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http://dx.doi.org/10.18419/opus-14662
Autor(en): | Bader, Christian Schwieger, Volker |
Titel: | Advancing ADAS perception : a sensor-parameterized mmplementation of the GM-PHD filter |
Erscheinungsdatum: | 2024 |
Dokumentart: | Zeitschriftenartikel |
Seiten: | 21 |
Erschienen in: | Sensors 24 (2024), No. 2436 |
URI: | http://nbn-resolving.de/urn:nbn:de:bsz:93-opus-ds-146813 http://elib.uni-stuttgart.de/handle/11682/14681 http://dx.doi.org/10.18419/opus-14662 |
ISSN: | 1424-8220 |
Zusammenfassung: | Modern vehicles equipped with Advanced Driver Assistance Systems (ADAS) rely heavily on sensor fusion to achieve a comprehensive understanding of their surrounding environment. Traditionally, the Kalman Filter (KF) has been a popular choice for this purpose, necessitating complex data association and track management to ensure accurate results. To address errors introduced by these processes, the application of the Gaussian Mixture Probability Hypothesis Density (GM-PHD) filter is a good choice. This alternative filter implicitly handles the association and appearance/disappearance of tracks. The approach presented here allows for the replacement of KF frameworks in many applications while achieving runtimes below 1 ms on the test system. The key innovations lie in the utilization of sensor-based parameter models to implicitly handle varying Fields of View (FoV) and sensing capabilities. These models represent sensor-specific properties such as detection probability and clutter density across the state space. Additionally, we introduce a method for propagating additional track properties such as classification with the GM-PHD filter, further contributing to its versatility and applicability. The proposed GM-PHD filter approach surpasses a KF approach on the KITTI dataset and another custom dataset. The mean OSPA (2) error could be reduced from 1.56 (KF approach) to 1.40 (GM-PHD approach), showcasing its potential in ADAS perception. |
Enthalten in den Sammlungen: | 06 Fakultät Luft- und Raumfahrttechnik und Geodäsie |
Dateien zu dieser Ressource:
Datei | Beschreibung | Größe | Format | |
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sensors-24-02436.pdf | 5,06 MB | Adobe PDF | Öffnen/Anzeigen |
Diese Ressource wurde unter folgender Copyright-Bestimmung veröffentlicht: Lizenz von Creative Commons