SDTagNet: Leveraging Text-Annotated Navigation Maps for Online HD Map Construction
Authors: Fabian Immel
Jan-Hendrik Pauls
Richard Fehler
Frank Bieder
Jonas Merkert
Christoph Stiller
Venue: NeurIPS 2025 poster ()
Keywords: Autonomous Driving
Birds-Eye-View Perception
Vectorized HD map construction
SD Map Prior
Natural Language Processing
Primary area: applications
TL;DR: We propose SDTagNet, the first NLP-based SD map encoder that can directly leverage text-annotated navigation maps for online HD map construction.

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.

SDTagNet: Leveraging Text-Annotated Navigation Maps for Online HD Map Construction
Fabian Immel
Jan-Hendrik Pauls"
Richard Fehler'
Frank Bieder'
Jonas Merkert
Christoph Stiller*
*Karlsruhe Institute of Technology (immel, fehler, bieder) [jan (jan-hendrik pauls, stiller)@kit. edu
FZI Research Center for Information Technology
Abstract
Autonomous vehicles rely detailed and accurate environmental information operate safely. High definition (HD) maps offer a promising solution, but their high maintenance cost poses 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 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- features, eliminating the dependency on predefined specifications or exhaustive class taxonomies. Second, we introduce 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. https: hhbb.b...... com immel - -
Introduction
Autonomous vehicles require a variety of information about their static environment drive safely. This information is usually provided in form of high definition (HD) maps that contain highly accurate and detailed lane-level road geometry, traffic lights, road signs, traffic rules, etc. The extension of self-driving fleets to larger and larger areas showed that the creation and maintenance of HD maps requires a huge amount of computational and manual effort, hence, posing major obstacle for scalable autonomous driving. To overcome this issue, variety of approaches have proposed to construct or perceive HD maps online from onboard sensor data 13 15 16).
Online HD map construction has the downside of being limited by the range and coverage of onboard sensors. A variety of prior information and representations can be used to mitigate this restriction and improve performance especially in the long range and for occluded areas. These priors can include data from previously visited scenes 226
well as data from existing prior maps 22 g 21 28 This prior map data is usually provided in the form of dense grids, sparse vectorized graph representations, or both [3] with different levels of richness.
32
27
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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Text-Annotated SD Maps
Multi-View Images
Predicted Map
Innwer
name:
fostwayy sidowalk width: 3
Crows Sireed
resriciion
nroesing uncomtrllled crossing markin ge: yea
turn
BEV Features
Map Decoder
BEV Module
Image
deform. CA
Backbone
Transformer
Decoder
PV to BEV
Tagged Polylines
NLP Tag Embeddings
SD Map Encoder
Cross-
Attention
[CLS]
Tagset Emb
Eic.
Transformer former Encoder
BERT Encoder
SiriCoe
Eise.
SaniCoe
Fiat
yes
El.
n
bdentibbess
Figure 1: Overview of the model architecture of SDTagNet. To fully exploit textual annotations and all element types in large public SD map databases like OpenStreetMap [22], SDTagNet introduces novel NLP tag embedding and SD map encoder modules. Text annotation embeddings are first computed with a BERT (5] embedding model. They are then fused with scene-level context in a SD map encoder, which uses graph transformer-like methods to flexibly encode points, polylines and element relations. The encoded information is finally supplied to the base model via cross-attention.
Using HD maps priors only partially alleviates the issues of their creation and maintenance and raises new issues like map change detection [2]. In contrast, standard definition (SD) maps, currently used for navigation and routing, have far lower demand accuracy and level of detail, hence, reducing the maintenance effort by orders of magnitude. This motivates the use of SD maps as prior for online HD map construction. With OpenStreetMap [22] crowd-soureed SD map data is freely available on a global scale and kept up-to-date by a community of volunteers.
As we show in this work, existing approaches for incorporating SD) map priors do not fully utilize all valuable data contained in OppnStreetMap, but are restricted to dense or sparse representations of subset of so-called ways. i.e. polylines of roads and streets, associated with only a few selected tagged text attributes 9 31) While these selective, manual SD map features already show great improvements, SpreStreetMap also contains point-level nodes, abstract relations, and huge amount of additional textual information annotated to its map elements. To the best of our knowledge, this majority of SD map data has not yet been used to boost online HD map construction performance.
Contributions
We present SetagNet, SD map prior encoding module that can fully utilize all information in SD maps, including text annotations, without manual feature engineering. Specifically:
We increase the SD map element encoder query resolution from polyline-leved point-level, allowing for greater expressiveness by additional integration of point map elements.
We complement it with explicit orthogonal random feature element identifiers used in graph transformers [0], effectively encoding points, polylines and relations a joint fashion.
We propose to use a BERT 5 natural language processing (NLP) encoder and contrastive pretraining to create NI.P tag embeddings and hence utilize the entirety of textual annotations in addition to geometric element features in end-to-end trainable manner.

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Evaluation on Argoverse 2 and nuScenes shows that SDTagNet outperforms existing SD map prior encoding modules by up to 20% and 35%, respectively, especially far range.
2 Related Work
Online HD Map Construction HDMapNet [3] pioneered in defining the task of online HD map construction, i.e. to predict set of vectorized map instances represented as polygons and polylines in 2D BEV grid, and proposed to solve it by inferring from intermediate semantic segmentation outputs in heuristic postprocessing step. Subsequent works, including VectorMapNet [[9] and MapTR [15], transitioned from this multi-stage pipeline to an end-to-end formulation using DETR- style transformers [2] for direct vectorized map instance prediction. While VectorMapNet adopts an autoregressive decoding strategy, MapTR accelerates inference through a hierarchical bipartite matching mechanism with fixed number of points per instance. Map [16] further advances the architecture by introducing auxiliary supervision, reformulating the point-to-point matching process, and integrating one-to-man mo-many query design to improve both detection performance and convergence.
has since become a baseline architecture for many follow-up research directions 3 4 33
Priors for Online HD Map Construction Recent work seeks to improve detection quality and range by integrating information from previously recorded scenes 26 32 23 27 or existing HD and SD maps [24 8 9 21 28). Information from previously visited scenes can either consist of recently recorded temporal information, e.g. in [3] 30]. or maintained and updated a global map prior data base (26). In addition to temporal memory, many real-world autonomous systems have access to pre-built maps ranging from SD maps to HD maps for tasks such as path planning [6]. Recent online map prediction approaches incorporate these priors complement incomplete outdated maps [24] g Among early works, MapEX (24) introduced a mechanism to directly fuse existing HD map elements embedded query tokens with classic learned queries and online sensor data. M3TR []] continues this line of work by exploiting query embeddings and training regimes to incorporate semantically diverse priors resulting from varying map degradation scenarios. Concerning SD maps, PMapNet [9] rasterizes OpenStreetMap (OSM) (22 road graphs into images and aims to predict HD map around it with online sensor information and a pre-trained HD map prior module trained to capture the distribution and structure of the target HD map representation. In contrast, SMERF [21 converts OSM-based SD maps into a sequence of polyline instances representing fixed set of seven semantic road types and encodes them with a transformer module. Furthermore, TopoSD 23 proposes a hybrid approach, encoding existing SD maps into both 2D feature grids and set of vectorized instance tokens. As in SMERF [21]. these are fused with BEV features from sensor data using a BEVFormer encoder [14] with cross- attention. Despite these advances, all of the above use only subset of available SD map content by hand-selecting attributes.
3 Method
An overview of SDTagNet is shown in Figure Beginning with an introduction of annotations in SD maps (Section 3.I. we describe the two main components SDTagNet: The NLP tag embedding module (Section 3.2 and the SD map encoder (Section 3.3)
3.1 Annotations in SD Maps
A key challenge in leveraging sonddard-defiittion (SD) maps for online HD map construction lies in understanding the spectrum between SD map and HD map representations. This distinction can be broken down into three main aspects. First, SD maps are less accurate and have lower resolution compared to HD maps, usually in the range of meters compared to centimeters. Like other works in this area 9 21, we assume that the SD map is roughly localized close to the target HD map and let remaining inaccuracies be compensated by the network during training.
Second, SD maps are often considered sparse content, with previous approaches only considering roads, e.g. by filtering for the highway tag in OpenStreetMap (OSM). However, as shown in Figure 2 SD maps like those widely available from the OSM 22 project contain many more kinds of elements. They contain points like traffic lights, bus stops, traffic signs, etc. as well many ways beyond
3

