Stability Under Scrutiny: Benchmarking Representation Paradigms for Online HD Mapping
Authors: Hao Shan
Ruikai Li
Han Jiang
Yizhe Fan
Ziyang Yan
Bohan Li
Xiaoshuai Hao
Hao Zhao
Zhiyong Cui
Yilong Ren
Haiyang Yu
Venue: ICLR 2026 Poster (2026)
Keywords: Online HD Map Construction
Temporal Stability
Benchmarking
Autonomous Driving
Evaluation Metrics
Primary area: datasets and benchmarks
TL;DR: We introduce the first benchmark to evaluate temporal stability (mAS) for online HD mapping models.

Abstract: As one of the fundamental intermediate modules in autonomous driving, online high-definition (HD) maps have attracted significant attention  due to their cost-effectiveness and real-time capabilities. Since vehicles always cruise in highly dynamic environments, spatial displacement of onboard sensors inevitably causes shifts in real-time HD mapping results, and such instability poses fundamental challenges for downstream tasks. However, existing online map construction models tend to prioritize improving each frame's mapping accuracy, while the mapping stability has not yet been systematically studied. To fill this gap, this paper presents the first comprehensive benchmark for evaluating the temporal stability of online HD mapping models. We propose a multi-dimensional stability evaluation framework with novel metrics for Presence, Localization, and Shape Stability, integrated into a unified mean Average Stability (mAS) score. Extensive experiments on 42 models and variants show that accuracy (mAP) and stability (mAS) represent largely independent performance dimensions. We further analyze the impact of key model design choices on both criteria, identifying architectural and training factors that contribute to high accuracy, high stability, or both. To encourage broader focus on stability, we will release a public benchmark. Our work highlights the importance of treating temporal stability as a core evaluation criterion alongside accuracy, advancing the development of more reliable autonomous driving systems. The benchmark toolkit, code, and models will be available at https://stablehdmap.github.io/.

Published as a conference paper at ICLR 2026
STABILITY UNDER SCRUTINY: BENCHMARKING REP- RESENTATION PARADIGMS FOR ONLINE HD MAPPING
Hao Shan 122,, Ruikai Li Han Jiang 1,2, Yizhe Fan 12 Ziyang Yan 12, Bohan Li
Xiaoshuai Hao Hao 7 Zhiyong Cuil?2 Vilong Ren 12 Haiyang Yu!.2
State Key Lab of Intelligent Transportation System
School of Transportation Science and Engineering, Beihang University
3 Shanghai Jiao Tong University
4 Ningho Institute of Digital Twin, Eastern Institute of Technology
$ Beijing Academy of Artificial Intelligence (BAAI)
6 Institute for AI Industry Research (AIR), Tsinghua University
Equal contribution
!Corespondding author
'Project leader
Ground Truth
Model
Model
Ground Truth
Model
Humans
76.0
72.0
83.6
MAS (Ours)
76.6
78.1
Figure I: Evaluating trustworthiness online mapping models using human judgment, tradi- tional mAP, and our mAS metric. In each case, the standard accuracy metric (mAP) fails to align with human judgment because evaluates only single-frame precision, disregarding stability across time. To address this limitation, we propose the first stability benchmark for online vectorized map construction and present large-scale analysis of contemporary models.
ABSTRACT
As one of the fundamental modules autonomous driving, online high-definition (HD) maps have attracted significant attention due to their cost-effectiveness and real-time capabilities. Since vehicles always cruise in highly dynamic environ- ments, spatial displacement of onboard sensors inevitably causes shifts in real- time HD mapping results, and such instability poses fundamental challenges for downstream tasks. However, existing online map construction models tend prioritize improving each frame's mapping accuracy, while the mapping stabil- ity has not yet been systematically studied. To fill this gap, this paper presents the first comprehensive benchmark for evaluating the temporal stability of on- line HD mapping models. We propose multi-dimennional stability evaluation framework with novel metrics for Presence, Localization, and Shape Stability, integrated into unified mean Average Stability (mAS) score. Extensive experi- ments on 42 models and variants show that accuracy (mAP) and stability (mAS) represent largely independent performance dimensions. We further analyze the impact of key model design choices on both criteria, identifying architectural and training factors that contribute to high accuracy, high stability, or both. To en- courage broader focus on stability, we will release a public benchmark. Our work highlights the importance of treating temporal stability as a core evaluation crite- rion alongside accuracy, advancing the development of more reliable autonomous driving systems. The benchmark toolkit, code, and models will be available at https:/sstablehdmap github.io..

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Published as a conference paper at ICLR 2026
INTRODUCTION
Road Boundary
Lane Divider
Podestrian Crossing
Puture
Predicted Trajectories
Scenario
Scenario
Waruing!
Dangeroust
Missing!
Missing!
Frame:
Frame:
Frame:
Frame:
Frame:
Prame:
Figure 2: The Impact of Unstable Map Elements on Downstream Tasks, In Scenario A, the ego vehicle attempts to overtake, but the forward lane divider suddenly disappears during the maneuver, causing the ego vehicle to steer toward the curb. In Scenario B, another vehicle attempts to change lanes, but due to flickering lane dividers in the ego vehicle's perception, the ego vehicle interprets the other vehicle's 's action as a collision course.
High-definition (HD) map is one of the fundamental component of autonomous driving, offering centimeter-level environmental details such as precise coordinates of map elements and vectorized topological structures (Hu al., 2023; Jiang et al., 2023; Liao et al., 2025a; Cao et al., 2025; Wei et al, 2026; Hao al., 2025a). Although traditional pre-built HD map provides highly accurate rep- resentations, its substantial production and maintenance costs, coupled with limited adaptability to dynamic road conditions, severely restrict large-scale deployment. To address these limitations, on- line HD mapping has recently emerged as a promising alternative (Li et al., 2022; Liao et al., 2022). By leveraging onboard sensors perceive the environment in real time, this approach dynamically constructs local vectorized maps, thereby reducing dependence on offline HD maps and paving the way toward scalable and generalizable autonomous driving systems.
Recent advances in online mapping have primarily aimed at improving accuracy and efficiency, giv- ing rise diverse set of approaches with distinct representational paradigms (Lilja et al., 2024; Liao et al., The community typically evaluates these methods using metries such as mean Average Precision (mAP) on benchmark datasets, which has driven continuous improvements in state-of-the-art performance. However, a critical yet underexplored issue in traditional evaluation is the stability of model outputs, a property essential for the safe deployment of autonomous driving systems, as illustrated in 22g.2. A model that achieves high average precision but produces flicker- ing map boundaries or fails entirely at complex intersections, acting like an "intermittently blind" guide, poses substantial safety risks (Gu et al., 2024; Zhang et al., 2025; Wan et al., 2025b; Zeng et al., 2025b). Despite its importance, the field currently lacks dedicated benchmarks and metrics to quantitatively assess stability in online HD mapping. This gap hinders systematic evaluation of how different representational paradigms respond to real-world disturbances, ultimately slowing progress toward more reliable next-generation mapping systems.
To bridge this gap, we present the first systematic investigation and benchmark for stability in online HD mapping, under the theme "Beyond Accuracy: Under Scrutiny of Stability". Our key contribu- tions are threefold:
A multi-dimensional stability evaluation framework. We propose novel temporal stabil- ity metrics, including Presence, Localization, and Shape Stability, to quantitatively capture the consistency of map elements across consecutive frames. These are integrated into a comprehensive mean Average Stability (mAS) score, enabling holistic model assessment.
Comprehensive benchmarking and analysis. We conduct large-scale experiments across diverse state-of-ther thetthe-rt models, revealing that accuracy (mAP) and stability (mAS) are largely independent performance dimensions. Our analysis examines how design choices in sensors, 2D backbones, BEV encoders, temporal fusion, and training regimens influence accuracy and stability as distinct evaluation aspects.
The first stability centric benchmark. We establish and will release public benchmark to catalyze community-wie focus on stability, providing the foundation for developing safer and more robust online mapping systems.
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Published as a conference paper at ICLR 2026
2 RELATED WORK
Online HD Mapping Models. Online HD mapping has become a critical and extensively stud- ied subtask in autonomous driving (Xie et al., 2025a; Zeng et al., 2024), Depending on the choice of sensor input, existing methods can be broadly categorized into camera-only (Qiao et al., 2023; Ding et al., 2023; Zhang et al., 2023; Liu et al., 2024a;b), LiDAR-only (Wang et al., 2023), and camera- LiDAR fusion (Li al., 2022; Liu et al., 2023; Liao et al., 2022; 2025b; Yuan et al., 2024; Zhang et al., 2024c) paradigms, each offering distinct strengths and weaknesses in perception ca- pability and environmental adaptability (Hao et al., 2024a; Kim et al., 2025; Yan et al., 2025; Li et al., 2025e; Kong et al., 2025). Generative frameworks have also been explored for large-scale map construction from multi- data (Yuan et al., 2025). Persistent autoregressive mapping with traffic rules has also been explored (Liang et al., 2025). Online HD-SD map association frameworks and benchmarks have also been explored (Wan et al., 2025a). Recent advances in point cloud Trans- formers further provide strong 3D feature extractors that can benefit LiDAR-based perception (Wan et al., 2025e; Zhang et al., 2024a). While these paradigms have driven notable progress in mapping accuracy and efficiency, current evaluation frameworks remain narrowly focused on mean Average Precision (mAP), overlooking the critical dimension of stability. This omission substantially limits the practical reliability and deployment of online mapping systems in downstream driving tasks.
Robustness in Autonomous Driving. Robustness to real-world perturbations has been extensively explored in core autonomous driving tasks. Established benchmarks exist for 2D (Wang et al., 2020) or 3D detection (Dong et al., 2023; Zhu et al., 2023; Paek et al., 2022), segmentation (Hong al., 2022), depth estimation (Kong et al., 2023), and vision-language navigation (Zeng et al., 2025c), where models are evaluated under conditions such as corruption, adverse weather, and occlusion. More recently, RoboBEV has extended to Bird's "rrd's-Eye-Vieee View (BEV) perception, revealing vulnera- bilities in view transformation techniques such as LSS and transformers (Xie et al., 2023; 2025b). In the context of online HD mapping, early efforts have examined sensor level robustness, demon- strating that mapping systems are highly sensitive to corrupted inputs (Hao al., 2024b; 2025c;b). However, these studies are restricted to static, single frame analyses and sensor-specific faults. Cru- cially, the temporal stability of mapping models under sequential perturbations and the comparative robustness of different representation paradigms remain unexplored, a gap which our benchmark aims to address.
Evaluation Metries for Online HD Mapping. Current evaluation metrics in the field of online HD map construction are often designed based on single frame geometric accuracy (Li et al., 2022; Liao et al.. 2022), primarily focusing on the geometric similarity between the predicted map and ground truth in given frame. Among typical existing metrics, mean Intersection over Union (mloU) measures the spatial overlap between the predicted map and the ground truth, while mean Average Precision (mAP) comprehensively considers both classification accuracy and the localization pre- cision of map elements. However, a critical yet previously overlooked issuc is that the impact of online mapping on downstream planning tasks depends not only per- framframe geometric accuracy, but also on the inter-frame dynamic stability of the vectorized map. Jitter in map elements across frames significantly impair the decision-making of autonomous driving systems (Zhang al., 2025; Gu et al., 2024; Jiang et al., 2023). More seriously, existing metrics completely ignore the temporal geometric stability of map elements, such as the magnitude of polyline edge jitter and the frequency of shape mutations, which are crucial safety factors. To the best of our knowledge, our work is the first to establish a publicly available benchmark dedicated to stability evaluation for online mapping.
3 MULTI-DIMENSIONAL MAP STABILITY EVALUATION FRAMEWORK
This section details the proposed framework for molti-diimensional stability evaluation in online HD mapping. The framework quantifies temporal stability through instance-level matching across con- secutive frames, specifically designed to assess three critical dimensions: detection consistency, ge- ometric jitter. and shape preservation. The entire pipeline, consists of four main stages: (1) temporal sampling of frame pairs, (2) cross-frame instance matching, (3) geometric alignment and resam- pling, and (4) stability metric computation.
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3.1 TEMPORAL SAMPLING
The temporal sampling stage constructs pairs of frames for analyzing stability over varying time intervals. Given sequence of L consecutive model output frames {Di, D Dr.} and pre- defined maximum temporal interval M, the process is as follows: for each anchor frame De (where S L M), subsequent frame De+k is randomly sampled from the futurre window [Dr+1...., D++M), } forming an evaluation pair (Dz. Di-k). Repeating this procedure for every valid anchor frame results in comprehensive sample S of size |S| I.M,, which provides the foundational inputs for subsequent stability analysis. Testing multiple M values (M E {2, 3, 5, 10)) allows our framework to adapt to different application scenarios. Detailed analysis of temporal sam- pling design and frame rate considerations is provided in Appendix C.4.3.
MapTR
BeMapNet
MGMap
GeMap
MapTR-T
MapTracker
#3
#3
33
8
#
#6
8
8
0
H/77
#7
R7
M-pTR-F
PivotNet
MapTRvz
MapQR
StreamMapNet
HRMapNet
#
#3
#1
5 #1
# #1
R
#
5
Figure 3: Radar chart for Basic HD map constructors covering eight evaluation metrics. The axes of the radar chart correspond to: #1 mAS, #2 Shape, #3 Loc, #4 Presence, #5 mAP, #6 Inference Memory Cost, #7 Parameter Count, #8 FPS.
3.2 CROSS-FRAME INSTANCE MATCHING
Establishing accurate correspondence between map elements across temporal frames is essential for stability assessment. Given the inherent inconsistencies in model predictions, direct matching approach is prone to error. Instead, a robust indirect strategy utilized, leveraging the consistent annotations of ground truth (GT) data as a reliable intermediary for association.
For each frame pair (D,, Dz+a), the matching process comprises two steps:
Predictions each frame are independently matched to their respective GT instances using the Hungarian algorithm, which optimizes cost function based on geometric and semantic similarity.
Frame-to-GT Matching:
2. GT-based Association:
The persistent identification GT elements across frames enables the linkage of corresponding predictions. Specifically, predictions matched to the same GT instance different frames are paired, thereby transferring the temporal consistency of the GT to the model outputs.
