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

Abstract

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.