Object detection is a core technology in the environmental perception of autonomous driving, and its task is to identify and locate key objects in the surrounding environment of the vehicle. To address the challenges of missed detection of small targets, misjudgment due to occlusion, and insufficient dynamic adaptability in autonomous driving scenarios, an improved object detection algorithm DPM-YOLO based on YOLOv11n is proposed. Firstly, in the Backbone network, a dynamic attention pyramid module is constructed to replace the original rapid spatial pyramid pooling structure. By utilizing parameter-shared dilated convolutions and a decomposed spatial attention mechanism, multi-granularity features are efficiently captured and the context information of each target can be better aggregated. Secondly, in the Neck network, a parallel fusion convolution module is designed. It can achieve channel-wise dynamic decoupling and multi-scale features fusion by heterogeneous multi-branch convolutions. Finally, an improved MPDWIoU loss function is proposed, which integrates a dynamic gradient focusing mechanism with corner constraints and optimizes bounding box localization accuracy by combining a batch statistics adaptive strategy. Experimental results demonstrate that the mAP@0.5 of DPM-YOLO on the KITTI dataset is 92.7%, which is 2.0 percentage points higher than that of YOLOv11n. The mAP@0.5 of DPM-YOLO on the Cityscapes and BDD100K datasets is improved by 1.6 and 0.8 percentage points respectively compared with YOLOv11n. The mAP@0.5 of DPM-YOLO on the extreme weather dataset Foggy-Cityscapes increases by 1.2 percentage points over YOLOv11n. The results indicate that the proposed method effectively balances detection accuracy and real-time performance in complex driving scenarios.