DriveMamba: Task-Centric Scalable State Space Model for Efficient End-to-End Autonomous Driving
Authors: Haisheng Su
Wei Wu
Feixiang Song
Junjie Zhang
Zhenjie Yang
Junchi Yan
Venue: ICLR 2026 Poster (2026)
Keywords: Autonomous Driving
Task-Centric Paradigm
Scalable State Space Model
Primary area: applications to robotics, autonomy, planning

Abstract: Recent advances towards End-to-End Autonomous Driving (E2E-AD) focus on integrating modular designs into a unified framework for joint optimization. Most of these advances follow a sequential paradigm (i.e., perception-prediction-planning) based on separable Transformer decoders and rely on dense BEV features to encode scene representations. However, such manual ordering design can inevitably cause information loss and cumulative errors, lacking flexible and diverse relation modeling among different modules and sensors. Meanwhile, insufficient training of image backbone and quadratic-complexity of attention mechanism also hinder the scalability and efficiency of E2E-AD system to handle spatiotemporal input. To this end, we propose DriveMamba, a Task-Centric Scalable paradigm for efficient E2E-AD, which integrates dynamic task relation modeling, implicit view correspondence learning and long-term temporal fusion into a single-stage Unified Mamba decoder. Specifically, both extracted image features and expected task outputs are converted into token-level sparse representations in advance, which are then sorted by their instantiated positions in 3D space. The linear-complexity operator enables efficient long-context sequential token modeling to capture task-related inter-dependencies simultaneously. Additionally, a bidirectional trajectory-guided "local-to-global" scan method is designed to preserve spatial locality from ego-perspective, thus facilitating the ego-planning. Extensive experiments conducted on nuScenes and Bench2Drive datasets demonstrate the superiority, generalizability and great efficiency of DriveMamba.

Published as a conference paper at ICLR 2026
DRIVEMAMBA: TASK-CENTRIC SCALABLE STATE SPACE MODEL FOR EFFICIENT END-TO-END AU- TONOMOUS DRIVING
Haisheng Su', Wei Wu', Feixiang Song", Zhang", Zhenjie Yang', Junchi Yan
1 Sch. of Computer Science & Sch. of Artificial Intelligence, Shanghai Jiao Tong University
2 SenseAuto, Corresponding Author
{a (suhaisheng yanjunchi 00s jtu. edu. cn
ABSTRACT
Recent advances towards End-to-End Autonomous Driving (E2E-AD) have been often devoted on integrating modular designs into unified framework for joint optimization e.g. UniAD, which follow sequential paradigm (i.e., perception- wrediction-planning based on separable Transformer decoders and rely on dense BEV features to encode scene representations. However, such manual ordering design inevitably cause information loss and cumulative errors, lacking flexible and diverse relation modeling among different modules and sensors. Mcanwhile, insufficient training of image backbone and quadratic-complexity of attention mechanism also hinder the scalability and efficiency of E2E-AD system to handle spatiotemporal input. To this end, we propose DriveMamba, Task-Centric Scalable paradigm for efficient E2E-AD, which integrates dynamic task relation modeling, implicit view correspondence learning and long-term temporal fusion into single-stage Unified Mamba decoder. Specifically, both extracted image fea- tures and expected task outputs are converted into token-level sparse representations in advance, which are then sorted by their instantiated positions in 3D space. The linear-complexity operator enables efficient long-context sequential token modeling to capture task-related inter-dependencies simultaneously. Additionally, bidi- rectional trajectory-guice spatial locality from ego-perspective, thus facilitating the ego-planning. Extensive experiments conducted on nuScenes and Bench2Drive datasets demonstrate the superiority, generalizability and great efficiency of DriveMamba.
"Yocal-to-global scan method designed to preserve
INTRODUCTION
Recent years witness significant progress in Autonomous Driving (AD), promoting the research trend of End-to-End learning. which unifies modular designs of various tasks (i.e., perception, prediction and planning) into a differentiable framework. Benefiting from the data-driven joint optimization, E2E-AD methods showcase great efficiency with convincing performance.
Current advances in E2E-AD 12 17
37 39 mainly follow the sequential Transformer paradigm as shown in Fig. (a) and (b), which is under the manual ordering of "perception-predictee prediction-planning modular systems. On hand, such sequential design will inevitably cause information loss and cumulative errors across successive modules. On the other hand, the diversity and flexibility of task relations are also restricted under this connection, hindering the possible task synergies learning. Instead, ParaDrive [43] increases the module flexibility through introducing a multi-task framework with parallel transformer decoders for specific tasks Fig. (c). However, still neglects the diverse task relation modeling between modules and lacks a closed-loop evaluation, thus failing to accurately reflect the actual planning performance under realistic driving conditions.
Corresponding author is also affiliated with Shanghai Innovation Institute. This work was in part supported by Scientific Research Innovation Capability Support Project for Young Faculty (U40) of the Ministry of Education of China (SRICSPYF-2Y2025019,

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Published as a conference paper at ICLR 2026
Detection
Modion Planzing
Prediction
Planting
Magping
Mapping
Derac BEV
Langtrr Memurr
x
Scatatilty
Spenial
Lang Leng-tere
Scalaidii
X
Images
Featuee
tmoges
Relation
Effklines
(4) BEV-Centric Sequential Trunsformer Pirxligm
(b) Query Centric Sequential Transfomerr Paradigm
Quers
Quarr
Desection
Detection
Unifeed
Mapsing
Mapping
Profictioon
Unifiie Mambhe
Profiction
Muli-ricw
Multi view
Images
Long-temn Merory
Sealahilty
Taak Rdaaion
Eilickncy
Hidtey Memory Quanae
Task Rolatton
(c) REV-Centric Panillel Transformer Paradigm
Task-Contric Scalable Maniha Panddigm
Figure I: Comparison of different end-to-end autonomous driving paradigms. (a) and (b) follow
the sequential Transformer paradigm based on dense BEV features [[2] and sparse query [39
respectively. (c) explores the multi-task BEV learning with parallel Transformer decoders 43 (d)
Our proposed Task-Centrie paradigm learns task-relations dynamically through a unified Mamba decoder, which directly leverages raw sensor inputs and history token memory for long-term spa- tiotemporal modeling, without construction of expensive BEV features, thus scalable and efficient.
Another challenge of E2E-AD system is handling spatiotemporal input with high efficiency, BEV- Centric methods 12 17 43 rely on dense BEV feature generation to learn scene representations, which are computationally expensive especially for long-range perception. Besides, BEV-based methods usually perform temporal fusion through storing history BEV features [[3] 21. which is impractical for long-term modeling. Meanwhile, the scalability of E2E-AD system also essential for scaling law exploration with exponential increase of driving data and model parameters. Although recent sparse Query-Centric Centric methods [16] 37: 39] are proposed to learn sparse representations, they still have limited scalability owing to the burden of quadratic-complexij attention and lack of efficient interaction order for downstream ego-planning.
We propose Drive a Task-Centric scalable paradigm for efficient end-to-end autonomous driving via unified Mamba decoders as shown in Fig. T (d). Instead of generating dense BEV features, DriveMamba is built upon sparse representations through image and task tokenization in advance. Then both task queries and image tokens are processed by a unified decoder to capture dynamic task-task relations and implicit task-sensor correspondence simultaneously in a parallel manner. Mcanwhile, a first-in-first-out memory queue is generated to store the history task queries for temporal modeling during the streaming process. Inspired by the adoption of Mamba (8] in NLP domain with its distinct selective state space model (SSM), 226 51 verify its success in vision tasks which leverage multi milti-directional SSMs for enhanced 2D image processing. Naturally, in this paper, we investigate its applicability to E2E-AD. Specifically, we introduce a unified Mamba decoder for parallel task relation modeling and view correspondence learning. To facilitate better spatial flecal-to-globut
locality preservation from ggo-perspective, a bidirectional Trajectory-Guides scan method is designed to handle the spatiotemporal tokens (i.e., Task and Sensor) for long-conten modeling with high efficiency. Thanks to its linear linear-complexty operator, which offers significant reduction of memory usage than Transformer, our proposed unified Mamba decoder seamless to scale up through simple stacking. After unified decoding and query enhancement, task queries send to different task heads for specific purposes. Notably, our DriveMamba-Ting exhibits great advantages as an efficient end-to-end planner, achieving 53.54 Driving Score on Bench2Drive and 0.1 0.13% Collision Rate on nuScenes datasets respectively, with 17.9 FPS running efficiency. The highlights are follows. We leave related works in Appendix Aor page limitation.
We propose Dambaa, a Task-Centric paradigm for end-to-end autonomous driving, which integrates view correspondence learning, task relation modeling and long-term temporal fusion into Unified framework with high efficiency and scalability.
We design a hybrid spatiotemporal sean method to capture task-related inter-dependencies from trajectory-guice "Local-to-Glohe and to-epporro bidirectionally, which preserves better spatiotemporal locality for ego-planning.
