Automatic Fault Detection using Cause and Effect Rules for In-vehicle Networks

Alexander Kordes, Sebastian Wurm, Hawzhin Hozhabrpour, Roland Wismüller

2018

Abstract

In-vehicle networks (IVNs) connect Electronic Control Units (ECUs) for automotive applications. Most of the communication on the IVNs directly affect the comfort or even the safety of the driver. Therefore, it is necessary to monitor these systems in order to find the cause and effect of a fault. Current developments use plausibility checks in automotive ECUs to enhance safety and security. Within the LEICAR project in cooperation with INVERS GmbH we focus on all sensors signals recorded directly from CAN bus IVNs for this positional paper. Even without the knowledge of the sensors semantics it is possible to extract cause and effect rules for all recorded sensor signal relationships of the vehicle, map them in a graph and extract certain situations. The proposed solution detects direct and slowly evolving changes even if they propagate across several involved sensor values. For the automatic fault containment we extract features from the cause and effect rules to train a machine learning model in order to make predictions on new data. Besides that it is possible to implement optimized error checking procedures for the involved ECUs.

Paper Citation

in Harvard Style

Kordes A., Wurm S., Hozhabrpour H. and Wismüller R. (2018). Automatic Fault Detection using Cause and Effect Rules for In-vehicle Networks.In Proceedings of the 4th International Conference on Vehicle Technology and Intelligent Transport Systems - Volume 1: VEHITS, ISBN 978-989-758-293-6, pages 537-544. DOI: 10.5220/0006792605370544

in Bibtex Style

@conference{vehits18,
author={Alexander Kordes and Sebastian Wurm and Hawzhin Hozhabrpour and Roland Wismüller},
title={Automatic Fault Detection using Cause and Effect Rules for In-vehicle Networks},
booktitle={Proceedings of the 4th International Conference on Vehicle Technology and Intelligent Transport Systems - Volume 1: VEHITS,},
year={2018},
pages={537-544},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006792605370544},
isbn={978-989-758-293-6},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 4th International Conference on Vehicle Technology and Intelligent Transport Systems - Volume 1: VEHITS,
TI - Automatic Fault Detection using Cause and Effect Rules for In-vehicle Networks
SN - 978-989-758-293-6
AU - Kordes A.
AU - Wurm S.
AU - Hozhabrpour H.
AU - Wismüller R.
PY - 2018
SP - 537
EP - 544
DO - 10.5220/0006792605370544