Flow Matching-Based Autonomous Driving Planning with Advanced Interactive Behavior Modeling
Authors: Tianyi Tan
Yinan Zheng
Ruiming Liang
Zexu Wang
Kexin ZHENG
Jinliang Zheng
Jianxiong Li
Xianyuan Zhan
Jingjing Liu
Venue: NeurIPS 2025 poster ()
Keywords: Autonomous Driving Planning
Flow Matching
Primary area: reinforcement_learning

Abstract: Modeling interactive driving behaviors in complex scenarios remains a fundamental challenge for autonomous driving planning. Learning-based approaches attempt to address this challenge with advanced generative models, removing the dependency on over-engineered architectures for representation fusion. However, brute-force implementation by simply stacking transformer blocks lacks a dedicated mechanism for modeling interactive behaviors that is common in real driving scenarios. The scarcity of interactive driving data further exacerbates this problem, leaving conventional imitation learning methods ill-equipped to capture high-value interactive behaviors. We propose Flow Planner, which tackles these problems through coordinated innovations in data modeling, model architecture, and learning scheme. Specifically, we first introduce fine-grained trajectory tokenization, which decomposes the trajectory into overlapping segments to decrease the complexity of whole trajectory modeling. With a sophisticatedly designed architecture, we achieve efficient temporal and spatial fusion of planning and scene information, to better capture interactive behaviors. In addition, the framework incorporates flow matching with classifier-free guidance for multi-modal behavior generation, which dynamically reweights agent interactions during inference to maintain coherent response strategies, providing a critical boost for interactive scenario understanding. Experimental results on the large-scale nuPlan dataset demonstrate that Flow Planner achieves state-of-the-art performance among learning-based approaches while effectively modeling interactive behaviors in complex driving scenarios.

Flow Matching-Based Autonomous Driving Planning with Advanced Interactive Behavior Modeling
Tianyi Tan Yinan Zheng Ruiming Liang Zexu Wang'
Kexin Zheng
Jinliang Zheng' Jianxiong Li' Xianyuan Zhan ! Jingjing Liu
Institute for AI Industry Research (AIR), Tsinghua University
2 Institute of Automation, Chinese Academy of Sciences
3 The Chinese University of Hong Kong tantyy2 2 2mmail tainghua. edu. zhanxianyuanOair wttpanaiiiriitiiiiiteeeeee tsinghua. edu. cn
Abstract
Modeling interactive driving behaviors complex scenarios remains a funda- mental challenge for autonomous driving planning. Learning- bhanid approaches attempt to address this challenge with advanced generative models, removing the dependency on over-engineered architectures for representation fusion. However, brute-force implementation by simply stacking transformer blocks lacks dedicated mechanism for modeling interactive behaviors that common in real driving scenarios. The scarcity of interactive driving data further exacerbates this problem, leaving conventional imitation learning methods ill-equipped to capture high-value interactive behaviors. We propose Flow Planner, which tackles these problems through coordinated innovations in data modeling, model architecture, and learning scheme. Specifically, we first introduce fine-grained trajectory tokenization, which decomposes the trajectory into overlapping segments decrease the complexity of whole trajectory modeling. With a sophisticatedly designed architecture, we achieve efficient temporal and spatial fusion planning and scene information, to better capture interactive behaviors. In addition, the framework incorporates flow matching with classifier-free guidance for multi-modal behavior generation, which dynamically reweights agent interactions during inference to maintain coherent response strategies, providing a critical boost for interactive scenario understanding. Experimental results on the large-scale nuPlan dataset and challenging interactive interPlan dataset demonstrate that Flow Planner achieves state-of-th of-the-arrt perfor- mance among learning-baid based approaches while effectively modeling interactive behaviors in complex driving scenarios. Official implementation can be found https://gi http//tttttttttttttege/gge
Planner
Introduction
Ensuring safe and reliable planning remains the highest priority for autonomous driving systems in real-world deployment [47]. However, exceptional challenges stem from complex interactions among
traffic participants exhibiting multi-modal driving behaviors [5 42 with the difficulty compounding
as the number of participants increases. While conventional rule-based approaches [16 50 can
"Equal Contribution.
Prroject Lead.
*Corresponding Author.
Work done during internships Institute for Al Industry Research (AIR), Tsinghua University.
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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effectively handle most driving scenarios through explicit human-defined constraints and numerical optimization, they are constrained by fundamental limitations, often demanding substantial human engineering efforts and exhibiting poor generalization capability in highly dynamic environments [8]. In contrast, learning-based methods aim to directly learn expert strategies for handling highly interactive scenarios from real-world driving data [3] 19]. These methods have emerged as the dominant choice in both academia [24] 57) and industry 27] with the expectation that they can
achieve reasonable planning through increased data volume and model parameters (5]
To model sophisticated interactive behaviors in complex traffic scenarios, a learning- baaned planner must generate trajectories that simultaneously address immediate interactions, anticipate future behaviors of critical traffic participants, and maintain temporally consistent kinematics [19] This process critically depends on effective temporal and spatial fusion with scene information. However, the heterogeneity of different elements, particularly static map information and dynamic agents' histories, imposes stringent requirements on the fusion mechanism. Early approaches 255 39 relied on human priors and over-engineered architectures to capture interactions among traffic participants, but these methods proved difficult to scale and often delivered suboptimal performance. While recent work has adopted transformer-based architectures [9] and generative models [57 35 to improve scalability and performance, vanilla transformer implementations often fail to effectively capture intricate interdependencies among heterogeneous information [14 54]. In complex scenarios, extensive redundant information can obscure critical traffic participant information during fusion (57), primarily because these architectures lack specialized designs for interaction modeling.
Moreover, the scarcity of high-quality interactive scenarios in training data leads to a critical lim- itation: naive behavior cloning methods may converge to biased distributions that fail to capture interactive driving behaviors (26), frequently resulting in safety-critical failures during closed-loop evaluation [3]. Although auxiliary losses can help penalize undesirable behaviors, as commonly done in imitation learning 01 8 29 6) they typically compromise training stability and require careful, case-by-case design. Besides, some methods incorporate prior knowledge, such as pre-searched refer- ence line [8) anchor trajectory 66 [6) and goal point [53), to guide more diverse driving behaviors, but often fail to account for legitimate interactive behaviors that conflict with these structural priors. Alternatively, reinforcement learning [32 4 can autonomously learn interactive behaviors through fral-and-error, -rror, but introduces new challenges including meticulous reward engincering BT 33 and safety assurance during exploration (56).
To address these challenges, we propose Flow Planner, an advanced sarming-basedd framework melding coordinated innovations in data modeling, architecture design, and learning schemes to enhance interactive driving behavior modeling for autonomous driving planning. Specifically, we first break down the complexity of full-trajectory modeling by decomposing it into overlapping segments. This fine-grained tokenization preserves kinematic continuity within each segment while enabling localized feature extraction through segment-specific token representations. Second, our framework enhances interactive behavior modeling through spatiotemporal fusion of scene and planning tokens. Inspired by scale-adaptive attention [38]. it dynamically optimizes receptive fields for each token to enable effective extraction of critical information. This process also projects hetero- geneous conditions from different scene inputs into unified representation space, further improving performance. Finally, to enable multi-moda behavior generation in complex driving scenarios, we adopt flow matching loss [37], which offers simpler implementation and faster convergence compared to diffusion- approaches [57]. Building upon classifier-free guidance 22 37], our framework dynamically reweights neighboring agent interactions during inference maintain coherent plan- ning strategies. Compared to naive behavior cloning approaches, this secne-inoormation-enll generation mechanism provides substantial improvements in interactive scenario understanding. Experimental results on the large-scale real-world benchmark nuPlan and challenging interactive benchmark interPlan show that Flow Planner establishes state-of-the-a art performance in closed-loop evaluation among learning- bearning-based planners, while demonstrating human-like interactive behaviors in complex traffic scenarios.
2 Related Work
Rule-based Planning Methods. Rule-based planners determine driving behaviors through manually specified rules, offering interpretable and practically viable solutions that have been validated in both simulation [I3] and real-world deployments []6]] 34 51). Despite their widespread use, these

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Finegraneed Tojectory Tolentation Ungecterng
manching with Charherrree Guidance
hupliaiig
Soae Digess
Soree Fanoder
Interaction Spadiotorporal Fusion
SBNN
SBUN
Spaziotomporal Rusion
Scale-Pdagine Seil Atsemton
Neantor Podiig
Notelal all icone information and planiing trajectorias come from rail dats real model ouspue
Final Layer
X
FFN
information
CFGigaced
Uncondsonoo
Figure 1: Overview of the Flow Planner framework.
methods rely heavily on handcrafted heuristics, which limits their scalability and effectiveness in complex interactive driving scenarios.
bourning-based Planning Methods, Conventional imitation learning-base methods for autonomous driving initially relied on CNN 22 21 or RNN [T] architectures. While modern transformer- based approaches [24] 27
offer improved scalability, they introduce new challenges in interactive behavior modeling. Specifically, effective interactive behavior modeling requires an efficient fusion mechanism to handle heterogeneous scene information, where spatial-temponn interactions between different scene elements (e.g.. lanes and neighboring agents) cannot be adequately resolved through simple parameter stacking [9] 46, 41]. While some approaches employ complex architectural these solutions often limit the transformer's scalability potential. Although auxiliary
29 6
designs 22 39
loss functions 8 29 6 can encourage safe behaviors in complex scenarios through penalties, they fundamentally fail to develop true interactive driving capabilities models and may result in overly conservative behaviors [56]. Reinforcement learning [3 33
provides solution achieve interactive behavior beyond data capabilities, but due to limitations such as safety exploration and reward design, the application of autonomous driving still remains at the simulation level 12 4 or simple real-world scenario [32 44 Thus, how to improve the model's ability to capture interactive driving behavior and further improve the performance of imitation learning remains challenge.
Generative Models for Plamning. Recent work has demonstrated generative models' strong expres- siveness for autonomous driving tasks [57 30 58). Autoregressive approaches [46] as class of generative modeling methods, enable each timestep's action to incorporate both environmental information and previous actions, thereby enhancing modeling capacity. But these methods inherently suffer from error accumulation over time. Diffusion models [45] 23] and flow matching models [37], as the most advanced generative models, share similar denoising processes for generating data from noise. Both demonstrate strong expressiveness in modeling complex distributions [IO), making them particularly suitable for autonomous driving behavior modeling. However, existing methods still rely heavily on prior knowledge (e.g., anchor trajectories 366 or goal points (53). Although such strong priors can stimulate behaviors with traffic participants. Diffusion Planner [57] attempts to improve interactive behavior modeling by jointly generating both the ego planning and predictions for neighboring vehicles without requiring prior knowledge. However, its interactions are constrained to a fixed number of nearest vehicles, which restricts potential interactions with important distant agents, and the architecture lacks specific designs for an effective fusion mechanism. Therefore, there still lacks design to further push the limits of generative models for interactive behavior modeling.
driving behaviors, they tend to cause the model neglect interactive
3 Method
In this section, we provide a brief introduction to trajectory generation in planning tasks and present Flow Planner, a novel approach that emphasizes interactive behavior modeling, as shown in Figure [] From the data modeling perspective, we propose fine-grained trajectory tokenization to achieve expressive trajectory modeling. Subsequently, we design well-curated architecture that enhances in- teractive behavior modeling through thorough spatiotemporal fusion. Finally, we adopt flow matching with classifier free guidance to further enhance multi-moda and interactive driving behaviors.
3

