Prediction and Prevention of Tripped Rollovers

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.