Extensive experiments are conducted on nuScenes 2 and Bench2Drive [[5] for both open and closed-loop planning evaluation, underscoring its prominent efficiency and scalability.
2

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Published as a conference paper at ICLR 2026
Outpat Saalt eegiets Preernets
Planiin Hleod
llea
Mapping llead
Predlition llead
Takes
AgrerToive
C EER
x
Unified Mamba Decoder
Pardaa Gobeddin
Image Tasl Tokenization
Forword
(a) DriveMamba
(b) Hybrid Spatiotemporal Scon
Figure 2: Framework of Drive.ammb. The multi-view images are encoded into token-level feature sequence and the spatiotemporal queries for different tasks are initialized respectively. Then we adapt the unified Mamba decoder with bidirectional serialization for simultaneous view correspondence learning, task relation modeling and long-term temporal fusion.
METHOD
2.1
PRELIMINARIES
The SSM-based model, Mamba 88 is discrete variant of continuous system, which establishes mapping between inputs sr(()= &M and output y(t) E IR through a hidden state vector l((l) R.V. This system can be preliminarily expressed as [9]:
(t())= = Ah(t) + Br((), v(t) Ch(t),
where A E NxN represents the learnable evolution parameters and B E RXI. CINN are the projection parameters. The zero-order hold (ZOH) method is used to convert A and B into discrete parameters A and B with a time scale parameter A, which can be defined as follows:
A = exp(AA), B = (Ax) AA) I) AB
Compared to previous SSMs, Mamba enhances contextual awareness by introducing a dynamic Selective Scanning Mechanism, abbreviated as S6. Specifically, its parameters B BBx CE R M and A Bxxx are directly obtained from the input x E xx for adaptive modulation. Thus, the discretized SSM can be rewritten as:
h4 A B Y/ Ch+-
The original Mamba block is designed for ID sequence modeling and neglects the spatial awareness when directly adapted to visual tasks. To this aim, Vision Mamba [5]] introduces bidirectional Mamba (B-Mamba) block in Fig. 4a). which adapts bidirectional sequence modeling for vision- specific applications. This block processes flattened visual sequences through simultaneous forward and backward SSMs, enhancing its capacity for spatially-aware processing. In this work, we extend the B-Mamba block as unified task decoder for end-to-end autonomous driving.
2.2 DRIVEMAMBA
Fig. shows the overall framework. We first introduce task-specific Positional Embeddings (PE) to distinguish spatiotemporal tokens from different tasks, sensors and timestamps, which consist of three components: spatial PE, temporal PE and task PE. After that, the token sequence is fed to the proposed DriveMamba with stacked unified decoders for view correspondence learning, task relation modeling and long-term temporal fusion simultaneously following the hybrid scan order.
2.2.1 TOKEN FORMULATION
Image Tokenization. To facilitate visual representation learning, multi-view sensor images are firstly encoded by off-the-shelf visual encoders (i.e., ResNet [10], ViT [. ViM (5I) separately to obtain sensor tokens T Teensor E N xxxxxxxxx where N. is the number of cameras, and H, W indicate the height and width extracted feature maps after patchification. Hence the sequence length input sensor tokens is G N. H W.

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Published as a conference paper at ICLR 2026
X/S
X/S
X
Ego Trijectory
Y/T
Y/T
(4) Horizontal/ Prrizontal//paatian SSpatial
(b) Vertical/ (Tricaal//emprall
Ego L2G
Trajectory L2G Bidirectional
(c)
(d)
First Bidirectional
-iist Ritirnctional
Bidirecctonal
Figure 3: Different bidirectional scan methods. Both spatial and temporal scan types are illustrated.
Task Tokenization. To conduct interaction modeling between the ego-vehicle and surrounding elements the driving scene, we pre-define three types of queries to extract task-specific information, namely Agent Queries, Map Queries and Ego Query. Specifically, agent queries represent dynamic objects, which will be used for object detection and motion prediction. Map queries are responsible for online mapping of static elements such lanes and crossings, etc. Ego query encodes both behavior intention and environmental interactions of the ego-vehicle for planning.
Token Initialization. Inspired by DAB-DETR [23], we initialize both sensor tokens and task queries with two parts respectively: semantic embeddings and positional embeddings. The semantic part of agent and map queries are randomly initialized learnable parameters Qegent NR xC and Qmap R Nm xC where Na. Nm are the number of agent and map queries. And the semantic part of sensor tokens sensor are obtained during the image tokenization process, while the ego query's s semantic embeddings Qego TR. XxC are initialized through encoding the canbus information with an MLP, which is composed of a linear layer and a ReL.U activation.
Three types of PE are considered to distinguish spatiotemporal tokens, where Spatial PE encodes the reference position of each token in BEV space, Temporal PE encodes the frame index, and Task PE encodes the task type. The reference positions of agent P Pagent E N. x2 and map queries Pmap E R.N,, x2 are initialized uniformly within the pre-defined perception range, while the ego- vehiele position Pego R1x2 initialized as origin. Then the reference positions and timestamps T are converted into SPE and TPE respectively through sine encoding. Additionally, each token is equipped with one of learnable task embeddings TE R4xC Formally, the PE of task token is:
PE,- (E()E TE.);
where C'at denotes concatenation and SE means sine encoding. Moreover, we adopt an additional depth prediction branch to estimate point-level depth for each sensor token respectively, instead of relying on the uniformly distributed ray-level 3D positions used in DriveTransformer (16), which cannot be directly employed for token sorting. Specifically, for each image token with pixel coordinate (u,, v.) of camera k. its corresponding location in 3D space is:
(5) where R& and K2 the extrinsic and intrinsic matrix of camera ks and di is the depth value of the predicted depth map. Besides, to model the object movements, the motion-aware layer normalization 42 is adopted for motion compensation implicitly before unified decoding.
P seensor (i,b) Kg [[, di U1 di d ER.
2.2.2 HYBRID SPATIOTEMPORAL SCAN
To apply the bidirectional Mamba (B-Mamba) block for spatiotemporal input, we extend the original 2D scan into different bidirectional 3D seans as shown in Fig. 3 (a) Horizontal/ppatial Firstt arrang- ing horizontal/spatial tokens by L-axis/Spatial-dimensiii cally/tepoorall;; (b) Verrical//emporal First, arranging vertical/temporal tokens by maxis/Temporia dimension BEV location then stacking them horizontally/spatiillyy (c) Ego- Local2Global,
BEV location then stacking them verti-
4

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Published as a conference paper at ICLR 2026
Urear Projation
Sequanee
@ Naliploeikss
Activation Nerm
Irandtemttionn
C Sunarue tostten
Agentsieserr Eelirccional Marlha
distrnctinall Mamnta
Fi
Badloarrd
Badonari
Carrr
SSM
May Scrsor Bidirecional Karrh
Map-llenary
Thak-quayy
Furward
Peevandi
Coze
SSM
Taok Belation Medeling
Egg Sanerr
Eyp Horzery
Daleestional Hariaa
Bidincttioaal Miamba
Cornesaanience
Long foras Temuporal
Fusion
(a) Bidirectional Mamba
(b) Unifted Mamba Decoder
Figure Detailed structure of Unified Mamba Decoder. (a) B-Mamba layer is the basic component
used for decoding. (b) We illustrate the divided modeling type here for clarity.
organizing spatial tokens by BEV location through spiral traversal around the ego origin (refer to Appendix[Djoo for more details); (d) Trajectory-Centric Local2Giobal: Considering the diversity and uncertainty of multi-modal ego-planning, we introduce a trajectory-guidde attention map to dynami- cally adjust the scan order based the relative distances between the interpolated future waypoints a// E RT of ego-vehicle and 3D positions Ptask of surrounding task queries (i.e., agents and maps) at intermediate layers. The importance wy of task query Q; with reference positions P, is:
w; = 11 - miin((]|PP) 1111)//))) max(fmin[[ - 1/2l2) N.*.
(6)
which indicates that the closest in-path query is prone to obtain the highest importance during the Ego-Env interaction process, conforming the driving attention distribution. Moreover, our experiments in Tab. l demonstrate that alternation of Horizontal/Verticall- across consecutive decoder layers can preserve better spatial locality for view correspondence learning between task queries and sensor tokens, while Prgjectory-Centiie Local2Global bidirectional scan is well designed for task relation modeling and motion planning from ego- gso-persppecive. Besides, the Spatial-Firs First bidirectional scan is the most effective yet simple for history memory token arrangement. Thanks to the linear complexity of Mamba, our Drive.ammb is capable of handling high-resolution and long-term memory tokens efficiently.
bidirectional scan
The motivation of our design is to preserve spatial locality suited to different tasks, rather than proposing more complex scan method. Previous SSM-based models in 2D vision tasks focus on designing well-structured scan patterns for semantic extraction 14: 52 Furthermore, our trajectory- guided soncclltt-Globlll scan considers the interaction order from ego--erspective which is intuitive for interactive- peaerattvv--llaningg but neglected in Transformer-ben based planners (i.e., Drive Driverransfommer,
Discussion.