This procedure yields a set matched instance pairs {(oly (c), (e)) | e E E} for each frame pair, where E represents the set of successfully tracked map elements. The complete algorith- mic details are provided in Algorithm 2 of the Appendix. GT serves as a matching medium rather than an absolute geometric benchmark. Minor GT annotation jitter does not affect stability assess- ment, as matching is based on persistent GT instance IDs. Detailed discussion on GT matching robustness and alignment noise handling provided in Appendix B.
3.3 GEOMETRIC ALIGNMENT AND RESAMPLING
Geometric alignment ensures fair and spatially consistent comparison between matched polylines (poly,4x poly, (e)) by transforming them into a common coordinate system and resampling them uniformly. This process consists of three sequential operations.
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Coordinate Transformation. The historical polyline pely,(e) is first transformed from the ego coordinate system of its original frame D, into the ego coordinate system of the current frame Deth- This spatial normalization is computed as:
poly,-t+a (e) worldd>t+ To-warkd (o)y,
where world and T world- + denote the transformation matrices from frame D, to the world frame and from the world frame frame Da+k, respectively.
Perception Range Filtering. The transformed polyline poly,-.44 is then clipped to the percep- tion range of the model in frame Dr A point p (z. y) is retained for subsequent analysis if and only if it satisfies:
Emin S x S Tmax and
1/min S y ymaxx
where [mii Fmax Mmin Ymax] defines the operational perceptual boundaries, ensuring evaluation consistency with the model's design.
Uniform Resampling. Finally, to enable precise point-wise comparison, both the current poly- line poly++* (e) and the transformed historical polyline poly,-x+xxee) are resampled uniformly. We employ a dynamic axis selection mechanism that adaptively determines the primary sampling axis based on local geometric orientation, rather than using fixed axis. This ensures robust resampling for polylines of arbitrary orientations. Detailed implementation is provided in Appendix B.
3.4 STABILITY METRIC COMPUTATION
Based on the aligned and resampled point sets poly;4k (e) and poly. samle of each matched map element is quantified from three perspectives.
Sample
(e). the temporal stability
Presence Stability. This metric evaluates the detection consistency an element across frames. Let score(e) denote the model's confidence for element and be detection threshold. The presence stability is defined as:
if score 2 r and (core; 2 T, or scorc++*(e) < and score, < T; otherwise (flickering).
Presence(e)
0.5,
higher average value across instances indicates better detection consistency.
Localization Stability. This metric quantifies the point-wise positional jitter of an element. For the resampled polylines, we compute the average L1 distance in the y-coordinate and map stability score:
N B N
Loc(e)
Σ [1+4(1;;) ve (x:)),
where is scaling parameter. The selection of 3 15 corresponds the map's short-range radius, representing the distance threshold for complete instability. Ablation studies and detailed justification are provided in Appendix C. The formula maps the average deviation score between 0 (unstable) and (stable).
Shape Stability. This metric assesses the consistency an element's geometric shape by compar- ing the curvature of the resampled polylines. We approximate the curvature of polyline as the average angle between consecutive segments:
(polyy)
1
Σ Oj, where 0, cos
0y71 |u
N
j=1
The shape stability is then defined as the normalized difference in curvature: (e)) k(poly. (e))
<(poly sample
sample
Shape(e) = 1
π
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Comprehensive Stability Index. bining the three metrics:
The overall stability for single instance e is computed by com-
Stability Presence(e) [w Loc(e) + (1 w) Shape(e)],
where w E [0, 1] a weighting parameter (default: 0.7). The class-wise stability the average over all instances of that class:
Stability class
Stability(e).
[Zelass]
eE
Finally, the overall model stability, mean Average Stability (mAS), is the mean of the stability scores across all classes:
mAS
Stability class'
C
classEC
This single score provides holistic measure of model's temporal stability.
4 EXPERIMENTAL ANALYSIS
In this section, we present comprehensive empirical evaluation of our proposed stability assessment framework. Our experiments are designed to answer the following key questions:
RQ1: How do statc-of-the-ar online HD mapping models perform in terms of both con- ventional accuracy (mAP) and our newly proposed temporal stability (mAS)? Is there an implicit correlation between them?
RQ2: How do different representational paradigms influence model stability?
RQ3: What are the specific strengths and weaknesses of each paradigm under temporal scrutiny, as revealed by our fine-grained stability metrics (Presence, Localization, Shape)?
Table 1: Basic Benchmarking of HD Map Constructors. Performance comparison of online HD mapping methods on nuScenes val set. Models grouped by temporal fusion mechanisms, input modality, BEV encoder and training epochs. "Temp" denotes the injection of temporal information. "L" and "C" represent LiDAR and camera respectively, while the 2D and 3D backbones employ ResNet50 (He et al., 2016) and SECOND (Yan et al., 2018), correspondingly.
Method
Venue
Tomp
Modal
BEV Encedur
Epech
mAPt
Presence
Lac
Shape
mASt
MapTR 2022)
ICLR'23
X
44,1
90.6
MapTR (1 in sl, 2022)
1CLR223
C&L
GKT
24
62.8
91.5
68.5
91.0
340
BeMtaphie
2013)
X
PootNee (Ding al, 2023)
1CCV223
X
C
Pens/wmmer
30
57.1
10000
71.4
91.2
34.3
MapiTRv2 (1 lan et 2025h)
UCY124
X
BEVPoel
GeMop (Chong 2024c)
BCCY"24
X
BEVFomen
51.3
MGMap (Lis al.,, 20243)
CVPR'24
BEVFomer
24
57.9
92.2
73,0
92.3
78.0
MapOR
BCCV124
X
BEVFumess3
MapIR 2022)
SacamMapNaa (Yum et 2024)
WACY'24
BEVFumaze-
63.3
96.6
97.7
92.3
91.9
MopTar (Chen 201.9
F0CY124
RFVFommms2
75.05
95.8
HRMipNer (Zhang 2024b)
800Y
BLVioomenn
67.2
4.1 BENCHMARK CONFIGURATION
Benchmark and Models. In this work, we conduct a comprehensive evaluation of 42 online HD map constructors and their variants, covering representative methods following diverse representa- tion paradigms, including BeMapNet (Qiao et al., 2023), PivotNet (Ding et al., 2023), MapTR (Liao al., 2022), MapTRv2 (Liao et al., 2025b), StreamMapNet (Yuan al., 2024), MGMap (Liu al., 2024a), GeMap (Zhang et al., 2024c), MapQR (Liu et al., 2024b), MapTracker (Chen et al., 2024), and HRMapNet (Zhang et al., 2024b). These models represent diverse design choices across input modalities, backbone architectures, BEV encoders, temporal fusion mechanisms, historical priors, and training epochs, allowing for a holistic analysis of representational paradigms. Model weights sourced from official code repositories retrained using default settings ensure faimesss. Un- fortunately, due to the unavailability of source code for several online mapping approaches, we were unable to include them in our full assessment.

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Published as a conference paper at ICLR 2026
Shape
90
50
Galdey
MoMay
Merth Vestin
Margh
75
Golir
M
70
Mestt
(a) mAP vs. Presence
(b) mAP vs.
Loc
(c) mAP vs. Shape
(d) mAP vs. mAS
Figure 4: The correlations between the Linglefrrame accuracy metrics mAP and the stability metrics Presence, Loc, Shape, and mAS. The bubble size represents the model's parameter count.
Evaluation Metrics. We evaluate each model using both the conventional mean Average Preci- sion (mAP) and our proposed multi-dimensional stability metrics: Presence, Localization (Loc), Shape stability, and the comprehensive mean Average Stability (mAS), as defined in Section 3.4. Additional metrics related to inference performance have been incorporated into the evaluation framework, As shown in Fig.3 More detailed evaluation metrics are provided in Section D of the Appendix. We also evaluate model stability under adverse weather conditions and provide compre- hensive ablation studies on hyperparameters. Results are detailed in Appendix C.
4.2 BASIC PERFORMANCE BENCHMARKING (RQI)
The basic benchmarking results summarized in Table 1. Our analysis reveals two key findings that challenge the sole reliance on accuracy for model evaluation:
Stability constitutes a distinct and critical performance dimension. A primary observation from our benchmark, illustrated in Fig.4, is the imperfect correlation between conventional curacy (mAP) and temporal stability (mAS). We observe that models with higher mAP do not nec- essarily achieve superior mAS, indicating that temporal stability not an automatic byproduct of high accuracy but rather a unique aspect of model performance. This aspect is crucial for real-world deployment yet is overlooked by conventional metrics.
Significant stability gaps exist among mainstream paradigms. We observe that the mAS scores span wide range from 71.6 (MapTR (Liao et al., 2022)) to 91.9 (StreamMapNet (Yuan et al., 2024)), indicating that the choice of representational paradigm profoundly impacts the consistency of the generated map. A majority of existing models, cluster in the lower to mid-range of mAS (71.6 78.0). This clustering suggests a common challenge faced by current approaches in maintaining output stability across consecutive frames.
4.3 IN-DEPTI ANALYSIS OF REPRESENTATIONAL PARADIGMS (RQ2 RQ3)
Table 2: Ablation the Input Modality.
Table 3: Ablation on the BEV Encoder.
Method
Modal
mAP
Presence
Loc
Shage
mAS
Methed
Encoder
L
Shape
MAS
MapTR...
65.4
MapTR
BEVFormer
31.3
MapTR
C&L
6288
91.5
68.6
91.0
74.0
MipTR o
GKT
44.1
91.2
05.4
98.6
71.6
GeMap
62.7
91.1
67.5
MapTR
BEVPoal
GuMap-
C&L.L
665
89.1
663
92.7
718
Impact of Sensor Modality. Our analysis reveals a nuanced relationship between sensor modality and temporal stability. As shown in Table 2, while LiDAR fusion consistently improves perception accuracy, increasing MapTR's 's (Liao al., 2022) mAP by 42.6% (from 44.1 to 62.8) and GeMap's (Zhang et al., 2024c) mAP by 6.1% (from 62.7 to 66.5), its effect on temporal stability demonstrates significant model dependence. MapTR (Liao et al., 2022) benefits from sensor fusion with a 3,4% improvement in mAS (71.6 to 74.0), suggesting that LiDAR's precise depth measurements can en- hance temporal consistency. In contrast, GeMap (Zhang et al., 2024c) experiences 3.9% decrease

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Published as a conference paper at ICLR 2026
in mAS (74.7 to 71.8) despite accuracy gains, indicating potential architectural limitations in lever- aging multi-modal signals for stable predictions. This divergence highlights that additional sensors alone cannot guarantee improved stability.
Influence of BEV Encoding Strategies. Our ablation study on MapTR (Liao et al., 2022) demon- strates that different BEV encoders achieve similar overall temporal stability, with mAS scores rang- ing from 71.3 to 71.9, despite significant variations in accuracy. where mAP values span from 41.6 to 50.1, as summarized in Table 3. Further analysis reveals distinct specialization patterns among encoders. The GKT (Chen al., 2022) encoder achieves superior Presence Stability 91.2, suring consistent detection map elements across frames. In comparison, BEVFormer (Li et al., 2024b) and BEVPool (Liu al., 2022) excel Localization Stability, with scores of 69.7 and 69.8 respectively, indicating their stronger capability in mitigating geometric jitter. These results high- light that BEV encoders embody characteristic preferences for different aspects of temporal stability, even within the same model architecture.
Table Ablation on Temporal Fusion.
Mothed
Tomp
Initial Map
Back.
BEY Encnder
Epoch
mAP
Presence
LacT
Shapet
mAST
MapTR
X
X
MapTR
X
R50
GKT
24
51.3
88.6
59.7
89.3
66.6
MapTR
R50
BEVFemer
24
41.6
89.6
69.7
90.6
71.3
MapTR
X
90.4
StrumMapNeet
X
X
DEViormer
30
51.7
87.0
91.8
83.8
StromMMaNNee
R50
BEVFvreer
30
63.3
96.6
91.7
92.3
91.9
MapTracker
X
R18
BEVisemer
72
62.8
95.3
91.3
85.9
87.4
MipTraceer
X
BEVKemar-2
MipTracker
X
R50
BEVFomas-2
72
68.3
94.5
97.9
93.8
9118
MapTraceer
R50
BEVismme
72
75.95
93.3
98.1
95.8
90.4
HRMapNett
X
92.3
IRMappett
Testing Map
BEVFomer
24
73.0
94,9
71.4
93.0
78.4
HRMapNet
Training Map
R50
BEVRema-1
24
83.6
89.9
75.9
93.2
76.7
44.1
(+11.) mAS 73.0 (+1.7)
mAS: 71.6
MAS 66.6 (-5.0)
MAS : 71.3
(a) Temp
(b) Temp
(c) Temp
Temp
(e) Ground
Figure 5: The dual effect of temporal fusion on MapTR with different BEV encoders.
Discussion of Temporal Fusion. As shown in Table 4, the effectiveness of temporal fusion is highly dependent architectural compatibility. Models with native temporal designs demonstrate robust performance: StreamMapNet (Yuan et al., 2024) achieves exceptional temporal stability (mAS: 91.9), while MapTracker (Chen et al., 2024) maintains strong stability (mAS: 90.4) alongside significant mAP improvement (+1 ++11.4%) 1..4%). In contrast, adding temporal fusion to architectures not originally designed for temporal processing yields inconsistent results. MapTR (Liao et al., 2022) exhibits divergent behaviors depending on its BEV encoder. With the GKT (Chen et al., 2022) encoder, temporal fusion degrades stability (mAS: -7.0%), whereas with BEVFormer (Li et al., 2024b), it provides balanced improvement (mAS: +2.4% mAP: +28.18 +2).1%). This contrast highlights the critical influence of the encoder's representation capacity on temporal integration. Furthermore, HRMapNet (Zhang et al., 2024b) demonstrates that while map priors substantially boost accuracy (mAP: +24.4% with training map priors), their impact on stability is more limited (mAS: +1.1% 1%). This suggests that dynamic temporal modeling contributes more significantly to consistency than static priors. These findings collectively emphasize that effective temporal fusion requires co-design of architectural components rather than simply appending temporal modules.
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Table 5: Ablation on the 2D Backbone.
Table 6: Ablation on the Training Epochs.
