Vision-Language Model-Based Risk Prediction and Explainable Decision-Making for Autonomous Driving in Complex Urban Environments
Keywords:
autonomous driving, vision-language models, risk prediction, explainable artificial intelligence, urban mobility, sociotechnical systems, governance, robustnessAbstract
Autonomous driving in complex urban environments requires more than accurate perception and low-level control. It demands anticipatory risk inference, socially legible decision-making, and governance mechanisms that can assign responsibility across distributed technical and institutional systems. This paper examines vision-language model-based risk prediction and explainable decision-making as an integrated sociotechnical architecture for urban autonomous driving. The discussion positions vision-language models as a unifying substrate that connects visual perception, textual reasoning, map semantics, traffic rules, and human-centered explanations. It then analyzes how risk prediction can be structured around multimodal grounding, temporal world modeling, and uncertainty-aware planning rather than isolated object detection. The paper argues that explainability must be treated as a system property rather than a post hoc interface, because explanations influence trust, liability, regulatory audit, and operational safety. Architectural trade-offs are considered across edge-cloud computation, modular and end-to-end pipelines, sensor fusion, redundancy, and real-time constraints. The paper further explores data infrastructure, deployment governance, robustness, fairness, sustainability, and policy implications. It concludes that successful deployment will depend on hybrid architectures that combine interpretable risk representations, auditable decision traces, infrastructure coordination, and adaptive regulation. The central contribution is a system-level research agenda for aligning vision-language risk prediction with explainable decision-making in complex urban mobility.
References
1. Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., & Polosukhin, I. (2017). Attention is all you need. In Advances in Neural Information Processing Systems (Vol. 30). Curran Associates.
2. Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J., & Houlsby, N. (2021). An image is worth 16x16 words: Transformers for image recognition at scale. In International Conference on Learning Representations.
3. Radford, A., Kim, J. W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., Krueger, G., & Sutskever, I. (2021). Learning transferable visual models from natural language supervision. In Proceedings of the 38th International Conference on Machine Learning (pp. 8748–8763). PMLR.
4. Li, J., Li, D., Xiong, C., & Hoi, S. (2022). BLIP: Bootstrapping language-image pre-training for unified vision-language understanding and generation. In Proceedings of the 39th International Conference on Machine Learning (pp. 12888–12900). PMLR.
5. Alayrac, J.-B., Donahue, J., Luc, P., Miech, A., Barr, I., Hasson, Y., Lenc, K., Mensch, A., Millican, K., Reynolds, M., Ring, R., Rutherford, E., Cabi, S., Han, T., Gong, Z., Samangooei, S., Monteiro, M., Menick, J., Borgeaud, S., ... Simonyan, K. (2022). Flamingo: A visual language model for few-shot learning. In Advances in Neural Information Processing Systems (Vol. 35, pp. 23716–23736). Curran Associates.
6. Geiger, A., Lenz, P., & Urtasun, R. (2012). Are we ready for autonomous driving? The KITTI vision benchmark suite. In 2012 IEEE Conference on Computer Vision and Pattern Recognition (pp. 3354–3361). IEEE.
7. Caesar, H., Bankiti, V., Lang, A. H., Vora, S., Liong, V. E., Xu, Q., Krishnan, A., Pan, Y., Baldan, G., & Beijbom, O. (2020). nuScenes: A multimodal dataset for autonomous driving. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 11621–11631). IEEE.
8. Xiong, Zhexiao, et al. "UniDrive-WM: Unified understanding, planning and generation world model for autonomous driving." European Conference on Computer Vision. Cham: Springer Nature Switzerland, 2026.
9. Codevilla, F., Müller, M., López, A., Koltun, V., & Dosovitskiy, A. (2018). End-to-end driving via conditional imitation learning. In 2018 IEEE International Conference on Robotics and Automation (pp. 4693–4700). IEEE.
10. Prakash, A., Chitta, K., & Geiger, A. (2021). Multi-modal fusion transformer for end-to-end autonomous driving. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 7077–7087). IEEE.
11. Hu, S., Chen, L., Wu, P., Li, H., Yan, J., & Tao, D. (2022). ST-P3: End-to-end vision-based autonomous driving via spatial-temporal feature learning. In Computer Vision – ECCV 2022 (pp. 533–549). Springer.
12. Li, Z., Wang, W., Li, H., Xie, E., Sima, C., Lu, T., Qiao, Y., & Dai, J. (2023). UniAD: Planning-oriented autonomous driving. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 17853–17862). IEEE.
13. Jiang, B., Chen, S., Xu, Q., Liao, B., Chen, J., Zhou, H., Zhang, Q., Liu, W., Huang, C., & Wang, X. (2023). VAD: Vectorized scene representation for efficient autonomous driving. In Proceedings of the IEEE/CVF International Conference on Computer Vision (pp. 8300–8310). IEEE.
14. Doshi-Velez, F., & Kim, B. (2017). Towards a rigorous science of interpretable machine learning. arXiv. https://arxiv.org/abs/1702.08608
15. Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nature Machine Intelligence, 1(5), 206–215. https://doi.org/10.1038/s42256-019-0048-x
16. Barredo Arrieta, A., Díaz-Rodríguez, N., Del Ser, J., Bennetot, A., Tabik, S., Barbado, A., García, S., Gil-López, S., Molina, D., Benjamins, R., Chatila, R., & Herrera, F. (2020). Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI. Information Fusion, 58, 82–115. https://doi.org/10.1016/j.inffus.2019.12.012
17. Bonnefon, J.-F., Shariff, A., & Rahwan, I. (2016). The social dilemma of autonomous vehicles. Science, 352(6293), 1573–1576. https://doi.org/10.1126/science.aaf2654
18. Awad, E., Dsouza, S., Kim, R., Schulz, J., Henrich, J., Shariff, A., Bonnefon, J.-F., & Rahwan, I. (2018). The Moral Machine experiment. Nature, 563(7729), 59–64. https://doi.org/10.1038/s41586-018-0637-6
19. Fagnant, D. J., & Kockelman, K. (2015). Preparing a nation for autonomous vehicles: Opportunities, barriers and policy recommendations. Transportation Research Part A: Policy and Practice, 77, 167–181. https://doi.org/10.1016/j.tra.2015.04.003
20. Nissenbaum, H. (1996). Accountability in a computerized society. Science and Engineering Ethics, 2(1), 25–42. https://doi.org/10.1007/BF02639315
21. Winner, L. (1980). Do artifacts have politics? Daedalus, 109(1), 121–136.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Computational Intelligence Systems
This work is licensed under a Creative Commons Attribution 4.0 International License.
This article is published under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.