Deep learning applied to scenario classification for lane-keep-assist systems

Halil Beglerovic, Thomas Schloemicher, Steffen Metzner, Martin Horn

Research output: Contribution to journalArticleResearchpeer-review

Abstract

Test, verification, and development activities of vehicles with ADAS (Advanced Driver Assistance Systems) and ADF (Automated Driving Functions) generate large amounts of measurement data. To efficiently evaluate and use this data, a generic understanding and classification of the relevant driving scenarios is necessary. Currently, such understanding is obtained by using heuristic algorithms or even by manual inspection of sensor signals. In this paper, we apply deep learning on sensor time series data to automatically extract relevant features for classification of driving scenarios relevant for a Lane-Keep-Assist System. We compare the performance of convolutional and recurrent neural networks and propose two classification models. The first one is an online model for scenario classification during driving. The second one is an offline model for post-processing, providing higher accuracy.

Original languageEnglish
Article number2590
JournalApplied Sciences
Volume8
Issue number12
DOIs
Publication statusPublished - 12 Dec 2018

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Keywords

  • Advanced driver assistance systems (ADAS)
  • Automated driving functions
  • Automated driving functions (ADF)
  • Deep learning
  • Scenario classification

ASJC Scopus subject areas

  • Materials Science(all)
  • Instrumentation
  • Engineering(all)
  • Process Chemistry and Technology
  • Computer Science Applications
  • Fluid Flow and Transfer Processes

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