Resource Efficient Classification of Road Conditions through CNN Pruning

Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review

Authors

  • Daniel Fink
  • Alexander Busch
  • Mark Wielitzka
  • Tobias Ortmaier

Research Organisations

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Details

Original languageEnglish
Title of host publication21st IFAC World Congress
Place of PublicationBerlin, Germany
Pages13958-13963
Number of pages6
Volume53
Edition2
Publication statusPublished - 2020
Event21st IFAC World Congress 2020 - Berlin, Germany
Duration: 12 Jul 2020 → 17 Jul 2020

Publication series

NameIFAC-PapersOnLine
PublisherIFAC Secretariat
ISSN (Print)2405-8963

Abstract

Towards autonomous driving, advanced driver assistance systems increasingly undertake basic driving tasks by replacing human assessment and interactions, when controlling the vehicle. The performance of these systems is directly related to knowledge of the vehicle’s state and influential parameters. In this respect, the road condition has a major influence on the tires’ traction and thus significantly affects the behavior of the vehicle. Therefore, a prediction of the upcoming road condition can improve the performance of the assistance systems which leads to an increased driving safety and comfort. The presented work aims to classify the road surface as well as its weather-related condition, based on images of the front camera view, using deep convolutional neural networks. In order to take computational limitations of vehicle control units into account, a pruning approach is investigated to reduce the network complexity.

Keywords

    Classification, Computer vision, Neural networks, Pruning, Road condition

ASJC Scopus subject areas

Cite this

Resource Efficient Classification of Road Conditions through CNN Pruning. / Fink, Daniel; Busch, Alexander; Wielitzka, Mark et al.
21st IFAC World Congress. Vol. 53 2. ed. Berlin, Germany, 2020. p. 13958-13963 (IFAC-PapersOnLine).

Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review

Fink, D, Busch, A, Wielitzka, M & Ortmaier, T 2020, Resource Efficient Classification of Road Conditions through CNN Pruning. in 21st IFAC World Congress. 2 edn, vol. 53, IFAC-PapersOnLine, Berlin, Germany, pp. 13958-13963, 21st IFAC World Congress 2020, Berlin, Germany, 12 Jul 2020. https://doi.org/10.1016/j.ifacol.2020.12.913
Fink, D., Busch, A., Wielitzka, M., & Ortmaier, T. (2020). Resource Efficient Classification of Road Conditions through CNN Pruning. In 21st IFAC World Congress (2 ed., Vol. 53, pp. 13958-13963). (IFAC-PapersOnLine).. https://doi.org/10.1016/j.ifacol.2020.12.913
Fink D, Busch A, Wielitzka M, Ortmaier T. Resource Efficient Classification of Road Conditions through CNN Pruning. In 21st IFAC World Congress. 2 ed. Vol. 53. Berlin, Germany. 2020. p. 13958-13963. (IFAC-PapersOnLine). doi: 10.1016/j.ifacol.2020.12.913
Fink, Daniel ; Busch, Alexander ; Wielitzka, Mark et al. / Resource Efficient Classification of Road Conditions through CNN Pruning. 21st IFAC World Congress. Vol. 53 2. ed. Berlin, Germany, 2020. pp. 13958-13963 (IFAC-PapersOnLine).
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