2.2.3 UNIFIED MAMBA DECODER
Our unified Mamba decoder (see Fig. consists of three types of B-Mamba layers for different usages: View Correspondence Learning. Task Relation Modeling and Long-term Temporal Fusion. We use joint modeling type with single-layer B-Mamba for each type discussed in Sec. 3.3
View Correspondence Learning. After bidirectional serialization according to reference positions of task queries P Pass we adopt a single B B-Mamba layer to allow task queries to directly extract task- without construction
specific semantics from raw sensor features with 3D position encoding 25 34
of dense BEV features and information loss:
Qresk VCL( PPrssk
T Tsensor + DEscnsor)),
where + denotes concatenation, and Qrask indicates the updated task query. Cross-view corre- spondence between desired task outputs and raw sensor inputs can be learned implicitly with linear attention during the joint training.
Task Relation Modeling, Benefiting from parallel paradigm of query design, we can further explore dynamic relations among different task queries with an additional B-Mamba layer, thus facilitating the task dependencies modeling (i.e. planning-orient perception) without manual connections (i.e., sequential paradigm):
Qrask TRM Qtask PPrssk)
(8)
5

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Published as a conference paper at ICLR 2026
Table Variants of DriveMamba difffer in sizes, mainly in the visual backbone and decoder layers.
Model
Decoder
Backbone
Resolution
#Parameters
#Latency
Tiny
3 Layers, 256 Hidden
ResNet-55
256 704
40.2M
55.8ms
Small
6 Layers, 256 Hidden
ResNet-50
384x 1056
48.2M
90.2ms
Base
6 Layers, 512 Hidden
VMamba-B/16
384x 1056
172.2M
164.3ms
Large
12 Layers, 768 Hidden
ViT-L/16
512x 1408
607.5M
599.
Besides, the task relation modeling capability can be seamlessly scaled up with more stacking layers and end-to-end training capture both intra-class (i.e., Agent-Agent, Map-Map) and inter-class (i.e., Ego-Agent, Ego-Map and Agent-Map) interactions. Meanwhile, the proposed Trajectory-guid -uritrryyggdddd Secal-to-Globar scan method also conforms to the natural interaction order of ego-centric planning.
Long-term Temporal Fusion. Inspired by [42], we further resort to Query-Centir temporal fusion through maintaining FIFO-style memory queue store history queries of different tasks respectively, rather than use history BEV features as 17, which are computationally expensive and storage unfriendly for long-term temporal modeling. The temporal fusion process can be formulated as:
f0 task
1.f (f J to Tuucue
where ig is the current time-step and T Tquene the length of temporal queue. this way, the temporal fusion can be conducted at each decoder layer and Top-K task queries from decoder output of previous timestamp are pushed into FIFO queues. Moreover, history task queries and positions are transformed into current ego coordinate system and are compensated for object movements with the Motion-aware Layer Normalization (MLN) 42 in advance.
2.2.4 ARCHITECTURE DETAILS
As shown in Tab. [ we design four different variants of DriveMamba architecture with different model sizes. Each variant includes specific visual backbone and task decoder with fixed number of layers as well as channel dimensions. For the image backbone, both CNN-based (ResNet-50), Transformer-basee basedd (ViT-L/16) and Mamba-based (VMamba B//6) networks are adopted. The 2D image features extracted by the backbone are passed to the unified Mamba decoder together with 3D positional encoding. The unified decoder consists of layers with C' channel dimensions. As for DriveMamba- Tiny, L is set to 3 and C is set to 256. Finally, the decoder outputs both perception, prediction and planning results after parallel decoding with iterative optimization.
2.2.5
TASK HEAD
Object Detection & Motion Prediction. In DriveMamba, the head for each task generally consists of 2 Fully Connected (FC) layers. After parallel decoding with the unified Mamba decoder, agent tokens pass through detection head to generate 3D boxes and classes. Subsequently, the agent tokens are augmented with mode embeddings and processed through a motion head, to predict multi-modal trajectories and corresponding probabilities.
Online Mapping. Map tokens are initially structured as point-level tokens. With the unified decoder, the updated map tokens processed by specific regression head to generate vectorized map instances, where each instance contains 20 points and instance-level class.
Planning. DriveMamba employs series of tokens represent future waypoints of ego vehicle, and each token encodes a discrete waypoint clue. After task interaction, waypoint tokens are processed R T. x2 with T. 6 continuous waypoints.
by planning head to predict future trajectory v0
Iterative Optimization. Outputs of every block the unified Mamba decoder are explicitly supervised by specific losses. Each block predicts relative offsets base the previous outputs, and then updates the task reference points and task position embeddings accordingly for next block.
Residual Learning. Before passing the updated task tokens to designed heads for output prediction, we combine the task position embeddings through element wise addition, so that the task heads can predict positional offsets more accurately.

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Published as a conference paper at ICLR 2026
Table Open-loop Planning on nuScenes val set under ST-P3 metric. i: the usage of ego-status.
L2 (m)) L
Collision (%)
Method
1s
2s
3s
Avg.
1s
2s
3s
Avg.
ST-P3 [1]
1.33
2.11
2.90
2.11
0.23
0.62
1.27
0.71
1.6
OccNet [40
1.29
2.13
2.99
2.13
0.21
0.59
1.37
0.72
UniAD 12
0.48
0.74
1.07
0.76
0.12
0.13
0.28
0.17
1.8
VAD-Base 17
0.41
0.70
1.05
0.72
0.07
0.17
0.41
0.22
4.5
BEVPlanner [22
0.27
0.54
0.90
0.57
0.04
0.35
1.80
0.73
DriveMamha- (Ours)
0.25
0.42
0.66
0.44
0.12
0.09
0.24
0.15
17.9
DriveMamha-Smann (Ours)
0.23
0.40
0.64
0.42
0.06
0.07
0.22
0.12
11.1
DriveMamba-Rard Base (Ours)
0.22
0.40
0.63
0.41
0.05
0.06
0.21
0.11
6.1
DriveMamba-Lagg (Ours)
0.21
0.38
0.62
0.40
0.01
0.04
0.21
0.09
AD-MLP 49
0.20
0.26
0.41
0.29
0.17
0.18
0.24
0.19
BEVPlanner++
0.16
0.32
0.57
0.35
0.00
0.29
0.73
0.34
SparseDrive-B 39
0.29
0,55
0.91
0.58
0.01
0.02
0.13
0.06
7.3
ParaDrive 43
0.25
0.46
0.74
0.48
0.14
0.23
0.39
DriveTransformer--ni
16
0.19
0.33
0.66
0.39
0.01
0.07
0.21
0.10
10.7
DriveTransffrmerr-Larr 16
0.16
0.30
0.55
0.33
0.01
0.06
0.15
0.07
4.5
DriveMamba-Tiny (Ours)
0.19
0.35
0.59
0.38
0.07
0.09
0.24
0.13
17.9
DriveMamba 5mall (Ours)
0.18
0.33
0,56
0,36
0.03
0.05
0.17
0.08
11.1
DriveMamba- (Ours)
0.17
0.33
0.54
0.35
0.02
0.04
0.17
DriveMamba-Lane Large (Ours)
0.16
0.30
0.52
0.33
0.00
0.03
0.16
0.06
1.7
2.2.6 END-TO-ENU TRAINING
Loss. Since the tasks in DriveMamba are equally prioritized with sequential dependencies, single-stage end-to-end training strategy is adopted during the training phase. The training loss is:
L Σ (Caet + S'mp Edepehh + mootion + Cpiin)s
(10)
where Ldet and Lmop indicate the perception loss for object detection and online mapping respectively, Emotion corresponds to the motion prediction loss. Ldepth adopted to supervise the depth prediction branch as [34]. Cpion is used for ego-trac ggo-trajectory regression. And L is the block number of the decoder. Meanwhile, vectorized planning constrains identified in [77 such as collision, overstepping and direction also included in Lptan for regularization. The weight terms Atask of different losses adjusted empirically to ensure the same magnitude.
3 EXPERIMENTS
3.1 DATASETS AND METRICS
NuScenes 2 is sophisticated outdoor dataset comprising 1000 driving scenarios lasting 20 seconds respectively. encompasses total of 1.4M 3D object annotations covering 10 categories, frequency of 2Hz. We evaluate the effectiveness of our approach on perception tasks (i.e., object detection and online mapping) using mAP and NDS metries respectively. And minADE (m) and minFDE (m) metrics are reported to assess the motion prediction results. Besides, the open-loop planning performance measured using Collision Rate (%) and 1.2 Displacement Error (DE) (m) metrics, following the conventions in [1: 17].
Bench2Drive [5] offers evaluating the multiple capabilities of E2E-AD systems in closed-loop manner. This benchmark comprises 1,000 clips, covering 44 interactive scenarios, 23 weather conditions, and towns in CARLA v2 6 Adhering to the official settings, 950 clips were utilized for training. while the remaining 50 clips were reserved for open-loop evaluation. For closed-loop evaluation, the trained model was deployed in CARLA across 220 test routes, and key metrics e.g. Driving Score (DS), Success Rate (SR), and Efficiency were caleulated accordingly.

