Abstract
This paper describes a novel approach to cope with driving scenarios in highly automated driving which are currently solved only by the driver’s control. The approach presented in this paper is currently being implemented as a prototype to be used in our test fleet. It combines techniques well established in robotics like Simultaneous Localization And Mapping (SLAM) as well as end-to-end protection and image compression algorithms with big data technology used in a connected car context. This allows enhancing the positioning of individual vehicles in their Local Environment Model (LEM). This is the next step to overcome current dependencies to in-vehicle sensors by using additional cloud-based sensor processing to gain information.
N. Beringer—Program Manager Highly Automated Driving.
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Acknowledgements
Special thanks to Holger Dormann, Olav Koska and to our project team who are responsible for the implementation of the approach presented in this paper.
Funding
Their work is partly funded by the BMVI Project PROVIDENTIA (FKZ: 16AVF1002D).
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Beringer, N. (2019). Improving the Environment Model for Highly Automated Driving by Extending the Sensor Range. In: Bertram, T. (eds) Fahrerassistenzsysteme 2018. Proceedings. Springer Vieweg, Wiesbaden. https://doi.org/10.1007/978-3-658-23751-6_2
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DOI: https://doi.org/10.1007/978-3-658-23751-6_2
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