3D point cloud-based object detection is a crucial topic in the field of computer vision. This paper addresses the issue of low detection accuracy in 3D object detection using LiDAR during autonomous driving by proposing an improved 3D object detection method based on PointPillars. Firstly, in the feature extraction stage of the PointPillars model, the CA attention mechanism is embedded in the backbone network to learn similarity information and focus on important features. By calculating attention in two spatial dimensions (height and width) separately, the spatial distribution characteristics in the image are captured more accurately, thereby more comprehensively capturing the dependency relationships between features. Secondly, when performing feature fusion on the extracted information, instead of simply adding or concatenating the feature maps directly, suitable weights are learned for each position to select features of different scales, enabling more effective fusion. This allows the model to more efficiently detect feature information at different scales. To verify the performance of the improved algorithm, experiments were conducted on the publicly available KITTI dataset. The experimental results show that the proposed optimization algorithm achieves certain improvements over other publicly available algorithms. Compared to the baseline algorithm PointPillars, the average precision was improved by 4.48%.