Title: Vehicle Dynamic Prediction Systems with On-Line Identification of Vehicle Parameters and Road Conditions
Authors: Hsu, Ling-Yuan
Chen, Tsung-Lin
Department of Mechanical Engineering
Keywords: dynamics predictions;sensor fusion system;vehicle parameter identifications;road condition identifications
Issue Date: 1-Nov-2012
Abstract: This paper presents a vehicle dynamics prediction system, which consists of a sensor fusion system and a vehicle parameter identification system. This sensor fusion system can obtain the six degree-of-freedom vehicle dynamics and two road angles without using a vehiclemodel. The vehicle parameter identification system uses the vehicle dynamics from the sensor fusion system to identify ten vehicle parameters in real time, including vehicle mass, moment of inertial, and road friction coefficients. With above two systems, the future vehicle dynamics is predicted by using a vehicle dynamics model, obtained from the parameter identification system, to propagate with time the current vehicle state values, obtained from the sensor fusion system. Comparing with most existing literatures in this field, the proposed approach improves the prediction accuracy both by incorporating more vehicle dynamics to the prediction system and by on-line identification to minimize the vehicle modeling errors. Simulation results show that the proposed method successfully predicts the vehicle dynamics in a left-hand turn event and a rollover event. The prediction inaccuracy is 0.51% in a left-hand turn event and 27.3% in a rollover event.
URI: http://dx.doi.org/10.3390/s121115778
ISSN: 1424-8220
DOI: 10.3390/s121115778
Journal: SENSORS
Volume: 12
Issue: 11
Begin Page: 15778
End Page: 15800
Appears in Collections:Articles

Files in This Item:

  1. 000311429500074.pdf