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[2602.12591] Vehicle behaviour estimation for abnormal event detection using distributed fiber optic sensing
Summary
This article presents a method for detecting vehicle lane changes to identify single-lane abnormalities using distributed fiber optic sensing, achieving 80% accuracy in real traffic data.
Why It Matters
As traffic congestion continues to be a significant urban issue, this research provides a cost-effective solution for monitoring vehicle behavior, which can enhance traffic management systems and improve road safety by identifying potential hazards early.
Key Takeaways
- The study introduces a novel method for detecting lane changes using distributed fiber optic sensing.
- Achieves 80% accuracy in identifying single-lane abnormalities that lead to congestion.
- Utilizes existing fiber infrastructure, making it a cost-effective solution for traffic monitoring.
Computer Science > Machine Learning arXiv:2602.12591 (cs) [Submitted on 13 Feb 2026] Title:Vehicle behaviour estimation for abnormal event detection using distributed fiber optic sensing Authors:Hemant Prasad, Daisuke Ikefuji, Shin Tominaga, Hitoshi Sakurai, Manabu Otani View a PDF of the paper titled Vehicle behaviour estimation for abnormal event detection using distributed fiber optic sensing, by Hemant Prasad and 4 other authors View PDF Abstract:The distributed fiber-optic sensing (DFOS) system is a cost-effective wide-area traffic monitoring technology that utilizes existing fiber infrastructure to effectively detect traffic congestions. However, detecting single-lane abnormalities, that lead to congestions, is still a challenge. These single-lane abnormalities can be detected by monitoring lane change behaviour of vehicles, performed to avoid congestion along the monitoring section of a road. This paper presents a method to detect single-lane abnormalities by tracking individual vehicle paths and detecting vehicle lane changes along a section of a road. We propose a method to estimate the vehicle position at all time instances and fit a path using clustering techniques. We detect vehicle lane change by monitoring any change in spectral centroid of vehicle vibrations by tracking a reference vehicle along a highway. The evaluation of our proposed method with real traffic data showed 80% accuracy for lane change detection events that represent presence of abnormalities...