Online Navigation Refinement: Achieving Lane-Level Guidance by Associating Standard-Definition and Online Perception Maps
Authors: Jiaxu Wan
Xu Wang
Mengwei Xie
Xinyuan Chang
Xinran Liu
Zheng Pan
Mu Xu
Hong Zhang
Ding Yuan
Yifan Yang
Venue: ICLR 2026 Poster (2026)
Keywords: online navigation refinement
geographic information systems
navigation
standard definition map
online perception map
Primary area: applications to computer vision, audio, language, and other modalities
TL;DR: We introduce Online Navigation Refinement that enabled low-cost real-time lane navigation by fusing SD maps with perception maps via a new transformer (MAT) and dataset (OMA).

Abstract: Lane-level navigation is critical for geographic information systems and navigation-based tasks, offering finer-grained guidance than road-level navigation by standard definition (SD) maps. However, it currently relies on expansive global HD maps that cannot adapt to dynamic road conditions. Recently, online perception (OP) maps have become research hotspots, providing real-time geometry as an alternative, but lack the global topology needed for navigation. To address these issues, Online Navigation Refinement (ONR), a new mission is introduced that refines SD-map-based road-level routes into accurate lane-level navigation by associating SD maps with OP maps. The map-to-map association to handle many-to-one lane-to-road mappings under two key challenges: (1) no public dataset provides lane-to-road correspondences; (2) severe misalignment from spatial fluctuations, semantic disparities, and OP map noise invalidates traditional map matching. For these challenges, We contribute: (1) Online map association dataset (OMA), the first ONR benchmark with 30K scenarios and 2.6M annotated lane vectors; (2) MAT, a transformer with path-aware attention to aligns topology despite spatial fluctuations and semantic disparities and spatial attention for integrates noisy OP features via global context; and (3) NR P-R, a metric evaluating geometric and semantic alignment. Experiments show that MAT outperforms existing methods at 34 ms latency, enabling low-cost and up-to-date lane-level navigation.

Published as a conference paper at ICLR 2026
ONLINE NAVIGATION REFINEMENT: ACHIEVING LANE- LEVEL GUIDANCE BY ASSOCIATING STANDARD- DEFINITION AND ONLINE PERCEPTION MAPS
Jiaxu Wan', Xu Wang² 9, Mengwei Xie?, Xinyuan Chang', Xinran Liu?, Zheng Pan",
Mu Xu², Hong Zhang! Ding Yuan!.4, Yifan Yang1.
(wanjiaxu, dmrzhang, dyuan, stephenyoung buaa.edu.en, /wx303649, xiemengwei.xww,
changxinyuan.cxx tom.lxr, panzheng.pan xumu.xm] @
'Scooo of Aerospace, BUAA AAmap, Alibaba Group 'Key Laboratory of Spacecraft Design
Optimization and Dynamic Simulation Technology, Ministry of Education *State Key Laboratory of High-Ffficiency Reusable Aerospace Transportation Technology
Github: https//gghabbcomm//wwllaaaWAAAA
ABSTRACT
Lane-level navigation is critical for geographic information systems and navigation- based tasks, offering finer- finer-grained grained guidance than road-level navigation by standard definition (SD) maps. However, currently relies expansive global HD maps that cannot adapt to dynamic road conditions. Recently, online perception (OP) maps have become research hotspots, providing real-time geometry as an alterna- tive, but lack the global topology needed for navigation. To address these issues, Online Navigation Refinement (ONR), a new mission is introduced that refines SD-map- map-based road-level routes into accurate lane-level navigation by associating SD maps with OP maps. The map-to-map association to handle many-to -to-one lane-to-road mappings under two key challenges: (1) no public dataset provides lane-to-road correspondences: (2) severe misalignment from spatial fluctuations, semantic disparities, and OP map noise invalidates traditional map matching. For these challenges, We contribute: (1) Online map association dataset (OMA), the first ONR benchmark with 30K scenarios and 2.6M annotated lane vectors; (2) MAT, a transformer with path-aware attention to aligns topology despite spatial fluctuations and semantic disparities and spatial attention for integrates noisy OP features via global context; and (3) NR P-R, a metric evaluating geometric and semantic alignment. Experiments show that MAT outperforms existing methods 34 ms latency, enabling low-cost and up-to-date lane-level navigation.
INTRODUCTION
Lane-level navigation has emerged as a critical capability geographic information systems (GIS) Hansson et al. (2020); Guo et al. (2025) and navigation-basel tasks (Png et al. (2025); Li al. Shan et al. (025), offering finer grainee guidance than traditional road-level navigation based on standard definition (SD) maps (Fig. T (a)) Zhang et al. (2024d). However, today's 'ss lane-level navigation systems mostly rely on builb HD maps, which are expensive to create and maintain. They often miss real-world changes Elghazaly et al. (2023)_ -like construction, rule updates, accidents-leading to outdated guidance and safety risks. Recently, online perception (OP) maps have become research hotspots within autonomous driving Li al. (2022); Liao et al. (2023b). Powered by vehicle sensors, the OP maps offer current and localized lane depictions. However, as shown in Fig. (b). they inherently lack global route topology and cannot support lane-level navigation Wong et al. (2020).
To achieve up-to-date and low-cost lane-level navigation, we introduce new mission called Online Navigation Refinement (ONR). The core objective of the mission is to transform the road-level
Corrrsppnndigg author.
Project Lead.

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Published as a conference paper at ICLR 2026
Road SD map
Lane in OP map
Lane with association Correct lane for navigation
End
End
End
Lane
Connect
Lane
Wrowig Lone
Road 2
Road
Start
Lone
Start
Wronn Lone
Start
Where is lane?
Which lane is correct?
Upper lane is correct!
(a) Stundard Definition (SD)
(b) Online Perception (OP) Map with map navigation
(e) Map Association between SD OP maps
map Navigation
Motivation for online navigation refinement and map association. The roads and lanes with the same color indicate that they are interconnected: (a) Standard Definition (SD) maps offer only road-level navigation without lane details. (b) Online Perception (OP) maps offer lane-specific details, yet they are not connected to SD maps and cannot identify the correct lane. (c) Associating SD and OP maps enables lane selection, achieving online navigation refinement and lane-level navigation.
Figure 1:
navigation derived from SD maps into precise lane-level navigation aligned with the OP maps. For fast and accurate process, ONR needs a paradigm of map-to-map matching, called map association, shown in Fig (c). Compared with map matching (MM) that bind GPS trajectories to static SD/HD
maps Chao et al. (2020) as path-to-map matching based on the HMM Newson (2009), seq-to -o---t--eq monel]Feng et al. 2020); Ren et al. (202T), I, graph model Liu et al. 2023b) and transtormers Tang et al.) (2025) map association uses map-to-map matching because SD maps and OP maps are heterogeneous and do not share one-to-one correspondence. Attempting directly align SD paths with OP maps, or the other way around, overlooks the semantic disparities between the two.
Specifically, map association faces key challenges. First, no public datasets provide structured, lane-to-road-les correspondences between SD maps and online perception (OP) maps. Although there exist auto-driving datasets (e.g., nuScenes Caesar al. (2020a). OpenLane Wang al.) (2023)) offer local lane geometries, and OpenStreetMap provides road-level topologies, none es- tablish explicit, learnable mappings between them. Secondly, SD) and OP maps display inherent heterogeneity due to diverse granularity. resulting in substantial spatial and semantic differences. robust association mechanism is required to manage spatial fluctuations (such as GPS drift and changes in scale) and semantic disparities (such as the differing number of lanes on road). It should also be capable of processing noise issues in OP maps real time, which include lane discontinuities, omissions, or errors in OP mapping. Figure in Appendix D iillustrates the complexity and challenge of map association.
To address these challenges, make three core contributions: (1) Dataset:
We introduce Online Map Association Dataset (OMA), the first open source benchmark for the online navigation re- finement mission in the paradigm of map association, derived from nuScenes Caesar al. (2020a) and OpenStreetMap josm, which contains more than 30K scenarios, 480K road paths and 2.6M lane vectors with manually annotated associations. (2) Baseline: We present MAT, a lightweight transformer- model for the real-time association of maps. To handle spatial and semantic differ- ences, MAT incorporates two key modules: path-aware attention and spatial attention. Path-aware attention reorders and group vector tokens by path index, implicitly encoding topological structure to align SD and OP maps despite spatial offsets. Spatial attention sorts and groups road/centerline tokens using a spatial curve, enabling global context modeling through cross- topologcall feature integration. (3) Metric: We propose Navigation Refinement P-R (NR P-R), a metric that measures alignment of the path and the correspondence through precision, recall and Fl specifically designed for map association. Traditional MM metrics only measure the accuracy of the matching, but the NR P-R evaluates both the geometric similarity and the accuracy of the matching. Moreover, NR P-R only utilizes annotation in ground -ruth perception maps, rendering the benchmark suitable for the evaluation of any map generation method.
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(1) Road in SD map
(2) Centerline and boundary OP map
(3) Mapping function
f:
Figure 2: (a) The schema of SD map input: Road R. (b) The schema of OP map input: Centerline C and boundary B. (c) The objective in task: Mapping function f.
Extensive experiments indicate that MAT achieves state-of tf-h--r---e-rtt performance with 34ms lateney on OMA, showing sigmificant improvements compared traditional map matching methods and deep learning map matching method, enabling low-cost and up-to-date lane-level navigation.
2 RELATED WORK
Map Matching. Map matching investigates how to link GPS tracks to SD or HD maps. Hansson al. (2020) utilizes hidden Markov model (HMM) considering trajectories as observations and the corresponding road segments as states, demonstrating superiority over earlier geometric matching al- gorithms. In the realm of deep learning, the main approach involves seeeesseeeeeeeeeee saaeenee-t--squenee (Seq2Seq) methods Feng et al. (2020); Ren et al. (2021). With an input trajectory consisting of sequence of geo-points, the encoder-decoder framework provides sequence of matched road segments. Recently, GraphMM Liu et al. (2023)) has used graph- approach that explicitly integrates all the corre- lations mentioned above, while EAM Tang et al. (2025) uses BERT-like transformer Alaparthi & Mishra (020) to achieve the best performance. Compared to the map matching method, MAT uses map-to-map paradigm to accommodate the many-to-one correspondence between lanes and roads in real time. Moreover, unlike the self-attention used in EAM MAT employs spatial and path- attention by incorporating the spatial curve and the path index, thus striking a balance between performance and latency.
Map Generation and Priors. The construction of online perception maps is popular topic in autonomous driving Hao et al. (2024); Zhang al.) (2024a); Li al. (2024) and is crucial for subsequent tasks Wan et al. (2025): Zhang et al. (2024b). HDMapNet Li et al. (2022) pioneered BEV-based map generation through sensor fusion. while LSS Philion & Fidler (2020) introduced
depth-aware BEV transformation. VectorMapNet Liu et al. (2023a) enabled end-to-end vector prediction, and the MapTR Liao al. (2023a) series introduced hierarchical query embeddings for instance-level construction. We adapt Map Liao et al. (2023b) for online HD construction using the annotations from our dataset and demonstrate its compatibility with the SD-HD association. Recent works such as SMERF et al. (2024), P-MapNet Diang et al.] (2024). and TopoSD Yang ctt al. (024) leverage SD map priors to mitigate sensor noise and refine perception geometry. Unlike these approaches that focus enhancing generation quality, MAT targets the explicit association phase, utilizing topological optimization to resolve assignment ambiguity arising from residual noise.
