{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,2]],"date-time":"2025-10-02T06:05:23Z","timestamp":1759385123975},"reference-count":37,"publisher":"Walter de Gruyter GmbH","issue":"7","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019,7,26]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>In autonomous driving, prediction tasks address complex spatio-temporal data. This article describes the examination of Recurrent Neural Networks (RNNs) for object trajectory prediction in the image space. The proposed methods enhance the performance and spatio-temporal prediction capabilities of Recurrent Neural Networks. Two different data augmentation strategies and a hyperparameter search are implemented for this purpose. A conventional data augmentation strategy and a Generative Adversarial Network (GAN) based strategy are analyzed with respect to their ability to close the generalization gap of Recurrent Neural Networks. The results are then discussed using single-object tracklets provided by the KITTI Tracking Dataset. This work demonstrates the benefits of augmenting spatio-temporal data with GANs.<\/jats:p>","DOI":"10.1515\/auto-2018-0124","type":"journal-article","created":{"date-parts":[[2019,7,10]],"date-time":"2019-07-10T09:03:16Z","timestamp":1562749396000},"page":"545-556","source":"Crossref","is-referenced-by-count":6,"title":["Strategies for supplementing recurrent neural network training for spatio-temporal prediction"],"prefix":"10.1515","volume":"67","author":[{"given":"Mark","family":"Schutera","sequence":"first","affiliation":[{"name":"Institute for Automation and Applied Informatics , Karlsruhe Institute of Technology , Karlsruhe , Germany"},{"name":"Corporate Research and Development , ZF Friedrichshafen AG , Friedrichshafen , Germany"}]},{"given":"Stefan","family":"Elser","sequence":"additional","affiliation":[{"name":"Corporate Research and Development , ZF Friedrichshafen AG , Friedrichshafen , Germany"}]},{"given":"Jochen","family":"Abhau","sequence":"additional","affiliation":[{"name":"Corporate Research and Development , ZF Friedrichshafen AG , Friedrichshafen , Germany"}]},{"given":"Ralf","family":"Mikut","sequence":"additional","affiliation":[{"name":"Institute for Automation and Applied Informatics , Karlsruhe Institute of Technology , Karlsruhe , Germany"}]},{"given":"Markus","family":"Reischl","sequence":"additional","affiliation":[{"name":"Institute for Automation and Applied Informatics , Karlsruhe Institute of Technology , Karlsruhe , Germany"}]}],"member":"374","published-online":{"date-parts":[[2019,7,6]]},"reference":[{"key":"2023033110104406735_j_auto-2018-0124_ref_001_w2aab3b7b2b1b6b1ab1b5b1Aa","unstructured":"Adamy, J.; Willert, V.: Cars become robots. 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