Look around when in doubt: Adaptive contextual reasoning for conformal and explainable road object detection with CAESAR++

Trustworthy object detection is a cornerstone for autonomous vision systems operating in safety-critical applications. This paper introduces CAESAR++ (Context-Aware Explanations via Semantic Attribution and Refinement), an adaptive contextual reasoning framework designed to enhance trustworthiness in road object detection. CAESAR++ integrates a two-step conformal prediction approach to obtain statistically grounded uncertainty estimates that determine when contextual reasoning is required, dynamically calibrates context window sizes according to that uncertainty, and employs dual-color saliency maps to disentangle bottom-up sensory evidence from top-down contextual cues within a unified explanation. Unlike methods that treat uncertainty estimation and explainability as separate components, CAESAR++ couples them in a single detector-agnostic pipeline without requiring retraining of the core model. Comprehensive experiments on TJU-DHD-Traffic, BDD100K, and Pascal VOC with ten state-of-the-art detectors demonstrate consistent improvements in detection accuracy, robustness, uncertainty calibration, and explanation quality, while preserving competitive computational efficiency. These contributions advance the development of transparent, robust, and responsible vision systems for autonomous navigation safety.