TASK DEFINITION
The goal of online navigation refinement is to transform rond-iopology-aligned navigation into nne-ttpollogyaalinee navigation. To achieve this, we utilize the map association as map-to-map match paradigm. This involves correlating SD maps with online perception maps, allowing us to translate any route from SD map into the corresponding route
online perception map.
Standard Definition Map (SD Map). As shown in Fig 2 (a). SD maps represent road networks that roads as primary primitives. Formally, a SD map is defined as graph GR (R. &R), where R = (r1, r2,-.., Tm denotes a set of roads and &R encodes their topological connectivity. Each road ry is parameterized by sequence of directed vectors:
rj (511 422933
Oik 1 976))
4ik E R2.
(1)
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Raw Data
(b) OMA Train'Val
OMA Test
Figure 3: (a) The visualization of SD map and GT OP map with association annotations of Boston in nuScenes. The same color implies an associative pair. (b) The visualization of OMA Train/Val set. (c) The visualization of OMA Test set.
where consecutive points define road segments through uniform spatial sampling.
Online Perception Map (OP Map). As shown Fig 2 (b), OP maps provide details of the lane level, primarily represented center line network. We model an OP map graph Gc (L, E2), where L = {41, .2.... ........,, Ln)
is a set of centerlines, each sampled at uniform intervals:
4= P.P. p R
Ec captures topological relations between the adjacent centerlines. In addition, we include road boundary vectors {b1, .m.....,,.... which reflect the extent and shape of the actual road. Each boundary by is specified as:
by haah,,,,y2hys... hyk- his E R
Objective. As shown in Fig 2 (c), given GR and Gc. the task is to learn mapping function f: L + R that assigns each centerline E C to its corresponding road r E R. The function satisfies two key constraints: 1. Uniqueness: Each centerline I corresponds to exactly
ground truth road T; 2. Multiplicity: A single road r E R may be associated with multiple centerlines 4 42, E L.
This formulation casts the alignment task as multi-to-one classification problem, where the number of classes equals the number of (R] and each centerline acts as an input sample. The goal is to maximize classification accuracy while preserving topological consistency between SD and OP maps. After we form this association, any path on the SD map allows us to locate all matching paths on the OP map through single topological sorting.
DATASET AND METRIC
4.1 DATASET OVERVIEW
The source of ground-truth OP maps is nuScenes Caesar al. (2020b), which includes locations in Boston and Singapore, featuring centerline geometries scanned with LiDAR. The SD maps were obtained from OpenStreetMap (OSM). We manually annotated the data to establish the association between the ground truth of the OP map and the SD map. The visualization is shown in Fig. 3 (a), which shows the global SD and ground-truth OP map of Boston with an association annotation.
The dataset is divided into training, validation, and test sets (see Fig. (b) and (c)). Within the nuScenes framework, we employ the pon-split of nuScene. This approach designates distinct areas for training and validation/testing datasets, effectively preventing data leakage. The validation and test data sets are generated from the same scenes, but they have a variety of OP maps. For the OP ground-truth maps with manual correspondence annotations.
map, both training and validation For testing, the OP map is generated by MapTRv2 and its annotation is the same as the scene in the val set. It essential explain that the validation set and test in OMA have equal significance.
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TP
FP
EN
Path Matching between Prediction and Ground Truth
thet mortap
targe prm
SD map
Case
Label Sequence Alignment Verification
Mistignel
Ground Truth map
Case 3: Loagth-Aware Overlap Assesoment
Label oredaa both patts are 67%G. TP for T-50% but FP T-95%.
Figure 4: Example of TP, FP and FN for evaluate Navigation Refinement Drecision-Recull
The purpose of the validation is to assess the association capability of the OP map under noise- free conditions, whereas the test set evaluates this capability when the OP map experiences significant noise. This distinction sets apart the validation/test split in OMA from that typical datasets. The specific analysis is provided in the Appendix D
4.2 EVALUATION METRIC
Standard map matching metrics, such Trajectory-Level Accuracy, assume one-to-one mappings and focus on path sequencing. contrast, map association requires evaluating noisy OP maps against ground truth. We propose the Navigation Refinement pr metric-adauee
from Reachability pr Lu et al. 2023 to assess map generation performance based exclusively on ground-truth annotations.
Navigation Refinement P-R: Fig. 4poovddes detailed description of typical scenarios encountered during the Navigation Refinement Precision Recall (P-R) evaluation. The examination protocol involves three consecutive stages:
Path Matching: Predicted paths are matched to GT via a Im bidirectional Chamfer distance threshold Lu et al. (2023). This step is vital only GT paths contain association annotations. Non-matches are labeled FP and excluded from SD-HD analysis. As in Fig. 4 (Row 1), low-deviation paths align (left), while high-deviation ones yield FPs (middle).
Label Sequence Alignment: Successful matches are converted into SD map link sequences. Alignment failures marked FP/EN. Case in Fig. showw sequence misalignment leading an FP (middle) and an unmatched GT as an FN (right).
Length-Aware Overlap: Semantic consistency is measured by Label Overlap score-the length ratio of Soadd--Dcoosiseenm segments to the total path. TP classification depends on threshold T. As shown in Fig. 4 (Case 3), 67% overlap is TP
50% but an at T 95%.
Following the mAP conventions Lin et al. 2014 we use T = |0.5 0.05 0.95) (10 thresholds)
and report mean P-R and F1 scores. To mitigate path-length bias, we separately compute metrics across 15 length intervals I, [[0, 5). [5, 10), .., [70, +oo)] before aggregation. The ablation study of distance threshold and length intervals shown in the appendix
5
METHOD
5.1
OVERALL ARCHITECTURE
As depicted in Fig. 5 the Map Association Transformer (MAT) is a transformer specifically designed for real-time map association. All inputs vectorized representations V (ū,, 02 ... UN where each vector J, is parameterized by two endpoints and direction: &o = [p Ph Piz V2 Og)
with 0; = arctan
P P.
and pa, P12 E R2 being the start/end points. The input maps are

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Asso. Head & Postprocess
Lase Token
Road Token
FFN
Path-aware Attention
(1) Road Token Pooling
(2) Cal. Sim. and Softmax
Poer. for Finul Result
Association Head Posiprocess
Layer Norm
*Road token for example
Pask
soaller
FFN
Spatial Attention
Group And Merge
Vector Serialization
based on Serialization
Layer Norm
Path-aware Attention
Sparial (o.g.
Vector Embedding
Start
Carve-based
(2) Vector order bused on Serialization
Vector Serialization
(3) Grouu Attention
centerline
road
boundary
Spatial Attention
Figure 5: Overview of Map Association Transformer (MAT). The framework processes vectorized roads in SD map and centerlines/bouddrres in OP map through N stacked layers containing Spatial Attention (for global context via curve-based serialization) and Path-Aware Attention (for topological alignment via path indexing). The Association Head then aggregates road features and calculates association probabilities with centerline tokens to generate final navigation refinement result.
composed of SD map, an OP map, and boundary. The SD map (GR) comprises road vectors which form a graph with topological edges &g. Each road ry is transformed into an ordered sequence of vectors R2- 931952 9,29;3,... through its parameterized segments. The OP map (Gc) consists of centerline vectors L = .1.....,mm. Im that represent the centerlines.
R {ri,..., Jm. Jm..
based on consecutive points p/. The boundary (B) includes the boundary vectors B {bi..... bm converted similarly to the roads: by > B, = hy1, hyz, hy2h33.... These vectors are processed by the vector embedding module, which maps each 5D vector é; to a digh-dimensional feature Fr, E RCC via a two-layer MLP. The outputs are aggregated into feature matrices: Froud E R XC Feenterline NCC and Fhoundory E NC where Ni.) denotes the total number of vectors (e.g. N, Σラ length(R,)))
Each centerline 4 is transformed into vectors L, = P.P. P.P.
Subsequently, the vector tokens are input into a transformer network. MAT consists of stacked MAT blocks to extract hierarchical features, each block containing Path-Aware Attention (PA), Spatial Attention (SA), and feed-forward network (FFN). In the association head, each road token requires the pooling obtain representative token Froud for the current road. The association of the centerline with the road is calculated by combining the attention between Froad and Feentertines generating the probability distribution of the associations of the SD-OP map. The association probabilities further refined by a post-pros pest-proeessig method to enforce topological constraints. The details of the implementation and the module are provided in Appendix
5.2 PATH-AWARE ATTENTION
Path-Aware Attention (PA) is designed to extract locally stable geometric features that preserve the structural arrangement of elements.

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Topological Ordering for Efficient Attention. To achieve real-time inference, our framework
employs Group Attention, which reduces computational complexity from ((N2) to linear O(N) by restricting interactions to local windows. However, this efficiency comes with a constraint: the effectiveness of group attention depends heavily on the semantic meaningfulness of token order. inductive bias. Unlike random
PA addresses this by introducing Topological Ordering ordering, which would fragment the graph into unrelated segments, PA explicitly models long-range dependencies by constructing paths from root leaf nodes. This ensures that topologically connected predecessor and successor nodes are placed adjacently in the sequence.
Mechanism. Specifically, we identify all valid paths from starting point an endpoint. We then reorder vector tokens to align with these path indices. This strict ordering allows the subsequent grouped attention (with size k) to focus solely on topologically relevant neighbors. Finally, tokens are reversed to their original sequence, and features of tokens appearing in multiple paths are averaged.
5.3 SPATIAL ATTENTION
While PA captures topological connectivity, it may miss interactions between geometrically adjacent but topologically distant segments (e.g.. parallel lanes or disconnected road boundaries). The Spatial Attention (SA) mechanism complements PA by capturing instance-level interactions across a wider spatial scope via Spatial Ordering.
Vector Serialization as Geometrie Clustering. Similar to PA, SA relies on a specific ordering strategy maximize the efficacy of group attention. However, instead of following graph connectivity, SA utilizes Vector Serialization to cluster tokens based on geometric proximity. This serves as spatial inductive bias: it forces physically adjacent entities even if they belong to different map layers or are disconnected- be placed adjacently in the 1D sequence. Consequently, when the sequence is sliced into groups, highly correlated tokens naturally fall into the same attention bucket, enabling the model to handle GPS offsets and map alignment robustly.
Motivation:
Mechanism: Attention with Vector Serialization. As illustrated in Fig. green part, we implement this spatial ordering through the following steps:
1) Coordinate Discretization: Each vector token ū. is encoded into 3D discrete coordinate (1, y, r). representing the quantized grid location and orientation of the vector.
2) Serialization via Space- Filling Curves: We employ space-filling curve funetion & (e.g., g., the Hilbert curve Hilbert (1935)) to map the 3D coordinates to single ID index. This mapping is crucial as it preserves spatial locality in the ID domain better than simple row-major scanning.
3) Grouped Attention & Restoration: Tokens are reordered based on their ID indices. Self-attention is performed within each group to aggregate spatial context, followed by an inverse operation to restore the original order.