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PMapNet
SMERF
SDTagNet
service: emergency access
lsnes: 2
oneway: yee
name: Grove Street
Footway sidewalk wieth: 3
crossing: uncontrolled crossing markings: yes
restriction: no. Jeft tur
Road
Pedestrian
Service road
Tags
Relations
Nodes
Figure 2: Visualization of the SD map prior input data utilized by existing methods. Existing approaches are limited to rasterized images or polylines with manually defined classes. SeTagNet the first method that can handle veee-vocayulary textual annotations and diverse element types such as points, polylines, and relational information.
roads, like pedestrian paths, building borders, etc. Virtual elements, so-called relations, even denote semantic or topological links between two elements, e.g. that no left turns are allowed.
Finally, there is wide variety of descriptive text information annotated to many of those elements. This valuable knowledge remains unexploited for online HD map construction. For instance, OSM maps contain so-called tags, i.e. key-value pairs, such as name: Park Avenue, oneway: yes, or lanes: 2. The last two examples already show that the textual information is highly informative for the task of online HD map construction. In total, at the point of writing, the global OSM map contains around 100k different keys and 168M values. This vast amount of tags as well as their unstructured nature obviously cannot be exploited by handerafted methods. Instead, we propose an NLP encoder to optimally interpret the full information contained in SD maps for the goal of HD map construction.
3.2 NLP Tag Embeddings
Our proposed NLP encoder takes inspiration from the area of sentence embedding natural language processing and can generate open-vocabulary embeddings for arbitrary textual annotations. By adopting training paradigms from sentence embedding. we require no extra labels for the encoder and pre-train it in fully self-supervised manner. Moreover, this makes the pre-training completely lask-agnostic, meaning the pre-trained encoder can in principle be used in any application which desires embeddings of textual SD map annotations, not just the area of online HD map perception.
For the embedding model choose the compact and well-studied BERT (5) architecture. SD map annotations are of considerably less complexity than the paragraphs of prose text normally used in sentence embedding, which renders large language models or other large embedding models unnecessary. A small model like BERT also enables us to run the encoder module in real time with the main online HD map perception model. The embedding dimension of the encoder is 144, with the [CLS] token selected as the tagset embedding, and one embedding is computed per SD map element.
Contrastive Pretraining Objective Common practice in natural language processing relies heavily on pre-trained large models that are then optionally finetuned for different use cases. However the data in SD map text annotation differs strongly from the text corpus for existing pre-trained sentence embedding models. The text consists of list of keywords rather than complete sentences and, contrary to general requirements for sentence embeddings, small changes in the text should make big difference in the embedding. For example, tagset with the tag lanes: 2 should have substantially different embedding from lanes: 3, even though only one letter changed.
Since a very large dataset for the use case exists as well with OpenStreetMap [22), we decided to train our embedding model from scratch. This also affords us more control of model hyperparameters such as embedding size and is necessary for reaching real-time performance. For our pre-training we adapt established methods and design a self self- supersuper contrastive learning objective based on the multiple negatives ranking loss [7]. Our customized objective is motivated by the fact that many annotation artifacts and semantically irrelevant tags exist in crowdsourced maps. For instance, many elements in OSM contain IDs and other cataloging information of national geodetic reference systems that are not informative for a downstream task. Our customized loss forces the model to ignore these irrelevant tags, which we identified using the main OSM tag database.

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lanes: 2,
lanes: 2,
lanes: 3,
footway: sidewalk,
oneway: yes,
oneway: yes,
oneway: yes,
highway: footway
name: Grove Street
name: : Davis Avenue
name: Brouge Avenue
Anchor
Positive
Negative
Figure 3: Example of the tag embedding contrastive pretraining objective. positive sample is selected from tagsets with the semantically meaningful tags, but different not meaningful ones (like the street name). Negative samples are selected from all other unique tagsets. The number of negative samples in practice is much larger than depicted here prevent unstable training.
Figure shows an example of training batch for the customized loss. Using the dataset of all unique tagsets from the OpenStreetMap planet map, the goal of the model is to minimize the distance of two tagsets with the same semantically meaningful tags while maximizing the distance towards other tagsets. A large batch size of negative samples is needed to keep the training stable [7]. 5120 in our case. We randomly sample 20 pairs positive samples per unique relevant tagset for the training data and choose training time of 4 epochs to ensure convergence. To facilitate further research in this area, the pre-trained encoder together with the extracted OSM annotation dataset used for training will be released alongside the main model.
Shows
3.3 SD Map Encoder
Following the NLP tag embedding, the SD map encoder combines these embeddings with the associated points and the scene-level context through transformer module. The SD map encoder module based on the architecture proposed by SMERF (21. however with key changes that sigmificantly improve performance in the online HD map construction task and allow for much more flexibility in its input representation. A detailed visualization of the SD map encoder design with all its components can be found in Figure All changes are investigated individually in Section 3 and Appendix I and we show that their combined application significantly increases performance.
Point Level Queries In SMERF 211, one query token represents one polyline, which is misaligned with the detection transformer architectures in HD map construction, that use detection query for each point instead [T5] T6, 3 4 30). To align the token design, we choose one query token per point. This also has the benefit an easier introduction of additional element types of points and relations. As all polyline elements in vectorized HD map construction architectures are detected with fixed point number, we also resample all SD map polylines the fixed point number of 10. Furthermore, we adopt the sin/cos positional encoding for point coordinates from SMERF (2 in our encoder and concatenate the tag embedding of the respective map element it.
ORF Element Identifiers While point-level queries better align the element representations and provide additional flexibility, they introduce problem: Without additional changes, context for the model is lost as to which element point belongs to, e.g. in the case of polyline features. We resolve this issue by taking inspiration from the field of graph transformers, where (10) show orthogonal random features (ORF) to be strong identifier features in the context of graph nodes and edges.
Following 10 and 29
retrieve ORF features from the rows of the random orthogonal matrix Q é IR mxn created from the QR decomposition of a random Gaussian matrix G E R nxn, Each element then assigned one ORF feature as its identifier, also visualized in Figure 4
As in 10 graph nodes, or our case, point features and polyline features, have the same ORF feature vector stacked twice in their query. Points are represented as a single query of the SD map encoder with two identical ORF identifiers. a tag embedding, and positional encoding. Polylines represented by list of multiple point-like queries that all share the same two identical ORF identifiers. Relations are represented using pairs of ORF identifiers, each corresponding to one of the members of relation edge, as done traditionally in graph-structured data with node IDs. This simple graph representation method for the SD map encoder is therefore not only a identifier for map element context, but also vastly broadens the element types that can be used for prior information.
Connection with Map Decoder There exist many possible ways to supply the encoded prior information to the main online HD map construction model, ranging from fusion with the BEV
5

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Map Embeddings
Input Scenario
Transformer Encoder
Binca Exi
SinCos
Sin/Cas Enc.
Exc
Tag
Tag
Tag Emb.
Tag
Tag
Tag
Emb.
Element Identifiers
2
3
2
2
3
3
orthonomall
Point Ft.
Polyline Feat.
Relation Feat.
Figure 4: Detailed design of the SD map encoder and its queries. Each point query is composed of the positional sin/cos encoding the point, the respective tag embedding and orthogonal random features (ORF) 29 that function as element identifiers and can model graph edges.
features to direct supply to the map decoder. We follow the approach of PMapNet 1 which uses an additional cross-attention layer in the map decoder the supply modality. Compared other approaches such as concatenation to the image BEV features, cross-attention is more expressive and can compensate spatial alignment issues better, resulting in the best performance in 9
Experiments
We evaluate method the Argoverse 25 and nuScenes ①1 datasets, viewing Argoverse as the main dataset for evaluation. We discuss our selected datasets and metric in Section and the implementation and baselines in Section 4.2 Section 443 contains the performance results of SDTagNet in comparison with existing methods and ablation studies to assess the contributions of individual components in the SD map encoder. More detailed ablation studies regarding the optimization strategies applied to the NLP tag embedding module and fine-tuning training regimes can be found in Appendix A.I
4.1 Datasets and Metric
To show the untainted performance of our model, we train and evaluate SDTagNet and all baselines on the geographically con-oaerlapping split proposed by [8] We put a focus on the more recent Argoverse 2 dataset since it is significantly larger than nuScenes (158k vs. 30k samples) and has to our experience more accurate maps. As ground truth HD maps, we use recently published labels g that contain topologically relevant centerline paths from [7 as well as semantically more challenging solid and dashed dividers, and correct many consistency errors the original labels 3
Additionally, as SD maps, SpenStreetMap data corresponding to the frame-wise local HD map label extracted from the planetary OSM database. We provide this data as SD map prior in two evaluation ranges for online HD map construction: near range (60 x 30 m) and far range (120m x 60 m). making sure to include all SD map elements that intersect that area. To the best of our knowledge, no previous method that evaluates on the increasingly important far range setting with a geographically disjoint train/val split 9 30 combines these important evaluation settings. Comparing Table of Appendix A.4 with Table shows that SD priors aggravate the localization overfit observed by [18] when train and evaluation data overlaps geographically. We assume that this is due to the additional leaked information available to the network when using SD maps as prior. All evaluation is carried out with the standard mean Average Precision (mAP) metric with 0.5, 1.0 and 1.5 meters thresholds for the Chamfer distance. This matches the standard mAp metric used by our baseline architecture [16).

