To meet the demand of efficient and accurate perception in autonomous driving systems, relying solely on cameras makes it challenging to achieve highprecision and robust 3D object detection. An effective solution to address this issue is to combine cameras with costeffective millimeterwave radar sensors, enabling more reliable multimodal 3D object detection. An effective approach to address this problem is to combine cameras with costeffective millimeterwave radar sensors, enabling more reliable multimodal 3D object detection, which not only improves the accuracy of environmental perception but also enhances the system's robustness and safety. In this paper, an autonomous driving perception algorithm based on the fusion of millimeterwave radar and cameras, named HPRDet (historical pillar of ray cameraradar fusion bird's eye view for 3D object detection) is proposed. Specifically, a radar BEV (bird's eye view) feature extraction module called RadarPRANet (radar point RCS attention net) is designed firstly. It comprises a dualstream radar backbone that extracts radar features with two representations, and an RCSaware BEV encoder that distributes radar features into the BEV space based on radarspecific RCS characteristics. Secondly, Historical radar of Object Prediction paradigm is adopted, designing both longterm and shortterm decoders that operate only during training, thus avoiding additional inference overhead. Due to the sparsity of the input data in this network, multimodal historical multiframe input is introduced to facilitate more accurate BEV feature learning. Lastly, the millimeterwaveoptimized ray denoising method is proposed, which utilizes the information from the current frame's millimeterwave radar point cloud as prior knowledge to assist in proposal generation, thereby enhancing the query feature representation for the camera. The proposed algorithm is trained and validated on the largescale public dataset nuScenes, with the NDS reaching 56.7% on the backbone of Resnet50.