Collision Warning System Using Naïve Bayes Classifier

Ahmed Tijani ()

Technium, 2022, vol. 4, issue 1, 39-56

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.

Date: 2022
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://techniumscience.com/index.php/technium/article/view/6653/2349 (application/pdf)
https://techniumscience.com/index.php/technium/article/view/6653 (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:tec:techni:v:4:y:2022:i:1:p:39-56

DOI: 10.47577/technium.v4i5.6653

Access Statistics for this article

Technium is currently edited by Scurtu Ionut Cristian

More articles in Technium from Technium Science
Bibliographic data for series maintained by Ana Maria Golita ().