Robust perception systems for automated, connected, and electrified vehicles: advances from EU project ArchitectECA2030

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Recekenzaun, Jakob
Solmaz, Selim
Goelles, Thomas
Hilbert, Marc
Weimer, Daniel
Mayer, Peter
Chromý, Adam
Hentschel, Uwe
Modler, Niels
Toth, Mate

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Mark

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Elsevier
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The perception supply chain (SC1) of the ArchitectECA2030 project investigates failure modes, fault detection, and residual risk in perception systems of electrified, connected, and automated (ECA) vehicles. This accounts for the needs of a reliable understanding of the surrounding environment. The three demonstrators of SC1, described in this paper, address steps of a typical ECA usage cycle: charge - drive - restart charging. The foreign object detection (FOD) demonstrator improves safety within a wireless charging system. The robust physical sensors demonstrator creates a more robust perception by detecting failures within fused and single sensor data. The position enhancement demonstrator improves vehicle localization in areas with reduced GNSS signal coverage. All demonstrators are linked to the challenges that occur during the ECA vehicle usage cycle
The perception supply chain (SC1) of the ArchitectECA2030 project investigates failure modes, fault detection, and residual risk in perception systems of electrified, connected, and automated (ECA) vehicles. This accounts for the needs of a reliable understanding of the surrounding environment. The three demonstrators of SC1, described in this paper, address steps of a typical ECA usage cycle: charge - drive - restart charging. The foreign object detection (FOD) demonstrator improves safety within a wireless charging system. The robust physical sensors demonstrator creates a more robust perception by detecting failures within fused and single sensor data. The position enhancement demonstrator improves vehicle localization in areas with reduced GNSS signal coverage. All demonstrators are linked to the challenges that occur during the ECA vehicle usage cycle

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Transportation Research Procedia. 2023, vol. 72, issue 12, p. 335-342.
https://www.sciencedirect.com/science/article/pii/S2352146523007093

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Peer-reviewed

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en

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