ARINBEV: Bird's-Eye View Layout Estimation with Conditional Autoregressive Model
Authors: Jiyong Kwag
Charles Toth
Alper Yilmaz
Venue: ICLR 2026 Poster (2026)
Keywords: BEV semantic Segmentation
Autonomous Driving
Autoregressive Generative Models
Primary area: applications to robotics, autonomy, planning
TL;DR: A single-stage, decoder-only autoregressive generative model for bird’s-eye-view layout estimation that exploits the conditioning property of next-token prediction.

Abstract: Recent advances in Bird’s Eye View (BEV) layout estimation have advanced through refinements in architectural and geometric design. However, existing methods often overlook the structured relationships among traffic elements. Components such as drivable areas, lane dividers, and pedestrian crossings constitute an interdependent system governed by civil engineering standards. For instance, stop lines precede crosswalks, which align with sidewalks, while lane dividers follow road curvature. To capture these interdependencies, we propose \textbf{ARINBEV}, an autoregressive model for BEV map estimation. Unlike prior generative approaches that rely on complex multiphase training or encoder-decoder architectures, ARINBEV employs a single-stage, decoder-only autoregressive design. This architecture enables semantically consistent BEV map estimation. On nuScenes and Argoverse2, ARINBEV attains 64.3 and 65.6 mIoU, respectively, while using $1.7\times$ fewer parameters and training $1.8\times$ faster than state-of-the-art models.

Published as a conference paper at ICLR 2026
ARINBEV: BIRD'S-EX. VIEW LAYOUT ESTIMATION WITH CONDITIONAL AUTOREGRESSIVE MODEL
Jiyong Kwag, Charles Toth, Alper Yilmaz The Ohio State University
ABSTRACT
Recent advances in Bird's Eye View (BEV) layout estimation have advanced through refinements in architectural and geometric design. However, existing methods often overlook the structured relationships among traffic elements. Com- ponents such as drivable areas, lane dividers, and pedestrian crossings constitute an interdependent system governed by civil engineering standards. For instance, stop lines precede crosswalks, which align with sidewalks, while lane dividers follow road curvature. To capture these interdependencies, we propose ARIN- BEV, an autoregressive model for BEV map estimation. Unlike prior genera- tive approaches that rely on complex multiphase training or encoder-decoder ar- chitectures, ARINBEV employs a single-stage, decoder-only autoregressive de- sign. This architecture enables semantically consistent BEV map estimation. On nuScenes and Argoverse2, ARINBEV attains 64.3 and 65.6 mloU, respectively, while using 1.7x fewer parameters and training 1.8x faster than state-of -ftt models.
INTRODUCTION
BEV perception aligns multi-view images into unified top-down representation support down- stream tasks in autonomous driving. Recent methods (Li et al., 2024 Liu et al.) 20236 Philionn & Fidler 2020), including depth-based and attention-based approaches, have achieved strong per- formance by leveraging camera parameters to fuse multi-view features. However, these methods often overlook the semantic structure of maps. Traffic elements such as drivable areas, lanes, pedes- trian crossings, and sidewalks are designed manually and are governed by structured dependencies. For instance, stop lines typically precede crosswalks. These conditional dependencies are formally specified by transportation standards. For instance, MUTCD Section 3A.05 (United States Depart- ment of Transportation (2009)) states: "Yellow markings for longitudinal lines shall delineate the separation of traffic traveling in opposite directions and white markings for longitudinal lines shall delineate the right-hand edge of the roadway." Similarly, Section 3B.16 (United States Depart- ment of Transportation 2009b) specifies: "Stop lines should be used indicate the point behind which vehicles are required to stop in compliance with a STOP (RI-1) sign, a Stop Here For Pedes- trians (RI-5b RI-5c) sign." " Such structured relationships align with the autoregressive next-token prediction paradigm, where the presence of a traffic element can be inferred from its surrounding context. This observation motivates us investigate conditional autoregressive modeling for BEV map estimation.
Existing generative approaches for BEV map estimation can be broadly categorized into two types: (1) two-stage frameworks (Zhang et al., 2024 Zhu et al. 2023 Zhao et al.) 2024) and (2) encoder- decoder generative frameworks (Zou et al. 2024 et al. (023). Figure provides an overview comparison. Following prior image generation methods, two-stage frameworks first learn a discrete representation of the ground-truth BEV map using VQ-VAE (Van Den Oord et al., 2017) recon- struction and codebook. In the second stage, a transformer (Vaswani et 20.1 Dosovisskiy 2020) trains on the learned discrete tokens to estimate the BEV map. While conceptually straight- forward, this approach is not well suited for BEV map estimation. The primary purpose of discrete representation learning with VQ- VAE is to map nigh-dimensional continuous representations of nat- ural images into discrete tokens. However, BEV maps not natural images; BEV maps highly structured, semantically constrained, and dominated by sparse backgrounds. This discrepancy often leads codebook under-urilization, feature rnder-representation, and weaker supervision for the
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Stage
Encoder
BEV Encoder
Generative Decader
Envoder
Stage 2
Encosdrr
(a) Two-stage
(b) Encoder decoder
(c) One-stage (ours)
Figure 1: Comparison of generative based BEV map estimation pipelines. (a) Two-stage models use discrete tokenization, which increases training complexity. (b) encoder-decoder models omit discrete tokenization but still separate the BEV encoder and generative decoder stages. (c) ARIN- BEV unifies both into single-stage, decoder-only autoregressive framework, reducing complexity while maintaining accuracy.
second-stage transformer. In contrast, encoder-decoder generative frameworks directly employ a BEV encoder to produce BEV features, which are then refined by generative decoder. Although more unified, this approach introduces additional complexity and longer training times due to the overhead of the generative decoder. Moreover, scaling the architecture further increases model com- plexity quadratically, as BEV perception encoding depends on generative decoding for improved BEV map estimation. The encoder extracts features only once, limiting the advantage of iterative refinement to the decoder.
