To address the challenge of balancing accuracy and real-time performance in existing 3D point cloud object detection models within autonomous driving scenarios, this paper proposes an efficient point cloud object detection method. The aim is to improve detection accuracy while meeting the requirements for real-time deployment on edge devices, thereby promoting the application development in autonomous driving and intelligent transportation fields. Based on a pillar-based point cloud detection framework, a multi-pillar feature fusion model is designed. The model enhances the pillar feature representation ability by introducing point cloud variance features and positional encoding. A multi-pillar feature fusion module is proposed to improve the learning of contextual information between adjacent pillars, and a backbone network combining residual connections and Dropout regularization is used to optimize feature extraction. Furthermore, a point cloud data augmentation strategy is improved by mixing point clouds of same-category objects to enhance the model’?s generalization ability to complex scenes. Extensive experiments on the KITTI dataset demonstrate that the proposed method achieves a 3D mAP of 68.93 and a BEV mAP of 75.02. The model maintains an inference speed of 55.65 FPS. It also demonstrates excellent performance on the DAIR-V2X dataset. The proposed model achieves a balance between detection accuracy and real-time performance through multi-pillar feature fusion and an optimized data augmentation strategy, showcasing its application potential in autonomous driving scenarios. Future work could further optimize the model architecture to adapt to embedded system deployment to meet practical application needs.