Two key technical improvements were proposed to address the issue of decreased target detection accuracy caused by target occlusion and fog interference in intelligent autonomous driving scenarios. Firstly, an improved detection method was proposed for the problem of target occlusion. A lightweight MobileNetV3_small with an integrated enhanced attention mechanism was used as the SSD backbone feature extraction network. The detection accuracy in occlusion scenarios was enhanced by combining a multi-scale feature fusion mechanism and an adaptive hyperparameter Soft-NMS algorithm. The confidence loss function was reconstructed by improving the adaptive Focal Loss to alleviate the imbalance between positive and negative samples and the sensitivity to noisy labels. Secondly, an improved lightweight AOD-Net defogging method was proposed to address the problem of target blurriness in foggy images. The image contrast was effectively enhanced and texture details were improved, by constructing a multi-scale feature extraction network based on depthwise separable convolution, optimizing the cross-layer connection structure, and introducing a boundary enhancement module. Finally, an end-to-end collaborative optimization of the defogging network and the detection network was achieved through a joint loss function, providing a more reliable optimization path for the detection task of occluded targets in foggy images. Experimental results showed that the joint optimization model improved the target detection performance in foggy occlusion scenarios, achieving efficient detection with an accuracy of 93.85% and a detection speed of 47.61 frames per second, and demonstrating excellent model robustness.