We begin with empirical analysis of discrete representation learning using recent different quantization-basee based VAE (Van Den Oord et al. [017] Mentzer et al. 2023 Yu et al., (202)) on the BEV map. Across all setups, we observe that quantization- codebook under-utilization and never achieve full codebook usage (Figure 3) However, this alone does not imply that inder-utilization directly causes feature under-represennatiin, which often cited as the primary reason for the degradation of discrete representation learning. To investigate further, we analyze the problem through Shannon entropy (Shannon 1948). High entropy reflects greater semantic complexity, while low entropy indicates confident assignments. Our analysis shows that entropy in BEV maps is extremely low, resulting in feature under- rnder-representation except in spe- cific central regions (Figure 2). This observation raises key question: Is sentation learning necessary for autoregressive BEV map estimation?
VAE consistently suffers from
discrete repre-
To this end, propose ARINBEV, single-stage, decoder-only autoregressive model for BEV map estimation. Designing such a model presents two key challenges. The first is how to construct discrete input tokens without the first stage training (Esser et al., 2021 Sun al. 2024 Yu al. 2021]. The second is that, while mask scheduling is essential for learning token dependencies, ran- dom mask strategies (Chang et al. 2022; Besnier al. 2025 Yu et al. (02I) developed for natural images may be suboptimal for BEV map estimation. To address these challenges, we introduce two core components: class encoding and entropy-guided mask scheduling. Class encoding directly incorporates ground-truth BEV map labels via a lightweight embedding layer, yielding an efficient input representation. Entropy-guid guided masking leverages the observation that central BEV map re- gions contain the most uncertain and informative content, ensuring that the autoregressive model allocates capacity to learning the most critical dependencies. Together, these designs enable ARIN- BEV to perform autoregressive BEV map estimation over informative regions, eliminating the need for a separate discrete representation learning stage. We summarize the main contributions this paper follows:
We demonstrate that discrete representation learning is unnecessary for generative BEV map estimation, and that encoder-decoder architectures introduce additional complexity.
We propose ARINBEV, single-stage, decoder-only autoregressive framework that re- moves the need for two-stage training and encoder-decoder architectures.
Our framework achieves 64.3 and 65.6 mloU nuScenes and Argoverse2, respectively, setting new ofate-of-the-an records in BEV map estimation while using 1.7x fewer param- eters and achieving 1.8x faster training.
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Entrapy Map
Entropy Map
(a) Individual Entropy map
(h) Average entropy map (c) Smoothed entropy map
Figure 2: Quantitative results nuScenes validation. (a) Ground-truth BEV map with its VQ- VAE entropy map. showing local uncertainty in codebook assignments. (b) Average entropy map across the dataset, highlighting regions of higher semantic variation. (c) Smoothed average entropy map, making central informative regions more visible.
2 RELATED WORK
We discuss the most relevant literature and compare the major differences between traditional and generative BEV map estimation.
Traditional BEV Map Estimation. The most straightforward approach BEV map estimation is the prediction of the monocular BEV map (Lu et al. 2019 Zhao et al.
2024 Gong et al. 2022]
Zou et al. 2024 Roddick Cipolla 2020). where each camera view processed independently.
However, this setting does not capture spatial correlations across views, leading to inefficiency and
limited perception. To overcome this, recent works (Philion Fidler 2020 Li et al.) 20224] Liu
al. 2023b Borse al.) 2023 Ge et al. 2023 Xie et al. 2002) have proposed unified frameworks that fuse multi-view images into a single BEV representation. enabling BEV map estimation on surround view. Pioneering approaches employ depth estimation (Philion & Fidler 2020 Liu al.,
2023b Lu et al. 2025) or iitention-based mechanisms (Zhou & Krähenbühl] 2022 Li et al. 2024)
project image features into a shared top-down view, showing strong performance in BEV map estimation. Building upon these foundations, subsequent works (Li et al. [2022; Liu et al. 2023a
Liao al. 2023) have introduced specialized decoders to convert dense BEV segmentation maps
into compact vectorized representations, addressing memory efficiency.
Generative based BEV Map Estimation. Recent research has explored generative modeling for BEV map estimation. TaDe (Zhao et al.) 2024) proposes two-stage architecture in which a ground truth map encoded in the first stage and then used to supervise second- stage generator. However, TaDe is limited to monocular structures and cannot generalize to multi-view settings. MapPrior (Zhu jet al., 2023) incorporates VQGAN-style (Esser et al. 2021 Goodfellow et al., 2014) model with discriminator to improve realism and structural detail. Although effective, it suffers from com- putationally expensive two-stage training and adopts encoder-decoder architecture in the second stage, increasing the cost of inference. Moreover, MapPrior relies GPT-style 1D token genera- tion, which is misaligned with the 2D nature of BEV maps. VQ-Map (Zhang et al., 2024) follows similar two-stage design MapPrior and TaDe, but supervises the second stage BEV feature ing embeddings from a lightweight first stage encoder without autoregressive decoder. Although more efficient than MapPrior, its performance remains constrained by the first-stage encoder's ca- pacity. Other generative approaches based on diffusion (Song et al., 20200: Ho et al. 2020 Ho & Salimans 2022 Rombach al. 2022) have also been explored. DDP (1i et al. 2023) proposes encoder decoder diffusion model to avoid two-stage training, but the diffusion process remains computationally heavy. DifiBEV (Zou al., (0024) similarly employs diffusion for the BEV map estimation, but is also limited to monocular input, restricting its applicability to multi-view systems the real world.
encoder-decoder
METHOD
Conventional two-stage models first learn discrete representations of the ground-truth truth BEV map via VQ-VAE (Van Den Oord et al. 2017). producing discrete tokens, which are then used for
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transformer- based token prediction. We revisit this approach by analyzing codebook utilization and entropy in VQ-VAE applied to BEV map. Our findings indicate that complex two-stage and encoder-decoder pipelines are unnecessary for BEV map estimation, and that entropy maps provide valuable guidance for designing effective mask scheduling to capture conditional dependencies.
3.1 DISCRETE REPRESENTATION LEARNING
Unlike language, where tokens carry clear semantics, individual image pixels do not. Discrete representation learning transforms complex continuous visual data into controllable discrete to- kenized forms, enabling transformer usage in vision tasks. However, BEV map labels are al- ready sparse and discrete, resembling tokens. This raises key question: Is discrete represen- tation learning necessary for autoregressive transformers when inputs are inherently token-like?
