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. The steering wheel angle and the vehicle’s longitudinal acceleration, lateral acceleration, and angular rate data were extracted and used to predict the vehicle position and heading over the duration of the VCH data record preceding the vehicle coming to rest. These predictions were generated by directly integrating the VCH data and by using the VCH data as inputs to PC-Crash simulations. The predicted positions and headings were then compared to the actual position and heading data measured using differential GPS synchronized to the VCH data record. The results of these analyses provide insights into the best methods for reconstructing vehicle kinematics from VCH data and estimates of the errors associated with different reconstruction techniques.