5.4 ASSOCIATION AND LOSS FUNCTION
Association. The association between the roads and the centerline is calculated through mechanism of cross-attention. For each road j, we first aggregate its token features {F) FyN
Froed) into
representative feature Froad 1 N Froed, where N denotes the number of road tokens roads ry and Froed e Rd. The association probability Proby between the centerline i and the road j is then calculated as:
Fel. Frood Vd
Fdcl Frood Vd
Proby exp
exp
where pdd dd is the token feature of the centerline, d is the dimension of the feature, and K represents the total number of roads. This formulation normalizes the similarity scores in all roads for each centerline i, ensuring valid probability distribution.
Loss Function. Followed by Liu et al. (202b);; Ren al. (2021), we optimize the model using a combination of cross-entropy loss (CE) and connection temporal classification (CTC) loss. The total loss a weighted sum:
Lioaal Lcs + 3 Lerc,
(5)
with hyperparameters a and balancing the two objectives. In practice, a 1,3 0.01.
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Table Result on OMA Val La. means latency. MM, PM, GM. MA means map matching, graph matching, point matching and map association method. P-M and M-M means the model is trained and inferred by path-to-map to-map or map-to-map.
Val
Methods
Present
Type
Paradigm
NR-FI 60:95
La./ms
HMM Newson 2009)
SIGSPATIAL'09
MM
P-M
70.1
465
DeepMM et al.
2020
TMC'20
MM
P-M
70,6
328
MTrajRec Ren el al. 2021
KDD'21
MM
P-M
71.2
849
GraphMM Liu ct al 202.5b
TKDE'23
MM
P-M
72.3
469
EAM Tang
2025)
TITS'25
MM
P-M
72.9
345
KNN Cochran
1965
GM
M-M
68.6
299
GMT He et al.
2024
TPAMP
GM
M-M
68.8
93
FastMAC Zhang ct al.
(2024c
CVPR
PM
M-M
70.3
62
MAT-T (Ours)
MA
M-M
34
MAT-L (Ours)
MA
M-M
78.7
70
Table 2: Result on OMA Test set. La. means latency. MM, PM, GM, MA means map matching. graph matching, point matching and map association method. P-M and M-M means the model is trained and inferred by path-to-map or map-to-map. SGG. means SegGrowGrpah Xie et al. (025a))
NR-F1 60:95
on Test Sets
Methods
Present
Type Paradigm
La/ms
MapTR
MapTRRv
SGG.
HMM Newson 2009)
SIGSPATIAL'09
MM
P-M
36.0
46.5
561
DeepMM Feng el al.
2020
TMC'20
MM
P-M
32.8
33.5
43.9
733
MTrajRec Ren el al.
2021
KDD'21
MM
P-M
34.1
36.8
48.9
1593
GraphMM Liu
20250
TKDE'23
MM
P-M
35.9
38.7
49.2
889
EAM Tang et al.
2025
TITS'25
MM
P-M
36.3
39.1
50.8
679
KNN Cochran 1965
GM
M-M
34.6
43.5
313
GMT He al.
2024
2P
GM
M-M
30.1
33.4
42.3
105
FastMACC/Zhang et (2024e)
CVPR'24
PM
M-M
32.0
35.8
45.0
81
MAT-T (Ours)
MA
M-M
41.5
44.8
54,8
35
MAT-L (Ours)
MA
M-M
41.9
45.0
54.9
74
5.5
TOPOLOGY POST-PROCESS
formalize topological decoding as structured prediction on the entire path of centerline P,, j E [1, [1. KK. K is the number of total paths. The two-stage decoding process operates as follows:
Token Initialization. For each centerline path Pj. we select the initial centerline Tima via:
T Tmax = argmax max P(l, r)
(6)
IEP,
FER
where P(I,r) is the probability of association from centerline I to road
Topological-constran Beam Search. Based on beam search, topological constraint beam search makes the following two improvements: 1. Modify the one-way search to implement bidirectional search starting at Tmaz- 2. When generating new predictions, instead of using the approach of taking the maximum value from all roads, we decode under the constraint of connectivity provided in the road network &,, thus ensuring that the connectivity of the road sequence corresponding to the lane path in the decoding result is consistent with the representation of the road network. Detailed expressions the topological constraints beam search, including formula descriptions, are included in the appendix F
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Table 3: Ablation study of structure, Post. means post Table Ablation study of loss and pool method.
process. Bd. means Boundary. La. means latency.
La. means latency.
SA PA
Bd.
Post.
NR-F1 50:95
La/ms
CE
CTC
Avg.
Max
NR-F1 50:95
La//ms
Baseline (PTv3)
61.8
59
78.4
70
62.1
77
67.7
70
74.1
61
v
v
v
78.5
70
77.8
64
V
78.7
70
78.5
69
78.7
70
EXPERIMENT
6.1
IMPLEMENTATION DETAILS
We train our models from scratch for total of 50 epochs using the Adam optimizer. A cosine- decay learming rate scheduler employed, incorporating a linear warm-up phase of epochs. The initial learning rate, weight decay, and batch size are set to 0.0001, 0.05, and 128, respectively. All experiments are conducted on NVIDIA A6000 GPUs. The latency is measured on NVIDIA A6000 GPU paired with Intel(R) Xcon(R) Platinum 8369B CPU. Note that MAT-T and MAT-L share identical architectural components and training configurations, differing only in the number of Transformer blocks to balance real-time efficiency and model capacity, which details are shown in Tab. 22 in Appendix.
6.2 RESULT
To evaluate the performance of different methods on the OMA dataset, we categorize existing approaches into map matching methods Newson (2009); Feng et al. (2020); Ren et al. (20T)) Lu (203bb; Tang et al. (2025), graph matching methods He et al. (2024), and point matching methods Zhang et al. (022e)). For map matching, we traverse every path on the OP map as a GPS
trajectory match against the SD map. For graph and point matching, we treat the SD and OP maps as separate graphs or point clouds.
Val set. As shown Table [ MAT achieves the optimal balance between precision and efficiency on the validation set. Specifically, compared to traditional, Seq2Seq, and graph-based map matching methods, MAT-T demonstrates significant improvements in NR-FI 50:95 outperforming HMM New-
son (009) by 8.1 %, DeepMM Feng et al. (2220) by 7.6%, MTrajRec Ren et al. (2021) by 7.0%.
and the recent 3AM Tang et al. (2025) by 5.3%. all while maintaining a low inference latency of 34 ms. Moreover, MAT-T achieves superior performance compared to graph matching techniques like GMT He et al.) (2024) and point matching approaches such as FastMAC /Zhang et al. (2024e). proving its effectiveness in handling the heterogeneity between SD and OP maps which typically challenges traditional matching or point cloud algorithms.
Test set. To further verify the model's robustness against varying noise patterns crucial for real-world deployment, we extended the evaluation on the Test set to include OP maps generated by three distinct methods: MapTR Liao et al.) MapTRv2 Liao et (2023b), and SegGrow Xie et al. (2025a), as summarized in Table 2 The results demonstrate that our proposed MAT method consistently outperforms state-of-the-aa baselines across these disparate generators without requiring specific fine-tuning. On the GeoGrow part, MAT-L achieves an NR-F1 50:93 of 54.9%, surpassing the strongest baseline EAM Tang al. (2025) by 4. %,, while on the more challenging MapTR dataset, which exhibits different topological error patterns, MAT-L maintains significant lead of 5.6% over EAM This consistent superiority confirms that MAT's architecture effectively generalizes across distinct noise distributions iistrbbttioss-raninng from fragmentation in MapTR to connectivity issues in growing- based methods thereby validating the strong cross geoss-gen generalization ability of the proposed Online Navigation Refinement framework.
6.3 ABLATION STUDY
The ablation study experiment is conducted with MAT-L in the val set OMA, with latency measured using a NVIDIA A6000.

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Table 5: Cross-validation between Boston and Singapore in the OMA dataset
City
Val
Test
Singapore + Singapore
77.5
49.5
Boston Singapore
77.3
48.7
Boston + Boston
79.4
42.9
Singapore Boston
79.0
42.6
Table 6: Data Efficiency experiment on OMA dataset.
Ratio
1%
2%
5%
10%
20%
50%
100%
Val / NR-F1 50:95
24.9
64.4
77.1
77.7
77.3
78.3
78.7
Test / NR-F1 50:95
22.3
41.5
44.5
44.6
46.6
44.7
45.0
Structure. As shown in Tab. 3 ablating path-aware (PA) and spatial attention (SA) reveals that PA+SA achieves the highest NR-FI 50:93 (+3.7% vs. PA-only, +15.7% vs. SA-only). PA-only outperforms SA-only (74.1%
62.1%) confirming the critical role of topological awareness. Boundary improve +0.7% with a 5ms latency cost. Post-processing further enhances accuracy (+0.2%) without efficiency trade-offs.
Loss Function and Road Pooling Method. Tab. shows an ablation study on loss functions and road pooling methods. The model exhibits strong robustness, with consistent performance across different settings, indicating low sensitivity to these components. Combining CE and CTC losses improves NR-FI by +0.3% over CE alone and by +11.0% over CTC alone, while all variants perform competitively. Average pooling outperforms maximum pooling by only +0.2%, further confirming the model's insensitivity to pooling strategy. These results suggest that the effectiveness of the method is due to its inherently robust design, rather than specific loss or grouping choices.
Cross-validation. To evaluate the generalization of the model in geographically and behaviorally distinct driving environments, we conducted cross-validation in Tab. using data from Boston and Singapore, two cities that differ markedly in road layout, traffic density, and regulations. The model maintains robust performance without fine-tuning: in the val set, the Boston- Boston-Singapore transfer incurred only a 0.4-point drop (79.4+79.0) and Singapore-Boor Bosttnn 0.2-point drop (77.5+77.3)); on the test set, the drops were similarly minor: 0.8 points (49.5 (49.5->48.7 7) and 0.3 points (42.9 +42.6), respectively.
Data Efficiency. To investigate the label efficiency of MAT and validate its potential for semi- supervised learning, we conducted experiments using varying subsets of the training data (ranging from 1% 100%) MAT-L. The results, summarized in Tab. 6 demonstrate exceptional data efficiency. In particular, with only 5% % of the annotated training data, the model achieves a N F F1:999 of 77.1% in the validation set and 44.5% in the set. These results are comparable to the performance achieved using 100% of the data (78.7% / 45.0%). This saturation in a low-data regime indicates that MAT effectively captures robust topological features without requiring massive amounts of dense annotations.
More Ablation Study, Visualization and Failed cases. Appendix G has shown more study of ablation study of hyperparameters of PA and SA, input size of SD map, beam width of post-process, visualizations, and failed cases.
CONCLUSION
We propose Online Navigation Refinement (ONR), new mission that fuses static SD maps with real-time perception for accurate, low-cost lane-level navigation. To achieve ONR, we make three core contributions: We release OMA, the first public benchmark for map associations, and MAT, transformer- based model for aligning noisy maps. We also introduce Navigation Refinement P-R, a metric evaluating both geometry and association accuracy. For limitations, will explore end-to-end integration with motion planners, cross-domain adaptation, and unsupervised association learning to reduce annotation dependence.
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ACKNOWLEDGMENTS
This work was supported by the National Natural Science Foundation of China (62433003, 62476017).
