FlowAD: Ego-Scene Interactive Modeling for Autonomous Driving
Authors: Mingzhe Guo
Yixiang Yang
Chuanrong Han
Rufeng Zhang
Shirui Li
Ji Wan
Zhipeng Zhang
Venue: ICLR 2026 Poster (2026)
Keywords: Autonomous Driving
World Model
End-to-End
Vision-Language-Action Model
Scene Flow
Primary area: applications to robotics, autonomy, planning

Abstract: Effective environment modeling is the foundation for autonomous driving, underpinning tasks from perception to planning. However, current paradigms often inadequately consider the feedback of ego motion to the observation, which leads to an incomplete understanding of the driving process and consequently limits the planning capability. To address this issue, we introduce a novel ego-scene interactive modeling paradigm. Inspired by human recognition, the paradigm represents ego-scene interaction as the scene flow relative to the ego-vehicle. This conceptualization allows for modeling ego-motion feedback within a feature learning pattern, advantageously utilizing existing log-replay datasets rather than relying on scenario simulations. We specifically propose FlowAD, a general flow-based framework for autonomous driving. Within it, an ego-guided scene partition first constructs basic flow units to quantify scene flow. The ego-vehicle's forward direction and steering velocity directly shape the partition, which reflects ego motion. Then, based on flow units, spatial and temporal flow predictions are performed to model dynamics of scene flow, encompassing both spatial displacement and temporal variation. The final task-aware enhancement exploits learned spatio-temporal flow dynamics to benefit diverse tasks through object and region-level strategies. We also propose a novel Frames before Correct Planning (FCP) metric to assess the scene understanding capability. Experiments in both open and closed-loop evaluations demonstrate FlowAD's generality and effectiveness across perception, end-to-end planning, and VLM analysis. Notably, FlowAD reduces 19\% collision rate over SparseDrive with FCP improvements of 1.39 frames (60\%) on nuScenes, and achieves an impressive driving score of 51.77 on Bench2Drive, proving the superiority. Code, model, and configurations will be released here.

Published as a conference paper at ICLR 2026
FLOWAD: EGO-SCENE INTERACTIVE MODELING FOR AUTONOMOUS DRIVING
Mingzhe Guo!
Yixiang Yang²
Chuanrong Han
Rufeng Zhang'
Shirui Li
Wan
Zhipeng Zhang
Baidu Inc.
2 Luto School of Artificial Intelligence, Shanghai Jiao Tong University
ABSTRACT
Effective environment modeling is the foundation for autonomous driving, un- derpinning tasks from perception to planning. However, current paradigms often inadequately consider the feedback of ego motion to the observation, which leads to an incomplete understanding the driving process and consequently limits the planning capability. To address this issue, we introduce novel ego-scene interac- tive modeling paradigm. Inspired by human recognition, the paradigm represents ego-scene interaction as the scene flow relative the ego-vehicle. This concep- tualization allows for modeling ego-motion feedback within a feature learning pattern, advantageously utilizing existing log-replay datasets rather than relying on scenario simulations. We specifically propose FlowAD, a general flow-based framework for autonomous driving. Within an ego-guided scene partition first constructs basic flow units to quantify scene flow. The ego-vehicle's forward di- rection and steering velocity directly shape the partition, which reflects ego mo- tion. Then, based on flow units, spatial and temporal flow predictions are per- formed to model dynamics scene flow, encompassing both spatial displacement and temporal variation. The final task-aware enhancement exploits learned spatio- temporal flow dynamics to benefit diverse tasks through object and region-level strategies. We also propose novel Frames before Correct Planning (FCP) metric to assess the scene understanding capability. Experiments in both open and closed- loop evaluations demonstrate FlowAD's generality and effectiveness across per- ception, end-to-end planning, and VLM analysis. Notably, FlowAD reduces 19% collision rate over SparseDrive with FCP improvements of 1.39 frames (60%) on nuScenes, and achieves an impressive driving score of 51.77 on Bench2Drive, proving the superiority. Code, model, and configurations will be released here
INTRODUCTION
Autonomous driving has achieved remarkable progress in recent decades Ly & Akhloufi (2020): Ki- ran al. (202T). A pivotal development is the shift from modular designs, with discrete perception, prediction and planning stages Zhu & Zhao (2021); Hussein et al. (2017). to end-to-ens (E2E) archi- tectures (adat al. (2020): Hu et al. (2023). This transition optimizes the flow of planning-centric information, minimizing intermediate losses and thereby significantly enhancing the performance. More recently, the escalating need for generalized scene understanding and reasoning has driven the adoption of Large Vision- LanguueL Models (LVLMs) within these systems Tian al. (2024); Diang cct al. (2024). The integration unlocks further potential in data-driven learning, endowing it with improved capabilities for holistic scene comprehension and more human-like llie ego-vehicle planning.
Upon reviewing existing architectures, is evident that the planning module consistently operates as the final computational step. contingent upon environmental messages from preceding modules. Each inference cycle culminates in an ego-plan, after which the pipeline resets for the next times- tamp (Fig. [.). Critically, however, such architecture largely neglects the profound impact of the ego-vehicle's 's own executed motion on its subsequent perception and decision-making. complete
"Correspondence Author

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Published as a conference paper at ICLR 2026
Tlanb
Time
Tkan
Time33
Dommand TURNLEPT
Vanilla Asto dradig System
Verpparal Aute-driing
Our Eye- lnterathe Anted
(a)
(6)
[c]
Figure 1: (a) Vanilla auto-driving system that performs isolated inference for each timestamp. (b) Temporal auto-driving system that integrates historical observations, yet incompletely captures the feedback of previous ego planning. (c) Our ego-scene interactive system that leverages previous planning to inform future observations, which benefits comprehension of dynamic driving process.
Table 1: Influence of the temporal fusion in UniAD Hu et al. (202)) the nuScenes validation set.
Method
Backbone
mAPt
Detection
NDS
Tracking AMOTA1
AMOTP.
1oU-- ame
Online Map
RoU-Raad!
Metion Prediction minADEL minFDE]
Avg. A.2 Avg Coll
Planning
UniAD Hu 2023
ResNet101
0,380
0
0.359
1,320
0.672
1.02
1.08
UniAD
2025
ResNei101
0.356
0.303
1.549-17%
0.788
0.648
1.08.9
driving process should involve two parts: planning with current observation, and more importantly, performing the control outputs that shape future sensory inputs. The absence of the second part, i.., the feedback of ego motion, leads to different open- and closed-loop environments CarooometttsCCssarrrr et al. 22200;: Dosovitskiy et al. (2017). The closed-loop environment Jia al. (2024) is mainly used for evaluation with real-time interactions between the ego vehicle and driving scene, hence allowing for more realistic testing of driving policies. In contrast, the open-loop one Contributors (2024) without ego-motion feedback is applied for large-scale training and testing of most current auto- driving systems due to its simplicity. With the fixed, pre-recorded datasets, planned trajectories are not physically realized, thereby severing the link between action and subsequent observation. This decoupling significantly impedes the model's ability to internalize the complex, dynamic interplay inherent in ego-scene interactions, ultimately constraining its planning acumen. Even temporal ar- chitecture (Fig. [T)) integrating historical states to model environmental changes, it often fails to fully capture the nuanced feedback from ego-actions to future states. The findings in Tab. [ underscore this point, that removing temporal fusion has negligible effect on planning, yet severely hampers tasks like tracking that depend on temporal continuity, suggesting that current temporal modeling does not adequately address this ego-centric feedback loop for planning.
To address the aforementioned limitation, we introduce a novel ego-scene interactive modeling ar- chitecture. This architecture is designed to explicitly incorporate the feedback of the ego-vehicle's motion by learning its influence within the latent feature space. As illustrated Fig. D our ap- proach leverages the planned ego trajectory from the preceding timestep to inform the reconstruction or prediction of subsequent environmental observations, thereby explicitly modeling the dynamic interplay between the ego-vehicle and its surroundings. The core intuition for this methodology is drawn from human perceptual-moter processes, particularly the concept of relative motion [Dvis) & Bobick (197)); Bobick Davis (200I). Humans inherently understand that they move, the environment appears to flow in the opposite direction. This perceived optic flow is crucial for an- ticipatory planning and navigation. We posit that this fundamental aspect of ego-scene interaction, manifested as relative motion, can be effectively captured and represented as a learnable "scene flow" within the latent space of the model. Crucially, this formulation enables the modeling of ego-motion feedback using readily available, pre-recorded datasets, obviating the need for complex simulations to generate varied observational outcomes.
More specifically, we introduce FlowAD, general flow-based framework for auto-driving, struc- tured around three core components: 1) Ego-guided scene partition addresses the inherent diffi- culty in quantifying holistic scene flow by decomposing the visual input into "flow units". This partitioning is strategically performed along the width milti-view images, aligning with the pre- dominant horizontal ego-scene relative motion, and its starting point and unit granularity dynami- cally adapt to the ego-vehicle's 's forward direction and steering velocity. To mitigate potential object fragmentation and capture scene flows across varied receptive fields, both local aggregation and
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Published as a conference paper at ICLR 2026
multi-level partition strategies are integrated. 2) Spatial and temporal flow prediction leverages these flow units to model the scene flow dynamics of spatial displacement and temporal variation. A spatial module infers the states of subsequent units based on information from antecedent ones, while a temporal module forecasts future unit states by exploiting historical ones. Mechanisms from world models Hafner al. (2020) (02T) are incorporated for refined modeling of flow dynamics. 3) Twsk-aware enhancement exploits the learned spatio-temporal flow dynamics to improve diverse downstream tasks. Specifically, object queries for object-level perception are augmented using formation from corresponding flow units, and region features for region-level analysis are boosted. This enhanced understanding scene dynamics facilitates more agile system responses.
Experiments in perception, open-/closed-loge E2E planning, and VLM analysis validate FlowAD's effectiveness and generality. Applied to baselines SparseBEV Liu al. (2023), SparseDrive [Sun et al. 2024), and SennaJjang assess the scene understanding capability, we also introduce novel Frames before Correct Planning (FCP) metric, which quantifies the frames elapsed until the planner initiates rational action in response to given command.
