Abstract
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.
Zusammenfassung
Im autonomen Fahren sind Vorhersagen aus komplexen räumlich-zeitlichen Daten notwendig. Dieser Artikel beschreibt die Untersuchung von Rekurrenten Neuralen Netzen (RNNs) zur Trajektorienvorhersage von Objekten im Bildraum. Die vorgeschlagenen Methoden verbessern die räumlich-zeitliche Vorhersagefähigkeit von Rekurrenten Neuronalen Netzen. Zu diesem Zweck werden zwei verschiedene Datenaugmentierungsstrategien und eine Hyperparametersuche implementiert. Eine konventionelle Datenaugmentierung und ein Generative Adversarial Network (GAN) werden auf ihre Fähigkeit hin analysiert, die Generalisierungslücke von Rekurrenten Neuronalen Netzen zu schließen. Die Ergebnisse werden unter Verwenden von Einzelobjekt-Trajektorien aus dem KITTI-Tracking Datensatz diskutiert. Diese Arbeit zeigt die Vorteile der Erweiterung von räumlich-zeitlichen Daten mit GANs.
About the authors
M. Sc. Mark Schutera is doctoral student in the field of deep learning for autonomous driving in the “Algorithms and Machine Learning Perception System” group at ZF Friedrichshafen AG and the Institute for Automation and Applied Computer Science at the Karlsruhe Institute of Technology.Research Interests: Deep Learning, autonomous driving, computer vision, data analytics and image processing
Prof. Dr. rer. nat. Stefan Elser works as professor for autonomous driving at the Hochschule Ravensburg-Weingarten.Research Interests: Machine learning, object detection, sensor fusion and their applications in autonomous driving
Dr. rer. nat. Jochen Abhau is team leader “Algorithms and Machine Learning Perception System” at ZF Friedrichshafen.Research Interests: Machine learning, image processing, data analytics, deep learning and autonomous driving
Apl. Prof. Dr.-Ing. Ralf Mikut is head of the research area “Automated Image and Data Analysis” of the Institute for Automation and Applied Computer Science at the Karlsruhe Institute of Technology.Research Interests: Machine learning, image processing, data analytics, computational intelligence, various applications in engineering and life sciences
PD Dr.-Ing. Markus Reischl is head of the research group „Machine Learning for High-Throughput and Mechatronics“ of the Institute for Automation and Applied Computer Science at the Karlsruhe Institute of Technology.Research Interests: Man-machine interfaces, image processing, machine learning, data analytics
Acknowledgment
With thanks to Katherine Quinlan-Flatter for proofreading the article.
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