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Recurrent Conditional GANsfor Time Series Sensor Modelling
RISE Research Institutes of Sweden, Digital Systems, Data Science.ORCID iD: 0000-0001-7856-113X
Zenuity, Sweden.
Zenuity, Sweden; Chalmers University of Technology, Sweden.
2019 (English)Conference paper, Published paper (Refereed)
Abstract [en]

Simulation of the real world is a widely researchedtopic in many different fields, and theautomotive industry in particular is very dependenton real world simulations. These simulationsare needed in order to prove the safety ofadvance driver assistance systems (ADAS) and autonomousdriving (AD). In this paper we proposea deep learning based model for generating timeseries outputs from sensors used in autonomousvehicles. We implement a Recurrent ConditionalGenerative Adversarial Network (RC-GAN) consistingof Recurrent Neural Networks that useLSTMs in both the generator and the discriminatorin order to generate sensor errors described astime series that exhibit long-term temporal correlations.The network is trained in a sequence-tosequencefashion where we condition the modeloutput with time series describing the environment,which enables the model to capture spatialand temporal dependencies. The RC-GAN is usedto generate time series describing the errors in aproduction sensor on a data set collected fromreal roads, and yields significantly better resultsas compared to previous works on sensor modelling.

Place, publisher, year, edition, pages
2019.
National Category
Natural Sciences
Identifiers
URN: urn:nbn:se:ri:diva-51870OAI: oai:DiVA.org:ri-51870DiVA, id: diva2:1518987
Conference
Time Series Workshop at International Conference on Machine Learning, Long Beach, California, 2019.
Available from: 2021-01-18 Created: 2021-01-18 Last updated: 2024-05-21Bibliographically approved

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Zec, Edvin Listo

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CiteExportLink to record
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Citation style
  • apa
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Output format
  • html
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  • asciidoc
  • rtf