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Verification, Testing, and Runtime Monitoring of Automotive Exhaust Emissions

17 pagesPublished: October 23, 2018

Abstract

Emission cleaning in modern cars is controlled by embedded software. In this context, the diesel emission scandal has made it apparent that the automotive industry is susceptible to fraudulent behaviour, implemented and effectuated by that control software. Mass effects make the individual controllers altogether have statistically significant adverse effects on people’s health. This paper surveys recent work on the use of rigorous formal techniques to attack this problem. It starts off with an introduction into the dimension and facets of the problem from a software technology perspective. It then details approaches to use (i) model checking for the white-box analysis of the embedded software, (ii) model- based black-box testing to detect fraudulent behaviour under standardized conditions, and (iii) synthesis of runtime monitors for real driving emissions of cars in-the-wild. All these efforts aim at finding ways to eventually ban the problem of doped software, that is, of software that surreptitiously alters its behaviour in certain circumstances – against the interest of the owner or of society.

In: Gilles Barthe, Geoff Sutcliffe and Margus Veanes (editors). LPAR-22. 22nd International Conference on Logic for Programming, Artificial Intelligence and Reasoning, vol 57, pages 1--17

Links:
BibTeX entry
@inproceedings{LPAR-22:Verification_Testing_and_Runtime,
  author    = {Holger Hermanns and Sebastian Biewer and Pedro R. D'Argenio and Maximilian A. K\textbackslash{}"ohl},
  title     = {Verification, Testing, and Runtime Monitoring of Automotive Exhaust Emissions},
  booktitle = {LPAR-22. 22nd International Conference on Logic for Programming, Artificial Intelligence and Reasoning},
  editor    = {Gilles Barthe and Geoff Sutcliffe and Margus Veanes},
  series    = {EPiC Series in Computing},
  volume    = {57},
  pages     = {1--17},
  year      = {2018},
  publisher = {EasyChair},
  bibsource = {EasyChair, https://easychair.org},
  issn      = {2398-7340},
  url       = {https://easychair.org/publications/paper/xgqz},
  doi       = {10.29007/6zxt}}

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