How Reliable Are Object Detection Models Beyond Clear Weather? A Deployment-Oriented Robustness Study for Autonomous Driving

Autonomous driving perception systems must maintain reliable object detection under diverse environmental conditions, not only in clear daytime scenes. However, the cross-weather robustness of modern detectors remains insufficiently characterised under source-only evaluation. This study evaluates representative object detection architectures trained exclusively on clear daytime driving scenes and tested without adaptation under unseen fog, rain, snow, and night conditions. The evaluation covers lightweight and medium-scale one-stage, two-stage, and transformer-based detectors within a unified protocol. Robustness is assessed using performance Drop (%), a Weather Robustness Index (WRI), and class-level sensitivity analysis for critical road participants, including cars, buses, trucks, and persons. Prediction confidence reliability under environmental domain shift is measured using Expected Calibration Error (ECE). Results reveal consistent performance degradation in adverse conditions, reaching 62.90% at night. Buses show the highest sensitivity to environmental variability, while cars remain comparatively stable across weather transitions. Comparisons indicate that YOLO-based detectors exhibit the lowest calibration mismatch, reflecting stronger agreement between predicted confidence and empirical detection correctness. In contrast, DETR achieves the lowest robustness score, with a WRI of 0.6859, indicating greater vulnerability to cross-weather domain shift. These findings offer deployment-oriented insights into detector robustness and confidence reliability for safety-critical intelligent vehicle perception systems.