Shope
Fiorch
Shape
MR
R18
$7.8
MigTR-18
32.4
75.1
72.8
R30
44.1
91.2
65.4
90.6
71.6
Mop1R-18
110
48.5
86.0
71.7
94.8
71.9
M o
R38
57.2
91.0
73.2
91.2
73.6
MigTR-50
24
44.1
91.2
65.4
98.6
71.6
MapTTRo2
R30
91.5
McpTIR-00
63.2
68.3
MapQK
Ki8
62.5
58.2
73.1
92.5
74.1
Map 50
24
06.4
91.8
75.6
91.6
77.8
MapQR
91.8
MapQR 50
72.6
759
BeMapNet
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60.7
1000
67.9
97.9
82.9
GcMap-50 50
24
51.3
92.3
69.7
92.6
75.5
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07.9
GcMop-50
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91.1
67.5
BeMopNet
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100.0
62.8
96.0
BeMoppeet
64.1
106.0
62.8
98.0
50.4
PioiNa e
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57.8
1000
71.8
97.2
64.5
BeMappies
110
68.3
108.0
64.11
98.2
$1.1
PionNet
R50
71.6
54.4
PhontNet
Swi 1
61.6
100.0
71.6
97.2
64.4
PiooNeer
110
66.4
108.0
72.1
97.4
$4.8
GoMup
91.1
MapTincker-B
69.3
94.8
98.2
93.8
90,5
G:Mip
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72.0
92.2
74.9
95.3
73.1
MayTracker-18
72
71.
92.9
98.5
94.8
69.9
G:Mip
V2-99
89.3
82.6
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91.
98.5
96.0
91.7
GeMop
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76.0
93.4
95.7
Maplitacker-00
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953
96.1
50.4
Influence of The 2D Backbone. The impact of the 2D backbone is model-specific as indicated in Table 5. A more powerful backbone consistently improves accuracy (mAP), as seen in MapTR (+36. 1%) (Liao et al., 2022) and MapQR (+6.6%) (Liu et al., 2024b). However, its effect stability (mAS) is less predictable, ranging from a slight decrease in MapTR (-1.6%) (Liao et al., 2022) to an increase in MapQR (+5.0%) (Liu et al., 2024b). We observe recurring trade-off: stronger backbones often enhance Presence Stability (e.g., +3.4% for MapTR (Liao et al., 2022)) but can reduce Localization Stability (-12.8% for MapTR (Liao et al., 2022)), suggesting potential focus on semantic over geometric consistency.
Impact of Training Regimen. Our analysis reveals distinct patterns in how extended training affects model performance across different architectures. As shown in Table 6, while longer training epochs consistently improve accuracy (mAP increases ranging from +4.3% to +40.1%), the effeets on temporal stability vary significantly. We observe three distinct learning behaviors. First, models like MapTR (Liao et al., 2022) exhibit stability erosion, where accuracy gains (+22.8% for MapTR- 50) come with stability degradation (-4.7% mAS). Second, architectures such as MapQR (Liu al., 2024b) and PivotNet (Ding et al., 2023) demonstrate stability saturation, maintaining or slightly improving mAS (+3.2% and +0.5% respectively) while achieving accuracy improvements. Third, complex temporal models like MapTracker (Chen et al., 2024) show optimization sensitivity, where extended training improves mAP (+3.7% +4.2%) but leads slight mAS reductions (-1.0% to -1.4%). These patterns underscore that temporal stability responds differently to extended training based on architectural inductive biases, suggesting that stability should explicitly optimized rather than expected to emerge from accuracy-focused training alone.
4.4 GENERAL DISCUSSION
Our benchmark reveals that temporal stability (mAS) is independent performance dimension from accuracy (mAP), challenging the prevailing focus on single-frame precision. Models with high mAP can exhibit significant instability, underscoring the need for dual optimization. Architectural choices induce distinct stability profiles. Multi-sensor fusion improves accuracy but affects sta- bility model-dependently. BEV encoders specialize differently: GKT (Chen al., 2022) favors detection consistency while BEVFormer (Li et al., 2024b) variants reduce geometric jitter. Tem- poral fusion effectiveness hinges on architectural compatibility, with native designs outperforming retrofitted modules. Training dynamics diverge by architecture. Extended training improves ac- curacy consistently but affects stability variably, revealing three patterns: erosion (MapTR (Liao et al., 2022)), saturation (MapQR (Liu et al., 2024b)), and sensitivity (Map Tracker (Chen et al., 2024)). This indicates stability requires explicit optimization rather than emerging implicitly from accuracy- focusee training.
These findings advocate for co-equal treatment of stability and accuracy in evaluation and de- sign. The substantial stability gaps among models (mAS: 66.6 66.6-91.9) highlight critical improvement opportunities. Future work should develop architectures that explicitly joint-optimize both criteria for trustworthy autonomous driving systems. mAS is designed to complement mAP, not replace it.

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Published as a conference paper at ICLR 2026
Models with high mAS but low mAP indicate gneudo-stabilig Detailed discussion is provided in Appendix C.
5 CONCLUSION
In this work, we address the critical yet overlooked aspect of temporal stability in online HD map- ping evaluation. While significant progress has been made in single-frame accuracy, the consistency of model outputs across sequential frames, which is essential for safe deployment, has remained largely unquantified. To bridge this gap, we introduce multi-dimensional stability evaluation framework with novel metrics for presence, localization, and shape stabiliiy, integrated into a unified mean Average Stability (mAS) score. Extensive benchmarking demonstrates that accuracy (mAP) and stability (mAS) represent independent performance dimensions, challenging the assumption that accuracy optimization alone ensures real-world reliability. Our analysis further reveals how archi- tectural choices, including temporal fusion strategies, sensor modality, training regimens, backbone designs, and BEV encoders, distinctly influence both accuracy and stability. By establishing the first stability-centric benchmark, we aim shift community focus beyond accuracy alone and inspire the development of next next-generattin online HD mapping systems that achieve both high accuracy and temporal consistency, thereby advancing more robust and trustworthy autonomous driving.
ACKNOWLEDGMENTS
This work was supported in part by the the Beijing Natural Science Foundation under Grant L,243008, in part by the Shandong Provincial Natural Science Foundation under Grant ZR2024LZN010, in part by the National Natural Science Foundation China (NSFC) under Grant 52441202, in part by the Ganwei Program of Beihang University under Grant WZ2024-2-16 16, and in part by the Fundamental Research Funds for the Central Universities.
Ethics Statement. This work presents a benchmarking framework for evaluating temporal sta- bility in online HD mapping. All experiments are conducted on the publicly available nuScenes dataset (Caesar al., 2020) under standard research protocols, with no human subjects involved. While the study itself does not collect sensitive data, we recognize that broader deployment of HD mapping technology raises important societal considerations: privacy risks from detailed environ- mental sensing, safety implications if models are overtrusted in autonomous systems, computational and environmental costs of large-scale model training, and potential performance disparities across diverse driving environments. We encourage the community to address these concerns through sponsible data practices, robust safety validation, efficient algorithms, and inclusive evaluation. This work adheres to the ICLR Code of Ethies and aims promote safer and more reliable autonomous systems through transparent and reproducible evaluation.
Reproducibility statement. To ensure the reproducibility of our work, we provide compre- hensive benchmark toolkit, including evaluation code and visualization script complete with full technical documentation, at https//stablehddmppiihhhe
The implementation details of all mod- els, training configurations, and hyperparameters are described in Section 4.1 and the Appendix. Our stability evaluation framework is fully detailed in Section 3, with algorithmic steps and metric formulations explicitly defined. The nuScenes dataset (Caesar al., 2020) used in this study is publicly available, and all preprocessing steps follow standard practices as cited. We encourage the community to released resources to facilitate fair comparisons and further research.
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Appendix
In the appendix, supply further details the proposed stability evaluation framework, the bench- mark setup, experimental analyses, and visualizations that are omitted from the main paper for brevity. The appendix is structured follows:
Sec. A provides additional statements on the use of Large Language Models (LLMs) in this work.
Sec, B presents implementation details of the stability evaluation algorithm pipeline.
Sec. C offers supplementary experimental setups and related ablation studies.
Sec. presents additional analyses from 10 different models.
Sec. E displays supplementary visualizations, encompassing additional mAP mAS com- parisons, and illustrations of how unstable predictions affect downstream tasks.
Sec. F discusses the limitations of our work and provides outlook future work.
A STATEMENT ON THE USE OF LARGE LANGUAGE MODELS (LLMS)
In the preparation this paper, large language models (LLMs) used solely assistive tool for writing refinement and polishing. The core research ideas, theoretical framework, experimental design, data analysis, and result interpretation were entirely conceived and conducted by the human authors. The LLM was employed after the intellectual substance of the work was fully established, specifically to assist with improving grammatical correctness, sentence fluency, and overall clarity of expression in certain parts of the manuscript. It did not contribute to the scientific ideation, method- ological development, or conclusions of the research. All final content was thoroughly reviewed, verified, and approved by the authors.
B STABILITY EVALUATION ALGORITHM WITH ADDITIONAL DETAILS
This section provides comprehensive algorithmic details for the multi-dimensional map stability evaluation framework introduced in Section 3. The complete pipeline consists of four main stages: temporal sampling, cross-frame instance matching, geometric alignment and resampling, and sta- bility metric computation. Each stage is implemented through carefully designed algorithms that ensure robust and reproducible evaluation of temporal stability in online HD mapping.
B.1 TEMPORAL SAMPLING
Algorithm implements the temporal sampling stage that constructs frame pairs for stability anal- ysis. The algorithm randomly selects subsequent frames within a predefined maximum tempo- ral interval M for each anchor frame, ensuring comprehensive coverage of temporal variations while maintaining computational efficiency. This approach generates a sampling set S of size M, providing the foundational inputs for subsequent stability analysis.
[S| L
Algorithm Temporal Sampling Algorithm
Require: Model output frame sequence {D1, Dz...., DL}. maximum temporal interval M
Ensure: Frame pair sampling set S
1: // Stage 1: Temporal Sampling
2: S x- 0 (Initialize sampling set)
3: for = 1 to T. Mf do
4: A: + RandomSample(1, M) {Random sampling within M] range)
5: S < S ((D,, De++)} (Add frame pair to sampling set)
6: end for
7: return S
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B.2 CROSS-FRAME INSTANCE MATCHING
Algorithm 2 implements the cross-frame instance matching stage that establishes correspondence between map elements across temporal frames.
Algorithm 2 Cross-Frame Instance Matching Algorithm
Require: Frame pair sampling set S
Ensure: Matched polyline pairs M
1: // Stage 2: Cross-Fis Frams- Instance Matching
2: M (Initialize matching result set}
3: for each (Dr, D2+*) S do
4: // Step 2.1: Frame-to-GT Matching
5: matches; <- Hungarian GT
6: matches:+ <- HungarrianMacching(D-ss GT(+R)
7: // Step 2.2: GT--based Association
8: < FindCommonGiTInstanees matchess, matches
9:
for each E E do
10: (ely t GerPolyline e)
poly (e) +- GelPolylinemaatchesss
12: M - M U M ((poly
end for
14:
end for
return M
The algorithm employs two-step strategy: first matching predictions to ground truth within each frame using the algorithm 3, then establishing temporal correspondence through ground truth-based association.
Algorithm Hungarian Matching Sub-algorithm
Require: Prediction frame D, ground truth frame GT, cost function C'
Ensure: Matching result matches
I: P t GetPolygons( D) {Get prediction polygons}
2: G <- GetPolygons (Get ground truth polygons}
3: n + IP]. +- G
4: // Build cost matrix
5: Cmatrix <- zeros(n (n x m))
6: for i 1 to n do
7: for j = to m do
8: Caannx [i, C(P(I, (Geometric and semantic similarity cost]
9: end for
10: end for
11: // Execute Hungarian algorithm
12: matches <- AlungarianAlgorihm((( (Cmatrix)
13: return matches
This indirect matching approach leverages the consistency of ground truth annotations to overcome the inherent inconsistencies in model predictions, yielding set of matched instance pairs for each frame pair.
B.3 GEOMETRIC ALIGNMENT AND RESAMPLING
Algorithm implements the geometric alignment and resampling stage that ensures spatially con- sistent comparison between matched polylines.
The algorithm transforms historical polylines into the current frame's coordinate system, applies algorithm 5 to ensure evaluation consistency, and performs uniform resampling along the r-axis. This process guarantees spatially aligned and comparable point sets for subsequent stability analy- sis, returning comprehensive set of matched and aligned polyline pairs, each annotated with the
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Algorithm 4 Geometric Alignment and Resampling Algorithm
Require: Matched polyline pairs M. perception range [Fmin; Pmax, Mminn Yerax resampling points N
Ensure: Matched aligned polyline pairs Maumple
1: // /Sage 3: Geometrie Alignment and Resampling
2: M <- 0 [Initialize sampled polyline pairs set]
3: for each (poly,,* (e), (oly,(e)) E M do
// Step 3.1: Coordinate Transformation
5:
poly.-r445 (e) t Tworks-224 - Ti-sworts (ely,(e)
6:
// Step 3.2: Perception Range Filtering
7: poly, poly,=r44e: (e) <- SnppTToPeeeetiinRaeeeeelllyy (to (e), [Tminn Tmax: Umins Umnx
8:
Step 3.3: Dynamic Axis Selection and Uniform Resampling
9:
DynamicAkissSelctton (p (e)) (Divide polyline into intervals with primary sampling axis]
{n, 1ー AAlocatSSamlinngPonnstt.... {Allocate N points proportionally intervals)
poly (e) 0, poly.mple(e
12:
for each interval I, E I with n, sampling points do if I is X-sampling interval then
13:
Generate ni uniform v--valuss in [r syas]]
15:
Compute corresponding g-coordinates via linear interpolation
16:
else
17:
Generate n, uniform y-values in Vann Vlnxl
18:
Compute corresponding z-coordinates via linear interpolation
19:
end if
20:
Add sampled points to poly244
sample (c) and poly?"pks (e)
21:
end for
22: M Miample
((poly:4e timlle
poly? smple (e), e)}
23:
end for
24:
return supple
corresponding map element identifier. The algorithm transforms historical polylines into the cur- rent frame's coordinate system, applies algorithm 5 to ensure evaluation consistency, and performs uniform resampling using a dynamic axis selection mechanism (detailed below). This process guar- antees spatially aligned and comparable point sets for subsequent stability analysis, returning a comprehensive set of matched and aligned polyline pairs, each annotated with the corresponding map element identifier.