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3.1 Problem Formulation
Our work primarily focuses on the planning module of autonomous driving systems, utilizing pro- cessed perception as input and evaluating planning capabiliitie through closed-loop testing [19]. The trajectory generation task can be formulated as conditional generation problem, where the trans- formation from source distribution p(TT) (typically standard Gaussian) to a target data distribution ((7]C) (e.g., planning trajectories 71 with scene information C)) represented by probability path. The model vo(+) is trained to predict the velocity along the path connecting sample pairs (To. T1) drawn from the source and target distributions, using the objective [37]:
C p..- cy)lva (CC) - Ve(TE, f)1??
where Tr (2T7) + To and the ground truth velocity Vy(Ti, 1) = F. â. + o the time derivative of the interpolation between source and target points. The distinction between ODE-based diffusion models and flow matching models lies in their respective designs of probability paths.
3.2 Model Architecture
Flow Planner is a transformer-based generative model for motion planning that effectively fuses noisy trajectory Tt with scene condition C. The architecture overview is presented in Figure [
Scene Encoder. We consider the vectorized driving scene information. For each neighboring agent (e.g., vehicle, pedestrian, cyclist), represent its past T timestep information Feegghoor X where Hueighhor is the dimension of state information including coordination, velocity, size, and the type of different agents. For each lane, we interpolate the centerline into N uniformly distributed points. Each point contains coordinates, corresponding boundary vectors, speed limit information, and traffic light status, represented as Fiane E N Huse, To maximize scenario information preservation while maintaining model efficiency, architectures 48 to encode both agent and lane features, following (57]
employ separate MLP-Mixer
== + MLP (F), F+ MLP, fear
where MLP beg and MLP feat are two separate MLPs operating on sequence and feature dimensions respectively, and F is the information of neighboring agents Fneighbor or lanes Flane- Additionally, we consider static objects, encoded using another MLP. The navigation information represented similarly to lane information and is processed by a separate MLP-Mixer.
Fine-grained Trajectory Tokenization. We start by rethinking the trajectory tokenization in autonomous driving planning, an area that has received inadequate research attention despite its critical importance. Prevalent methods [57 9 36) use single token represent the whole trajectory, which maintains kinematic consistency (28] but suffers from inefficient scene context fusion due to over- compression. Alternative approaches utilizing either discrete timestep tokens [[]] or autoregressive generation 446] successfully achieve temporal fusion within trajectories. However, these methods inevitably encounter compounding error accumulation, presenting a fundamental limitation for closed-loop autonomous driving applications.
To address these limitations, propose balanced modeling framework featuring fine- graiiad trajectory tokenization that preserves temporal interaction patterns while ensuring consistent behavior across all planning horizons. Specifically, the noised trajectory 72 (01,,22 .... 21.)) introduced in Section [3. which comprises T. points in total, is first divided into K segments, each containing Teeg points. Additionally, the neighboring segments share an overlap with a length Loveriop to ensure the consistency and smoothness of the resembled trajectory. Next, a shared MLP is used to transform the noised segments into the ego-trajectory tokens:
Fb MLP ((I,,, Tye +1 x,,)) k 1,2,.., K,
where 1t (A: 11 Loverlap) is the start timestep of the trajectory segment, and 1. (k: - 1)(Lseg - -Loverlap) + Leeg is the end timestep. Note that timesteps in trajectories differ conceptually from noise step t in generative models. After that, adopt the sinusoidal position encoding [52] to inject temporal information. Finally, we concatenate all the segment tokens along the sequence dimension obtain the ego feature: Pego Concat (Fijo-... ..., F.k)))
Interaction-enhanced Spatiotemporal Fusion. Although fine-grained trajectory tokenization provides more expressive representations for behavior modeling, the efficient fusion of spatiotemporal

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information remains unresolved. This process requires bidirectional interaction among numerous information- sparse tokens to enable comprehensive scene understanding and behavior modeling. Another critical challenge lies in effectively fusing these heterogeneous modalities, such as the static information of lanes and the dynamic information of agents. These challenges are what vanilla fails to accomplish effectively.
transformer attention p 8 57
where cross-modality feature fusion is performed in a unified space, we first process heterogeneous features through separate adaptive LayerNorm (adaLN) modules [4], projecting them into a shared latent space where both timestep conditions and navigation information are injected via modulation mechanisms [57]. Then the processed tokens from one scenario, including the features of lanes Fione: neighboring agents F Fneighhor and ego planning trajectory Fegos are concatenated along the sequence dimension:
Inspired by recent work in text-to-image generation [14
gigobol Concat (adaLN (Fiane) adaLNN (Fneighhor) adaLN (Fego))
(4)
For the subsequent fusion of all tokens Fgiobal, critical observation reveals that interaction intensity between traffic participants correlates strongly with their spatial distances. For instance, excessively distant roads and vehicles may introduce noise that degrades planning performance [57]. Inspired by scale-adaptive self-attention (38), we employ learnable receptive fields for individual tokens to incorporate spatial guidance during feature fusion. This is achieved through spatial distance-scaled attention score adjustment:
F Fglabal WQ (F stobal (FatobaWWk) W X
F Fglobul Softmax(
D) Fg WV
global
(5)
Vd
where W W WV are the pre-projection weights used to generate query, key, and value for the attention mechanism [52]. D is the pairwise Euclidean distance matrix of the tokens, and À is the receptive scaler generated by a simple linear projection of the tokens. Intuitively, tokens representing traffic participants beyond a certain distance are assigned smaller attention scores, making them less influential in the computation and enabling more adaptive resource allocation. Then, the global feature is decomposed into modelity-specifie tokens, where each modality undergoes distinct adaL. and FFN projections to further mitigate the heterogeneous modality gap:
Flane: Fniighhrr; F Fego = Chunk (Fgtobat),
(6)
Fione V (Ftane). Fneighhor = (Fnnighhor) Fego V (Fego)
where Ti is the abbreviation of the FFN(adaLN(.)). Building upon the foundation established in Eq. (4)-66). our architecture employs stacked transformer blocks to progressively refine spatiotemporal interactions. Finally, a standard self-attention layer is used for final aggregation. The features then processed through token pooling to obtain ego planning tokens, followed by a final layer V that transforms these representations into the ego vehicle's planned trajectory.
[3
57
3.3 Guided Trajectory Generation via Flow Matching
With the enhanced model architecture established, we want to push the upper- bound performance for imitation learning in trajectory generation. Compared to conventional behavior cloning, gen- erative models show better expressiveness modeling multi-modal and interactive driving behav- iors [57]. The most compelling aspect of generative models lies in their ability to dynamically reweight conditional signals during inference through classifier-free guidance (22), thereby amplify- ing conditional influence to achieve superior conditioned generation. Specifically, we enhance the conventional behavior cloning approach by combining the cecne-conditioned planning trajectory distribution ((T(C) with unconditioned distribution q(T.). yiclding an enhanced distribution q(7 ((TiIC)))g (()) (-([)) (O)T, where w > 1 is the weighting parameter that controls the condition signal strength. Intuitively, the model learns both the planning behavior without scene conditions and the behavior with scene conditions, enabling it to implicitly capture the behaviors induced by scene conditions. This approach strengthens the understanding of complex driving scenarios and improves interactive behavior modeling. Building upon this framework, we can sample from distribution ((m[C) using the guided velocity field as follows (22
(((T,,t(C) = (1 w)e f) + woe(T,,t(C).
The remaining challenge involves training the model to estimate velocities in Eq. 77. We address this by training a single model with Bernoulli-masked conditions as follows (55
L 2t.-vo..,.--...xmmm....... (Te,t((1 tt((1 b) C+ b Ø n1.
(8)
5

