Rash Driving Detection

  • Komal Shete
Keywords: Mobile Sensing, Smartphone, IMU, Data, Driving Behavior, Insurance

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.

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.
Published
2018-04-15
How to Cite
Shete, K. (2018, April 15). Rash Driving Detection. ASIAN JOURNAL FOR CONVERGENCE IN TECHNOLOGY (AJCT ) -UGC LISTED, 4(I). Retrieved from http://asianssr.org/index.php/ajct/article/view/476
Section
Computer Science and Engineering