Edge AI-Enabled In-Vehicle System for Comprehensive Remote Surveillance

Abstract:

The system outlined here presents a novel in-vehicle system designed for continuous monitoring of a vehicle by detection of traffic violations and aggressive driving. The system integrates a range of sensing modules and data processing algorithms within the in-vehicle unit to continuously capture and process vehicle data. The system transmits data, including violation states and aggressive driving state to a remote server in real time to provide secure and immediate access to the data. The system’s continuous detection and monitoring functionality is a significant improvement in remote vehicle surveillance technology.

• Periodical: Advances in Science and Technology (Volume 177)
• Pages: 13-24
• DOI: https://doi.org/10.4028/p-lEt9Iy
• Online since: May 2026
• Authors: Nethumina Pananwala*, Nethmi Deheragoda, Isini Rathnayake, Lakmini Malasinghe
• Keywords: Aggressive Driving Detection, Deep Learning, Fleet Management, Law Enforcement, Remote Monitoring, Traffic Violation Detection

References

[1] Wang, X., Pan, J., Jiang, M., Meng, G. (2013, December 1). A video-based traffic violation detection system. IEEE Xplore. https://ieeexplore.ieee.org/document/6885246

[2] Franklin, R. J., Mohana. (2020, June 1). Traffic signal violation detection using artificial intelligence and deep learning. IEEE Xplore. https://ieeexplore.ieee.org/abstract/document/9137873

[3] Chen, Z., Yu, J., Zhu, Y., Chen, Y., Li, M. (2015). D3: Abnormal driving behaviors detection and identification using smartphone sensors. In 2015 12th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON) (pp.524-532

DOI: 10.1109/INISTA.2019.8778416

[5] Yoo, H., Yang, U., Sohn, K. (2013). Gradient-enhancing conversion for illumination-robust lane detection. IEEE Transactions on Intelligent Transportation Systems, 14(3), 1083-1094

DOI: 10.1109/TITS.2013.2252427

[6] Lee, S., Kim, H., Kim, J., Lee, J., Yoon, J. (2017). VPGNet: Vanishing point guided network for lane and road marking detection and recognition. In 2017 IEEE International Conference on Computer Vision (ICCV) (pp.1965-1973). IEEE

DOI: 10.1109/ICCV.2017.215

[7] Yoo, S., Jung, M., Kim, S., Park, J., Hwang, H. (2020). End-to-end lane marker detection via row-wise classification. In 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) (pp.4335-4343). IEEE

DOI: 10.1109/CVPRW50498.2020.00511

[8] Qin, Z., Wang, H., Li, X. (2020, April). Ultra Fast Structure-aware Deep Lane Detection. arXiv

[9] Foucher, P., Feron, R., D'Orazio, T. (2011, October). Detection and recognition of urban road markings using images. In 2011 14th International IEEE Conference on Intelligent Transportation Systems (ITSC) (pp.1747-1752). IEEE

DOI: 10.1109/itsc.2011.6082840

[10] Mathibela, B., Mporokoso, A., Sefara, R., Nxumalo, T. (2015). Reading the road: Road marking classification and interpretation. IEEE Transactions on Intelligent Transportation Systems, 16(4), 2072-2081

DOI: 10.1109/tits.2015.2393715

[11] Hoang, T. M., Phan, H. Q., Luong, T. M., Tran, A. T. (2020). Enhanced detection and recognition of road markings based on adaptive region of interest and deep learning. IEEE Access, 7, 109817-109832

DOI: 10.1109/access.2019.2933598

[12] Yu, B., Zhang, W., Cai, Y. (2008). A lane departure warning system based on machine vision. In 2008 IEEE Pacific-Asia Workshop on Computational Intelligence and Industrial Application (pp.197-201). IEEE

DOI: 10.1109/PACIIA.2008.142

[13] Jung, C. R., Kelber, C. R. (2004). A lane departure warning system based on a linearparabolic lane model. In IEEE Intelligent Vehicles Symposium, 2004 (pp.891-895). IEEE

DOI: 10.1109/IVS.2004.1336503

[14] Bilal, H., Yin, B., Khan, J., Wang, L., Zhang, J., Kumar, A. (2019). Real-time lane detection and tracking for advanced driver assistance systems. In 2019 Chinese Control Conference (CCC) (pp.6772-6777). IEEE

DOI: 10.23919/ChiCC.2019.8866334

[15] Lee, C., Moon, J.-H. (2018). Robust lane detection and tracking for real-time applications. IEEE Transactions on Intelligent Transportation Systems, 19(12), 4043-4048

DOI: 10.1109/TITS.2018.2791572

[16] Pan, X., Shi, J., Luo, P., Wang, X., Tang, X. (2017, December). Spatial As Deep: Spatial CNN for Traffic Scene Understanding. arXiv. https://arxiv.org/abs/1712.06080

[17] Veness, C. (2019). Calculate distance and bearing between two Latitude/Longitude points using haversine formula in JavaScript. Movable-type.co.uk. https://www.movabletype.co.uk/scripts/latlong.html[18] Balzer, P., Trautmann, T., Michler, O. (2014, January). EPE and speed adaptive extended Kalman filter for vehicle position and attitude estimation with low cost GNSS and IMU sensors. Proceedings of the 6th International Conference on Indoor Positioning and Indoor Navigation (IPIN 2015)

DOI: 10.5220/0005023706490656

[19] Du, L., Sun, Q., Bai, J., Wang, J. (2021). A verification method for traffic radar-based speed meter with target position determination in road vehicle speeding enforcement. IEEE Transactions on Vehicular Technology, 70(12), 12374-12388

DOI: 10.1109/tvt.2021.3116110