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An IMM Algorithm for Tracking Maneuvering Vehicles in an Adaptive Cruise Control Environment  

Kim, Yong-Shik (Department of Mechanical and Intelligent Systems Engineering, Pusan National University)
Hong, Keum-Shik (School of Mechanical Engineering, Pusan National University)
Publication Information
International Journal of Control, Automation, and Systems / v.2, no.3, 2004 , pp. 310-318 More about this Journal
Abstract
In this paper, an unscented Kalman filter (UKF) for curvilinear motions in an interacting multiple model (IMM) algorithm to track a maneuvering vehicle on a road is investigated. Driving patterns of vehicles on a road are modeled as stochastic hybrid systems. In order to track the maneuvering vehicles, two kinematic models are derived: A constant velocity model for linear motions and a constant-speed turn model for curvilinear motions. For the constant-speed turn model, an UKF is used because of the drawbacks of the extended Kalman filter in nonlinear systems. The suggested algorithm reduces the root mean squares error for linear motions and rapidly detects possible turning motions.
Keywords
Adaptive cruise control; constant-speed turn model; extended Kalman filter; hybrid estimation; interacting multiple model; nonlinear filtering; unscented Kalman filter;
Citations & Related Records
Times Cited By KSCI : 4  (Citation Analysis)
Times Cited By Web Of Science : 14  (Related Records In Web of Science)
Times Cited By SCOPUS : 17
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