TY  - JOUR
A1  - Belaid, Mohamed Karim
A1  - Rabus, Maximilian
A1  - Krestel, Ralf
T1  - CrashNet
BT  - an encoder-decoder architecture to predict crash test outcomes
JF  - Data mining and knowledge discovery
N2  - 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.
KW  - Predictive models
KW  - Time series analysis
KW  - Supervised deep neural
KW  - networks
KW  - Car safety management
Y1  - 2021
U6  - https://doi.org/10.1007/s10618-021-00761-9
SN  - 1384-5810
SN  - 1573-756X
VL  - 35
IS  - 4
SP  - 1688
EP  - 1709
PB  - Springer
CY  - Dordrecht
ER  -