A Testing Framework for Predictive Driving Features with an Electronic-Horizon
Authors:
Mohamed Elgharbawy, Andreas Schwarzhaupt, Rainer Arenskrieger, Michael Frey, Frank Gauterin
Keywords:
predictive driving features, electronic horizon, map-based fusion algorithms, hardware-in-the-loop co-simulation, robustness testing
Abstract:
This paper proposes a novel approach for automated functional testing of map-based fusion algorithms in complex vehicle networks. The hybrid data representation of detailed digital maps and physical automotive sensors provides an extended view of the ego-vehicle environment and thereby facilitates improved inferences and more competent decision-making. It has therefore been instrumental in the ongoing development of predictive assisted driving features, e.g. fuel-efficient driving, traffic sign recognition and highway-pilot. The presented approach utilises a closed-loop Hardware-in-the-Loop (HiL) co-simulation framework to evaluate the performance of the decision level fusion algorithms. The method contains both the structural design and resource-efficient integration into the HiL test bench in the example of traffic sign recognition. In the real environment discrepancy between visual and map data is omnipresent due to map errors, outdated map data or optical detection failure. Through fault injection, defined inconsistencies can be produced within the HiL simulation environment. Amongst others, the fault injection covers the placement and value of traffic signs. These failures can be used for worst-case robustness testing of the fusion algorithms. In summary, the results show that the extended HiL environment is capable of generating electronic Horizon data which can easily be adapted or extended.
Cite this article
Elgharbawy M.; Schwarzhaupt A.; Arenskrieger R.; Frey M. and Gauterin F. A Testing Framework for Predictive Driving Features with an Electronic-Horizon In: Proceedings of the Driving Simulation Conference 2017 Europe VR, Driving Simulation Association, Stuttgart, Germany, 2017, pp. 17-18
@inproceedings{Elgharbawy2017,
title = {A Testing Framework for Predictive Driving Features with an Electronic-Horizon},
author = {Mohamed Elgharbawy and Andreas Schwarzhaupt and Rainer Arenskrieger and Michael Frey and Frank Gauterin},
editor = {Andras Kemeny and Florent Colombet and Frédéric Merienne and Stéphane Espié},
issn = {0769-0266},
year = {2017},
date = {2017-09-06},
booktitle = {Proceedings of the Driving Simulation Conference 2017 Europe VR},
pages = {17-18},
address = {Stuttgart, Germany},
organization = {Driving Simulation Association},
abstract = {This paper proposes a novel approach for automated functional testing of map-based fusion algorithms in complex vehicle networks. The hybrid data representation of detailed digital maps and physical automotive sensors provides an extended view of the ego-vehicle environment and thereby facilitates improved inferences and more competent decision-making. It has therefore been instrumental in the ongoing development of predictive assisted driving features, e.g. fuel-efficient driving, traffic sign recognition and highway-pilot. The presented approach utilises a closed-loop Hardware-in-the-Loop (HiL) co-simulation framework to evaluate the performance of the decision level fusion algorithms. The method contains both the structural design and resource-efficient integration into the HiL test bench in the example of traffic sign recognition. In the real environment discrepancy between visual and map data is omnipresent due to map errors, outdated map data or optical detection failure. Through fault injection, defined inconsistencies can be produced within the HiL simulation environment. Amongst others, the fault injection covers the placement and value of traffic signs. These failures can be used for worst-case robustness testing of the fusion algorithms. In summary, the results show that the extended HiL environment is capable of generating electronic Horizon data which can easily be adapted or extended.},
keywords = {electronic horizon, hardware-in-the-loop co-simulation, map-based fusion algorithms, predictive driving features, robustness testing},
}
TY - CONF TI - A Testing Framework for Predictive Driving Features with an Electronic-Horizon AU - Elgharbawy, Mohamed AU - Schwarzhaupt, Andreas AU - Arenskrieger, Rainer AU - Frey, Michael AU - Gauterin, Frank C1 - Stuttgart, Germany C3 - Proceedings of the Driving Simulation Conference 2017 Europe VR DA - 2017/09/06 PY - 2017 SP - 17 EP - 18 LA - en-US PB - Driving Simulation Association SN - 0769-0266 L2 - https://proceedings.driving-simulation.org/proceeding/dsc-2017/a-testing-framework-for-predictive-driving-features-with-an-electronic-horizon ER -
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