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Vehicle rollovers are complex events that can be difficult to reconstruct. The goal of this study was to explore whether different vehicle trip models could identify when during the trip phase a vehicle possesses the dynamic conditions needed to rollover. We used three sport utility vehicles (SUVs) with either absent or disabled electronic stability control to conduct six tests involving a steer-induced control loss on a large flat concrete surface. Vehicle kinematics were measured using a GPS speed sensor, tri-axial accelerometers, tri-axial angular rate sensors, and both drone- and land-based video cameras. Four vehicle trip metrics were derived and evaluated using the vehicle dynamics between steer onset and the end of the trip phase. During three tests, one or more of the vehicle’s tires lifted off the ground but the vehicle did not roll. In the other three tests, the vehicle rolled.

Modern vehicles record dynamic data from a number of on-board sensors for events that could precede a crash. These data can be used to reconstruct the behavior of a vehicle, although the accuracy of these reconstructions has not yet been quantified. Here, we evaluated various methods of reconstructing the vehicle kinematics of a 2017 and a 2018 Toyota Corolla based on Vehicle Control History (VCH) data from overlapping events generated by the pre-collision system (PCS), sudden braking (SB) and anti-lock brake (ABS) activation. The vehicles were driven towards a stationary target at 32-64 km/h (20-40 mph) and then after the pre-collision alarm sounded the vehicle was steered sharply right or left and braked rapidly to rest. VCH data for PCS event were recorded at 2 Hz and for the sudden braking and ABS activation events at 6.7 Hz.