TY - CHAP A1 - Müller, Marcus A1 - Botsch, Michael A1 - Böhmländer, Dennis A1 - Utschick, Wolfgang ED - Klaffke, Werner T1 - A Simulation Framework for Vehicle Safety Testing T2 - Aktive Sicherheit und Automatisieres Fahren : 3. Interdisziplinärer Expertendialog (IEDAS) T2 - Ein Simulationsframework für die Absicherung von Fahrzeugsicherheitsfunktionen Y1 - 2017 SN - 978-3-8169-3405-9 SP - 147 EP - 167 PB - expert Verlag CY - Renningen ER - TY - JOUR A1 - Müller, Marcus A1 - Botsch, Michael A1 - Böhmländer, Dennis A1 - Utschick, Wolfgang T1 - Machine Learning Based Prediction of Crash Severity Distributions for Mitigation Strategies JF - Journal of Advances in Information Technology N2 - In road traffic, critical situations pass by as quickly as they appear. Within the blink of an eye, one has to come to a decision, which can make the difference between a low severity, high severity or fatal crash. Because time is important, a machine learning driven Crash Severity Predictor (CSP) is presented which provides the estimated crash severity distribution of an imminent crash in less than 0.2ms. This is 63⋅ 103 times faster compared to predicting the same distribution through computationally expensive numerical simulations. With the proposed method, even very complex crash data, like the results of Finite Element Method (FEM) simulations, can be made available ahead of a collision. Knowledge, which can be used to prepare occupants and vehicle to an imminent crash, activate and adjust safety measures like airbags or belt tensioners before of a collision or let self-driving vehicles go for the maneuver with the lowest crash severity. Using a real-world crash test it is shown that significant safety potential is left unused if instead of the CSP-proposed driving maneuver, no or the wrong actions are taken. UR - https://doi.org/10.12720/jait.9.1.15-24 KW - crash severity KW - vehicle safety KW - reliable prediction KW - machine learning Y1 - 2018 UR - https://doi.org/10.12720/jait.9.1.15-24 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-24644 SN - 1798-2340 VL - 9 (2018) IS - 1 SP - 15 EP - 24 PB - Engineering and Technology Publishing CY - Rowland Heights (CA) ER - TY - CHAP A1 - Müller, Marcus A1 - Long, Xing A1 - Botsch, Michael A1 - Böhmländer, Dennis A1 - Utschick, Wolfgang T1 - Real-Time Crash Severity Estimation with Machine Learning and 2D Mass-Spring-Damper Model T2 - 2018 IEEE Intelligent Transportation Systems Conference UR - https://doi.org/10.1109/ITSC.2018.8569471 KW - computer crashes KW - vehicle crash testing KW - machine learning KW - two dimensional displays KW - forestry KW - accidents KW - acceleration Y1 - 2018 UR - https://doi.org/10.1109/ITSC.2018.8569471 SN - 978-1-7281-0323-5 SP - 2036 EP - 2043 PB - IEEE CY - Piscataway ER - TY - CHAP A1 - Müller, Marcus A1 - Nadarajan, Parthasarathy A1 - Botsch, Michael A1 - Utschick, Wolfgang A1 - Böhmländer, Dennis A1 - Katzenbogen, Stefan T1 - A statistical learning approach for estimating the reliability of crash severity predictions T2 - 2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC) UR - https://doi.org/10.1109/ITSC.2016.7795911 KW - vehicle crash testing KW - reliability KW - computer crashes KW - vehicles KW - mathematical model KW - accidents KW - predictive models Y1 - 2016 UR - https://doi.org/10.1109/ITSC.2016.7795911 SN - 978-1-5090-1889-5 SN - 2153-0017 SP - 2199 EP - 2206 PB - IEEE CY - Piscataway ER -