000			03239nam a22003257a 4500
003			OSt
005			20250715164832.0
008			250714b |||||||| |||| 00| 0 eng d
040			_aTUPM _bEnglish _cTUPM _dTUPM _erda
050			_aBTH TK 870 _bA45 2025
100			_aAl-hajri, Sarah Bint Mubarak F. _eauthor
245			_aWireless 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)/ _cSarah Bint Mubarak F. Al-hajri, Elizhea C. Camonias, Jason D. Del Rosario, Alexandre Jr. Dela Cruz, and Ivan James Gabriel R. Yray.--
260			_aManila: _bTechnological University of the Philippines, _c2025.
300			_axvii, 177pages: _c29cm.
336			_2rdacontent
337			_2rdamedia
338			_2rdacarrier
500			_aBachelor's thesis
502			_aCollege of Engineering.-- _bBachelor of science in electronics engineering: _cTechnological University of the Philippines, _d2025.
504			_aIncludes bibliographic references and index.
520			_aThis study presents the development and evaluation of a Wireless Blind-Spot Monitoring System with Real-Time Motion Detection and Risk Estimation using YOLOv11 and Optical Flow on the NVIDIA Jetson Orin Nano, designed specifically for utility vehicles such as the Toyota Hilux 2.4 FX 4x2 M/T. The system aims to enhance driver awareness and safety by providing real-time object detection and motion tracking in the vehicle’s blind spots through a non-intrusive Heads-Up Display (HUD). It integrates four ESP32-CAM modules for video capture, wirelessly transmitting data to the Jetson Orin Nano for processing. YOLOv11 is utilized for object detection while a CUDA- accelerated optical flow algorithm, in conjunction with OBD-based vehicle speed input, is employed for estimating the relative motion of surrounding objects. Experimental results demonstrate strong object detection performance with a mean Average Precision ([email protected]) of 0.78 and consistent tracking of moving objects. Real- time processing was achieved with an average inference time of 0.06 seconds per frame, though memory constraints on the Jetson Orin Nano presented limitations. Speed estimation tests yielded mean absolute percentage errors ranging from 4.4% to 69.1%, depending on object distance and motion complexity. A bird’s-eye view (BEV) map was integrated into the HUD for intuitive spatial awareness. The system was deployed and evaluated across 30 road trips in urban environments, with user surveys indicating improved driving confidence and reduced distraction. This study concludes that the proposed system offers a scalable, affordable, and real-time solution to blind-spot monitoring, contributing to safer road conditions and intelligent vehicle systems in developing transportation infrastructures.
650			_aReal time motion detector
650			_aBird's-eye view (bev)
650			_aMonitoring system
700			_aCamonias, Elizhea C. _eauthor
700			_aDel Rosario, Jason D. _eauthor
700			_aDela Cruz, Alexandre Jr. _eauthor
700			_aYray, Ivan James Gabriel R.
942			_2lcc _cBTH COE _n0
999			_c30353 _d30353