A Probabilistic Model for Driving-Style-Recognition-Enabled Driver Steering Behaviors
Zejian Deng, Duanfeng Chu, Chaozhong Wu, Shidong Liu, Chen Sun, Teng Liu, Dongpu Cao
Abstract
This article presents a framework to determine driving style and design a driver steering model considering driver characteristics. First, principal component analysis (PCA) and <inline-formula> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula>-means clustering are utilized to classify 30 participants into cautious, moderate, and aggressive drivers. Subsequently, a generic steering model is established based on the model predictive control method. Thereafter, the maximum lateral acceleration is extracted as a crucial indicator to represent driver characteristics, and it is calibrated through probabilistic models using the dataset, which consists of the classified drivers. Besides, point estimation model and interval estimation model are leveraged to determine driving style and adjust constraints in the stochastic programming-based steering model. Finally, simulation experiments present the variations of actual output trajectories between the aggressive drivers and the cautious drivers.