Title: A new driving condition identification method for heavy duty vehicles based on HHMM
Authors: Tianjun Zhu; Bin Li; Changfu Zong
Addresses: College of Mechanical and Equipment Engineering, Hebei University of Engineering, Handan, 056038, China ' CONCAVE Research Center, Department of Mechanical and Industrial Engineering, Concordia University, Montreal, H3G2W1, Canada ' College of Mechanical and Automotive Engineering, Zhaoqing University, Zhaoqing, 526061, China
Abstract: Aiming to improve active safety of heavy duty vehicles, a new dynamic driving condition identification method is developed in this paper through incorporating Hierarchical Hidden Markov Models (HHMM) into the rollover warning system for heavy duty vehicles to assist the driver to be aware of the driving conditions. The corresponding data under typical driving conditions are first collected and then put into test with Student's t-test method and Grubbs's test method (T-G test method). The outliers filtered by T-G test from the data are detected and eliminated. K-Means algorithm, used to set up the rollover threshold value and Baum-Welch algorithm for optimising the proposed rollover warning model, are discussed in detail. Computer simulations under different driving conditions are carried out to verify the optimised HHMM. The simulation results demonstrate that the proposed driving condition identification method can effectively identify the driving status with a high accuracy under a variety of driving conditions and could be used for real-time rollover warning control.
Keywords: HHMM; hierarchical hidden Markov model; heavy duty vehicles; driving condition identification; rollover warning system.
International Journal of Heavy Vehicle Systems, 2019 Vol.26 No.3/4, pp.334 - 350
Received: 19 Jan 2017
Accepted: 05 Aug 2017
Published online: 11 Aug 2019 *