Jiang et al. (2024), our framework yields consistent performance gains. To
In summary, contributions are three-fold: 1) We observe the limited planning capability caused by neglecting the feedback of ego motion, and propose an ego-scene interactive modeling paradigm to build the influence by learning the scene flow in latent space; 2) We design general flow- based framework for autonomous driving, which builds and exploits the flow dynamics to benefit diverse downstream tasks; 3) We prove the effectiveness and generality of our method with SOTA performance in both open- and closed-loop evaluations, as well as the proposed FCP metric.
RELATED WORK
2.1 END-TO-END AUTONOMOUS DRIVING
In contrast to the traditional auto-driving systems that perform staged and rule-based planning Zhu
& Zhao (021), the end-to-end architecture Hu et al. (2023) directly outputs the planning results with the input environmental observation. From the seminal work ALVINN Pomerleau (1888) to
modern approaches such as ST-P3 Hu al. (202b)). dedicated modular designs introduced to integrate auxiliary information. UniD Hu et al. (2023) and VAD Jiang et al. (2023) demonstrate impressive performance with Transformer pipelines. After that, more progress has been achieved, e.g. sparse modeling in Sparse/rive (Snn et al.) (2024), probabilistic planning in VADv2 et al. (2024). Most recently, the capabilities of commonsense reasoning and interoperability in LVLMs are employed. The pioneering DriveGPT4 Xu et al. (2024) converts sensor inputs into control com- mands via VLMs. Building on hybrid designs, Drive VLM Tian al.) (2024) and Senna Jiang et al. (2244) integrate LVLMs with planners to achieve unified high-level reasoning and low-level con- trol. In this work, we further consider the feedback of ego motion and model ego-scene interactive dynamics to benefit the driving comprehension.
2.2 WORLD MODEL
With the formulated representation of the world, the world models Ha & Schmidhuber (2018b ap aim learn the transition dynamics and predict the future states. The studies either gaming or robotics are widely explored] Haftner Hafner et al.) (2020) 2021 2023). Recently, learning world models have been introduced into autonomous driving Hu et al. (2022a); Wang et al. 2022bb a). MILE Hu et al. 2022a) incorporates world modeling within the BEV segmentation through imitation learning. The successive DriveDreamer Wang et al. (224a)) ADriver- Jia et al. (202)) and Drive- WM Wang et al. (2024b) explore the training of driving world models with diffusion designs. In this work, employ the mechanism of the world model to learn apatio-tempore dynamics the scene in the latent space, which helps to understand the ego-scene interaction and improve the planning capability.
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Egp
Agregation
1d,d Ido
Filee
0000
Sturiing Point
Adjastaeet Pactition
Figure 2: Illustration of Ego-guided Scene Partition. The feedback of ego motion is reflected in Starting Point of Partition (forward direction) and Dynamic Adjustment of Partition Size (velocity). Then Multi-level Partition and Local Aggregation divides basic flow units and fuses local messages.
METHOD
3.1
EGO-GUIDED SCENE PARTITION
The ego-guided scene partition aims to construct the basic units to model the ego-scene interactive dynamics based the ego motion and input multi-view images, in Fig. 2 The partition is performed on the multi-viww image features Fimg E NRNXXWW XC built with the backbone network.
Starting Point of Partition. The ego-vehicle's forward direction determines the start from which the driving scenario flows through relative motion. Thus, we first consider the starting point of partition to introduce the guidance of ego motion. As shown in Fig. 2 we assume the ego-vehicle at time t is located at the origin of the coordinate system, with six camera planes arranged at the edges of the perception range. The ego-vehicle's 's forward direction is formed constructed with ego positions at time t - 1/t. Then the intersection (red point) between the forward vector and multi-view planes serves the start of partition, which divides the ego-left/right scenes.
vector (red arrow),
Dynamic Adjustment of Partition Size. During steering maneuvers, the flow speeds of the ego- vehicle's ' left/right scenes vary due to different lateral movement velocities. In such cases, partition- ing the bilateral scenes with the same size P does not conform to the kinematic characteristics of ego-scene interaction. We then design strategy to dynamically adjust the partition size illustrated in Fig. 2 Assuming the steering trajectory of the ego-vehicle is part of a circle Wang et al. (2005); Park al. (2015), the ego positions { 2t= (3t-1, Vt-1). (Ir,, yt) }} are adopted determine the center (Ze; Me) and radius r (please refer to appendix for details). With the ego-vehicle width Wego- the steering radiuses of the ego-left/right motion are achieved. As shown in the turning-right case of Fig. the values are + " /r Meeg respectively. We posit that the original partition
size P corresponds to the steering radius r, then the size for the ego-left is Pieft Px (++Wego/ (2)
and Pright P (r==) for the ego-right. The varied partition sizes endow more fine- fine-grained ego-scene interactive modeling, which helps to understand the feedback of ego motion to the envi- ronmental observation.
Multi-level Partition and Local Aggregation. As the relative motion of the ego-vehicle to the scene is mainly reflected in the horizontal dimension, we perform partition along the width of multi- view images. As shown in Fig. 2 each image feature Fing E IR Hx x (1 S & N) divided into multiple items, i.e., flow units Fi
umit E ER KRH PxC with the predefined partition size P (W S I.} from the backbone network are exploited K x P). The multi-level features (Fimgl) < S
to explore the flow dynamics of different receptive fields with varied partition sizes 1 < / < L}. Notably, the objects may be unexpectedly split and induce fragmentary information. We thus design local aggregation to handle this issue. The messages of adjacent flow units are fused, which helps to increase the receptive field as well as improve the correlation between multi-view images. Specifically, each flow unit f
unit TR PxC (1 < k & K) of F Funit is concatenated with adjacent
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Spottal Fioee
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Figure 3: (a) The pipeline of Spatial Flow Prediction. (b) The pipeline of Temporal Flow Prediction.
two flow units (Fig. 2). which forms the local flow feature funit attention Vaswani et al. (017) is performed on the 3P dimension to fuse local messages, followed by linear layers to reduce the dimension and output Fé finit = The process formed as,
1:k+1 fl:11:++1 #33x3 Then, self-
wnit = MLP (SffAAttttttntii (f
(1)
The flow units F unir x after local aggregation are input to the successive spatial and temporal flow prediction modules to build the ego-scene interactive dynamics.
3.2 SPATIAL AND TEMPORAL FLOW PREDICTION
With the flow units F umir after ego-guided scene partition, it's capable of quantifying the scene flow of the ego-scene relative motion. The scene flow includes: 1) spatial displacement: the scene moves from one flow unit to another; 2) temporal variation: the scene of the same flow unit changes over time. Therefore, we propose spatial and temporal flow prediction modules to capture the ego-scene interactive dynamics in latent space.
Spatial Flow Prediction Module. We propose the spatial flow prediction module to learn the spatial dynamics of the ego-surround driving scenario. As shown in Fig. 3a the module captures the dynamics from ahead flow units and forecasts the rear ones, which endows the capability of spatial flow prediction by supervision with GT states. Specifically, trainable spatial flow queries Qspat E RR xC which represent transition dynamics of flow units, are initialized. As the scene flows on both sides of the ego-vehicle, the flow units and queries are arranged into two parts from xH xxxC and Qupat E 2x x A/xx xC, For each Lppa E R22x in Qepat. the ahead flow unit f/-1 E 2 in F Funit is exploited to auto-regressively update the cached motional messages through a gated recurrent unit (GRU) CCung et al. (0014).
the partition start, i.e., F Funit E 2x A
á a
GRU( (a-l))
(2)
Notably, the ahead flow unit 7o unit of the first spatial flow query alpat does not exist. Thus, we take the first flow unit of the last frame f1,6-1 as the substitute. As the spatial flow prediction aims to capture the scene flow dynamics, it is a prerequisite to infer the states of subsequent flow units. The output Pipar. is exploited to predict the rear flow unit Pi based on 7
unit with cross-attention,
fJ unst Cross Attettott Fj-1 kU P.pat)
(3)
After iterations of all flow units, the predicted units from Eq. are arranged the spatial flow feature f spat XX which represents spatial dynamics of the scene flow.
Following the loss design in world models Hafner et al. 2200 202T), we respectively map each predicted/GT flow units / /F/ to latent states of {P 6 spat | d'pat E x
with MLP layers, and then minimize their KI. divergence. The predicted state from po unit is regarded
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Input
Sge-Sonn Interactive Modeling
Taskowarr Enhancement
Eoe-wiinted Serna Partikn
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The architecture of our FlowAD. For the input stage, the image features of multi-view videos are extracted with the backbone network. Then, the ego-scene interactive modeling intro- duces the feedback of the ego-motion and builds the spatio-temporal scene flow feature. Finally, the flow feature serves for the downstream tasks with the task-aware enhancement.
Figure 4:
as a forecast of the spatial dynamics. And the GT state from f represents the distribution from the real observation. The KL divergence measures their gap. We expect to enhance the cognition ability of spatial flow by optimizing the spatial prediction loss Lspat.
Lspat KL((Pspat Apput (u pppat)).
(4)
Temporal Flow Prediction Module. The temporal variation of the scene flow modeled by the proposed temporal flow prediction module. The pipeline is illustrated in Fig. 3b Different from the spatial flow prediction within each flow unit of single frame, the temporal How is forecasted x s T} with trainable tempo- ral flow queries {Qiem E NR-XXXO < S T}. For each iteration, the flow units time [ i.e. Ft-1 are exploited to provide temporal prior for updating the flow query Qfem with a GRU, "nit
through sequence of multi-view images (F) iimg E
GRU Vtem: Flmit)
tem
Similarly, the updated temporal flow query Qiem is exploited to predict the flow units of the next frame F unit based on unit with a atoss-attention module,
F
F unit
CrossAttcntionnqq== = Ft-1 lev Q
The output temporal flow feature of the last iteration, i.e., Fr F is employed for the downstream tasks, which provides temporal dynamics the ego-scene interaction. The temporal prediction loss fiem is computed with the predicted state {Atem 6%cm IR NK XXXxxxxxX" from F Funit and GT state (Khem otem E X KX from Ft unit mapped with MLP layers,
Liem KT(((item: otem I1 (Ptem: oeem
Flow Feature Fusion. After the spatial and temporal flow predictions, the output F spat and FT are concatenated by flow units and fused with a self-attention, which forms F juse E R Nx PxC.
