Title: SDTagNet: Leveraging Text-Annotated Navigation Maps for Online HD Map Construction
Venue: NeurIPS 2025
Accepted: yes
Keywords: Autonomous Driving

Birds-Eye-View Perception

Vectorized HD map construction

SD Map Prior

Natural Language Processing
Abstract: Autonomous vehicles rely on detailed and accurate environmental information to operate safely.
High definition (HD) maps offer a promising solution, but their high maintenance cost poses a significant barrier to scalable deployment. 
This challenge is addressed by online HD map construction methods, which generate local HD maps from live sensor data.
However, these methods are inherently limited by the short perception range of onboard sensors. 
To overcome this limitation and improve general performance, recent approaches have explored the use of standard definition (SD) maps as prior, which are significantly easier to maintain.
We propose SDTagNet, the first online HD map construction method that fully utilizes the information of widely available SD maps, like OpenStreetMap, to enhance far range detection accuracy. 
Our approach introduces two key innovations.
First, in contrast to previous work, we incorporate not only polyline SD map data with manually selected classes, but additional semantic information in the form of textual annotations.
In this way, we enrich SD vector map tokens with NLP-derived features, eliminating the dependency on predefined specifications or exhaustive class taxonomies.
Second, we introduce a point-level SD map encoder together with orthogonal element identifiers to uniformly integrate all types of map elements.
Experiments on Argoverse 2 and nuScenes show that this boosts map perception performance by up to +5.9 mAP (+45%) w.r.t. map construction without priors and up to +3.2 mAP (+20%) w.r.t. previous approaches that already use SD map priors.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper proposes SDTagNet, an innovative method for online HD map construction, which leverages text-annotated SD maps (e.g., OpenStreetMap) as priors to improve the performance of high-definition (HD) map generation. The method integrates semantic information from textual annotations with geometric features to enhance long-range detection accuracy. Experiments on two prominent datasets (Argoverse 2 and nuScenes) demonstrate significant improvements in map perception accuracy.\n\nSTRENGTHS AND WEAKNESSES:\nPros:\n1. The integration of NLP-derived features into SD map encoding is a novel approach that expands the utility of publicly available SD maps, particularly OpenStreetMap, for autonomous vehicle navigation and online HD map construction.\n2. The proposed SDTagNet architecture achieves substantial improvements over previous methods, particularly in the far range, demonstrating its effectiveness in real-world autonomous driving applications.\n3. The experiments cover two well-established datasets, Argoverse 2 and nuScenes, providing a robust evaluation of the proposed method in different contexts. This strengthens the generalizability of the approach.\n4. The adoption of contrastive pretraining for NLP tag embeddings allows SDTagNet to leverage a vast amount of unannotated map data, reducing the dependency on handcrafted feature engineering and improving scalability.\n\nCons:\n1. While the paper introduces an interesting approach, the novelty of the work may be viewed as incremental rather than groundbreaking. The idea of using prior map data for HD map construction is not new, and other works have already explored using SD maps, though without the same emphasis on textual annotations.\n2. Although SDTagNet excels in far-range detection, it shows limited improvement in near-range scenarios. This could be seen as a limitation if the method is to be applied in real-world driving conditions where near-range perception is equally important.\n3. The method heavily relies on the quality and comprehensiveness of the SD map priors, such as OpenStreetMap. However, these maps are not always fully accurate or complete, especially in certain geographic areas where data might be sparse. The paper does not address how the method handles situations where SD map priors are missing critical details or contain erroneous information.\n4. The paper does not explore the computational complexity of the model, nor does it discuss the feasibility of deploying SDTagNet in real-time systems with high computational constraints. The inference speed and memory requirements, especially with large-scale map data, are important factors for practical deployment, and they need to be addressed more thoroughly.\n5. While the introduction of the point-level SD map encoder is a key innovation, the methodology lacks detailed explanation on how it processes complex map elements (points, polylines, relations) and how it handles the integration of these diverse map features at a computational level.\n\nQUESTIONS:\n1. Given the complexity of incorporating textual annotations into the SD maps and the need for real-time map construction in autonomous driving systems, what is the expected computational cost (e.g., inference time, memory usage) of SDTagNet in a real-time setting? \n2. The method relies heavily on SD map priors (e.g., OpenStreetMap) to construct HD maps. How does the method handle scenarios where SD maps are incomplete, outdated, or contain errors (such as missing or incorrect features)?\n3. The evaluation is conducted on datasets like Argoverse 2 and nuScenes, but these datasets may not fully represent the diversity of geographic and infrastructural conditions that SDTagNet would encounter in real-world deployment. How well does the method generalize to different geographic regions with varying levels of map detail and urbanization?