ETHICS STATEMENT
This work uses only publicly available autonomous driving datasets (e.g., nuScenes) and Open- StreetMap (OSM), following their licenses and usage terms. We rely solely map-level and sensor-derived geometric information and do not use or release any personally identifiable data such as faces, license plates, raw trajectories linked to individuals. All additional annotations were produced by trained annotators on de-identified map representations. Our method and dataset are intended for research purposes to improve road safety and navigation; any real- woal-world deployment should include thorough testing, safety checks, and compliance with relevant regulations to avoid potential harms from incorrect misleading guidance.
REPRODUCIBILITY STATEMENT
This work is based entirely on publicly available datasets, specifically the nuScenes autonomous driving dataset and OpenStreetMap (OSM). We describe our model architectures, training objectives, and optimization hyperparameters, evaluation metrics in the main text and appendix to enable independent verification. Upon publication, will release code, including scripts for data preprocessing, map association construction, model training, and evaluation, together with configuration files specifying all hyperparameters, random seeds, and implementation details (e.g., framework and library versions). We will also release the processed haa-aasscciatin annotations introduced in this paper. together with the code of our models and trained checkpoints.
well dataset preprocessing, train/validation/test splits, and
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CONTENTS OF APPENDIX
LLM Usage
17
Question and Answer
17
B I Motivation
17
Distribution
17
B3
Maintenance
17
B.4
Composition
18
B.5
Collection Process
18
B.6
Use
18
Related Work
18
End-to-end Autonomous Driving
18
Mission Comparison
19
D Dataset
19
D.I Revised Analysis.
19
D.2 Range of Sample
21
D.3 Pon split and OP maps
D.4
Geographical generalization
21
Annotation Workflow)
D.6
Handling Transition Ambiguity
21
D.7
Future Expansion Strategy
21
Metric
22
Details of NR-PR
22
E.2
Curvature-based Weighted Metric
22
Method
24
Path-aware attention
24
F22
Spatial attention
25
Post Process
25
Ablation Study
26
G.1 Hyper Parameter
26
G.2 Robustness to Imperfect SD Maps
28
Robustness under Environmental Noise and Occlusion
29
isualization
30
Attention Map
30
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H.2 Result Compare
31
Failed Cases
H,4 Generalization on Different OP Generators
33
Implement Details
34
Model Settings
34
Train Configuration
34
1.3 Data Augmentations
34
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A LLM USAGE
The paper has employed LLM tools such as GPT and Writefull for enhancing and refining writing in the abstract, main text, and appendix.
B QUESTION AND ANSWER
We have included some Q&A to enable readers to grasp the information in our dataset. Every response follows the double- blind principle.
B.1 MOTIVATION
For what purpose was the dataset created? We introduce this dataset to refine online navigation by transforming road-level paths on SI) maps into lane-level paths on online perception maps. This approach facilitates cost-effective and highly real-time lane-Ievel navigation. Current lane-level navigation systems are highly dependent expensive and globally updated HD maps, which are costly and lag in real-time efficiency. Online perception maps, a significant focus in autonomous driving research, create local HD maps near vehicles using real-time onboard sensor data. However, they lack global topology connections, making them unsuitable for navigation. This dataset envi- sions aligning SD maps with online perception maps through map-to-map association paradigm, translating road-level paths on SD maps into lane-level paths on online perception maps.
What is the relationship between this paper and planning methods? The task and methodology of this paper focus on delivering road-level navigation on vehicles and drivers' devices. Addresses the issue of road-level navigation provision in context where HD maps are no longer used. Recently, research like NavigScene Peng et al. (2025) has incorporated SD navigation data into autonomous driving planning modules. We suggest that our approach can act as a preliminary stage for NavigScene, enhancing the SD navigation data to offer lane-level guidance. This grants NavigScene's 's planning module more detailed navigation details, thereby increasing planning precision.
B.2 DISTRIBUTION
Will the dataset be distributed third parties outside the entity (e.g,, company, institution, organization) on behalf of which the dataset was created? Yes, the dataset can be accessed publicly on the Internet.
How will the dataset be distributed (for example, tarball on website, API, GitHub)? The dataset will be released on GitHub and Huggingface.
B.3 MAINTENANCE
Who will be supporing/Issttgnmmiittiinng the dataset? The authors will be supporting, hosting, and maintaining the dataset.
How can the owner curator manager of the dataset be contacted (e.g., email address)? You contact the author by email which will be included in the accepted version of the paper.
Is there an erratum? No. We will make a statement there is any.
Will the dataset be updated (e.g., to correct labeling errors, add new instances, delete instances)? Yes. we intend to refresh OMA's on-line HD map with more robust baselines in the future. The new method will be selected based on comprehensive criteria that include performance measures such as accuracy, efficiency, and generalization.
Will older versions of the dataset continue to be supportedhnsteddmaiiiiinneet Yes.
If others want to extend/augment/build on/contribute to the dataset, is there a mechanism for them to do so? Yes. We will make guide document the website of OMA.
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(SD) Centerline (OP)
Boundary (OP)
Shift
Noise
(a) GPS shift
(b) Semantic disparities
(c) Missing lane
(d) OP map noise
Figure 6: Challenge for map association. (a) GPS shift, (b) Semantic disparities, (c) missing lane, (d)
noise in OP map
B.4 COMPOSITION
What do the instances that comprise the dataset represent?In OMA, the basic element vector that encompasses the road vectors, the lane vectors, and the boundary vectors. Each vector is defined by points that indicate its position the map. We have normalized each vector in OMA match the ego perspective used in real-world perception settings. Furthermore, each vector is equipped with of connectivity relationships that specify the other vectors which is connected within the road network. Road vectors include an additional road ID to identify their associated road. Lane vectors have been manually marked show their association with a specific road.
How many instances are there in total (of each type, if appropriate)? OMA contain over 30K scenarios, 480K road path and 2.6M lane vector with manually annotated associations.
Are relationships between individual instances made explicit? On the SD map, all roads, and on the GT OP map, all lanes within the training and validation sets have thorough descriptions and associated annotations. For the test set, we offer descriptions of roads and lanes, but without associated annotations. Nevertheless, the test set can still be evaluated using annotations from ground truth OP map in the validation set, thanks to the NR P-R metrics introduced in our paper.
Are there recommended data splits (for example, training, development / validation, testing)? Yes. We have already partitioned our dataset into three distinct splits: training, validation, and testing.
B.5 COLLECTION PROCESS
Who involved in the data collection process (e-g., students, crowd workers, contractors)? The annotations are provided by experienced annotators and multiple validation stages.
B.6 USE
What (other) tasks could the dataset be used for? This dataset is primarily used for map association, a new task for online navigation refinement.
C RELATED WORK
C.1 END-TO-END AUTONOMOUS DRIVING
Planning, being essential component for numerous fields Zhang et al. (2025a 2024c 2025b): Bai
ct al. (2024), is recognized as one of the pivotal research domains in the realm of autonomous driving at present. VAD Jiang et al. 2023) utilizes
ego query mechanism to forecast individual mode trajectories, whereas VADv2 Chen et al. (2024) enhances this by adopting probabilistic framework that considers multiple trajectories. Drive Sun et al. (2025) is innovative in its creation of parallel motion and planning modules, effectively decreasing the computational requirements of the BEV features. DiffusionDrriel Liao al. (2025) employs truncated diffusion policy to improve the
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Mission: Obtain the from map.
Mission: Generate the OP by
Mission: March the trujectory
Mission:
Online Navigntion Refinement by matching OP map
the path
map. Ads; of low cost lane anee-level navigation of Somt and position from SD map mup-to-map matching.
Problem: lane-level navigation
Problem:
No start'end position on a local lane-level OP map
Problems: Patl--oo-mpp menching
overlcols the semantic disporibies
Between SD and mape.
(a) SDD map Navigation
(b) Map Generation
(c) Map Matching
(d) Map Association (Ours)
Figure 7: The comparison of (a) SD map navigation, (b) mapping generation, (c) map matching and (d) map association. The blue word the input of the task and the purple word is the output of the lask
probabilistic depiction of trajectories, while MomAD Song et al. (2025) aims to enhance stability and maintain consistency throughout sequential planning decisions. Significantly, unlike other end-to-end planning techniques, NavigScene Peng et al. (2025) uses navigation data based on SD) maps, achieving accurate and reliable autonomous driving, thus underscoring the significance of navigation data. Consequently, we propose that the refinement of lane-level navigation through MAT could further enhance the capabilities of current autonomous driving methodologies.
C.2 MISSION COMPARISON
Fig. compares four tasks: SD map navigation, map generation, map matching, and map association. Initially, navigation based on the standard definition (SD) map achieved a road-level path, which is now widely integrated into navigation apps. However, road-level navigation often does not provide precise directions for specific lanes and directions. As result, the development of economical and quick lane-level navigation systems has emerged as a significant online perception (OP) maps are generated using data from vehicle sensors by map generation. Unfortunately, such maps do not have a global topological framework, making challenging to form route from the start of the navigation to the endpoint. Subsequently, the task of map matching attempts to align GPS path with global SD or HD maps by following path-to-map matching model. However, this model falls short when trying to match SD and OP maps due to disregarding the differences between these types. Finally, the map association task seeks to create linkages between SD and OP maps via map-to-map association model. Using the SD map, the map association identifies a global route from the origin to the destination. Then this global route meticulously translated into the OP map at the lane level through the association of the SD and OP maps, resulting in comprehensive lane-level navigation path.
of GIS. Next, lane-level
D DATASET
D.1 REVISED ANALYSIS.
As detailed in Section 4.1, the dataset is partitioned into OMA train, val, and test set, with statistics summarized in Tab. 7 The OMA training and val set comprises 26,111 111 training scenarios and 5,613 validation scenarios, totaling 31,724 samples, while the test set contains only 5,573 test scenarios due to the exclusion of low-quality predictions. Both data sets share identical spatial coverage, with HD maps covering (=15m, #30m) and SD maps extending to (#75m, =75m). Notably, the SD map's road density in OMA train and val set decreases from 15.1 roads/scene during training to 10.8 in validation/test splits, suggesting potential domain shifts between training and evaluation environments.
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Table 7: Statistics of OMA train, val and test
Split
Train
Val
Test
HD map Range
15m, =30m)
SD map Range
75m, #75m)
Scene Segment
26111
5613
5573
Avg. lane per scene
81.3
75.8
310.34
Avg. lane path per scene
7.40
7.97
322.06
Avg. boundary per scene
3,48
3.31
9.65
Avg. length per lane
114
3.19m
2.32m
Avg. length per boundary
44.81
43.64m
32.17m
Avg. road per scene
15.1
10.8
10.8
Avg. length per road
38.2m
50.3m
50.3m
Avg. Connection per lane
2.0
2.1
2.9
Avg. Connection per road
2.0
1.8
1.8
Avg. Connection per boundary
2.0
2.0
2.0
Table 8: Refinement statistics for different scenes in OMA
Scene name
Number of centerlines
Number of refinements
Refinement ratio
Boston
1,205,661 910,140
2,341
0. 0.194% 0.404%
Singapore
3,682
Quantitative discrepancies between train, val and test set reveal systemic geometric and topological inconsistencies predicted maps. The set predicts an average of 310 lanes/scene, more than four times that of the Train and Val set (73.8), with significantly shorter mean lane lengths (2.32 m vs 3.16 m in trainval), indicating both over-segmennation and false positives. This fragmentation further amplified by test's ss prediction of 322.06 lane paths/scene (vs. 7.69 in Train), where ground-truth lanes are frequently split into disconnected fragments. Boundaries exhibit similar degradation: test set detects 9.65 boundaries/scene (vs 3,40 in trainval) with reduced mean lengths (32.17m) 17m vs 44.23m), reflecting fragmented boundary detection. Meanwhile, validation data show slight degradation compared to training splits (e.g. 75.8 81.3 lanes/scene), highlighting inherent variability in real-world map quality.
