Driving Style Classification Using Deep Temporal Clustering with Enhanced Explainability
Yuxiang Feng, Qiming Ye, Fahmy Adan, Luís Marques, Panagiotis Angeloudis · 2023
The driving styles of human drivers exhibit a diverse range of observed driving patterns and manoeuvres, influenced by their habitual choice of driving behaviours. Understanding these driving styles is essential for enhancing road safety and reducing emissions. In this paper, we propose a deep temporal clustering approach to classify driving styles, providing enhanced explainability. A comprehensive car-following dataset was collected, incorporating extensive feature parameters. Subsequently, we developed a deep temporal clustering-based classification method that considers the variations in driving style within a single trip. The performance evaluation employed K-Shape clustering, and the significance of various features was assessed using SHAP values, enhancing the interpretability of the model. Our findings contribute to the advancement of driving style classification methods, promoting a deeper understanding of driving behaviours for improved road safety measures.