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Published as a conference paper at ICLR 2026
Irokiro VAB-Te
Konde lesel
Marbbooudd
Belter Paster
30g
50.55 3%% caantti
11:5
Sufer
finter
GPU
Resolution (heigh))
Resolution (height
Speed Comparison
(b) Memory Comparison
(6) Comparison
Figure 5: Comparisons between Transformer-based and our Mamba-based E2E-AD methods.
Table 3: Planning Performance in Bench2Drive 15
Avg. 1.2 averaged over future seconds.
Method
Open-loop Metric
Closed-loop Metries
Latency (ms)
Avg. 1.2
Driving Score
Success Rate (%)
Efficiency
Comfortness
AD-MIP 49
3.64
18.05
0.00
48.45
22,63
3.0
663.4
VAD 17
0.91
42.35
15.00
157.94
46.01
278.3
ED
60.33
DrieeTransSormoe-Laage
16
0,62
63.46
35.01
100.64
20.78
211.7
DriveMambe- -iay (Ours)
0.77
53.54
27.27
137.88
20.55
55.8
DriveMamha-Smalll (Ours)
0.75
60.78
31.82
150.46
16.52
90.2
DriveMambee-Bsc (Ours)
0.71
65.50
36.82
158.33
21.51
164.3
DriveMamba-Long (Ours)
0,70
66.82
37.73
152.91
18.77
599.1
Table 4: Design of Unified Mamba Decoder. Open- Table Evaluation of Planning-oriented
loop planning nuScenes val set is reported.
Perception Performance nuScenes val set. ResNet-50 adopted as visual backbone.
Method
Planning
2x
35
(ne)
Avg.
1s
Planning (%)
3r
Avg.
Decoder Layers
Detection
Online Mapping
Joint Dec.
0.25
0.42
0.66
0.12
0.09
0.24
0.15
Divided Doc.
0.26
0.69
0.12
0.25
mAP1 T
NDS
mAP
W/o VCL.
0.34
0.58
0.91
0.61
0.23
0.33
0.1
0.39
3
64.7
w/o
$1.2
62.5
67.0
w/o TRM
0.23
0.41
0.66
0.44
0.15
0.15
0.32
0.20
53.6
65.1
70.4
weo IO
0.25
0.44
0.71
0.47
0.42
0.31
0.41
0.38
12
55.1
68.3
72.2
0.68
3.2 MAIN RESULTS
Open-loop Planning Evaluation. As shown Tab. 2. our DriveMamba exhibits notable advantages over previous works in both planning performance and running efficiency even with shallower visual backbone and fewer decoder layers. Specifically, DriveMamba-Tin 42.1% and 38.9% compared with UniAD 12 and VAD [17, along with 11.8% and 31.8% reduction of average collision rate respectively. Besides, benefiting from our efficient sparse representation learning and parallel task decoding. we only consume 55.8 ms during inference, achieving 17.9 FPS (10x and 4x faster accordingly). Through deepening the visual encoder and task decoder with more stacking layers, the open-loop planning performance can be further boosted as expected, showeasing the great advantages of our proposed unified yet scalable Mamba paradigm for interactive planning.
reduces the average L2 error by
Closed-loop Planning Evaluation. To further evaluate the interactive planning ability, we also examine the closed-loop planning performance in Bench2Drive [15] as shown in Tab. 3 It can be observed that AD-MLP [49] has a high L2 error and bad closed-loop planning performance using merely ego status input, which is different from findings in nuScenes (2), demonstrating the behav- ior diversity in Beneh2Drive. Besides, UniAD [22 has a lower L2 error compared to VAD [17] but with worse closed-loop planning performance discussed in 22 Recently, DriveTransformer (16) achieves the lowest 1.2 error with convincing closed-loop driving score. However, our DriveMamba exhibits superior performance with fewer parameters and higher efficiency (i.e., DriveMamba- vs. Driverrranformee--aare underscoring its promising scaling capability.
3.3 A ABLATION STUDY
Modular Study. In Tab. we first study the modular effectiveness of our proposed unified Mamba decoder exhaustively. Three components ablated respectively, namely View Corresponding
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Table Study of DriveMamba Scalability. Open-loop perception scalability is evaluated on the nuScenes val set, while the closed-looe planning scalability is conducted under the Dev10 benchmark of Bench2Drive
[15] for quick validation. DS: Driving Score, SR: Success Rate.
Visual
Decoder Layers
Detection
Mapping mAP
Planning
Encoder
mAP
NDS f
DS
SR
ResNet-50
3
34,8
45.4
50.3
51.1
10
ResNet- 101
37.5
46.6
52.1
51.6
10
VMamba- BB16
3
37.1
44.8
53.5
52.6
10
ViT-L/16
50.8
59.9
64.8
55.8
20
ResNet-50
33.6
44.9
50.3
58.8
20
ResNet-50
9
33.7
45.4
49.4
63.4
30
ResNet-50
33.1
45.4
46.6
66.5
40
Table 7: Effect of Different Sean Methods. Both perception and planning performance reported on nuScenes val set with different scan types. EC: Ego-Centric, TC: Trajectory- Centrrc.
Detection mAP f
Mapping mAP f
Planning
Spatial
Temporal
NDS t
L2(m) 4
Coll. (%) +
Horizontal-Finn
Spatial-First
31.9
41.8
48.7
0.55
0.21
Vertical-First
Spatial-Firs
32.8
43.4
46.3
0.52
0.22
Horizontal- Vertical
Spatial-First
33.9
44.6
50.8
0.49
0.21
EC Local2Global
Spatial-First
30.4
40.6
43.2
0.46
0.19
TC Local2Global
Spatial-First
26.0
35.1
39.8
0.45
0.17
Hybrid
Spatial-First
34.8
45.4
50.3
0.44
0.15
Hybrid
Temporal- 34.7
44.3
50.2
0.48
0.16
Learning (VCL), Long-term Temporal Fusion (LTF), and Task Relation Modeling (TRM). The VCL layer is the most important operation for randomly initialized queries to extract rich context from sensor tokens. And the LTM conducts efficient temporal modeling with the help of streaming process, while the TRM layer learns dynamic inter-task and intra-tac tasa- relations for better planning.
Paradigm Study, We further study the advantages of our parallel paradigm in many aspects, (1) Modeling Type: As shown in Fig. three types of B-Mamba layers are illustrated respectively for clarity. However, we compare the performance joint modeling with single B-Mamba layer and divided modeling with a shared B- B-Mamba layer in Tab. where the joint modeling can yield better planning performance without introducing additional parameters. (2) Modeling Order: Existing methods [12] 17 (3] usually follow the sequential modeling order, which is not conducive to learn diverse task inter- dependeceesss and thus inferior as in Tab. and 3 (3) Attention Type: Compared to cunsformer-basee end-to-end planners, our DriveMamba adopts linear complexxt SSM to learn ego-centric driving attention from sequential task tokens and achieves 3.2x increase in processing speed and requires 68.8% less GPU memory consumption when scaling up input resolution directly as shown in Fig. 5 demonstrating its efficiency and effectiveness in handling long sequential tokens.
Scalability Study. In Tab. 6 study the framework scalability through scaling up both encoder and decoder respectively. (1) Encoder: When scaling up the visual backbone from ResNet-50, ResNet- 101 to VM and ViT-L/16, we can observe that the open-loop perception performance is increased significantly, while the closed-loop planning performance exhibits slight improvement. (2) Decoder: Benefiting from the parallel design. we can scale up the decoder through stacking more layers directly. And we can find that the decoder scaling contributes most on ego-planning while causing a little drop of perception performance, which maybe because our Task- Cesktientric E2E-AD model learns planning- ooriene perception rather than general perception.
Planning-oriees oriented Perception Learning. evaluate the planning-oriented perception performance, we conduct an additional ablation regarding the perception on Closest In-Path Objects (CIPO) using the nuScenes val set, as shown in Tab. 3] CIPO defined as all objects and lanes within 5m around each Ground-Truth future waypoint. The results reveal that CIPO perception performance improves monotonically with additional stacked layers of our unified Mamba decoder, confirming DriveMamha's effectiveness in learning planning-orienter task synergy and dynamic relations.

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Published as a conference paper at ICLR 2026
Table Initialization of Trajectory Prior.
Table 9: Sensitivity Prediction Errors.
Depth
mAP1 1
Detection
NDS
Mapping mAP1
L20m) 4
Plunning
Coll. (%) 4
Depth
Detection ND6
Mapping
1.2eet
Planning
Coll.
Origin
34.8
45.4
0.15
Calibration
Uniform
34.7
45.3
50.3
0.46
0.16
Normal Prediction
Noisy Production
34,8
45.4
50.3
0.4
0.45
0.15
Random
34.6
45.1
50.2
0.14
GT
42.3
30.5
54,8
0.22
0.13
Table 10: Necessity of Trajectory-Centeee Scan on Bench2Drive.