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In Eq. (8) we employ the reparameterization trick to directly predict ground-truth trajectories. This formulation proves mathematically equivalent to the velocity prediction loss in Eq. T [37] Additionally, we employ the optimal transport path 40 49 widely used in flow matching, where the velocity estimation follows u0(TT, 1|C) (To(Te, [[C) To) /t. The same approach applies to
unconditioned velocities (g(TT, f). Finally, we obtain the guided velocity from Eq. 7 and employ an ODE solver for trajectory generation.
Notably, the conditioned information can be flexibly selected in our framework. In practice, we specifically mask neighboring vehicle information in Eq. (8), as we empirically find this be most critical for interactive driving behavior. As shown in Figure ! the unconditioned model fails to account for neighboring vehicles, while the conditioned model overreacts to the nearest vehicle with overly conservative planning behavior. In contrast, our classifier-fre guided approach successfully generates reliable planning results that appropriately respond to neighboring vehicle dynamics.
3.4 Implementation Details
In our implementation, the entire scenario is first transformed into an ego-centric front-left-up coordinate. To ensure training stability and input consistency, input data well ground truth trajectories are first normalized using static measures extracted from training data. In addition, data augmentation [57] has been demonstrated as effective in improving the robustness of planning outputs. During training, the ego vehiele's state at the current frame first randomly perturbed, and a quintic polynomial is used for interpolating
trajectory, serving as the ground truth of the augmented sample. During inference, the second-order midpoint method [37] is used for the flow ODE solver. In addition, ensure the consistency of the generated trajectory, introduce extra consistency loss Lconsist on the segment overlap during training. Specifically, given the predicted segments, we apply L2 loss on the overlap of neighboring predicted segments:
Lconsist
K
where fksk+1 denotes the portion of the predicted trajectory from segment that overlaps with segment 1, and +**1 represents the portion of the predicted trajectory from segment + 1 that overlaps with segment k. This consistency loss is added the flow matching loss with a loss scaling hyperparameter a > 0 to form the final objective = Lylow + a Ccomeist- Note that the converged model is equivalent to the one supervised by merely the vanilla flow matching objective. During inference, the predicted values of the overlapping areas are averaged a straightforward manner to generate the final prediction.
4 Experiments
In this section we conducted extensive experiments to demonstrate the performance of our model. We will start by briefly introducing the benchmarks on which the experiments are performed, and the primary baselines we compared our model with. Next, we will focus on several representative cases that intuitively illustrate the advantage of our method, comparing it with the previous state-of-the art method in specific scenarios. Then, we will delve into the details of our designs and showcase their effectiveness through further ablation study.
t= 1s
t t= 7s
t = 9s
= 13s
Figure 2: A typical out-of-distribution scenario in interPlan benchmark: nudging around crashed vehicles. Flow Planner demonstrate strong scene understanding ability and generation adaptability situation totaly unseen the training data.

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4.1 Experimental Setup
Benchmarks. In this study, our model is trained on nuPlan [9 featuring large-scale real-world driving dataset collected across four different cities. This dataset covers up to 75 scenario types, including diverse scenarios with multi- interaction and complex lane structures. Specifically, the model is trained on the IM training split, following [57]. For evaluation, the model is tested on both nuPlan and interPlan (0),, and we mainly focus on the closed-loop performance of the planners, where an LQR controller is used for simulation. We test the model in both non-reactive and reactive settings using the following three benchmarks in nuPlan: (1) Val14 [3]. validation dataset with 1118 scenarios in total; (2) 4-stldorandom [ over 200 randomly selected scenarios from the scenario types assigned by the nuPlan Planning Challenge; and (3) Test 4-haadd [9]: a collection of the worst-performing scenarios by rule-based PDM [13, comprising 272 scenarios. Each of the three benchmarks covers 14 different types of scenarios respectively. We argue that the Val14 benchmark evaluate model's 's performance and capability more comprehensively owing to its sufficient capacity. while Testl4-random benchmark may introduce extra uncertainty, hindering the fairness of evaluation. Whereas Flow Planner achieves overall state-of-the-arr performance across three benchmarks, we primarily leveraged Val14 for further experiments. In addition, we evaluate our model using the full-scale interPlan benchmark, which contains 335 challenging interactive scenarios with specially augmented traffic agents quantity and behavior, as shown in Fig. 2 in reactive mode. This benchmark highlights model's capability of modeling interactive behavior in complex and unexpected scenarios, providing more appropriate evaluation of our method.
Baselines. For comprehensive analysis of the effectiveness of our method, conduct comparative experiments with prevailing methods, including based, imitation learning, and hybrid methods that refine the learning- -based model with rule-based trajectory post-processing. Specifically, compare our method with the following baselines:
IDM [50]:a : classic rule-based method, also used for neighboring vehicles control in closed-loop reactive evaluation;
PDM 3 the first place of nuPlan contest, which proposed rule-based model (PDM-Closed), learning-baad based model (PDM- (oM-Open)xx as well hybrid version (PDM-Hybrid), all relying on road centerline;
PLUTO an imitation learning-bast based model with extra contrastive objectives and post-
processing;
GameFormer 25
a transformer-based model for interactive prediction based on game-theory. with extra refinement process;
Diffuusion Planner [57] the state-of-the-art model based on diffusion model for multi-modal trajectory generation.
Difiusion Planner
00s
10s
Flow Planner (ours)
8s
10s
8s
10s
(a) Lane Change
(b) Unprotected Left Turn
Figure 3: Visualization of interaction behaviors. Two challenging scenarios with distinctive interactions in closed-loop testing, including: (a) changing lane and (b) umprotected left turn in the closed-loop test. The trajectories illustrated here include: the future planning ego vehicle, the ego history, and neighbor history.
7

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Table 1: The overall performance Flow Planner other learning-based baatin baseline models. Evaluation conducted both non-reactive and reactive mode. The highest score of each benchmark is marked with and the second-bos best scores appear in bold. In addition, we use to mark the methods directly leveraging road centerline extracted from the map.
Val14
Testl 4-hard
Test14
Type
Planner
NR
R
NR
R
NR
Expert
Log-replay
93.53
80.32
85.96
68.80
94.03
75.86
IDM
75.60
77.33
56,15
62.26
70.39
74,42
PDM-Closed
92.84
92.12
65.08
75.19
90.05
91.63
PDM-Hyhrid
92.77
65.99
76.07
90.10
91.28
Rule-based Hybrid
GameFormer
79.94
79.78
68.70
67.05
83.88
82.05
PLUTO
92.88
76.88
80.08
76.88
92.23
90.29
Diffusion Planner refine.
94.26
92.90
78.87
82.00
94.80
91.75
Flow Planner w/ refine. (Ours)
94.31
92.38
78.64
80.25
94.79
92.40
PDM-Open*
53.53
54.24
33.51
35.83
52.81
57.23
Gamel'ormer w/o refine.
13.32
8.69
7.08
6.69
11.36
9.31
PlanTF
76.95
69.70
61.61
85.62
79.58
Leaming-based
PL.TOO w/o refine.*
88.89
78.11
70.03
59.74
89.90
78.62
Diffusion Planner
89.87
82.80
75.99
69.22
89.19
82.93
Flow Planner (Ours)
90.43
83.31
76.47
70.42
89.88
82.93
Table 2: Performance different planners in specific scenarios of Val14, where "Intersection" cor- responds to the "Starting straight traffic light intersection traversal" scenario.
Planner
Starting right turn Starting left turn
Intersection Waiting for pedestrian
PLUTO (wf refinement)
80. 19
86.51
90.08
84.65
Diffusion Planner
78.
87.96
94.57
91.65
Flow Planner (Ours)
83.23
90.60
94.81
93.25
4.2 Main Results and Case Study
Main Results. The overall evaluation results are shown in Table Flow Planner achieves state-of- the-art performance in the learning-base settings, without rule-based refinement. It is noteworthy that Flow Planner achieves 90.43 on Val14, which is the largest and most representative benchmark of the three. As far as we know, it is the first barming-basee method to surpass the 90-score mark without any prior knowledge on this benchmark, while other learning-basee based models require extra rule-based refinement to reach 90+ performance. With the similar post-processing module in [57], our model Flow Planner w/ refine. achieved competitive performance on the nuPlan benchmark compared with previous rule- based and hybrid methods. To find out the specific improvement brought by our method, further report the performance of Flow Planner and baseline methods in several specific types of scenarios in nuPlan. As is shown in Table 2 Flow Planner outperforms the two strong baselines significantly scenarios where interaction with neighboring vehicles frequent, including unprotected left turns and traffic light intersections. In addition, on the interPlan benchmark in which most scenarios require dense interaction with the environment, as is shown Table 3 we achieve 8.92-point improvement over Diffusion Planner, demonstrating the strong capability of interactive modeling. It can also be inferred from the table that Flow Planner exhibits robust interactive behavior modeling ability even when interacting with jaywalking pedestrians, whose behavior is tricky to predict due to the lack of relevant data and their volatile movement.
Case Study. To further demonstrate the effectiveness of our method, we select representative interactive scenarios to compare the behavior different models in the closed-loop testing. As is shown in Figure we selected two scenarios from nuPlan benchmark, and report the different behavior of our Flow Planner and Diffusion Planner. Specifically, in (a), Diffission Planner failed to account for vehicle approaching quickly from the rear-left. It changed lanes despite being

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Table 3: Performance of different planners on interPlan and scores on specific scenarios
Planner
Overall Score
Nudge Around
High Traffic Density
Jaywalk
PlanTF
47.70
49.40
58.85
33.94
PLUTO (w/o refinement)
58.47
71.56
67.25
25.48
Diffusion Planner
52.90
60.48
49.71
26.20
Flow Planner (Ours)
61.82
72.96
67.21
43.57
slower than the approaching car, resulting in a collision. In contrast, Flow Planner recognized the fast-approaching vehicle and, realizing that the safety distance was insufficient, aborted the lane change to avoid collision; while in (b), Diffusion Plamnrr did not consider right-turning vehicle from the oncoming lane. It entered the turn low speed, leading to collision. Flow Planner, however, identified that the right-turning vehicle would arrive later and chose higher entry speed, safely completing the turn. More closed-loop planning results where Flow Planner exhibits interactive behavior modeling capability are shown in Appendix
4.3 Ablation Studies
We ablate the key designs our method further study their effects on model performance. The overall ablation path is shown in Table where we start with base model by simply stacking self attention layers to form the decoder while keeping the scene encoder unchanged. The model is then gradually enriched to form the final architecture, so to better reflect the improvements gained from each component.
Table 4: Ablation path of key components nuPlan( Vall4) and interPlan, and performance on specific interPlan scenarios. Below use NA for Nudge Around, HTD for High Traffic Density and JW for Jaywalk
Components
nuPlan( Val14)
interPlan
NA
HTD
JW
Base
88.10
41.27
50.61
38.18
27.99
Trajectory Tokenization in Eq.
88.33
44.14
43.27
48.11
52.73
Scale-Adaptive Attention in Eq.
88.77
46.25
51.31
44.28
34.42
Separate adaLN & FFN in Eq. Classifier-free Guidance in Section
89.54
58.22
64.46
60.08
43.16
90.43
61.82
72.96
67.21
43.57
Table 5:
Ablation on
Long 80
the number of trajectory segments on nuPlan Val14 Benchmark.
Length
Score
1.g0
88.48
20
89.43
16
89.27
L
90.43
40
89.32
80
87.75
Figure 4: Illustration of the influence of token number trajectory quality.
Trajectory Tokenization. The choice trajectory segment number and the length of overlap directly affect the balance of consistency and flexibility of the generated trajectory. Therefore, we trained different models with different choices of segment number, and evaluated them on Val14, as shown in Table and Figure 4 Note that the overlap length is set to 0 when the segment length is (fully scattered) or (whole trajectory), and half the length of the trajectory segment other cases. can be revealed from the table and figure that the length of segments increases, the smoothness of the

