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

arXiv: https://arxiv.org/abs/2404.00019 · HTML: https://mlsanity.com/paper/2404.00019/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 58 of its 292 references that are in the map.
- First posted on arXiv 19 Mar 2024.

## Builds on (most cited first, 30 of 58)

- [Explanation in Artificial Intelligence: Insights from the Social Sciences](https://mlsanity.com/paper/1706.07269/) (2017)
- [A Survey of Autonomous Driving: Common Practices and Emerging Technologies](https://mlsanity.com/paper/1906.05113/) (2019)
- [A Survey on Multimodal Large Language Models for Autonomous Driving](https://mlsanity.com/paper/2311.12320/) (2023)
- [NuScenes-QA: A Multi-modal Visual Question Answering Benchmark for Autonomous Driving Scenario](https://mlsanity.com/paper/2305.14836/) (2023)
- [Metrics for Explainable AI: Challenges and Prospects](https://mlsanity.com/paper/1812.04608/) (2018)
- [Textual Explanations for Self-Driving Vehicles](https://mlsanity.com/paper/1807.11546/) (2018)
- [Interpretable Learning for Self-Driving Cars by Visualizing Causal Attention](https://mlsanity.com/paper/1703.10631/) (2017)
- [PlanT: Explainable Planning Transformers via Object-Level Representations](https://mlsanity.com/paper/2210.14222/) (2022)
- [Explainable Object-induced Action Decision for Autonomous Vehicles](https://mlsanity.com/paper/2003.09405/) (2020)
- [Explanations in Autonomous Driving: A Survey](https://mlsanity.com/paper/2103.05154/) (2021)
- [Explainability of deep vision-based autonomous driving systems: Review and challenges](https://mlsanity.com/paper/2101.05307/) (2021)
- [Explainable Artificial Intelligence: a Systematic Review](https://mlsanity.com/paper/2006.00093/) (2020)
- [Explainable Artificial Intelligence for Autonomous Driving: A Comprehensive Overview and Field Guide for Future Research Directions](https://mlsanity.com/paper/2112.11561/) (2021)
- [Receive, Reason, and React: Drive as You Say with Large Language Models in Autonomous Vehicles](https://mlsanity.com/paper/2310.08034/) (2023)
- [Explainable AI for Safe and Trustworthy Autonomous Driving: A Systematic Review](https://mlsanity.com/paper/2402.10086/) (2024)
- [Computing Systems for Autonomous Driving: State-of-the-Art and Challenges](https://mlsanity.com/paper/2009.14349/) (2020)
- [Scene-Graph Augmented Data-Driven Risk Assessment of Autonomous Vehicle Decisions](https://mlsanity.com/paper/2009.06435/) (2020)
- [Explaining Autonomous Driving by Learning End-to-End Visual Attention](https://mlsanity.com/paper/2006.03347/) (2020)
- [To Explain or Not to Explain: A Study on the Necessity of Explanations for Autonomous Vehicles](https://mlsanity.com/paper/2006.11684/) (2020)
- [GRIT: Fast, Interpretable, and Verifiable Goal Recognition with Learned Decision Trees for Autonomous Driving](https://mlsanity.com/paper/2103.06113/) (2021)
- [Joint Interaction and Trajectory Prediction for Autonomous Driving using Graph Neural Networks](https://mlsanity.com/paper/1912.07882/) (2019)
- [SCOUT: Socially-COnsistent and UndersTandable Graph Attention Network for Trajectory Prediction of Vehicles and VRUs](https://mlsanity.com/paper/2102.06361/) (2021)
- [Commonsense Visual Sensemaking for Autonomous Driving: On Generalised Neurosymbolic Online Abduction Integrating Vision and Semantics](https://mlsanity.com/paper/2012.14359/) (2020)
- [roadscene2vec: A Tool for Extracting and Embedding Road Scene-Graphs](https://mlsanity.com/paper/2109.01183/) (2021)
- [Autonomous Driving using Safe Reinforcement Learning by Incorporating a Regret-based Human Lane-Changing Decision Model](https://mlsanity.com/paper/1910.04803/) (2019)
- [Explainability in AI Policies: A Critical Review of Communications, Reports, Regulations, and Standards in the EU, US, and UK](https://mlsanity.com/paper/2304.11218/) (2023)
- [Look Who's Talking Now: Implications of AV's Explanations on Driver's Trust, AV Preference, Anxiety and Mental Workload](https://mlsanity.com/paper/1905.08878/) (2019)
- [Behavior Planning at Urban Intersections through Hierarchical Reinforcement Learning](https://mlsanity.com/paper/2011.04697/) (2020)
- [Driving Behavior Explanation with Multi-level Fusion](https://mlsanity.com/paper/2012.04983/) (2020)
- [The Reasonable Crowd: Towards evidence-based and interpretable models of driving behavior](https://mlsanity.com/paper/2107.13507/) (2021)

## Built on by (most cited first, 6 of 6)

- [Out-of-Distribution Detection for Safety Assurance of AI and Autonomous Systems](https://mlsanity.com/paper/2510.21254/) (2025)
- [All You Need for Object Detection: From Pixels, Points, and Prompts to Next-Gen Fusion and Multimodal LLMs/VLMs in Autonomous Vehicles](https://mlsanity.com/paper/2510.26641/) (2025)
- [X-Blocks: Linguistic Building Blocks of Natural Language Explanations for Automated Vehicles](https://mlsanity.com/paper/2602.13248/) (2026)
- [VAGNet: Vision-based Accident Anticipation with Global Features](https://mlsanity.com/paper/2604.09305/) (2026)
- [Exploring human-SAV interaction using LLMs: The impact of psychological factors on user experience](https://mlsanity.com/paper/2504.16548/) (2025)
- [What Do People Want to Know About Artificial Intelligence (AI)? The Importance of Answering End-User Questions to Explain Autonomous Vehicle (AV) Decisions](https://mlsanity.com/paper/2505.06428/) (2025)

## Abstract (arXiv)

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.