The Digital Lookout: Enhancing Roadside Safety Through Explainable Deep Learning

Each year, about 3% of the world’s GDP, and 1.3 million people, die in traffic accidents. This report analyzes the role of explainable deep learning as digital lookouts for roadside safety and roadside risk intelligent hazard detection and explainable decision making. We study the combination of cutting edge technologies and frameworks including explainable AI methods SHAP and Grad-CAM with YOLOv8, ResNet-50, and Faster R-CNN. We show YOLOv8 as the best for our metrics with 96.2% detection accuracy for 95 FPS. Also, SHAP values reach 92.5% on interpretability. Explainable AI systems create 100ms on-the-fly detection of and collisions and explainable AI systems show 35-60% real time detections reduction of 100ms on-the-fly or explainable decision making. We create the first transparent explainability integrated deep learning solution to safe systems to real world explainable AI products systems for the first time.