Scan
Driving Score
Success Rate
Ego-Centric L.2G
50.27
25.45 27.27
Srajectory-Centee L2G
53.54
Design Choice Study. Moreover, we explore different design choices in Tab. and Tab. 7 including Scan Type, Iterative Optimization (IO) and Residual Learning (RL). To conclude. Vorizzntal-Vertical Vertical spatial scan useful for sensor correspondence learning and Trajectory- spatial scan is more reasonable for task relation modeling and ego-centric planning. And hybrid of them can lead ultimate performance. Mcanwhile, Iterative Optimization and Residual Learning also demonstrate their necessities for better performance especially for model scaling up evaluation.
Local2Global
3.4 ROBUSTNESS ANALYSIS
Initialization of Trajectory Prior. To examine the sensitivity of trajectory prior, we try to ablate different initialization ways of waypoint positions used for token sorting across decoding layers, and the results are shown in Tab. g where "Origin", "Uniform" and "Random" indicate zero offset, fixed offset (A,=0, A,=1) and random offset (μ = 0, = 1) initialization of future waypoints of ego vehicle, respectively. And we can find that though with different initialized waypoints, both perception and planning performance exhibit trivial changes, showcasing the training stability of DriveMamba and robustness of our proposed egocentric design for task synergy learning.
Sensitivity to Depth Prediction Errors. As shown Tab. 9 we further evaluate the sensitivity of overall model to various qualities of depth, which is indispensable for sensor token sorting in the view correspondence learning layer during unified decoding. In short, when utilizing the Ground-Truth depth map for sensor token positioning, improvement of perception performance (+4.9 NDS) as expected. However, we observe relatively smaller improvement of planning performance (-0.02% Collision Rate), demonstrating the robustness of our Drive in handling inaccurate or uncertain detections. Besides, when adding the Gaussian noise either camera extrinsic parameters (incorrect calibration) or predicted depth map directly, the perception performance drops more significantly than planning performance respectively. It might be because DriveMamba avoids the sequential modeling and construction of BEV features as previous methods, which are sensitive to perception inputs. On the contrary, DriveMamba directly interacts with raw sensor features and thus be able ignore those failure or noisy inputs and demonstrates better robustness ego-planning.
DriveMamba can obtain noticeable
Generalization of Trajectory-Centri Scan. To evaluate the generalizability of our proposed trajectory-guiie guidedd scan method, we have also conducted the ablation of Trajectory-Centrie L2G scan in Task BaskyQuery B-Mamba layer for task relation modeling on Bench2Drive dataset as shown in Tab. TO which further demonstrates the effectiveness and generalizability of our trajectory- scan.
4 CONCLUSION
We have proposed task-centric scalable paradigm based on Mamba architecture for efficient E2E- AD, termed as DriveMamba. DriveMamba integrates dynamic task relation modeling, implicit view correspondence learning and long-term temporal fusion into unified decoder, which is easy to scale up with simply stacking. Built upon sparse representations, a hybrid spatiotemporal scan method is further designed capture task-related inter-dependencies without spatial locality loss. Extensive experiments conducted on the nuScenes and Bench2Drive datasets demonstrate the great effectiveness, convincing efficiency and promising scalability of our proposed DriveMamba.
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A RELATED WORK
A.1 END-TO-EN. TO--DO-ENDDDA PLANNING
Recent end-to-end planning works can be categorized into implicit and explicit classes. The implicit
type of methods 4 S 22 31
36 44
45
46
30
32 20 perform direct optimization on planning
task, which lack interpretability and controllability in realistic applications. [1] 17 enhance the model interpretability through integrating perception, prediction and planning tasks into a unified BEV-based framework explicitly and sequentially. Differently, VAD [17] vectorizes the perception output for interactive planning. thus achieving superior efficiency. ParaDrive [4 removes all explicit task links and proposes a multi-task framework with parallel Transformer decoders. GraphAD (50)
utilizes interaction scene graphs to capture object interactions. In addition, some works [ 3T: 33
study how enhance models' capability through using multiple sensors or incorporating safety- enhanced rules. More recently, there emerges some query-based methods [39 37 [6] built upon
sparse representations. However, most of these methods still overlook the dynamic lask-relation modeling and suffer from inefficiency as well as inflexibility to scale up. Particularly, we notice that DriveTransformer [16] is the most similar work which unifies sparse representation, task parallelism and streaming processing into a Transformer decoder for parallel modeling, demonstrating promising model scaling trend. However, still suffers from limited scalability with quadratic increasing of GPU memory consumption. Besides, DriveTrannforrng [16] neglects the importance of interaction or- der from ego-perspe geo-perspecttve, which employs global uniform attention with a quadratic-complest operator, hindering the continuous scalability study and efficient -rrinte modeling for long-time/ high-resolution input and larger models. Unlike other Transformer based methods, we did not extensively explore data processing order, but instead integrated the "Task- Spatial- Local-to-Glonn
Temporal Positional Embeddings", "Trajectory-cemts Linear Attention" into unified Mamba decoder for efficient modeling, which already achieved the best performance with prominent efficiency for further scaling of autonomous driving models.
Scan" and "Query-centric
A.2 STATE SPACE MODELS
State Space Models (SSMs) have recently demonstrated considerable effectiveness in capturing the dynamic dependencies within language sequences through state space transformation. A structured state-space sequence model is introduced [9] model long-range dependencies, which inspires various further attempts, such as S5 (35) H3
7 GSS [29] and Mamba [8. Differently, Mamba stands out with its selective mechanism using parallel scan (S6). Due to its linear complexity operator, Mamba excels at long sequence modeling compared to Transformer [41] based on quadratic- complexity attention. The great potential of Mamba motivates series of works 14 5T in vision tasks, where Vim [51] particularly introduces a bidirectional SSM and position embeddings to learn enhanced 2D image features. Better performance and higher GPU efficiency than Transformer can be obtained on visual downstream tasks like object detection, image classification and semantic segmentation. MambaBEV 47 designs temporal Mamba model for BEV-level feature fusion and Mamba-DETR DETR head for 3D object detection. DRAMA (48) adopts Mamba fusion module to fuse camera and LiDAR features in the BEV space, and introduces a Mamba- Transformer decoder to plan deterministic trajectory. As prior works which employs SSM for efficient sequence modeling in 2D vision tasks (e.g.. VisionMamba vs. ViT), we are the first to explore a pure SSM-based decoder for visual End-to-End Autonomous Driving (E2E-AD). Our decoder jointly learns implicit view correspondence, dynamic task relations, and long-term temporal dependencies within unified, fully sparse, ego-centric design. To adapt SSMebased framework to E2E-AD, we introduce three key innovations to achieve convincing performance: (1) Accurate depth estimation of sensor tokens to facilitate spatial positional embeddings construction and reliable spatial/temporal token sorting; (2) Hybrid scan strategies designed for different tasks of various purposes: better spatial locality preservation for semantic perception and trajectory-guider locality preservation for ego-centric planning; (3) Planning- saanning-oriented task synergy learning. Extensive ablations and experiments showcase the great efficiency, superiority and scalability of DriveMamba for end-to-end autonomous driving.
B EVALUATION METRICS
Perception. The evaluation for object detection and online mapping follows standard evaluation protocols [8 19 [8]. For detection, we use mean Average Precision (mAP), mean Average Error of Translation (mATE), Scale (mASR), Orientation (mAOE), Velocity (mAVE), Attribute (mAAE)
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and nuScenes Detection Score (NDS) evaluate the model performance. For online mapping, calculate the Average Precision (AP) three mäp classes: lane divider, pedestrian crossing and road boundary, then average across all classes to get mean Average Precision (mAP).
Motion Prediction. Following the standard motion prediction protocols, we adopt minADE (mini- mum Average Displacement Error), minFDE (minimum Final Displacement Error) and MR (Miss Rate) as evaluation metrics. Similar to the prior works, these metrics are only calculated within matched TPs, and set the matching threshold we set the miss FDE threshold to 2.0m. Similarly to the prior works 12] [9], we merge the car, truck, construction vehicle, bus, trailer, motorcycle, and bicycle as the vehicle category. and all the motion prediction metrics are measured on the vehicle category only.
