{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,24]],"date-time":"2026-02-24T20:59:18Z","timestamp":1771966758770,"version":"3.50.1"},"reference-count":0,"publisher":"TechForum Publishing Group","issue":"1","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Bull. Comput. Data Sci."],"published-print":{"date-parts":[[2022,12,30]]},"abstract":"<jats:p>The Line Monitoring Algorithm (LMA) has demonstrated remarkable efficiency in motion detection for specific line areas in driving test supervision. However, its limitation lies in detecting only whether a line is crossed without providing contextual information about what crossed the line or which vehicle part was involved. This paper proposes a novel hybrid framework that integrates the computational efficiency of LMA with the contextual understanding of deep learning-based object detection. Our approach uses LMA as a high-efficiency trigger mechanism to activate a lightweight YOLO-based object detector only when line crossing events occur. This architecture maintains the real-time performance of LMA while gaining rich contextual awareness. Experimental results on driving test videos show that our hybrid system achieves 96.8% accuracy in line crossing detection while reducing false positives by 73% compared to standalone LMA, with only an 8% decrease in frame processing rate. The system successfully identifies specific vehicle components (tires, body) involved in boundary violations, providing comprehensive evaluation metrics for driving test assessment.<\/jats:p>","DOI":"10.71448\/bcds2231-3","type":"journal-article","created":{"date-parts":[[2026,2,24]],"date-time":"2026-02-24T20:01:42Z","timestamp":1771963302000},"page":"22-33","source":"Crossref","is-referenced-by-count":0,"title":["Hybrid Line-Monitoring and Event-Driven Deep Learning for Reliable Automated Driving Test Evaluation"],"prefix":"10.71448","volume":"3","author":[{"name":"Government College University, Pakistan","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Waqas","family":"Nazeer","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Iftikhar","family":"Ahmad","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"name":"University of Agriculture Faisalabad (UAF), Pakistan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"52394","published-online":{"date-parts":[[2022,12,30]]},"container-title":["Bulletin of Computer and Data Sciences"],"original-title":[],"deposited":{"date-parts":[[2026,2,24]],"date-time":"2026-02-24T20:01:43Z","timestamp":1771963303000},"score":1,"resource":{"primary":{"URL":"https:\/\/bcds.ch\/hybrid-line-monitoring-and-event-driven-deep-learning-for-reliable-automated-driving-test-evaluation\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,12,30]]},"references-count":0,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2022,12,30]]},"published-print":{"date-parts":[[2022,12,30]]}},"URL":"https:\/\/doi.org\/10.71448\/bcds2231-3","relation":{},"ISSN":["3072-2926"],"issn-type":[{"value":"3072-2926","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,12,30]]}}}