We begin by reviewing discrete representa- tion learning for BEV map using VQ-VAE. The VQ- VAE architecture consists three main components: the encoder, the vector quantization layer, and the de- coder. The encoder E() is a deterministic mapping that takes an input BEV map M where C denotes the number of semantic classes, and produces a latent representation (M)) The vector quantization layer (-)) computes the discrete latent variable via nearest-neighhor lookup in the codebook E R R using the encoder output & (MM) Here, K the number of codebook entries, and each codebook vector is denoted E RD for i 1,..., K. For- mally, the quantization is defined as:
VQ-VAE.
Codktook USI ostor vo Codebect Siee
Recerstoctis m660 vo, Codibeek Sie
vo
(0,1)cxA*W))
Genneo the
(a)
(b)
Figure 3: Quantitative results on nuScenes validation. (a) Utilization drops with larger codebooks. (b) Reconstruction mloU improves with codebook size, showing no collapse.
{1 if = arg min, [[EMM) -e]l2- O( = M) (0 otherwise.
The decoder D(-) reconstructs the BEV map M from the quantized latent 2. VQ- VAE training includes three losses: the reconstruction loss |M M] :. the codebook loss 3 [|sg[[(M)] el
to update codebook vectors, and the commitment loss [|E(M) - sgíc]lž to encourage the encoder output remain close to the selected codebook vector. Here, sg[-] denotes stop-gradient, and /3 is hyperparameter, typically set in the range [0.25, 2]. The overall optimization objective is:
((M) = M - +
3 (M - sg sg(e]]l [|sg[E(M)] - el
(2)
Codebook Utilization. We analyze codebook utilization in VQ- VAE trained ground-truth BEV maps. Utilization measures the number of unique codebook entries used during validation: higher rates indicate greater representation diversity, while low rates suggest codebook collapse lim-
ited input variation, both of which hinder transformer-based generative modeling. Figure [3 reports utilization rates for various discrete representation methods (Van Den Oord et al., 2017 Mentzer eet al. 2023 Yu et al., 2023). Across all methods, utilization declines as codebook size increases, and full usage never achieved without a drop in reconstruction mloU, even for small codebooks. These results highlight a key insight: the inherent sparsity of BEV maps limits the semantic richness of latent representations. Large background regions contribute little diversity, leading codebook under-utilization. This finding further suggests that discrete representation learning may be unnec- essary for BEV map estimation.
Shannon Entropy Analysis. Low codebook utilization can arise from either true feature under- representation or codebook collapse, where only a few entries are used regardless of input diversity. To assess representational quality, we compute the Shannon entropy (Shannon 1948) of the soft assignment probabilities between encoder features and codebook entries. Higher entropy reflects greater semantic diversity and uncertainty in the assignments, whereas lower entropy indicates more confident assignments. Hence, entropy serves as a proxy for the representational richness of BEV maps. Formally, the Shannon entropy of discrete probability distribution p (Pi,...,PI, over
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Image Encoder
Camera
Calibration
For Inference
Loss
Self- Attention
Cross Attention
Deformable
CLS Emc. Masking
Emb.
Comp.
Global
Decomp
Input
Binary Map M
Map
Pred. Map M
AR BEV Decoder
X steps
Figure 4: Overview of the proposed ARINBEV architecture. Multi-view images are first pro- cessed by image encoder and projected into BEV space using camera calibration. The semantic BEV map is masked and compressed before being passed through transformer blocks. The output subsequently decompressed to reconstruct the complete BEV map. During inference, fully masked map is iteratively refined estimate the BEV map.
K codebook entries is defined
K Σ 1=1
H(P) =
Pt log
(3)
where y3 denotes the probability of assigning feature to the i-th codebook entry. Let D be the encoder feature map (M). To compute entropy at each spatial location (h, w), we define a probability distribution over the feature vector Xh. RDD This distribution must satisfy p: 2 0 for
all i E (1,..., K} and Σ = 1. Since raw feature vectors do not satisfy these constraints, we first compute the cosine similarity between the normalized feature vector Xt,w and each codebook entry e; IR" from the learned codebook {e.)
Xh,
(h,,, w)
(4)
||x,
|e
These similarities are transformed into valid probability distribution via the softmax function: Pi(h, w) soflmax(s;(h,, w)). Finally, the Shannon entropy at each location (h, w) computed as
K
H(h, w) = Σ (h, w) log (h,,
(5)
Figure 2 Jillustrates the entropy map for a single BEV sample, average entropy map of the entire Baussian-smoothed dataset, and its Gaussian smoothed version. Background regions exhibit consistently low entropy, while semantically meaningful areas-such as intersections, roads, and boundaries-shon higher entropy. This confirms the semantic sparsity of BEV maps, where large portions contribute little information. Consequently, forcing discrete representation learning may be unnecessary or even detrimental.
3.2 AUTOREGRESSIVE MODELING
In BEV maps, stage-one discrete representation learning often fails due to low-entropy regions, leading to poor codebook utilization and weak transformer (Vaswani et al. (017)) supervision. This suggests that discrete quantization may not be necessary. However, it raises key question: Without learned discrete tokens, how can we construct semantically meaningful index sequences for autore- gressive BEV map modeling?
Class Encoding, To bypass discrete representation learning and provide semantically grounded in- put, we begin with binary ground-truth BEV map M. We construct class-conditioned embeddings
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Lart
Right
Tront
Fron
Right
Back Lert
Back
Back Right
Front
Front Right
(a) Surround- View Images
(b) Ground-truth (c) BEVFusion
(d) ARINBEV
Figure 5: Prediction results under different conditions (day, rainy, night; top bottom) on nuScenes validation. ARINBEV yields more plausible outputs, even in occluded regions, while reducing artifacts. Color scheme follows (Liu et 2023b).
using a learnable embedding table E E [CC+))xD where C' is the number of classes and D is the embedding dimension. An additional entry is reserved for the mask token. To encode the class strength, we define a multiplicative factor F E IR RIxxxIx 1xl assigning each channel a weight equal to its class index. The weighted label map is computed as C M o F. The class index of each pixel in C is used to index in the embedding table, producing S E CXCHxxXD We then average over the class dimension:
S Sag
C Σ S [t....]
