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Three-dimensional target detection is one of the key technologies for intelligent driving, but problems like large amount of processed data and many preset feature parameters still exist. To remedy the problem of poor correlation between 3D feature selection settings and actual target features, a detection method that incorporates point-piilars networks and DETR is proposed. Firstly, piilar coding is employed to offset the influence of redundant point clouds, which improves both computational efficiency and the match of feature extraction. Secondly, the prediction module based on the DETR decoder adopts a multi-headed attention mechanism to establish the correlation mapping between global features and the prediction set, and the prediction results with the strongest correlation are produced in parallel, avoiding the uncertainty caused by manual reliance on the intervention parameters of priori knowledge. Finally, the method is validated on the publicly available dataset, and the mAP is increased by 19. 14% and FPS by 3 compared with the original point-piilars network. Also, they both improve substantially when compared to other typical algorithms. [ABSTRACT FROM AUTHOR]