3.3 GENERAL FLOW-BASED FRAMEWORK
With the ego-guided scene partition and the spatial/temporal flow predictions, the spatio-temporal dynamics of the ego-scene interaction are represented as F fuee. This enables the auto-driving sys- tem to understand the feedback of ego motion to the surrounding scenario, which benefits down- stream tasks. We then propose a general flow-based framework for perception, end-to-end planning and VLM analysis, as shown in Fig. 4 Our framework consists of three main parts: Input, Ego- Scene Interactive Modeling, and Task-e Enhancement. Taking multi-view videos as input, the image backbone first extracts image features. Then, the spatio-temporal flow features are built with

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Published as a conference paper at ICLR 2026
Table Object detection results on nuScenes Caesar et al. (2020) and Bench2Drive Jia 2024). The baseline is SparseBEV.
Method
Backbone Input Size
Dutuset
MAPt NDS1
MATE! MASE. mAOE, mAVE! mAAE)
StreaPETTR Wang al. 2023
ResNet50 256 704
nuScenes
0.432 0,540
0.581
0.272
0.413
0.295
0.195
SparseBEV Lau et al
2025
ResNet50 256 704
nuScenes
0,445 0,553
0.605
0.278
0.389
0.253
0.189
FlowAD (Ours)
ResNe150 256 704
nuScenes
0.475 0.574
0.585
0.265
0.361
0.236
0.184
BEVDepth Li et al.
2023a
ResNet101 512 x 1408
nuScenes
0.412 0.535
0.565
0.266
0.358
0.331
0.190
SparseBEV
ResNet101 512 1408
nuScenes
0.501 0,592
0.562
0.265
0.321
0.243
0.195
FlowAD (Ours)
ResNet101 512 x 1408
nuScemes
0,528 0.607
0.552
0.253
0.311
0.230
0.192
BEVFormer Li et
2022
ResNet101 900 x 1600 Bench2Drive
0,634 0.671
SparseBEV Lau et al.
2025
ResNet101 512 x 1408
Bench2Drive
0.641 0.689
0.386
0.165
0.124
0.189
FlowAD (Ours)
ResNet101 1408
Bench2Drive
0.673 0.723
0.346
0.150
0.117
0.184
the Ego-Scene Interactive Modeling, serving as the input of the Task-aware Enhancement. As the downstream tasks are generally classified into object-level (e.g., object detection) and region-level ones (e.g.. VLM to analyse the driving scenario), we design corresponding enhancement strategies.
Object-level Enhancement. For object-level tasks that employ M object queries Qabj E RMMx for inference (e.g., detection and motion prediction). enhance the perception capability by ap- plying the spatio-temporal flow feature. As in Fig. 4 object queries are regressed to the sampling points p E M XxC and projected to multi-view image planes. The flow units covering the sam- pling points of an object query are exploited to enhance the query embedding with a cross-attention, which injects the spatio- teapooltemporal dynamics.
Region-level Enhancement. For region-level tasks that perform inference based on the feature of the driving scenario (e.g., constructing the ego query with the observation feature in ego planning, generating scene descriptions in the VLM analysis), we design the region-level enhancement strat- egy. As shown in Fig. 4 the region features are directly concatenated with the corresponding flow units, with a convolution layer to reduce the feature channels. This helps the model better understand the dynamics of the ego-scene interaction and make more robust decisions.
4 EXPERIMENT
4.1 EXPERIMENTAL SETUP
Baselines. To demonstrate the generality and effectiveness of the proposed Flo we conduct experiments on the tasks of perception, E2E planning, and VLM analysis. 1) For perception, we
apply our framework on SparseBEV Liu et al. (2023). which perceives the scene with sparse object queries. 2) For E2E planning, Sparse Drive Sun et al. (2024) and DiffusionDrive Liao al. (2025) are adopted as the baselines. 3) For VLM analysis, we employ Senna Jiang et al. (2024) as the
baseline. It should be noted that the official checkpoint trained on the DriveX dataset which not released. Thus, we finetune the official Senna on the nuScenes dataset Cacsar al. (2020) to ensure fair comparison. All the inputs and parameters are aligned with the official settings of the baselines.
Datasets. We evaluate FlowAD on nuScenes Caesar et al. (2000) and Bench2Drive Jia et al. (2024). 1) The nuScenes dataset comprises 1,000 driving scenes, with 700 and 150 sequences allocated for training and validation. We perform tasks of perception, end-to-end planning (open- loop) and VLM analysis on this dataset. The metries are aligned with the baseline methods. 2) The Bench2Drive dataset is benchmark to evaluate driving abilities in a closed-loop manner, with 2 million frames collected from CARLA Dosovitskiy al. (2017). We conduct experiments of per- ception and end-to to--tt--ndd planning (both open- and closed-loop) on this dataset. Following the official setting, the 950 clips are allocated for training and 50 clips for validation (i.e., perception and open- loop planning). The closed-loop planning evaluation is performed on the official 220 routes. All metries are aligned with previous works.
Evaluation for Scene Understanding. Reviewing commonly used planning metrics (e.g.. L2 er- and collision rate Caesar et al. (2020)) the capability of scene understanding is not specifically
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Table Comparison of 3D occupancy prediction on the Oce3D-nuSeenes The baseline is SparseOce Liu et al. 2024).
Tian al. (2023) dataset.
Method
Backbone
RayloU
RayIoU m. 4m
SimpleOoc Gan et
2024
ResNet101
22.5
17.0
22.7
27.9
9.7
BEVFormer Li et 2022
ResNet101
32.4
26.1
32.9
38.0
3.0
BEVDet-Ool Huang Hining
ResNet50
32.6
33.1
38.2
FB-Ooc Li 20236
ResNet50
33.5
26.7
34.1
39.7
10.3
SparseOee Luu al. 2024
ResNer50
35.7
29.3
36.5
41.4
17.3
FlowAD (Ours)
ResNer50
38.4
32.2
39.3
43.8
14.6
Table 4: Results of perception, motion prediction and planning on the nuScenes Caesar et al. (2020) validation set. The FPS is measured on an RTX3090 GPU. The baselines are SparseDrive Sun al. 2024) and Difv Liao et al. (2025). denotes using ego status in the planning module.
nuScenes(Caesar
Method
Backbone
Detection mAP] NDS
AMOTA! AMOTPU
Tracking
Online Map)
0.476 mAA1
minADE) minFDE.
Motion Prediction
Planning Avg.
Arg.Coll, PCP|
FTS
VAD Jang al
2023
ResNec50
0.72
0.21
3,07
5.2
ReaNer5O
Drive Wane &
2024b
0.80
0.26
LAW Lieran 2022
0.61
0.50
UncAD
2125
ResNec50
0.60
0.07
41263
ResNec50
GenAD Zacig 3ia 5.66
ResNec50 0.200
0.58
0.16
1.78
6.7
ORION (VE c.. RU25
0.63
0.57
SpurseDrive Sun 2022
ResNa50 0.418
0.525
0.386
1.254
0.551
0.62
0.99
0.61
0.08
2.55
9.0
ResNer50 0.448
0.555
DiflusioDriss Liau
2025
ResNec50 0,412
0.522
0.574
1,216
0.559
0.61
1.01
0.57
0.08
1.35
6.8
FlowADDir FawnADaar//ttttiiiii (Ouns)
ResNe150 0.477
0.567
0.466
1.141
0.605
0.61
0.97
0.54
0.04
1.05
5.4
UniAD Hu a 2023
RasNe101 0,380 0.498
0.359
1.320
0.71
1,02
0.69
0.12
2.69
1.8
Sparsel
ResNer101
0.588
1.0K5
FlouAD SruaADapaaaammmm Drar (OBIS)
ResNer101
DrimeDre Wang et
3024.
0.29
0.15
SSR &i&
RexNer50
Drive Tannsormrr
Iin el
2023
ResNer50
0.40
0.11
FlowAD Sparee
(Ours)
RecNec50
0.554
evaluated, which is critical for robust maneuvering. Then, we introduce novel Frames before Correct Planning (FCP) metric. As a practised planner can quickly scan the scenario and make rea- sonable plans with the given command, the frames elapsed until the initiation of a rational action serve as the measurement,
Namd F Nemd Σ
FCP
1P G22 0.5m)),
where Nomd the number of given commands, P/ and G denote the predicted/GT ego trajecto- ries at 3s in the fith frame of the n-th video clip (1 < h F.). 0.5m is the threshold judging if the planned results accord with the command (more thresholds and an average version are provided in the appendix). The FCP metric provides statistical view reflect planner's comprehension of the driving process with ego-scene interaction, which helps to complement the evaluation system.
4.2 COMPARISON WITH STATE-OF-THE-ARE
Perception of Object Detection. Tab. 2 compares the results of 3D object detection on the nuScenes and Bench2Drive datasets. Our FlowAD achieves superior performance in both real driving scenarios (nuScenes) and simulated worlds (Bench2Drive), i.e., 52.8% mAP and 60.7% NDS, respectively. Notably, by applying our framework, the baseline SparseBEV Liu jet al. (2023) with the ResNet50 achieves impressive 3.0% mAP and 2.1% NDS gains on the nuScenes dataset. It proves the effectiveness of our ego-scene interactive modeling to improve the perception capability.
Perception of 3D Occupancy Prediction. The proposed ego-scene interactive modeling helps to improve the scene comprehension by learning the flow dynamics. We further verify this on vision- centric 3D occupancy prediction, which focuses partitioning 3D scenes into structured grids. As shown in Tab. 3. our FlowAD brings gains of 2.7% RayloU to the baseline SparseOce Liu et al. 2024), proving its effectiveness.
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Table 5: Planning results on the Bench2Drive dataset Jia et al. (2024). The baseline method is
SparseDrive Sun et al. 2024.
Method
Backhone
Openslopp
Avg. L21
DS1
SKI
Clountllop
Dffi
Smooth?
Merging!
Overtaking
Emergency Brake]
Closet-lop Ahiliy
Give Way?
Traffic Sign!
Meant
UniAD stall 2003
ResNes101
0.73
45.81
16.36
129.21
43.58
14,10
17.78
21.67
10.00
14.21
15.55
VAD dian
2024
ResNee50
0.1
42:35
15:00
157.4
46.01
8.11
24.84
18.64
20.00
19.15
18.07
pu San SE 2024
ResNes50
0.33
44.54
16.71
170.21
48.63
12:50
17.50
2000
20,00
23.05
18.50
Mon Song scar 2065
ResNee5U
0:52
47.91
18.11
174.1
51
FlowAD (CRP9)
0.73
51.77
2242
178.42
53.89
20000
28.87
25,00
25,00
28.42
25.42
Table 6: Results of VLM high-level planning the nuScenes Caesar al. (2020) validation set.