Dynamic Axis Selection Mechanism. For map instance composed of point sequence (Ii, v1), (e), (:M)...,,,MMMMM, we do not perform resampling along fixed axis. Instead, we dynamically determine the primary sampling axis by analyzing its local geometric orientation. The specific procedure is as follows:
Calculation of Local Direction Vectors: For each consecutive segment 8] in the instance, formed by points Pý and Pj+1. its direction vector is computed as tīj = (dx dy,) (Iz+1 - -z 3/+1 - yj)-
2. Determination of the Primary Sampling Axis: We compute the absolute changes of the segment along each coordinate axis:
Ar, = ldz5l Ayj = Idy,
If Ay; > Azj, the segment is considered vertically oriented and categorized as an "X- sampling interval". Conversely, Az, > Ayj. it is considered horizontally oriented and categorized
"Y-sampling interval".
3. Determination of the Number Sampling Points per Interval: The number of sampling points in each interval is determined based on its length. First, the instance is divided into K intervals using the dynamic axis selection method described previously. For each interval
j. we calculate its length along the primary sampling axis:
For an X-sampling interval: L; = -.haxx I
For Y-sampling interval: L.j Vlaxx Vin
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Subsequently, sampling points allocated proportionally based on the relative length of each interval. The initial number of sampling points ny for each interval calculated as follows:
L
?i round
N
2t=1
where round(-) denotes rounding to the nearest integer.
4. Total Adjustment Mechanism: Since rounding may cause the total number of points Σ n4 + N, we introduce an adjustment mechanism:
If EEE n4 N: Points incrementally allocated intervals in descending order of their length ratio Σt, until the total number of points equals N.
If Ei= n4 N: Points are sequentially removed from intervals ascending order of their length ratio until the total number of points equals N.
5. Axial Resampling Execution:
Within an X-sampling interval, we uniformly generate n, sample points x; across the x-value range [ mmal and compute the corresponding y-coordinates via linear interpolation:
24 min + (i 1).
min
=1,,...., ..,,,..,
T1
polysumple ((x; (r:)))
Within Y-sampling interval, we uniformly generate n, sample points y/ across the y-value range Wiini Vmaxl. and compute the corresponding r-coordinates via linear interpolation:
= Vmni + (i 1). Fina Vinin
i i= 1,2,..., ?,
n2
pamplemple [((())) 34)
This mechanism ensures that, regardless of the map element's orientation, uniform sampling is al- ways performed along its locally dominant direction of extension, thereby establishing fair and ro- bust benchmark for point-to-point geometric comparison. Specifically designed for the piecewise- smooth polyline structures prevalent in online HD maps.
Algorithm Perception Range Filtering Sub-algorithm
Require: Transformed polyline poly (e), perception range [Tmin Emax: Mmins Vmaxx]
Ensure: Filtered polyline polynitered
1: polynitered t- /
2: for each point (z. 3) polly do
3: if TTmin S S Tmax AND Umin S y S Unax then
4: polynteerdd +- polysitered {(x, }))
5: end if'
6: end for
7: return poly fotered
B.4 STABILITY METRIC COMPUTATION
Algorithm 6 implements the core stability metric computation that quantifies temporal stability across three dimensions: presence, localization, and shape. The algorithm processes each matched polyline pair to compute individual stability scores, then aggregates these scores at the class and model levels. The presence stability evaluates detection consistency, the localization stability quan- tifies positional jitter, and the shape stability assesses geometric consistency through curvature com- parison.
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Algorithm 6 Multiddimensional Map Stability Evaluation Framework Stability Metric Computa-
tion
Require:
Matched and aligned polyline pairs M detection threshold т, weighting parameter w, scaling parameter
Ensure: Overall model stability score mAS
1: // Stage 4: Stability Metric Computation
2: C + GeLAlIClasses(M (MMppll {Get all classes)
3: each class C do
Touus f- GeednstanesOFFllssssMM" class)
5: stability class <
each (poly c+ (e), poly? 2mmple (c).e) E Telass
7:
// Compute Presence Stability
8:
if score:+> (e) 2 7 AND score, (e) OR scorer+e(e) < AND score,(e) <. 7 then
Presence(e)
else
11:
Presence(e) + 0.5 [Flickering case)
12:
end if
13:
// Compute Localization Stability
14:
avg deviation < x Σ (14+((xs)) - (x:))
15:
Loc(e) t- 3 devvaeeitioon
16:
// Compute Shape Stability
17:
Ke++l +
(e))
18:
< ComputeCurvatureppul (e))(())) (e))
19:
Shape(e) + 1 |
20:
// Compute Comprehensive Stability
21:
Stability(e) k- Presence(e) [w Loc(e) + (1 w) Shape(e)]
22:
stability clacs f- stability + Stability(e)
23:
end for
24: Stability class
sability
25: end for
26: // Compute Overall Model Stability
27: mAS [e] Eetsgge Stability class
28: return mAS
The specific implementation of the ComputeCurvature function mentioned in Algorithm 6 is detailed in Algorithm 7, which approximates polyline curvature by computing the average angles between consecutive segments, thereby providing a robust geometrie measurement method with translation and rotation invariance.
Algorithm/ Curvature Computation Sub-algorithm
Require: Resampled polyline poly sample {(x,, yi) i 1,2,..... N]
Ensure: Curvature
1: K +-
2: for
1 to N do
3: 6 f- (I - Fz. Ms+1 vj) (Compute vector (
4:
Ufi1 + (I,+2 Iz-1: Uj+2 4/+1) (Compute vector vyZ1}
5: Of cos-1 شلتي
[Compute angle)
6: K t K + 0
7: end for
8: K N-1
x
(Compute average curvature)
9: return
This survature-based approach enables effective comparison of geometric consistency across tem- poral frames while capturing subtle shape variations that may indicate instability the model's predictions.
Upon completion of all sub-metric evaluations, the final mean Average Stability (mAS) score is computed, providing holistic measure of the model's temporal stability across all evaluated classes.
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C FURTHER DETAILS ON THE EXPERIMENTAL SETUP
C.I SUPPLEMENTAL DETAILS ON THE EXPERIMENTAL SETUP
Prior to conducting experiments, it is necessary to configure certain hyperparameters. This section primarily elaborates on the detailed configurations of these hyperparameters adopted in our study, along with the rationale for these choices:
Maximum frame interval (M=2): The configuration of different frame intervals essen- tially represents distinct evaluation scenarios for stability assessment, each carrying unique implications. Therefore, in addition to the experiments with M=2 presented in the main text, as shown in chapter C.4.3, we have conducted supplementary experiments with M=3, M=5, and M=10 provide as comprehensive a stability evaluation possible for existing models.
Number of resampling points (N=100): The purpose of resampling is to adjust the distribu- tion of map points on two instance polylines to be identical, thereby facilitating the caleu- lation of stability metries. Thus, the value of N should not be set too small to avoid undue influence from individual outliers on the instances in subsequent computations. However, beyond a sufficient threshold, variations in N do not significantly affect stability evaluation outcomes. Conversely, excessively large values of N may substantially reduce computa- tional efficiency. Balancing resampling granularity and computational cost, ultimately set = 100. This value can be appropriately adjusted in different experimental settings.
Position Stability Scaling Factor (3=15.0): The specific implication of this scaling factor is that when the distance between two matched map points (i.e., points sampled at identical x- x-values on matched map instances) in adjacent frames equals 39 their positional stability is considered zero. Consequently, the value of 3 corresponds to the distance threshold representing extreme instability. Typically, we define such extreme cases using the shorter map radius (half the length of the map's 's shorter side). In prevailing map construction paradigms, the standard map range is generally defined as x E [-15, 15].y E [-30, 30). Therefore, in our experiments, 3 is set to 15.0.
C.2 DESIGN RATIONALE FOR STABILITY METRICS AND PARAMETER SELECTION
C.2.1 SHAPE STABILITY DESIGN MOTIVATION
Capturing Perceptual Shape Instability Rather Than Precise Differential Geometric Properties Our primary objective is to evaluate the temporal stability of map element shapes, rather than to compute their precise differential geometric curvature. In the context of online mapping, instability typically manifests as noticeable fluctuations or jitter in the polyline contours between consecutive frames. A typical example would smoothly curved road being temporarily predicted an unnaturally sharp corner in certain frames. This type of macro-level shape flickering represents exactly the kind instability that downstream planning modules are particularly sensitive to.
Design Motivation:
The Average Inter-Segment Angle Effectively Captures Macroscopic Instabilities: The
average inter-segment angle, defined as k(poly) Rー. Ej= 0j. essentially measures the cumulative rate of directional change along the entire curve. Any sudden and unstable sharp corner or jagged fluctuation will significantly increase the value of > Oj. and will therefore be effectively captured by our Shape Stability metric AAnl/T)
The sensitivity concern raised by the reviewer is sub- stantially mitigated through our fixed and reasonable sampling strategy. By employing fixed sampling point count (N=100), we ensure uniform and consistent sampling for all instances. Under such uniform and consistent sampling conditions, minor variations in segment angles caused by sampling artifacts tend to be smoothed out through the averaging process, while genuine shape instabilities emerge significant signals.
Robustness to Microscopic Noise:
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Critical Balance Between Practicality and Computational Efficiency Online HD mapping sys- tems place high demands on evaluation efficiency, particularly in large-scale benchmarking. It is essential to strike a balance between the discriminative power of metric and its computational cost.
Regarding the Fréchet distance, while it is an excellent metric for curve similarity, its computa- tional complexity is relatively high as it typically requires dynamic programming. This becomes bottleneck in benchmarking scenarios that require rapid evaluation of tens of thousands of instance pairs.
Concerning curvature signatures, these require denser sampling and more complex local computa- tions. They present similar challenges in computational efficiency and implementation complexity, while also introducing the need for additional parameter selection such as determining the appropri- scale for Gaussian smoothing.
In contrast, our method based on the average inter-segment angle offers distinct advantages. It demonstrates high computational efficiency since it involves only vector dot products and inverse trigonometric calculations, with a complexity of O(N). This makes it highly suitable for large-scale evaluation. Furthermore, it maintains clear physical interpretation as the output, represented by average angular change, is straightforward to understand and visualize, providing intuitive feedback for model diagnosis.
C.2.2 PRESENCE STABILITY DESIGN RATIONALE
We wish to emphasize that one of the core design objectives of our proposed presence metric is precisely to directly capture various types of prediction inconsistencies, including semantic flickering.
First, the "semantic flickering" mentioned would lead to a significant decrease in the presence metric, thereby reducing the mAS. When a model exhibits semantic flickering. For example, if a map instance recognized as "lane divider" at frame t but "road boundary" at frame + 1. Our presence metric is heavily penalized once such a mismatch occurs.
Furthermore, the presence metric also accurately captures another form of instability: even after accounting for range effects, a map instance is detected at frame t but missed at frame + 1, the presence metric similarly incurs strong penalty.
C.2.3 LOCALIZATION STABILITY PARAMETER SELECTION
Regarding the scaling coefficient in the localization stability formula: Loc(e) 1
NI Li=i ( y (a:)). The mAS metric necessarily decreases monotonically decreases. The selection of 3 = 15 our experimental setup was based on the following reasoning:
First, clarify the physical meaning of B. As shown in the formula, when the average distance between corresponding map points of two instances reaches 1 Σ ( yx(x;) = B, then Loc(e) = 0. Therefore, f3 represents the distance threshold at which our framework applies the max- imum penalty localization stability. Specifically, when the average distance between map points of the same instance across two frames reaches 3, our metric considers this instance "completely unstable" between these frames.
This threshold corresponds to the extreme case of "complete instability." We therefore define this threshold as the map's short-range radius, which in the models we evaluated corresponds to = 15. This value represents distance at which localization errors would be considered critically significant for autonomous driving applications.
Experiments with different values of B were conducted, and the results align with the theoretical reasoning, demonstrating that the mAS metric increases monotonically as the value of 3 increases.
Based on the theoretical analysis and experimental results presented above, argue that the value of 3 should not be determined through experimental selection alone, but should instead be chosen physically meaningful and appropriately justified value.
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C.2.4 COMPREHENSIVE STABILITY INDEX PARAMETER SELECTION
Regarding the balancing coefficient used in the comprehensive stability caleulation, it serves to balance the relative importance between Localization Stability and Shape Stability. Our selection of 0.7 was based on the following rationale: autonomous driving, the localization stability of map elements (such as the lateral position of lane lines or boundaries) directly impacts path planning, vehicle control, and safety. For instance, localization jitter may cause the vehicle to deviate abruptly from its lane, whereas shape variations (such as subtle changes in curvature) generally have lesser impact, unless severe distortion occurs. Therefore, we assign a higher weight (70%) to localization stability to reflect its greater importance in real-world driving scenarios.
C.3 EVALUATION UNDER ADVERSE WEATHER CONDITIONS
To ensure a fair and consistent evaluation, we did not introduce new dataset but instead followed the established practice from prior online HD mapping work, PivotNet, by curating subset of challenging scenarios from the nuScenes dataset. Specifically, we selected scenes under overcast, rainy, and nighttime conditions, resulting in a total of 78 scenes for evaluation.
Table 7: Evaluation Results Under Adverse Weather Conditions.
Modal
Temp
Backbone
BEV
Epoch
m m.ASpre
m 1 mSA9toc
m.A shape mASghhae
m. S.S
Only
RI8
0.5773
0.7035
0.8556
0.4457
Only C
X
R18
GKT
110
0.5668
0.6958
0.8883
0.4442
Only C
R50
GKT
0,5683
0.6062
0.8538
0.4149
Only C
X
GKT
110
0,5762
0,7033
0.874
0.446
Only C
R50
BEVFormer
24
0,5683
0.7195
0.8543
0,4416
Only
BEVPool
0.5639
0.699
0.4319
Only C
R50
GKT
0.5624
0.7224
0.872
0,4419
Only C
R50
BEVFormer
0.5663
0.7324
0.8673
0.4475
C+L
x
R50
GKT
0,5662
0.7173
0.858
0.4391
Based on the results in the table above, it can be observed that all stability metrics show significant decrease under adverse weather conditions. Moreover, while the stability metrics decrease markedly, they also tend converge across different models. This occurs because under adverse weather conditions, model instability manifests more prominently in terms of instance presence stability, that is, whether instance is detected or persists As result, the presence stability metric drops significantly and becomes similar across models. This finding aligns with the expected performance drop of existing models under challenging weather conditions.
time, rather than in positional or shape variations.
