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Enhanced Full-State Estimation and Dynamic-Model-Based Prediction for Road-Vehicles

[+] Author Affiliations
Aliakbar Alamdari, Javad Sovizi, Venkat N. Krovi

SUNY at Buffalo, Buffalo, NY

Paper No. DETC2014-34453, pp. V003T01A018; 8 pages
doi:10.1115/DETC2014-34453
From:
  • ASME 2014 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference
  • Volume 3: 16th International Conference on Advanced Vehicle Technologies; 11th International Conference on Design Education; 7th Frontiers in Biomedical Devices
  • Buffalo, New York, USA, August 17–20, 2014
  • Conference Sponsors: Design Engineering Division, Computers and Information in Engineering Division
  • ISBN: 978-0-7918-4634-6
  • Copyright © 2014 by ASME

abstract

In this paper, we address the enhanced state estimation and prediction system for automobile applications by fusing relatively low-cost and noisy Inertial Navigation System (INS) sensing with Global Positioning System (GPS) measurements. An unscented Kalman filter is used to merge multi-rate measurements from GPS and INS sensors together with a high-fidelity vehicle-dynamics model for state-predictions. The high-fidelity motion model (including suspension-effects) for the vehicle motion trajectory on uneven terrain is captured by a 20-state system of nonlinear differential equations. Computer simulation results illustrate the effectiveness of sensor-fusion (building upon the merger of an inexpensive INS sensing with GPS based measurements) to accurately estimate the full system-state. The relative ease of implementation, accuracy and predictive performance with low-cost sensing will facilitate its use in various electronic control and safety-systems, such as Electronic Stability Program, Anti-lock Brake Systems, and the Lateral Dynamic Stability Control.

Copyright © 2014 by ASME

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