Modeling and Explanation of Driver Steering Style: An Experiment under Large-Curvature Road Condition

Abstract

Understanding driver’s maneuver behavior is an important prerequisite for providing drivers with different levels of assistance in the collaborative driving system. Aiming at establishing a general and interpretable model of driver steering styles, 38 drivers’ data are collected by a driving simulator platform, where a U-shaped experimental scene is built. To reduce data redundancy, Principal Component Analysis (PCA) is utilized to extract key features. Vali-dated by both Elbow Method and Silhouette Coefficient, the features are classified by k-means cluster. Finally, three driving styles with different characteristics are defined, and the corresponding original data are compared to make a reasonable explanation. The results can be used as a design basis for customizing shared steering controllers in collaborative driving.