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Table I: Comparison of SD map prior encoding methods Argoverse 2 25 with the geographical split of [8] With the classes from [21] in the input features, which are not used the original work. f: With OSM nodes in the input features, which not used in the original work. All models are trained for 24 epochs.
Dutaset: Argoverse
Near Range (60 m 30 m)
Method
dsash
AP sol
AP
AP cen
AP ped
mAP
16
MapTRv2 16
37.9
55.0
49.7
41.7
46.5
+ PMMpNNet
9
36.7
55.4
49.7
49.5
43.3
46,9
+0.4
+ PMMpNNet* (all info.)
39.4
54.6
50.6
47.6
42.7
47.0
+0.5
+ SMERF 21
39.4
54.9
49.4
49.0
39.0
46.3
-0.2
+ SMERF 21
info.)
54.4
50.0
48.5
38.2
45.9
-0.6
+ -SDTagNet
36.0
55.2
53.3
52.6
43.3
48.1
+1.6
Far Range
(120
60 m)
MapTRv2 116
9.5
15.0
11.3
17.5
11.7
13.0
NetppNet
9
9.7
16.4
13.9
19.6
16.8
15.3
42.3
PMMapNet*
(all info.)
9.3
15.1
15.0
20.0
18.9
15.7
+2.7
+ SMERF
21
8.7
14.2
10.9
11.5
12.2
-0.8
+ SMERF
21
(all info.)
9.1
15.5
14.0
19.0
13.5
14.2
+1.2
+ SDDaagNet
13.0
18.4
17.7
22.6
22.9
18.9
+5,9
4.2 Implementation Details and Baselines
Our baseline SD map encoding approaches the recent works PMapNet and SMERF 21 that represent the two general existing approaches towards SD map encoding: Rasterization into BEV image in the module in the case of SMERF [2]] To minimize the influence from other priors like temporal information, we select the popular single-shot architecture MapTRv2 [66] as a base model. For fair comparison, all SD map encoder modules have been ported to the MapTRv2 base architecture, thus isolating any performance differences to the SD map encoder module. All models are trained on H100 Nvidia GPUs for 24 epochs on Argoverse 2 and 1 110 epochs for nuScenes.
of PMapNet [9] and encoding of the vectorized polylines via transformer
Both baselines are evaluated in settings: With their original input data settings and with all information that is available in the OSM map, to the extent that the encoding method permits its integration. For PMapNet [9, we add the more detailed road label classes from SMERF to the raster image and for SMERF [21] we introduce node features that are not used in the original work. This additionally eliminates any differences between available input data and compares SDTagNet with the fairest versions of existing SD map encoding methods. A detailed discussion of baseline implementation details and hyperparameters can be found in Appendix A.2
4.3
Online HD Map Construction Performance
In this section we first discuss our results Argoverse and nuScenes in the near range and far range setting in comparison with previous work. We also present ablation studies that show the effectiveness of the proposed architectural changes and NLP tag embedding module.
Table shows the results of SDTagNett on Argoverse 2 25 in the near range and far range setting in comparison with the SD map encoding basclines PMapNet 9 and SMERF (21). applied to the base architecture MapTRv2 [16].
Results on Argoverse 2
In the far range, all approaches using SD prior maps improve upon the prior-less MapTRv2 baseline. This shows the benefit of prior knowledge to drive safely and comfortably even at higher velocities, requiring farther perception range where camera resolution and road visibility diminish. Surpassing all other approaches, SDTagNet outperforms MapTRv2 by +5.9 mAP (+45%). Furthermore, SDTagNNtt beats existing map prior methods by +3.2 mAP (+20%), even when they are extended to use all available information. Comparing the SD map baselines, PMapNet shows better performance than SMERF, suggesting that the SMERF encoder originally used lane topology prediction ill-adapted to perform vectorized HD map construction.

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Ground Truth
PMapNet
SMERF
SDTagNet
dashed divider
sold divder
cententine
pedestion road
Figure 5: Qualitative comparison of SDTag with PMapNet (all info.) and SMERF (all info.) on Argoverse 2 in the far range setting. Both SMERF and PMapNet fail to identify the one-way road and hallucinate a standard two-way crossing topology instead. SDTaaNeet can translate the information in the SD map tags to correct one-way topology prediction.
In the near range no significant performance benefit compared to the baseline can be observed for any method, even slightly reducing performance in some cases. This can be attributed to geographic overlap between train and val split for both PMapNet and SMERF as well as weaker base architectures of HDMapNet [13] and MapTR [15] in case of PMapNet. These differences emphasize the importance of using geographically non-overlapping splits, especially for models using map priors.
To compare SDTagNet with recent otate-of-the-art non SD map prior methods, we also evaluated it in the more limited label setting of [3, the results of which are shown in Table 2 SDTagNet maintains competitive performance in the near range, while significantly outperforming existing approaches in the far range, acheiving a +6.1 mAP gain vs. MapTTracke B.. This is despite SDTagNet being the only single-shot method in this comparison other than HIMap
The results show the strengths of SeTagNet in fully exploiting all information contained in SD prior maps, significantly outperforming ofate-of-the-are map encoders, even when beneficial changes are introduced to them. A qualitative comparison of SDTagNet with PMapNet and SMERF on Argoverse in the far range setting is displayed in Figure
SD map prior methods and existing SD
Ablation Studies We conduct ablation studies on the Argoverse 2 dataset to assess the contributions of individual components in the SD map encoder, displayed in Table 3 Only the full configuration of SDTagNet, combining all proposed modifications, yields significant performance gains compared to the SMERF (2I) baseline. The results further highlight the importance of ORF identifiers, without which the performance even drops when switching to point level queries. This suggests that the provided element identifiers are essential for enabling effective point-level queries.
To investigate the viability of fusion approaches for the map decoder connection as proposed by TopoSD (28), investigated combination of direct cross-attention to the SD map encoder tokens and cross-attention to additional SD map BEV features, shown as "+ BEV Ft." in Table The SD map BEV features are rasterized similarly to PMapNet []] but contain processed SD map encoder tokens instead of grayscale images. Despite class-level differences, this combined connection showed no overall performance gain. Our results indicate that, when evaluated on geographic split with the more diverse labels of M3TR [ regular cross-attention alone is sufficiently expressive on its own.
Results on nuScenes Table shows the evaluation results on the nuScenes dataset. The relative improvement of SDTagNet in the far range setting is even stronger, increasing performance compared to MapTRv2 by 14.1 mAP (+105%) and compared This coincides with the general drop in mAP for all methods on the nuScenes geo split [18) and shows the benefits of our approach even on smaller datasets with less generalization capability. SMERF outperforms PMapNet here, indicating that these methods may be more susceptible to dataset differences than SDTaageet, which shows similar performance benefits in both cases. the near range, the results mostly mirror those on Argoverse 2, with method able to provide significant performance gain. On nuScenes in particular, we observed some significant differences between the roads and structures contained in the SD map and the ground truth HD map (see Section 5). We
existing methods by +2.1 mAP (+35%).
8

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Table 2: Comparison of SeTagNet with recent sate-of-the-ar non SD map prior methods the more limited label setting of [3. evaluated on the Argoverse 2 original split and the geographic split of 30
only total mAP reported in the original paper. t: values taken from Map'rracker [3]
Dutaset: Argoverse 2
Near Range (60 x 30 m)
Method
Split
Epochs
APaiv
A Dou
AP Aed
mAP
HIMap
3
Og. Split
24
72.4
73.2
72.4
72.7
MapUnveiler
Og. Split
30
74.2
71.9
72.5
72.9
StreamMapNet
80
Og. Split
72
74.2
66.1
70.5
70.3
MapTracker
Og. Split
35
80.0
73.7
77.0
76.9
SDTagNet
Og. Split
24
81.7
76.3
76.1
78.0
StreamMapNet
30
Geo Split
72
68.2
63.2
61.8
64.4
MapTracker
Geo Split
35
75.1
68.9
70.0
71.3
SDTagNet
30
Geo Split
24
72.0
67.5
64.0
67.8
Far Range (100 m x 50 m) from 30
MapUnveiler
11
Og. Split
30
67.9
62.6
71.7
67.4
StreamMapNet
30
Og. Split
30
57.7
SDTagNeet
Og. Split
24
80.2
72.9
81.7
78.3
StreamMapNeef
30
Geo Split
72
56.1
47.5
60.1
54.6
MapTrrccker R
Geo Split
35
64.6
58.5
71.2
64.8
SDTaggNet
30
Geo Split
24
69.5
68.0
75.1
70.9
Table 3:
Ablation study of different encoder components on the Argoverse 2 dataset. All experiments are in the near range setting and all models are trained for 24 epochs. With OSM nodes in the input features, which are not used in the original work. + BEV Ft.: With BEV features as an additional prior mode similar
(28
Base Enc.: SMERF (2I) (all info.)
AP
Pt Lvl. NLP Emb. ORF Id.
BEV Ft.
mAP
dsh.
sol.
bou.
cen.
ped.
45.9
38.0
54.4
50.0
48.5
38.2
44.3
34.9
53.0
49.5
45.5
38.7
45.8
37.0
55.4
47.9
49.2
39.3
46.3
39.3
53.3
49.6
48.5
40.8
45.0
33.6
53.2
50.2
47.7
40.6
48.1
36.0
55.2
53.3
52.6
43.3
48.1
38.2
56.3
52.6
50.7
42.3
hypothesize this is one of the main causes of the lacking performance in the near range, where the visibility of road features much higher and the model less reliant on SD map features.
5 Limitations
Despite SDTagNet's strong performance, we observe several limitations. First, like all SD map prior methods, SDTaagNet assumes congruency SD map and HD map ground truth. Depending on level of detail and quality of dataset ground truth and SD map, number of map discrepancies can be observed. This is especially the case for crowdsourced databases like OSM, as shown for Argoverse 2 by (20] and nuScenes by [9]. We also noted significant differences concerning structures and roads that are missing from the ground truth HD map. Figure 6b shows examples of tunnel and smaller service roads that are not included in the nuScenes map, impacting model accuracy and training data quality.
nuScenes, particularly
Another limitation concerns the case of one-way way roads. SDTaggNet is able to correctly identify one- way roads from the map tag information, however some cases the driving direction is predicted wrongly. An example of this is displayed in Figure 6al Since OSM encodes one-way direction via polyline point order, this likely stems from the lack point order information in the element identifiers. Adding point order identifiers for polyline features could help remedy this limitation.

