AUTONOMOUS AND EXPLAINABLE DETECTION OF SUSPICIOUS BEHAVIORS IN CONNECTED VEHICLE ENVIRONMENTS THROUGH MULTI-SENSOR VISION
DOI:
https://doi.org/10.33480/4z0rn547Keywords:
Connected Vehicles, Explainable Artificial Intelligence, Real-Time Embedded Vision, Reinforcement Learning, Suspicious Behavior DetectionAbstract
The safety of connected and autonomous vehicles requires intelligent systems capable of detecting suspicious behaviors in real time while providing clear explanations to human operators. This paper presents an innovative framework for the autonomous and explainable detection of suspicious activities around connected vehicles, combining multi-sensor vision, multi-agent reinforcement learning (MARL), and explainable artificial intelligence (XAI). The system relies on lightweight deep learning models (YOLO-tiny, MobileNet) for perception, along with spatio-temporal reasoning to identify abnormal events such as prolonged parking, restricted area crossings, or the placement of suspicious objects. Cooperative decision-making between vehicles and roadside units (RSUs) is managed through MARL. In parallel, an XAI module generates visual and textual explanations to enhance transparency and user trust. The framework has been implemented and evaluated in simulation (CARLA, SUMO/Veins) and on embedded platforms (Jetson Nano/Orin). Results demonstrate an F1-score of 0.91, real-time performance at 7.5 FPS, and a 40% reduction in false positives, confirming the robustness of the proposed system for the cyber-physical security of intelligent transportation systems.
References
Alahdal, N. M., Abukhodair, F., Meftah, L. H., & Cherif, A. (2024). Real-time object detection in autonomous vehicles with YOLO. Procedia Computer Science, 246, 2792–2801. https://doi.org/10.1016/j.procs.2024.09.392
Al-Maamari, M. R., Ramteke, R., Al-Hejri, A. M., & Alshamrani, S. S. (2025). Integrating CNN and transformer architectures for superior Arabic printed and handwriting characters classification. Scientific Reports, 15(1), 1–17. https://doi.org/10.1038/s41598-025-12045-z
Almehdhar, M., et al. (2024). Deep learning in the fast lane: A survey on advanced intrusion detection systems for intelligent vehicle networks. IEEE Open Journal of Vehicular Technology, 5, 869–906. https://doi.org/10.1109/OJVT.2024.3422253
Alonge, A. M., & Isreal, O. (2025). Explainable AI techniques for real-time VANET intrusion detection.
Kiran, B. R., Sobh, I., Talpaert, V., Mannion, P., Al Sallab, A. A., Yogamani, S., & Pérez, P. (2022). Deep reinforcement learning for autonomous driving: A survey. IEEE Transactions on Intelligent Transportation Systems, 23(6), 4909–4926. https://doi.org/10.1109/TITS.2021.3054625
Bukola, A. C., Owolawi, P. A., Du, C., & Van Wyk, E. (2024). A systematic review and comparative analysis approach to boom gate access using plate number recognition. Computers, 13(11). https://doi.org/10.3390/computers13110286
Chanus, T., & Aubertin, M. (2023). Exploring the application of large language models in infrastructure as code. HAL. https://hal.science/hal-04192999
Cheng, J., Zhang, X., Chen, X., Ren, M., Huang, J., & Luo, P. (2022). Early detection of suspicious behaviors for safe residence from movement trajectory data. ISPRS International Journal of Geo-Information, 11(9). https://doi.org/10.3390/ijgi11090478
Dazeley, R., Vamplew, P., & Cruz, F. (2023). Explainable reinforcement learning for broad-XAI: A conceptual framework and survey. Neural Computing and Applications, 35(23), 16893–16916. https://doi.org/10.1007/s00521-023-08423-1
Daull, X., et al. (2025). Répondre aux questions complexes : Limites des LLM et solutions hybrides. HAL. https://hal.science/hal-05173655
Elallid, B. B., Benamar, N., Hafid, A. S., Rachidi, T., & Mrani, N. (2022). A comprehensive survey on the application of deep and reinforcement learning approaches in autonomous driving. Journal of King Saud University – Computer and Information Sciences, 34(9), 7366–7390. https://doi.org/10.1016/j.jksuci.2022.03.013
Grace, R., V., H., & Ponrani, M. A. (2024). Leveraging MobileNet and YOLO algorithm for enhanced perception in autonomous driving. International Journal of Innovative Science and Research Technology, 2056–2060. https://doi.org/10.38124/ijisrt/ijisrt24mar1535
Long, Z., Yan, H., Shen, G., Zhang, X., He, H., & Cheng, L. (2024). A transformer-based network intrusion detection approach for cloud security. Journal of Cloud Computing, 13(1). https://doi.org/10.1186/s13677-023-00574-9
Maisonhaute, T., Michel, F., & État, J. S. (2025). État de l’art des approches en apprentissage par renforcement multi-agent. HAL. https://hal.science/hal-04932606
Rezaei, M., & Azarmi, M. (2023). Deep learning-based anomaly detection in video surveillance: A survey. Sensors, 23(11), 5024. https://doi.org/10.3390/s23115024
Nwakanma, C. I., et al. (2023). Explainable artificial intelligence (XAI) for intrusion detection and mitigation in intelligent connected vehicles: A review. Applied Sciences, 13(3). https://doi.org/10.3390/app13031252
Puder, A., Rumez, M., Grimm, D., & Sax, E. (2022). Generic patterns for intrusion detection systems in service-oriented automotive and medical architectures. Journal of Cybersecurity and Privacy, 2(3), 731–749. https://doi.org/10.3390/jcp2030037
Dinneweth, J., Boubezoul, A., Mandiau, R., & Espié, S. (2022). Multi-agent reinforcement learning for autonomous vehicles: A survey. Autonomous Intelligent Systems, 2, Article 27. https://doi.org/10.1007/s43684-022-00045-z
Wang, B., Li, W., & Khattak, Z. H. (2024). Anomaly detection in connected and autonomous vehicle trajectories using LSTM autoencoder and Gaussian mixture model. Electronics, 13(7). https://doi.org/10.3390/electronics13071251
Zhao, J., Mao, X., Zhao, L., & Li, X. (2022). Intelligent and connected vehicles: Current status, enabling technologies, and challenges. IEEE Consumer Electronics Magazine, 12(1), 59–69. https://doi.org/10.1109/MCE.2021.3081515
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