(6)
i=1
To normalize the embedding, we apply a bounded nonlinearity:
Z = (2 (Sww 1)
where (-)) is the sigmoid function used for normalization, and 0.01 is a scaling factor for numerical stability. The resulting Z serves as the initial BEV feature, enabling the direct injection of semantic labels into the transformer. This formulation eliminates the need for stage-one quanti- zation. Additional discussion is provided in the Appendix
B
The goal of our autoregressive BEV decoder is to learn parameters that maximize the likelihood of token sequence x = {21, f2,, ø2,... Is} conditioned on multi-view image features in a bidirectional manner. We decompose the token distribution according to a mask scheduling strategy S:
Autoregressive BEV Decoder.
9 p(x c) II p(x, x2s,
(8)
where x, denotes the set of tokens predicted step according to the schedule S, and x repre- sents the tokens already predicted. Our architecture builds upon BEVFormer (Li et al. 2024) and DETR (Carion et al., 2020), using pre-normalized attention layers (Dosovitskiy et al. 2020). The input embedding Z is initialized from ground-truth BEV map labels via learnable embedding ta- ble E. To reduce the cost of deformable cross-attention (Zhu et al., 2020), we compress Z from 200 x 200 to 25 25, followed by decompression layer to restore resolution. Given the sparsity of compressed tokens, deformable self-attention is replaced with global self-attention. For cross-view interaction, retain deformable cross-attention sets and camera parameters. Binary focal loss (Lin et al. 2017) is applied to mitigate class imbalance in per-token classification. Figure provides overview of the proposed architecture.
access relevant image features using learned off-

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Table 1: State-of-the-art comparison for surround-view BEV map estimation on the nuScenes validation set. All methods are evaluated under the camera-only setting. The results include per- class loU (%) scores for six semantic categories and the mean IoU.
IoU t (%)
Methods
Drivable
Ped. Cross.
Walkway
Stopline
Carpark
Divider
Mean
OFT
74.0
35.3
45.9
27.5
35.9
33,9
42.1
LSS
75.4
38.8
46.3
30.3
39.1
36.5
44.4
CVT
74.3
36.8
39.9
25.8
35.0
29.4
40.2
B
77.2
40.5
BEVFusion
81.7
54.8
58.4
47.4
50.7
46.4
56.6
MapPrior
81.7
54.6
58.3
46.7
53.3
45.1
56.7
X-Align
82.4
55.6
59.3
49.6
53.8
47.4
58.0
MetaBEV
83.3
56.7
61.4
50.8
55.5
48.0
59.3
DDP
83.6
58.3
61.6
52.4
51.4
49.2
59.4
VQ-Map
83.8
60.9
64.2
57.7
55.7
50.8
62.2
ARINBEV
85.0
62.4
66.5
60.8
59.7
51.2
64.3
Guided Masking Strategy. In BEV map estimation, Figure shows that semantic in- formation concentrated near the center, while peripheral regions are mostly background. To guide learning toward informative areas, adopt spatial masking strategy based on Halton sequence (Halton 1964). inspired by Halton MaskGIT (Besnier et al., (025) with entropy-guided selection. To mitigate overfitting dataset-specific entropy, employ fixed Gaussian prior map S E R centered on the grid with standard deviation = 0.5. The masking ratio is obtained by sampling u0, 1) and transforming it via the arccos distribution:
2
Pb arccos(r).
Next, we generate N candidate coordinates {Vk, Ix) ) using a random Halton sequence and assign selection probabilities proportional the Gaussian prior map S:
Pk:
(10)
2j=1 Syg Syymy
We then sample Les H-W positions without replacement according to pı to construct a mask B. To further prevent overfitting to sparse unmasked regions, this stochastically guided entropy masking applied with probability p = 0.5, combined with random arccosine masking as in MaskGIT.
EXPERIMENTS
Table 2: Computational overhead analysis. Training time is measured in GPU hours using single NVIDIA A100 on the nuScenes training set. ARINBEV achieves the shortest training time compared to prior work, requires fewer parameters than two-stage models VQ-Map and MapPrior, and incurs lower computational cost (MACs) than encoder-decoder decoder model DDP.
Methods
Params (M)
MACs (G)
Train Time (h)
mloU (%)
BEVFusion
50.1
155.5
100
56.6
MapPrior
719.1
396.0
> 200
56.7
DDP
53.6
614.1
160
59.4
VQ-Map
108.3
231.6
131
62.2
ARINBEV
63.4
215.8
73
64.3
4.1 EXPERIMENTAL. SETTINGS
Dataset. We evaluate ARINBEV on BEV map estimation from surround-views images using two
large-scale public datasets: nuScenes (Caesar al. 2020) and Argoverse2 (Wilson al. 2023

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Table Comparison of surround-view BEV map estimation on the Argoverse2 validation set. All methods are evaluated under the camera-only setting. A constant IoU threshold of 0.5 is used across all methods ensure fair comparison. Results marked with * are reproduced from their respective official implementations.
IoU t (%)
Methods
Drivable
Ped. Cross.
Divider
Mean
MapPrior*
78.5
50.0
41.1
56.5
BEVFusion*
80.6
53.1
47.0
60.2
VQ-Map*
79.7
56.9
46.9
61.2
DDP*
83.5
58.1
48.8
63.5
ARINBEV
83.9
61.4
51.6
65.6
nuScenes provides RGB images from six cameras covering 360 views across 1000 scenes in Boston and Singapore, with 28K frames for training and 6K for validation. Argoverse2 offers seven-camera 360 views from six US cities, comprising 11K training and 24K validation frames.
Evaluation Protocol. We follow the standard evaluation protocol from prior work (Liu et al. 2023b Zhang et al. 2024 Ji et al. (023). The perception range is set to [-50.0 m, 50.0 m] along both the Y-axes, with segmentation resolution of 0.5 meters per pixel, resulting in a 200 x 200 BEV grid. Map construction quality is assessed using Intersection-over-Uniiine
X-
and X-and
(IoU) with threshold of 0.5. For nuScenes, six binary segmentation categories are evaluated: drivable area, lane, pedes- trian crossing, stop line, sidewalk, and parking area. For Argoverse2, three binary segmentation categories are used: drivable area, lane, and pedestrian crossing.