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Table 4: Comparison of SD map prior encoding methods nuScenes I with the geographical split of 18 With the 7 classes from [21] the imput features, which are not used in the original work. t: With OSM nodes in the input features, which are not used in the original work. All models trained for 110 epochs.
Dutaset: nuSneees
Near Range (60 m 30 m)
Method
dshsh
AP sol
AP bou
AP ccn
AP ped
mAP
16
MapTRv2 16
12.5
19.1
32.4
29.1
21.6
22.9
+ PMapNNet 9
13.4
20.9
32.5
30.2
22.2
23.9
+1.0
+ PMapNNet* (all info.)
12.6
19.8
32.6
28.7
21.1
23.0
+0.1
+ SMERF 21
14.6
19.3
34,2
28.9
21.6
23.7
+0.8
+ SMERF 21
info.)
20.2
33.0
28.4
19.8
23.4
+0.5
+ SSDTagNet
19.3
19.8
31.3
30.4
15.6
23.3
+0.4
Far Range (120
60 m)
MapTRv2 116
2.7
3.7
4.6
5,4
2.9
3.9
NetppNet
9
2.4
3.5
5.0
5.8
2.9
3.9
+0.0
PMMapNet*
(all info.)
2.6
3.6
4.7
5.6
2.7
3.8
-0.1
+ SMERF
21
4.5
5.6
7.6
+1.9
+ SMERF
21
(all info.)
4.1
6.6
7.8
7.5
3.4
5.9
+2.0
+ SDDTagNet
8.3
6.6
10.3
9.3
5.3
8.0
+4.1
Ground Truth
SDTagNet
Ground Truth
OSM Map
Ground Truth
OSM Map
deshed divider
solid divider
lane pertefline
pedestrion crossing road border
(a) Pred, false one-war direction.
(b) Discrepancies between the SD map and ground truth.
Figure Examples for observed limitations of SDTagNet in Argoverse 2 (left) and nuScenes (right). Figure ba. Even when correctly recognizing way road, SDTagNet can fail to predict its correct driving direction. Figure [6b] Discrepancies between dataset HD maps and OSM SD maps like missing tunnels (left) or small service roads (left, right).
Conclusion
We present SDTagNet, the first online HD map construction approach that fully utilizes commonly available SD maps, including all elements such as points, polylines and relations conjunction to their descriptive textual information. This is achieved by incorporating embeddings of the textual annotations for each point, polyline, and relation in addition to their geometric information, encoding their kaluv tags with a contrastively pre-trained and fine-tuned BERT encoder. For scene-level encoding of SD map priors with their geometric data we increase the query resolution from polyline- level to point-level using orthogonal element identifiers, allowing for greater expressiveness by integration point and relational SD map elements in addition to the established polylines.
The combination of these encoding methods results in 20-35% increase in performance on Argov- erse and nuScenes, respectively, when evaluated on a perception range. This makes SDTagNett the first SD map prior based online HD map construction model free from manually selected features, with scalable self-supervised pre-training on planet-scale SD map data, improving the performance of existing methods by large margins.
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7 Acknowledgements
The authors gratefully acknowledge the computing time provided on the high-performance computer HoreKa by the National Pigfoneerfom Computing Center KIT (NHR @ KIT). This center is jointly supported by the Federal Ministry of Education and Research and the Ministry of Science, Research and the Arts of Baden- Meneen-Württtee Württemberg, part of the National High- Pigh-Peerforance Computing (NHR) joint funding program (https://www.nnt www.nhh-eerii.deee. verein.de/en/ourr partners.. HoreKa is partly funded by the German Research Foundation (DFG). SD map data copyrighted OpenStreetMap contributors and available from https//www.opensteeema..ppp
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Table 5: Ablation studies concerning the NLP tag embedding module on the Argoverse dataset. All experiments are in the near range setting and all models are trained for 24 epochs. rel. tag CL relevant tag contrastive learning, meaning whether our contrastive learning objective based on relevant tags is used during pretraining not. If not, irrelevant tags are removed in preprocessing and the positive sample also the anchor for pretraining.
AP
Fine-tuning LR Rel. Tag CL.
mAP
dsh.
sol.
bou.
cen.
ped.
Frozen
45.6
36.5
54.2
49.8
48.4
39.2
x 0.5
47.1
35.7
53.5
52.0
50.0
44.3
0.1
47.5
35.2
54.5
51.9
50.9
45.0
0.1
48.1
36.0
55.2
53.3
52.6
43.3
Table 6: Ablation studies of the SD map encoder cross attention on the Argoverse 2 dataset. All experiments are in the near range setting and all models are trained for 24 epochs.
AP
Att. Heads
Dropout
MAP
dsh.
sol.
bou.
cen.
ped.
0.2
46.9
35.7
53.7
50.6
52.0
44.3
4
0.1
47.7
38.6
54.3
51.9
51.4
42.3
0.1
48.1
36.0
55.2
53.3
52.6
43.3
A Technical Appendices and Supplementary Material
The supplementary material focuses three key aspects: Additional ablation studies all in- dividual components of SDTagNer (appendix [ and additional experiments on non-geo split (appendix A.4), implementation details of the baselines and NLP pretraining (appendix A.2 and appendix A.3) as well as more qualitative examples and visualizations (appendix A.7
A.1 Ablation studies
Additional ablation studies concerning the optimization strategies applied to the NLP tag embedding module and fine-tuning training regimes, conducted on the Argoverse 2 dataset, summarized in T and Table 6
For the NLP tag embedding module, observe in Table that fine-tuning the pretrained language model with small learning rate is crucial to align the self-supervised embeddings with the specific objectives of the downstream task. Additionally, removing objective results in degraded performance. This confirms the utility of our training scheme, which guides the model focus semantically meaningful tag representations.
custom contrastive pretraining
Additionally, we tested different numbers attention heads and different values of dropout for the SD map encoder cross-attention in Table settling on 8 attention heads and dropout of 0.1 as the configuration with the best performance.
A.2 Implementation Details of Baseline Approaches
We reproduce our baselines PMapNet 9 and SMERF [2]] according to their publicly available source code, with few changes to adapt to different experiment setting and base architecture.
For the PMapNet base setting, we create BEV grid with the same resolution as the main image BEV features and rasterize point and polyline features on this grid. Polyline and point features are drawn with fixed width of an 5 grid cells, respectively. Like in the PMapNet reference implementation. each element gets assigned a descending grayscale value from 0 to 255, without any additional labels. For the enhanced version PMapNet (all info.), we additionally add the classes defined SMERE, plus an additional other class for elements that do not belong to the predefined classes. Follwing the
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Table 7: Comparison of SD map prior encoding methods on the Argoverse 2 data set, with the original geographically overlapping split. t: With OSM nodes and relations in the input features, which are not used in the original work. All models trained for 24 epochs.
Dataset: Argoverse 2, og. split
Near Range (60 m 30 m)
Method
APash
APsol
AP
AP
APp
mAP
vs. [6
MapTRv2 16
61.1
66.8
67.9
61.9
58.6
63.3
SMERF 21 (all info.)
60.6
66.6
67.8
60.1
60.5
63.1
-0.2
+ Net
59.5
67.7
70.7
65,5
62.1
65.1
+1.8
Far Range (120 m 60 m)
MapTRv2 116
26.7
33.4
22.9
30.7
30.6
28.9
SMERF [1] (all info.)
30.7
37.9
28.9
33,9
35.3
33.4
+4.5
SDTagNett
39.6
45.1
38.7
43.8
45.5
42.5
+13.6
original source, the feature grids of both PMapNet versions get processed by a simple 3-layer CNN before being supplied the main map decoder.
For the SMERF base setting, we keep all hyperparameters as found in the original source code, except for also including the additional other class for elements that do not belong the predefined classes. We also select a fixed polyline point number of 10 like in the other experiments instead of 11 for easier preprocessing. For the enhanced version SMERF (all info.), we add point features to the encoder which also get assigned the other class. To fit the point features into the original polyline-level query token, we repeat the single point for 10 times. We do not include relation features in both PMapNet and SMERF, as the encoder architectures make this not possible without major changes.
A.3 Implementation Details Contrastive Pretraining Objective
In this section, we describe in more detail the selection process for the non-relevant tags used in the contrastive pretraining objective. The selection was performed with the help of the central OSM tag database located at /ttp::://taiinn o. openstreetmen eeeaap......... org/ and for all tags that were used least 100,000 times. For comparison, as of the time of writing, the most used tag key is building with around 650 million uses, or around 6% of total objects. As a rule, the selection was performed lask-agnostically and did not consider whether tag was particularly relevant for the task of online HD map construction. This preserves the general applicability of the proposed embedding model. Two main categories of tags are considered not informative. First, tags that are names and other identifying information of places like telephone numbers or website links. Second, map annotation artifacts, primarily IDs and other data from various government geodetic databases that were the original source of the data. As an example, most OSM elements in the United States contain IDs from the Topologically Integrated Geographic Encoding and Referencing system (TIGER) by the US Census Bureau, from which these elements were imported. The full list contains around 110 tag keys in total and can be found in the accompanying source code.
A.4 Argoverse 2 Original Split
To examine the differences to a non-geographic split setting, we furthermore evaluated reduced model set MR22 SMERF (all info.), and SDTaaNett on the original Argoverse split with about 40% geographic overlap [8]. The results in Table 7 that SDTagNett maintains superior performance in both range settings, with a much higher absolute mAP gain in the far range of +9.1 mAP compared to SMERF. It worth noting that the base performance is already 15 to 20 mAP higher in both settings, confirming the findings in [18] and indicating that significant geographic overfitting still takes place in the training of online HD map construction models.
A.5
SD Map Augmentation Methods
Motivated by the findings above and general trend of increased overfitting we observed with SD map priors, explored augmentation methods for SD map priors that could increase the performance
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Table Comparison of different SD map prior augmentation methods on the Argoverse dataset. All experiments are in the near range setting and all models are trained for 24 epochs. Loc. Const. locally constant, meaning all map elements one frame are augmented with the same offset and rotation.
Pos. Noise
AP
El. Drop Rate
Loc. Const.
Otrans
rot
mAP
dsh.
sol.
bou.
cen.
ped.
0.1
m
2
43.8
40.2
54.6
47.3
43.6
33.3
0.1
<
1 1m
2
45.6
37.3
54.5
48.8
47.4
39.9
48.1
36.0
55.2
53.3
52.6
43.3
Table 9: Comparison of different SD map prior tag masking augmentation methods on the Argoverse 2 dataset. All experiments are in the near range setting and all models are trained for 24 epochs. El. Aug. Rt. Rate of elements where tag masking is applied. Non-Rel. Only Tag masking is only applied to non-relevant tags. NLP Ft. LR Fine-tuning learning rate multiplier for the NLP encoder.
Tag Masking
AP
El. Aug. Rt.
Tag Drop Rt.
Non-Rel. Only
NLP Ft.
LR
mAP
dsh.
sol.
bou.
ped.
0.5
0.4
x 0.1
44.7
37.4
54.2
47.5
46.7
37.9
0.5
0.6
v
x 0.1
47.2
36.2
54.6
51.6
49.2
44.7
0.5
0.6
v
0.5
47.4
34.7
55.3
52.1
51.5
43.2
48.1
36.0
55.2
53.3
52.6
43.3
outside of the geographic training area. [28] showed that position noise augmentation for the SD map prior input during training increases performance when noisy priors are also there during validation. In this section however, our goal is to examine whether SD map prior augmentation can increase the general performance of a model, similar to the common use of augmentation techniques in computer vision. Additionally, with our direct use of the text annotations, we have an additional avenue for augmentation available in modifying the text content of these annotations.
We focused two main augmentation modes: Position noise and element dropping, shown in Table 8) and text annotation tag masking, shown in table 9 Unfortunately, in both cases, we were unable to note performance benefit when evaluated on the validation set without any added noise. For the element-level noise modes in Table B both augmentation variants worsened performance instead, even when all SD map elements one frame were moved with the same offset to simulate localization noise. The tag masking augmentation in table Juses uses an element augmentation rate to randomly select what percentage of elements will be modified and a tag drop rate select how many tags will be dropped. An additional parameter considered whether only non-relevant tags are dropped or not. The performance of this augmentation mode is higher than with position noise, especially when only non-relevant tags are dropped. Nonetheless, tag masking was also not able to improve performance compared the baseline without any augmentation. We believe that method to prevent geographic overfitting via augmentation techniques in other ways would significantly increase generalization and overall performance in online HD map construction, particularly when priors are also available.
A.6 Inference Time and Model Size
SDTagNet designed with real-time capability in mind: the NLP tag encoder based on a compact BERT model (embedding dim 144), and the SD map encoder is lightweight compared to the main perception backbone. To evaluate the real time capability of SDTTagNet, we have investigated its inference time in comparison with no prior and with other SD map encoders. The parameter count and FPS of SDTaaNeet and baselines on an NVIDIA H100 GPU can found in Table 10 The additional inference time for SDTTagNet is less than 7 ms per frame, and the memory overhead is marginal relative to the base model. We thus consider our approach suitable for real-time deployment in autonomous driving systems, with negligible difference other SD map encoding methods.
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Table 10: Comparison of model parameter count, FPS, and VRAM with MapTRv2 and two SD map baseline methods. All experiments are on the Argoverse 2 dataset with the far-range setting, averaged over 10 samples and tested on NVIDIA H100 GPU.
Model
Param. Count
FPS
VRAM [MB]
MapTRv2 [6] (no prior)
42.12 12.12 M
16.0
3654
MapTRv2 + PMapNet [9]
50.48 M
13.9
3698
MapTRv2 + SMERF (2
44.62 M
14.9
3662
MapTRv2 + SDTagNet
50.54 M
14.5
3686
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Figure Larger resolution example of the OSM SD map input data on Argoverse used for SDTagNet. All text annotations are used as-is, without any preprocessing or filtering. This makes the design of SDTagNet highly scalable and adaptive.
A.7 Additional Qualitative Examples and Visualizations
Example of OSM Text Annotations To provide a better understanding of the format and content of text annotations in OpenStreetMap, include a larger resolution example of the OSM elements for a frame fig. 7 Every element has annotated information, this includes buildings and roads with their names, oneway information number of lanes for roads, pedestrian ways, pedestrian crossing points, etc. The road annotations on the lower left also contain some of the aforementioned TIGER geodetic database identifiers.
Additional Examples of nuScenes Map Discrepancies Figure shows additional examples of HD and SD map discrepancies we observed in nuScenes. These consist of inconsistent road borders that include loading and parking areas, differences in annotated crossing topology and annotation ranges that are too short and leave parts of the road unannotated. We hypothesize that these differences play a role in the reduced performance of all SD map prior methods in the nuScenes near range setting, where information from the camera images is more plentiful.
Additional Qualitative Comparisons of SDTagNet In fig. 9 and fig. 10 we present additional comparisons of SDTagNett with the baselines PMapNet (9] and SMERF [2 on Argoverse 2 and nuScenes, respectively. Figure 9 contains examples that show how SDTagNet can utilize text- annotated information such as oneway roads or the number of lanes and transfer this into improved prediction results. Figure[TO displays the increased accuracy and topological consistency of SDTagNet on nuScenes compared to previous methods.
24

