Zusammenfassung
Deep Learning and AI has become the standard model for object detection and recognition such as situation understanding, prediction and planning. In this chapter we explore how AI can be used to improve parts of the classical ADAS algorithm chain. Based on this, we investigate the full Automated Driving pipeline. Firstly, we describe the building blocks of the pipeline composed of standard computer vision tasks. We provide an overview of use cases for automated driving based on the authors’ experience in commercial deployment, e.g. Sensor-Fusion, Perception, SLAM or End-2-End Driving. Finally, we discuss the opportunities of using AI and Deep Learning to improve upon state-of-the-art classical methods.
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Milz, S., Schrepfer, J. (2020). Is artificial intelligence the solution to all our problems? Exploring the applications of AI for automated driving. In: Bertram, T. (eds) Automatisiertes Fahren 2019. Proceedings. Springer Vieweg, Wiesbaden. https://doi.org/10.1007/978-3-658-27990-5_10
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DOI: https://doi.org/10.1007/978-3-658-27990-5_10
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