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generated trajectory increases as well as the model performance. However, when the trajectory is coarsely segmented, the trajectory tokens become cumbersome to model the multi-modis distribution of interactive behavior since single token is responsible for relatively long horizon of trajectory, which can contain several different interactions with neighboring agents.
Scale-Adaptive Attention and Separate AdaLN and FFN. Scale-adaptive attention enables more efficient feature fusion between numerous tokens extracted from the scenario. However, there exists inherent heterogeneity between the features collected from different types of instances in the scenario, and thus the straightforward implementation of adaptive attention does not introduce visible improvement. With the separate adaLN and FFN modules projecting the heterogeneous features into shared space, a prominent performance improvement can be seen from Table 4 when the scale-adaptive attention is cooperatively implemented.
Classifier-free Guidance. The scale of classifier-free guidance is a flexible hyperparameter that can be tuned at inference. Therefore, we ablated different choices of guidance scale using the same check- point. The results are illustrated in Table 6 It can be seen from the table that as the scale of guidance increases, the model's per- formance also improves. However, an overwhelmingly large scale can also lead to deterioration in performance, since the norms of the conditioned and unconditioned velocity are not perfectly aligned, and scale too large may lead to irreversible deviation on the flow path. Ideally, proper scale applied during inference can induce even better performance compared with the fully conditioned generation, as is shown in Figure
Table 6: Ablation on the scale of classifier-fres guidance on Val14,
CFG Scale
Score
1.65
89.64
1.70
89.89
1.75
90.14
1,80
90.43
1.85
90.00
1.90
89.63
5 Conclusion
We introduce Flow Planner, a novel dearning-based framework for autonomous driving planning that advances interactive behavior modeling through three coordinated innovations. First, fine-grained trajectory tokenization enables expressive behavior representation. well-designed architecture then facilitates comprehensive spatiotemporal fusion to enhance interaction modeling. Finally, flow matching combined with classifier-free guidance captures the multi-modal nature of real driving behaviors, which further attunes to interactive behavior during inference. Over the nuPlan benchmark, Flow Planner achieves state-of-the-an closed-loop performance among imitation-learning methods, while demonstrating capability in modeling interactive behaviors in complex driving scenarios. Due to space limit, more discussion on limitations and future direction can be found Appendix
Acknowledgement
This work is supported by National Key Research and Development Program of China under Grant 2022YFB2502904, and funding from BYD Automotive New Technology Research Institute, Wuxi Research Institute of Applied Technologics, Tsinghua University, under Grant 20242001 120. and Xiongan Al Institute.
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Appendix
A Visualization of Closed-loop Planning Results
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Figure 5: Interaction cases in closed-loop results (part 1).
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Figure 5: Interaction cases in closed-loop results (part 2).
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- 5s
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Figure 6: Interaction cases in interPlan closed-loop results.
B Implementations of Training
Data Preprocessing. scenario in nuPlan contains versatile scene information, whereas all of is used for model prediction. In our method, the model takes the lanes, neighboring agents (vehicles, pedestrians, cyclers etc.), navigation information static objects from the scenario as input. These scenario inputs are collected into tensors once and for all, and can be reused during training.
Lanes: lane is represented by sequence of 20 points sampled from the past seconds at 10 Hz, each of which contains the coordinates of the lane center line and lane boundary line, as well as the connecting vector of center line and lane traffic signal; up to 70 lanes are fed into the model;
Agents: the nearest 32 agents to ego vehicle encoded as the model input, and each agent contains the historical trajectories in the past 2 seconds at 10Hz, and states in the current frame;
Navigation: represented by lanes along the assigned direction, saved the same form as lanes;
Static Objects: the static obstacles in the scenario, represented by vector containing the position the ego centric coordinates at the current frame and the specific type the obstacle;
Transformation and Normalization. The original data in nuPlan recorded in the world coordinates, in which the starting point of the ego vehicle appear in any place, introducing unnecessary

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complexities for training. Therefore, to unify the numerical value of model input, we first transform the entire scenario into the ego-centric front-left-up coordinate, which the state of the ego vehicle at the first frame of each scenario is (X 0, 0, 0 0), and the coordinates of other scenario instances are transformed accordingly. Specifically, given the world coordinates of the ego vehicle (X, Y, Y), the transformation from the original coordinates (x..) v) to the transformed coordinates (x', y can be formulated as
x V'
cos 0 sin 0
- X cos 0 - ysin 0 sin 0 Y cos 0
x
sin 0
cos 0
(10)
O 0
1
In addition, the original scale of coordinates varies in different scenario. To ensure the numerical stability, we normalized the coordinates and trajectories with staties from training data via z-score along the x-axis and scaled along the y-axis [9 [7]. The model directly predicts the normalized future trajectory, and it is then denormalized into the scale the real-world trajectory.
Data Augmentation. Following previous practice [57]. we applied data augmentation for more robust generation. The initial states, including coordinates, velocities and heading angle, of the ego vehicle are first perturbed with random offsets. Then the perturbed initial states and the states at the 20-th frame are used as the boundary condition to solve a new quintic polynomial replace the first 20 frames of the original ground truth trajectory. Intuitively, this augmentation forces the model to output a feasible future trajectory even when the ego vehicle is not properly placed on the road, enabling self-correction when the vehicle is driving off the road.
C Experimental Details
The training is conducted 8 NVIDIA A6000 GPUs, using the IM training data split from nuPlan. The model is trained for over 200 epochs with a batch size of 2048. We used Adam optimizer for training, and the learning rate is set to be x 10-4. In addition, (EMA) to stablize the training process, with 0.999 weight decay. During inference, we used a simple midpoint solver solve the flow ODE, with only four steps of ODE simulation. The frequency of model inference is approximately 12Hz. The details for training and inference can be found in Table. 7
used exponential moving average
Table 7: Hyperparameters of Flow Planner
Type
Parameter
Symbol
Value
Num. neighboring vehicles
32
Num. past timestamps
Dim. neighboring vehicles
D Dneighbor
11
Num. lanes
70
Num. points per polyline
20
Training
Dim. lanes vehicles
D
12
Num. navigation lanes
25
Num. encoder block
Num. decoder block
Dim. encoder hidden layer
192
Dim. decoder hidden layer
256
Num. multi-head
8
Len. trajectory segment
Leeg
20
Len. trajectory overlap
Loverlop
10
Flow path
Conditional OT
Temperature
1.0
Inference
Flow ODE simulation step
4
D Benchmarks and Baselines
Benchmark Details. For the nuPlan benchmark, we used the Val14, Test 4-andom and 4-ar14-hard benchmarks for evaluation stated in Section 4 Each of the benchmarks contains the 14 scenarios
25

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specified by the nuPlan leaderboard respectively. The models are tested in two modes: non-reactive and reactive. In the non-reactive settings, the neighboring vehicles and other traffic participants strictly follow their trajectories in the record and will not react to the ego vehicle; on the contrary,
the reactive mode, the neighboring vehicles are controlled by the rule-based IDM [50]. However, the reaction generated by IDM can sometimes be suboptimal and lead to unnatural behavior of surrounding vehicles. For the interPlan benchmark, we chose the full-scale test set, with 335 specially augmented out-of-distribution scenarios. The models are all evaluated in reactive mode.
Baseline Reproduction. We followed [57] reproduce the baselines on the nuPlan. addition, we picked three competitive baselines for further compare on interPlan. For Diffusion Planner, we used the official implementation reproduce the results, and the model is trained for 500 epochs, which is more than twice the number of Flow Planner.
E Limitation & Discussion & Future Work
The inference speed of Flow Planner currently represents its main limitation. While our shift from diffusion flow matching has improved sampling efficiency, the system achieves only speed marginally faster than 10 Hz on an A6000 GPU, where 10 Hz is the requirement for industrial deployment [16]. The main reason leading to low sampling efficiency is that the spatiotemporal self-attention mechanism remains computationally intensive. Future work will explore acceleration techniques like the shorteut model [17 to address these constraints without sacrificing planning quality. Besides, for the interactive behavior modeling, we still use an implicit design to enhance this capability. The whole method is still imitation learning-based method, facing the problem of data quality and data forgetting issues. Maybe we can use the reinforcement learning approach to further enhance the capability of interactive behavior modeling, but there is still long way to go tackle problems for real-worke vehicle implementation.
26