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SD Map
ILADET PHIA
(a) Road border of the ground truth mistakenly includes loading and parking areas.
Ground Truth
SD Map
(b) Non-existent crossing arm included the ground truth.
Ground Truth
SD Map
(c) Ground truth annotation cutoff too short.
Figure 8: Additional examples of discrepancies between the ground truth HD maps and OSM SD maps in the far range setting. Inconsistent road borders, wrong crossing topologies and too short of an annotation cutoff cause inconsistencies.
25

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SD Map
Ground Truth
PMapNet
SMERF
SDTagNet
Map
Ground Truth
PMapNet
SDTagNet
SD Map
Ground Truth
PManNet
SMERF
SDTagNet
SD Map
Ground Truth
PMapNet
SMERF
SDTagNet
pedestrian crossing
Figure 9: Further qualitative comparison of SDTagNet with PMapNet (all info.) and SMERF (all info.) on Argoverse 2 in the far range setting. SDTagNet is able to utilize text-annotated information such number of lanes and oneway roads to improve prediction results.
26

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SD Map
Ground Truth
PMapNet
SMERF
SDTagNet
SD Map
Ground Truth
PMapNet
SMERF
SDTagNet
SD Map
Ground Truth
PMapNet
SMERF
SDTagNet
SD Map
Ground Truth
PMapNet
SMERF
SDTagNet
dashed divider solid divider
+ lane centerlne
pedestrian crossing road border
Figure 10: Qualitative comparison of SDTagNet with PMapNet (all info.) and SMERF (all info.) on nuScenes in the far range setting. SDTagNet shows less errors and more consistent topology compared to previous methods.
27


