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

Halil Beglerovic, Thomas Schloemicher, Steffen Metzner, Martin Horn

Publikation: Beitrag in einer FachzeitschriftArtikelForschungBegutachtung

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.

Originalspracheenglisch
Aufsatznummer2590
FachzeitschriftApplied Sciences
Jahrgang8
Ausgabenummer12
DOIs
PublikationsstatusVeröffentlicht - 12 Dez 2018

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learning
Advanced driver assistance systems
Recurrent neural networks
sensors
Sensors
Heuristic algorithms
Time series
inspection
vehicles
Inspection
Deep learning
Processing

Schlagwörter

    ASJC Scopus subject areas

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

    Dies zitieren

    Deep learning applied to scenario classification for lane-keep-assist systems. / Beglerovic, Halil; Schloemicher, Thomas; Metzner, Steffen; Horn, Martin.

    in: Applied Sciences, Jahrgang 8, Nr. 12, 2590, 12.12.2018.

    Publikation: Beitrag in einer FachzeitschriftArtikelForschungBegutachtung

    Beglerovic, Halil ; Schloemicher, Thomas ; Metzner, Steffen ; Horn, Martin. / Deep learning applied to scenario classification for lane-keep-assist systems. in: Applied Sciences. 2018 ; Jahrgang 8, Nr. 12.
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