Conference paper

Safety Application Car Crash Detection Using Multiclass Support Vector Machine

M. Schwarz (Tech. Hochschule Mittelhessen - Univ. of Applied Sciences, Germany), A. Buhmann (Robert Bosch GmbH, Germany)

In this paper, the application of Support Vector Machine (SVM) on multiple car crash situations for improved decision of saftey applications, e.g. airbag control systems is presented. The intention of the paper is to show how state of the art products use ML for safety critical applications. It is the goal to avoid the deployment of an airbag. We use a (Multiclass) Support Vector Machine to account for an improved classification. Various multiclass classification methods are rated and the two methods One-Versus-Rest and One-Versus-One are benchmarked in terms of quantities as test error, training time, memory consumption and misclassified crashes. All methods are applied to real measurement data of car crashes for the type of full frontal crashes in various conditions. We will show that One-Versus-One performs best. The method is able to classify car crash situations and improve the detection possibility. This allows for active and passive occupant safety components in the automotive area.

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Receipt of papers:

March 15th, 2021

Notification of acceptance:

May 11th, 2021

Registration opening:

May 17th, 2021

Final paper versions:

May 31th, 2021