Connectivity metrics expose deeper structural errors in test predictions. The average lane con- nectivity in test set reaches 2.9, substantially higher than trainval's 2.0/2.1, revealing widespread mislinking of spatially disjoint lanes. Similarly, the validation data show reduced road connectivity (1.8 vs. 2.0 in training), suggesting a domain bias toward simpler topologies in training scenarios. Se- mantic associations between roads and lanes also degrade significantly. Training roads are associated with 1,547 lanes on average, collapsing to 945 in validation splits, which implies degradation of the hierarchical structure in complex scenarios.
These discrepancies have critical implications for benchmarking perception systems. The severe over- prediction and fragmentation in test set highlight the need for metrics penalizing false positives and discomnected paths (e.g., nathllenth--weigheed scores). Furthermore, the mismatch between training and validation/test distributions (e.g., road count/length differences) necessitates domain adaptation strategies to ensure generalization. Finally, the collapse of semantic hierarchies in validation data suggests that end-to-end models may struggle to learn robust associations between roads and their constituent elements without explicit struetural constraints. Together, these findings underscore the importance of a connectivity- association method to avoid overestimating performance on fragmented mislinked predictions.
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D.2 RANGE OF SAMPLE
The model is trained on nuScenes using synchronized LiDAR and camera imputs with official configuration. For each sample, an SD map cropping measure 150 m 150 centered around the ego vehicle preserves the adjacent topological context followed by the sensor setting of nuScenes Caeear al. (2020a). For the OP map, the cropping measure of the OP map 30 m x 60 m was centered around the vehicle of the ego, as referenced in
Liao et al. (023b): Liu et al. 2023a et al. 2022).
D.3 PON SPLIT AND OP MAPS
Both Train, val and test set apply the pon split Roddick & Cipolla (2020) of the nuScenes dataset Cac sar et al. (2020b), ensuring that there is no leakage between the training and validation datasets. For consistent lane prediction segmentation with OMA test
re-trained MapTRRv2 Liao et al. (20236) using the nuScenes dataset with the pon split. Drawing inspiration from the private protocol MOT17/MOT20 Dendorfer al. (202T), we suggest that future research evaluates the test with an enhanced centerline prediction network, without relying on MapTRv2 as baseline. Furthermore, we will establish an open evaluation protocol allowing submissions include their own OP maps. thereby reducing reliance on a single model.
D.4 GEOGRAPHICAL GENERAL
Regarding the geographical generalization of OMA, we elaborate from two aspects: First, OMA's data originates from the nuScenes dataset, which widely used in the autonomous driving field and contains data from two different countries, Singapore and Boston. Given that nuScenes has become a core dataset in the autonomous driving field for perception, mapping, and planning since its introduction, we believe that the nuScenes data itself possesses certain representativeness and generalizability. Second, we conducted cross-validation ablation experiments in the main text. The cross-experimennal results of MAT OMA demonstrate that MAT models trained different geographical regions possess certain cross-regional generalization capabilities, reflecting the good geographical generalizability of the OMA dataset itself.
D.5 ANNOTATION WORKFLOW
The OMA data annotation process divided into two primary stages: First, we performed coarse alignment based on the GPS coordinates from the SD map of OSM and the GT OP map of NuScenes. Second, we employed a skilled annotation team to correlate the SD map with the real perception map. The specialists were then engaged to evaluate and improve the annotations, ensuring their ultimate quality. Tab. presents the total count and percentage of data changes various regions in Phase 3. It is evident that the alteration rate for Singapore and Boston remained less than I 1,, suggesting generally high standard of data annotation quality.
D.6 HANDLING TRANSITION AMBIGUITY
Addressing the concern regarding transition lines, we acknowledge that assigning roads in such transition areas presents inherent ambiguity due to the heterogeneity between SD and OP Maps. To mitigate potential impacts on training stability and evaluation fairness. we implemented specific strategies: (1) Standardized Annotation Protocol: We established unified rule where the assignment of a Lane Vector at an SD Link transition boundary is determined by the SD Link closest to the Lane Vector's 's start point, ensuring topological consistency. (2) Tolerance in Evaluation Metrics: Our metric assesses the precision of level associations across 11 thresholds (50% to 95%). Crucially, the 5% tolerance buffer included even at the strictest threshold is specifically designed to accommodate inevitable semantic ambiguity at transition boundaries.
D.7 FUTURE EXPANSION STRATEGY
We acknowledge the importance of scaling the benchmark to encompass more diverse datasets (e.g.. Argoverse). To minimize annotation costs during future expansion, we propose a "Human-in-ther Loop" iterative annotation strategy. This approach is strongly supported by our data efficiency
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experiments as shown in Tab. which demonstrated that MAT can achieve competitive performance with as little as 5% of the training data. The specific pipeline as follows:
1. Model-Assisted Pre-annotation: We utilize the MAT model trained on the existing OMA (nuScenes) dataset to perform zero-shot or few-shot inference on unlabeled new data. This generates initial "draft" associations (e.g.. SD-to-HI correspondences and topology) and significantly reduces the cold-start problem.
2. Lightweight Manual Correction: Instead of labeling from scratch, annotators focus solely verifying and correcting the model's high-confidence predictions. Given the model's high data efficiency, the pre-annotation quality improves rapidly even with small set of initial corrections.
3. Closed-Loop Iteration: The corrected data is immediately integrated into the training set to fine-tune the model. The updated model is then used to pre-annotate the subsequent batch of data, creating positive feedback loop that progressively reduces manual workload.
E METRIC
E. DETAILS OF NR-PR
In the main article, present narrative explanation of the Navigation Refinement P-R accompanied by a schematic diagram. To elucidate the calculation of Navigation Refinement P-R more thoroughly, we include the pseudo-code for computing Navigation Refinement P-R, as depicted in Alg. [
Furthermore, the formula for NR-P NR-R 50:95 and NR-F1 th NR- NR--50:96.. as follows:
NR-P TR-P30:95-
NR-P
NR- 50:95
NR:950:95
NR-R
thET
thET
NR-R th NR-P + NR-R
2NR-p4h
2NR-
p..pp 50:95
NR-R 50:95
NR-FIth th
NR-F] 50:95
NR- p50:95
NR-R 50:95
where th the thresholds in association P-R [0.5 0.05 0.95 (10 thresholds). Additionally, in the validation set, because NR-R is always 1, we use the NR-P value directly as NR-FI.
E.2 CURVATURE-BASER WEIGHTED METRIC
Although the length-inten interval stratification the proposed NR-PR metric effectively mitigates bias towards short paths, it does not explicitly account for the geometric complexity of roads. In real-world datasets, straight roads are statistically more frequent than complex curved roads, potentially masking model deficiencies in handling high-curvature topologies. To address this and evaluate the model's fairness across diverse road types, we introduce Curvature-based Weighted Metric.
Complexity Measure. Defining complexity via average angular changes can highly sensitive to point sampling rates. Therefore, we employ the Discrete Fréchet Distance between the actual path and the straight line connecting its endpoints as robust measure of geometric complexity. higher Fréchet distance indicates greater deviation from a straight line, representing higher curvature or irregularity.
Stratification Strategy. As shown in Fig. similar to length-bases based approach, we adopt stratified statistical method to handle the long-tailed distribution of road complexity. We identify the 95th percentile of complexity scores in the dataset as upper bound and uniformly divide the range [0, 95th percentile] into 10 bins. The final metric computed by averaging the NR-FI scores across these bins, ensuring that complex road geometries contribute equally the final score, rather than being overshadowed by the dominant straight roads.
Quantitative Analysis. We evaluated the MAT model on the OMA dataset using this weighted metric. Table 9 illustrates the impact of stratification on the overall score. As the number of bins increases forcing the metric weigh complex roads equally to straight ones the overall
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Algorithm Evaluate Navigation Refinement P-R
1: function EVALMETRIC(pred Mn gt_ seneeeerrlee, threshold, acc_ ciss]
2: Input: pred_ centeeene,,,,e, gt_centerline, threshold, acc_list
3: Step 1: Point Matching
4: EXTRACTPOINTSSpred (ttprinttsspeede_ccttiinnn
5:
EXTRACTPOINTTS(L )
6:
ATC _amplle gg_sample_poim threshold)
7:
Step 2: Path Matching
8:
WNTIALIZECOUNTESSCT FP, FN, acc acc_list) for all point pairs (i,j) in matched points do
9:
10:
Find pred_path and gt_ath between points i,j PATHMATCH(pred _ath_paahhhhhh gt path, threshold) if paths match then
12:
Check sequence consistency and accuracy for all acc E acc_lis do
14:
15:
Update TP/FP based on accuracy vs acc end for end
16:
17:
18:
end for
19:
Step 3: Count Unmatched Paths
20:
for all unmatched gt path do
21:
for all occ E acc acc_list do FN L2V[cco] end for end for
FN [ace]]k] +
23:
25:
Step 4: Calculate Precision and Recall
26:
Initialize: Precision + (, Recall + 0
27:
for all acc E acc_list do denominalor p Plapac |ace + F lac)]
28:
29:
denominator
TP acc + FN [ace)
T Pl (ace) denominator / p if denominator-p otherwise
30:
Precision[acc)
0
T P [acee|/denmminatorr.
if denominator_
31:
Recall[acc)
0
otherwise
32:
end for
33:
Return: TP, FP, FN, Precision, Recall
34:
end function
Table 9: Impact of Complexity Stratification on NR-F1. The metric score decreases we enforce equal weighting for complex road types (increasing N), revealing the dominance of simple roads in the unweighted score.
Complexity Bins (N)
2
5
10
Val Set Test Set
81.6
70.2
62.7
60.3
51.8
38.7
33.5
32.5
performance drops significantly (e-g., from 81.6% to 60.3% on the Val set). This confirms that the datasct is dominated by simple geometries where the model performs exceptionally well, masking the challenges posed by complex topologies in unweighted metrics.
Table 10 details the performance in specific complexity intervals. The results reveal strong negative correlation between road complexity and association precision. On simple straight roads (Bin 1), the model achieves high precision (87.2% Val). However, performance degrades drastically high-complexity paths (dropping to 43.1 % Bin 10). This trend is even more pronounced in the Test set, indicating that while MAT is robust, geometric complexity remains significant challenge
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Distances
Cumulative Distribution
Ner: E329
Com-lattee Trobutaltty
Number S4npple
80
1.3
0.0
parcortle 5.54
22 22 20
33
Ditance
Figure Statistics of Fréchet distances across all paths in the OMA dataset. (1) Histogram of Fréchet distances. A substantial number of paths exhibit Fréchet distances concentrated within the range of [0, 1], revealing a pronounced long-tail distribution the overall data. (2) Cumulative probability of Fréchet distances. Consistent with (1), the distribution displays significant long-tail characteristic, underscoring the importance block-wise statistics for the NR-PR metric.
