Personalised Driver Risk Assessment With Adaptive Feedback Using Crowdsensed Telemetric Data

Abstract

This paper presents a comprehensive, data-driven framework for personalised driving risk assessment, designed to enhance driver safety within intelligent transportation systems. By leveraging crowdsensed telemetric and road environment data, the framework captures diverse driving behaviours and contextual factors to provide real-time, individualised risk insights. The two-phase framework combines Gaussian Mixture Model (GMM) clustering, Deep Embedded Clustering (DEC), and Fully Connected Network (FCN) for accurate risk classification and prediction, while Deep Q-Learning (DQN) delivers adaptive feedback that encourages safer driving practices. Extensive evaluation shows that our approach outperforms traditional models in both accuracy and adaptability with an accuracy score of 95% and an average F1-score of 0.94, demonstrating its value in capturing complex driver behaviour patterns and contributing a scalable solution for transportation safety.