1.0m in all of our experiments. As for the MR,
Planning. We adopt commonly used L2 error and collision rate evaluate the planning performance. The evaluation of L. eтror is aligned with VAD [17]. For collision rate, there are two drawbacks implementation, resulting inaccurate evaluation in planning performance. On one hand, previous benchmark convert obstacle bounding boxes into occupancy map with grid size 0.5m, resulting in false collisions certain cases, e.g. ego vehicle approaches obstacles that smaller than single occupancy map pixel [49] (2) The heading of ego vehicle is not considered and assumed to remain unchanged [22]. To accurately evaluate the planning performance, we account for the changes in ego heading by estimating the yaw angle through trajectory points, and assess the presence of a collision by examining the overlap between the bounding boxes ego vehicle and obstacles. We reproduce the planning results our benchmark with official checkpoints [12 17 for a fair comparison.
in previous [2 17
C IMPLEMENTATION DETAILS
DriveMamba is implemented upon Bench2DriveZoo (15] Following the previous methods [17] we set the perception range to 60mx300m longitudinally and laterally, and adopt ResNet-50 as the default backbone to encode image features. DriveMamba plans a 3 seconds (2Hz) future trajectory of ego-vehicle using 2 seconds history information as input. We have four variants of DriveMamba with different sizes of backbone, image resolution and decoder layer as shown Tab. I namely DriveMamba- Tiny, DriveMamba-Sinn Small, DriveMamba- Base and DriveMamba-Lan number of agent query and map query are set to 900 and 125 x2, while the memory queue length set to gque = (2 seconds) with Top-K = 256 query propagating continuously (42). We train DriveMamba for 90 epochs on nuScenes and 30 epochs on Bench2Drive respectively. All experiments conducted using 8 Tesla A800 GPUs, utilizing AdamW (28) optimizer and Cosine Annealing (27) scheduler to train Driba with weight decay 0.01 and initial learning rate x 10 4, TV. is set to 30 for trajectory interpolation.
The default
D MORE DETAILS
Formulation of Ego-Centric Local2Global Scan. For N x N matrix, the layer is the minimum distance from the border to the location (Ir, y):
min(I, y, N N, 1- 9).
(11)
and the index of location (z. y) can be calculated as:
AI(NN - 1) + (y -
I = I
41(N - 1) + (N - -)) +x 1),
= N -
k
(12)
4I(N- - +)) 22N 11-1)++ (N--NN111 -1-1-- x
41(NV - 1) + 3(N - 21 (N
y =1
Data Augmentation. During training phase, we conduct global data augmentation strategy to improve the model stability. Specifically, random rotation, translation and flipping (Y-axis) are applied to both bounding boxes of agent/ map instances and future trajectories of agents / eeo-vehicle respectively. Meanwhile, we adjust the camera extrinsic parameters accordingly to ensure spatial alignment between the surround-vier view images and the noise-perturbed targets.
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Table 11: Comparisons of different training strate-
Table 12: Study of BEV Size. Different sizes
gies. E2E training showcases better results.
used for position sorting in 3D space.
Method
Planning 1.
2x
(8)
Avg.
1y
Planning Coll. (%)
2x
3o
Avg.
BEV Size
1s
Planning 1.2 (m))
Avg.
Plansing Coll, (%)
End End Training
0.25
0.42
0.66
0.44
0.12
0.09
0.24
0.35
25
Two Siage Training
0.24
0.41
0.44
0.31
0.46
0.23
50x50
0.23
0.41
0.65
0.43
0.02
0.05
0.24
0.10
Plaaning-Oaly Training
0.25
0.44
0.16
0.25
0.30
75:255
0.23
0.40
0.65
0.43
0.09
0.13
0.26
0.16
Dense Supervisson
0.44
0.42
0.38
100x:100
0.23
0.41
0.67
0.44
0.13
0.15
0.28
0.19
Table 13: Necessity of Trajectory-Guided Scan. Both predicted and Ground-Trun future trajectories are adopted respectively for attention map generation during the task relation modeling process.
Planning L2 (m) 4
Planning Coll.
(%)
L
Method
1s
2s
Avg.
2s
3r
Avg.
Predicted Trajectory
0.23
0,41
0.65
0.43
0.02
0.05
0.24
0.10
GT Trajectory
0.17
0.26
0.35
0.26
0.03
0.05
0.13
0.07
Generation of Trajectory Prior. As stated the token initialization section, each pre-defined task query is equipped with both semantic embeddings and positional embeddings. And we initialize the BEV location of ego query as origin (0, 0). Specifically, Drive.ammb employs series of tokens (e.g. T. =) to represent future waypoints of ego vehicle, where each token encodes a discrete waypoint position. Then the future waypoints are iteratively refined with predicted offsets across consecutive decoding layers through planning head, facilitating the adaptive token sorting from ego- -go-perspective.
Hybrid Combination of Spatiotempoal Scan. Empirically, we find that an alternation of Horizontal- First and Vertical-Firs bidirectional scan, named as Horizontal Horizontal-Vertical,, across consecutive decoding layers (i.e. Lo: H-First, L1: V-First, L2: H-First, .... etc) can preserve better spatial locality for view correspondence learning, leading to accurate perception. Besides, Trajectory-Centric L2G spatial scan exhibits superior planning performance from ego perspective during the task relation modeling, While the spatial-first temporal scan excels at long-term temporal fusion. Intuitively, we combine these three scan strategies into unified decoder for different purposes of B B-Mamba layers, termed as hybrid spatiotemporal scan. Moreover, the alternation of H/V-First spatial scan contributes to the training convergence and stability of DriveMamba for structural representation learning from sensor tokens, thus providing high-quality task queries for relation modeling and temporal fusion. And Trajectory-Centric L2G scan can further facilitate ego-planning with adapative interaction order. Each ablation about hybrid scan combination has been carried out with three repeated experiments independently. demonstrating the consistent conclusion.
E
MORE ABLATION STUDY
Training Strategies. As show in Tab. 11 we find that the end-to-end training can achieve superior performance than divided training, which is different to findings of previous methods [12] [7] following sequential paradigm. And the dense supervision contributes the model convergence and benefits model scaling up. Besides, without perception supervision, the planning-only training can also result in considerable performance through extracting information directly from raw sensors.
Effect of BEV Size. The BEV size is important for indexing the task queries in 3D space with instanti- ated reference positions. As shown in Tab. [2] we compare different BEV sizes of DriveMamba-Smin from small to large. And we find that when the BEV size is set to 50x 50, our DriveMamba can achieve the best planning performance. The smaller size may cause the indistinguishable importance among different queries, and the larger size will inevitably lead to discontinuity of spatial information.
Necessity of Trajectory-Guideer Scan. As shown in Tab. 13 trajectory guidance for dynamic attention map generation used for position sorting, When utilizing the ground-truth future trajectories for upper-limit evaluation, our DriveMamba- extremely excellent planning performance (0.26m average L2 error with 0.07% average collision rate), demonstrating that the siecctory-guider scan can contribute to the high-quality query sorting, which is essential for accurate trajectory planning, and vice versa.
we further evaluate the necessity of
can obtain the
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Table 14: Performance comparison of different E2E-AD methods on middle tasks, including both BEV-Centric, Query-Centric and Task Rask-Centric paradigms. t: Reproduced with official checkpoint.
Detection
Online Mapping
Motion Prediction
mAP
NDS
APpeat
APaivider
Al APwondory 1
mAP
minADE
minFDE. J
MR
UniAD
38.0
49.8
0.71
1.02
0.15
VAD!
50.5
SpivseDrive
39
49.6
58.8
53,2
56.3
59.1
56.2
0.60
0.96
0.13
Drive Tranffrrnf
16
49.9
59.3
0.61
0.95
0.13
DriveMamba
50.8
60.4
65.8
68.1
0.55
0.76
0.08
Table 15: Module runtime statistics. The inference speed is measured for Drive NVIDIA GeForce RTX 3090 GPU as [7]
Module
Latency (ms)
Proportion (%)
Backhone
5.1
9.1
Depth Prediction
4.0
7.2
Bidirectional Serialization
1.2
2.2
View Correspondence Learning
4.4
7.9
Long-term Temporal Fusion
11.6
20.8
Task Relation Modeling
3.9
7.0
Task Head Inference
7.8
13.9
Temporal Memory Propagation
17.8
31.9
Total
55.8
100.0
Comparison on Middle Task Performance. To study the generalizability our proposed Drive- Mamba, also compare the performance of middle tasks on nuScenes validation in Tab. 14 It can be observed that our proposed DriveMamba surpasses all existing end-to-end autonomous driving methods by notable margin, including both BEV-Centric [2 17 and Query- Centric [39] paradigms. The probable reason maybe owing to the proposed effective yet efficient parallel paradigm with Task-Centric scalable Mamba decoding. Diverse task synergies and planning- oriented optimization can be simultaneously leveraged for single-stage end-to-end training.
shown
Efficiency Analysis. Finally, we analyze the efficiency DriveMamba with module runtime statistics as shown in Tab. 15 Among all modules and operations, the long-term temporal fusion and temporal memory propagation occupy the most proportion (52.7%), which contribute to the streaming process for parallel decoding. Thanks to the linear-complexig attention operation and parallel decoding, our Mamba achieves great efficiency and scalability.
F DISCUSSION
Comparison to Transformer-basee E2E Planners. Traditional attention-based E2E planners mainly adopt sequential Transformer paradigm based either dense BEV features [7] sparse query set [39]. However, sequential design relying on manual order can inevitably cause information loss and cumulative errors across modules. Besides, diverse relation modeling among different modules also neglected for task-oriented optimization. Moreover, lack of a unified decoder hinders the scalability for building up a large-scal scale model. Recently, ParaDrive 4 explores a multi-task BEV framework with parallel Transformer decoders to study the necessity of different modules and the impact of their connectivity. However, BEV-based view transformation is computational expensive and training insufficient owing to sparse gradients. Although with elaborate query design, the quadratic complexity attention mechanism adopted Drive Transforaer [16] still suffers from dramatic GPU memory consumption when scaling up gradually. In contrast, our DriveMamba processes spatiotemporal tokens with linear complexity and urajectory-guided hybrid sean, outperforming both BEV. Centric and Query-Centar end-to-end planners all tasks of nuScenes dataset (see Tab. and 14.