In metric design, since the location and shape stability metrics only reflect the stability of instances that are consistently present, we incorporated the presence stability as a weighting coeffi- cient in the overall metric. This ensures comprehensive and accurate representation of stability in terms of presence location, and shape. Consequently, when the presence stability is low, variations in location and shape stability have diminished impact on the overall metric. This design leads to the overall mAS scores exhibiting a distribution similar to that of the presence metric, showing lower values that are closely clustered across different models.
C.4 ABLATION STUDIES ON HYPERPARAMETERS
C.4.1 ABLATION STUDY ON THE LOCALIZATION STABILITY SCALING FACTOR 3
Experiments with different values of 3 were conducted, and the results align with the theoretical reasoning, demonstrating that the mAS metric increases monotonically as the value of 3 increases.
C.4.2 ABLATION STUDY ON THE RESAMPLING DENSITY
Concerning the resampling density (N), the design principle is to ensure it is significantly higher than the number of points in the vast majority of original map instances. In current mainstream
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Table 8: Ablation Study on the Localization Stability Scaling Factor 3.
Medel
Shrap
MapTRv]
0.4097
0.4097
0.8776
0.6788
0.886
0.697
noTemp
R18-GKT-24
0.8776
0.3499
0.3552
0.7285
MapTRy]
0.3601
0.856
0.011
0.551
0.9044
06637
neTemp
0.912
0.6544
0.903
0.7158
R50 GKT224
0,7052
0.9044
0.7393
MapTity]
Temp
10
0.8961
0.5017
0.996
0.6355
0.8861
0.5974
0.8988
0.6877
RNO-OKT-24
0.836]
0.6325
0.886
0:4885
MapTikv]
Temp
10
0,9025
0.6411
0.913
0.7091
0.6947
0.121
R50- 240-BevFammer-24
20
0,9035
0.7931
0.913
n.7751
MapTRv2
0.5231
09145
0.9138
0.6251
00148
0.714
moTanp
R50 BevPool 24
20
0.9138
0.7255
0.9155
1.7579
MGMMp
0.3195
03256
0.9213
0.6645
0.9266
0.7428
noTany
R50-Fumme--44
20
0,9013
0.800
0.9266
08009
0.9184
0.5865
0.9257
0.7088
MR
0.9188
0.6937
0.9257
0.253
noTemp
0.7556
Former
20
0.9184
0.7951
0.9257
0.8021
GBMap
0,9227
0.4976
0.929
D66677
0.616
0.999
0.7988
noloomp
0.9227
0.6975
0.2588
0.7546
20
0.9227
0.7635
0.929
0.7831
paradigms, this instance point count is typically fixed at 20. The core objective is to avoid compu- tational instability caused by insufficient number of random samples, thereby ensuring the consistency and repeatability of the stability metric. If N is too small, random variations in indi- vidual sample points can disproportionately influence the overall stability score. At the same time, we aim to prevent computational inefficiency from an excessively large N. Our experiments con- finm that the map stability metric shows no significant variation once the resampling density reaches N > 100. Therefore, balancing sampling density against computational cost, we selected N 100 to achieve the best overall performance.
C.4.3 ABLATION STUDY ON THE TEMPORAL SAMPLING INTERVAL
Temporal sampling serves as the initial step in the stability evaluation benchmark. In the default configuration, the time interval M is set to assess the granularity of map changes, as delineated in Table 1. To provide a more comprehensive illustration of our evaluation framework's ' performance different temporal sampling intervals, we conducted further experiments with M values of 3, 5, and 10. The results are presented in Tables 10, 11, and 12 respectively. All other experimental settings remain consistent with Table
The ablation studies reveal several important patterns regarding temporal stability assessment. First, as the temporal interval M increases from 2, 3, 5, 10, most models exhibit progressive decline in stability scores across all metrics, particularly in Localization Stability. This pattern is consistent across different architectural paradigms and demonstrates the challenge of maintaining consistency over longer time horizons. For instance, MapTR (Liao et al., 2022) with camera-only input shows a reduction in mAS from 71.6 (M=2) to 61.9 (M=10), primarily driven by decreasing Localization Stability. Similar trends observed for other non-temporal models, with BeMapNet (Qiao et al., 2023) maintaining superior Presence Stability but experiencing significant Localization Stability degradation from 65.8 (M=2) to 41.6 (M=10).
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Table 9: Ablation Study the Resampling Density N.
Model
Presence
mAS
MapTRvi
50
0,5776
0.7499
05852
0,7283
noTemp
0.2317
RIS-IKT-34
150
0.5783
0.753
08862
0.73
0.8789
0.7105
0.9107
0.6334
0.908
0.7145
MapTRv]
noTanp
100
0.9111
0.647
0.9022
0.7112
ROAGKE-
0.9107
0.907
0.7119
0.111
0.6488
0.2113
0,6644
MapiRvi
50
0.8961
0.9914
0.8928
0.667
Temp
R50-GKT-34
150
0.8965
0.5702
0.991
0,6625
200
0,8967
0.509
0.8959
0.6014
30
0.88
0.7169
0.9199
0.7284
MapTRv]
Teup
100
0.9012
0.7326
0.9142
0,7481
0.002
0.7502
200
0.903
0.7344
0.146
0,7507
0.9156
0.7342
Mapiiiv2
50
0:0147
0.686
0.905
0.7196
noTemp
0.9149
0.7414
190
0:9147
0.6868
09152
0.7426
200
0.9145
0.6848
0.9187
0.2411
0:0188
0.7437
0.349
0.7786
noTemp
100
0:9217
0.7492
0.925
0.7802
R50-Fommer24
0.9212
0.7789
200
0.922
0.7476
0.253
0.7801
0.9154
0.7839
MapQR
50
0.9182
0.7556
0.9155
0.7781
noTemp
9.7826
R50=Fomer224
150
0:9173
0.7532
0.926
0.7818
0.913
0.999
0.9144
0.6985
0.9277
0,752
0.7566
no'emm
100
0.922
0.899
0.931
0.7099
0.9233
0.9315
0,5232
0.6937
0.9309
0.7504
Table 10: Ablation Study on the Temporal Sampling Interval (M=3)
Veane
Temp
Epoch
Shapet
MapTR
ICERR23
GKT
24
44.1
89.3
59.9
90.6
67.6
MapTR
X
ReMapNer
CVPR'23
X
IPM-PE
PivooNer
10CV123
X
C
Pensformer
30
57.1
100.0
45.2
98.3
71.7
MRp2Rv2
UCC"24
X
BEVPoul
24
61.4
90.6
39.5
91.8
69.0
FCCV"94
X
REVRomms-1
MGMap
CVPR'24
X
BEVRemer-]
24
57.9
91.8
68.8
92.3
74,4
MaрQR
BCCV"34
X
REVRomees3
24
66.4
89.4
66.9
91.1
73.4
MapTR
ICL.2'3
GRT
24
51.3
86.8
55.2
89.1
62.9
Mtpea
WACV"24
BEViommes-]
30
63.3
96.9
96.6
95.8
93.2
Map Trackr
HKMaрNet
BCCV224
BEVioreer-1
24
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Table 11: Ablation Study on the Temporal Sampling Interval (M=5)
Method
Venue
Temp
Modal
BEY Encoder
Epoch
mAP
Presence?
Loct
Shape?
mAS1
MapTR
ICL8'23
X
64.7
6888
MиртR
ICLK'23
X
C&L
GKT
24
62.8
89.4
69.2
91.0
72.1
BeMapNet
23
K
IPM-PE
61.4
100.0
50.0
97.5
73.7
PivoNNet
X
PorsFormer
1000
700
MapTR-2
14CV24
X
BEVPool
61.4
89.0
5288
90.8
64.8
GeMap
BCCY'24
C
BEVFormer-
51.3
88.8
61.7
91.6
69.3
MGMap
CVPR'24
X
BEYFormer-
67.1
MapQR
ECCV'24
X
BEVFurmer-
66.4
88.1
59.9
89.6
67.4
MapTR
[C
GKT
24
51.3
85.0
55.8
90.3
61.8
HRMMapNet
ECCV'24
BEVormer-
67.2
89.6
70.0
92.1
738
Table 12: Ablation Study on the Temporal Sampling Interval (M=10)
Method
Venue
Temp
Modal
Eneader
Presencef
Loct
Shape
mAS?
MipTR
X
MapTR
X
C&L
66.5
BeMapNet
C3PR/23
X
C
IPM-PE
61.4
1000
41.6
97.5
69.5
PivotNet
ICCV/23
C
PersFormer
57.1
1000
28.3
98.9
63.6
MapTR>2
X
BEVPool
GaMap
BDCY224
X
BEVFormer-
51.3
85.4
61.2
92.3
656
MGMpp
CVPR'24
C
BEVFormer-
24
$7.9
89.7
57.5
92.4
68.1
MayQR
ECCY"224
X
BEVFormer.3
590
MapTR
ICLR'13
GKT
51.3
41.0
92.8
62.3
MRMMppNe
ECCV'24
BEVFurmer
24
67.2
83.8
55.8
92.8
62.4
The comparative analysis reveals distinctive robustness characteristies across representation paradigms. Models with inherent temporal modeling capabilities, such as StreamMapNet (Yuan al., 2024) and MapTracker (Chen et al., 2024), demonstrate remarkable resilience increas- ing temporal intervals. StreamMapNet maintains exceptional stability with mAS of 93.2 at M=3, significantly outperforming non-temporal counterparts. This performance advantage particularly pronounced in Localization Stability, where temporal models consistently exceed 96.0 even at larger intervals, compared to the substantial degradation observed in static models.
The studies also reveal paradigm-specifie specifie sensitivity patterns. Geometric- catneetrcc-rioor--e prior-based models like BeMapNet (Qiao et al., 2023) and PivotNet (Ding et al., 2023) maintain perfect Presence Stabil- ity across all intervals but exhibit considerable vulnerability in Localization Stability. In contrast, learning-based bbased BEV representation models show more balanced degradation across stability dimen- sions. The performance variations across intervals provide additional evidence that accuracy (mAP) and stability (mAS) represent independent evaluation dimensions, as models with comparable mAP scores exhibit dramatically different stability characteristics under extended temporal intervals.
These findings underscore the importance of evaluating temporal stability across multiple time scales, as different representation paradigms exhibit distinct degradation patterns. The comprehen- sive interval analysis reinforces our central thesis that temporal stability constitutes fundamental performance dimension that requires explicit consideration in online HD mapping system design and evaluation.
D COMPREHENSIVE EVALUATION RESULTS WITH FURTHER DETAILS
We present detailed analysis of the performance of various online HD mapping models in terms of both accuracy (mAP) and temporal stability (mAS), based on the comprehensive results summarized in Tables 13 Table 22. Our analysis highlights how different architectural choices, including back- bone networks, temporal modeling, sensor modalities, and BEV encoders, affect these two critical performance dimensions.
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Table 13: Evaluation Results of MapTR
Backduue
Temp
Modal
BEY Enoader
Epuch
mAP1
Presence
Loct
Shupe?
mAS1
Parameters
R18
X
GKT
75.0
88.5
72.8
15.4M
KIS
X
C
GKT
110
45.5
86.0
71.7
94.8
718
15.4M
R,50
X
C
GKT
24
44.1
91.2
65.4
90.6
71.6
36.2M
R,50
X
GKT
89
63.2
91.0
68.2
36.2M
R50
X
C
BEVFormer1 I
24
41.6
89.6
69.7
90.6
71.3
36.3M
R50
X
REVPool
24
50.1
89.3
69.8
83.5
71.9
32.3M
R50
59.7
89.3
36.2M
R50
C
BEVFormer
24
53.3
90.
69.5
91.2
73.0
36.3M
R50 & Sec.
X
C L
GKT
24
62.8
90.1
75.2
90.8
74.9
40.1M
D.I
COMPREHENSIVE ANALYSIS TOWARDS MAPTR
As presented in Table 13, the extensive variants of MapTR (Liao et al., 2022) provide controlled setting to dissect how distinct representation paradigms influence model behavior. By altering key components while holding the core architecture constant, we can isolate their effects on both accu- racy (mAP) and temporal stability (mAS).
Temporal Fusion. The effect of incorporating temporal fusion is not uniform but is mediated by the underlying BEV representation. When applied to the GKT-based (Chen et al., 2022) represen- tation, temporal fusion disrupts its strength. Presence Stability drops from 91.2 to 88.6, and Localization Stability plummets from 65.4 to 59.7, leading to a significant decrease in mAS (71.6 to 66.6). This indicates that the representation formed by GKT is not easily aligned or integrated across time; the temporal module may introduce noise rather than useful context. Based on our experimen- tal data and model architecture analysis, the core reason lies in the fundamental incompatibility between the static spatial features generated by the GKT encoder and the dynamic represen- tations required for temporal fusion.
Specifically, the decline in stability stems from the following three interrelated underlying mecha- nisms:
1. The feature space solidifies the static scene representation, making dynamic alignment challenging. The core strength of GKT lies in its efficient construction of single-frame BEV features through geometric priors. However, this makes its features highly dependent on the instantaneous coordinate system at the time of construction. When historical frame features are introduced, due the cumulative error of ego-motion, these statically gen- erated features from different coordinate systems cannot be precisely aligned in the BEV space. This inherent misalignment at the feature level directly leads representa- tional confusion after fusion, thereby causing detection jitter (drop in Presence stability) and positional jumps (drop Localization stability) of map elements.
2. Temporal aggregation of localized fine-grained features amplifies high-frequency jit- ter. GKT excels at extracting local, high-resolution geometric details. However, when a simple temporal fusion module (e.g.. basic convolution attention mechanisms) attempts to aggregate these highly localized features, it not only fails to smooth out their inherent inter-frame variations but even amplifies such high-frequency jitter. This manifests as instability in the position and shape of map elements after decoding.
