Hasso-Plattner-Institut
Prof. Dr. Felix Naumann
  
 

31.05.2021

Article accepted for Journal Track of ECML/PKDD 2021

The joint research of Mohamed Karim Belaid (University of Passau), Maximilian Rabus (Porsche AG) and Ralf Krestel (HPI) on simulating destructive car crashes for safety management has been accepted for publication in Springer's Data Mining and Knowledge Discovery journal. As part of the special issue for one of the leading machine learning and data mining conferences ECML/PKDD 2021, the authors present CrashNet: an encoder–decoder architecture to predict crash test outcomes based on a deep neural network.

Abstract

Destructive car crash tests are an elaborate, time-consuming, and expensive necessity of the automotive development process. Today, finite element method (FEM) simulations are used to reduce costs by simulating car crashes computationally. We propose CrashNet, an encoder–decoder deep neural network architecture that reduces costs further and models specific outcomes of car crashes very accurately. We achieve this by formulating car crash events as time series prediction enriched with a set of scalar features. Traditional sequence-to-sequence models are usually composed of convolutional neural network (CNN) and CNN transpose layers. We propose to concatenate those with an MLP capable of learning how to inject the given scalars into the output time series. In addition, we replace the CNN transpose with 2D CNN transpose layers in order to force the model to process the hidden state of the set of scalars as one time series. The proposed CrashNet model can be trained efficiently and is able to process scalars and time series as input in order to infer the results of crash tests. CrashNet produces results faster and at a lower cost compared to destructive tests and FEM simulations. Moreover, it represents a novel approach in the car safety management domain.

  • CrashNet: an encoder–decoder architecture to predict crash test outcomes. Belaid, Mohamed Karim; Rabus, Maximilian; Krestel, Ralf in Data Min Knowl Disc (2021).