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Table 16: Comparisons of different Drive and DriveTransformer T6 Bench2Drive DevI0 benchmark.
variants over sizes under
Model
Backbone
#Parameters
Latency
Driving Score
Triverrraaffoom--Smmaa
ResNet-50
47.41M
93.8ms
45.0
TriveTTranfooome-BBaee
ResNet-50
178.05M
139.6ms
60.5
Transffrrrrmmeeeeaggg
ResNet-50
646.33M
221.6ms
68.2
DriveMamba- Tiny
ResNet-50
42.2M
55.8ms
51.1
DriveMamba-Snenn
ResNet-50
48.2M
90.2ms
58.8
DriveMamba-
VMamba-B/16
172.2M
164.3ms
68.5
DriveMamba-Larg
ViT-L/16
607.5M
599.1ms
70.3
Furthermore, DriveMamba achieves 3.2x increase processing speed and requires 68.8% less GPU memory for long- wtwp//ittttoootiionnn image sequences (see Fig. 5). demonstrating its efficiency and effectiveness handling high- resolution sensor data and performing long-horizon tasks.
[16]. Unlike other Transformer-based methods, we did not extensively explore data processing order (i.e., Sequential or Parallel), but instead integrated the "Task-centric SpatialTemporal Positional Embeddings"", "Trajectory-centrr Local-to-Globa Scan" and "Query-centric Linear Attention" into a unified decoder for parallel modeling, which already achieved the best performance with promising efficiency for E2E-AD model scaling. And our propose DriveMamba mainly differs from previous Transorranerrmer in design motivation, token initialization, modeling type and attention pattern.
Comparison to DriveTransfore
Specifically, we perform View Correspondence Learning between 2D sensor tokens and 3D task tokens through point-level depth estimation and hybrid token sorting in 3D space, which are fed to the linear-complexity SSM layer for efficient feature extraction. However, Sparse Represen- tation adopted in Tranverranformer is constructed through combining the 2D image features and 3D ray-level positional embeddings following PETR series [24] (25), then the task tokens decode semantics from the pseudo 3D features with transformer layers. Besides, we conduct Task Relation Soccll-o-Globall geometric prior, which is
Modeling through introducing the trajectory demonstrated facilitate the ego-centric interactive planning with extensive experiments. Differ- ently, Task Parallelism proposed in DriveTTrasffon Transformer simply models the task relations through global-wise quadratic-complexit self-attention, neglecting the importance of interaction order from ggo-perspective, leading to inferior planning performance and efficiency. Further, we conduct the Long-term Temporal Fusion through bidirectional SSM, which has shown great capacity of se- quential modeling in natural language processing (NLP) domain. Differently, Streaming Processing in Drive Transformer simply follows StreamPETR [42 to conduct computationally expensive self- attention in temporal domain. In sum, propose pure, unified yet scalable SSM- -Ssed decoder to learn implicit view correspondence, dynamic task-relations and long-term temporal dependencies with the ego-centric design for End-to-End Autonomous Driving (E2E-AD).
Quantitatively, we compare different variants of DriveMamba with DriveTraansinn Transformer as shown in Tab. [6] which showcases great efficiency of our DriveMamba with superior planning performance. For example, DriveMamba-Tee Tiny outperforms Prriverransfomme-SSmaee by 6.07 in Driving Score while reducing latency by 40.5% (55.8ms vs. 93.8ms). And DriveMamba-Sman is comparable with Transformer-rBBB with fewer parameters and higher efficiency.
Comparison DRAMA 48). Recent works such as DRAMA also proposes an end-to-end motion planner with Mamba design. which seeks for a combined Mamba-Tran Transformer decoder to improve the planning performance. Specifically, DRAMA targets at using Mamba Fusion module to encode multi-modal BEV features from both cameras and LiDAR. Besides, its decoder follows Gamba-Transforme structure to enhance the attended features for planning output. Differently, our DriveMamba introduces pure Mamba decoder for visual end-to-end autonomous driving, which integrates the view correspondence learning, task-relation modeling and long-term temporal fusion into unified decoder with fully sparse query design. maute-of-the-ee performance different tasks (i.e., perception, prediction and planning) are achieved on both open-loop and closed-loop benchmarks with great efficiency.
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(a) Ego vehicle brakes w when encounters slow -oving hazard blocking part of the lane and the side lane not availuble
(b) Eggo vehicle maneuvers next to a lane of traffic moving in the same direction to avoid when the side lane is available
Figure 6: Qualitative results of DriveMamba on closed-loop routes of representative interactive scene "HazardAtSideLann" Task outputs of DriveMamba are illustrated including perception, motion, and planning trajectories.
G LIMITATIONS AND SOCIAL IMPACT
Limitations, Though our proposed DriveMamba achieves enormous advancement and showcases great potential of parallel Mamba paradigm for end-to-end autonomous driving, the excessive entanglement of the framework will also constrain flexible problem localization and debugging. A combination of joint and divided modeling with parallel decoders worth further research.
Social Impact. DriveMamba could be easily deployed on mass mrosuued car chips with different limitations of computing resources, and thus can be served as plug-ant and-play software to assist human drivers in decision-making and safe driving.
H VISUALIZATION
As show in Fig. we visualize the closed-loop results on the Bench2Drive test routes. An typical interactive scenario named as "HazardAtSidelaans is illustrated across different timestamps. Specifi- cally, in Fig. (a), the ego vehicle decides to brake when encounters slow-moving hazard blocking part the lane, meanwhile the side lane not avaliable owing to the congested traffic. And in Fig. (b), when the side lane is available, the ego vehicle start to maneuver next lane of traffic moving forward to avoid the hazard for efficient driving, which demonstrates the great interactive planning capacity of our DriveMamba considering both safety, comfort and efficiency.
19


Reviews:
[{"review_id": "nzeqIX7YwU", "reviewer": "Reviewer_KbT8", "summary": "This paper identifies key challenges in current End-to-End Autonomous Driving (E2E-AD) systems. It argues that dominant methods, which often use sequential Transformer decoders (e.g., perception-prediction-planning), suffer from information loss, cumulative errors, and inflexible task modeling. Furthermore, the reliance on dense BEV features and the quadratic complexity of Transformer-based attention mechanisms create bottlenecks in efficiency and scalability. To address these issues, the paper proposes DriveMamba, a \"Task-Centric Scalable paradigm\" for E2E-AD. The central idea is the replacement of Transformer decoders with a Unified Mamba decoder. This decoder is based on State Space Models (SSMs), which have linear complexity, to improve efficiency and scalability.\n\nDriveMamba is a single-stage, parallel framework. It tokenizes multi-view images and task-specific queries (Agent, Map, Ego) into sparse representations. These tokens, along with positional embeddings derived in part from a predicted depth map, are fed into the unified decoder. This decoder is designed to simultaneously learn view correspondence, dynamic task relations, and long-term temporal fusion.\nA key component is the hybrid spatiotemporal scan method, which is required to apply the 1D Mamba model to the 3D driving scene. The paper introduces a \"bidirectional trajectory-guided 'local-to-global' scan\". This method dynamically sorts tokens based on their proximity to an intermediate predicted ego-trajectory, aiming to preserve spatial locality from an ego-centric perspective.\n\nExperiments are conducted on the nuScenes and Bench2Drive datasets for both open-loop and closed-loop evaluation. The results show that DriveMamba models achieve lower L2 displacement error and collision rates compared to previous methods, while also demonstrating significant improvements in inference speed (FPS) and reductions in GPU memory consumption. The paper includes ablation studies on the decoder's modular components , scan methods , and scalability.", "questions": "Please see weakness above", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- The paper clearly articulates two significant problems in E2E-AD: 1) The limitations of sequential, manually-ordered pipelines, such as information loss and error accumulation. 2) The efficiency and scalability constraints imposed by the quadratic complexity of attention in Transformer models.\n-  The idea to replace the Transformer decoder with a Mamba-based (SSM) decoder  directly addresses the efficiency and scalability problem. The linear complexity of SSMs is a clear advantage for processing long spatiotemporal sequences. Figure 5 provides a clear comparison of this, showing a 3.2x speed increase and 68.8% less memory use at higher resolutions.\n- The paper proposes a \"Trajectory-Centric Local2Global\" (TC-L2G) scan method. The idea of dynamically sorting tokens based on their relevance to the predicted future ego-path is an explicit attempt to inject an ego-centric bias, which is relevant for the planning task.\n- The approach moves away from dense BEV feature maps and instead uses sparse tokenized representations for both sensor inputs and task outputs. These are processed in parallel by a unified decoder, which is designed to enable dynamic modeling of task relationships.