Implementation Details. We adopt Swin Liu et al. [2021) the image backbone for fair
comparison with the prior work (Liu et al. 2023b] /Zhu et al. 2023 Ji et al. (023). We employ the
AdamW optimizer (Loshchilov & Hutter 2017) with weight decay of 0.01. The model is trained for 20 epochs, including 8 warm-up epochs, on 4 NVIDIA A100 GPUs with batch size of per GPU. The initial learning rate is set to 5 x 10-5 and scheduled using the one-cycle policy (mith 2017). During inference, we apply 3 sampling steps with the Halton scheduler(Besmier et al. 2025 Additional training details are provided in the Appendix A
Results. We report BEV map estimation results on nuScenes and Argoverse2 in Table and Table 3 and computation results in Table 2 Figure presents qualitative visualization comparisons. nuScenes, ARINBEV's simplified architecture mottivateddd by insights from extensive analysis achieves the fastest training time among prior works. also achieves substantially lower parameter count compared to two-stage models such MapPrior and VQ-Map (Zhu et al. [023] Zhang al.) 2024). By unifying the BEV encoder and generative decoder into a single-stage model, ARINBEV further reduces the number of mulltppy--ccummunt operations (MACs) relative
incoder-decodee baselines (ji et al., (003). while maintaining high efficiency. Despite its simplicity, ARINBEV complex architectures across all map element categories. On Argoverse2, both MapPrior and VQ-Map exhibit substantial performance degradation. These models rely heavily on discrete representation learning, which less effective when limited to the three map element classes available in Argoverse2. In contrast, ARINBEV does not depend on two-stage quantization and remains robust, achieving strong performance without suffering from this degradation.
outperforms
4.2 ABLATION AND ANALYSIS
To investigate the contribution of individual components, we conduct an ablation study on the nuScenes validation set, shown in Table
Masking Strategy. To evaluate the impact of entropy-guided masking, we compare three variants: (a) arccosine random masking, (b) pure entropy-guided masking, and (c) hybrid masking, which switches between random and entropy-guided strategies with probability method (c) achieves the highest performance, outperforming both (a) and (b). This suggests that integrating entropy awareness improves model performance. All experiments use (g) as the default feature compression method.
= 0.5. The hybrid
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Table Ablation study on the nuScemes validation set evaluating the impact of different mod- ules. Rows (a)-(c) compare various masking strategies, while (d)-(g investigate different fea- ture compression techniques. Row (h) evaluates an alternative embedding formulation. The best- performing and final configuration is marked with *
IoU t (%)
Drivable
Ped. Cross.
Walkway
Stopline
Carpark
Divider
Mean
(a)
83.6
61.5
64.5
59.1
58.3
50.1
62.9
(b)
82.0
59.4
62.5
57.8
56.7
48.1
61.1
(c)*
85.0
62.4
66.5
60.8
59.7
51.2
64.3
(d)
84.5
62.0
66.3
60.3
59.2
51.0
63.9
(e)
84.6
62.1
66.4
60.5
59,4
51.1
64.0
(f)
83.0
60.4
65.5
59.3
58.3
50.0
62.8
(g)*
85.0
62.4
66.5
60.8
59.7
51.2
64.3
(h)
83.0
59.1
61.2
58.3
56.6
47.5
61.0
Convolution vs. Interpolation. To assess the impact of different feature compression strategies, fix the BEV embedding shape to 25 and compare the following configurations: (d) three sequential 3 x 3 convolutional layers, (e) single 8 x convolutional layer with stride and zero padding, (f) pure bilinear downsampling by factor 8, and (g) a hybrid approach combining bilinear downsampling followed by 3 3 convolution. Among these, (g) yields the highest mloU, slightly outperforming the convolution based variants (d) and (c), and significantly surpassing the pure interpolation (f). These results highlight the effectiveness of combining smooth interpolation with learnable convolution for producing compact BEV embeddings. For all experiments in this group, use the masking strategy (c) and apply bilinear upsampling followed by xx3 convolution as the default decompression layer.
Different Class Encoding. Beyond our proposed structured class encoding, we evaluated alter- native prototype: (h) channel-wise binary encoding. In this formulation, each spatial location on the binary vector across channels and converted into a integer index via a dot product with weight vector of two power [20-] .... 2" Although this approach simpler, it produces a lower mean loU of 61.0%. For fair comparison, we use the same default configuration with masking strategy (c) and feature compression method (g).
binary BEV map M is interpreted
ACKNOWLEDGMENTS
This research/project is supported in part by the U.S. Department of Transportation under Grant 69A3552348327 for the CARMEN+ University Transportation Center.
6 CONCLUSION
In this work, introduced ARINBEV, simplified autoregressive BEV map estimation frame- work. Through codebook utilization and entropy analysis, we show that discrete representation learning in BEV semantics exhibits sparse, low-entropy structure, suggesting substantial redundancy discrete token modeling. Through this observation, we design a single-stage, decoder-only au- toregressive model with lightweight class encoding mechanism and entropy-guided masking schedule to focus capacity on informative regions. As a result, ARINBEV achieves state-of-the-art art results on both the nuScenes and Argoverse2 benchmarks with compact architecture and low train- ing time. We hope our work can inspire future research on efficient BEV map estimation training.
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APPENDIX
A IMPLEMENTATION DETAILS
Architecture Detail. We mainly follow archtecture design from BEVFormer and DETR. How- ever, we made several modification on architecture, Our autoregressive BEV decoder contains layers with dimension of 512 and 8 attention heads using pre-normalization. BEV embeddings are downsampled by 1/8 and upsampled by 8x using bilinear interpolation, each followed by a 3 x convolution enhance local context. Global self-attention is applied on the compressed grid of size 25 x 25. For deformable cross cross-attentin attention, we sample 2 anchors from 4 heights, 2 depths, and 2 width, each predicting 2 learned offsets per scale. Figure 6 ililusttsrte specific deisgn of our architecture.
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BEV Token Pred.
Decomp.
Camera
Calibration
FFN
Reference
Layer Norm
Points
Deformable
Cross-Attention
Image Encoder
Layer Norm
Global
Self-Attention
Layer Norm
Compression
Token Query
Figure 6: More detail overview of autorcgressive BEV deocder. Our model is consisted of global self-attention, deformable cross-attention, and feedforward network with pre-normalization.
