Modeling and Explanation of Driver Steering Style: An Experiment under Large-Curvature Road Condition
Authors: Puheng Shao, Zhenwu Fang, Jinxiang Wang, Zhongsheng Lin, Yin Guodong
Abstract: Before the maturation of vehicle’s self-driving, human-vehicle shared control would be a dominant solution in a certain period. Understanding driver’s maneuver behavior is an important prerequisite for providing drivers with different levels of assistance in the collaborative driving system. This research aims to classify the characteristics of drivers’ maneuver modes and establish a general model of driver steering styles.Firstly, an experiment is designed to collect the behavioral data under a certain circumstance of the drivers. As driving simulating has significant advantages over the real vehicle, for instance, the replicability and stability of the testing scene, this experiment is conducted on a driving simulator platform with six degrees of freedom (6-DOF). 38 participants (21 males, 17 females) with different personalities and driving experiences are required to drive through a U-shaped testing scene. Meanwhile, data such as velocities, lateral deviations, and steering wheel torques are recorded at a frequency of 60Hz. Secondly, in the data processing part, the Principal Component Analysis (PCA) is utilized to extract key features from original data, aiming to reduce redundancy between steering characteristics. Two principal components are calculated to represent the original features. And then, determining the clustering number as three by both Elbow Method and Silhouette Coefficient, three types of driving styles are classified by the K-means cluster. Finally, after the explanation based on the corresponding original data of lateral deviations, steering wheel torques, and its change rate, the drivers’ proficiencies and path tracking abilities are compared, and the three styles are defined as moderate, radical, and conservative types. In the result analyzing, the driver’s path tracking ability is reflected by smaller lateral deviation, a middle steering wheel torque represents higher proficiency, and the change rate of the torque can show the extent of radicalness. The results show that the moderate driver type has high proficiency in vehicle control, who has more direction adjustments and strong path tracking accuracy. The radical type drivers also manipulate the steering wheel a lot, but their routes have relatively violent fluctuations. While the conservative drivers operate the steering wheel carefully, which displays their lack of driving adeptness.This study identifies the specific characteristics of drivers’ steering behaviors and obtains the parametric boundary of driving styles. In further work, the results can be used as a design basis for customizing shared steering controllers for different driver types in collaborative driving. After identifying the driving style by measuring certain steering indexes, a personalized co-drive mode can be confirmed, which makes the driver feel “the vehicle drives like him/herself”, then the human-vehicle trust and driving experience can be greatly improved.
Keywords: Driving Style, Personalized Driving, Clustering
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