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AR-CP: Uncertainty-Aware Perception in Adverse Conditions with Conformal Prediction and Augmented Reality For Assisted Driving

dc.contributor.authorDoula, Achref
dc.contributor.authorMühlhäuser, Max
dc.contributor.authorSanchez Guinea, Alejandro
dc.date.accessioned2024-04-30T16:10:57Z
dc.date.available2024-04-30T16:10:57Z
dc.date.issued2024-04-14
dc.identifier.urihttps://tudatalib.ulb.tu-darmstadt.de/handle/tudatalib/4219
dc.descriptionDeep learning models play a crucial role in improving driver assistance systems and environmental perception. However, their tendency toward overconfident predictions poses risks to driver safety, particularly in adverse conditions. To address this, we propose AR-CP, an uncertainty-aware framework integrating conformal prediction and augmented reality (AR). AR-CP starts with a conformal prediction step, generating an uncertainty-aware prediction set. Then, AR simplifies and clarifies the visualization of the closest common parent class, reducing misinformation. We present a rigorous formulation and theoretical analysis, evaluating AR-CP on the ROAD dataset. Results demonstrate superior performance compared to existing methods, ensuring safer driving experiences with reduced mental load and heightened situation awareness, as validated by an immersive user study involving 15 participants.de_DE
dc.language.isoende_DE
dc.rightsOpen Data Commons Attribution License (ODC-By) v1.0
dc.rights.urihttps://opendatacommons.org/licenses/by/1.0/
dc.subjectUncertainty Quantificationde_DE
dc.subjectDeep Learningde_DE
dc.subjectAugmented Realityde_DE
dc.subjectUrban Scene Understandingde_DE
dc.subject.classification409-05 Interaktive und intelligente Systeme, Bild- und Sprachverarbeitung, Computergraphik und Visualisierungde_DE
dc.subject.ddc004
dc.titleAR-CP: Uncertainty-Aware Perception in Adverse Conditions with Conformal Prediction and Augmented Reality For Assisted Drivingde_DE
dc.typeSoftwarede_DE
dc.description.version0.1de_DE
tud.unitTUDa


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Solange nicht anders angezeigt, wird die Lizenz wie folgt beschrieben: Open Data Commons Attribution License (ODC-By) v1.0