Reviews:
[{"review_id": "gUuXIrSgSj", "reviewer": "Reviewer_yVKT", "summary": "This paper proposes structural improvements across trajectory representation, heterogeneous context fusion, and training \nmethodology to enhance interactive behavior modeling in autonomous driving planners. First, the authors introduced a Fine-grained Trajectory Tokenization method, which segments the ego vehicleʼs future trajectory into multiple tokens. This design allows for a more precise representation of spatiotemporal interactions. Second, the paper presents an Interaction-enhanced Spatiotemporal Fusion Block that accounts for the statistical heterogeneity across different modalities such as lanes, neighboring agents, and the ego vehicle itself. This block incorporates modality-specific normalization (adaLN, distance-aware attention mechanism (scale-adaptive attention), and separate feed forward networks for each modality, enabling more efficient and focused information fusion. Lastly, to improve training efficiency, the \nauthors adopt a Flow Matching framework and apply Classifier-Free Guidance (CFG) at inference time. By jointly training conditioned and \nunconditioned outputs within a single model, the approach enables adjustment of interaction sensitivity during inference. The proposed \nmodel achieves a state-of-the-art CLS-NR score of 90.4 on the nuPlan Val14 benchmark.", "questions": "Please refer to the weakness part above. Authors could respond to my concerns.", "limitations": "yes.", "rating": 3, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 2, "strengths_and_weaknesses": "Strength\n- This paper structurally adapts and integrates several techniques originally developed for diffusion-based generative models—such as \nFlow Matching, Classifier-Free Guidance (CFG), and adaptive Layer Normalization (adaLN) to the task of autonomous driving planning.  These methods are incorporated effectively within the proposed framework.\n- The paper introduces a novel Fine-grained Trajectory Tokenization approach that segments the ego vehicleʼs future trajectory into multiple \ntemporally ordered segments, each treated as an individual token. This enables the Transformerʼs attention mechanism to capture \nspatiotemporal interactions more precisely. The proposed design is structurally meaningful and brings novelty in improving interactive behavior modeling.\n\nWeakness\n- Limited Novelty and Unclear Effectiveness of the Interaction-enhanced Spatiotemporal Fusion Block:  While the proposed Fusion Block introduces scale-adaptive attention to the planning task for the first time, the method itself has already been explored in other domains and thus lacks structural novelty. \n- Moreover, computing attention scores solely based on pairwise distances between entities may not sufficiently capture qualitative relationships or context-aware complex interactions between agents.  As a result, the contribution of this module to improving interactive \nbehavior modeling appears limited, and the structural design does not offer a novel solution.\n- Limited Relevance of Flow Matching & Classifier-Free Guidance to Interactive Behavior Modeling: Flow Matching, originally proposed to improve training efficiency in diffusion-based models, is applied here to the planning task. However, it does not directly address or improve interactive behavior modeling in a structural sense. CFG is used during inference to blend conditioned and unconditioned outputs, amplifying the influence of  scene-level conditioning. While this technique is effective in some planning scenarios, it may not a fundamentally novel or targeted solution for modeling interactive behavior.", "was_revised": true, "final_justification": "It remains unclear whether the proposed methods effectively achieve the goal of improved interaction modeling. Therefore, I will maintain my current rating.", "rebuttal": "We thank the reviewer for the constructive comments. To further address the concerns of reviewer, we conducted extra experiments and provide the following response.\n\nWe used interPlan [1], a closed-loop driving benchmark extended from nuPlan, which contains 335 long-tail traffic scenarios including jaywalkers (*Jaywalk*), accidents (*Nudge Around*), and dense-lane-change situations (*High Traffic Density*) etc, **posing a huge challenge in interactive behavior modeling**. **We achieved SOTA performance on the interPlan benchmark**.\n\n\n| Methods                                 | Overall Score | Nudge Around | High Traffic Density | Jaywalk  |\n| --------------------------              | ----------    | ---------    | -------------------- |--------- |\n| PlanTF                                  | 47.70         | 49.40        | 58.85                | 33.94    |\n| PLUTO w/o refine.*                      | 58.47         | 71.56        | 67.25                | 25.48    |\n| Diffusion Planner                       | 52.90         | 60.48        | 49.71                | 26.20    |\n| **Flow Planner**                            | **61.82**         | 72.96        | 67.21                | 43.57    |\n\n*\\*: prior knowledge is used for the model*\n\nWe conducted further ablation studies on the interPlan by adding the modules onto the base model. We also compared our model with an vanilla imitation learning-based model *Vanilla IL*, which shares the same model architecture but directly regresses the trajectory, and a Diffusion adaption of our model. **The proposed modules bring significant performance improvement**, and the result demonstrates the superiority of Flow Matching-based paradigm compared with vanilla imitation learning and Diffusion for interactive behavior modeling.\n\n| Methods                                 | Overall Score | Nudge Around | High Traffic Density | Jaywalk  |\n| --------------------------              | ----------    | ---------    | -------------------- |--------- |\n| Vanilla IL                              | 32.39         | 50.54        | 25.19                | 32.37    |\n| Diffusion Adaptation                    | 40.71         | 25.55        | 44.21                | 40.71    |\n| **Base Model**                              | 41.27         | 50.61        | 38.18                | 27.99    | \n| **+Fine-grained Trajectory Tokenization**   | 44.14         | 43.27        | 48.11                | 52.73    |\n| **+Scale-adaptive Attention**               | 46.25         | 51.31        | 44.28                | 34.42    |\n| **+Separate adaLN & FFN**                   | 58.22         | 64.46        | 60.08                | 43.16    |\n| **+CFG (Flow Planner)**                           | 61.82         | 72.96        | 67.21                | 43.57    |\n\n> **W1 & W2: Limited Novelty and Unclear Effectiveness of the Interaction-enhanced Spatiotemporal Fusion Block and Scale-adaptive Attention**\n\nWe propose a **coordinated solution** for interactive behavior modeling in autonomous driving, with our Interaction-enhanced Spatiotemporal Fusion Mechanism serving as a key component. \n- As a subcomponent, **our architecture is not naively implemented**. Crucially, it coordinates with Fine-grained Trajectory Tokenization to enable richer bidirectional interactions among heterogeneous tokens. Besides, with the help of Scale-adaptive Attention, traffic participants within a certain distance are assigned higher attention scores, enabling each trajectory token to capture spatiotemporal interactions more precisely and adaptively. All these design enhances the expressiveness of interactive behavior modeling while serving as the foundation for flow matching and classifier-free guidance designs.\n- Previous methods attempt to enhance interactive behavior modeling by employing complex game-theoretic models [2] or topology-based representations [3] to capture intricate interactive behaviors. In contrast, **we pursue an elegant yet effective architecture** that demonstrates its effectiveness through our method's SOTA performance (90.43), **achieving marked improvement** over vanilla Transformer approaches (PlanTF [4], 84.27) and complex specialized architectures (GameFormer [2], 13.32). Our model also achieves SOTA performance (61.82) on interPlan benchmark with the significant performance gain (**+14.08**) brought about by the two key designs.\n\n> **W3: Limited Relevance of Flow Matching & Classifier-Free Guidance (CFG) to Interactive Behavior Modeling**\n\n\n\n- `W3.1 Limited Relevance of Flow Matching` Generative models demonstrate superior multimodal behavior modeling capabilities, which are fundamental for interactive behavior modeling [5][6]. On top of this, Flow Matching provides more stable training and faster convergence, as noted by the reviewer. As is shown in the table above, when incorporated with our architecture, our method (**61.82**) outperforms the imitation-learning based model (32.39) and the diffusion adaptation (40.71) with a significant lead **in interactive scenarios**. Note that the Diffusion adaptation is trained with the same epochs (200 epochs) with Flow Planner, only **half** the steps of Diffusion Planner [5], indicating the advantage of faster convergence of Flow Matching.\n- `W3.2 Limited Relevance of CFG` While CFG is widely used in diffusion models, our work **novelly conditions on neighboring vehicles**, effectively enhancing interactive behavior modeling for autonomous driving. Unlike typical CFG use in tasks with explicit conditions (e.g., text prompts [7] or rewards [8]), **interaction in driving lacks clear definitions**. We find that neighboring vehicles provide a powerful implicit condition. By learning both unconditioned and scene-conditioned behaviors, CFG captures interaction patterns—**offering a novel solution for generating interactive behaviors**. As shown, our model achieves **61.82** on interPlan vs. 58.22 without CFG, with notable gains in *Nudge Around* (**+8.50**) and *High Traffic Density* (**+7.13**).\n\n\n\n[1] Hallgarten, Marcel, et al. \"Can vehicle motion planning generalize to realistic long-tail scenarios?.\" 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2024.\n\n[2] Huang, Zhiyu, Haochen Liu, and Chen Lv. \"Gameformer: Game-theoretic modeling and learning of transformer-based interactive prediction and planning for autonomous driving.\" Proceedings of the IEEE/CVF International Conference on Computer Vision. 2023.\n\n[3] Liu, Haochen, et al. \"Reasoning multi-agent behavioral topology for interactive autonomous driving.\" Advances in Neural Information Processing Systems 37 (2024): 92605-92637.\n\n[4] Cheng, Jie, et al. \"Rethinking imitation-based planners for autonomous driving.\" 2024 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2024.\n\n[5] Zheng, Yinan, et al. \"Diffusion-Based Planning for Autonomous Driving with Flexible Guidance.\" The Thirteenth International Conference on Learning Representations.\n\n[6] Liao, Bencheng, et al. \"Diffusiondrive: Truncated diffusion model for end-to-end autonomous driving.\" Proceedings of the Computer Vision and Pattern Recognition Conference. 2025.\n\n[7] Ramesh, Aditya, et al. \"Hierarchical text-conditional image generation with clip latents.\" arXiv preprint arXiv:2204.06125 1.2 (2022): 3.\n\n[8] Ajay, Anurag, et al. \"Is conditional generative modeling all you need for decision-making?.\" arXiv preprint arXiv:2211.15657 (2022)."}, {"review_id": "Cd6pWQ4ebS", "reviewer": "Reviewer_SPYV", "summary": "Previous learning-based AD planning methods adopt brute-force implementation, which is not enough for  modeling interactive driving behaviors  In this work, the authors propose Flow Planner, which include coordinated designs in terms of data modeling, model architecture, and learning scheme to better model interactive driving behaviors in data. Fine-grained trajectory tokenization, temporal and spatial fusion, flow matching with classifier-free guidance are integrated into the planner and bring significant gain. Experimental results on  nuPlan dataset demonstrate the method achieves state-of-the-art performance among learning-based approaches.", "questions": "See the part of Weakness.", "limitations": "1. This work is like a combination of several incremental designs, The novelty  is limited. It's the main concern.