Validation of Event Data Recorders in High Severity Full‑Frontal Crash Tests

Tsoi, Ada; Hinch, John; Ruth, Richard; Gabler, Hampton C.

doi:10.4271/2013-01-1265

Cited by 26 publications

(2 citation statements)

References 9 publications

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“…The uncertainty of the ΔV determined using the CRASH3 method has been analyzed [14,16]. Comparison of the uncertainty reported in those references with the uncertainty of the ΔV long determined from ACM measurements [19] provides the rationale for the use of measured ΔV versus calculated the ΔV using CRASH3.…”

Section: Discussionmentioning

confidence: 99%

Nonlinear Optimization in Vehicular Crash Reconstruction

Brach

Mink

2015

SAE Int. J. Trans. Safety

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Recent advances in the collection of data by the on-board electronic systems, on both light vehicles and heavy trucks, have changed the scope of the physical evidence available to reconstructionists. In addition to (or, sometimes in place of) traditional physical evidence such as tire marks, residual crush, rest positions, etc., the list of physical evidence in crashes involving vehicles with an EDR may

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Section: Discussionmentioning

confidence: 99%

Nonlinear Optimization in Vehicular Crash Reconstruction

Brach

Mink

2015

SAE Int. J. Trans. Safety

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“…There are also a few publications directly dealing with the accuracy of determining the parameters that describe the vehicle motion, but they mainly concern the strict collision phase and the determining of the vehicle velocity immediately before and after the collision or the vehicle velocity change during the collision, denoted by ∆V. As examples of such publications, [9][10][11][12][13][14][15][16] may be mentioned. A review of the materials showing the errors that occur when the pre-and post-impact velocities and ∆V are determined has been provided in [17], indicating the possible reasons for the inconsistencies having occurred.…”

Section: Introductionmentioning

confidence: 99%

Are EDR Devices Undoubtedly Helpful in the Reconstruction of a Road Traffic Accident?

Guzek

Lozia

2021

Energies

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All over the world, the vehicles introduced now into the market are usually provided with EDRs (Event Data Recorders), intended to measure and record the parameters that characterise the vehicle motion in the pre-, during-, and post-accident phases. The EDRs are to facilitate the description and reconstruction of possible road accidents. They are patterned on aircraft “black boxes” (flight recorders). Many of them have simplified design, disregarding three (of six) vector components that describe the motion of the vehicle body solid. In the paper presented, the authors used simulation models built by themselves to represent motor vehicle dynamics and the reconstruction of vehicle trajectory and velocities based on records obtained from two EDR types: “aircraft” one (EDR1) and “simplified” one (EDR2). Using a simulation method, they examined the impact of the said simplifications mentioned above on the quality of reconstruction of vehicle motion for four typical manoeuvres in road traffic. The calculation results obtained for input data adopted to rep-resent a medium-class passenger car have shown that the simplifications may cause considerable reconstruction errors. This particularly applies to the manoeuvres where significant changes took place in the roll and pitch angles of the vehicle body solid (to which the EDR was fixed) or where the changes were characterised by absence of symmetry in the parameters that describe the manoeuvre and by the constant sign of the vehicle body roll angles.

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Comparison and Validation of Injury Risk Classifiers for Advanced Automated Crash Notification Systems

Kusano

Gabler

2014

Traffic Injury Prevention

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Logistic regression slightly outperformed the machine learning algorithms based on sensitivity and specificity of the models. Previous studies on AACN risk curves used the same data to train and test the power of the models and as a result had higher sensitivity compared to the cross-validated results from this study. Future studies should account for future data; for example, by using cross-validation or risk presenting optimistic predictions of field performance. Past algorithms have been criticized for relying on age and sex, being difficult to measure by vehicle sensors, and inaccuracies in classifying damage side. The models with accurate damage side and including age/sex did outperform models with less accurate damage side and without age/sex, but the differences were small, suggesting that the success of AACN is not reliant on these predictors.

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Validation of Event Data Recorders in High Severity Full‑Frontal Crash Tests

Cited by 26 publications

References 9 publications

Nonlinear Optimization in Vehicular Crash Reconstruction

Nonlinear Optimization in Vehicular Crash Reconstruction

Are EDR Devices Undoubtedly Helpful in the Reconstruction of a Road Traffic Accident?

Comparison and Validation of Injury Risk Classifiers for Advanced Automated Crash Notification Systems

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