The FPS is measured on an NVIDIA RTX3090 GPU. The baseline is Senna Jiang et al. (2024). denotes finetuned nuScenes dataset.
Method
Planning Accuracy
Straigh
Path (FI Scure)
Turn Right
Keep
Accelerate
Speed (Fl Score)
Decelerate
Stop
Senna [ang et al. 2024
18.67
67.8 67.81
6.99
0.00
78.11
0.00
2.12
31.31
043
Seuna
88.54
96
30.53
46,94
96.50
0.00
0.00
90.82
0.43
93.55
Table 7: Ablation on the proposed modules in FlowAD. The FPS is measured on the detection baseline SparseBEV Liu et (2023). "#3s" denotes the planning metric 3s.
guided Scene Partition Aggre. Adjust
Flow Prediction
Detection mAP"
Plunning 3x), FCP]
VLM Analysis
Sputial Temporal
Planning Acc?
0.96
2.55
88
21.7
2
0.454
0.93
2.23
89.13
20.9
0.463
0.88
1.31
90.22
20.2
30
0.466
0.87
1.16
90.30
19.4
0.475
0.84
1.03
90.99
18.9
End-to-Enu Planning. The open- and closed-loop planning results are listed in Tab. and Tab. 5 respectively. In the open-loop nuScenes dataset, our ShowADsparseDrii and achieve lower 1.2 error and collision rates compared with the baseline SpirseDrive Sun et al. (2024) and DiffusionDrive Liao et al. (2025), well other SOTAs. This proves the effectiveness of our spatio-temporal flow dynamics to help understand the driving process. Notably, the FCP of our FlowAD FlowADsparseeDinn SparseDriee is only 0.91 frames, indicating the improved response speed to the given command with our ego-scene interactive modeling. Our planners also excel in the preceding subtasks, which demonstrates the generality. We further evaluate in the closed-loop Bench2Drive dataset, which covers 44 interactive scenes (e.g., cut-ins, overtaking, detours) and 220 routes across diverse weather conditions. Our method impressively improves the driving score (DS) by 7.23% and success rate (SR) ) by 5.31 % over the bascline SparseDrive, as well as the SOTA perfor- mance on various metrics. This proves the enhanced planning capability by modeling the ego-scene interactive dynamics in the latent feature space.
FlowAD fusion pissioonnnnooooioii
Liao
VLM Planning. Tab. 6 lists the results of VLM high-level planning. Compared with the baseline Senna Jiang et al. (2024) finetuned on nuScenes, our FlowAD improves the accuracy by impressive 2.45%. Notably, in safety-critical steering scenarios (i.e., turning left/right), the Fl scores signifi- cantly increase from 30.53% /4%.94% to 60.71 %%68. 17%. This proves the effectiveness of our design help understand the driving process and make reasonable plans.
4.3 COMPONENT-WIS WISE ABLATION
We analyze the influence of each component based on FlowAD of ResNet50, as in Tab. 7 We first ablate the spatial and temporal flow predictions (#T)), which model the core ego-scene interactive dynamics to benefit downstream tasks. Notably, the ego-guided scene partition is not performed, in which flow units are directly divided from multi-view image features (L). By applying the spatial flow prediction, obtains considerable gains 0.9% mAP and 0.32 FCP (2 v.s. D). The temporal flow prediction further improves the performance with outstanding 45.9% mAP and 1.87 FCP (3).
This verifies that perceiving the scene flow helps to understand the driving scenario and enhance the planning capability. We then ablate the influence of the ego-guided scene partition (+)). It

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Ground Truth
Ours
SparseBEV
BEVFormer
Figure 5: Qualitative results of multi- view object detection on nuScenes Caesar et al. 2020). The baseline is Bp Liu et al. 2023
the
NaRlhau
Figure 6: Qualitative results of end-to-end planning and VLM analysis on nuScenes Caesar et al. 2020).
surprises us that the simple starting point of partition significantly improves for 0.4% mAP and 0.56 FCP with only 0.1 FPS cost (0 v.s. @). This proves the necessity of considering the feedback of ego motion, which contributes to superior driving cognition and planning acumen. The multi-level partition, local aggregation and dynamic adjustment also bring considerable gains. The version with all the modules (7) has the best performance with 47.5% mAP, 0. 0.14% collision rate at 3s and 90.99% planning accuracy, showing the cooperation of our designs.
4.4 VISUALIZATION
We visualize the results of perception, E2E planning and VLM analysis for deeper insights into our designs. As shown in Fig. | our detector reveals stronger perception ability compared the baseline SparseBEV Liu et al. (2023) and BEVFormer Li et al. (2022). Notably, with the learned flow dynamics, our FlowAD is capable of handling its superiority. Fig. shows the predicted ego trajectories and high-level VLM planning. Benefited from the enhanced scene understanding, our method could plan a more reasonable ego route under challenging steering scenarios. Besides, the scene description of our method accurately captures surrounding elements and makes reliable planning meta-actions. These further demonstrate the effectiveness of our design to improve diverse tasks by introducing the feedback of the ego motion.
objects (front-left camera), proving
Due to space limitations, we provide additional analyses and experiments in the appendix, covering scene flow, the ego-guided scene partition module, the flow prediction module and the FCP metric. We also demonstrate the effectiveness and robustness of FlowAD and present more visualizations.
CONCLUSION
We propose FlowAD, a general flow-based framework with ego-scene interactive modeling for au- tonomous driving. The feedback of ego motion is formulated as the scene flow relative to the ego vehicle. This makes possible to model the latent interactive dynamics through feature learning with off-the-shelf datasets, instead of simulating. The ego-guided scene partition quantities the scene flow into basic flow units, as well as manifesting the ego motion. The subsequent spatial and temporal flow predictions capture the transition dynamics of spatial displacement and temporal variation based on flow units. Finally, the built spatio-tempore flow dynamics are exploited to ben- efit object- and regiln-level tasks. Extensive experiments in both open- and closed-loop evaluations prove the generality and effectiveness of our framework on tasks of perception, end-to-end planning and VLM analysis, as well as the proposed FCP metric.
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ETHICS STATEMENT
This work focuses on ego-scene interactive modeling for autonomous driving. All experiments are conducted on publicly available benchmarks, including NuScenes and Bench2Drive. No human subjects, sensitive personal data, or privacy-related information are involved in this research. The proposed method does not present foreseeable risks of harmful misuse, and study has no conflicts of interest or commercial sponsorship that could bias the results. We adhered to the ICLR Code of Ethics to ensure fairness, transparency, and academic integrity throughout the research process.
REPRODUCIBILITY STATEMENT
We have made extensive efforts to ensure the reproducibility of our results. The experimental setup including datasets (NuScenes and Bench2Drive) and evaluation metrics (e.g., L2 error and collision rate), is clearly described in the main text. Comparative baselines (VAD, SparseDrive, etc.) and detailed ablation studies are reported to validate our claims. The relevant source code the core components in this paper has been submitted anonymous supplementary material. Upon accep- tance, we will release code and related resources through a public code repository on GitHub.
LLM USAGE STATEMENT
The authors utilized LLMs such as Google's Gemini to refine the language and enhance the readabil- ity of this paper. important to state that these models were not used for generating ideas or the conceptual framework. All authors have reviewed the final manuscript and take full responsibility for its content and all claims.
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A APPENDIX
The appendix presents additional designing and explaining details of our general flow-based frame- work for autonomous driving (FlowAD) in the manuscript.
Methodoloiical Elaborations Discussions
Motivation Scene Flow: We discuss the motivation of the scene flow, which originates from the relative motion between the ego vehicle and the surrounding driving scenario.
Difference with World-Model-Stylee Approaches: We elaborate the methodological differences between our design and other wordd-modellbased methods.
Detailed Analysis Ego-guided Scene Partition
Determination of Steering Circle: We explain the process of determining the steering circle in the Dynamic Adjustment of Partition Size, which decides the adjustment ratios for the ego-leftrright scenes.
Different Partition Sizes:
We compare the performance of different partition sizes to man- ufacture the basic flow units in the proposed Ego-guided Scene Partition.
Influence of Dynamic Adjustment Parameter: We ablate different coefficients in the dy- namic adjustment of partition size to better introduce the ego-motion messages.
Different Ranges of Local Aggregation: We explore the influence of different ranges in the Local Aggregation to fuse messages for the possible object fragmentations.
We explore an adaptive aggregation strategy with De- formable Attention, which dynamically fuses local messages.
Adaptive Aggregation Strategy:
3. Detailed Analysis of Flow Prediction Module
Decoupling of Transition State and Observation Feature: We explore the influence of decoupling transition state and observation feature in the proposed flow prediction module, which is clearly different from standard world models.
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Different Levels for Flow Prediction: We compare features of different levels to perform spatial and temporal flow predictions, which aims to achieve a balance between efficiency and computation cost.
Influence of Flow Prediction Loss: We ablate the self-supervised prediction losses of the spatial and temporal flow prediction modules, which enable the model to forecast the scene flow dynamics.
4. Details on FCP Metric
Different Thresholds for FCP Metric: We apply different thresholds for the proposed FCP metric, which reflects the scene understanding of planner to make quick responses with the given command.
Extension of FCP Metric Closed-loop Scenarios: We extend the FCP metric by elimi- nating the requirement of the GT trajectory, which is flexible for both open-loop and closed- loop evaluations.
5. Effectiveness and Robustness Analysis
We explore the influence training with different scales of data, which demonstrates the strong capability of our method to learn from limited samples. Accuracies of Different Scenarios in E2E Planning: We evaluate the performance of different driving scenarios in E2E planning, which proves the reliability under challenging steering scenarios.
Scale of Training Data:
We detail the accuracy of differ- ent driving scenarios VLM analysis, which demonstrates the influence training data distribution.
Accuracies of Different Scenarios in VLM Analysis:
Rohustness Analysis: We assess the robustness of our planner by perturbing the input multi-view images, calibration parameters, and sampled timestamps.
Qualitative Visualizations
Visualizations of Various Tasks: We provide more visualization results for the tasks of perception, end-to-end planning and VLM analysis.
Visualizations of Challenging Cases: We present several visualization cases to illustrate
the robustness of our method under complex driving scenarios.
Visualization of Activation Maps: We visualize the activation maps before and after our flow prediction, which illustrates the positive influence of concentrating critical objects.