Train Detail. For nuScenes and Argoverse2, we use distinct pipeline as each dataset has distinct multi-view camera setups and images. For nuScenes, the six images are resized from 1600 900 to 256 705 and follow same augmentation pipeline from BEVFusion for a fair comparisonss with the prior work (Liu al.) 2023b Ji et al.] 2023 Zhang al. 2024). For Argoverse2, the front view images 1550 x 2048 and other Six view images 2048 x 1350 are padded 2048 x 2048, then uniformly resized by factor of 0.3. Data augmentation follows the MapTT pipeline Liao et al. 2023) to accommodate the different setup of Argoverse2.
Discrete Representation Learning Detail. For discrete representation learning, adopt the VQ- Map (Zhang et al., 2024) encoder. The 200 x 200 BEV map is divided into x 8 patches, yielding 8. These are passed through three 3 x 3 convolutional layers with 64, 128, and 128 channels, followed by a 2 x 2 max pooling layer. A linear projection reduces the channel dimension to 16 to improve codebook utilization Yu al. (202I). Vector quantization uses exponential moving average (EMA) updates with decay rate of 0.99 and 10-5 for stability with codebook size of 512. The decoder, following VQGAN Esser al. (202T), consists of five convolutional layers and three residual blocks.
an input of shape 625
B
MORE EXPERIMENTS
Class Encoding. Class encoding allows our model bypass the need for discrete representation learning, thereby reducing both training time and overall model complexity. As shown in Eq. 7 appropriate scaling is crucial, since different choices directly affect the stability of training. Table Sa reports results for various scaling factors: scale of 0.01 exhibits the best performance, whereas larger values degrade accuracy. We also evaluated a learnable scale, but it consistently produced the lowest performance among all settings.
Entropy-Guided Masking Strategy. In entropy-guided masking, selecting an appropriate for the Gaussian prior map is crucial to ensure stable training. Table 5b reports results for different values. The black square represents a masked token. A setting of a 0.3 leads to training failure, whereas performance improves from o 0.4 onward. The best performance is observed at 0.5, with only a marginal decrease thereafter. To evaluate the diversity of masking distributions, Table 6a reports the dynamic changes in the values during training. Moreover, Figure 7 visualizes entropy- guided masking compared with random masking at the same masking ratio using o = 0.5. We also provide Pyrch implementation of masking strategy in F
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Sampling Drift. A common explanation of declining performance after step 3 is sampling drift, where the distribution during inference differs from training. During training, the model leams to invert masked ground-truth BEV map, whereas during inference it must iteratively unmask its own imperfect predictions under a fully masked input. As these errors accumulate, the input distribution progressively diverges from the ground-truth distributions, typically after step 3. To reduce sampling drift, we adopt the same training strategy used in DDP. We construct Zmodel from the model's predictions using unmasked inputs and then apply masking to Zmodet during the final 9K iterations (1 epoch). However, accuracy consistently stabilizes around step 3, and additional steps do not yield further improvement Tbb
0.2335
0.367
0.521
0.733
Figure 7: Comparison of entropy-guided masking with o = 0.5 (top) and random masking (bottom) randomly chosen masking ratios of 0.2335, 0.367, 0.521, and 0.733. The masking primarily concentrated in the central region, where the most informative areas reside.
Table 5: Comparisons of scaling factors in class encoding and a values in untropy-guided mask- ing.
Scale
mloU
mloU
0.01
64.3
0.3
62.1
0.1
63.2
0.4
63.8
1.0
61.2
0.5
64.3
learned
57.2
0.6
64.0
(a) Comparison of different scales class encoding.
(h) Comparison of different values entropy masking.
Table 6: Comparison of a ranges for entropy-guided masking and the impact of iterative sam- pling steps.
step mloU
mIoU
59.8
0.25-0.75
63.9
63.4
0.30-0.60
64.2
64.3
0.40-0.70
64.3
64.3
(a) Effect different ranges on masking performance.
(b) Effect of iterative sampling steps.
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C MORE VISUALIZATIONS
In this section, provide additional visualizations on the nuScenes validation set. Figure presents autoregressive predictions of the BEV map, demonstrating how the model progressively refines its estimates based on previous predictions with our efficient masking strategy. Figure Dillustrates fur- ther examples of entropy maps on the validation set. As previously observed, background regions exhibit the lowest entropy. Moreover, certain inner regions of the road, where only drivable eas exist, also show low entropy or a consistent codebook assignment. This further strengthens our claim that discrete representation learning unnecessary. Figures T0, [T show additional visualiza- tions across diverse scenarios (daytime, rainy conditions, and nighttime) on the nuScenes validation. We consistently observe that ARINBEV exhibits strong robustness despite occlusion and nighttime conditions, owing to dependency learning that enables better prediction using only observed traffic elements.
1=0
3
Figure 8: Autoregressive BEV layout predictions over steps. The model progressively refines structure and semantics.
Labee
HEW
1507
Figure 9: More quantitative results nuScenes validation. Ground-truth BEV map with its VQ-
VAE entropy map, showing local uncertainty in codebook assignments.
D LIMITATIONS AND FUTURE WORK
Inference Speed. We evaluate the effect of varying the autoregressive inference steps. Figure ?? shows how performance changes with different step counts. mloU rises sharply from step to step 3, reaching 64.3%, but gradually declines thereafter. These results suggest that early autoregressive finements capture essential structural dependencies, whereas excessive iterations introduce overfit- ting, degrading prediction quality. FPS is highest at the beginning (18 FPS), approaching real-time
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Bugh
Enont Right
Left
Bock
Back Right
Fromt
Front Right
Back Luft
Back Right
(a) Surround- View Images
(b) Ground-truth
(c) BEVFusion
(d) ARINBEV
Figure 10: More Prediction results under different conditions (day, rainy, night; top to bottom)
nuScenes validation.
Back Right
Front Left
Front
Front Right
Back Right
Tront Lett
hont
Front Right
Back
(a) Surround Burround-View Images
(b) Ground-truth
(c) BEVFusion
(d) ARINBEV
Figure 11: More prediction results under different conditions (day, rainy, night; top to bottom) on the nuScenes validation set. We provide more challenging cases, where cameras are heavily occluded by vehicles and rain, and in night-time scenes with minimal lighting.
performance, and decreases as the number of steps increases. The best mloU corresponds to 7.5 FPS.