3. The training objective optimized for single-frame performance conflicts with that of the temporal module. As non-temporal model, MapTR's GKT encoder and decoder are strictly optimized to achieve optimal accuracy on individual frames. When a fusion mod- ule without temporal consistency constraints is introduced, the entire network falls into an optimization dilemma: the single-frame supervision signal forces the features to main- tain overfitting to instantaneous observations, while the newly added temporal parameters, lacking explicit guidance, disrupt the originally well-optimized feature distribution for single frames. This ultimately leads performance degradation the temporal dimen- sion.
The validity of this analysis is confirmed unequivocally by our controlled experiments on MapTR- BEVFormer. As shown in Table 4 of the paper, BorFrrr inherently employs deformable atten-
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tion for temporal modeling, meaning its features designed from the outset for cross-frame alignment and fusion. Consequently, when a temporal fusion module is incorporated. its features can be integrated smoothly and consistently, leading to simultaneous improvements in both accu- racy and stability (mAP increased from 41.6 16 to 53.3, while mAS rose from 71.3 to 73.0). This demonstrates that temporal fusion acts complementary enhancement when applied repre- sentations that are already designed for spatiotemporal modeling. This demonstrates that temporal fusion most effective when the base representation is inherently compatible with processing se- quential data. The effectiveness of temporal fusion is not standalone property but is contingent on the representational capacity of the BEV encoder. It amplifies the capabilities of temporally-wwe aware representation (BEVFormer) but can degrade the performance of primarily ppacual one (GKT). Therefore, temporal fusion serves as force multiplier for representations already predis- posed to temporal modeling, but can be detrimental to those that are not.
2D Backbone. Changing the 2D backbone from ResNet-18 18 to ResNet--50 shifts the model's 's repre- sentational focus towards more complex visual patterns. This shift has clear effect: it consistently improves mAP (32.4 to 14.1) by leveraging higher-capacity feature extraction. However, this comes with a redistribution of stability properties: Presence Stability improves (87.8 to 91.2), but Local- ization Stability worsens significantly (75.0 to 65.4). The deeper network appears to learn rep- resentation that more sensitive to semantic content but potentially more susceptible to per-frame variations in texture lighting, which harm geometric consistency. The backbone network in- fluences the type of features that form the basis of the map representation. More powerful backbones enhance semantic discrimination but can introduce high-frrequ high-frequency noise that undermines geometric stability, suggesting that representations favoring stability may require features that are invariant to superficial appearance changes.
Table 14: Evaluation Results of BeMapNet
Backsuue
Temp
Modal
BEV Encoder
Epuch
mAP
Presence
Loc!
Shape1
mAS1
Parameters
Edbo
x
IP
30
60.7
100.0
67.9
97.9
82.9
55.4M
R35
IPM-PE
30
61.4
100.0
65.8
97.9
$1.9
73 SM
SwinT
IPM.PE
64.1
100.0
98.0
R550
X
IPM-PE
RM
SwinT
IPM-PE
110
68.3
100.0
64.0
98.2
81.1
79.6M
Table 15: Evaluation Results PivotNet
Backbone
Temp
Modal
BEV Faceder
Epuch
mAP
Presence
Lact ?
Shape?
mASt
Parsmeters]]
Penshormer
57.8
R.50
x
PendFormer
30
57.1
100.0
71.4
97.2
81.3
41 2M
Swin-T
PensFormer
30
61.6
100.0
71.6
97.2
84.4
44 SM
Swin-T
PersPormer
110
D.2 IN-DEPTH ANALYSIS BEMAPNET AND PIVOTNET
As shown in Table 14 and Table 15, both BeMapNet(Qiao et al., 2023) and PivotNet(Ding et al., 2023) demonstrate stable mAP and mAS performance, with BeMapNet achieving mAp scores of 60.7 68.3 and mAS values of 80.2 to 82.9, while PivotNet demonstrates mAP values of 57.1 to 66.4 and mAS scores of 84.3 to 84.8. indicats that these two models are insensitive to backbone network selection and training epoch configurations. The difference lies in the fact that PivotNet achieves its highest mAS when using Swin-T(Liu et al., 2021) the backbone, while BeMapNet attains its peak mAS value with an Efficien(Net-B((Tal & Le, 2019) backbone.
It should be specifically noted that both BeMapeet and PivotNet adopt "dynamic vectorized quence" " representation for map encoding, which explains their consistently perfect presence metrics (100%). However, this representation format severely limits the localization stability of map in- stances, resulting in significantly lower performance compared to models like GeMap(Zhang et al., 2024c), MGMap(Liu al., 2024a), and MapQR(Liu et al., 2024b).
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Table 16: Evaluation Results of MapTRv2
Backduuee
Temp
Modal
BEV Encoder
Epuch
mAP1
Presence
Lect
Shape
mAS1
Parameters]
R118
BRVPaol
24
27.9M
R30
HKVPool
24
61.4
91.5
68.6
91.0
74.0 40.6M
D.3 EVALUATION AND ANALYSIS OF MAPTRV2
Map2Rv2 (Liao al., 2025b) improves upon MapTR (Liao et al., 2022) with higher baseline mAP (57.2-61 1-61.44 61) and mAS (74.0-75.6) 75.6). Interestingly, the R R18 backbone achieves higher mAS (75.6) than R50 (74.0), despite lower mAP (57.2 vs. 61.4), as illustrated in Table 13 and Table 16, reinforcing the independence of accuracy and stability.
Table 17: Evaluation Results of GeMap
Backbone
Temp
Modal
BEV Encoder
Epoch
mAP1
Presence
Loct
Shapet
mASt
Parameters.
R30
X
c
BEVFomer-1
24
51.3
92.3
69.7
92.6
75.5
44.1M
R50
X
c
BEVFumer-
110
62.7
67.5
94.5
74.7
46.1M
X
BEVFumerr
V2-99
X
BEVFumer-1
72.0
89.2
71.5
92.6
74.2
92.6M
V2-9IDDDD
C
BEVFomeer
110
76.0
93.4
66.9
93.7
75.1
92.6M
second
X
BEYFomeer
D.4 DISCUSSION ON GEMAP
As illustrated in Table 17, GeMap (Zhang et al., 2024c) presents a particularly instructive case for examining the complex relationship between accuracy and stability. The model demonstrates strong capacity for high accuracy, with its mAP score scaling significantly from 51.3 to top score 76.0 when employing a powerful V2-99 (Lee al., 2019) backbone and extended training. However, this pursuit of accuracy often introduces instability, evidenced by its mAS scores, which range from a modcrate 71.8 to a more competitive 78.1.
A critical observation is the divergent effect of LiDAR fusion. While integrating LiDAR data with a ResNet-50 (He et al., 2016) backbone yields a predictable improvement mAP (+6. 1,, from 62.7 to 66.5). conversely leads decrease in mAS (-3.9%, from 74.7 to 71.8). This result chal- lenges the conventional wisdom that more sensor data invariably leads to more robust perception. suggests that GeMap's architecture, while effectively leveraging LiDAR for geometric precision in a single frame, may lack the necessary mechanisms to harmonize the potentially noisy or asyn- chronous multi-modal signals across time, leading increased jitter flickering.
Furthermore, the model exhibits high sensitivity to backbone design. The Swin-T (Liu et al., 2021) backbone strikes the most favorable balance, achieving the highest mAS (78.1) alongside high mAP (72.0). In contrast, the larger V2-99 (Lee et al., 2019) backbone, despite achieving the peak mAP (76.0), produces lower mAS (75.1). The degradation in Localization Stability (from 74.9 with Swin-T (Liu et al., 2021) to 66.9 with V2-99 (Lee et al., 2019)) is especially notable, implying that the increased representational power of the larger backbone may overfit single frame features at the expense of temporal coherence. This pattern underscores that for stability, simply scaling up model capacity not sufficient strategy and may even be counterproductive without explicit temporal regularization.
Table 18: Evaluation Results of MGMap
Bacisbone
Temp
Modal
BEV Encoder
Epoch
mAP"
Presence
Loc?
Shapet
mAS?
Parumeters.
R30
BEViormas:1
24
38.0
92.2
75.0
92.3
78.0
55.9M
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D.5 ANALYSIS FOR MGMAP
MGMap (Liu et al., 2024a) achieves balanced profile (mAP: 58.0, mAS: 78.0) with a ResNet-50 backbone (He et al., 2016) and BEVFormer (Li et al., 2024b) encoder, presented in Table 18. Its strong Localization and Shape Stability scores (75.0 and 92.3, respectively) suggest robustness against geometric jitter.
Table 19: Evaluation Results of MapQR
Backdooee
Temp
Modal
BEV Encoder
Epoch
mAP?
Presence
Lect
Shapet
mAST
Parameters.
x
BFVRemas-3
24
88.2
74.1
1126M
R.50
BEVFomers3
24
66.4
91.8
75.6
91.6
77.8
125.4M
850
BEVFomes-3
110
72.6
92.4
75,9
96.4
80.3
125
D.6 STUDY OF MAPQR
As shown in Table 19, MapQR (Liu et al., 2024b) shows clear positive scaling trend: larger backbones and longer training improve both mAP (62.3 to 72.6) and mAS (74.1 to 80.3). This indicates that the model's architecture supports stable learning under increased capacity.
Table 20: Evaluation Results of Sapeet
Backbone
Temp
Modal
BEV Encoder
Epuch
mAP1
Presence
Lact
Shape
mAS1
Parameters.
R.50
BEVFormer
30
51.7
87.0
97.8
95.1
83.8
56.0
B18
REV
30
27.8
87.1
98.4
94.6
85.0
42.5M
R50
BEVFomee-1
63.4
96.5
97.7
91.9
56
BEVRomaa-1
97.0
56
D.7 TOWARDS COMPREHENSIVE ANALYSIS OF STREAMMAPNET
StreamMapNet (Yuan et al., 2024) stands out as the paradigm for temporally stable online mapping, achieving the highest mAS scores in our benchmark, ranging from 83.8 to exceptional 94.4. This performance is primarily driven by its native temporal architecture, which explicitly designed to model consistency across frames.
The most striking feature of StreamMapNet its near-perfect Localization Stability (97.7-98.5), the highest among all models, as shown in Table 1 and Table 20. This indicates an exceptional ability to suppress the positional jitter of map elements over time, a critical factor for downstream planning tasks. The analysis clearly shows that temporal fusion is not merely an optional add-on but
the core determinant of its performance. Enabling temporal modeling (comparing the R50, / /vs. R50, X configurations) results in a dramatic improvement both mAP (+22.6%, from 51.7 to 63.4) and mAS (+9.7%, from 83.8 to 91.9). This dual improvement confirms that effectively leveraging historical context can simultaneously enhance per-frame accuracy and inter-frame consistency.
Table 21: Evaluation Results of Map
Temp
Epoch
mAP
Loc"
Shapet
Parameters
R18
REVRomas22
72
62.8
95.3
97.3
85.9
87.4
60.7M
R50
RFVRemee-3
72
68.3
94.5
97.9
R18
BEVRomeas2
69.3
94.8
68.7M
R18
BEViormer
72
71.9
92.9
98.5
94.8
39.9
60.7M
R50
3
48
73.0
91.7
98.5
91.7
P4
D.8 A COMPREHENSIVE ANALYSIS OF MAPTRACKER
Map Trackecker (Chen et al., 2024) represents another strong temporal model that successfully balances state-of ohettr-rrtttttt accuracy with high stability, which is shown in Table 1 and Table 21. It achieves the
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highest overall mAP (76.0) in our benchmark while maintaining mAS scores above 87.4, peaking 91.7.
Similar to StreamMapNet (Yuan al., 2024). MapTracker's ' integration of temporal fusion ("Temp- (") consistently boosts mAP (e.g., from 62.8 69.3 for R18) while preserving high mAS. This re- inforces the conclusion that architectures designed with temporal reasoning in mind from the ground up are essential for perforeeformance online mapping. The model also exhibits very strong Local- ization and Shape Stability, often exceeding 98.0 and 94.0, respectively, which is characteristic of models that effectively aggregate information over time.
However, MapTracker reveals nuanced trade-off related to training duration. For both the R18 and R50 backbones, extending training from 48 to 72 epochs leads to a further increase mAP but slight decrease in mAS (e.g., R50: mAP 73.0 to 76.0, mAS 91.7 to 90.4). This pattern, which we term optimization sensitivity, suggests that as the model continues to minimize a primarily eecuracy-orennte loss function, it may gradually overfit to single-frame frame details, thereby sacrificing some temporal smoothness. This highlights key area for future research: the development of loss functions regularization techniques that explicitly penalize temporal instability during training to prevent this erosion.
Table 22: Evaluation Results HRMapNet
Backhone
Temp
Initial Map
Modal
BEV Encoder
Epach
mAPt
Presence
Lect
Shape?
maSt
Parameterss
R55
X
RFVFirment
24
67.3
92.3
30.5
91.5
75.9
47.3M
R550
Testing Map
C
BEVFuemes
24
13.0
94.9
71.4
93.0
784
47.3M
R50
Training Map
C
REVFummes
24
83.6
75.9
98.2
76.7
R50
X
C
BBV
110
73.5
90.5
74.1
92.7
75.9
47.3
D.9 DETAILED INVESTIGATION OF HRMAPNET
As presented in Table 22, HRMapNet (Zhang et al., 2024b) incorporates a distinct representation paradigm by integrating static map priors into a temporal mapping framework. The performance variations across its configurations provide critical insights into the interaction between dynamic sensory input and static prior knowledge.
The most pronounced effect is observed single frame accuracy. Utilizing a map prior during training (e.g., linear priors (Li et al., 2024a)) yields a substantial improvement in mAP, elevating the score from 67.2 to 83.6. This result indicates that the model's 's representation effectively internalizes the structural constraints provided by the high quality offline map, leading to superior geometric precision in individual frames.
In contrast, the impact of map priors on temporal stability is more complex and less direct. The con- figuration employing prior only during testing achieves the highest mAS of 78.4 and the highest Presence Stability of 94.9. This suggests that an externally provided prior can serve as a stabiliz- ing reference during inference, enhancing detection consistency without being fully baked into the model parameters.
