The DOI matches its Crossref record; 176 of the 176 references it cites were found in a registry or an index.
Explainable Artificial Intelligence for Autonomous Driving: A Comprehensive Overview and Field Guide for Future Research Directions
Open accessAbstract
Autonomous driving has achieved significant milestones in research and development over the last two decades. There is increasing interest in the field as the deployment of autonomous vehicles (AVs) promises safer and more ecologically friendly transportation systems. With the rapid progress in computationally powerful artificial intelligence (AI) techniques, AVs can sense their environment with high precision, make safe real-time decisions, and operate reliably without human intervention. However, intelligent decision-making in such vehicles is not generally understandable by humans in the current state of the art, and such deficiency hinders this technology from being socially acceptable. Hence, aside from making safe real-time decisions, AVs must also explain their AI-guided decision-making process in order to be regulatory-compliant across many jurisdictions. Our study sheds comprehensive light on the development of explainable artificial intelligence (XAI) approaches for AVs. In particular, we make the following contributions. First, we provide a thorough overview of the state-of-the-art and emerging approaches for XAI-based autonomous driving. We then propose a conceptual framework considering the essential elements for explainable end-to-end autonomous driving. Finally, we present XAI-based prospective directions and emerging paradigms for future directions that hold promise for enhancing transparency, trustworthiness, and societal acceptance of AVs.
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Cited by
Explainable AI (XAI): A systematic meta-survey of current challenges and future opportunities
Saeed, W., & Omlin, C. (2023) · Knowledge-Based Systems · doi:10.1016/j.knosys.2023.110273 · Cited by 783
A Review of Trustworthy and Explainable Artificial Intelligence (XAI)
Chamola, V., Hassija, V., Sulthana, A. R., Ghosh, D., Dhingra, D., & Sikdar, B. (2023) · IEEE Access · doi:10.1109/access.2023.3294569 · Cited by 352
VLAAD: Vision and Language Assistant for Autonomous Driving
Park, S., Lee, M., Kang, J., Choi, H., Park, Y., Cho, J., Lee, A., & Kim, D. (2024) · 2024 IEEE/CVF Winter Conference on Applications of Computer Vision Workshops (WACVW) · doi:10.1109/wacvw60836.2024.00107 · Cited by 49
Klüver, C., Greisbach, A., Kindermann, M., & Püttmann, B. (2024) · Security and Safety · doi:10.1051/sands/2024020 · Cited by 8
Nomological Deductive Reasoning for Trustworthy, Human-Readable, and Actionable AI Outputs
Hakizimana, G., & Ledezma Espino, A. (2025) · Algorithms · doi:10.3390/a18060306 · Cited by 2
Metadata
- Authors
- Atakishiyev, S., Salameh, M., Yao, H., & Goebel, R.
- Venue
- IEEE Access
- Published
- 2024
- Volume
- 12
- Pages
- 101603-101625
- ISSN
- 2169-3536 (ISSN-L)
- OpenAlex
- W4400810536
- Cited by
- 323
- References
- 176
- FWCI
- 43.75
- Type
- journal article
- Language
- en
- Open access
- gold
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- Atakishiyev, S., Salameh, M., Yao, H., & Goebel, R. (2024). Explainable Artificial Intelligence for Autonomous Driving: A Comprehensive Overview and Field Guide for Future Research Directions. IEEE Access. https://doi.org/10.1109/access.2024.3431437
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