Abstract
Driver Monitoring System (DMS) is a promising IoT application in Intelligent Transport Systems (ITS) research field. DMS assists car drivers by monitoring their driving activities, sensing incidents to cause possible dangers, and alerting the drivers to prevent accidents. We aim to realize a new DMS that is inexpensive and highly effective. This paper proposes a method for detecting any incidents based on machine learning. The proposed method firstly configures a detector by training in-car environment data and driver’s vital signs gathered from multiple sensors. Then, the detector is embedded in a self-contained edge computer for monitoring a driver in a car. The device is always connected to the information communication network by radio waves. Those data obtained by monitoring are stored in the cloud server. The server learns and analyzes the stored data using processing such as machine learning. As a result, we acquire knowledge leading to safe driving. The edge computer uses these knowledge to process the sensor data in real time, observe the driver, sense the danger, and call attention. These mechanisms prevent occurrence of troubles such as traffic accidents. The paper describes the proposed system overview, implementation method, and initial evaluations.
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References
Haramaki, T., Nishino, H.: A network topology visualization system based on mobile AR technology. In: Proceedings of the 29th IEEE International Conference on AINA-2015, pp. 442–447 (2015)
Haramaki, T., Nishino, H.: A device identification method for AR-based network topology visualization. In: Proceedings of the 10th International Conference on BWCCA-2015, pp. 255–262 (2015)
Haramaki, T., Nishino, H.: A sensor fusion approach for network visualization. In: Proceedings of IEEE International Conference on 2016 ICCE-TW, pp. 222–223 (2016)
Haramaki, T., Shimizu, D., Nishino, H.: A wireless network visualizer based on signal strength observation. In: Proceedings of IEEE International Conference on 2017 ICCE-TW, pp. 23–24 (2017)
Yatsuda, A., Haramaki, T., Nishino, H.: An unsolicited heat stroke alert system for the elderly. In: Proceedings of IEEE International Conference on 2017 ICCE-TW, pp. 345–346 (2017)
Acknowledgments
This work was supported by JSPS KAKENHI Grant Number 15K00277.
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Haramaki, T., Nishino, H. (2018). An Edge Computer Based Driver Monitoring System for Assisting Safety Driving. In: Barolli, L., Xhafa, F., Javaid, N., Spaho, E., Kolici, V. (eds) Advances in Internet, Data & Web Technologies. EIDWT 2018. Lecture Notes on Data Engineering and Communications Technologies, vol 17. Springer, Cham. https://doi.org/10.1007/978-3-319-75928-9_57
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DOI: https://doi.org/10.1007/978-3-319-75928-9_57
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