Intelligent Transportation Systems Institute, Center for Transportation Studies, University of Minnesota
Vehicle rollovers account for a significant fraction of highway traffic fatalities, causing more than 10,000
deaths in the U.S. each year. While active rollover prevention systems have been developed by several automotive
manufacturers, the currently available systems address only untripped rollovers. This project focuses on the
development of a new real-time rollover index that can detect both tripped and un-tripped rollovers.
A new methodology is developed for estimation of unknown inputs in a class of nonlinear dynamic
systems. The methodology is based on nonlinear observer design and dynamic model inversion to compute the
unknown inputs from output measurements. The developed approach can enable observer design for a large class
of differentiable nonlinear systems with a globally (or locally) bounded Jacobian.
The developed nonlinear observer is then applied for rollover index estimation. The rollover index
estimation algorithm is evaluated through simulations with an industry standard software, CARSIM, and with
experimental tests on a 1/8th scaled vehicle. The simulation and experimental results show that the developed
nonlinear observer can reliably estimate vehicle states, unknown normal tire forces, and rollover index for
predicting both un-tripped and tripped rollovers. The final chapter of this report evaluates the feasibility of rollover
prevention for tripped rollovers using currently available actuation systems on passenger sedans.
Department of Mechanical Engineering; University of Minnesota
Phanomchoeng, Gridsada; Rajamani, Rajesh.
Prediction and Prevention of Tripped Rollovers.
Intelligent Transportation Systems Institute, Center for Transportation Studies, University of Minnesota.
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