This paper presents an interpretable object detection framework using Kolmogorov-Arnold networks to enhance trustworthiness in autonomous vehicle perception systems. The approach addresses the critical limitation of limited transparency in confidence scores during visually degraded or ambiguous driving scenarios. A Kolmogorov-Arnold network serves as an interpretable post-hoc surrogate model for YOLOv10 detections, utilizing seven geometric and semantic features to assess detection reliability. The additive spline-based architecture enables direct visualization of feature contributions, revealing when confidence scores are well-supported versus unreliable. Experimental validation on COCO dataset and University of Bath campus images demonstrates accurate trustworthiness estimation for autonomous driving perception.