New Rollover Index for the Detection of Tripped and Untripped Rollovers

Abstract

Accurate detection of the danger of an impending rollover is necessary in order to effectively use active vehicle rollover prevention. A real-time rollover index is an indicator used for this purpose. A traditional rollover index utilizes lateral acceleration measurements and can detect only untripped rollovers that happen due to high lateral acceleration from a sharp turn. It fails to detect tripped rollovers that happen due to tripping from external inputs such as forces when a vehicle strikes a curb or a road bump. Therefore, this paper develops a new rollover index that can detect both tripped and untripped rollovers. The new rollover index utilizes vertical accelerometers in addition to a lateral accelerometer and is able to predict rollover in spite of unknown external inputs acting on the system. The accuracy of the developed rollover index is evaluated through simulations with industry-standard software CARSIM and experimental tests on a one-eighth-scaled vehicle. In order to show that the scaled vehicle experiments can represent a full-sized vehicle, the Buckingham π theorem is used to show dynamic similarity. The simulation and experimental results show that the new rollover index can reliably detect both tripped and untripped rollovers.