Next Gen Road Safety: Harnessing YOLOv11 Explainable AI and Cloud Aware Context

Road safety is a critical concern, and unsurfaced roads are a special case where potholes and cracks are among the greatest hazards to human-driven and autonomous vehicles. To follow through with the issue of tackling road hazards in real time, we propose a real-time road hazard detection system that incorporates deep learning, explainable AI (XAI) techniques, and context-aware detection to enhance the Advanced Driver Assistance Systems (ADAS). The YOLOv11 model is used to detect road anomalies quickly and easily available for hazard identification. YOLOv11 was selected after comparative evaluation with YOLOv8, Single Shot Detector (SSD), and Attention based Convolutional Neural Networks due to its higher detection accuracy, faster inference and better performance under occlusion. It outperformed other models in terms of mean Average Precision (mAP), reduced false positives, and maintained superior inference speed under occlusion, shadow, and low-light conditions. Furthermore, its architecture supports efficient edge deployment, crucial for real-time ADAS systems. Edge detection, coupled with occlusion sensitivity analysis, yields context-aware detection, which detects partially exposed potholes and cracks, thereby reducing false negatives and providing the overall robustness of the system under difficult road conditions. Stereo vision with the use of multi- camera fusion methodology is used to estimate depths and effectively handle occlusions caused by other vehicles as well as provide better spatial localizations of hazards. The field of view of the vehicle is used to divide the location of the hazards into left, centre, and right zones. A real-time voice alert mechanism helps in acting ahead of time in a larger perspective of situational awareness. The proposed system is cloud-integrated in order to achieve large-scale deployment and to continuously keep improving the model with model updates and sharing of the hazard data in the cloud at remote nodes for collective intelligence. This research integrates deep learning, explainable AI, and contextual analysis, rooted in knowledge garnered from the intrinsic knowledge of its generation, and builds a future generation of knowledge-enabled intelligent transportation systems for the accurate, interpretable real-time road hazard detection for applications of ADAS and drivers at large, improving road safety for all users.