Vehicle behaviour estimation for abnormal event detection using distributed fiber optic sensing
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866911445010087936 |
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| author | Prasad, Hemant Ikefuji, Daisuke Tominaga, Shin Sakurai, Hitoshi Otani, Manabu |
| author_facet | Prasad, Hemant Ikefuji, Daisuke Tominaga, Shin Sakurai, Hitoshi Otani, Manabu |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_12591 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Vehicle behaviour estimation for abnormal event detection using distributed fiber optic sensing Prasad, Hemant Ikefuji, Daisuke Tominaga, Shin Sakurai, Hitoshi Otani, Manabu Machine Learning 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. |
| title | Vehicle behaviour estimation for abnormal event detection using distributed fiber optic sensing |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2602.12591 |