Al-hajri, Sarah Bint Mubarak F. <br/><br/>
Wireless blind-spot monitoring system with real-time motion detection and risk estimation using yolov11 and optical flow on nvidia jetson orin nano for toyota hilux 2.4 fx 4x2 mt (w/rear a/c)/ 
Sarah Bint Mubarak F. Al-hajri, Elizhea C. Camonias, Jason D. Del Rosario, Alexandre Jr. Dela Cruz, and Ivan James Gabriel R. Yray.-- 
 - Manila:  Technological University of the Philippines,   2025. 
 - xvii, 177pages:  29cm. 
<br/><br/> Bachelor's thesis 
<br/><br/>College of Engineering.-- 
<br/><br/>Includes bibliographic references and index. 
<br/><br/> This study presents the development and evaluation of a Wireless Blind-Spot<br/>Monitoring System with Real-Time Motion Detection and Risk Estimation using<br/>YOLOv11 and Optical Flow on the NVIDIA Jetson Orin Nano, designed specifically for<br/>utility vehicles such as the Toyota Hilux 2.4 FX 4x2 M/T. The system aims to enhance<br/>driver awareness and safety by providing real-time object detection and motion tracking in<br/>the vehicle’s blind spots through a non-intrusive Heads-Up Display (HUD). It integrates<br/>four ESP32-CAM modules for video capture, wirelessly transmitting data to the Jetson<br/><br/>Orin Nano for processing. YOLOv11 is utilized for object detection while a CUDA-<br/>accelerated optical flow algorithm, in conjunction with OBD-based vehicle speed input, is<br/><br/>employed for estimating the relative motion of surrounding objects.<br/>Experimental results demonstrate strong object detection performance with a mean<br/><br/>Average Precision (mAP@0.5) of 0.78 and consistent tracking of moving objects. Real-<br/>time processing was achieved with an average inference time of 0.06 seconds per frame,<br/><br/>though memory constraints on the Jetson Orin Nano presented limitations. Speed<br/>estimation tests yielded mean absolute percentage errors ranging from 4.4% to 69.1%,<br/>depending on object distance and motion complexity. A bird’s-eye view (BEV) map was<br/>integrated into the HUD for intuitive spatial awareness.<br/>The system was deployed and evaluated across 30 road trips in urban environments,<br/>with user surveys indicating improved driving confidence and reduced distraction. This<br/>study concludes that the proposed system offers a scalable, affordable, and real-time<br/>solution to blind-spot monitoring, contributing to safer road conditions and intelligent<br/>vehicle systems in developing transportation infrastructures. 
<br/><br/><label>Subjects--Topical Terms: </label><br/>Real time motion detector<br/>Bird's-eye view (bev)<br/>Monitoring system
<br/><br/><label>LC Class. No.: </label>BTH TK 870  / A45 2025