[Submitted on 1 Oct 2026]
Title:Confidence-Controlled XAI Auditing for Pedestrian Detection under Domain Shift
View a PDF of the paper titled Confidence-Controlled XAI Auditing for Pedestrian Detection under Domain Shift, by Ruben Dario Florez-Zela
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Abstract:Explainability is increasingly required for perception models in intelligent vehicles, yet whether explanations remain faithful under driving domain shift is still poorly understood. This work audits post-hoc explanations of a fixed YOLOv8s pedestrian detector across PIE and JAAD using ROI-based D-Deletion, frozen confidence terciles, rank-based tests, bootstrap intervals, and Holm correction. The audit shows that deletion-based faithfulness is strongly coupled to detection strength at explanation time, with Spearman correlations between 0.70 and 0.82 for D-RISE, making naive confidence-stratified comparisons unreliable. After controlling for detection strength within fixed f0 bins, D-RISE faithfulness remains domain-dependent in the central f0 range, with PIE showing higher D-Deletion than JAAD and Holm-adjusted significance. A non-perturbative EigenCAM baseline is less faithful than D-RISE but also exhibits score coupling, suggesting that the effect is not specific to D-RISE and is related to the deletion-based evaluation setup. These results motivate confidence-controlled XAI audits for safety-critical perception under domain shift.
Comments: Accepted at the 2026 IEEE International Conference on Vehicular Electronics and Safety (ICVES 2026). 6 pages, 4 figures
Subjects:
Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2610.02364 [cs.CV]
(or arXiv:2610.02364v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2610.02364
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Ruben Dario Florez-Zela [view email] [v1] Thu, 1 Oct 2026 18:39:57 UTC (1,171 KB)
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