3D Occupancy Prediction. The current work focuses BEV map estimation in 2D space, but the dependency-baie prediction scheme underlying ARINBEV provides a natural foundation for extension to 3D occupancy prediction, where modeling height variations is essential. Nonetheless, applying autoregressive modeling directly to dense 3D voxel grids may impose prohibitive computa- tional costs. Future work could leverage ARINBEV's efficiency in 2D and investigate more scalable
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vs. Steps
18
16
14
mleu
-12
med1
62-
60
Inference Staps
Figure 12: mloU and FPS vs. inference steps. mloU peaks at early steps and diminishes thereafter.
strategies, such sparse voxelization, hierarchical decoding, hybrid 2D-3D representations, to enable practical autoregressive occupancy prediction.
Discrete Diffusion. While ARINBEV employs discrete mask tokens for autoregressive BEV map estimation, an alternative direction is to investigate discrete diffusion with continuous noise. Dif- fusion models operate Gaussian noise, which may provide stronger generative capacity than masked-toker strategies. Ex- ploring this paradigm could offer principled way to combine the advantages of diffusion modeling with dependency-base baaed prediction schemes.
the entire representation space while gradually introducing scheduled
E
LLM USAGE
Our primary use of LLMs is to refine our writing and receive recommendations on word choices. We did not use LLMs for retrieval discovery of ideas, such finding related work, did use them for research ideation. Instead, all conceptual development, methodological design, and experimental analysis
conducted independently by the authors. The role of LLMs was limited to improving clarity, grammar, and presentation without influencing the scientific contributions of this work.
F PYTORCH IMPLEMENTATION OF ENTROPY-GUIDED MASKING STRATEGY
import torch
from scipy. stats import qmc
4
def eet_haatonnccoree (H, W, n_points):
Gonerate random halton sequence sampler qmc Halton (d=2, scramble-True)
sample sampler, random (n=n_points)
coords torch, tensor (sample) forch.tensan [[H, W])
coords torch. fl flror (coords) long ()
Remove duplicates
coords torch. unique (coords, dim=0) ys, xs coordsl;, 01, coordsl:, 1|
13
return ys, xs
def antropy_halttn.e (gaussian_prior, code, mask_id, n_points): B, C, H, w code.shaa
17
18
= ==
arccos scheduler
torch.
22
val_to_mask torch. arceos (r. clamp (0, 1)) / (math.pi 0.5)
masked_code code.clanel.
mask torch. ( (H, H, W), dtype=tarch.boo!)
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for range (B)
28
Get Halton candidate points ys, xs get_halton_coords (H, W, n_points)
Compute entropy probabilities scores gaussian_priorrys, xs] prob = scores / (scores. 1e-8)
33
Get number of tokens mask num_tokens int (val_to_mass [b] item())) Nj
num_tokens min (num_tokens, (prcb))
Sample mask location based probablities idx sel torch. march.multinomial (prob, nun_tokens, replacement-Palse ys_sel [sixx_seel
42
xs_sel xs [idx_sco]]
mask[b, ys_sel, xs_sel] True
mask mask. uaskuuns (1) exxand (B, C, H, W)
Apply mask
masked code maskk] mask id
return masked_code, mask
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Reviews:
[{"review_id": "xs4yYnnxnv", "reviewer": "Reviewer_Wj3i", "summary": "This paper presents an empirical analysis showing that current generative BEV map estimation methods contain unnecessary complexity: in particular, discrete representation learning exhibits low codebook utilization and extremely low entropy on BEV maps, leading to feature under\u0002representation. Building on this, the authors propose ARINBEV, a conditional autoregressive model with a single-stage, decoder-only design. It directly injects ground-truth BEV map labels into the embedding layer via Class Encoding, thereby avoiding the discrete quantization stage. In addition, the paper introduces an entropy-guided masking schedule that leverages the low-entropy property of BEV maps to focus learning on information-rich regions and capture the conditional dependencies among traffic elements. ARINBEV surpasses existing SOTA methods on nuScenes and Argoverse2, achieving clear improvements.", "questions": "Q1. Beyond the best setting at σ=0.5, if the Gaussian prior center is shifted, widened, or narrowed —or if the prior weight pp is varied from 0.5 to {0.25,0.75}—how does the performance trend? Do you observe noticeable fluctuations for classes in the peripheral regions?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 2, "contribution": 2, "strengths": "S1. This work brings a novel perspective to BEV layout estimation: through Shannon-entropy analysis and empirical measurements of codebook utilization, it systematically—for the first time—demonstrates the inefficiency of discrete representation learning on BEV maps.\nS2. It proposes a single-stage, decoder-only autoregressive BEV generation framework, abandoning the prevalent two-stage VQ-VAE→Transformer and diffusion-based encoder–decoder pipelines; moreover, using Class Encoding, it feeds the labels directly as tokens, a design that is novel and well aligned with the semantic sparsity of BEV maps.\nS3. The paper presents the method with clear detail: the autoregressive factorization, embedding normalization, masking schedule, and the compression to 25×25 for global self-attention as well as the cross-attention sampling settings are all documented in the main text and appendix. The appendix also provides PyTorch pseudocode, making the work reproducible.", "weaknesses": "W1. The inference procedure relies on iterative autoregressive steps, which leads to higher latency; as noted in the paper, the best case reaches only 7.5 FPS, lagging behind non-generative baselines such as BEVFusion. This setup may constrain deployment in dynamic driving scenarios.\nW2. The entropy-guided masking uses a fixed Gaussian prior with p=0.5p=0.5, but the ablation study shows sensitivity to this parameter—for example, the appendix notes that σ=0.3 leads to training failure. This introduces potential instability, and the heavy reliance on a nuScenes-like distribution may degrade on datasets with different entropy patterns.", "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": "v06xnXcAMw", "reviewer": "Reviewer_Qyij", "summary": "This paper proposes ARINBEV, a single-stage, decoder-only autoregressive model for BEV map layout prediction. The authors' key contribution is to demonstrate that learning discrete representations in existing two-stage generative models (such as VQ-VAE) is unnecessary given the semantic sparsity of BEV maps. To replace the discrete representations, the authors introduce class encoding and entropy-guided masking based on entropy maps for more efficient and concise training. Experimental results show that ARINBEV achieves new state-of-the-art performance on nuScenes and Argoverse2, while requiring faster training time and fewer model parameters.", "questions": "1. Representational Capacity of Category Encoding: The authors convert discrete labels M into embeddings $S_{avg}$ through a learned embedding table E. Please elaborate on how this simple pixel-wise class encoding effectively captures the structural dependencies of traffic elements in the BEV map. How does this \"hard-coding\" ensure that its expressiveness is sufficiently robust compared to the contextually semantic discrete tokens learned by VQ-VAE?