A Real-Time Multisensor Fusion Verification Framework for Advanced Driver Assistance Systems
Authors:
Mohamed Elgharbawy, Andreas Schwarzhaupt, Marco Weiskopf, Michael Frey, Frank Gauterin
Keywords:
multisensor data fusion, dynamic behavior testability, mahalanobis distance, hardware-in-the-loop co-simulation
Abstract:
This paper presents a novel approach for the verification of multisensor data fusion algorithms in complex automotive sensor networks. Multisensor fusion plays a central role in enhancing the interpretation of traffic situations, facilitating inferences and decision making. It has therefore been instrumental in the ongoing innovation of Advanced Driver Assistance Systems (ADAS) which paves the way to autonomous driving. We introduce a real-time framework which can benchmark the performance of the fusion algorithms at the electronic system level using a Hardware-in-the-Loop (HiL) co-simulation. The presented research provides a quantitative approach for a trade-off between physical realism and computational efforts of the real-time synthetic simulation. The proposed framework illustrates a generic architecture of ADAS sensor error injection for robustness testing of the System under Test (SuT). We construct a lemniscate model for errors to find multivariate outliers with the Mahalanobis distance. A critical driving scenario considering road users in urban traffic describes the dynamic behavior testability of the fusion algorithms. The industry-proven framework facilitates a functional verification of multisensor-fusion-based object detection precisely and more efficiently on the target electronic control unit (ECU) in the laboratory.
Cite this article
Elgharbawy M.; Schwarzhaupt A.; Weiskopf M.; Frey M. and Gauterin F. A Real-Time Multisensor Fusion Verification Framework for Advanced Driver Assistance Systems In: Proceedings of the Driving Simulation Conference 2016 Europe, Driving Simulation Association, Paris, France, 2016, pp. 145-149
@inproceedings{Elgharbawy2016,
title = {A Real-Time Multisensor Fusion Verification Framework for Advanced Driver Assistance Systems},
author = {Mohamed Elgharbawy and Andreas Schwarzhaupt and Marco Weiskopf and Michael Frey and Frank Gauterin},
editor = {Andras Kemeny and Frédéric Merienne and Florent Colombet and Stéphane Espié},
issn = {0769-0266},
year = {2016},
date = {2016-09-07},
booktitle = {Proceedings of the Driving Simulation Conference 2016 Europe},
pages = {145-149},
address = {Paris, France},
organization = {Driving Simulation Association},
abstract = {This paper presents a novel approach for the verification of multisensor data fusion algorithms in complex automotive sensor networks. Multisensor fusion plays a central role in enhancing the interpretation of traffic situations, facilitating inferences and decision making. It has therefore been instrumental in the ongoing innovation of Advanced Driver Assistance Systems (ADAS) which paves the way to autonomous driving. We introduce a real-time framework which can benchmark the performance of the fusion algorithms at the electronic system level using a Hardware-in-the-Loop (HiL) co-simulation. The presented research provides a quantitative approach for a trade-off between physical realism and computational efforts of the real-time synthetic simulation. The proposed framework illustrates a generic architecture of ADAS sensor error injection for robustness testing of the System under Test (SuT). We construct a lemniscate model for errors to find multivariate outliers with the Mahalanobis distance. A critical driving scenario considering road users in urban traffic describes the dynamic behavior testability of the fusion algorithms. The industry-proven framework facilitates a functional verification of multisensor-fusion-based object detection precisely and more efficiently on the target electronic control unit (ECU) in the laboratory.},
keywords = {dynamic behavior testability, hardware-in-the-loop co-simulation, mahalanobis distance, multisensor data fusion},
}
TY - CONF TI - A Real-Time Multisensor Fusion Verification Framework for Advanced Driver Assistance Systems AU - Elgharbawy, Mohamed AU - Schwarzhaupt, Andreas AU - Weiskopf, Marco AU - Frey, Michael AU - Gauterin, Frank C1 - Paris, France C3 - Proceedings of the Driving Simulation Conference 2016 Europe DA - 2016/09/07 PY - 2016 SP - 145 EP - 149 LA - en-US PB - Driving Simulation Association SN - 0769-0266 L2 - https://proceedings.driving-simulation.org/proceeding/dsc-2016/a-real-time-multisensor-fusion-verification-framework-for-advanced-driver-assistance-systems ER -
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