Citation

Alsayaydeh, Jamil Abedalrahim Jamil and Bacarra, Rex and Ibrahim, Ahamed Fayeez Bin Tuani and Farid, Mazen and Al-Hilali, Aqeel and Herawan, Safarudin Gazali (2026) DriveRight: An Embedded AI-Based Multi-Hazard Detection and Alert System for Safe and Sustainable Driving. International Journal of Advanced Computer Science and Applications, 17 (1). ISSN 2158-107X

Text 7.pdf - Published Version Restricted to Repository staff only Download (930kB)

Abstract

Abstract: 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.

Item Type:	Article
Uncontrolled Keywords:	Embedded AI, computer vision, intelligent transportation, IoT-based ADAS, deep learning, real-time object detection, Raspberry Pi
Subjects:	H Social Sciences > HD Industries. Land use. Labor > HD28-70 Management. Industrial Management > HD30.2 Electronic data processing. Information technology. Including artificial intelligence and knowledge management
Divisions:	Faculty of Information Science and Technology (FIST)
Depositing User:	Ms Suzilawati Abu Samah
Date Deposited:	02 Mar 2026 01:42
Last Modified:	02 Mar 2026 01:42
URII:	http://shdl.mmu.edu.my/id/eprint/15395

Downloads

Edit (login required)