Using Data Mining in Automotive Safety

Authors: Carine Cridelich, Pablo Juesas Cano, Emmanuel Ramasso, Noureddine Zerhouni, Bernd Weiler

Abstract:

Safety is one of the most important considerations when buying a new car. While active safety aims at avoiding accidents, passive safety systems such as airbags and seat belts protect the occupant in case of an accident. In addition to legal regulations, organizations like Euro NCAP provide consumers with an independent assessment of the safety performance of cars and drive the development of safety systems in automobile industry. Those ratings are mainly based on injury assessment reference values derived from physical parameters measured in dummies during a car crash test. The components and sub-systems of a safety system are designed to achieve the required restraint performance. Sled tests and other types of tests are then carried out by car makers and their suppliers to confirm the protection level of the safety system. A Knowledge Discovery in Databases (KDD) process is proposed in order to minimize the number of tests. The KDD process is based on the data emerging from sled tests according to Euro NCAP specifications. About 30 parameters of the passive safety systems from different data sources (crash data, dummy protocol) are first analysed together with experts opinions. A procedure is proposed to manage missing data and validated on real data sets. Finally, a procedure is developed to estimate a set of rough initial parameters of the passive system before testing aiming at reducing the number of tests.

Keywords: KDD process, passive safety systems, sled test, dummy injury assessment reference values, frontal impact

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1337891

References:

[1] http://www.planetoscope.com/mortalite/1270-mortalite—morts-d-accidentsdela- route-dans-le-monde.html 16.09.2014
[2] EuroStat Persons killed in road accidents by sex (CARE data) Last update: 21-01-2014
[3] Aral Aktiengesellschaft Aral Studie Trends beim Autokauf, Brochure page 12 2013
[4] H. Johannsen Unfallmechanik und Unfallrekonstruktion ATZ/MTZ-Fachbuch 2013
[5] Internet Website Euro NCAP, The official site of the European New Car Assessment Programme, www.euroncap.com, version October 2014.
[6] Internet Website Wikipedia Euro NCAP, consulted November the 5th, 2014.
[7] Internet Website TRW, consulted November the 30th, 2014.
[8] G. Batista and M.C. Monard, An Analysis of Four Missing Data Treatment Methods for Supervised Learning 2003
[9] S. Tseng, K. Howang and C.Lee A pre-processing method to deal with missing values by integrating clustering and regression techniques Taylor & Francis, 2003.
[10] X. Wu, V. Kumar, J. Quinlan, J. Ghosh, Q. Yang, H. Motoda, G. McLachlan, A. Ng, B. Liu, P. Yu, Z. Zhou, M. Steinbach, D. Hand and D. Steinberg Top 10 algorithms in data mining Knowl Inf Syst, 2008.
[11] G.F. U¨ c¸tug, N.E.Kabakc, O. Bugu Bekdikhan and B. Akyu¨rek Multi-Criteria Decision Making-Based Comparison of Power Source Technologies for Utilization in Automobiles Vol. 3, No.3, May 2015. Journal of Clean Energy Technologies
[12] A. Pirdavani, T. Brijs, G. Wets A multiple criteria decision making approach for prioritizing accident hotspots in developing countries in the absence of crash data. 2009.