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Recurrent Conditional GANsfor Time Series Sensor Modelling
RISE Research Institutes of Sweden, Digitala system, Datavetenskap.ORCID-id: 0000-0001-7856-113X
Zenuity, Sweden.
Zenuity, Sweden; Chalmers University of Technology, Sweden.
2019 (Engelska)Konferensbidrag, Publicerat paper (Refereegranskat)
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.

Ort, förlag, år, upplaga, sidor
2019.
Nationell ämneskategori
Naturvetenskap
Identifikatorer
URN: urn:nbn:se:ri:diva-51870OAI: oai:DiVA.org:ri-51870DiVA, id: diva2:1518987
Konferens
Time Series Workshop at International Conference on Machine Learning, Long Beach, California, 2019.
Tillgänglig från: 2021-01-18 Skapad: 2021-01-18 Senast uppdaterad: 2024-05-21Bibliografiskt granskad

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Zec, Edvin Listo

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