This paper introduces a novel approach called Stream Query Denoising (SQD) for temporal modeling in high-definition map (HD-map) construction. The SQD strategy is designed to facilitate the learning of temporal consistency among map elements within the streaming model. The methodology involves denoising the queries that have been perturbed by the addition of noise to the ground-truth information from the preceding frame. This denoising process aims to reconstruct the ground-truth information for the current frame, thereby simulating the prediction process inherent in stream queries. The SQD strategy can be applied to streaming methods such as StreamMapNet to enhance the temporal modeling. The proposed SQD-MapNet is the StreamMapNet equipped with SQD. Extensive experiments on nuScenes and Argoverse2 show that our method is remarkably superior to other existing methods across all settings of close range and long range. The paper explores the challenges of constructing vectorized HD-maps using onboard sensors and proposes SQD to address these challenges. SQD involves two main components: normal query denoising and stream query denoising. Normal query denoising considers three distinct noise strategies for curves: line shifting, angular rotation, and scale transformation. Stream query denoising incorporates temporal adaptive matching to establish an explicit one-to-one correspondence between historical ground truths and current ones. A dynamic query noising mechanism is devised to address warped errors inherent in temporal and current elements. The SQD-MapNet framework is evaluated on two large-scale datasets, nuScenes and Argoverse2, and shows significant improvements in performance compared to existing methods. The results demonstrate that SQD-MapNet achieves superior performance in both short- and long-range settings, with mAP scores of 63.9 and 74.0, respectively. The method is effective in enhancing the temporal modeling of HD-map construction, providing a versatile strategy for improving the network's temporal understanding. The paper concludes that SQD significantly enhances the temporal modeling of HD-map construction, validated on nuScenes and Argoverse2. The proposed approach is expected to contribute to the development of more accurate and efficient HD-map construction methods for autonomous driving.This paper introduces a novel approach called Stream Query Denoising (SQD) for temporal modeling in high-definition map (HD-map) construction. The SQD strategy is designed to facilitate the learning of temporal consistency among map elements within the streaming model. The methodology involves denoising the queries that have been perturbed by the addition of noise to the ground-truth information from the preceding frame. This denoising process aims to reconstruct the ground-truth information for the current frame, thereby simulating the prediction process inherent in stream queries. The SQD strategy can be applied to streaming methods such as StreamMapNet to enhance the temporal modeling. The proposed SQD-MapNet is the StreamMapNet equipped with SQD. Extensive experiments on nuScenes and Argoverse2 show that our method is remarkably superior to other existing methods across all settings of close range and long range. The paper explores the challenges of constructing vectorized HD-maps using onboard sensors and proposes SQD to address these challenges. SQD involves two main components: normal query denoising and stream query denoising. Normal query denoising considers three distinct noise strategies for curves: line shifting, angular rotation, and scale transformation. Stream query denoising incorporates temporal adaptive matching to establish an explicit one-to-one correspondence between historical ground truths and current ones. A dynamic query noising mechanism is devised to address warped errors inherent in temporal and current elements. The SQD-MapNet framework is evaluated on two large-scale datasets, nuScenes and Argoverse2, and shows significant improvements in performance compared to existing methods. The results demonstrate that SQD-MapNet achieves superior performance in both short- and long-range settings, with mAP scores of 63.9 and 74.0, respectively. The method is effective in enhancing the temporal modeling of HD-map construction, providing a versatile strategy for improving the network's temporal understanding. The paper concludes that SQD significantly enhances the temporal modeling of HD-map construction, validated on nuScenes and Argoverse2. The proposed approach is expected to contribute to the development of more accurate and efficient HD-map construction methods for autonomous driving.