing research, and proposes a comprehensive roadmap emphasizing user-centered, ethical, and technological considerations for future development.

Contribution

It provides a detailed categorization of explainability research in AVs and introduces a holistic research roadmap to address current challenges and future directions.

Findings

Existing literature categorized into explanatory tasks, information, and communication.

Identified key research directions including privacy, ethics, real-time analytics, and human-centric design.

Proposed a roadmap emphasizing responsible innovation and stakeholder needs.

Abstract

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 the 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.