<p>Rapid and accurate Advanced Driver Assistance Systems (ADAS) Object Detection in foggy environments is crucial for autonomous driving tasks. Existing methods typically enhance images before detection, which compromises real-time performance. Popular detectors also rely on stacks of static convolutions for feature extraction and fusion, making it hard to capture details in fog-degraded images and reducing accuracy. We therefore propose a lightweight Adaptive Feature-domain Defogging Dynamic Convolutional Network (AFD-YOLO) for object detection in foggy scenes. AFD-YOLO integrates a flexible convolutional architecture and dynamic scale selection throughout training, while adopting lightweight designs across the backbone, the Feature Pyramid Network, and the detection head. First, a Context-Guided Downsampling (CGD) module suppresses haze-induced scattering and restores discriminative details in the feature domain, thereby replacing complexity-increasing preprocessing steps. Second, to alleviate texture diffusion, a Cross Stage Partial Parallel Groupwise Multi-scale Convolution (CSP-ParGMC) module uses parallel multi-scale hybrid convolutions to adaptively aggregate cross-scale information from shallow layers. To address attenuation of structural orientation, AFD-YOLO introduces a Cross Stage Partial Dynamic Kernel Mixture Bottleneck that employs hierarchical kernel configurations and Dynamic Kernel Convolution (DKConv2d) for Scale and Orientation Selection; together with an Efficient Bidirectional Lightweight Feature Pyramid Network (EBLFPN), the flexible architecture transmits diverse gradients to the detection head with lower computation. Finally, experiments on synthetic and real-world foggy datasets demonstrate the effectiveness and efficiency of AFD-YOLO for real-time deployment. On the RTTS dataset, AFD-YOLO reduces parameters by 36% while improving mAP@0.5 by 3.08%.</p>