A Probabilistic Model for Driving-Style-Recognition-Enabled Driver Steering Behaviors

被引：45
|
作者：
Deng, Zejian [1 ,2 ]
Chu, Duanfeng [1 ]
Wu, Chaozhong [1 ]
Liu, Shidong [3 ]
Sun, Chen [2 ]
Liu, Teng [2 ]
Cao, Dongpu [2 ]
机构：
[1] Wuhan Univ Technol, Intelligent Transportat Syst Res Ctr, Wuhan 430063, Peoples R China
[2] Univ Waterloo, Dept Mech & Mechatron Engn, Waterloo, ON N2L 3G1, Canada
[3] China Automot Technol & Res Ctr Co Ltd, Dev Dept 1, Tianjin 300300, Peoples R China
来源：
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS | 2022年 / 52卷 / 03期
基金：
中国国家自然科学基金;
关键词：
Vehicles; Acceleration; Stochastic processes; Support vector machines; Predictive models; Probabilistic logic; Principal component analysis; Driver steering model; driving style; model predictive control; probabilistic modeling; stochastic programming; OPTIMAL PREVIEW CONTROL; PREDICTIVE CONTROL; RISK; ACCELERATION; STRATEGY; COMFORT; DEFINE; SPEED; SAFE;
D O I：
10.1109/TSMC.2020.3037229
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
This article presents a framework to determine driving style and design a driver steering model considering driver characteristics. First, principal component analysis (PCA) and K-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.
引用
收藏
页码：1838 / 1851
页数：14
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