Table 10: Performance per Complexity Bin (N 10). Bin I represents straight roads, while Bin 10 represents high-curvature/ireguua roads. The results indicate performance degradation as geometric complexity increases.
Bin Index
3
4
5
6
8
9
10
Val Set 87.2
63.2
56.7
50.7
49.9
49.1
46.5
45.2
42.7
43.1
Test Set
57.9
50.4
32.0
21.2
16.8
14.7
10.7
10.6
10.4
11.6
for map association tasks. This analysis serves as a complement to the length-based metric, offering more comprehensive view of the robustness of the model.
F METHOD
In this section, delve deeper into the technical aspects of the model, including Path-aware attention, spatial attention, and the model's post-procee post-processing.
F.1 PATH-AWARE ATTENTION
The particular design of path-aware attention (PA) can be seen in Fig 9 (a). The fundamental framework of PA is made up of four components: computing order and its inverse, reorganizing tokens, calculating attention, and inverting tokens.
Path-aware attention uses paths determine the sequence of tokens. Initially, we define the network of roads or centerlines and then identify all complete paths from a starting point (with no incoming connections) an endpoint (with no outgoing connections). We concatenate these paths generate path- based token sequence. During the reordering of path-aware attention, a token may appear across various paths simultaneously, requiring us to duplicate the token. Then, we compute attention by segregating tokens based on their paths, ensuring that interactions occur only among tokens within the same path. After attention calculation, the tokens are reversed match the original input sequence. If multiple tokens exist within the path of single original token, they are averaged.
should be highlighted that the model organizes paths internally to prevent overly lengthy paths. If a path surpasses the patch size, it is divided into several groups, where each group (aside from the final one) matches the patch size. PA's worst-case time complexity (kr²),, where & is the patch size and is the number of tokens, if and only if 3 centerline road are either starting points ending points. Clearly, this is highly improbable in real-world scenarios, and its average time complexity remains (k2 which is linear.
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Computing order and inverse by path
Computing order and inverse by spatial curve
Re-order Tokens
Group Attention
Inverting Tokens
Re-order Tokens
Group Attention
Inverting Tokens
(a) Path-aware Attention (PA)
(b) Spatial Attention (SA)
Figure 9: Overview of Path-aware attention and spatial attention.
F.2
SPATIAL ATTENTION
Fig. 9 (b) provides detailed description of the architecture of the SA model. Similarly to pa, the fundamental structure of SA includes four main components: computing sorting and its reverse, rearranging tokens according to the sorted order, calculating attention, and then reverse sorting the tokens again.
In order of SA. apply space filling curve p 2 - E to serialize the vectors in ID sequence, preserving spatial locality. Four curves are used: Z-order, Transposed-Z, Hilbert, and Transposed Hilbert. To avoid bias toward specifie curve types, randomly select one curve per training iteration. In addition, similar to PA, there are multiple tokens that belong to single token after the coordinate calculation, will average the multiple tokens in the sort and copy that token to all the corresponding tokens in the reverse sort.
During the group stage, tokens partitioned based the sorted sequence, each partition matching the patch size. Following this, the model executes self-attention computations within these partitions. Similar PA, the self-attention (SA) algorithm has time complexity of Okk)), where is the patch size and n is the number of token, indicating that the complexity of SA remains linear.
F.3
POST PROCESS
In the main manuscript, we offer narrative explanation of the post post-processing. To enhance clarity, we also present mathematical formulation of the post-processing details. Let R denote the road as the vocabulary in the traditional beam search with size [R], and let & = 4 represent the width of the beam. At each step f, the algorithm maintains set B, of candidates path k, each associated with score shh) defined as the sum of logarithmic conditional probabilities. The search begins by selecting the token w" with the maximum initial probability P(wlx:) given as input v, forming the singleton initial set:
Bo = Top, (R, log P(wlz)),
(8)
which simplifies to:
Bo ([w*)) where log P(w"la (x) max log P(wlaa).
weR
This initialization bypasses conventional fixed start tokens and prioritizes high-probability seeds.
In iteration t. > 1, each sequence h E Bi-1 generates 2|R| candidates by appending a token E R to the left (w /)) or right (h w) of h, forming the expanded candidate set:
C, {w h B,-1, w R.) U (h- h Br-1, w R]
(10)
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Table 11: Ablation study of patch size of PA and SA.
Table 12: Ablation study of group method of PA.
Patch Size
64
256
1024
2048
00
NR-F1 50:95
77.8
78.4
78.7
78.5
78.4
Group Method
N.G
Category
Path
Latency/ms
77
68
70
72
72
NR-F1 50:95
78.0
78.2
78.7
Latency/ms
75
73
70
Table 13: Ablation study of Input size of SD Map
Input size of SD Map
NR-FI 50:95
Latency/ms
60 x 60m
78.6
67
90 X 90m
78.6
69
120 X 120m
78.7
69
150 150m
78.7
70
h extension updates the sequence score using irrection-specifie conditional probabilities:
s(h') (h) + log P(wlh, left, x:), if h' w shh + log P(w h, right, 2),
(11)
if ht' w
The top-k candidates from C₁ are retained to form Br:
B. = Topp (Ce, s(-)),
(12)
i.e.,
B, = (11 My...., [4), with sM1) 2 s(h/) .... s(1))-
(13)
The process ends at a predefined maximum length T or when all sequences emit an end-of- sequence token, with the final output h selected as:
h = arg max
s(h).
(14)
hEU?_
G ABLATION STUDY
For the hyperparameters we proposed, additional ablation studies were executed to exhibit their stability.
G.1 HYPER PARAMETER
Patch Size of and SA. Tab. shows that precision plateaus at patch size 256 (NR-F1 50:95 78.4%) but increases slightly at 1024 (+0.3%) with latency trade-off (+2 ms).
Group Method of PA. For grouping (Tab. 12 path-based grouping surpasses non-grouping
(+0.7%) and category-based baselines (%). likely due to reduced cross-path interference.
Input size of SD map. Table 13 presents results of the adjustment findings reveal that alterations in the SD input size exert minimal influence on NR-FI (S 0.1).
SD map input size. The
Sampling size of SA. Table [4] shows the findings for the spatial sample size When 5 set at 1m, there is drop in model accuracy, which may be attributed to the challenge of differentiating nearby lane lines with such a substantial sampling size. When is below 0.5m, the accuracy of the model is decreased (< 0.6). Decreasing the interval further to 0.01 does not improve the accuracy of the model.
Hyperparameter of loss function. As illustrated in Table 15], the ablation studies for 3ce and Bate indicate that high beta values make CTC the main loss, which reduces the precision of the model. In contrast, lower beta values allow CE loss predominate, leading stabilized NR-F1 (< 0.3).
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Table 14: Ablation study of
Table 15: Ablation study of Bee and Bete loss
function
8
NR-F1 50:95
Latency
Bee Bete
NR-F1 50:95
Latency
Im
74.6
65
0.5m
78.1
68
1:1
68.4
70
0.1m
78.7
70
1:0.1
78.5
70
0.01m
78.7
79
1:0.01
78.7
70
1:0.001
78.4
70
1:0
78.4
70
Table 16: Ablation study of space curve in SA.
Table 17: Ablation study of beam width A: in post process.
Spatial Curve
NR-F1 50:95
Latency/ms
Beam Width
NR-F1 50:95
Latency/ms
Z
77.6
70
Z TZ
78.1
70
78.2
69
H+TH
78.2
70
78.6
70
Z+TZ+ TH
78.7
70
78.7
70
78.7
78
Space Curve of SA. Table 16 displays the result of ablation studies focusing spatial curves in SA. The findings suggest that adding more diverse spatial curves improves the model's performance. is crucial to emphasize that including multiple spatial curve types does not affect model latency, given that each layer utilizes only a single spatial curve. Thus, the variety of spatial curve types does not increase the frequency of spatial curve sorting.
Beam width of post-process. Table 17 shows the results of the ablation test regarding the hyperpa- rameter of beam width during post-processing. The study reveals that setting to 4 results in the best trade-off between accuracy and processing time.
Length intervals of NR P-R. To further investigate the influence of the path length distribution the NR-FI metric, we performed a stratified evaluation based on length intervals. The motivation for introducing length intervals stems from our observation that within the dataset, short-distance paths occur far more frequently than long-dit dige-ncstc ones, and their corresponding FI scores tend to be higher. Without appropriate handling, this imbalance could bias the overall NR-F1 score toward performance on short-distance paths. Inspired by the bize-based categorization strategy adopted the MS COCO benchmark [in et al.) (2014), we categorize paths into discrete length intervals, compute the Fl score within each interval, and subsequently aggregate the results into unified NR-FI measure. This procedure ensures that each length range contributes proportionally to the final metric, thus mitigating the dominance of short-distance paths in the evaluation. As shown in Tab, [8 omitting this stratification (that is, computing the metric on all paths without length- based grouping) produces substantially inflated overall score. In contrast, increasing the number of length intervals causes the aggregated metric to gradually decrease and eventually converge to a stable value. These findings suggest that the length interval-based computation allows NR-FI provide more balanced and fair evaluation on both the short- and long-distance paths, resulting in more comprehensive evaluation of the model's ability to capture relational associations.
Distance Threshold of NR P-R. Tab. 19 highlights the effect various distance thresholds on the final metrics in NR P-R Step 1. These thresholds originate from those used in Reachability P-R. is important to note that Val uses the ground-truth OP map for predictions, thus all roads are deemed true roads, while the distance threshold applies solely to the test set. Ablation experiments reveal that distance thresholds significantly influence the accuracy outcome of NR-FI. With strict threshold of 0.5m, most the lane lines on the OP map are regarded false detections, as they do not meet the requirement. In contrast, more lenient threshold of 2.0 allows most lane lines to be perceived as correct, bringing the NR-FI to a value comparable to that of the Val set. This also indicates that there is domain difference between the Test and Val sets; discrepancies in results threshold settings. However, considering that the accuracy of the lane regression is also pivotal, we opted for balanced threshold of 1.0m judge whether the OP map correctly represents lane.
due the
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Table 18: Ablation study of length intervals of NR P-R.
Number of length intervals
2
3
10
15
NR-F1 50:96 / Val
81.8
79.0
78.5
78.2
78.7
78.7
NR-FI 50:95
Test
51.6
47.6
47.2
45.3
44.9
45.0
Table 19: Ablation study of distance threshold of NR P-R.
Distance threshold
0.5m
1.0m
1.5m
2.0m
NR-FI 50:95
Val
78.7
78.7
78.7
78.7
NR-FI 50:95 /est
6.6
45.0
68.2
72.4
G.2 ROBUSTNESS TO IMPERFECT SD MAPS
In real-world applications, Standard Definition (SD) maps often suffer from imperfections caused by GPS localization errors, sensor noise, or map construction delays. To quantitatively evaluate the robustness of MAT and the proposed metrics against such imperfect inputs, we conducted additional ablation studies on the OMA Validation set.
