Vehicle intrusion detection in highway work zones using inertial sensors and lightweight deep learning

Moein Younesi Heravi, Ayenew Yihune Demeke, Israt Sharmin Dola, Youjin Jang, Inbae Jeong, Chau Le

Published version (DOI: 10.1016/j.autcon.2025.106291) Download PDF

PDF: author accepted manuscript. © 2025. This manuscript version is made available under the CC-BY-NC-ND 4.0 license (https://creativecommons.org/licenses/by-nc-nd/4.0/). The published version is available via the DOI above.

Summary

Inertial measurement unit (IMU) sensors attached to traffic cones, combined with a lightweight LSTM deep learning model, distinguish real vehicle intrusions into highway work zones from non-hazardous events such as manual handling and wind displacement. The model reached 96% accuracy and 97% recall for actual intrusions and runs in real time on edge devices, reducing false alarms and alarm fatigue.

Abstract

Highway work zones are prone to intrusion events that threaten workers' safety and disrupt operations. Existing intrusion detection systems often produce high false alarms, causing alarm fatigue and reduced responsiveness. To address this, a data-driven intrusion detection method is proposed to distinguish real vehicle intrusions from non-hazardous events using inertial measurement unit (IMU) sensors attached to traffic cones. Acceleration and angular velocity data were collected through field experiments involving vehicle collisions, manual handling, and wind displacement. After preprocessing and data augmentation, a lightweight Long Short-Term Memory (LSTM) model was trained and optimized for real-time performance on edge devices. Evaluation yielded a 96% accuracy and a 97% recall for actual intrusions. Resultant acceleration and angular velocity are recognized as key features. This cost-effective, scalable solution enhances safety by effectively identifying actual hazards, minimizing false alarms, and mitigating the negative impact of alarm fatigue in highway work zones.

Keywords: Highway work zones, Intrusion detection, False alarms reduction, Worker safety, LSTM

How to cite

Younesi Heravi, M., Demeke, A. Y., Dola, I. S., Jang, Y., Jeong, I., & Le, C. (2025). Vehicle intrusion detection in highway work zones using inertial sensors and lightweight deep learning. Automation in Construction, 176, 106291. https://doi.org/10.1016/j.autcon.2025.106291

BibTeX

@article{heravi2025vehicle,
  title   = {Vehicle intrusion detection in highway work zones using inertial sensors and lightweight deep learning},
  author  = {Younesi Heravi, Moein and Demeke, Ayenew Yihune and Dola, Israt Sharmin and Jang, Youjin and Jeong, Inbae and Le, Chau},
  journal = {Automation in Construction},
  volume  = {176},
  pages   = {106291},
  year    = {2025},
  publisher = {Elsevier},
  doi     = {10.1016/j.autcon.2025.106291}
}