\n2. The experiment parts are not convincing enough.", "rating": 2, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 3, "significance": 2, "originality": 2, "strengths_and_weaknesses": "Strength:\n1. The paper is well written and easy to follow.\n2. Impressive and intuitive figures to show the detailed designs.\n\nWeakness:\n1.  This work is like a combination of several incremental designs, like flow matching mechanism, trajectory tokenization manner, and classifier-free guidance,  and feature fusion manner.  And most of the designs are already well studied and proved to be effective in other works, especially flow matching and classifier-free guidance, which cannot be regarded as contributions of this work. Overall, the novelty of this work is limited and incremental. It's the main concern.\n2. The experiments are not adequate to show the effectiveness of the paper, only on one dataset with comparisons with few baseline methods. And the ablations in Table 3 show that many of the proposed components have marginal improvements.\n3. The layouts of table 4 and figure 3 can be improved.", "was_revised": true, "final_justification": "Thank the authors for the response.\nThe additional experiments on interplan are less convincing. The results show  Flow matching based model  significantly outperform diffusion based model. But as pointed out by researchers of Google DeepMind, diffusion and flow matching are similar.\nAnd in the field of end-to-end autonous driving, diffusion-based methods achieve similar and even better  performance compared with flow matching-based method. The diffusion-based model may not be well tuned to achieve its performance upper bound. \nSeparate adaLN & FFN improves the score from 46.25\t to 58.22, the gain of this design is much significant than other designs. It's strange.\n\n[1] https://diffusionflow.github.io  Diffusion Meets Flow Matching: Two Sides of the Same Coin.\n[2] Diffusiondrive: Truncated diffusion model for end-to-end autonomous driving.\n[3] GoalFlow: Goal-Driven Flow Matching for Multimodal Trajectories Generation in End-to-End Autonomous Driving\n\nAs for the novelty,   \n1. Fine-grained Trajectory Tokenization makes sense to me. But the other designs are incremental.\n2. Interaction-enhanced Spatiotemporal Feature Fusion is just minor network modification.\n3. Flow matching, as discussed above, is not superior to diffusion, and cannot be regarded as a contribution of this work.\n4. Classifier-free guidance is well studied. Conditioning on neighboring vehicles is an incremental design.\nOverall, I still hold the concern about novelty and 'will keep the original rating.", "rebuttal": "We thank the reviewer for the constructive comments. Regarding the concerns of the reviewer, we provide the following responses with extra experiments.\n\nTo further demonstrate the effectiveness of our design, we used interPlan [1], a closed-loop driving benchmark extended from nuPlan, which contains 335 long-tail traffic scenarios including jaywalkers (*Jaywalk*), accidents (*Nudge Around*), and dense-lane-change situations (*High Traffic Density*) etc, **posing a huge challenge in interactive behavior modeling**. We remain **SOTA** on this benchmark, with a significant lead over previous methods.\n\n| Methods                                 | Overall Score | Nudge Around | High Traffic Density | Jaywalk  |\n| --------------------------              | ----------    | ---------    | -------------------- |--------- |\n| PlanTF                                  | 47.70         | 49.40        | 58.85                | 33.94    |\n| PLUTO w/o refine.*                       | 58.47         | 71.56        | 67.25                | 25.48    |\n| Diffusion Planner                       | 52.90         | 60.48        | 49.71                | 26.20    |\n| **Flow Planner**                            | **61.82**         | 72.96        | 67.21                | 43.57    |\n\n*\\*: prior knowledge is used for the model*\n\nIn addition, we conducted further ablation studies on the interPlan benchmark, and compared our model with a vanilla imitation learning-based model *Vanilla IL*, which directly regress the target trajectory, and a Diffusion adaptation. **The proposed modules bring significant performance improvement in interactive scenairos**, and our method outperforms the imitation learning-based and diffusion adaptation with a prominent margin.\n\n| Methods                                 | Overall Score | Nudge Around | High Traffic Density | Jaywalk  |\n| --------------------------              | ----------    | ---------    | -------------------- |--------- |\n| Vanilla IL                              | 32.39         | 50.54        | 25.19                | 32.37    |\n| Diffusion Adaptation                    | 40.71         | 25.55        | 44.21                | 40.71    |\n| **Base Model**                              | 41.27         | 50.61        | 38.18                | 27.99    | \n| **+Fine-grained Trajectory Tokenization**   | 44.14         | 43.27        | 48.11                | 52.73    |\n| **+Scale-adaptive Attention**               | 46.25         | 51.31        | 44.28                | 34.42    |\n| **+Separate adaLN & FFN**                   | 58.22         | 64.46        | 60.08                | 43.16    |\n| **+CFG (Flow Planner)**                           | 61.82         | 72.96        | 67.21                | 43.57    |\n\n> **W1 & L1: The novelty of this work is limited and incremental.**\n\nInteractive behavior modeling remains a fundamental challenge in autonomous driving. Our core contribution is an integrated framework where each component is carefully designed to enhance interactive behavior modeling. \n\n<!-- It is unfair to claim there is a lack of novelty. -->\n\n- `Fine-grained Trajectory Tokenization` Our Fine-grained Trajectory Tokenization (acknowledged by reviewer yVKT & bpBq) enables kinematically continuous trajectory representation while supporting localized feature extraction, providing **a solution to the overcompression problem in existing methods [2][3][4]**. The Fine-grained Trajectory Tokenization enables high-quality and flexible trajectory generation (Fig. 3) and **brings performance improvement in interactive scenarios**, as is indicated in the table above.\n- `Interaction-enhanced Spatiotemporal Feature Fusion` Our Interaction-enhanced Spatiotemporal Feature Fusion (acknowledged by reviewer eK33) effectively integrates heterogeneous scene information through cooperative design with trajectory tokenization, **contrasting with brute-force transformer blocks[5] or unnecessarily complex architectures in prior work[6]**. This mechanism prominently enhances model's robustness, with a **+14.08** gain in overall performance on interPlan.\n- `Flow matching` Flow Matching-based generative models excel at capturing multi-modal driving behavior, essential for modeling complex interactive scenarios in autonomous driving[2][3]. Their straightforward loss function leads to faster convergence, more stable training, and higher inference efficiency than diffusion models [7]. As shown in the table, our Flow Matching model outperforms both the vanilla imitation learning model (**+29.43**) and the Diffusion adaptation (**+21.11**), **demonstrating the effectiveness of Flow Matching-based model in modeling interactive behavior**. Notably, the Diffusion adaptation is trained for the same 200 epochs as Flow Planner—only **half** the steps used by Diffusion Planner [2]—highlighting Flow Matching’s training efficiency.\n- `Classifier-free guidance` While CFG is widely used in diffusion models, our work **novelly conditions on neighboring vehicles**, effectively enhancing interactive behavior modeling for autonomous driving (acknowledged by reviewer bpBq & eK33). Unlike typical CFG use in tasks with explicit conditions (e.g., text prompts [8] or rewards [9]), **interaction in driving lacks clear definitions**. We find that neighboring vehicles provide a powerful implicit condition. By learning both unconditioned and scene-conditioned behaviors, CFG captures interaction patterns—**offering a novel solution for generating interactive behaviors**. As shown, our model achieves **61.82** on interPlan vs. 58.22 without CFG, with notable gains in *Nudge Around* (**+8.50**) and *High Traffic Density* (**+7.13**).\n\nIn hope of better addressing the reviewer's concerns, we welcome any further, more specific questions or comments regarding the novelty of our work. \n\n> **W2 & L2: The experiments are not adequate to show the effectiveness of the paper.**\n\n- We evaluate our method on nuPlan, the largest open-source real-world driving dataset supporting closed-loop testing. As the standard benchmark for planning methods, nuPlan better aligns with real-world applications. Our comparisons already have included existing SOTA methods.\n- To demonstrate the effectiveness of our designs, we further compared our method with existing competitive models on interPlan. We remain SOTA performance on this benchmark, with a significant lead (**61.82**) over existing models, demonstrating the effectiveness and robustness **in interactive behavior modeling** of our designs. The additional ablation on interPlan further indicates the effectiveness in interactive scenarios of each module we propose, as shown in the table above and discussion on W1 & L1.\n\n> **W3: The layouts of table 4 and figure 3 can be improved**\n\nWe appreciate the constructive suggestion from the reviewer. We will improve the layouts of the mentioned table and figure in the final version of our paper.\n\n[1] Hallgarten, Marcel, et al. \"Can vehicle motion planning generalize to realistic long-tail scenarios?.\" 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2024.\n\n[2] Zheng, Yinan, et al. \"Diffusion-Based Planning for Autonomous Driving with Flexible Guidance.\" The Thirteenth International Conference on Learning Representations.\n\n[3] Liao, Bencheng, et al. \"Diffusiondrive: Truncated diffusion model for end-to-end autonomous driving.\" Proceedings of the Computer Vision and Pattern Recognition Conference. 2025.\n\n[4] Cheng, Jie, Yingbing Chen, and Qifeng Chen. \"Pluto: Pushing the limit of imitation learning-based planning for autonomous driving.\" arXiv preprint arXiv:2404.14327 (2024).\n\n[5] Cheng, Jie, et al. \"Rethinking imitation-based planners for autonomous driving.\" 2024 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2024.\n\n[6] Huang, Zhiyu, Haochen Liu, and Chen Lv. \"Gameformer: Game-theoretic modeling and learning of transformer-based interactive prediction and planning for autonomous driving.\" Proceedings of the IEEE/CVF International Conference on Computer Vision. 2023.\n\n[7] Lipman, Yaron, et al. \"Flow matching for generative modeling.\" arXiv preprint arXiv:2210.02747 (2022).\n\n[8] Ramesh, Aditya, et al. \"Hierarchical text-conditional image generation with clip latents.\" arXiv preprint arXiv:2204.06125 1.2 (2022): 3.\n\n[9] Ajay, Anurag, et al. \"Is conditional generative modeling all you need for decision-making?.\" arXiv preprint arXiv:2211.15657 (2022)."}, {"review_id": "YNpm43UaPF", "reviewer": "Reviewer_eK33", "summary": "The paper proposes a transformer based generative network for the planning module of an autonomous vehicle. It focuses on interactive behavior modeling by using spatiotemporal fusion from processed perception inputs and a fine-grained tokenized trajectory. In the end the model uses a classifier-free guidance by using a flow matching model to enhance interactive driving behavior by having the model predict the needed velocity to achieve two sample pairs drawn from the source and target distributions. Testing on the nuPlan dataset the model achieves SOTA results.", "questions": "1. How do the authors define interactive driving behavior? It is left undefined as a whole and seems quite subjective.