\n- The model is evaluated on both open-loop (nuScenes) and closed-loop (Bench2Drive)  benchmarks. This dual evaluation provides a more complete picture of the model's planning capabilities, as open-loop metrics do not always correlate with real-world driving performance.", "weaknesses": "I have the following concerns from this work\n\n- The \"Trajectory-Centric Local2Global\" scan (L232) creates a potential circular dependency. The scan order, which is an input to the decoder layers, is determined by an importance weight $w_i$ calculated from an intermediate predicted ego-trajectory $\\psi^{\\prime}$. This means the decoder's output (the trajectory) is required to define its input (the scan order). The paper does not specify how this intermediate trajectory is generated or analyze the stability of this co-dependent design. Table 10  shows that using a Ground-Truth trajectory improves results, but this does not resolve the question of how the model functions in practice. \n\n- The scalability study in Table 6 presents conflicting data. As the decoder is scaled from 3 to 12 layers, closed-loop planning performance improves (51.1 to 66.5). However, open-loop perception performance (Detection mAP, NDS, and Mapping mAP) decreases (e.g., Detection mAP drops from 34.8 to 33.1, Mapping mAP drops from 50.3 to 46.6). This suggests a performance trade-off between tasks, not uniform scalability. The paper's explanation that the model learns \"planning-oriented perception\" is an interpretation that requires more evidence, as it implies general perception is being sacrificed.\n- To support the \"planning-oriented perception\" claim, the paper provides Table 5 showing that perception of \"Closest In-Path Objects (CIPO)\" improves with more decoder layers. However, this table only shows CIPO metrics. It does not show the general perception metrics for the same models. To make a convincing argument, the paper should present both CIPO and general perception metrics side-by-side to demonstrate that while general perception degrades (as suggested by Table 6), CIPO-specific perception improves. The current presentation disconnects these two key results.\n- The 3D position of sensor tokens, $P_{sensor}$, is a critical component for token sorting and view correspondence. This position is entirely dependent on a predicted depth value $d_{i,k}$, which comes from an auxiliary depth prediction branch. This introduces a significant potential failure point. If the predicted depth is inaccurate, the 3D positions of sensor tokens will be incorrect, leading to flawed token sorting and feature extraction. An ablation study on the accuracy of this depth branch or the sensitivity of the overall model to depth prediction errors is needed.", "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": "kwezr7VL7Y", "reviewer": "Reviewer_pyiN", "summary": "This paper proposes DriveMamba, a Task-Centric Scalable State Space Model for efficient end-to-end autonomous driving. The core innovation lies in replacing the traditional attention-based Transformer architecture with a Unified Mamba Decoder, which jointly models perception, prediction, and planning in a single-stage pipeline with linear complexity. The authors further introduce several technical components — Hybrid Spatiotemporal Scan (HSS), task-centric tokenization, 3D sensor token localization, and long-term memory fusion — aiming to enhance scalability, efficiency, and task-level relational modeling. Experiments on nuScenes and Bench2Drive demonstrate consistent performance improvements in L2 and collision metrics, with notable inference speed.", "questions": "1. How is the trajectory prior in Trajectory-L2G obtained? Any risk of future information leakage?\n2. How sensitive is the method to depth noise or calibration errors?\n3. Could the authors clarify the HSS layer configuration and its effect on stability?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. Unified Mamba Decoder: Achieves linear complexity while jointly processing perception, map, and planning queries, showing clear scalability advantages on high-resolution multi-camera inputs.\n2. Hybrid Spatiotemporal Scan (HSS): Cleverly alternates between spatial and ego-centric scanning to balance locality preservation and long-range temporal consistency.\n3. Task-centric tokenization: Structured query design (ego/map/agent) improves modular interpretability and relational learning.\n4. 3D sensor token localization: Replacing uniform ray sampling with depth-predicted projection enhances geometric accuracy and spatial reasoning.\n5. Comprehensive experiments: Covers multiple benchmarks and provides results with and without ego-status, supporting generalizability and robustness claims.", "weaknesses": "1. Insufficient HSS details: The paper lacks explicit layer-wise configurations or stability studies when varying scan order; the contribution of each H/V-first and L2G layer is not isolated.\n2. FPS reporting: Experimental FPS comparisons are unclear due to missing details on resolution, camera count, and hardware setup.\n3. Depth branch robustness: No analysis of depth estimation noise, calibration error, or trade-offs between uniform-ray and learned-depth methods.\n4. Trajectory prior ambiguity: The source of trajectory guidance (e.g., ego-pose history vs. future leakage) is not clarified, raising potential fairness concerns.\n5. Limited interpretability of task relations: The “shared Task Query B-Mamba” strategy lacks ablation or visualization, leaving its contribution unclear.\n6. Efficiency gap: The Large model exhibits a steep FPS drop, suggesting scalability bottlenecks at high model capacity.", "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": "O2Z2zdEnWL", "reviewer": "Reviewer_RMQD", "summary": ">This paper proposes DriveMamba, a novel Task-Centric and scalable State Space Model paradigm designed for efficient End-to-End Autonomous Driving. DriveMamba aims to address the limitations of conventional Transformer-based E2E-AD systems, which suffer from quadratic complexity and sequential (Perception-Prediction-Planning) design-induced cumulative errors. The core innovation is the Unified Mamba Decoder, which leverages the linear-complexity Mamba architecture to concurrently integrate dynamic Task Relation Modeling, View Correspondence Learning, and Long-term Temporal Fusion in a single stage. Crucially, DriveMamba utilizes sparse, token-level representations instead of dense BEV features and introduces a Hybrid Spatiotemporal Scan guided by the ego-vehicle's trajectory. This scanning mechanism enables efficient long-range context modeling and Ego-planning. Experimental results on the Bench2Drive and nuScenes datasets confirm that the DriveMamba-Tiny model achieves both superior performance and high efficiency, demonstrating the model's scalability and efficacy.", "questions": ">Could the authors clarify the reason for omitting the performance results of the DriveMamba-Base model in Table 2? Including this would allow for a clearer understanding of the model's scalability and the performance trend across different model sizes (Tiny, Base, Large).", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": ">S1. By replacing the quadratic-complexity Transformer with a Mamba-based decoder (SSM), the method effectively solves the major bottleneck of E2E-AD systems. This design drastically reduces memory consumption and makes the decoder easily scalable through simple layer stacking, which is a critical contribution to the exploration of scalable E2E-AD systems.\n\n>S2. The ablation study rigorously confirms that simply stacking the decoder layers monotonically improves CIPO (Closest In-Path Objects) perception performance. This quantitatively validates that DriveMamba effectively learns perception specifically optimized for planning, rather than general scene perception.\n\n>S3. The experiments are very dense and well-constructed. The proposed method demonstrates superior performance compared to existing baselines across the majority of in-domain scenarios.", "weaknesses": ">The work is well-executed, and I have only one significant concern regarding the robustness properties of the proposed architecture.\n\n>W1. As shown in Table 10, the performance of the trajectory-guided scan appears to be highly dependent on the accuracy of the predicted trajectory. This suggests that the model might perform poorly and lack robustness in Out-of-Distribution (OOD) or extreme scenes with significant domain gaps, potentially causing planning failures. Given that robust operation in diverse and challenging deployment environments is a critical requirement for autonomous driving, the authors should have conducted more comprehensive generalization experiments (e.g., cross-dataset validation) to address this concern, which seems to be missing.", "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": "GdPZHULnqd", "reviewer": "Reviewer_qoTq", "summary": "This paper proposes DriveMamba, a novel task-centric and scalable paradigm for end-to-end autonomous driving that replaces traditional sequential or Transformer-based modules with a unified Mamba decoder built on selective state-space models for linear-time complexity. Its key contributions include a sparse token representation that integrates image features and task queries (for perception, prediction, and planning) with 3D positional encoding, a hybrid spatiotemporal scan method—featuring a trajectory-guided \"local-to-global\" scan—that preserves spatial locality and enhances ego-centric planning, and a unified architecture that simultaneously learns view correspondence, dynamic task relations, and long-term temporal fusion. Extensive experiments on nuScenes and Bench2Drive show that DriveMamba achieves state-of-the-art planning accuracy and efficiency.", "questions": "See weaknesses.", "limitations": "", "rating": 6, "confidence": 5, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "1. Brief framework: The paper presents a scalable and unified framework for end-to-end autonomous driving, characterized by its simplicity, effectiveness, and elegant design.\n\n2. Clear presentation and comprehensive experiments: The paper is clearly written and well-structured, with extensive experiments that convincingly demonstrate the effectiveness of the proposed approach.", "weaknesses": "1. **Novelty**: This paper largely follows the framework of DriveTransformer, sharing a similar architecture, experimental setup, and overall structure. The main modification lies in replacing the Transformer module with Mamba, which leads to only marginal improvements in performance and efficiency. Therefore, while the paper does not exhibit major flaws, its contribution is relatively incremental and may receive only a moderate level of interest.", "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)