Introducing Compressed Mixture Models for Predicting Long-Lasting Brake Events
Paper in proceeding, 2018

With tougher restrictions on emissions the automotive industry is in dire need of additional functionality to reduce emissions. We conduct a case study trying to predict long-lasting brake events, to support the decision-making process when the engine can beneficially be put to idle or shut down to achieve emission reduction. We introduce Compressed Mixture Models, a multivariate and mixed variate kernel density model featuring online training and complexity reduction, and use it for prediction purposes. The results show that the proposed method produces comparable prediction results as a Random Forest Classifier and outperform a Support Vector Classifier. On an urban road a prediction accuracy of 87.4 % is obtained, while a prediction accuracy of 76.4 % on a highway segment using the proposed method. Furthermore, it is possible to use a trained Compressed Mixture Model as a tool for statistical inference to study the properties of the observed realization of the underlying random variables.

probabilistic models

model complexity reduction

information theory

Machine learning

recursive algorithms

prediction methods


Emil Staf

Volvo Cars

Tomas McKelvey

Chalmers, Electrical Engineering, Signal Processing and Biomedical Engineering


24058963 (eISSN)

Vol. 51 31 840-845

5th IFAC Conference on Engine and Powertrain Control, Simulation and Modeling, E-COSM 2018
Changchun, China,

Subject Categories

Bioinformatics (Computational Biology)

Other Civil Engineering

Probability Theory and Statistics



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7/7/2021 1