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.

通过全面回顾已有文献，本研究对解释性任务、解释性信息和解释性信息沟通这三个主要话题进行了分类，提出了一个综合的研究路线图，旨在指导解释性自动驾驶车辆的开发和部署，以确保在用户需求、技术进步、合规性和伦理考虑等方面具有更安全和可信赖的自主驾驶体验。