A new algorithm was proposed based on a shared backbone network to meet the autonomous driving requirements and to improve the synergy effect among multiple models. An axial attention mechanism was added to the backbone network, and connections between global key points were established while maintaining lightweight feature extraction to enhance the location representation of the model. The adaptive weight allocation method, along with the implementation of a three-dimensional attention mechanism, was devised to mitigate the information conflict that emerges from the diverse scale features present in the multi-scale information extraction phase. The loss function was optimized based on the challenging sample region, and the capacity of the proposed algorithm to capture intricate details in the difficult sample region was strengthened. Experimental results in the BDD100K dataset showed that compared with YOLOP, the proposed algorithm improved the mean average accuracy in the traffic target detection task (at IoU=50%) by 3.3 percentage points, the mIoU in road drivable area segmentation task by 1.0 percentage points, the accuracy of lane line detection by 6.7 percentage points, and the reasoning speed was 223.7 frames per second. The proposed algorithm demonstrates excellent performance in traffic target detection, drivable area segmentation, and lane line detection, and achieves a good balance between detection accuracy and reasoning speed at the same time.