\n2. What is the multi-modality of the model? Is it just in reference to unconditioned and conditioned classifier-free guidance?", "limitations": "yes", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 2, "significance": 3, "originality": 3, "strengths_and_weaknesses": "1. Quality - The paper is well written with all the claims in the paper being backed by experimental results. The experiments done are appropriate, thorough and complete. A drawback might be that the model was tested only on the nuPlan dataset and no others, but seeing that it's a standard benchmark dataset it isn't too big of a problem. Figures and graphs are legible, tables are informative.\n2. Clarity - The clarity lacks a bit but it is likely due to the fact that the paper had to say so much with limited page size. The introduction leaves a bit of the proposed changes somehow undefined, while the rest of the paper answers only a portion of them.\n3. Significance - The results are significant in the field in using flow matching models for generating trajectories which is a build up on diffusion models. The tokenization, spatiotemporal fusion and architectural changes to the classifier-free trajectory proposition are likely to be built upon further. The results themselves are a bit worse than, as the authors call it, rule based & hybrid models, but is the best out of the models that actually learn \n4. Originality - The paper is original in the sense that it takes a base of learning trajectory planners and builds upon referenced works, while introducing modified methods for parts of the architecture. It provides new insights into trajectory planning and the way it builds upon previous work makes sense.", "was_revised": true, "final_justification": "The authors answered the questions and addressed the issues from my review sufficiently. Thus, I keep my rating and propose the paper should be accepted.", "rebuttal": "We really appreciate the reviewer for the constructive comments and positive feedback on our paper.\n\n> **W1: The model was tested only on nuPlan dataset and no others.**\n\n- We thank the reviewer for the constructive comment and understanding. To provide a more comprehensive evaluation of the effectiveness of our method, we conducted extra experiments on interPlan [1], a closed-loop driving benchmark extended from nuPlan, which contains 335 long-tail traffic scenarios including jaywalkers (*Jaywalk*), accidents (*Nudge Around*), and dense-lane-change situations (*High Traffic Density*) etc, **posing a huge challenge in interactive behavior modeling**. We achieved SOTA performance on this benchmark over the existing baselines with a significant lead (**61.82**). \n\n| Methods                                 | Overall Score | Nudge Around | High Traffic Density | Jaywalk  |\n| --------------------------              | ----------    | ---------    | -------------------- |--------- |\n| PlanTF                                  | 47.70         | 49.40        | 58.85                | 33.94    |\n| PLUTO w/o refine.*                       | 58.47         | 71.56        | 67.25                | 25.48    |\n| Diffusion Planner                       | 52.90         | 60.48        | 49.71                | 26.20    |\n| **Flow Planner**                            | **61.82**         | 72.96        | 67.21                | 43.57    |\n\n*\\*: prior knowledge is used for the model*\n\n- In addition, we ablated our key designs on this benchmark to further demonstrate the effectiveness of our method in highly interactive scenarios. The proposed modules bring a total gain of **+20.55**, with a prominent improvement in interactive scenarios *Nudge Around* (**+22.35**), *High Traffic Density* (**+29.03**) and *Jaywalk* (**+15.58**) respectively. We also compared our Flow Planner with a vanilla imitation learning-based (*Vanilla IL*), which directly regresses the target trajectory, and Diffusion adaptation. Our Flow Matching-based model significantly outperforms the imitation learning (**+29.43**) and Diffusion adaptation (**+21.11**). It is worth noting that the Diffusion adaptation is trained with the same epochs of Flow Planner, which is **half** the steps of Diffusion Planner [2], indicating the advantage of fast convergence of Flow Matching.\n\n| Methods                                 | Overall Score | Nudge Around | High Traffic Density | Jaywalk  |\n| --------------------------              | ----------    | ---------    | -------------------- |--------- |\n| Vanilla IL                              | 32.39         | 50.54        | 25.19                | 32.37    |\n| Diffusion Adaptation                    | 40.71         | 25.55        | 44.21                | 40.71    |\n| **Base Model**                              | 41.27         | 50.61        | 38.18                | 27.99    | \n| **+Fine-grained Trajectory Tokenization**   | 44.14         | 43.27        | 48.11                | 52.73    |\n| **+Scale-adaptive Attention**               | 46.25         | 51.31        | 44.28                | 34.42    |\n| **+Separate adaLN & FFN**                   | 58.22         | 64.46        | 60.08                | 43.16    |\n| **+CFG (Flow Planner)**                           | 61.82         | 72.96        | 67.21                | 43.57    |\n\n> **W2: Clarity issues.**\n\nWe appreciate the constructive suggestion from the reviewer. We will improve the clarity of paper in the final version.\n\n\n> **Q1: How do the authors define interactive driving behavior? It is left undefined as a whole and seems quite subjective.**\n\nWe appreciate the reviewer's question and apologize for any confusion caused by unclear definitions. In this work, 'interactive driving behavior' specifically refers to a vehicle's ability to perceive and respond to other road users. As previously mentioned, we conducted additional interactive scenario testing to validate the effectiveness of our design on interPlan, providing a more objective and comprehensive evaluation of our method.\n\n> **Q2: What is the multi-modality of the model? Is it just in reference to unconditioned and conditioned classifier-free guidance?**\n\n- Multi-modality driving behavior describes the diverse possible actions a driver may take in a given scenario (e.g., turning left or right to avoid an obstacle). Conventional imitation learning struggles with this as it regresses to mean ground truth values. This limitation motivates our use of probabilistic generative models like Flow Matching, whose advantage is shown in the experiment results above. \n- In terms of the Classifier-Free Guidance, it is adopted to amplify the awareness of the existence of neighboring vehicles in trajectory generation via combining the unconditioned and conditioned velocity field during inference. This novel implementation is proved to be beneficial in enhancing model's interactive modeling capability, as is shown in Table 3 and Table 5, as well as the extra experiments we provided above.\n\n\n\n[1] Hallgarten, Marcel, et al. \"Can vehicle motion planning generalize to realistic long-tail scenarios?.\" 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2024.\n\n[2] Zheng, Yinan, et al. \"Diffusion-Based Planning for Autonomous Driving with Flexible Guidance.\" The Thirteenth International Conference on Learning Representations."}, {"review_id": "lKW1rYPPIj", "reviewer": "Reviewer_bpBq", "summary": "This paper proposes Flow Planner, a learning-based autonomous driving planner combining trajectory tokenization, spatiotemporal fusion, and classifier-free guidance. The method achieves state-of-the-art performance on nuPlan benchmarks, particularly in dense interaction scenarios like unprotected left turns. Key contributions include fine-grained trajectory decomposition and dynamic interaction reweighting during inference.", "questions": "N/A", "limitations": "While the method shows technical soundness, its advancements over existing approaches (Diffusion Planner, UniAD) remain incremental. The lack of OOD testing and marginal benchmark improvements fail to justify the architectural complexity. However, the closed-loop interaction analysis provides value for future research directions.", "rating": 6, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 4, "clarity": 4, "significance": 4, "originality": 4, "strengths_and_weaknesses": "Major Strength\nArchitectural Coherence\nThe integration of overlapping trajectory segments (L=20 in Table 4) provides localized interaction modeling while maintaining kinematic consistency through overlap constraints (Eq. 9). This addresses over-compression issues in prior work like Diffusion Planner.\n\nPractical Deployment Insights\nThe 12Hz inference speed (Sec. 4.3) marginally exceeds industrial requirements, demonstrating awareness of real-world constraints despite computational bottlenecks.\n\nMajor Weakness\nIncremental Technical Contributions\nThe core innovations—trajectory tokenization and scale-adaptive attention—constitute architectural refinements rather than paradigm shifts. Table 3 reveals cumulative gains of just 2.33 points from four components, suggesting diminishing returns compared to prior work, such as UniAD's 5.1-point gain over baselines.\n\nBenchmark Limitations\nExclusive reliance on nuPlan (Table 1) raises concerns, given its known deficiencies in interaction modeling. The claimed 1.2-point improvement on Test14-hard (76.47 vs 75.22) becomes statistically marginal when considering test set variance\n\nMinor Weaknesses\nOOD Generalization Blindspot\nNo evaluation on navigation-focused benchmarks like Navsim, despite claims about interactive capability. This contrasts with recent works (e.g., GoalFlow'25) demonstrating 89.3 EPDMS on Navsim through explicit goal conditioning.\n\nSafety-Critical Scenario Analysis\nWhile Figure 2 shows collision avoidance cases, there's no quantitative breakdown of failure modes. The 27% reduction in collision rates (Table 5) lacks context regarding the remaining risk levels in production systems.", "was_revised": false, "final_justification": "", "rebuttal": "We would like to first highlight our appreciation to the acknowledgement to our work from the reviewer. Regarding the remaining concerns of the reviewer, we provide the following response.\n\n> **W1: Architectural refinements rather than paradigm shifts**\n\nWe propose a coordinated solution for interactive behavior modeling in autonomous driving, with architectural refinements as a core component. Our Fine-grained Trajectory Tokenization enables kinematically continuous trajectory representation while supporting localized feature extraction. When integrated with CFG, which dynamically reweights neighboring interactions during inference, this framework establishes a new paradigm for interactive behavior generation.\n\n> **W2, W3 & L1: Marginal improvement and Exclusive Reliance on nuPlan**\n\n- To our knowledge, Flow Planner is the first learning-based method to achieve a score above 90 on nuPlan without post-processing or prior knowledge. This accomplishment is particularly significant as the scores are averaged across 1,000+ val14 scenarios, making further improvements exceptionally challenging.\n- To further demonstrate the effectiveness of our method, we conducted additional experiments on the interPlan [1], a benchmark tailored to better evaluate the capability and robustness of algorithms **in interactive scenarios** with specially modified scenarios. Our method remained SOTA on this augmented benchmark, with a significant lead over existing methods.\n\n| Methods                                 | Overall Score | Nudge Around | High Traffic Density | Jaywalk  |\n| --------------------------              | ----------    | ---------    | -------------------- |--------- |\n| PlanTF                                  | 47.70         | 49.40        | 58.85                | 33.94    |\n| PLUTO w/o refine.