Open Access Open Access  Restricted Access Subscription or Fee Access

A Study in Cloud based Driver Drowsiness and Aggressiveness Detection

Edith Luhanga, Eliab Z. Opiyo

Abstract


In this paper a novel advanced driver assistant system is modelled. This driver assistant model detects the driver abnormal habit via a smart device and alert it to the remote person and passengers for their own safety. The proposed model interacts via the cloud-based web application which alert the passenger and the owner of the vehicle for safety. The system detects the drowsiness of the driver and send alert to the passengers and vehicles owner as smart alert. The evaluation of the system is performed in real time cloud system.


Keywords


Drowsiness Detection, Driver Assistant System, Smart Driver Supportive Device.

Full Text:

PDF

References


Chhabra, R., Verma, S., & Krishna, C. R. (2019). Detecting Aggressive Driving Behavior using Mobile Smartphone. In Proceedings of 2nd International Conference on Communication, Computing and Networking (pp. 513-521). Springer, Singapore.

Kashevnik, A., Lashkov, I., & Gurtov, A. (2019). Methodology and Mobile Application for Driver Behavior Analysis and Accident Prevention. IEEE Transactions on Intelligent Transportation Systems.

Alvarez-Coello, D., Klotz, B., Wilms, D., Fejji, S., Gómez, J. M., & Troncy, R. (2019, June). Modeling dangerous driving events based on in-vehicle data using Random Forest and Recurrent Neural Network. In 2019 IEEE Intelligent Vehicles Symposium (IV) (pp. 165-170). IEEE.

Peris-Lopez, J. Hernandez-Castro, J. Estevez-Tapiador, and A. Ribagorda, “LMAP: A real lightweight mutual authentication protocol for low-cost RFID tags,” inProc. 2nd Workshop RFID Secur.,2006, pp. 27–36

Yao, C. Chu, and Z. Li, “The adoption and implementation of RFID technologies in healthcare: A literature review,” J. Med. Syst., vol. 36, no. 6, pp. 3507–3525, 2012.

H. Chien and C. Chen, “Mutual authentication protocol for RFID conforming to EPC class 1 generation 2 standards,” Comput. Stand. Interfaces, vol. 29, no. 2, pp. 254–259, 2007.

Peris-Lopez, J. Hernandez-Castro, J. Estevez-Tapiador, and A. Ribagorda, “EMAP: An efficient mutual authentication protocol for low-cost RFID tags,” in Proc. OTM Federated Conf. Workshop: IS Workshop, 2006, pp. 352–361

S. Weis, S. Sarma, R. Rivest, andD. Engels, “Security and privacy aspects of low-cost radio frequency identification systems,” inProc. Int. Conf. Secur. Pervasive Comput., 2003, pp. 454–469.

Yi, D., Su, J., Liu, C., Quddus, M., & Chen, W. H. (2019). A machine learning based personalized system for driving state recognition. Transportation Research Part C: Emerging Technologies, 105, 241-261.

Menegazzo, J., & Wangenheim, A. V. (2018). Vehicular Perception and Proprioception Based on Inertial Sensing: a Systematic Review. Brazilian National Institute for Digital Convergence-Technical Reports.

Małecki, K., Forczmański, P., Nowosielski, A., Smoliński, A., & Ozga, D. (2019, May). A New Benchmark Collection for Driver Fatigue Research Based on Thermal, Depth Map and Visible Light Imagery. In International Conference on Computer Recognition Systems (pp. 295-304). Springer, Cham.

Shafaei, S., Hacizade, T., & Knoll, A. (2018, December). Integration of Driver Behavior into Emotion Recognition Systems: A Preliminary Study on Steering Wheel and Vehicle Acceleration. In Asian Conference on Computer Vision (pp. 386-401). Springer, Cham.

Kleiman, M. A., Jones, T., Miller, C. J., & Halperin, R. (2018). Driving while stoned: Issues and policy options. Journal of Drug Policy Analysis, 11(2).

Kim, J., Sato, K., Hashimoto, N., Kashevnik, A., Tomita, K., Miyakoshi, S., & Boyali, A. (2018). Impact of the face angle to traveling trajectory during the riding standing-type personal mobility device. In MATEC Web of Conferences (Vol. 161, p. 03001). EDP Sciences.

Kim, J., Sato, K., Hashimoto, N., Kashevnik, A. M., Tomita, K., Miyakoshi, S., ... & Boyali, A. (2019). Context-based rider assistant system for two wheeled self-balancing vehicles. Труды СПИИРАН, 18(3), 583-614.


Refbacks

  • There are currently no refbacks.


Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.