An Interacting Multiple-Model-Based Algorithm for Driver Behavior Characterization Using Handling Risk

• Published in: IEEE transactions on intelligent transportation systems Vol. 20; no. 12; pp. 4308 - 4317
• Main Authors: Hong, Sanghyun, Lu, Jianbo, Panigrahi, Smruti R., Scott, Jonathan, Filev, Dimitar P.
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.12.2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Acceleration; Active control; active safety; Algorithms; Control systems; Driver behavior; Drivers; Dynamics; Handling; Heuristic algorithms; intelligent vehicles; Interacting multiple model; Kalman filter; Mathematical models; Risk management; Tires; Trucking industry; Vehicle dynamics; Vehicles
• ISSN: 1524-9050; 1558-0016
• DOI: 10.1109/TITS.2016.2633254

Performance of vehicle control systems, such as active safety systems and driver assistance systems, can be significantly improved by taking driver behavior information into consideration. This paper implements a handling limit-based algorithm for driver behavior characterization by introducing stochastic perspective with the interacting multiple model (IMM) estimation theory. The proposed algorithm constructs mathematical models for four vehicle dynamics categories. The IMM estimator is designed for each vehicle dynamics category to evaluate driver scores. The proposed algorithm is compared with an existing handling limit-based algorithm through experimental tests, and the results illustrate advantages of the proposed algorithm.