B METHODOLOGICAL ELABORATIONS & DISCUSSIONS
B.1 MOTIVATION OF SCENE FLOW
Drawing inspiration from the human perceptual- -motor processes, particularly the concept of relative motion Davis & Bobick (1997); Bobick & Davis (2001), we represent the ego-scene interaction as the scene flow relative to the ego-vehicle. As shown in Fig. when the ego vehicle moves forward, the surrounding backward along the left and right sides. The flowing driving scenario, including the involved object (e.g., the car and pedestrian), reflects the feedback of the ego motion, hence helping the auto-driving system to understand the driving process by learning the dynamics. We then propose the ego-scene interactive modeling architecture to perceive the optic flow the latent feature space for anticipa- tory planning and navigation. The scene is divided into multiple flow units with the guidance of ego motion, which provides the foundation to capture the spatio-temporal transition dynamics, followed by the enhancement strategies benefit diverse downstream tasks. Notably, local aggregation and multi-level partition strategies are integrated to mitigate the potential object fragmentation and cap- ture scene flows across varied receptive ficlds. This pipeline enables the modeling of ego-mos mooooooonn feedback using readily available, pre-recorded datasets, obviating the need for complex simulations to generate varied observational outcomes.
will separate from the center of the front-view camera and move relatively
B.2 DIFFERENCE WITH WORLD-MODEL-STT APPROACHES
As described in the manuscript, we exploit the mechanism of the world model to forecast the tran- sition dynamics in both spatial and temporal dimensions. Here further clarify the difference
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Egn Forward Direction
Relative Motion
Flow Direction Ego-Left Scene
Flow Directio Egp-Left Sone
Flow Direction Egn-Right Scene
Rekative Motion
Multi-Levd
Partition
Figure 7: Visualization of the scene flow caused by the relative motion of the ego vehicle.
compared with other world-model-style approaches (e,g. DriveDreamer Wang al. (2024a) and Drive-WM Wang et al. (2024b)). These planners regard the whole driving environment as a single feature embedding and directly predict the future state driven by the action embedding. Although achieving remarkable driving performance, it neglects the fine- fine-grained environmental messages and the physical influence of ego motion on the camera observations. In contrast, our method divides the driving scenario into multiple parts and extracts the dynamics by flow units. The feedback of ego-motion is explicitly captured by adjusting the start point and ego-left/riht sizes of the flow unit partition. These make our ego-scene interactive modeling more sensitive to surrounding dynamics and help the planner understand the driving process and predict robust ego trajectories. In addition, scene flow performs on both temporal and spatial dimensions, which further benefits the spatial perception the environment (Tab. 7 of the manuscript). The performance comparison with the mentioned world- methods in Tab. also corroborates these advantages.
C DETAILED ANALYSIS OF EGO-GUIDED SCENE PARTITION
C.I DETERMINATION OF STEERING CIRCLE
In the Dynamic Adjustment of Partition Size of Sec. 3.1 the manuscript, we assume the steering trajectory of the ego-vehicle is part of circle Wang et al. (2005); Park et al. (2015). The radius r is exploited to dynamically adjust the partition sizes of the ego-left/right scenes with the ego width Wego, which helps cognize the feedback of ego motion. Here we detail the computational process. As common sense, three points decide the formulation of a circle. Thus, we exploit the ego posotions ((r,,
144-1.9-111 (Ir-2, 3)-2)) time t/t 1/t (Ire Yc) and radius r of the steering circle. The derivation results are listed as follows, with key items replaced by letters,
determine the center
a = 2(x - Kt x1-1); = 2(yr - yt-1),
c x/ + y x!- - 1/-11
c = 2(x(11 - x1-2); f = 2( 11111 - 32-2),
g = 2 + 1/1 4/-2 v2-
g xx c
a X
e x a ax f Dc
a x f x e
r / (x. 2² + (yo 3.
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Table 8: Ablation on the base partition size / on the nuScenes
Caesar et al.
2020
validation set.
Multi-level Partition Sizes
mAPt
NDS1
mATE)
mASE
mAOE
mAVE.
mAAE
FPS1
Stagel
Stage2
Stage3
Stage4
3
0
0
0.462
0.562
0.600
0.272
0.382
0.240
0.198
21.1
8
0.475
0.574
0.585
0.265
0.361
0.236
0.184
18.9
16
0.594
0.268
0.363
0.244
3 24
12
0.459
0,562
0.601
0.271
0,380
0.243
0.199
19.8
Table Results of perception, motion prediction and planning on the nuScenes Caesar et (2020)
validation set. The baseline method is SparseDrive (unn et al. (2024). denotes using the last FPN feature for multi-level partition and peatialtemmoral flow prediction. indicates fusing the transition state and forecasted in the flow prediction module.
Method
Backtoone
Desection mAP:
NDS
Tooking AMUTAT
AMOTP)
Online May mAP
Motion Prediction minADE.
minFDE]
Avg. L.2.
Planning Avp.Col]
PCP.
FPS
SparseDrive
0.386
0.62
FlowAD (Ours)
ResNe150
0.488
0.555
0.427
1,181
0.582
0.58
0.95
0.56
0.06
1.03
7.5
FlomAD" (Ouns)
ResNe150
0.444
0.544
0.416
1,198
0.567
0.59
0.96
0.57
0.06
1.45
8.5
Flow AI
ResNe150
0.429
Table 10: Ablation on the power coefficient in the dynamic adjustment of partition size on the nuScenes Caesar et 2020) validation set.
Power Coefficient
mAPt
NDS
mATE!
mASE.
mAOE/
mAVE!
mAAE.
Linear
0.469
0.569
0.595
0.271
0.363
0.240
0.190
2
Quadratic
0.475
0,574
0,585
0.265
0.361
0.236
0.184
39
Cubic 0.474
0.572
0.589
0.256
0.354
0.237
0.187
Then the radius r of the steering circle is exploited to compute the dynamic adjust ratios for the scenes in ego-left/rigt. This makes the modeling way conform to the kinematic characteristics of ego-scene interaction, which benefits the understanding of the driving process.
C.2 DIFFERENT PARTITION SIZES
Following the design the FPN network Ren et al. (2015). we propose a multi-level partition to model the flow dynamics of different receptive fields. Here explore the influence of different partition sizes as Tab, 8 It shows that applying the multi-level partition brings the performance gains of considerable 1. 5 mAP and 1.2% NDS with patch sizes of (8, 4, 2, 1} for the four-level image features (2) v.s. @). The effectiveness of our design is proven, which helps to understand the ego-scene interaction with flow units of different sizes. It should be noticed that increasing the partition size would instead degrade the performance (Φ3 v.s. @)) The reason lies in the oversized flow unit, which makes hard to quantify the scene flow dynamics. This also proves the necessity of our partition strategy to construct basic items for dividing and modeling the scene flow.
Considering the multi-level flow prediction is the primary bottleneck causing the FPS degradation, to optimize this trade-off, we perform the ego-scene interactive modeling only on the last stage of multi-view features. The result listed in Tab. shows that the lightweight FlowAD maintains its leading performance while incurring only minimal drop of 0.5 FPS, achieving balance between effectiveness and efficiency.
C.3 INFLUENCE OF DYNAMIC ADJUSTMENT PARAMETER OF PARTITION SIZE
In Sec. 3.1 we choose the quadratic power for the coefficients ego-lefUright partition sizes. Here we further explore the influence by ablating the power, in Tab. 10 The results show that linear power not sufficient to reflect the different motion speeds of ego-left/right, which is inferior to the one with quadratic power. Notably, the cubic power is overlarge to balance the partition sizes of flow units on both sides. The orientation- metrics are further improved (c.g., mASE and MAOF), while the overall mAP/NDS is inferior to the quadratic one.
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Table 11: Ablation on the range of local aggregation on the nuScenes Caesar al. (2020) validation
set.
Range Local Aggregation
mAPt
NDS?
mATE,
mASE
mAOEf
mAVE,
mAAE,
FPST
0.460
0.563
0.605
0.270
0.365
0.239
0.200
19.2
3
0.475
0.574
0.585
0.265
0.361
0.236
0,184
18.9
5
0.471
0.575
0.574
0.269
0.350
0.238
0.196
18.8
7
0.464
0.567
0.588
0.271
0,366
0.242
0.194
18.6
Table 12: Ablation of aggregation strategies on the nuScenes Caesar et al. 2020
validation set.
Method
Aggregation
mAPt
NDS1
mATE,
mASE,
mAOE!
mAVE,
mAAE!
FPSt
SpaneBBV Liu al. 2023
0.445
0.553
0.605
0.278
0.389
0.253
0.189
21.7
FlowAD (Owis)
Local Deformable
0.475
0,574
0.585
0.265
0.361
0.236
0.184
18.9
FlowAD (Ours)
0.480
0,580
0,582
0.273
0.367
0.241
0.185
16.7
C.4 DIFFERENT RANGES OF LOCAL AGGREGATION
As mentioned in Sec. 3.1 the manuscript, the proposed local aggregation fuses adjacent flow units to handle possible object fragmentations. We then ablate different ranges to explore the influence, as shown Tab. I Compared with the version without local aggregation, fusion with the range of 3 brings considerable gains of 1.5% mAP (Ø v.s. ①), proving its effectiveness for environmental modeling. Notably, it does not bring further improvements when further increasing the fusion range (43 v.s. @). The underlying reason is that the overbroad fusion would weaken the unique distribu- tion of each flow unit and distract the learning of scene flow dynamics. We adopt the fusion range of 3 the default setting in our implementation.
C.5 ADAPTIVE AGGREGATION STRATEGY
We explore the adaptive aggregation by applying the Deformable Attention Xia et al. (2022) which dynamically aggregates the surrounding environmental information for each partitioned patch. No- tably, the initial sampling locations uniformly distributed in local flow units, which are then dynamically adjusted by the predicted offsets. Considering the computation costs caused by the high resolution of features in earlier FPN stages, the aggregation is only performed on the last two stages. The result is listed in Tab. [2] It shows that the mAP/NDS further improves, while the FPS degrades for 2.2 FPS. We will explore more efficient dynamic aggregation strategy in future work.