Reviews:
[{"review_id": "2OQ1vAmdXV", "reviewer": "Reviewer_iYXD", "summary": "This 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.", "questions": "I’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?", "limitations": "The 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.", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 2, "significance": 2, "originality": 2, "strengths_and_weaknesses": "Strengths:  \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?", "was_revised": true, "final_justification": "Based on the reviewers comments and my reading of the other reviews, I stand by my original assessment.", "rebuttal": "# Author Response to Reviewer iYXD\n\nWe thank the reviewer for their thoughtful and constructive feedback, as well as for recognizing the strengths of our work, including the generality and flexibility of our SD map encoding, the novel semantic tag embedding, the thorough evaluation, and discussion of limitations.\n\n## Response to Specific Weaknesses\n\n### W1: Modest Gains in Near Range, Low Absolute Accuracy in Far Range\n\n> 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\nWe appreciate the reviewer’s concern regarding the significance of the reported gains, particularly in the far range setting where absolute mAP values are low. We would like to clarify several points:\n\n- As Reviewer kgfW also noted, for the near range scenario, the high-quality sensor data in the immediate vicinity is likely sufficient information for some element classes. However SDTagNet still shows significant gains for road boundaries and lane centerlines, even in the near range. The Response to Reviewer kgfW contains a detailed analysis of the performance difference per element class. The far range scenario in contrast is inherently more challenging due to limited sensor visibility, occlusions, and lower image resolution of visible far-range areas. \n\n\n- The low absolute mAP in the far range is also an artifact of our chosen evaluation metric. The mAP in online HD map construction is usually calculated via the averaging of Chamfer distance at specific thresholds, commonly \\[0.5m, 1m, 1.5m\\] in the near range setting. We adopted the same thresholds for both near and far range setting to make the values directly comparable and show the benefits of SDTagNet in a hard evaluation context. \n\n- This however causes the absolute mAP values to appear quite low. For comparison, StreamMapNet [a], MapTracker [b], and other approaches use higher thresholds of \\[1m, 1.5m, 2m\\] in their far range setting. To make SDTagNet comparable with their evaluations, we provide the mAP values for Argoverse 2 in far range with higher thresholds in the table below. The absolute mAP values of all models increase drastically, with the performance gain of SDTagNet increasing proportionally, totaling +10.1 mAP vs. no prior and +6.4 mAP vs. the best other SD map prior encoding method.\n\n#### Table 1: Far range with higher mAP thresholds\n\n| Model | AP_div | AP_sol | AP_bou | AP_cen | AP_ped | **mAP** | vs. MapTRv2 |\n|-------|--------|--------|--------|--------|--------|---------|-------------|\n| MapTRv2 (no prior) | 14.1 | 23.3 | 23.5 | 27.3 | 27.0 | **23.0** | - |\n| MapTRv2 + PMapNet  | 14.3 | 25.2 | 27.8 | 30.4 | 35.6 | **26.7** | +3.7 |\n| MapTRv2 + SMERF    | 13.3 | 21.9 | 22.7 | 25.8 | 26.5 | **22.0** | -1.0 |\n| MapTRv2 + SDTagNet | 19.0 | 28.7 | 36.2 | 36.5 | 44.9 | **33.1** | **+10.1** |\n\n*Evaluation on Argoverse 2 dataset with geographical split, far range (120m × 60m) setting, higher mAP thresholds \\[1m, 1.5m, 2m\\] (like [a, b]).*\n\n### W2: Significance of SD Map Priors for HD Map Generation\n\n> 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\n- SD maps (e.g., OSM) are globally available, change much less often and are significantly easier to maintain (both compared to HD maps). This makes them a practical prior in many use cases. The flexible, task-agnostic design and pre-training of the encoder complements this, making it suitable for applications beyond online HD map construction that also benefit from map context, such as trajectory prediction.\n\n- As we show above, absolute mAP values and gains strongly depend on the specific evaluation setting. When using easier thresholds that are commonly used \\[a, b\\] and correspond to the navigational flexibility at greater range, absolute mAP values increase drastically.\n \n- SDTagNet is furthermore able to correctly predict elements that are not visible in sensor data, for example crossing topologies, which we show in our qualitative examples. The accuracy gains in these settings are not achievable with other approaches, even other SD map encoders that do not incorporate semantic tags. This makes SDTagNet an important part in solving the HD map generation problem.\n\n### W4: Baseline Performance, Especially PMapNet\n\n> 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\n- MapTRv2 is indeed a much stronger baseline than HDMapNet. In the original MapTRv2 [c] paper, the authors report a mAP of 18.8 for HDMapNet and 67.4 for MapTRv2 on Argoverse 2. The evaluation in MapTRv2 was not performed on a geographic split (roads from the training set also appear in the validation set), and with 3 instead of 5 classes, so the values are not directly comparable to ours. However, the mAP difference should give an indication of baseline model performance strength.\n- Furthermore, PMapNet does not use a geographic split between train and val data like SDTagNet does, which enables overfitting on specific areas and roads - a problem which increases with the usage of priors like SD maps in a model.\n- The fact that the (all info) baselines don’t really do much better than the originals is consistent with our findings in the ablation studies of SDTagNet (Table 3 in the Appendix).\nWe observed that simply \"dumping\" all possible information into the encoder is not enough, the encoder architecture plays a big role in model performance. For example, the inclusion of ORF element identifiers, which none of the competing encoders do, is very important for mAP results.\nWithout the combination of all proposed architectural changes in SDTagNet, no baseline method is able to provide a significant performance gain over MapTRv2 in the near range on Argoverse 2.\n\n[a] Yuan, Tianyuan, et al. \"Streammapnet: Streaming mapping network for vectorized online hd map construction.\" WACV, 2024.\n\n[b] Chen, Jiacheng, et al. \"Maptracker: Tracking with strided memory fusion for consistent vector hd mapping.\" ECCV, 2024.\n\n[c] Liao, Bencheng, et al. \"MapTRv2: An End-to-End Framework for Online Vectorized HD Map Construction\" IJCV, 2024"}, {"review_id": "jijQ2OHXXO", "reviewer": "Reviewer_Xgo9", "summary": "The 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.", "questions": "The 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.", "limitations": "yes", "rating": 4, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "**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.", "was_revised": true, "final_justification": "My concerns have been adequately addressed through the authors' feedback. Thus, I will maintain my initial positive rating.", "rebuttal": "# Author Response to Reviewer Xgo9\n\nWe thank the reviewer for their valuable feedback and for recognizing the strengths of our work, including its main innovation of using language processing, the flexibility of our SD map encoding, and our contribution towards reproducibility. We are encouraged by the reviewer's assessment that our approach 'will inspire many related follow-up studies,' which validates the significance of our contribution.\n\n### W1: Comparison to Recent Methods (HIMap, MapUnveiler, MapTracker)\n\n> 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\nand\n\n>Since the method leverages additional information from SD maps, it would be expected that the proposed method outperforms existing HD map construction approaches.\n\n\nWe appreciate the suggestion to include these recent methods and agree they present relevant advances in their setting.\n\n- MapTracker \\[b\\] (and its predecessor StreamMapNet \\[a\\]) marks a major shift in HD map construction through its use of end-to-end temporal modeling (MOTR-based tracking). This complements our direction, which focuses on leveraging static SD maps—a distinct and orthogonal source of information. SD map priors aim to enhance map construction performance even in previously unseen areas, including those far into the future range. These two mechanisms could potentially complement each other in the future.\n- Simply comparing the reported results of HIMap, MapUnveiler, or MapTracker is not feasible due to inconsistent validation splits and ground truth label sets. Each paper applies different HD map references or simplified label spaces (e.g., 3-class vs. 5-class).\n- For HIMap and MapUnveiler sadly no public code is available (git repositories are empty), which we would require to incorporate SDTagNet and evaluate them on our expanded and consistent ground truth. We have contacted the authors of HIMap regarding the code while working on SDTagNet but unfortunately did not receive a response.\n- In particular, integrating SDTagNet with temporally-enhanced architectures such as MapTracker presents promising future work. However, as shown in our ablations, effective integration of SD priors requires careful architectural design. MapTracker’s multi-stage, multi-loss, memory-bank-based system introduces complexity that we consider outside the scope of this paper.\n \n> Additionally, I think the proposed method would be performed on top of various baselines, not only on top of MapTRv2.\n> \n\n- We evaluate against two prominent SD map prior baselines, SMERF and PMapNet, both re-implemented and integrated on top of the same single-shot HD map construction model for fair comparison. This ensures consistent evaluation conditions and isolates the effects of the SD map prior encoders.\n-  Rather than comparing reported numbers across incompatible setups, we take a more rigorous approach by rebuilding prior methods on a unified baseline that is significantly stronger than the original base architectures of SMERF and PMapNet.