\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-06-17"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nExisting methods for generating real-time high-definition (HD) maps for autonomous vehicles do not fully utilize the rich textual and structural data available in standard-definition (SD) maps like OpenStreetMap. The authors introduce SDTagNet, a novel approach that uses a natural language processing model to interpret textual annotations and a point-level encoder to uniformly integrate various map elements such as points, polylines, and their relationships. This method significantly enhances the accuracy of online HD map construction, demonstrating substantial performance gains on the Argoverse 2 and nuScenes datasets, especially at far perception ranges.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n1. This paper's key strength lies in its innovative use of textual annotations from Standard Definition (SD) maps, a rich source of information previously neglected in online HD map construction. The authors ingeniously employ a pre-trained BERT-based NLP encoder to interpret this vast and unstructured text data, eliminating the need for manual feature engineering and predefined class taxonomies that limited prior work. \n2. This paper demonstrates a crucial strength in significantly boosting map perception performance at far ranges, a scenario where sensor data quality typically diminishes. This improvement is vital for autonomous driving, as accurate long-distance environmental models are necessary for safe operation at higher speeds. \n3. This paper's move to a point-level query resolution for the SD map encoder is a significant architectural strength. This design choice creates better alignment with the underlying HD map construction model, which also operates on a per-point basis, and provides far greater flexibility and expressiveness than previous polyline-level methods.\n\nWeaknesses:\n1. This paper presents multiple significant improvements simultaneously, including a finer point-level query resolution and a novel method for leveraging textual tag annotations. While the authors rightly conduct ablation studies to parse the effectiveness of each individual component, this crucial analysis is relegated to an appendix. Placing this vital experimental breakdown in the appendix weakens the main paper, as it requires readers to consult supplementary materials to fully understand the specific contribution and importance of each innovation to the final impressive results\n2. A notable limitation of this paper is the minimal performance improvement in the near-range setting, where the proposed method provides only a marginal gain over the baseline on both the Argoverse 2 and nuScenes datasets. This is likely because the high-quality sensor data available for the immediate vicinity is sufficient for the baseline model, reducing the reliance on SD map priors. Consequently, this indicates the primary utility of SDTagNet is to enhance perception where sensor data is sparse or unreliable\n3. While the paper effectively demonstrates the benefits of its proposed method on the MapTRv2 architecture, its claims of efficacy could be strengthened by testing the SDTagNet module on a wider range of baseline HD map construction models.\n\nQUESTIONS:\nI would like to increase my score if the authors can address my 1st and 3rd points in weaknesses\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 5, "CLARITY": 4, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-01"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper proposes an approach for online high-definition (HD) map construction that leverages the rich semantic and textual information available in standard-definition (SD) maps like OpenStreetMap, overcoming the limitations of previous methods that only used a restricted subset of SD map features. By introducing a BERT-based natural language processing (NLP) encoder for open-vocabulary tag embeddings and a point-level SD map encoder with orthogonal element identifiers, SDTagNet can uniformly integrate points, polylines, and relations, thus eliminating the need for manual feature engineering or predefined class taxonomies. This architecture enables the model to utilize all available semantic information, including diverse textual annotations, and supports scalable, self-supervised pre-training on large-scale SD map data, making it robust and adaptable for real-world autonomous driving scenarios. Experimental results on the Argoverse 2 and nuScenes datasets demonstrate that SDTagNet improves far range HD map construction performance, achieving state-of-the-art performance.\n\nSTRENGTHS AND WEAKNESSES:\n**Strengths**\n1. The idea of leveraging text-annotated standard-definition (SD) maps is highly innovative, offering a promising approach that is well-suited for real-world deployment in autonomous driving scenarios.\n\n2. The overall model design is well-structured and logically justified, integrating natural language processing for tag embeddings and a flexible SD map encoder to effectively utilize diverse map elements.\n\n3. Experimental results demonstrate that the proposed method significantly outperforms previous approaches that utilize SD maps, achieving superior performance in online high-definition (HD) map construction tasks.\n\n4. The code submission enhances the reproducibility and trustworthiness of the experimental results, allowing other researchers to verify and build upon the work more effectively.\n\n**Weaknesses**\n1. The comparison table in the paper is incomplete, as it does not include several recent and relevant methods for online vectorized HD map construction such as HIMap [a], MapUnveiler [b], and MapTracker [c]; since the proposed method additionally leverages SD maps, it is important to directly compare its performance against these state-of-the-art approaches to robustly validate its effectiveness.