However, when the model is trained with the map prior, a different pattern emerges. While this con- figuration achieves the highest mAP, its mAS of 76.7 is lower than the testing prior variant. Notably, its Presence Stability decreases to 89.9. This indicates that deep integration of the static prior during training may lead to representation that is overly reliant on persistent features, potentially at the expense of robustness to real world variations encountered in a temporal sequence. The model may become less adept at handling cases where the prior is imperfect or where dynamic scenes deviate from the stored map.
Furthermore, extending training to 110 epochs without any initial map prior improves mAP to 73.5 but leaves mAS unchanged at 75.9. This stability saturation effect underscores that prolonged train- ing on single frame accuracy objective has diminishing returns for temporal consistency. The gain in stability achieved through the intelligent use of a testing time prior surpasses that achieved by simply training the baseline model longer.
In summary, HRMapNet demonstrates that static map priors constitute a powerful representation for enhancing perceptual accuracy. Their utility for improving temporal stability, however, is contingent
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on the method of integration. A prior used dynamic guidance signal at inference bolster consistency, whereas deeply embedding the prior into the model through training may introduce a trade off, favoring accuracy over stability. This highlights that the effective fusion of dynamic and static representations remains a key challenge for robust online mapping.
D.10 SUMMARY OF COMPREHENSIVE EVALUATION
The comprehensive evaluation of representative online HD mapping models demonstrates that different representation paradigms induce distinct performance characteristics along the accuracy- stability spectrum. Our analysis reveals that these two performance dimensions are independently influenced by specific architectural choices and their underlying representational biases.
Models incorporating strong geometric priors, such BeMapNet (Qiao al., 2023) and PivotNet (Ding et al., 2023), achieve exceptional Presence Stability due to their structure-aware representa- tions, In contrast, architectures based on learned view transformations like BEVFormer (Li al., 2024b) exhibit superior Localization Stability, benefiting from their spatially coherent bird's eye view representations.
Temporal modeling effectiveness shows fundamental dependence on representational compatibility. Architectures with native temporal designs demonstrate that explicit sequence modeling produces the highest stability scores. However, the integration of temporal modules requires careful alignment with the base representation, as evidenced by the varied outcomes when adding temporal compo- nents to different BEV encoders.
Multi-modal integration exhibits model-dependent effects on stability. While sensor fusion generally enhances accuracy, its impact on temporal consistency varies across architectures, indicating that effective multi-modal representation
reppersenttt requires specialized design beyond simple feature combination.
The relationship between model capacity and performance reveals consistent patterns. Larger back- bones produce substantial accuracy gains but yield inconsistent effects on stability, suggesting that representational capacity alone cannot address temporal consistency requirements.
The comparison between static priors and dynamic modeling highlights their complementary roles. While static priors significantly boost accuracy, dynamic temporal modeling proves essential for achieving temporal stability, indicating that these two approaches address distinct aspects of the mapping problem, and future priors can further enhance ahead-aware online mapping (Li et al., 2025b).
These findings collectively suggest that accuracy and stability are governed by different aspects of representation design. This understanding points to the need for future architectures that can simultaneously support high-fidelity spatial representation and robust temporal consistency through integrated design principles.
D.11 ON THE SYNERGISTIC USE MAS AND MAP
We would like to clarify that the primary intention behind designing mAS was never to provide standalone metric for evaluating map construction performance in isolation. Instead, it aims to reveal the "temporal stability" dimension that conventional mAP overlooks.
Crucially, any model employing strategies like copy pasting or blind temporal smoothing would in- evitably suffer a significant drop precision metrics like mAP, even if it achieved high localization stability score. This is because both copy pasting and heavily smoothing predictions across frames introduce substantial deviations from the ground truth for the affected instances.
Therefore, when mAS is examined in conjunction with mAP, such pscudo stable models are ily exposed. They exhibit an anomalous profile of high mAS but very low mAP. Identifying and cautioning against this exact type of behavior is key objective of our evaluation framework.
Thus, the value of mAS lies in its synergistic use with inference accuracy metrics like mAP. Together, they define pursue models that achieve both high accuracy and high stability, rather than over optimizing for either metric alone.
comprehensive trustworthiness evaluation quadrant, guiding the community to
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D.12 COMPARISON WITH BASELINE METHODS
Comparison with Chamfer Distance First, regarding "inter-frrat framme Chamfer Distance," our pro- posed mAS is a comprehensive evaluation metric, whereas traditional geometric measures like Chamfer Distance typically capture only a single dimension of stability. As explained in Section 3.4 of the paper, mAS consists of three dimensions: Presence Stability, Localization Stability, and Shape Stability. Chamfer Distance essentially measures the overall positional deviation between two point sets, roughly corresponding to the "Localization Stability" in our framework. However, it completely fails to detect "presence flickering" the critical instability where map elements ap- pear and disappear intermittently, and cannot effectively quantify temporal changes in geometric shape, such as curvature. A model may exhibit low Chamfer Distance (i.e., minimal positional jitter), but if its predicted lane boundaries frequently appear or vanish (poor presence stability), it remains hazardous and unreliable for downstream planning tasks. mAS is specifically designed to comprchensively diagnose these different types of instabilities.
Comparison with Temporal Consistency Loss Second, regarding "temporal consistency loss," mAS and "temporal consistency loss" serve different purposes and are complementary. The "temporal consistency loss" you mentioned is an optimization objective used during model train- ing, aimed at penalizing temporal jitter in model outputs through regularization terms. In contrast, mÄS is an offline evaluation metric for assessing model performance. Their roles and appli- cations are fundamentally distinct. An optimized loss function does not automatically constitute a comprehensive evaluation system. In fact, our mAS metric can provide precise evaluation guid- ance and feedback for designing and optimizing such temporal loss functions. For example, by analyzing the three sub-metrics of mAS, we can determine whether a temporal loss function effectively improves localization stability or inadvertently sacrifices presence stability.
Advantages of mAS Design Furthermore, the design of mAS is more aligned with the safety requirements of autonomous driving. The stability issues in online HD map construction manifest their risks in multiple aspects. As demonstrated in Figures 2, 6, and 7 of the paper, as well as the downstream task impact analysis in Appendix E.1, the disruption caused by presence flickering and shape mutations to the planning module is often critical The comprehensiveness of mAS enables it to simultancously alert against these three different types of risks, whereas single geometric metries like Chamfer Distance overlook key safety-related information.
minor positional jitter.
In summary, we do not dismiss the value of traditional geometric metrics but highlight their inherent limitations in evaluating complex temporal stability. The proposal of mAS aims to establish a more comprehensive and safety-aware evaluation benchmark, capable of uncovering instability patterns that are overlooked by conventional methods.
E SUPPLEMENTAL VISUALIZATION AND ANALYSIS
E. IMPACT OF UNSTABLE MAP PREDICTIONS ON DOWNSTREAM TASKS
In this part, we visualize how temporal fluctuations in online mapping predictions impact down- stream tasks, as shown in Figures 6 and 7.
Figure illustrates the effect map changes between consecutive frames on these tasks.
In Scenario A at time the ego vehicle fails to detect an intersection ahead due to occlusion by leading vehicle, leading it to predict the vehicle will turn left. At time t+1, the ego vehicle success- fully identifies the intersection, resulting in a corrected prediction of the leading vehicle's 's trajectory.
In Scenario B, occlusion by three vehicles directly ahead prevents the ego vehicle from detecting the road boundary behind them, causing it to plan straight path that would collide with the curb. At time t+1, 1, after moving forward, the ego vehicle observes the previously hidden map element and correctly plans a right turn.
In Scenario C at time t, the vehicle does not observe the crosswalk ahead and plans to continue straight. After advancing and detecting the map element, the ego vehicle adjusts its plan accordingly.
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Scenario A
Scenarto
Scenario
Scenario
Road Boundary
Lane Divider
Pedestrian Crossing
GT Puture
Predicted Trajectories
Agent History
Figure Impact of Temporal Inconsistency in Map Element Presence on Downstream Tasks
Scenario
Missing!
Warning!
Frame: t-
Frame: t
Frame: t
Scenario F
Missing!
Warningg
Frame: 1
Frame:
Frame: t
Road Boundary
Lane Divider
Pedestrian Crossing
GT Puture
Predicted Trajectories
Agent History
Figure 7: Impact of Flickering in Predicted Map Elements Downstream Tasks.
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In Scenario D's initial frame, occlusion by other vehicles prevents the ego vehicle from predicting the road boundary to its left, leading to a straight path plan that risks a curb collision. Upon detecting the map element on the left in the next frame, it adjusts its trajectory for safe navigation.
Based on the analysis of Figure 6, a key conclusion can be drawn: map elements are critical for autonomous systems to perform downstream tasks such as trajectory prediction and planning. in- cluding in VLA-based driving systems (Cai et al., 2025). Temporal instability in the perception of these elements can lead to significantly different and potentially unsafe predictions and plans.
Quantitative Evaluation on Trajectory Prediction Tasks. To quantitatively assess the impact of map stability downstream tasks, we integrate map predictions from different models into trajectory prediction pipelines. We evaluate two representative trajectory prediction methods: HiVT (Zhou et al., 2022), which leverages map context for multi-agent trajectory forecasting, and Dense (Gu al., 2021), which uses map topology for goal-oriented trajectory prediction.
Table 23: Downstream Task Performance: Trajectory Prediction with Dense TNT.
Methods
config
DenseTNT
modality
Temporal
Backhone
BEV Encoder
Epoch
minADE
minFDE
MR
Only C
R50
GKT
L1228
2,151
0.5726
71.
NectmMapNet
Osly C
R50
BEVPomer
24
L,039
2,3430
0.3412
83.8
Table 24: Downstream Task Performance: Trajectory Prediction with HiVT.
Methods
config
INVT
modality
Tempural
Backhone
BEV Encader
Fpoch
minADE:
minFDR
MR
Only
R:50
0.4015
0,5404
0.0060
StramMMapNe
Only e
R80
RFV RFViomer
24
D. 196966
D. 0,8223
0.0023
838
The experimental results, summarized Tables and 23, demonstrate clear correlation between map stability (mAS) and downstream task performance. StreamMapNeet with its superior temporal stability (mAS: 83.8), consistently outperforms MapTR (mAS: 71.6) across all evaluation metrics in both trajectory prediction methods. Specifically:
Stpea achieves lower minADE (0.3963 vs. 0.4015), minFDE (0,8223 vs. 0.8404), and Miss Rate (0.0923 vs. 0.0960), indicating more accurate and reliable trajectory predictions.
HiVT Results:
DenseTNT Results: Similarly, StreamMapNet demonstrates superior performance with lower minADE (1.0639 vs. 1.1228), minFDE (2.1430 vs. 2.2151), and Miss Rate (0.3412 vs. 0.3726).
These quantitative results validate that temporal stability in map construction directly translates to improved performance in downstream autonomous driving tasks. Unstable map predictions intro- duce noise and inconsistency that propagate through the pipeline, degrading trajectory prediction accuracy and increasing the risk of unsafe maneuvers. This finding reinforces the critical impor- tance of evaluating and optimizing temporal stability alongside traditional accuracy metrics.
As shown in Figure we provide further visualization of how flickering map elements impact downstream tasks over time.
In Scenario frame t-1, the ego vehicle proceeding normally. However, at frame t, flicker occurs in the predicted road boundary to the right of the lead vehicle, caused by instability in the online mapping model. This leads the ego vehicle to perceive an opportunity overtake on the right, resulting in planning decision to steer right and attempt pass. By frame t+1, the model correctly perceives the road boundary again, causing the ego vehicle to abort the maneuver and resume a straight path.
In Scenario F at frame t-1, the ego vehicle observes the lane divider ahead and plans a normal trajectory. At frame however, the predicted lane divider suddenly disappears, causing the planning module to become uncertain and unable to confidently decide between a lane change or continuing straight.
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E.2 MAP vs. MAS
In this section, we present additional cases where mAP proves to be misleading indicator for evaluating temporal stability, whereas our proposed mAS correctly assesses temporal stability, as demonstrated in Figure 8 and Figure 9. These examples clearly show that mAP should not be used as a criterion for temporal stability evaluation, whereas mAS provides a more accurate assessment of temporal stability.
Model
Ground Truth
Model
Humans
mAP
51.3
44.1
(Ours)
66.6
71.6
Model
Ground Truth
Model
Humans
mAP
76.0
72.0
mAS (Ours)
75.1
78.1
Model E
Ground Truth
Model F
Humans
MAP
83.6
72.0
mAS (Ours)
76.6
78.1
Figure 8: Evaluating trustworthiness of online mapping models using human judgment, tradi- tional mAp, and our mAS metric.
As shown in Figure 8, model A represents the MapTR (Liao et al., 2022) model integrated with temporal features, employing ResNet-50 backbone (He et al., 2016) and GKT encoder (Chen
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et al., 2022), trained for 24 epochs. Model B represent the MapTR Liao et al. (2022) model without temporal features, utilizing the same ResNet-50 backbone (He et al., 2016) and GKT encoder (Chen et al., 2022), also trained for 24 epochs. Although model A achieves a relatively high mAP of 51.3, its stability is inferior to that of model B. Specifically, in the visualization results of model A, a crosswalk flickers into view on the road, and additionally, the leftmost lane divider visualized by model A flickers frequently, In contrast, although model B has lower mAP compared to model A, it does not produce sudden flickering of other map elements in the middle of the road and is able to consistently predict the lane divider on the roadside in nearly every frame.
As illustrated in Figure 8, both model C and model D represent GeMap (Zhang et al., 2024c) models. Model C was trained for 110 epochs using the Swin-T (Liu et al., 2021) backbone network and the BEVFormer encoder. Model D was trained for 110 epochs using the V2-99 (DD3D) (Lee et al,, 2019) backbone network and the BEVFormer encoder (Li et al., 2024b). Although the performance indicators of model C are superior to those of model D, our mAS evaluation indicates that the stability of model C is relatively poor. By visually comparing the outputs of the two, we find that model C occasionally detects non-existent pedestrian crossings in individual frames, which is a manifestation of poor field stability. This observation result confirms that the stability of model C is indeed weaker than that of model D, which is consistent with the mAS evaluation result.
