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2024 | OriginalPaper | Chapter

8. Driver Skill Profiling Using Machine Learning

Authors : Nadeem Akhtar, Mithun Mohan

Published in: Infrastructure and Built Environment for Sustainable and Resilient Societies

Publisher: Springer Nature Singapore

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Abstract

This chapter delves into the critical issue of road safety and the importance of evaluating driving skills, particularly among young and novice drivers. It introduces a machine learning-based scoring system to quantify drivers' abilities, focusing on parallel and reverse parking skills. The study involves a self-assessment questionnaire and field tests, with data analyzed using both supervised and unsupervised machine learning techniques. The Random Forest algorithm is employed for predicting driving skills, while Fuzzy C-means clustering is used to group drivers based on their self-assessment responses. The chapter highlights the discrepancies between self-assessed and actual driving skills, offering valuable insights into improving road safety through targeted training and evaluation. The combination of machine learning approaches provides a robust method for assessing and enhancing driving skills, making it a significant contribution to the field of transportation safety.

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Metadata
Title
Driver Skill Profiling Using Machine Learning
Authors
Nadeem Akhtar
Mithun Mohan
Copyright Year
2024
Publisher
Springer Nature Singapore
DOI
https://doi.org/10.1007/978-981-97-1503-9_8

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