\n\n2. Entropy Guidance and Gaussian Prior: In Section 3.2, the authors mention using an \"entropy-guided masking strategy,\" but actually employ a fixed Gaussian prior map S (Equations 9-10). This Gaussian prior map S simply treats the central area as a high-information region, overlooking the significant differences in traffic scenarios within the BEV coordinate system (e.g., highways vs. complex intersections). The lack of an adaptive masking strategy for complex scenes may limit the model's performance in highly variable environments.\n\n3. Purpose of Using a Fixed Gaussian Prior: As stated in L343, the purpose of employing a fixed Gaussian prior is to prevent the model from overfitting to the entropy distribution of a specific dataset. However, the map prediction network itself is already overfit to a particular dataset. There is no experimental evidence demonstrating that a fixed Gaussian prior is less prone to overfitting compared to an empirical entropy map derived from training data.\n\n4. Decline in Multi-Step Inference Performance: Figure 6 shows that as the number of inference steps increases from 3 to 5, the mIoU declines, while FPS continues to decrease. What is the reason for the drop in mIoU? Is it due to overfitting (as you mentioned), or does the model introduce erroneous information in subsequent iterations?", "limitations": "", "rating": 4, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- Simplicity and Efficiency: The authors have successfully simplified the complex two-stage or encoder-decoder generative framework into a single-stage, decoder-only autoregressive model, significantly reducing model complexity.\n\n- Novel Motivation and Analysis: Through an in-depth analysis of the VQ-VAE codebook utilization and Shannon Entropy of BEV maps, the authors compellingly argue that the semantic sparsity of BEV maps renders discrete representation learning unnecessary.\n\n- Sota Performance: ARINBEV achieves state-of-the-art mIoU performance on both the nuScenes and Argoverse2 datasets, with a training time of 73 hours, which is significantly less than the SOTA baseline (e.g., 131 hours for VQ-Map), while also having fewer parameters, demonstrating an impressive improvement in efficiency.", "weaknesses": "- Limited Novelty in Core Architecture: While the removal of VQ-VAE is a reasonable simplification, the core architecture of ARINBEV is essentially based on the decoder-only Transformer from BEVFormer and DETR, and it adopts the autoregressive paradigm of the Masked Generative Image Transformer (MaskGIT). The main innovation—category encoding—essentially maps sparse labels directly to Transformer input embeddings, and its complexity and generalizability still require further validation.\n\n- Generalizability of Entropy-Guided Masking: The \"Gaussian Prior Map S\" (Equation 10) used for training is a fixed and predetermined prior that focuses on the central area of the BEV. It is merely an empirical, heuristic design rather than a dynamic strategy based on real-time semantic entropy. This undermines the rigor of the \"entropy-guided\" approach.", "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": "YmqRceQ1Bl", "reviewer": "Reviewer_FeCg", "summary": "This paper addresses the oversight of structured relationships among traffic elements in existing BEV layout estimation methods by proposing ARINBEV, a single-stage, decoder-only autoregressive model. Unlike two-stage or encoder-decoder frameworks, ARINBEV leverages class encoding and entropy-guided mask scheduling to bypass discrete representation learning. Evaluated on nuScenes and Argoverse2 datasets, it achieves state-of-the-art mIoU scores (64.3 and 65.6 respectively) with 1.7× fewer parameters and 1.8× faster training compared to existing models, demonstrating effectiveness in capturing semantic dependencies of traffic elements.", "questions": "1. Why is BEVFormer not included in the comparative experiments? Including it would better contextualize ARINBEV’s performance in the existing landscape.\n2. For different ego trajectory types (e.g., straight, left turn, right turn, high-speed driving, start-stop), does the model’s performance improvement vary? If so, what factors contribute to these differences?\n3. How does the Gaussian prior’s standard deviation (σ=0.5) in the entropy-guided masking strategy generalize to other datasets or scenarios beyond nuScenes and Argoverse2?\n4. The paper mentions that excessive autoregressive inference steps lead to performance degradation. Is there a theoretical or empirical explanation for this phenomenon (e.g., overfitting to local dependencies)?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 4, "strengths": "1. The core idea of exploiting conditional autoregressive modeling to capture structured dependencies among traffic elements (e.g., stop lines preceding crosswalks) aligns well with real-world transportation standards, bringing valuable domain awareness to BEV map estimation.\n2. The single-stage, decoder-only architecture eliminates the inefficiencies of discrete representation learning and separate encoder-decoder stages, achieving superior performance with fewer parameters and faster training, which is practically meaningful for autonomous driving applications.\n3. Comprehensive experiments on two large-scale datasets (nuScenes and Argoverse2) and detailed ablation studies (on masking strategies, feature compression, etc.) effectively validate the contribution of each component in the proposed framework.", "weaknesses": "1. The model’s advantage in this paper seems to lie in utilizing historical information via autoregressive modeling, but the authors fail to clarify whether baseline models use the same length of historical data or are video-based. This ambiguity makes it hard to attribute performance gains solely to the autoregressive design.\n2. While the model achieves better results with fewer parameters and lower computation, the paper lacks in-depth insights explaining why the autoregressive paradigm inherently outperforms two-stage or encoder-decoder architectures in efficiency-performance trade-off.\n3. Inference speed is critical for autonomous driving, yet the paper only reports training time and MACs. It does not compare end-to-end inference overhead (e.g., latency) across methods, which limits the evaluation of its practical applicability.\n4. The ablation study on feature compression and masking strategies, though detailed, does not sufficiently discuss how these components interact with the autoregressive core, leaving gaps in understanding the model’s holistic working mechanism.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)