We categorized SD map imperfections into three distinct types and injected varying levels of noise (Low, Medium, High) into the input SD maps. The noise generation protocols are defined as follows:
Simulates GPS localization errors by applying uniform distribution offset relative to the global map range. We tested noise ratios of 10%, 20%, and 50%. Crucially, for each SD map sample, the same random offset vector applied to all road vectors, preserving the internal shape but shifting the absolute position.
Global Shift:
Element Noise:
Simulates geometric construction errors by applying random perturbations relative to the global map range. We tested noise ratios of 5%, 10%, and 20%. Unlike Global Shift, the noise here is generated independently for each vector within the SD map, resulting in geometric jitter and shape distortion.
Element Absence: Simulates map construction incompleteness by randomly masking out road vectors. We evaluated omission ratios of 10%, 20%, and 30%, where vectors randomly discarded from the SD map input.
The results, measured by the NR-F1 50:95 score, summarized in Tab. 20 The results demonstrate the resilience of the proposed method under varying noise conditions:
Robustness to Shift: Even under a high global shift of 50%. the model maintains reasonable performance (74.6 / 41.4). This indicates that our Path-Aware Attention (PA) successfully captures the relative topological structure of the road network, reducing dependency on absolute coordinate alignment.
Robustness to Element Noise: The performance remains highly stable, dropping only marginally from 78.3 44.8 77.5 44.1 despite 20% geometric jitter. This suggests that the Spatial Attention (SA) mechanism effectively aggregates global contextual information, thereby mitigating the impact of local geometric inaccuracies.
Robustness to Missing Flements: The model exhibits strong adaptability to missing data, maintaining a score of 75.5 43.1 even when 30% of the SD map vectors are absent. The Transformer architecture effectively infers associations based on the remaining context and the overall graph topology, ensuring that the association process does not fail catastrophically when individual segments are missing.
These experiments confirm that MAT maintains strong robustness when dealing with imperfect SD maps, effectively handling geometric misalignment, shape distortion, and topological incompleteness inherent in real-world navigation tasks.
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Table 20: Ablation study of robustness to imperfect SD Maps on OMA Val Test set with varying noise intensities. The metric reported is NR-F1 50:95.
Noise Type
Low
Medium
High
Global Shift (10%, 20%, 50%) Element Noise (5%, 10%, 20%) Element Absence (10%, 12%, 30%)
77.5 43.3
76.0 / 42.9
74.6 / 41.4
78.3 44.8
77.5 / 44.3
77.5 44.1
77.5
76.4 / 43.7
75.5 43.1
Table 21: Zero-shot robustness evaluation on nuScenes-C under different environmental corruptions.
Condition
Easy
Mid
Hard
Rain
44.7
42.1
40.6
Fog
44.2
43.4
42.1
Dark
44.0
43.8
41.6
G.3 ROBUSTNESS UNDER ENVIRONMENTAL NOISE AND OCCLUSION
To evaluate the robustness of MAT against sensor failures caused by environmental factors and severe occlusions, we conducted extended experiments focusing on two aspects: quantitative evaluation on corrupted inputs and qualitative analysis of occlusion scenarios.
Robustness against Environmental Noise. We utilized nuscenes-CC C Xie et al. (2025b), a benchmark designed to assess robustness against input corruptions, simulate sensor degradation. Specifically, we used MapTTRv2) 2 et al. (2023b) to perform inference on the nuScenes-C validation set with pon split, generating OP maps under three specific weather conditions: Rain, Fog and Dark (Night). These conditions were categorized into three difficulty levels: Easy, medium, and Hard followed by the nuScenes-C setting.
Crucially, adopted zero-shot inference setting: the MAT model used for association trained solely on clean data and was not retrained on these corrupted samples. This setting serves as a rigorous stress test for the generalizability of the model.
Table 21 reports the quantitative results with NR-F1 50:95 score. As observed, MAT maintains robust performance across all conditions. Even in "hard" settings, where visual features significantly compromised, the performance drop is minimal. For example, in Fog scenarios, the metric only decreases from 44.2 (Easy) 42.1 (Hard), This stability demonstrates that, by taking advantage of the topological priors of SD maps, MAT effectively mitigates the impact of sensor noise.
Visualization of Environmental Degradation. To intuitively display the challenge, Fig. iluttrrtss the synthesized degradation samples of the OP maps at different difficulty levels. Fig. II and Fig. 12 further compare the inference results under the "Hard" setting. Despite the severe noise in the input OP maps (c.g. missing boundaries or phantom lanes due to fog/rain), MAT successfully recovers the correct lane topology by aligning it with the SD map.
Analysis of Severe Occlusion. by the lack of ground-trus annotations, we analyze it physically. Vehicle occlusion typically manifests itself in OP maps as lane break or missing segments. Since MAT is explicitly designed to model global topology, it is inherently robust such defects.
While the explicit quantitative evaluation of occlusion is constrained
Fig. 12 presents case study of severe vehicle occlusion. As shown in the visualization, MapTRv2 manages to reconstruct coherent global road structure despite significant visual blockage by vehicles. We attribute this capability to the temporal modeling design of the upstream perception model (MapTRv2), where single-frame frame occlusion does not disrupt the map modeling derived from the overall temporal sequence. Taking advantage of the inherent robustness of MapTRv2 against occlusion, our downstream MAT model is consequently largely unaffected by dynamic vehicle occlusion, ensuring stable association performance.
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aa) None
(b) Easy
(c) Mid
(d) Hard
(1) Rain
(2) Fog
(3) Dark
Figure 10: Visualization of OP map degradation samples from nuScenes- The columns represent different environmental conditions (Rain, Fog, Dark), and the rows represent increasing difficulty levels (Easy, Mid, Hard). These noisy inputs simulate realistic sensor failures.
H VISUALIZATION
This section presents the visualization our model, featuring path-aware attention (PA) and spatial attention (SA) alongside the model's results. Furthermore, we examine the failed case with an analysis of our model.
H.1 ATTENTION MAP
The upper portion of Fig 13 presents visualizations of Spatial Attention (SA) maps at different stages of the model. As revealed by the analysis, SA provides extensive receptive fields in the early stages, enabling tokens to capture global contextual information. Specifically, during Stage and Stage the SA attention distributions exhibit highly dispersed patterns, allowing each query token uniformly attend to global regions across the input space. later stages, functional role of SA transitions to facilitating coss-cat token interactions. For example, in Stages 3-5, distinct attention patterns emerge where tokens primarily interact with their semantically corresponding road elements. Notably, this interaction not strictly confined to the road tokens directly associated with the centerline token significant attention weights also develop between the centerline token and adjacent road segments. As exemplified in Stage 4, the tokens establish prominent attention links with multiple road tokens along the same path. We posit that this expanded interaction mechanism constitutes critical component for precise centerline localization. The propagation of attention observed in later stages effectively enables the maintenance of geometric coherence between spatially distributed road elements while preserving discriminative semantic information through longerange dependencies.
In contrast, the lower part of Fig 13 visualizes the Path-aware Attention (PA) maps at different stages of the model. The visualization reveals that primarily focuses on neighboring tokens adjacent to the target path tokens, effectively serving as a local information extractor. Experimental results demonstrate that this localized information extraction capability plays a pivotal role in the model performance, exhibiting marked contrast with the global perception mechanism of SA. We posit that SA specializes in capturing global contextual patterns while PA emphasizes localized feature extraction. This dual-attention paradigm establishes synergistic interplay between global and local perception, achieving an optimal balance between comprehensive understanding and fine- grained detail processing, thereby substantially enhancing the model's overall effectiveness.
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(1) Normal
(2) Rain
(3) Fog
(4) Dark
Figure 11: Qualitative results under "Hard" environmental conditions. The figure compares the ground truth (GT) with MAT's predictions under severe Rain, Fog, and Dark conditions. The model demonstrates strong zero-shot robustness, effectively associating noisy OP lanes with SD paths.
H.2 RESULT COMPARE
Fig 14 illustrates comparisons model ground truth on KNN, HMM, MAT and MAT w/ postprocess. The top of (d) and (e) exhibit post-processing module's effectiveness. MAT predictions initially display incorrect topological connections where roads are mistakenly linked (marked with red circles). Our postprocessing, which utilizes topology-aware beam searching, rectifies this by eliminating -equeeuulttaal transitions and reconstructing precise topological paths. The second row demonstrates MAT's superior handling of complex topologies. Although HMM targets sequential path associations, its single path paradigm often underperforms in complex topologies with intersections. In contrast, our model uses spatial attention to grasp global information and cross-path associations, facilitating adaptive learning of complex topological patterns for accurate connectivity inference. The third row showcases our model's improved ability to localize associations. Using path-aware attention, the model emphasizes detailed extraction of local features along paths. This targeted local perception ensures precise associations at challenging points, such as junctions, where HMM is typically short due limited contextual understanding.
Fig. 15 illustrates a comparison of ground truth results for KNN, HMM, MAT, and MAT with post- processing. Significantly, the visualization demonstrates that model excels in map association in noisy scenarios with inaccurate centerline predictions, surpassing KNN and HMM by integrating the complementary benefits of global association (SA) and local detail refinement (PA).
H.3
FAILED CASES
illustrates the failed cases of the MAT. Our study reveals that the key challenge is the localization errors associated with spatial misalignment between the predicted paths and the actual labels. This discrepancy significantly affects the accuracy of the association, particularly at critical junctures where complex path interactions create ambiguous topological patterns. Although all baseline methods exhibit substantial association eтrors under these difficult conditions, our model achieves notable error reduction due to its dual-attention framework. However, discrepancies between
Fig. 16
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aanto
(1) Normal
(2) Rain
(3) Fog
(4) Dark
Figure 12:
Qualitative results under "Hard" environmental conditions and severe occlusion. Similar to the setup in Fig. I0 this figure displays the inference results under Rain, Fog, and Dark scenarios, but specifically highlights a case of severe vehicle occlusion. As observed, the model maintains robust lane reconstruction capabilities even when the visual information is corrupted by extreme weather or significantly obstructed by other vehicles.
Ground Truth
SA
Model Prod
PA
Stage
Stage 2
Stage
Stage 4
Stage
Figure 13: Visualization attention map of Path-aware attention and spatial attention. SA means Spatial Attention. PA mean Path-aware Attention. The red triangle represents the token corresponding to the current attention map.
predictions and the ground truth remain, indicating potential for further enhancement. We propose that improving the path-aware attention (PA) mechanism by incorporating local operators such convolutional kernlss could be advantageous. This hybrid approach would preserve model
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Adept comples topo
precisc sssociation
(a) Ground Truth
(b) KNN
(c) HMM
(d) MAT
(c) MAT Post Process
Figure 14: Visualization of result in OMA Val set.
Ground Truth
(b) KNN
HMM
(d) MAT
(e) MAT w/ Post Process
Figure 15: Visualization of result in OMA Test set.
efficiency while allowing for more precise opatial-tempont feature extraction at path intersections, thus improving local association accuracy without compromising inference speed.
H.4 GENERALIZATION ON DIFFERENT OP GENERATORS
To further validate the robustness of MAT against varying noise patterns inherent to different map generation paradigms, we visualize the association results the OMA Test using three distinct baselines: MapTR (Liao et al. 2023a), MapTRv2 (Liao et al. 2023b), and SeqGrowGraph (SSG) (Xie et al. 2025a).
