To address the challenge of missed detections for distant small objects and occluded targets in complex autonomous driving scenarios, this paper proposes an improved algorithm based on YOLO11n. First, an efficient multi-scale attention mechanism is introduced to replace the original C2PSA module in the backbone network. It performs group reshaping and parallel multi-scale fusion without reducing channel dimensions, thereby enhancing the model′s focus on small objects. Second, an additional 160×160 detection layer is incorporated to leverage detailed spatial information from shallow features for the precise localization of small targets. Furthermore, a context-guided module, designated C3k2-CGB, is designed to augment feature fusion with global contextual information, improving the recognition capability for occluded objects. Finally, the Wise-IoU loss is adopted for bounding box regression, which suppresses the harmful gradients from low-quality examples. Experimental results on the KITTI dataset demonstrate that the improved model achieves a 5.9% increase in mAP and a 10.3% gain in recall, while reducing the number of parameters by 10.1%. It outperforms several mainstream YOLO variants, showing significant improvements in detecting small and occluded objects.