D DETAILED ANALYSIS OF FLOW PREDICTION MODULE
D. DECOUPLING OF TRANSITION STATE AND OBSERVATION FEATURE
Different from the standard world models Hafner et al. 2020); Wang et al. (2024b) that regard constantly-updtted transition state as the predicted environmental feature, our flow prediction module decouples the two concepts to unleash the modeling potential. Specifically, the transition state modeled with GRU (same as the standard world model) is exploited to backward update the observation feature with cross-attention (Fig. 3 of the manuscript). The ablation in Tab, g further proves that the decoupling helps to better extract environmental information and benefit planning.
D.2 DIFFERENT LEVELS FOR FLOW PREDICTION
The multi-level partition in Sec. 3.1 of the manuscript is performed capture the flow dynamics of different receptive fields. We further explore the influence by ablating the feature levels for flow prediction, as shown in Tab. the mAP/NDS metric improves by considerable 1.5%/0.7% (2 v.s. 1), respectively. It proves the effectiveness of our design to help object perception. An interesting observation is that the perfor- mance of the version with flow prediction the fourth level feature (3) is better than the one the first level feature (D). This reveals that the high-level feature with more semantic messages brings more benefits environmental modeling compared with the low-level ones with more tex-
With the spatio-temporal flow prediction on the first level feature,
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Table 13: Ablation on the levels for flow prediction on nuScenes
Caesar et
2020
validation set.
Levels for Flow Prediction
mAPi
NDS1
mATE!
mASE
mAOE
mAVE.
mAAE
FPS1
Stagel
Stage?
Stage3
Stage4
0.445
0.553
0.605
0.278
0.389
0.253
0.189
21.7
0.460
0.560
0.599
0.272
0.388
0.247
0.190
19.8
0.466
0.475
0,574
0.585
0.266
0.361
0.236
0.184
18.9
0.479
0.580
0.581
0.266
0.357
0.232
0.183
17.2
0.481
0.583
0.578
0.263
0.356
0.233
0.184
15.1
Table 14: Ablation on the flow prediction losses on the nuScenes Caesar (2020)
validation set.
Flow Prediction Loss
mAPt
NDS1
mATE!
mASE.
mAOE.
mAVE!
mAAE|
Spatial
Temporal
0.444
0.548
0.619
0.280
0.392
0.256
0.192
(20
0.459
0.562
0.596
0.273
0.379
0.244
0.187
/
0.456
0.559
0.601
0.276
0.381
0.246
0.189
/
0.475
0,574
0,585
0.265
0.361
0.236
0.184
Table 15: Influence of the flow prediction losses the nuScenes The default train epoch is 24, following the baseline SparseBEV
Caesar al. (2020)
validation set.
Liu et al. (023)).
Method
Train Epoch
Flow Spatial
Temporal
mAP-
NDS!
mATE!
mASE.
mADEL
mAVE,
mAAE.
SparseBEV Li clal
2023
18
0.377
0.541
0.625
0.283
0.401
0.255
0.191
SpurscBEV
0.445
0.553
0.389
0.189
SparseBEV
0.554
FO (Ours)
18
0.393
0.199
0.671
0.294
0.404
0.275
0.198
FlowAD (Ours)
24
0.444
0.548
0.619
0.250
0.392
0.256
0.192
PlowAD
0.570
0.189
FD (Ours)
0.454
0.557
0.611
0.270
0.385
0.244
0.187
FlowAD AD (Ours)
24
0.175
0.574
0.585
0.265
0.361
0.236
0.184
88
FlowAD (Ours)
0.483
0,580
0.578
0.263
0.355
0,233
0.183
tural information. The version that performs flow prediction all-Ievel features achieves the best performance with outstanding 48. 1% mAP and 58.3 98.3% NDS (@), yet causes more computation costs (i.e., 21.7 FPS of ① + 15.1 FPS). To balance the performance and efficiency, only perform flow prediction on the third- and feurth-level features (+) in our default implementation.
D.3 INFLUENCE OF FLOW PREDICTION LOSS
Following the loss design in world models Hafner et al. 22000 2021 predicted/GT flow unit to latent states and minimize their KL divergence. This endows the model with the capability of forecasting scene flow dynamics, hence benefiting the understanding of the feedback of ego motion to the driving scenario. We then ablate the spatial and temporal flow predic- tion losses to explore the influence. As shown in Tab. 14, the performance of the version removing all flow prediction losses is even inferior to the baseline BE Liu et al. (2023) (Q). This reveals the necessity of explicit supervision learn effective environmental messages. When ap- plying the spatial or temporal flow prediction losses, the performance improves for 1.5% 1.2% mAP respectively (2:3 v.s. D), proving the effectiveness. Notably, the spatial supervision brings more performance gains than the temporal one (3 v.s. @ It demonstrates that spatial knowledge is more helpful for scene understanding. Applying both losses contributes the best version with 47.5% mAP and 57.4 47.4% NDS, showing the cooperation of the spatio-temporal modeling.
respectively map each
We further explore the influence of flow prediction losses by ablating them, í.e., without the explicit supervision of the predicted transition states. As shown in Tab. 15 our model without world model loss benefits more from the training epochs compared with the baseline, which achieves superior performance at the training epoch 30. In contrast, the with explicit supervision reaches conver- gence much faster. These reveal that the world model loss helps the model to learn realistic future driving environment, well the optimization potential of our design.
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Table 16: Planning results in terms of L2 error, collision rate, and the proposed FCP metries with different thresholds on the nuScenes Caesar et al. (2020) validation set. The baseline method is SparseDrive Sun et 2024). denotes the extended FCP metric.
Backbone
Collision
(%).
FCP (frame) J
PCP2 (frame)
2x
Avg.
1s
2s
36
Avg
0.25m
0.50m
0.75m
Avg.
frume
frame
frame
Avg.
Uni all R028
R0
10:44 0.67
0.69
0.04
0.08
0.23
0.12
5.41
2.69
0.93
3.01
0.73
2.02
L8
VAD Ramret di pISsA
ResNar50
0.44 0.70
1.05
0.72
0.7
0.17
0.41
0.22
6.44
3.07
1.42
3.64
0.87
2.30
4.12
2.46
5.2
Mont Song et 2025
ResNet50
0.31
0.57
091
0.60
0.11
0.05
0.22
0.09
7.8
Spurse Sun 2024
ResNet50
0.29 0.58
0.6
061
001
0.05
0.18
0.08
4.87
2.55
1,09
2.84
0.54
1.88
3.02
1.48
9.0
HDwAD (Obi)
KesNa150
0.55
0.56
0.01
0.06
0.48
1.21
0.37
0.90
7.6
E DETAILS ON FCP METRIC
E.I DIFFERENT THRESHOLLD FOR FCP METRIC
Having observed the capability of the scene understanding within an auto-driving system is not specifically evaluated in current metrics (e.g. L.2 error and collision rate Caesar et al. (2220), ). we propose the FCP metric to provide a statistical perspective. The frames elapsed until the iitiiaiio of a rational action are exploited as the measurement, with the threshold 0.5mm to judge if the planned results accord with the command. Here we ablate more thresholds for deeper exploration, as listed in Tab. I6. The results show that, with a stricter threshold of 0.25m, the FCP of our method dra- matically increases from 1.03 frames (0.5m) to 2.12 frames. It indicates the slowed response speed of our method under more rigorous measurement standards. Even though, our FlowAD surpasses other SOTA planners with large performance margin, e.g., our 2.12 frames 4.87 frames of the baseline SparseDrive Sun et al. (2024), demonstrating the superiority. The less restrictive threshold of 0.75m leads better performance of the FCP metric. The average version of the three thresh- olds reflects a general comprehension of the driving scenario for planner, in which our Flow AD still performs best with outstanding 1.21 FCP. This proves the effectiveness of our method help understand the driving process and make quick responses to the given command.
E.2 EXTENSION OF FCP METRIC TO CLOSED-LOOP SCENARIOS
We propose the FCP metric to evaluate planner's s reaction speed to the given command by comput- ing the errors compared with the GT trajectory. While reliance on Ground Truth (GT) is standard for open-loop metrics (e.g.. L2 Error), it inherently limits the assessment of intrinsic correctness and the multi-modal nature of human-like planning. To overcome this limitation and facilitate a more precise evaluation of reaction speed, extend the FCP metric to operate beyond simple GT match- ing. Specifically, a "successful reaction" is defined based on the trajectory's 's compliance with the high-level command rather than its proximity to the GT. For given command (applicable in both open- and closed-loop settings), the model is considered responsive if its predicted trajectory at the 3s horizon aligns with the command for predefined sequence of q frames. For instance, regarding a "Tur Right" command, adopting the threshold from UniAD Hu et al. (2023), planner is deemed successful if the lateral displacement of the ego trajectory exceeds 2.0m for q consecutive frames. The extended metric is formulated as,
Nemd
F., max (0.
FCP*
E 1(P), -o) n=1 f-1 hm1
(10)
Nemd
We then perform comparison using the extended FCP metric, as shown in Tab. 16 The results indicate that our method consistently maintains its superiority over competing approaches across varying thresholds of q, validating the robustness of our planner. Moreover, by eliminating GT dependence and accommodating diverse docision-making behaviors, this refined metric offers the flexibility to be deployed in both open-loop and closed-loop settings. We hope FCP serves as a standard, robust benchmark for more holistic assessment of autonomous driving capabilities.
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Table 17: Ablation on the scale of training data on the nuScenes Caesar al. 2020)
validation set.
#
Scale of Training Data
mAPt
NDS
mATEJ
mASE
mAOE
mAVEJ
mAAE!
25%
0.391
0.497
0,691
0.275
0.519
0.297
0.200
50%
0.448
0.554
0,593
0.269
0.389
0.257
0.194
75%
0.466
0.569
0.587
0.266
0.367
0.244
0.188
100%
0.475
0.574
0.585
0.265
0.361
0.236
0.184
Table 18: Performance comparison under different driving scenarios on the nuScenes Caesar et al.
2020
validation
Porf. gu stroight
Perf. num left 4
Perf. tum right (409 sumples)
Perf. Overill
Method
Backhone
(5309 samples)
(301 samples)
(6019 samples)
Arg. 1.2 (m) Arg. Col. (%6 Avg L.2 (m) Avg. Col. (56 Avg. (m) Avg. Cal. (56)
L.2 (m) Aug. Col. (86
SparseDDrive Sun et
2024
ResNec50
0.58
0.08
0.29
0.86
0.08
0.61
0.09
FlowAD PlowADsperae Drites
ResNet50
0,53
0,06
0.77
0.08
0.89
0.09
0,56
0.06
SpursDrriv(Suneeu Sun
2024
ResNet011
0.56
0.06
089
0.13
0.81
0.10
0.58
0.06
FlowAD SewAADperre.Dree SperscDrive
ResNet101
0.50
0.05
0.74
0.08
0.63
0.08
0.52
0.05
DifTusionDrive Lino et al.