\n- We selected MapTRv2 as the base model because it is a widely adopted and strong baseline for online HD map construction. Many recent methods, including HIMap, build on its foundation, reinforcing our choice to use it for isolating the effect of SD map priors in a stable setup.\n\n### W2: Evaluation on StreamMapNet Split and Consistent GT\n\n>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**Response:**\n- We chose the geographically non-overlapping split from [17] (Lilja et al., CVPR 2024) to avoid overfitting to specific locations, which is especially important when using map priors. As shown in our experiments (see supplementary material, Table 6), models using SD map priors are more prone to geographic overfitting compared to models without any prior. Additionally, the geo split provides a more realistic assessment of generalization to unseen areas.\n\n- The StreamMapNet split is geographically disjoint as well. At the time we started this work, it was not clear whether the split from [17] or the StreamMapNet split would prevail. To facilitate comparisons, we provide results on both splits, showing that our method maintains strong performance in either setting. The performance of all baselines and our method trained on the StreamMapNet split can be found in Table 2.\n\n#### Table 2: StreamMapNet Split \n\n| Model                                | AP_div | AP_sol | AP_bou | AP_cen | AP_ped | **mAP**    | Δ mAP to no prior |\n|--------------------------------------|--------|--------|--------|--------|--------|------------|-------|\n|    MapTRv2 (no prior)                | 5.5 | 14.5 | 12.9 | 16.3 | 16.3 | **13.1** | 0.0       |\n|    MapTRv2 + PMapNet                 | 6.6 | 15.3 | 16.4 | 19.1 | 20.8 | **15.6** | +2.5      |\n|   MapTRv2 + SMERF                    | 5.6 | 14.8 | 17.0 | 18.4 | 16.0 | **14.4** | + 1.3     |\n| MapTRv2 + SDTagNet                   | 7.8 | 19.7 | 24.7 | 24.0 | 28.2 | **20.9** | **+7.8**      |\n\n*Evaluation on Argoverse 2 dataset with StreamMapNet geographical split, far range (120m × 60m) setting, 6 epochs training.*\n\n- We agree that consistent ground truth is essential for thorough evaluation. As noted in Section 4.1, SDTagNet uses the improved labels from M3TR \\[g\\], which to our knowledge is the most comprehensive and most challenging ground truth. They include topologically relevant centerlines and correct the same errors in the original labels as Maptracker did for the consistent GT. Table 3, reproduced from M3TR, provides an overview of proposed ground truths of different online HD map construction approaches. \n  \n#### Table 3: Comparison of Ground Truth Labels\n\n| **Method**                | Divider Types | Lane Centerlines | 3D Instances | Fixed GT Artifacts | Geo. Split |\n|---------------------------|:-------------:|:-------------:|:-------------:|:------------------:|:----------:|\n| VectorMapNet          |      -        |      -        |      -        |        -           |     -      |\n| MapTRv2           |      -        |   ✓           |   ✓           |        -           |     -      |\n| StreamMapNet         |      -        |      -        |      -        |        -           |   ✓        |\n| MapTracker            |      -        |      -        |      -        |      ✓             |   ✓        |\n| MapEX                 |      -        |      -        |      -        |        -           |     -      |\n| PriorDrive            |      -        |      -        |      -        |        -           |     -      |\n| **M3TR**      |   ✓           |   ✓           |   ✓           |      ✓             |   ✓        |\n\n---\n\n### References\n\n\\[a\\] Yuan, Tianyuan, et al. \"Streammapnet: Streaming mapping network for vectorized online hd map construction.\" WACV, 2024.\n\n\\[b\\] Chen, Jiacheng, et al. \"Maptracker: Tracking with strided memory fusion for consistent vector hd mapping.\" ECCV, 2024.\n\n\\[c\\] Liao, Bencheng, et al. \"MapTRv2: An End-to-End Framework for Online Vectorized HD Map Construction\" IJCV, 2024\n\n\\[d\\] Luo, Katie Z et al. \"Augmenting lane perception and topology understanding with standard definition navigation maps.\", ICRA, 2024\n\n\\[e\\] Jiang, Zhou et al. \"P-mapnet: Far-seeing map generator enhanced by both sdmap and hdmap priors.\" RA-L, 2024\n\n\\[g\\] Immel et al. *M3TR: A Generalist Model for Real-World HD Map Completion.* arxiv, 2025.\n\n## Additional Notes\n\nWe have added our code to the submission and will release it along with pre-trained models upon publication. We aim to promote reproducibility and encourage further innovations in SD map prior integration—potentially also in conjunction with temporal models like Maptracker.\n\n\nWe would like to thank the reviewer again for their positive assessment and valuable suggestions. We will incorporate our additional clarifications and evaluations in the final version."}, {"review_id": "s4gfDBwGiL", "reviewer": "Reviewer_kgfW", "summary": "Existing 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.", "questions": "I would like to increase my score if the authors can address my 1st and 3rd points in weaknesses", "limitations": "Yes", "rating": 4, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 4, "clarity": 4, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths:\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.", "was_revised": true, "final_justification": "Based on the rebuttal and other reviewers' comment, I am maintaining my score.", "rebuttal": "# Author Response to Reviewer kgfW\n\nWe thank the reviewer for their valuable and constructive feedback and for recognizing the strengths of our work, including its main innovation of fully exploiting the rich information contained in SD maps, its architectural contributions, generalization capabilities, and significant improvement especially for far-range online HD map construction.\n\n### W1: Placement of Ablation Studies\n\n> 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\n\n* In the camera-ready version, we will move as much of the main ablation results into the main paper as space allows and summarize the main findings. Additionally, we will reference explicitly to the appendix for further details, avoiding the full ablations to go unnoticed.\n\n### W2: Near Range Performance\n\n> 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.\n\n* Regarding the limited improvement in the near-range setting, we agree with the reviewer's analysis: the baseline MapTRv2 already performs strongly where sensor data is reliable, and the main benefit of SD maps is in far-range scenarios where areas are sparsely visible.\n* SD maps can still provide valuable prior knowledge even in near range. When we take a detailed look at the class-wise evaluation metrics we can conclude that based on the kind of information that SD maps typically provide.\n* For instance, detailed lane divider positions and marking types (dashed vs solid) are clearly visible in near range sensor data, thus SD map prior information contributes little in these cases or even slightly decreases the performance in the near range due to differing labeling guidelines between OSM and Argoverse 2.\n* But for classes such as lane centerlines, that have corresponding prior information in OSM, like upcoming turn directions (lane topology at intersections), lane count, road width, lane and road types, we see a notable improvement of the detection performance. Analogously, road boundary detections improve since OSM contains building outlines, pedestrian walkways, rough positions of roads, and their intersections, aiding the HD map construction in the near range as well.\n\n#### Table 4: Per-class breakdown of near range performance\n\n| Method | AP_dsh | AP_sol | AP_bou | AP_cen | AP_ped | mAP | vs. MapTRv2 |\n|--------|--------|--------|--------|--------|--------|-----|-------------|\n| MapTRv2 | 37.9 | 55.0 | 49.7 | 48.2 | 41.7 | 46.5 | \\- |\n| \\+ SDTagNet | 36.0 | 55.2 | **53.3** | **52.6** | 43.3 | 48.1 | **+1.6** |\n| Δ in AP | \\-1.9 | +0.2 | **+3.6** | **+4.4** | +1.6 | 48.1 |  |\n\n*Per-class comparison of performance in the near range setting of MapTRv2 vs. SDTagNet. Values are taken from Table 1 in the paper.*\n\n### W3: Generalization to Other Architectures:\n\n> 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\n* We evaluate against two prominent SD map prior baselines, SMERF and PMapNet, both re-implemented and integrated on top of the same single-shot HD map construction model for fair comparison. This ensures consistent evaluation conditions and isolates the effects of the SD map prior encoders.\n* Rather than comparing reported numbers across incompatible setups, we take a more rigorous approach by rebuilding prior methods on a unified baseline that is significantly stronger than the original base architectures of SMERF and PMapNet.\n* We selected MapTRv2 as the base model because it is a widely adopted and strong baseline for online HD map construction. Many recent methods build on its foundation, reinforcing our choice to use it for isolating the effect of SD map priors in a stable setup.\n* Other recent work like HIMap \\[h\\], MapUnveiler \\[i\\], and MapTracker \\[c\\] contains relevant advances in their setting, but either has no public code available, or, in the case of MapTracker, introduce complexity for effective integration hat we consider outside the scope of this paper. A more detailed discussion of these works can also be found in the response to reviewer Xgo9, who shared similar feedback.\n\n\\[b\\] Chen, Jiacheng, et al. \"Maptracker: Tracking with strided memory fusion for consistent vector hd mapping.\" ECCV, 2024.\n\n\\[d\\] Luo, Katie Z et al. \"Augmenting lane perception and topology understanding with standard definition navigation maps.\", ICRA, 2024\n\n\\[e\\] Jiang, Zhou et al. \"P-mapnet: Far-seeing map generator enhanced by both sdmap and hdmap priors.\" RA-L, 2024\n\n\\[h\\] Zhou, Yi, et al. \"Himap: Hybrid representation learning for end-to-end vectorized hd map construction.\" CVPR, 2024.\n\n\\[i\\] Kim, Nayeon, et al. \"Unveiling the Hidden: Online Vectorized HD Map Construction with Clip-Level Token Interaction and Propagation.\" NeurIPS, 2024."}, {"review_id": "cMxrCocK4A", "reviewer": "Reviewer_CvWw", "summary": "The 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.", "questions": "1. 