\n\n2. The proposed method is evaluated only on the geographical split of [17], whereas many prior works opt for StreamMapNet split [d]; conducting experiments on this commonly used split would enable fair and comprehensive comparisons with a broader range of existing methods and further strengthen the empirical validation of SDTagNet. Furthermore, the author may consider using a consistent GT used in MapTracker [c] for a better thorough experiment.\n\n[a] Zhou, Yi, et al. \"Himap: Hybrid representation learning for end-to-end vectorized hd map construction.\" CVPR, 2024.\\\n[b] Kim, Nayeon, et al. \"Unveiling the Hidden: Online Vectorized HD Map Construction with Clip-Level Token Interaction and Propagation.\" NeurIPS, 2024.\\\n[c] Chen, Jiacheng, et al. \"Maptracker: Tracking with strided memory fusion for consistent vector hd mapping.\" ECCV, 2024.\\\n[d] Yuan, Tianyuan, et al. \"Streammapnet: Streaming mapping network for vectorized online hd map construction.\" WACV, 2024.\n\nQUESTIONS:\nThe idea proposed in this paper is interesting, as it makes full use of practically available information from SD maps, which is highly relevant for real-world applications. I expect this approach will inspire many related follow-up studies. However, my main concern lies with the experimental setup. Since the method leverages additional information from SD maps, it would be expected that the proposed method outperforms existing HD map construction approaches. Additionally, I think the proposed method would be performed on top of various baselines, not only on top of MapTRv2. While I currently stand positive, my score could be changed if my concerns are not addressed.\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 5, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-02"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes a method to incorporate map data from a standard-definition map data set like OSM into a high-definition map-generation pipeline (where a high-definition map includes detailed road and lane boundary information). The key technical contributions are:\n\n- A way to incorporate natural language road \"tags\" that are common in OSM, such as annotations about the number of lanes or road types, based on an NLP (BERT) encoder trained with a contrastive loss metric to predict if two annotations are “semantically similar,” i.e., they encode a road segment with the same number of lanes and the same direction of travel.\n\n- An overall encoding of SD map data that includes these semantic embeddings and also differs from prior work by encoding road segments as points instead of polylines and a method for encoding relational features (such as the fact that two points belong to the same segment).\n\nEvaluation is done on the nuScenes and Argoverse datasets, showing that the method can improve over a method that relies only on a method that does not use SD map features (MapTRv2), and that it generally outperforms existing methods that add SD map features to the HD map generation.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:  \nS1 Clever method for encoding semantic tags and other road metadata.  \nS2 Overall SD map encoding also seems quite general and flexible and appears to be better than competing methods.  \nS3 Evaluation against state-of-the-art baselines on real-world data sets shows some gains.  \nS4 Paper and supplementary material do a good job of listing weaknesses and comparing against a number of alternatives.\n\nWeaknesses:  \nW1 Overall gains are modest; in the “near range” setting, the method helps only marginally, in the “far range” setting it does somewhat better, but the overall accuracy in this setting is quite low (so it’s unclear if the gains are really meaningful from the point of view of, e.g., autonomous vehicle navigation).  \n\nW2 While I appreciate the methods that the authors have developed, I found it hard to be excited about the problem here — overall the contribution is modest, and I’m not sure whether the whole line of “add SD Map Data to HD Map data” is going to ultimately deliver the types of significant accuracy gains that are needed to solve the HD map generation problem.  \n\nW3 I found the paper a bit hard to follow in places. For example, Figure 1 shows that images are used to produce BEV features, but the paper doesn’t really talk about this part of the architecture at all. It wasn’t until I got to the experiments that I realized that the image features are all based on a pre-trained image/lidar to HD Map model (MapTRv2). In other places, there is jargon — for example, “BEV” is used without being defined, and phrases like “we also resample all SD map polylines to the fixed point number of 10” don’t mean anything to me.  \n\nW4 I don’t quite follow why the baselines, especially PMapNet, don’t seem to show gains in this evaluation. On nuScenes, the PMapNet paper suggests that it offers significant performance boosts vs. HDMapNet — is it just that MapTRv2 is a much stronger baseline than HDMapNet? Why do you think it is that the (all info) baselines don’t really do much better than the originals?\n\nQUESTIONS:\nI’d like to see the authors response to W1/W2/W4.  \n\nFor W1/W2, given the overall low MAP and small absolute gains over MapTRv2, is SD map augmentation really an important component forsolving the HD Map generation problem?\n\nLIMITATIONS:\nThe authors do a good job of describing limitations, which illustrates qualitative examples where the method doesn't work, and in the body of the text where they talk about why the gains over the baseline are not huge.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 2, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-07-02"}