2025
ResNet50
0.55
0.07
0.12
0.59
0.10
0.57
0.08
FlowAD D.f Drive
ResNet50
0.54
0,03
0.02
0.54
0,07
0.54
0,06
F EFFECTIVENESS AND ROBUSTNESS ANALYSIS
F.1 SCALE OF TRAINING DATA
The quality of ego-scene interactive modeling is deeply influenced by the training data volume. We then explore the influence by training our FlowAD with different scales of data and the results are presented in Tab. 17 The default setting for the scale of training data is noted as "100%". It shows that as the scale of training data reduces (i.e., "75%" "50%" and "25%" the overall performance gradually decreases (e.g., 47.5% + 46.6% + 44.8%
39.1% of mAP on the nuScenes datasct), demonstrating that more data helps improve the model capacity of understanding the driving process of ego-scenne interaction. Notably, even with only 50% of the training data, our FlowAD has achieved comparable performance with the baseline SparseBEV Liu et al. (2023), proving the effectiveness of our design.
F.2 ACCURACIES OF DIFFERENT SCENARIOS IN E2E PLANNING
We evaluate the benefit of our ego-scene interactive modeling under different driving scenes, as shown in Tab. 18 According to the given driving command (Le., go straight, furn left and turn right), we divide the 6,019 validation samples in nuScenes [Cassar et al.] (2020) into three parts, which contain 5,309, 301 and 409 ones, respectively. As expected, all methods perform better under go straight scenes than the steering scenes. When applying the proposed ego-scene interac- tive modeling, our planners achieve considerable gains in steering scenes, proving its robustness. Furthermore, the ability metrics more challenging Bench2Drive (Tab. 5 of the manuscript) also confirm the stability of our approach diverse and extreme maneuver scenarios.
F.3 ACCURACTES OF DIFFERENT SCENARIOS IN VLM ANALYSIS
The ego-scene interactive modeling is proposed to enhance the comprehension of the driving process and improve the planning capability. We demonstrate the superiority of our proposed architecture by evaluating the accuracies of different driving scenarios in Tab. [9 It shows that our FlowAD achieves consistent performance gains compared with the baseline Senna Jiang et al. (024)). No- tably, the nuScenes dataset Caesar et al. (2020) is dominated by the straight scenes (i.e., 4724 sam- Straight" all 5119 samples). The performance of steer- ing scenarios is less reflected in the overall planning accuracy metric, yet critical for safe maneu- vering. In such "Left/Right Turn" cases, our method still demonstrates superior accuracy, proving the effectiveness of the scene flow modeling benefit scene understanding and planning.
ples of
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Table 19: Accuracy of high-level planning on the nuScenes Caesar et al. (2200) validation set. The
baseline is Senna Jiang et al. (2024). denotes finetuned on the nuScenes dataset.
Planning Accuracy (5119 samples)
Keep Right Turn (287 samples)
Keep Left Turn (139 samples)
Keep Straight (3704 sumples)
Accelenae Right Tur.
Accelerate Left Turn
Accelerate Struigt (62 sampless
Decelerate Straight (45 sumpless
Stup Straight (977 sumples))
Method
FPS
(5 samples)
(4 samples)
Senra es al. 20144
18.67
0.50
18.03
37.53
0.00
0.50
0.00
2.22
26.56 88. 13
0.43
Senna
ping cm 2005
58, 54
35 58
28.78
92. 28
0:00
0.00
0.00
6,67
0.43
(OWADTOUSS)
90.99
69.52
69.06
94,71
66.67
25,00
43.55
17.78
90.87
0.39
Table 20: Robustness analysis on the nuScenes Caesar al. (220)) validation set. The baseline method is SparseDrive Sun et al. (202)) with ResNet101.
Gau Calibraton Timestapp
Map
Motion Preduction minADD)
Plonning
Unit
MAP] NDS
AMOTA! AMOTP]
mAP1
Avg.L.1
Avl ICPL
Sp (Olr))
0.585
1,855
0.90
0.55
0.52
Spansebri flowDD (dur)
0.555
1.159
0.535
0.63
1.05
0.50
2.34
0,517
0,59%
1,053
0,585
0.96
0,53
0.85
StnndDiuuS et FlowAD (Oirs)
2034
/
0.438
0.540
0.466
1.211
0.509
0.64
1.07
0.65
0.08
1.20
8.456
11:70
1.498
1,00
0.543
0.58
0.99
0.56
0.86
1.55
SpanaDreuu ol FlowAD (Ohrs)
01470
0.565
0-483
1.107
0.598
0.61
1.00
0.00
0.07
1.91
0.506
11.590
0.508
1,856
01569
0.56
0.85
0.53
0.88
1.98
Spu et FloAD (Oir))
2024
0.419
01553
0.457
1.244
0,485
0.65
1.30
0.66
0.08
4.50
0.465
0.558
0.476
1.119
0.520
0.60
1.02
0.57
0.06
2.17
F.4 ROBUSTNESS ANALYSIS
Here we evaluate the robustness of our planner by (1) injecting Gaussian noise into the observed images, (2) perturbing camera intrinsicseextrinsics and ego-pose parameters by multiplying ran- dom coefficient with the range of 0.95 ~ 1.05, and (3) introducing temporal drift by randomly sampling the input frame from - 2 to t. The results Tab. 220 show that our method consistently demonstrates stronger robustness compared with the baseline SparseDrive Sun al. (2024).
G QUALITATIVE VISUALIZATIONS
G.1 VISUALIZATIONS OF VARIOUS TASKS
We visualize the results of perception, E2E planning and VLM analysis for deeper insights into our designs, which demonstrate the generality and effectiveness of our designs. As depicted in Fig. 8 our reveals stronger perception ability of the surrounding environment and localizes the objects, compared with the baseline SparseBEV Liu et al.] (2023) and BEVFormer Li et al.) (022). should be noticed that our FlowAD is capable to capture hard cases, e.g., part occlusion, small and dense objects. It proves the superiority of our design to enhance the perception ability. Fig. Fig./ccompe compares the planned ego trajectories and high-level VLM planning with recent SOTA planners. With the proposed ego-scene interactive modeling, FlowAD could quickly understand the driving scenario and plan a more reasonable ego route under various scenarios. Besides, the scene descriptions of our method accurately capture surrounding elements and make reliable planning meta-actions. We also visualize the planned routes in the closed-loop Bench2Drive dataset Jia al.) (2024), which performs real-time interactions between the ego vehicle and driving scene with the CARLA simulator Dosovitskiy et al. (2017). The Fig. [T] shows that our FlowAD is able to notice the critical driving elements (e.g., pedestrians, cars, and traffic lights) and drive the vehicle in a safe manner. These further prove the effectiveness of our design to improve diverse tasks by learning the feedback of the ego motion in the latent space.
exploiting the learned flow dynamics
G.2 VISUALIZATIONS OF CHALLENGING CASES
Non-ego-centric Driving Scenario. FlowAD's scene flow modeling captures object dynamics be- yond static background through local aggregation that handles object fragmentation. Therefore, our approach essentially models the interactive dynamics between the vehicle, the static environment, and moving objects. This enhances the model's understanding of autonomous driving and its pacity to capture scene dynamics. Consequently, our method can handle non-ego-centric centric dynamics, such as the "cut-in" scenario. We further demonstrate this by the case in Fig. [T As a leading
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GA TRGHT
CAR
Ground Truth
Ours
SparseBEV
BEVFormer
Figure 8:
Qualitative results of multi view object detection on nuScenes Caesar et al. (020). Results show the strong capability of our method to handle occluded objects with scene flow dynamics.
vehicle performs a cut-in, our planner has already inferred its motion state from historical observa- tions and produces salient response, corroborating the above hypothesis. Besides, the superiority of the ability metrics in Bench2Drive Jia et al. (2024) (e.g., Merging and Emergency Brake that include many cutting-in scenarios, Tab. 5 of the manuscript) substantiates our method's advantage in modeling vehicles that are not centered ego-motion.
Extreme Dynamic Driving Scenario. The model's stability further evaluated under challenging longitudinal and topographical conditions, including rapid acceleration, deceleration, and travers- ing slopes. As shown in Fig. 12 the proposed FlowAD exhibits superior robustness in these hard scenarios. Fig. 13 demonstrates the superior steering capability of our planner with the proposed ego-scene interactive modeling. These qualitative observations align with the quantitative ability metrics reported Bench2Driiee Jia al. (2024) (Tab. 5 of the manuscript), which validate the system's stability under diverse and extreme dynamic maneuvers.
G.3 VISUALIZATION OF ACTIVATION MAPS
With the aim better illustrating the mechanism the proposed flow prediction, we visualize the activation maps of multi-view image features before and after design using GradCAM, in
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Figure 9: Qualitative results of end-to-end planning and VLM analysis on the nuScenes Caesar al. 2020) dataset. Our method performs more robust planning under different driving scenarios with enhanced comprehension by scene flow dynamics.
Figure 10: Visualization of the planning results on the Bench2Drive dataset Jia al. 2024) with
CARLA simulator.
Fig. 14 It shows that the standard image features fail to observe the crucial vehicles, which could potentially lead to serious traffic accidents. In contrast, the features with ego-scene interac- tive modeling produce a highlighted response to key dynamic targets. Besides, the activation of background clutter is much reduced by design. These demonstrate that the proposed module successfully guides the model to comprehend the driving environment and focus on critical objects, thereby enhancing the overall robustness and stability of the auto-driving system.
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Ground Truth
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Cutting-in
Figure 11: Visualization of challenging cutting-in case. Observing the front vehicle with an abrupt lane change (marked by the yellow arrow), our planner captures the spatio-temporal dynamics and performs the brake, proving the capability to handle complex driving interactions.
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Figure 12: Visualization of planning stability under challenging longitudinal dynamies. Under the complex scenarios including hard braking (Left, -2.54m/8 and rapid acceleration (Right, +2.65m/ /2²), our method is capable of making robust planning compared with other methods.
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Figure 13: Visualization of planning robustness in steering case Our method could plan a more reasonable ego trajectory under the safety-critical turning scenario, proving the effectiveness.