As illustrated in Figure each generator exhibits unique geometric and topological characteristics:
Row 1 (MapTR): Often produces fragmented centerlines with localized geometric jitter, creating challenge ensure topological continuity.
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(a) Ground Truth
(b) KNN
(c) HMM
(d) MAT
(c) MAT w/ Post Process
Figure 16: Visualization of failed cases.
Row 2 (Map'Rv2)): Offers improved geometric stability, but still suffers from occasional detection gaps and instance discontinuities.
Row (SegGrowGraph): Generates highly connected graphs via expansion, which reduces fragmentation, but may introduce erroneous topological links (over-connection) in complex intersections.
Despite these substantial domain gaps, MAT consistently establishes accurate associations (indicated by the consistent coloring of This qualitative evidence reinforces the quantitative results in Tab. [ demonstrating that our proposed Path-Aware and Spatial Attention mechanisms effectively generalize across disparate upstream perception generators without requiring generator-specifie fine-tuning.
lane instances that match the SD map topology) across all three inputs.
I IMPLEMENT DETAILS
1.1 MODEL SETTINGS
Tab. summarizes the architectural configurations of our proposed MAT variants (MAT-T and MAT-L.). All variants adopt identical channel dimensions. attention head counts, spatial curve orders and hybrid attention mechanisms combining spatial attention (SA) and path-aware attention (PA). In particular, parameters such patch sizes, MLP ratios (matching spatial curve orders), and stochastic depth rates are uniformly inherited across architectures, reflecting ablation study results that optimized these values for balanced securacy-lateng trade-offs. A distinctive design choice lies in the shuffling strategy, where MAT-T/L progressively refine the shuffle order to enhance token mixing in spatial attention, aligning with their increasing computational budgets. This structured configuration hierarchy enables systematic evaluation of model capacity versus efficiency, as validated by the ascending La metrics (34 70) corresponding deeper transformer layers.
1.2 TRAIN CONFIGURATION
The details of the implementation are summarized in Tab. 23 Our training protocol uses the Adam optimizer with a base learning rate x 10-4 and cosine learning rate decay, operating batch size of 128. The weight decay regularization is set to 5 x 10-3, The training process spans 50 epochs with 2-epoch warm-up phase for learning rate initialization. Model optimization combines CrossEntropy loss for classification tasks and CTC loss for sequence alignment objectives.
1.3 DATA AUGMENTATIONS
For data augmentation as shown in Tab. 23 we implement a series of randomized transformations that include axis-aligned rotation around the z-axis with + angular variation at 50% application
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SSG. (2) MapTRv2 (1) MapTR
Figure 17: Visualization of MAT association results across different Online Perception (OP) map generators on the OMA Test set. Rows correspond to (1) MapTR, (2) MapTRv2, and (3) Seq- GrowGraph (SSG). The columns display diverse scenarios including intersections and straight roads. Consistent coloring between lane segments indicates that MAT successfully associates fragmented or noisy OP elements with the correct semantic road instances from the SD map, demonstrating strong cross-generator robustness.
Table 22: Model settings.
Parameter
MAT-T
MAT-L
Blocks
[2, 2, 2,
[4, 4, 4, 12, 4]
Attention Head
[4, 4, 8, 8,
MLP Ratio
4, 4, 4]
Drop Path
[0.3. 0.3, 0.3, 0.3]
Channels
[96, 192, 384, 768, 1536]
Path Size
[1024, 1024, 1024, 1024, 1024]
Attention Order
('Spatial Attention". "Path-aware Attention"] ["2", "z-trans", "hilbert", "hilbert-trans"]
Spatial Curve
Shuffle
[Shufile Order, Shuffle Order, Shuffle Order, Shuffle Order, Shuffle Order]
Latency/ms
Table 23: Train Configuration and Data augmentations.
Training Configuration
opt inimmii zer scheduler learning rate criteria
AdamW W Cosine
batcc size weight decay epochs
128
5e-3
le-4 CrossEntropy, CTC Loss
50
warmup epochs
Data Augmentation
random rotate
axis: z, angle: [-1, 1], p: 0.5 p: grid size: [0.1, 0.1, x/16]
random scale
scale: [0.9, 1.1]
random flip
random jitter
sigma: 0.005, clip: 0.02
grid sampling
probability, isotropic scaling within the range (0.9, 1.1), random flipping with equal probability
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50%, point cloud jittering characterized by 0.005 and a clip limit of 0.02, and grid sampling with spatial discretization parameters set to [0.1, 0,1, These enhancement strategies were systematically validated through ablation studies to optimize the balance between model accuracy and computational efficiency while ensuring robustness to input variations.
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Reviews:
[{"review_id": "MTLbEzG0vF", "reviewer": "Reviewer_cbHh", "summary": "This work focuses on the task of Online Navigation Refinement, which aims to transform the road-level navigation derived from SD maps into precise lane-level navigation aligned with the online perception maps. Specifically, the authors introduce the Online Map Association Dataset (OMA), which is developed from nuScenes, and a corresponding transformer-based model for the real-time map association task. In addition, to measure the alignment of the paths and the precision of correspondence, the authors also proposed a new metric named Navigation Refinement P-R (NR P-R). MAT is verified on the OMA dataset and achieved rather low latency (34ms) while maintaining comparable performance improvements compared to previous methods.", "questions": "- What’s the difference between MAT-T and MAT-L?", "limitations": "", "rating": 4, "confidence": 4, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "- Although existing autonomous driving datasets, such as nuScenes and OpenLane-V2, provide fine-grained local lane geometry annotations, and OpenStreetMap provides road-level SD Map annotations, the mappings between them are less explored. The OMA dataset proposed by this work greatly mitigates this data gap.\n- Path-aware attention is introduced to align the topologies under the distractions introduced by spatial fluctuations and semantic disparities. By forcing each token can appear only once in the path, cycles are prevented from happening and interactions are ensured to occur only between tokens within the same path.\n- The authors also proposed a Spatial Attention module that enhances feature interactions at the instance level across a wider spatial scope.", "weaknesses": "- Figure 5 is not self-contained. The concepts of path-aware and spatial attention are hard to grasp from the visual alone, as the figure lacks sufficient illustrative descriptions or a detailed caption.\n- Abbreviations are sometimes unclear or non-standard. For example, \"Para.\" in Table 1 is ambiguous and should be explicitly defined (e.g., as \"Parameters\").\n- The section on \"attention with vector serialization\" is underdeveloped. The explanation is too brief, lacking the detail needed for the reader to fully understand the proposed mechanism.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "eoPLswwQWC", "reviewer": "Reviewer_9aX1", "summary": "Targeting the pain points regarding cost and real-time performance in lane-level navigation, this paper proposes a new task of Online Navigation Refinement (ONR). By associating Standard-Definition Maps (SD) with Online Perception Maps (OP), it upgrades road-level navigation to lane-level navigation, while simultaneously addressing the core issues that existing solutions rely on expensive HD maps and OP maps lack global topology.", "questions": "1. The OMA dataset’s annotations are manually completed. Is there a plan to introduce semi-supervised/unsupervised annotation tools to reduce costs for future dataset expansion?\n2. The metric aggregates results across 15 length intervals to avoid short-path bias. However, it does not consider road complexity (e.g., straight roads and curved roads). Could we adding a \"complexity weight\" to improve the metric’s fairness in evaluating model performance on diverse road types?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. It directly tackles the core pain points of existing lane-level navigation solutions. The proposed ONR task bridges SD and OP maps to achieve low-cost, up-to-date lane-level guidance, which is highly relevant to real-world needs in GIS and autonomous driving.\n2. The paper provides a \"dataset-model-metric\" trinity solution to fill research gaps:OMA Dataset, MAT Model and NR P-R Metric.\n3. The paper provided solid experimental validation.", "weaknesses": "1. The test set's OP map noise only comes from MapTRv2.  It does not evaluate MAT’s performance under other common OP noise types (e.g., severe lane occlusion by vehicles, sensor failure in heavy rain/fog), I think it should be evaluated to improve the model's robustness.\n2. The test set uses OP maps generated solely by MapTRv2. If other OP map generators produce different noise patterns, whether MAT’s performance will degrade or not remains untested, i think the cross-generator generalization ability should be evaluated.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "joFhiXsqNv", "reviewer": "Reviewer_FhYL", "summary": "This paper proposes a new and important task for Online Perception Maps and SD map association. It first introduces Navigation Refinement P-R to evaluate the geometric and association accuracy, and then proposes a model named MAT to take centerlines, SDMap roads and boundary lines as input to predict road-lane association. \nIn the MAT model, two modules - PA and SA are carefully designed to consider topological and spatial features learning. \nThe paper also proposes the online map association dataset (OMA). Overall, this work focuses on a new problem of map association for low-cost and up-to-date lane-level navigation.", "questions": "1. In Figure 4, the caption states that “Label overlap in both paths is 67%. TP for T=50% but FP for T=95%.” I am wondering how the value of Label overlap is computed — is it defined as the ratio of correctly matched (or overlapping) edges to the total number of edges? How is an edge seen as the correct one?\n2. Why does the order of road tokens or centerline tokens matter in the MAT attention modules?", "limitations": "", "rating": 8, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 4, "strengths": "1. This paper proposes a new and important task for the online perception maps association, which is valuable for real-world applications in autonomous driving. Assigning prediction lanes to SD road elements is essential for building topological relationships between lanes and roads, further benefiting navigation and planning. Building a corresponding benchmark and evaluation metric fills the gap in the field of online map construction, as far as I know.\n2. The evaluation metrics are carefully designed to cover various cases, which reflect the distance matching, connection assignment, and length accuracy ratio.\n3. The contrastive experiments, ablation study and analysis are thorough, which demonstrates the effectiveness of each part of the model and post-process algorithm.", "weaknesses": "1. For the subfigure Fig 2.(3), there are two junction curve lines connecting two crossroads. The mapping of such transition\n lines is ambiguous due to their connection attributes. How to solve this problem when constructing the ground truth assignment labels?\n2. As this paper also studies the noise of road elements and assignment with predicted lanes, a line of related works regarding the use of SDMaps and the noise problem for map perception should be included and discussed, such as SMERF [1],  TopoSD [2], P-MapNet [3] and etc.\n3. We know there are inevitably some cases with the absence of the SDMap road elements or shifting road geometry,  caused by the errors of GPS localization or map construction.  We would like to know how these factors affect the association process and how the proposed metrics maintain robustness when dealing with imperfect SDMaps. In particular, this concern applies to cases where the generated online maps are geometrically or topologically inaccurate, even though they are otherwise precise.\n\n[1] Luo K Z, Weng X, Wang Y, et al. Augmenting lane perception and topology understanding with standard definition navigation maps[C]//2024 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2024: 4029-4035.\n[2] Yang S, Jiang M, Fan Z, et al. Toposd: Topology-enhanced lane segment perception with sdmap prior[J]. arXiv preprint arXiv:2411.14751, 2024.\n[3] Jiang Z, Zhu Z, Li P, et al. P-MapNet: Far-seeing Map Generator Enhanced by both SDMap and HDMap Priors[J]. arXiv preprint arXiv:2403.10521, 2024.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)