Method to evaluate safety enhancement of the past and methodology to predict future enhancement based on vehicle deformation depth

doi:10.16511/j.cnki.qhdxxb.2016.22.002

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Abstract Accident velocity information is difficult to obtain with accident reconstruction used to compute velocities, primarily using energy-based estimates. However, the different stiffnesses of the vehicles are difficult to estimate, which leads to significant uncertainty in the computed velocity. This is a major problem because most approaches for estimating the effect of driver assistant systems need the velocities of the involved vehicles. A method is given here to evaluate past safety enhancements to predict future enhancement based on the vehicle deformation depth. Analyses of German accident data show that for vehicles of significantly different stiffnesses, the vehicle deformation depth more truly reflects the severity of the accident than the velocity change. Vehicle safety enhancements over the past 30 years are then calculated based on the deformation depth. The results are in good agreement with historical data, which verifies the safety enhancement evaluation method using deformation depth information. A deformation-depth-based safety impact prediction method is also given for driver assistance systems. This prediction method uses the accurate deformation depth information in the accident database to improve evaluations of driver assistance systems.

Keywords safety impact evaluation driver assistance system vehicle deformation index accident reconstruction

ZTFLH:

U461.91

Issue Date: 15 February 2016

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	CHEN Long
	Robert Zobel
	LI Keqiang
	WANG Hongyan
	CHEN Junyi

Cite this article:

CHEN Long,Robert Zobel,LI Keqiang, et al. Method to evaluate safety enhancement of the past and methodology to predict future enhancement based on vehicle deformation depth[J]. Journal of Tsinghua University(Science and Technology), 2016, 56(2): 124-129.

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http://jst.tsinghuajournals.com/EN/10.16511/j.cnki.qhdxxb.2016.22.002 OR http://jst.tsinghuajournals.com/EN/Y2016/V56/I2/124

[1] Eriksson H, Jacobson J, Hérard J. ActiveTest-121214- D34-V30-FINAL [R]. European Community's 7th Framework Programme, 2013.
[2] Becker H, Busch S, Zobel R. Methods for the evaluation of primary safety measures by means of accident research [C]//30th FISITA World Automotive Congress. Barcelona, 2004.
[3] Fildes B, Keall M, Thomas P, et al. Evaluation of the benefits of vehicle safety technology: The MUNDS study [J]. Accident Analysis & Prevention, 2013, 55: 274-281.
[4] Burgett A, Srinivasan G, Ranganathan R. A Methodology for Estimating Potential Safety Benefits for Pre-Production Driver Assistance Systems, DOT HS 810945 [R]. Washington DC: National Highway Traffic Safety Administration, 2008.
[5] Bálint A, Fagerlind H, Kullgren A. A test-based method for the assessment of pre-crash warning and braking systems [J]. Accident Analysis & Prevention, 2013, 59: 192-199.
[6] Lee J D, McGehee D V, Brown T L, et al. Collision warning timing, driver distraction, and driver response to imminent rear-end collisions in a high-fidelity driving simulator [J]. Human Factors, 2002, 44(2): 314-334.
[7] Aoki H, Aga M, Miichi Y, et al. Safety Impact Methodology (SIM) for Effectiveness Estimation of a Precollision System (PCS) by Utilizing Driving Simulator Test and EDR Data Analysis [R]. SAE Technical Paper 2010-01-1003, 2010.
[8] Brännström M, Coelingh E, Sjöberg J. Model-based threat assessment for avoiding arbitrary vehicle collisions [J]. IEEE Transactions on Intelligent Transportation Systems, 2010, 11(3): 658-669.
[9] Kim T, Jeong H. Crash probability and error rates for head-on collisions based on stochastic analyses [J]. IEEE Transactions on Intelligent Transportation Systems, 2010, 11(4): 896-904.
[10] Siegel A W. The Vehicle Deformation Index: A Report from the International Ad Hoc Committee for Collision Deformation and Trauma Indices [R]. Los Angeles, CA: School of Medicine, University of California at Los Angeles, 1969.
[11] Niehoff P, Gabler H C. The accuracy of winsmash delta-V estimates: The influence of vehicle type, stiffness, and impact mode [J]. Annu Proc Assoc Adv Automot Med, 2006, 50: 73-89.
[12] Jones I S, Champion H R. Trauma triage: Vehicle damage as an estimate of injury severity [J]. Journal of Trauma, 1989, 29(5): 646-653.