Vehicle behaviour estimation for abnormal event detection using distributed fiber optic sensing
This work addresses traffic monitoring challenges for urban infrastructure by providing a method to detect lane-specific issues, though it is incremental as it builds on existing sensing technology.
The paper tackled the problem of detecting single-lane abnormalities that cause traffic congestion by monitoring vehicle lane changes using distributed fiber-optic sensing, achieving 80% accuracy in lane change detection with real traffic data.
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.