*                       | 58.47         | 71.56        | 67.25                | 25.48    |\n| Diffusion Planner                       | 52.90         | 60.48        | 49.71                | 26.20    |\n| **Flow Planner**                            | **61.82**         | 72.96        | 67.21                | 43.57    |\n\n*\\*: prior knowledge is used for the model*\n\n- In addition, we ablated our key designs on this benchmark to further demonstrate the effectiveness of our method in highly interactive scenarios. The proposed modules bring a total gain of **+20.55**, with a prominent improvement in interactive scenarios *Nudge Around* (**+22.35**), *High Traffic Density* (**+29.03**) and *Jaywalk* (**+15.58**) respectively. We also compared our Flow Planner with a vanilla imitation learning-based (*Vanilla IL*), which directly regresses the target trajectory, and Diffusion adaptation. Our Flow Matching-based model significantly outperforms the imitation learning (**+29.43**) and Diffusion adaptation (**+21.11**). It is worth noting that the Diffusion adaptation is trained with the same epochs of Flow Planner, which is **half** the steps of Diffusion Planner [2], indicating the advantage of fast convergence of Flow Matching.\n\n| Methods                                 | Overall Score | Nudge Around | High Traffic Density | Jaywalk  |\n| --------------------------              | ----------    | ---------    | -------------------- |--------- |\n| Vanilla IL                              | 32.39         | 50.54        | 25.19                | 32.37    |\n| Diffusion Adaptation                    | 40.71         | 25.55        | 44.21                | 40.71    |\n| **Base Model**                              | 41.27         | 50.61        | 38.18                | 27.99    | \n| **+Fine-grained Trajectory Tokenization**   | 44.14         | 43.27        | 48.11                | 52.73    |\n| **+Scale-adaptive Attention**               | 46.25         | 51.31        | 44.28                | 34.42    |\n| **+Separate adaLN & FFN**                   | 58.22         | 64.46        | 60.08                | 43.16    |\n| **+CFG (Flow Planner)**                           | 61.82         | 72.96        | 67.21                | 43.57    |\n\n- We appreciate the reviewer's suggestion to evaluate on NavSim. However, this benchmark focuses on pseudo-closed-loop testing for end-to-end driving systems, which differs from our work's scope. We would like to leave the end-to-end adaptation of our method for future study.\n\n> **W3: Inadequate Safety-Critical Scenario Analysis.** \n\nWe appreciate the emphasis on safety in autonomous driving. As noted earlier, we have expanded our evaluation to include safety-critical interactive scenarios. In addition, we are confused with the reviewer's discussion on Table 5 and would like to be provided with further comments from the reviewer.\n\n\n[1] Hallgarten, Marcel, et al. \"Can vehicle motion planning generalize to realistic long-tail scenarios?.\" 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2024.\n\n[2] Zheng, Yinan, et al. \"Diffusion-Based Planning for Autonomous Driving with Flexible Guidance.\" The Thirteenth International Conference on Learning Representations."}, {"review_id": "ifJtwwPKUS", "reviewer": "Reviewer_hKs2", "summary": "This paper introduces Flow Planner, a learning-based framework targeting the challenge of modeling interactive driving behaviors in complex scenarios. The authors identify key limitations in existing transformer-based and imitation learning approaches, particularly regarding their inability to effectively capture agent interactions due to architectural simplicity and limited interactive data. To address these gaps, the proposed method combines fine-grained trajectory tokenization, a purpose-built architecture for spatiotemporal fusion, and flow matching with classifier-free guidance to enhance multi-agent behavioral modeling. They evaluated the models on the nuPlan dataset and obtained the state-of-the-art performance.", "questions": "Q1. In the trajectory tokenization process, how do you ensure that each trajectory can be divided into exactly K segments with L_seg points per segment? How is this handled in cases where trajectories vary in length or have fewer points than required?\n\nQ2. According to Table 1, several \"Rule-based & Hybrid\" planners achieve higher scores compared to the learning-based approach. Could you elaborate on the advantages of adopting a learning-based framework in this context, despite the relatively lower performance? What are the trade-offs in terms of generalizability, scalability, or adaptability?\n\nQ3. The learning-based baselines in the paper are primarily focused on imitation learning. Could you clarify why optimization-based planning methods, such as reinforcement learning–based approaches, are not included for comparison? Including such baselines could provide a more comprehensive evaluation of the proposed method's effectiveness.", "limitations": "yes", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "S1. The paper is well-organized and presented.\n\nS2. The proposed schemes are well-examined in ablation studies.\n\nPlease check the questions part for the reviewer's confusion.", "was_revised": true, "final_justification": "The authors' response has mainly addressed my concern. I would like to upgrade the score to 5.", "rebuttal": "We appreciate the constructive comments from the reviewer. In hope of addressing the reviewer's confusion, we provide the following response.\n\n> **Q1: How do you ensure that each trajectory can be divided into exactly K segments with L_seg points per segment? How is this handled in cases where trajectories vary in length or have fewer points than required?**\n\nIn practical implementation, we adopt fixed trajectory lengths for both training and testing, consistent with real-world vehicle deployment. On top of this, given the predefined segment length and overlap length as hyperparameters, we can divide the trajectory into desired segments with overlap accordingly (see Section 3.2 for details). If handling variable-length inputs is required, standard padding and masking techniques can be employed to ensure consistent input dimensions across all samples.\n\n> **Q2: Could you elaborate on the advantages of adopting a learning-based framework in this context, despite the relatively lower performance? What are the trade-offs in terms of generalizability, scalability, or adaptability?**\n\n- Learning-based frameworks have largely replaced rule-based methods in academia [1] and industry [2] due to their scalability and data-driven generalization capabilities. However, trade-offs still exist: while data scales up, the scarcity of high-quality data leads to suboptimal performance in interactive scenarios and increasing computational demands when scaling up models. To address this, we propose Flow Planner—an efficient framework that achieves SOTA performance on limited nuPlan training data while maintaining fast inference speeds.\n- We further note that the **benchmark could potentially be hacked** by rule-based/hybrid methods through metric optimization - they could select trajectories specifically to maximize scores [3][4]. Flow Planner could similarly employ a post-processing module for this purpose. We implemented the same post-processing module as in [5] and achieved competitive performance with existing rule-based and hybrid methods.\n\n| Methods                     | Val14 (NR) | Val14 \\(R\\) | Test14-hard (NR) | Test14-hard \\(R\\) | Test14 (NR) | Test14 \\(R\\) |\n| --------------------------- | ---------- | ----------- | ---------------- | ----------------- | ---------------- | ----------------- |\n| GameFormer                  | 79.94      | 79.78       | 68.70            | 67.05             | 83.88            | 82.05             |\n| PLUTO                       | 92.88      | 76.88       | 80.08            | 76.88             | 92.23            | 90.29             |\n| Diffusion Planenr w/ refine.| 94.26      | 92.90       | 78.87            | 82.00             | 94.80            | 91.75             |\n| **Flow Planner w/ refine.**    | 94.31      | 92.38       | 78.65            | 80.25             | 94.79            | 92.4              |\n\n\n> **Q3: Could you clarify why optimization-based planning methods, such as reinforcement learning–based approaches, are not included for comparison?**\n\nAs discussed in Related Works, RL has limitations such as safe exploration and reward design, which restrict its application in real world autonomous driving. In the nuPlan simulation, RL methods can leverage sufficient interactions and carefully designed rewards to achieve higher evaluation scores. However, imitation learning directly mimics the data without incorporating additional prior knowledge, making it inappropriate for direct comparison with RL approaches.\n\n> **Extra Experiment Results**\n\nTo further demonstrate the effectiveness of our model in interactive behavior modeling, we conducted extra experiments on interPlan [6], a closed-loop driving benchmark extended from nuPlan, which contains 335 long-tail traffic scenarios including jaywalkers (*Jaywalk*), accidents (*Nudge Around*), and dense-lane-change situations (*High Traffic Density*) etc, **posing a huge challenge in interactive behavior modeling**. We achieved **SOTA** performance on this benchmark.\n\n| Methods                                 | Overall Score | Nudge Around | High Traffic Density | Jaywalk  |\n| --------------------------              | ----------    | ---------    | -------------------- |--------- |\n| PlanTF                                  | 47.70         | 49.40        | 58.85                | 33.94    |\n| PLUTO w/o refine.*                       | 58.47         | 71.56        | 67.25                | 25.48    |\n| Diffusion Planner                       | 52.90         | 60.48        | 49.71                | 26.20    |\n| **Flow Planner**                            | **61.82**         | 72.96        | 67.21                | 43.57    |\n\n*\\*: prior knowledge is used for the model*\n\nIn addition, we conducted ablation studies on interPlan to further demonstrate the improvement brought about by each key designs, proving the effectiveness of these modules in interactive behavior modeling.\n\n| Methods                                 | Overall Score | Nudge Around | High Traffic Density | Jaywalk  |\n| --------------------------              | ----------    | ---------    | -------------------- |--------- |\n| **Base Model**                              | 41.27         | 50.61        | 38.18                | 27.99    | \n| **+Fine-grained Trajectory Tokenization**   | 44.14         | 43.27        | 48.11                | 52.73    |\n| **+Scale-adaptive Attention**               | 46.25         | 51.31        | 44.28                | 34.42    |\n| **+Separate adaLN & FFN**                   | 58.22         | 64.46        | 60.08                | 43.16    |\n| **+CFG (Flow Planner)**                           | 61.82         | 72.96        | 67.21                | 43.57    |\n\n\n[1] Hu, Yihan, et al. \"Planning-oriented autonomous driving.\" Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2023.\n\n[2] Hwang, Jyh-Jing, et al. \"Emma: End-to-end multimodal model for autonomous driving.\" arXiv preprint arXiv:2410.23262 (2024).\n\n[3] Cheng, Jie, Yingbing Chen, and Qifeng Chen. \"Pluto: Pushing the limit of imitation learning-based planning for autonomous driving.\" arXiv preprint arXiv:2404.14327 (2024).\n\n[4] Dauner, Daniel, et al. \"Parting with misconceptions about learning-based vehicle motion planning.\" Conference on Robot Learning. PMLR, 2023.\n\n[5] Zheng, Yinan, et al. \"Diffusion-based planning for autonomous driving with flexible guidance.\" arXiv preprint arXiv:2501.15564 (2025).\n\n[6] Hallgarten, Marcel, et al. \"Can vehicle motion planning generalize to realistic long-tail scenarios?.\" 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2024."}]

Author rebuttal:

Meta-review: The paper has received a mixture of opposite reviews ranging from reject to strong accept. All reviewers acknowledge the clarity of the paper, and the benefits of the results. There is, however, a incremental novelty concern shared among different reviewers. Nevertheless, overall feels like a solid paper.
Decision: Accept (poster)