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The results of a traffic crash reconstruction often include vehicle speeds to address causation and changes in velocity to indicate crash severity. Since these results are related, they should be modeled in a probabilistic context as a joint distribution. Current techniques in the traffic crash reconstruction literature assume the the input parameters and results of an analysis are independent, which may or may not be appropriate. Therefore, a discussion of uncertainty propagation techniques with correlation and Monte Carlo simulation of correlated variables is presented in this paper. The idea that measuring a parameter with a common instrument induces correlation is explored by examining the process of determining vehicle weights. Also, an example of determining the energy from crush is presented since the A and B stiffness coefficients are correlated. Results show the difference between accounting for correlation and assuming independence may be significant.

The Monte Carlo method is a well-known technique for propagating uncertainty in complex systems and has been applied to traffic crash reconstruction analysis. The Monte Carlo method is a probabilistic technique that randomly samples input distributions and then combines these samples according to a deterministic model. However, describing every input variable as a distribution requires knowledge of the distribution, which may or may not be available, and the time and expense of determining the distribution parameters may be prohibitive. Therefore, the most influential parameters from the input data, such as mean values, standard deviations, shape parameters, and correlation coefficients, can be determined using an analytical sensitivity calculation based on the score function.

Recovery of snapshot data recorded by Caterpillar engine control modules (ECMs) using Caterpillar Electronic Technician (CatET) software requires a complete snapshot record that contains information gathered both before and after an event. However, if an event is set and a crash ensues, or a crash creates an event, then it is possible for the ECM to lose power and not complete the recording. As such, the data may not be recoverable with CatET maintenance software. An examination of the J1708 network traffic reveals the snapshot data does exist and is recoverable. A motivational case study of a crash test between a Caterpillar powered school bus and a parked transit bus is presented to establish the hypothesis. Subsequently, a digital forensic recovery algorithm is detailed as it is implemented in the Synercon Technologies Forensic Link Adapter (FLA).

There are numerous activities in the automotive industry in which a vehicle drives a pre-defined route multiple times such as portable emissions measurement systems testing or real-world electric vehicle range testing. The speed profile is not the same for each drive cycle due to uncontrollable real-world variables such as traffic, stoplights, stalled vehicles, or weather conditions. It can be difficult to compare each run accurately. To this end, this paper presents a method to compare and quantify the repeatability of real-world on-road vehicle driving schedules using dynamic time warping (DTW). DTW is a well-developed computational algorithm which compares two different time-series signals describing the same underlying phenomenon but occurring at different time scales. DTW is applied to real-world, on-road drive cycles, and metrics are developed to quantify similarities between these drive cycles.