As depicted in Figure 8, model E represents the HRMapNet (Zhang et al., 2024b) model with training map as initial map, employing a ResNet-50 backhone (He et al., 2016) and BeyFormer encoder (Li et al., 2024b), trained for 24 epochs. Model F represents the GeMap model (Zhang et al., 2024c), employing a Swin-T backbone (Liu et al., 2021) and BevFormer encoder (Li et al., 2024b), trained for 110 epochs. Model E achieves higher mAp value than model F, yet according to our mAS metric evaluation, model E exhibits inferior stability compared to model F. A visual comparison of their inference results reveals that the crosswalks predicted by model E show more pronounced geometric jitter, while other instances remain similar between the two models. This observation confirms that model E's stability is indeed poorer than Model F's, consistent with the assessment provided by the mAS metric.
As can be seen from Figure 9, both model G and model H represent GeMap (Zhang et al., 2024c) models. Model G was trained for 110 epochs using the Swin-T backbone network (Liu et al., 2021) and the BEVFormer encoder (Li et al., 2024b), while model H was trained using the V2-99 (DD3D) backbone network (Lee et al., 2019) and the BEVFormer encoder (Li et al., 2024b). It was also trained for 110 epochs. Although the performance indicators of model G are superior to those of model H. our mAS evaluation indicates that the stability of model G is relatively poor. Through visual comparison of the outputs of the two, we find that model G has significant spatial offset and morphological fluctuation in the prediction of road boundary lines in consecutive frames, which is a manifestation of poor field stability. This observation result confirms that the stability of Model G indeed weaker than that of model H. which is consistent with the mAS evaluation result.
As illustrated in Figure 9, both model and model J represent Ge models (Zhang et al., 2024c) Model I was trained for 110 epochs using the Swin-T backbone network (Liu et al., 2021) and the BEVFormer encoder (Li et al., 2024b). Model J was trained for 110 epochs using the V2-99 (DD3D) backbone network (Lee et al., 2019) and the BEVFormer encoder (L. al., 2024b). Model I achieves higher mAP value than model J, yet according to our mAS metric evaluation, model I exhibits inferior stability compared to model J. Model J demonstrates superior delineation in the demarcated regions, with map instances exhibiting more precise spatial localization, while main- taining comparable performance to other models in remaining areas. This observed enhancement in output quality confirms model J's higher stability, which aligns consistently with the mAS evaluation results.
As shown in Figure 9, model K represents the MapTR (Liao et al., 2022) model integrated with temporal features, employing a ResNet-50 backbone (He et al., 2016) and GKT encoder (Chen al., 2022), trained for 24 epochs. Model I. represent the Map'TR Liao al. (2022) model without temporal features, utilizing the same ResNet-50 backbone (He et al., 2016) and GKT encoder (Chen al., 2022), also trained for 24 epochs. Model K achieves a higher mAp value than model L, yet according to our mAS metric evaluation, model K exhibits inferior stability compared to model L. Model produces clearer map results in the demarcated areas with more accurate spatial positioning of map instances, while maintaining similar performance to other models in remaining regions. This demonstrates Model I's superior stability, which is consistent with the mAS evaluation outcomes.
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Model
Ground Truth
Model
Ewe
RERWY BERWWRED
Humans
mAP
76.0
72.0
mAS (Ours)
75.1
78.1
Model
Ground Truth
Model
Humans
mAP
76.0
72.0
mAS (Ours)
75.1
78.1
Model
Ground Truth
Model
Humans
mAP
51.3
44.1
MAS (Ours)
66.6
71.6
Figure 9: Evaluating trustworthiness online mapping models using human judgment, tradi- tional mAP, and our mAS metric.
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F LIMITATIONS AND FUTURE WORK
This study presents the first dedicated benchmark for temporal stability evaluation in online HD mapping, yet several limitations indicate directions for future research. The current benchmark is constrained by the scope of existing datasets, particularly in representing complex real-world scenarios. Our evaluation primarily relies on standard driving sequences from the nuScenes dataset (Caesar et al., 2020), which lacks systematic coverage of challenging conditions such as extreme weather, adverse illumination, and intentional adversarial scenarios (Lou al., 2025). Consequently, the current assessment may not fully reflect model stability under critical edge cases that are essential for safe autonomous driving.
Another limitation stems from the rapid evolution of this research field. While our benchmark encompasses 42 model variants representing major architectural paradigms, new methodologies continue to emerge at rapid pace. The current static snapshot of model comparisons requires continuous updates to maintain relevance and comprehensiveness.
To address these limitations, outline two primary directions for future work. First, will estab- lish a continuously maintained benchmark platform that systematically incorporates new research developments. This living benchmark will implement standardized evaluation protocols for emerg- ing methodologies, ensuring fair comparisons and tracking progress over time. The platform will feature regular updates model implementations, evaluation metrics, and dataset expansions, fos- tering community-win collaboration (e.g., decentralized vehicular crowd sensing for online partici- pant recruitment (Jiang et al., 2025)) and providing reliable foundation for assessing advancements in temporal stability.
Second, we will expand the benchmark include diverse challenging scenarios that better reflect real-world complexity. This expansion will incorporate data from multiple geographic regions with varying road infrastructures and traffic patterns. Specifically, we will integrate specialized datasets containing extreme weather conditions (heavy rain, snow, fog), low-light and night-time driving scenarios, and challenging urban environments with complex intersections and dense traffic. Fur- thermore, will develop evaluation protocols for synthetic adversarial scenarios (e.g., occupancy- centric driving scene generation (Li et al., 2025a), controllable video generation (Ma et al., 2025) and diffusion transformer for image editing (Feng al., 2025)) designed to stress-test model stabil- ity, such as sensor degradation simulations and challenging weather transitions(Zeng et al., 2025a; Xiao et al., 2025). These enhancements will provide robustness under critical conditions.
comprehensive assessment of model
We believe these efforts will significantly advance the development of more reliable and robust online HD mapping systems.
39


Reviews:
[{"review_id": "dZn3EF7TRb", "reviewer": "Reviewer_DMdK", "summary": "This paper presents the first benchmark for temporal stability in online HD mapping, introducing the mAS metric with Presence, Localization, and Shape components, and shows through 42 model variants that accuracy (mAP) and stability (mAS) are largely independent dimensions.", "questions": "1. Can you provide ablation studies on β, ω, and N to show that mAS rankings are stable across reasonable parameter choices?\n\n2. What happens to your GT-based matching when annotations themselves have temporal jitter - have you measured annotation consistency in nuScenes?\n\n3. Could you add experiments showing mAS correlation with downstream planning metrics to validate that it actually predicts safe behavior?\n\n4. How would your results change on challenging datasets with weather corruptions or sensor degradation?", "limitations": "", "rating": 4, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 3, "strengths": "1. The problem is well-motivated - existing metrics focus on single-frame accuracy while ignoring temporal consistency that's critical for safe autonomous driving.\n\n2. Testing 42 model variants across different backbones, BEV encoders, and temporal fusion strategies provides solid empirical evidence for the mAP-mAS independence claim.\n\n3. Breaking stability into Presence, Localization, and Shape gives more actionable insights than a single number would.", "weaknesses": "1. Using ground truth as a bridge for cross-frame matching (Algorithm 2) assumes perfect GT consistency, but annotation noise in consecutive frames could bias your measurements - have you considered direct matching with learned features like in SORT [1] or MOT approaches [2]?\n\n2. Your uniform resampling along x-axis only works for roughly vertical polylines; this breaks for curved roads or horizontal boundaries, whereas arc-length parameterization [3] would handle arbitrary shapes more robustly.\n\n3. Computing curvature as average angles between segments (Algorithm 7) is quite sensitive to sampling density - why not use more robust shape metrics like Fréchet distance [4] or proper curvature signatures [5]?\n\n4. The choices of β=15.0 and ω=0.7 seem arbitrary without ablation studies - how sensitive is mAS to these values, and do they generalize to different map ranges or vehicle types?\n\n5. Only using nuScenes limits your conclusions, especially since it has relatively benign weather - what happens on corruption benchmarks like nuScenes-C [6] or RoboDrive [7] where models might fail differently?\n\n6. While your qualitative examples are compelling, there's no quantitative link between mAS and actual planning metrics like collision rate or trajectory smoothness - does higher mAS actually lead to safer planning?\n\n7. Testing M∈{2,3,5,10} is a start, but you're randomly sampling rather than systematically studying how stability degrades with time or identifying worst-case temporal transitions like occlusion events.\n\n8. You don't compare against simpler alternatives like frame-to-frame Chamfer distance [8] or temporal consistency losses - how do we know mAS is actually better than these baselines?\n\n## References\n\n[1] Bewley et al. \"Simple online and realtime tracking.\" ICIP 2016.\n\n[2] Voigtlaender et al. \"MOTS: Multi-object tracking and segmentation.\" CVPR 2019.\n\n[3] Pottmann et al. \"Geometry of the squared distance function to curves and surfaces.\" Visualization and Mathematics III, 2003.\n\n[4] Alt & Godau. \"Computing the Fréchet distance between two polygonal curves.\" IJCGA, 1995.\n\n[5] Mokhtarian & Mackworth. \"Curvature-based shape representation for planar curves.\" IEEE TPAMI, 1992.\n\n[6] Xie et al. \"RoboBEV: Towards robust bird's eye view perception under corruptions.\" arXiv:2304.06719, 2023.\n\n[7] Kong et al. \"Robo3D: Towards robust and reliable 3D perception.\" ICCV 2023.\n\n[8] Achlioptas et al. \"Learning representations and generative models for 3d point clouds.\" ICML 2018.", "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": "FQPooEkZNy", "reviewer": "Reviewer_dHha", "summary": "This paper addresses temporal stability for HD Mapping, which is a critical and long-overlooked problem. The authors argue that current mainstream evaluation metrics, such as mAP, focus exclusively on single-frame geometric accuracy. To address this gap, the paper introduces the benchmark specifically designed to evaluate the temporal stability of online HD maps. The core contribution is a new, multi-dimensional metric named mAS (mean Average Stability).", "questions": "1. Could the authors elaborate on the sensitivity of the mAS metric to GT quality and ego-motion noise? For example, if varying levels of noise are injected into the GT or ego-motion data, how much do the model rankings change? This seems key to building trust in this new benchmark.\n2. How are near-vertical polylines (which would run parallel to the x-axis in the BEV-like coordinate system mentioned) handled during the resampling process? Does this strategy risk ignoring the stability evaluation for these critical elements, such as lane lines?\n3. Does the current mAS framework (particularly the instance matching stage) account for the stability of classification labels? If an instance is stable in geometry and location but its class label flickers between frames, is this captured by the metric?\n4. An interesting finding is that adding temporal fusion (Temp=4) to a non-temporal model like MapTR-GKT significantly degrades stability (mAS from 71.6 to 66.6). The authors speculate this is due to \"noise\". Could the authors provide a deeper analysis or hypothesis at the feature level? For instance, is it possible that the temporal module struggles to align the spatial features produced by GKT, leading to feature-level aliasing or confusion?\n\nI would be happy to raise the score if the authors provide more analysis.", "limitations": "", "rating": 6, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The task of this paper is meaningful, which systematically address and quantify the critical evaluation blind spot of \"temporal stability\" in the online HD mapping domain.\n2. The proposed mAS metric is well-designed and comprehensive.\n3. The paper is written clearly and with a strong logical flow.", "weaknesses": "1. The effectiveness of mAS relies on the GT stability. If the GT annotations themselves are inconsistent between frames (e.g., \"jitter\" or \"flicker\" from human labelers, which is common in large datasets), the mAS metric would unfairly penalize a model that produces a stable (and potentially more correct) output.\n2. Before stability calculation (Sec 3.3), the framework must perform \"geometric alignment\" using the vehicle's pose data. If the vehicle's localization data is noisy, it will introduce artificial \"instability\" during the alignment process, again leading to an incorrect penalty on the perception model's stability.\n3. The paper mentions (Sec 3.3) using uniform resampling along the x-axis to compare polylines. This method may becomes highly unstable or fails for vertical line segments (i.e., those running parallel to the vehicle's direction of travel). The authors do not clarify how this common and critical case (e.g., lane lines) is handled, which could lead to biases in the evaluation.\n4. The current mAS focuses on geometry and presence. However, another significant failure mode is \"semantic flickering\" where an element's position and shape are stable, but its classification label jumps between categories (e.g., \"lane\" and \"road boundary\"). This is equally detrimental to downstream tasks, but the mAS framework does not appear to capture this.", "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": "EFRDltvnQt", "reviewer": "Reviewer_1LUQ", "summary": "The paper introduces mAS, a comprehensive metric suite for evaluating the temporal stability of lane elements in online HD mapping. It measures Presence, Localization, and Shape Stability across frames and aggregates them into a unified score. Evaluation across various baseline methods is also proposed.", "questions": "N/A", "limitations": "", "rating": 4, "confidence": 3, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "- The proposed mAS is a well-designed, multi-dimensional metric (Presence, Localization, Shape) that directly targets temporal stability in online HD mapping, addressing a clear gap in prior evaluations.\n\n- The paper presents a thorough, large-scale comparison across diverse baselines, making temporal consistency differences easy to assess and helping the community select appropriate baselines.\n\n- The manuscript is clear and well-structured; metric definitions and the evaluation protocol are easy to follow.", "weaknesses": "- `Reliance on accurate inter-frame alignment:`\nThe mAS metric depends on precise ego-motion/pose transformations to align frames. Errors from localization, calibration, or time sync can propagate into the stability score, introducing variance unrelated to the model’s intrinsic stability.\n\n- `Vulnerable to “copy–paste” or over-smoothing strategies:`\nA model could boost mAS by copying or heavily smoothing predictions across frames, inflating stability without being correct or responsive to scene changes.\n\n- `Coupling with accuracy and interpretability of rankings:`\nBecause stability can rise even as accuracy drops, comparing models solely on mAS may be misleading. Low-mAP models could appear “stable” but be persistently wrong. This reduces the value of the submission.\n\n- `Sensitivity to implementation choices:`\nPolyline resampling density, curve parameterization, and discretization can affect shape and localization stability. Results may shift with frame rate and temporal window length.", "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)