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Collision Warning System Using Naïve Bayes Classifier

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  • Ahmed Tijani

Abstract

Motor vehicle crashes can lead to traumatic experiences. High-impact collisions usually cause severe injuries or fatalities. A collision warning system that analyzes driving behaviors and warns drivers of impending crashes can prevent road collisions and save lives, so increasing traffic safety. An application of the Naïve Bayes classifier model to determine the potential for rear-end collisions between highway vehicles is presented. The Naïve Bayes classifier is a supervised machine-learning model based on Bayes’s theorem. Two vehicles are utilized, with one vehicle following the other. The parameters studied are speed, distance, acceleration, and deceleration. Training examples involving over 100 potential collision scenarios have been evaluated. Simulation results show that the model successfully responds to and correctly predicts potential collisions.

Suggested Citation

  • Ahmed Tijani, 2022. "Collision Warning System Using Naïve Bayes Classifier," Technium, Technium Science, vol. 4(1), pages 39-56.
    • Handle: RePEc:tec:techni:v:4:y:2022:i:1:p:39-56
    DOI: 10.47577/technium.v4i5.6653
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    JEL classification:

    • R00 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General - - - General
    • Z0 - Other Special Topics - - General

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