Recent advances in Artificial Intelligence (AI) and Computer Vision have significantly enhanced the potential of Advanced Driver Assistance Systems (ADAS). However, existing solutions remain limited by high computational cost, single-function design, and dependence on expensive sensors such as radar and LiDAR. This study presents DriveRight, an embedded AI-based driver-assistance system that integrates multi-scenario hazard detection and real-time object detection and alerting using a single low-cost vision sensor on a Raspberry Pi platform. The system leverages a simulation-to-deployment pipeline, combining CARLA-based synthetic training environments with TensorFlow deep learning models, including SSD Inception v2, MobileNet-SSD, and Faster R-CNN. Experimental results show that Faster R-CNN achieved 92.1% detection accuracy for vehicles and 90.3% for traffic signs, while MobileNet-SSD achieved real-time performance at 14.6 frames per second (FPS) with minimal latency of 2.8 seconds on embedded hardware. Field tests validated the system’s ability to accurately detect and classify stop signs, vehicles, and lane deviations under varying lighting and motion conditions, triggering timely alerts to the driver. The prototype demonstrates a cost-effective and energy-efficient AI solution (< 12 W) for intelligent transportation systems. The findings establish the feasibility of deploying IoT-based ADAS and deep learning–driven driver-assistance technologies in low-cost, sustainable embedded platforms, bridging the gap between research-grade ADAS and practical real-world deployment.
Alsayaydeh et al. (2026) studied this question.