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Before FlowAD
After FlowAD
Figure 14: Visualization of activation maps The proposed flow prediction helps to concentrate on the critical dynamic objects and suppress the background clutter.
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Reviews:
[{"review_id": "CY2G74mDvs", "reviewer": "Reviewer_sdh6", "summary": "This paper proposes FlowAD, a novel modeling paradigm for autonomous driving that aims to address the lack of feedback from ego-motion to future observations in current models. The authors argue this gap leads to an incomplete understanding of the driving process. FlowAD's core idea is to model this \"ego-scene interaction\" as a \"scene flow\" relative to the ego-vehicle. The paper also introduces a new FCP metric.", "questions": "1. Can the authors provide analysis linking a better FCP score (open-loop) to a specific, measurable improvement in closed-loop driving (e.g., faster reaction to hazards), beyond the overall driving score?\n2. Could the authors provide comparisons against more recent end-to-end planners (e.g., GenAD (ECCV24), DriveTransformer (ICLR25), MomAD (CVPR25), ORION (ICCV25), etc.) on both open-loop and closed-loop?", "limitations": "", "rating": 4, "confidence": 5, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "1. Modeling the ego-scene interaction as \"scene flow\" in the feature space, inspired by human optic flow, is an interesting approach to the problem.\n2. The paper is well written.", "weaknesses": "1. The newly proposed FCP metric judges \"correctness\" using L2 distance to the Ground Truth (GT) trajectory. This is a strong and potentially flawed assumption, as \"closeness to GT\" does not equal \"correct\" in planning. A safer, more conservative plan (e.g., braking earlier) might deviate from the GT but would be unfairly penalized by FCP as a \"slow\" or \"wrong\" response.\n2. The paper does not demonstrate how FCP, an open-loop metric based on GT, translates to or predicts better closed-loop performance. Its value in a closed-loop context is not well-argued, only discuss in open-loop is some how meaningless.\n3. The paper's SOTA claim is weakened by its comparison to relatively dated baselines (e.g., SparseDrive, UniAD). This makes it difficult to assess FlowAD's true performance.\n4. The \"scene flow\" seems to primarily model the relative motion of the static background caused by ego-motion. It is unclear how this \"ego-centric\" flow model effectively handles the more critical, non-ego-centric dynamics, such as another vehicle suddenly cutting in while the ego-vehicle is driving straight.", "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": "Xfp6q4ZTAg", "reviewer": "Reviewer_8pZR", "summary": "This paper proposes FlowAD, a general flow-based framework for autonomous driving that addresses a key limitation in existing approaches: the inadequate modeling of ego motion feedback into scene observation. Drawing inspiration from human perception and relative motion, FlowAD introduces an ego-scene interactive modeling paradigm, representing ego-scene interaction as scene flow relative to the ego vehicle. The methodology centers around an ego-guided scene partition, spatial and temporal flow prediction modules, and task-aware enhancement for downstream perception, planning, and VLM  analysis tasks. A new evaluation metric, FCP, is proposed to quantify scene understanding. The framework is evaluated across several benchmarks and tasks, demonstrating consistent gains over baselines on multiple metrics.", "questions": "- Could the authors provide a more detailed comparison and discussion with world-model–based methods to clarify the statement made in the introduction? Furthermore, could comparisons with more recent approaches, such as the perception-free methods SSR and LAW, be included to strengthen the evaluation?\n- How sensitive is the final performance to the chosen partition size and the parameters within the dynamic adjustment module? In particular, what is the model's robustness in the presence of inaccurate ego-motion estimates?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "- The paper explicitly addresses the gap of neglecting ego vehicle feedback by proposing to model this effect as a “relative scene flow,” balancing physical intuition with practical data-driven learning from logged data.\n- FlowAD is built with end-to-end components, making it pluggable into various autonomous driving baselines.\n- Extensive experiments covering open-loop and closed-loop scenarios, multiple tasks, ablation studies, and introducing the FCP metric, effectively demonstrate the method's efficacy and its potential advantages over the baselines.", "weaknesses": "- Many world-model–style approaches like DriveDreamer [1] and Drive-WM [2] exploit action-guided future status to optimize the trajectory. The claim that previous methods “ignore feedback” seems somewhat absolute and would benefit from a more rigorous comparison and clear delineation of the differences.\n- The estimation of the partition starting point and turning radius depends heavily on the accuracy of the ego vehicle’s pose/odometry. It would be better to give more analysis of sensitivity to noise, calibration errors, or timestamp drift. Besides, partitioning exclusively along the image width assumes that the dominant relative motion is horizontal. The method’s performance under conditions with significant changes in longitudinal dynamics (e.g., on slopes or during acceleration/deceleration) or notable camera pitch variations is not analyzed.\n- The chosen baseline methods are primarily from 2023 to 2024, and it would be beneficial to include comparisons with more recent approaches, such as the perception-free methods SSR [3] and LAW [4].\n\n[1] DriveDreamer: Towards Real-world-driven World Models for Autonomous Driving. arXiv. 2309.09777.\n\n[2] Driving into the Future: Multiview Visual Forecasting and Planning with World Model for Autonomous Driving. arXiv. 2311.17918.\n\n[3] Navigation-guided sparse scene representation for end-to-end autonomous driving. arXiv:2409.18341.\n\n[4] Enhancing end-to-end autonomous driving with latent world model. arXiv preprint arXiv:2406.08481.", "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": "R3F1eYt3O7", "reviewer": "Reviewer_n4vr", "summary": "This paper introduces FlowAD, a unified ego-scene interactive modeling framework for autonomous driving. Unlike standard BEV-based approaches that mostly treat ego-motion as an external factor to compensate for, FlowAD attempts to integrate ego trajectory information directly into the representation space through a flow-unit partitioning scheme. The method includes an ego-guided dynamic partition, spatial-temporal flow prediction, and a task-aware enhancement module, which together aim to improve perception and planning by better capturing the interaction between the ego vehicle and the surrounding scene. A new evaluation metric, FCP (Flow Consistency Precision), is proposed to measure alignment between ego-motion and scene evolution. Experiments on standard benchmarks show consistent improvements over SparseDrive.", "questions": "How does FlowAD compare with a token-based BEV fusion baseline and a compact world-model/diffusion-style planner under matched training and evaluation conditions?\n\nHow does FCP correlate with standard closed-loop planning metrics such as driving score, collision rate, and comfort across multiple runs or datasets?", "limitations": "", "rating": 8, "confidence": 4, "soundness": 4, "presentation": 3, "contribution": 3, "strengths": "I appreciate the clarity with which the authors formulate ego-scene interaction as a modeling problem. This is a conceptual contribution with potential impact. The flow-unit partitioning is elegant and physically interpretable. The method’s modular design allows integration into various downstream tasks, which enhances its practical value. The introduction of FCP as a metric is also interesting, as it focuses on ego-scene alignment, something that existing metrics often overlook. Empirical results are consistent across multiple tasks, lending credibility to the claims.", "weaknesses": "Robustness is also under-characterized: the method hinges on ego-guided partition but offers little evidence for stability under sharp turns, high speeds, or other extreme maneuvers. The proposed FCP metric is interesting yet its external validity remains unclear—there is no systematic analysis of how it correlates with closed-loop planning metrics across routes/seeds or datasets.", "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": "cBPLnIRKY9", "reviewer": "Reviewer_17iF", "summary": "This paper introduces FlowAD, a novel framework for autonomous driving that explicitly models the ego-vehicle's motion feedback on the perceived environment. The core idea is to represent this interaction as a learnable \"scene flow\" in the latent feature space. The method is structured around three main components: ego-guided scene partition to create flow units, spatial and temporal flow prediction modules to model dynamics, and task-aware enhancement strategies for downstream tasks. The paper also proposes a new metric, Frames before Correct Planning, to evaluate a planner's scene understanding speed. Extensive experiments on nuScenes and Bench2Drive demonstrate state-of-the-art performance across perception, planning, and VLM-based tasks.", "questions": "1)The dynamic adjustment of partition size relies on fitting a circle to past ego positions. How robust is this method to noisy or high-frequency steering inputs? Did the authors experiment with simpler or more robust ways to incorporate steering velocity?\n2)It seems that the local aggregation uses a fixed range of 3 flow units. Was any other adaptive mechanism considered for this aggregation to handle objects of varying sizes more effectively?\n3)The paper mentions using \"mechanisms from world models\" and the KL loss. Could the authors elaborate on how their spatial and temporal prediction modules differ from a standard recurrent world model applied patch-wise? The novelty in the prediction architecture itself could be clarified.", "limitations": "", "rating": 8, "confidence": 3, "soundness": 3, "presentation": 4, "contribution": 3, "strengths": "1）In this work, the authors introduce a general flow-based framework for autonomous driving. While most existing methods mainly study the planning with current observation, the authors focus on performing the control outputs that shape future sensory input, which is a relatively absence part of existing methods.\n2) The proposed FlowAD framework is elegantly designed and demonstrates remarkable generality. According to the experiment, it is successfully applied to diverse baselines (such as SparseBEV, SparseDrive, and Senna) across multiple task types (detection, planning, VLM analysis).\n3) The paper is well-written and easy to understand. The experimental section is thorough and convincing. \n4) The authors propose a novel and valuable metric, FCP. This new metric addresses a genuine gap in evaluation by quantifying the responsiveness and scene understanding speed of a planner, complementing traditional metrics like L2 error and collision rate.", "weaknesses": "1) While the performance are promising,  the proposed method introduces a degradation on the computational cost, as evidenced by the drop in FPS compared to baselines (e.g., SparseDrive 9.0 FPS vs. FlowAD 7.6 FPS with ResNet50). It is recommended to add more detailed discussion on the trade-off between performance and efficiency, and potential avenues for optimization.\n2) The concept of \"scene flow\" is central to the paper. While the intuition is clear, a more precise definition or visualization of what these learned flow features represent in the latent space could strengthen the methodological explanation. For instance, how do these features qualitatively differ from standard BEV or image features?\n3) The use of KL divergence loss from world models is presented as a key component for training the flow prediction modules. However, I am curious about the experimental results presented in Table 11. It shows that removing both spatial and temporal losses leads to performance worse than the baseline. This suggests that architecture without proper supervision might be harmful. It is recommended to provide a deeper analysis of why this happens and how critical the specific world model loss formulation is.", "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)