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?", "limitations": "Yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 3, "significance": 2, "originality": 2, "strengths_and_weaknesses": "Pros:\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.", "was_revised": true, "final_justification": "The author address my concerns, so I decide to raise the rating.", "rebuttal": "# Author Response to Reviewer CvWw\n\n\n### W1: Incremental Novelty of Using SD Maps and Textual Annotations\n\n>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.\n\nWe fully agree that SDTagNet is not the first paper to use SD maps as prior information and indeed use other SD map approaches as baselines. However, previous methods (e.g., PMapNet, SMERF) are limited to polylines with a small, hand-selected set of attributes or classes. This disregards the majority of available SD map data, including topological hints or textual information.\n\nThe groundbreaking idea of SDTagNet is to fully leverage the rich, open-vocabulary textual annotations available in SD maps like OpenStreetMap, i.e. combining online HD vector map construction and natural language processing techniques for the first time. \nSDTagNet introduces a contrastively pre-trained NLP tag embedding module, enabling the use of all textual annotations *without* manual feature engineering.\n\nOur experimental results show that adding an NLP encoder is not only a substantial conceptual step forward in utilizing the full potential of SD maps. It also significantly improves performance especially for far-range detections. We therefore believe that SDTagNet represents a considerable advance beyond prior art.\n\n### W2: Limited Improvement in Near-Range Scenarios\n\n>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.\n\n   We agree that the improvement in near-range perception is limited. As noted by reviewer kgfW, this is expected as near-range regions are covered well by onboard sensors, making the additional value of SD map priors naturally less pronounced.\nHowever SDTagNet is still able to provide a substantial benefit for classes like road boundaries or lane centerlines. We have included a more detailed performance discussion in the response to Reviewer kgfW.\n\nHence our main focus is on still insufficient far-range perception, where SDTagNet demonstrates substantial gains (+45% mAP over no prior, +20% over previous SD map prior methods). This is particularly relevant for real-world autonomous driving, where long-range perception of HD maps is critical for safe and comfortable planning, especially at higher speeds.\n\n### W3: Reliance on SD Map Quality and Handling Incomplete/Erroneous Priors\n\n>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.\n\nWe acknowledge that SDTagNet, like all methods using map priors, depends on the quality and completeness of the prior (SD) map. \nWe have already investigated missing and noisy SD maps as augmentation techniques in Table 7 and 8 in the Appendix. However, we can use the same techniques, which cover normally distributed point noise, element and textual attribute dropout, to further analyze SDTagNet's performance in real-world scenarios with noisy or incomplete SD map data. \n\nTo that end, we evaluated the original version of SDTagNet (trained without noisy SD map data) on SD maps with positional noise and/or dropout. The results are shown in the Tables 5 and 6 below. One can observe that SDTagNet generally maintains its strong performance with noisy data, even when not explicitly trained on it.\n\n#### Table 5: Influence of Noisy SD Maps\n\n| Noise in train | El. Drop Rate | Loc. Const. | σ_trans | σ_rot | **mAP** (perfect SD map prior in validation) | **mAP** (**with** noise added to SD map prior in validation ) |\n|---------------|---------------|-------------|---------|-------|---------|-------|\n|       ✓       | 0.1           | -           | 1 m     | 2°    |  **43.8**   | **43.8**   |\n|       ✓       | 0.1           | ✓           | 1 m     | 2°    |  **45.6**  | **45.6** | \n|       x       | 0.1           | -           | 1 m     | 2°    | **48.1**   |  **46.9**   |\n|       x       | 0.1           | ✓           | 1 m     | 2°    | **48.1**   |  **47.6**   | \n\n*Investigation of effects of SD maps with positional noise when used as augmentation during training (rows 1-2) as well as only at test time (rows 3-4). All experiments are on the Argoverse 2 dataset in the near range setting and all models are trained for 24 epochs. Loc. Const. = locally constant, meaning all SD map elements in one frame are distorted by the same offset and rotation*\n\n---\n\n#### Table 6: Influence of Incomplete SD Maps\n\n| Noise in train | El. Aug. Rt. | Tag Drop Rt. | Non-Rel. Only | NLP Ft. LR | **mAP** (perfect SD map prior in validation) | **mAP** (**with** element dropout added to SD map prior in validation) |\n|---------------|-------------|-------------|---------------|------------|---------|-------|\n|       ✓       | 0.5         | 0.4         | -             | × 0.1      | **44.7**    | **44.7** |\n|       ✓       | 0.5         | 0.6         | ✓             | × 0.1      | **47.2**    | **47.2** |\n|       ✓       | 0.5         | 0.6         | ✓             | × 0.5      | **47.4**    | **47.4** |\n|       x       | 0.5         | 0.4         | -             | -      | **48.1**    | **47.1** |\n|       x       | 0.5         | 0.6         | ✓             | -      | **48.1**    | **47.7** |\n\n*Investigation of effects of incomplete SD maps when used as augmentation during training (rows 1-3) as well as only at test time (rows 4-5). All experiments are on the Argoverse 2 dataset in the near range setting and all models are trained for 24 epochs. El. Aug. Rt. = Rate of elements where tag dropout is applied. Non-Rel. Only = Tag dropout is only applied to non-relevant tags. NLP Ft. LR = Fine-tuning learning rate multiplier for the NLP encoder.*\n\n### W4: Computational Complexity and Real-Time Feasibility\n\n>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.\n\nSDTagNet is designed with efficiency in mind: the NLP tag encoder is based on a compact BERT model (embedding dim 144), and the SD map encoder is lightweight compared to the main perception backbone. We have included the parameter count and FPS of SDTagNet and the baselines on an NVIDIA H100 GPU below. The additional inference time for SDTagNet is less than 7 ms per frame, and the memory overhead is marginal relative to the base model.\nWe thus consider our approach suitable for real-time deployment in autonomous driving systems, with a negligible difference to other SD map encoding methods. We will include these results in the final version of the Appendix.\n\n#### Table 7: Computational Complexity and Real-Time Feasibility\n\n| Model                   | Param. Count | FPS  | VRAM [MB] |\n|-------------------------|--------------|------|------|\n|    MapTRv2 (no prior)   |  42.12 M     | 16.0 |   3654  |\n|    MapTRv2 + PMapNet    |  50.48 M     | 13.9 |   3698  |\n|   MapTRv2 + SMERF       |  44.62 M     | 14.9 |   3662  |\n| MapTRv2 + SDTagNet      |  50.54 M     | 14.5 |   3686  |\n\n*Comparison of model parameter count, FPS, and VRAM with MapTRv2 and two SD map baseline methods. All experiments are on the Argoverse 2 dataset with far-range setting, averaged over 10 samples and tested on NVIDIA H100 GPU.*\n\n### W5: Methodological Details: Processing of Complex Map Elements\n\n>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\nAll map elements are encoded at the point level, resulting in identical processing steps, with orthogonal random feature (ORF) identifiers (think of IDs in vector space) enabling the model to distinguish and relate different element types.\nAs depicted in Figure 4, points are represented as a single query of the SD map encoder with two identical ORF identifiers, a tag embedding, and positional encoding.\nPolylines are represented by a list of multiple point-like queries that all share the same two identical ORF identifiers. Relations are represented using pairs of ORF identifiers, each corresponding to one of the members of a relation edge, as done traditionally in graph-structured data with node IDs. \nWe will highlight the commonality of processing and add further explanation to the camera-ready version.\n\nAs mentioned in our reply to reviewer kgfW, substantial parts of the ablations done in Table 5 will be moved to the main paper. This ablation highlights the impact of ORF identifiers on detection performance and the symbiotic nature of combining it with the point-level representations and NLP-based tag embeddings.\n\n---\n\n**Remark on Generalization to Diverse Geographic Regions**\n\n- **Generalization to Diverse Geographic Regions:**\n We agree that further evaluation on more diverse regions would strengthen the work. Our use of geographically disjoint splits (Section 4.1) is a step towards realistic generalization assessment. We will extend our evaluation to additional regions and newly released datasets as they become available in future work."}]

Author rebuttal:

Meta-review: This paper presents an online high-definition map reconstruction method called SDTagNet, which utilizes text-annotated standard-definition maps such as OpenStreetMap as prior knowledge. The authors introduce a natural language processing model to interpret textual annotations and a point-level SD map element encoder to uniformly integrate various map elements. Extensive evaluations on the Argoverse 2 and nuScenes datasets confirm that the method achieves gains in map perception accuracy.

The paper received four “Borderline Accept” ratings.

Reviewer iYXD and Reviewer Xgo9 had concerns about the performance of SDTagNet and baseline methods. They both finally gave the "Borderline Accept". However, the authors further presented a detailed reply about these concerns.

Reviewer kgfW maintained the  “Borderline Accept” rating after reading the rebuttals and other reviewers' comments.  

Reviewer CvWw thought the authors had processed all his/her concerns. Reviewer CvWw finally gave the "Borderline Accept".

The majority of the reviewers' concerns had been addressed. There is a consensus on the positive evaluation. This manuscript is recommended for acceptance.
Decision: Accept (poster)