Rash Driving Detection

  • Komal Shete

Abstract

Abnormal driving behaviors observation might be an anchor to improving driving safety. Existing works on driving behaviors observation using smart phones only supply a coarse-grained result, i.e. characteristic abnormal driving behaviors from normal ones. to boost drivers’ awareness of their driving habits therefore on stop potential car accidents, we would like to think about a fine-grained observation approach, that not only detects abnormal driving behaviors but in addition identifies specific varieties of abnormal driving behaviors, i.e. Weaving, Swerving, side slippery , fast reversion, Turning with a large radius and sudden braking. Recognizing this observation, we tend to further propose a fine-grained abnormal Driving behavior Detection and identification system to perform real-time high-accurate abnormal driving behaviors observation using smart phone sensors. We tend to extract effective choices to capture the patterns of abnormal driving behaviors. After that, a pair of machine learning ways, rash driving, or officially driving under the Influence (DUI) of alcohol, can be a serious reason for traffic accidents throughout the globe. In this, we tend to tend to propose a extraordinarily efficient system geared toward early detection and alert of dangerous vehicle maneuvers typically related to rash driving. The whole solution wants only a mobile placed in vehicle and with accelerometer device. Once any proof of rash driving is present, the mobile will automatically alert the driver or sends a message to predefined number in application for help well before accident actually happens.

Keywords: Mobile Sensing, Smartphone, IMU, Data, Driving Behavior, Insurance

References

[1] World.Health.Organisation. The top ten causes of death. [Online].Available: http://www.who.int/mediacentre /factsheets/fs310/en/ [2] C. Saiprasert and W. Pattara-Atikom, “Smartphone enabled danger- ous driving report system,” in Proc. HICSS, 2013, pp. 1231–1237. [3] M. V. Yeo, X. Li, K. Shen, and E. P. Wilder-Smith, “Can svm be used for automatic eeg detection of drowsiness during car driving?” Elsevier Safety Science vol. 47, pp. 115–124, 2009.
Statistics
38 Views | 31 Downloads
How to Cite
Shete, K. (2018). Rash Driving Detection. Asian Journal For Convergence In Technology (Founded by ISB &M School of Technology )), 4(I). https://doi.org/10.33130/asian journals.v4iI.476
Section
Computer Science and Engineering