Trajectory Data Driven Driving Style Recognition for Autonomous Vehicles Using Unsupervised Clustering

Abstract

Introduction: This research focuses on enhancing the understanding and classification of vehicle driving styles by analyzing extensive trajectory data. Recognizing and categorizing different driving behaviours is crucial, particularly for the development of autonomous vehicles, which must predict and respond to the diverse actions of human drivers. Understanding these driving styles is essential for improving road safety and ensuring that autonomous systems can navigate mixed traffic environments effectively.

Objectives: The primary objective of this study is to classify vehicle driving styles into distinct categories like aggressive, moderate, and traditional by leveraging unsupervised learning techniques. This classification aims to improve the predictive capabilities of autonomous vehicles and enhance overall road safety by providing a more nuanced understanding of driving behaviours.

Methods: The study begins by applying Principal Component Analysis (PCA) to simplify complex trajectory data, reducing multiple characteristic indexes into two principal components that encapsulate the most significant features related to driving behaviour. To determine the optimal number of driving style categories, the "Elbow rule" and Silhouette analysis are employed, followed by the application of the K-means clustering algorithm. This approach allows for the effective grouping of driving styles based on the processed data.

Results: The analysis identified three distinct driving styles: aggressive, moderate, and traditional. Aggressive driving is characterized by higher velocities, greater acceleration, and increased jerk, along with shorter space and time headways. Traditional driving styles exhibit more conservative behaviours, with lower speeds, reduced acceleration, and greater following distances. Moderate driving styles lie between these two extremes, reflecting a balanced approach in terms of speed, acceleration, and headway distances.

Conclusions: The findings of this study have significant implications for the development and operation of autonomous vehicles. By accurately classifying driving styles, autonomous systems can better anticipate and react to the behavior of surrounding vehicles, thereby enhancing safety in mixed traffic environments. The research also demonstrates the potential for extending the proposed unsupervised learning approach to other driving scenarios and datasets, offering a scalable solution for ongoing advancements in intelligent transportation systems.