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Modelling of Safe Driving Assistance System for Automotive and Prediction of Accident Rates

Modelling of Safe Driving Assistance System for Automotive and Prediction of Accident Rates

Debraj Bhattacharjee, Prabha Bhola, Pranab K. Dan
Copyright: © 2019 |Volume: 10 |Issue: 1 |Pages: 17
ISSN: 1941-6237|EISSN: 1941-6245|EISBN13: 9781522565062|DOI: 10.4018/IJACI.2019010104
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MLA

Bhattacharjee, Debraj, et al. "Modelling of Safe Driving Assistance System for Automotive and Prediction of Accident Rates." IJACI vol.10, no.1 2019: pp.61-77. http://doi.org/10.4018/IJACI.2019010104

APA

Bhattacharjee, D., Bhola, P., & Dan, P. K. (2019). Modelling of Safe Driving Assistance System for Automotive and Prediction of Accident Rates. International Journal of Ambient Computing and Intelligence (IJACI), 10(1), 61-77. http://doi.org/10.4018/IJACI.2019010104

Chicago

Bhattacharjee, Debraj, Prabha Bhola, and Pranab K. Dan. "Modelling of Safe Driving Assistance System for Automotive and Prediction of Accident Rates," International Journal of Ambient Computing and Intelligence (IJACI) 10, no.1: 61-77. http://doi.org/10.4018/IJACI.2019010104

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Abstract

This research article attempts to analytically determine the factors, significant for safety, in connection with driving of automotives as well as to develop a conceptual model of the driving assistance system, using the knowledge about such factors. Millions of casualties due to road accidents, happen worldwide every year and the annual average of lives lost in India alone is about hundred and fifty thousand. The causes of such accidents are attributed to road characteristic and condition, driving faults, driving conditions or traffic environmental factors and defects or functional failure in vehicle mechanism. Studies have focused primarily on these factors without associating the ‘weather' which has been reported as in a work but as an isolated factor without including the above three. This work includes all the four stated factors in modelling the driver assistance system for automatic speed control with warning system module. Further, to predict accident rates in a particular region a model using adaptive neuro fuzzy inference system (ANFIS) is proposed in this work, which may be used by the vehicle manufactures to select the right product variant to minimise accidents.

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