mlsanityData to 14 Oct 2026

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

arXiv 2404.00019 ↗First posted 19 Mar 2024cs.HC cs.AI cs.LGcited by 6

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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.

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

58 of 292 references.

References · 58

  1. 2017Explanation in Artificial Intelligence: Insights from the Social Sciencescited by 1,206
  2. 2019A Survey of Autonomous Driving: Common Practices and Emerging Technologiescited by 379
  3. 2023A Survey on Multimodal Large Language Models for Autonomous Drivingcited by 290
  4. 2023NuScenes-QA: A Multi-modal Visual Question Answering Benchmark for Autonomous Driving Scenariocited by 262
  5. 2018Metrics for Explainable AI: Challenges and Prospectscited by 260
  6. 2018Textual Explanations for Self-Driving Vehiclescited by 226
  7. 2017Interpretable Learning for Self-Driving Cars by Visualizing Causal Attentioncited by 107
  8. 2022PlanT: Explainable Planning Transformers via Object-Level Representationscited by 105
  9. 2020Explainable Object-induced Action Decision for Autonomous Vehiclescited by 88
  10. 2021Explanations in Autonomous Driving: A Surveycited by 81
  11. 2021Explainability of deep vision-based autonomous driving systems: Review and challengescited by 80
  12. 2020Explainable Artificial Intelligence: a Systematic Reviewcited by 79
38 more
  1. 2021Explainable Artificial Intelligence for Autonomous Driving: A Comprehensive Overview and Field Guide for Future Research Directionscited by 73
  2. 2023Receive, Reason, and React: Drive as You Say with Large Language Models in Autonomous Vehiclescited by 72
  3. 2024Explainable AI for Safe and Trustworthy Autonomous Driving: A Systematic Reviewcited by 53
  4. 2020Computing Systems for Autonomous Driving: State-of-the-Art and Challengescited by 47
  5. 2020Scene-Graph Augmented Data-Driven Risk Assessment of Autonomous Vehicle Decisionscited by 34
  6. 2020Explaining Autonomous Driving by Learning End-to-End Visual Attentioncited by 25
  7. 2020To Explain or Not to Explain: A Study on the Necessity of Explanations for Autonomous Vehiclescited by 21
  8. 2021GRIT: Fast, Interpretable, and Verifiable Goal Recognition with Learned Decision Trees for Autonomous Drivingcited by 19
  9. 2019Joint Interaction and Trajectory Prediction for Autonomous Driving using Graph Neural Networkscited by 17
  10. 2021SCOUT: Socially-COnsistent and UndersTandable Graph Attention Network for Trajectory Prediction of Vehicles and VRUscited by 16
  11. 2020Commonsense Visual Sensemaking for Autonomous Driving: On Generalised Neurosymbolic Online Abduction Integrating Vision and Semanticscited by 15
  12. 2021roadscene2vec: A Tool for Extracting and Embedding Road Scene-Graphscited by 15
  13. 2019Autonomous Driving using Safe Reinforcement Learning by Incorporating a Regret-based Human Lane-Changing Decision Modelcited by 14
  14. 2023Explainability in AI Policies: A Critical Review of Communications, Reports, Regulations, and Standards in the EU, US, and UKcited by 13
  15. 2019Look Who's Talking Now: Implications of AV's Explanations on Driver's Trust, AV Preference, Anxiety and Mental Workloadcited by 12
  16. 2020Behavior Planning at Urban Intersections through Hierarchical Reinforcement Learningcited by 12
  17. 2020Driving Behavior Explanation with Multi-level Fusioncited by 12
  18. 2021The Reasonable Crowd: Towards evidence-based and interpretable models of driving behaviorcited by 12
  19. 2022Learning Interpretable, High-Performing Policies for Autonomous Drivingcited by 11
  20. 2020Building Trust in Autonomous Vehicles: Role of Virtual Reality Driving Simulators in HMI Designcited by 10
  21. 2022Towards Explainable Motion Prediction using Heterogeneous Graph Representationscited by 10
  22. 2020Towards Better Performance and More Explainable Uncertainty for 3D Object Detection of Autonomous Vehiclescited by 9
  23. 2023Explainable Action Prediction through Self-Supervision on Scene Graphscited by 9
  24. 2019Algorithmic decision-making in AVs: Understanding ethical and technical concerns for smart citiescited by 8
  25. 2022From Spoken Thoughts to Automated Driving Commentary: Predicting and Explaining Intelligent Vehicles' Actionscited by 8
  26. 2020Building Proactive Voice Assistants: When and How (not) to Interactcited by 7
  27. 2022A Human-Centric Method for Generating Causal Explanations in Natural Language for Autonomous Vehicle Motion Planningcited by 7
  28. 2023X-CANIDS: Signal-Aware Explainable Intrusion Detection System for Controller Area Network-Based In-Vehicle Networkcited by 7
  29. 2024Safety Implications of Explainable Artificial Intelligence in End-to-End Autonomous Drivingcited by 7
  30. 2021Grounded Relational Inference: Domain Knowledge Driven Explainable Autonomous Drivingcited by 6
  31. 2021Human-Vehicle Cooperation on Prediction-Level: Enhancing Automated Driving with Human Foresightcited by 6
  32. 2021Towards explainable artificial intelligence (XAI) for early anticipation of traffic accidentscited by 6
  33. 2023Textual Explanations for Automated Commentary Drivingcited by 6
  34. 2021An Investigation of Drivers' Dynamic Situational Trust in Conditionally Automated Drivingcited by 4
  35. 2023On the Forces of Driver Distraction: Explainable Predictions for the Visual Demand of In-Vehicle Touchscreen Interactionscited by 3
  36. 2023FeedbackLogs: Recording and Incorporating Stakeholder Feedback into Machine Learning Pipelinescited by 3
  37. 2024Designing for Projection-based Communication between Autonomous Vehicles and Pedestrianscited by 3
  38. 2021Reason induced visual attention for explainable autonomous drivingcited by 2

50 most cited of 58 All 58 (JSON)

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 4; 2026: 2.

6 papers.

  1. 2025Out-of-Distribution Detection for Safety Assurance of AI and Autonomous Systemscited by 2
  2. 2025All You Need for Object Detection: From Pixels, Points, and Prompts to Next-Gen Fusion and Multimodal LLMs/VLMs in Autonomous Vehiclescited by 2
  3. 2026X-Blocks: Linguistic Building Blocks of Natural Language Explanations for Automated Vehiclescited by 1
  4. 2026VAGNet: Vision-based Accident Anticipation with Global Featurescited by 1
  5. 2025Exploring human-SAV interaction using LLMs: The impact of psychological factors on user experiencecited by 0
  6. 2025What Do People Want to Know About Artificial Intelligence (AI)? The Importance of Answering End-User Questions to Explain Autonomous Vehicle (AV) Decisionscited by 0