BridgeDrive: Diffusion Bridge Policy for Closed-Loop Trajectory Planning in Autonomous Driving
Authors: Shu Liu
Wenlin Chen
Weihao Li
Zheng Wang
Lijin Yang
Jianing Huang
YipinZhang
Zhongzhan Huang
Ze Cheng
Hao Yang
Venue: ICLR 2026 Poster (2026)
Keywords: Diffusion policy
closed-loop planning
end-to-end autonomous driving
Primary area: applications to robotics, autonomy, planning

Abstract: Diffusion-based planners have shown strong potential for autonomous driving by capturing multi-modal driving behaviors. A key challenge is how to effectively guide these models for safe and reactive planning in closed-loop settings, where the ego vehicle's actions influence future states. Recent work leverages typical expert driving behaviors (i.e., anchors) to guide diffusion planners but relies on a truncated diffusion schedule that introduces an asymmetry between the forward and denoising processes, diverging from the core principles of diffusion models. To address this, we introduce BridgeDrive, a novel anchor-guided diffusion bridge policy for closed-loop trajectory planning. Our approach formulates planning as a diffusion bridge that directly transforms coarse anchor trajectories into refined, context-aware plans, ensuring theoretical consistency between the forward and reverse processes. BridgeDrive is compatible with efficient ODE solvers, enabling real-time deployment. We achieve state-of-the-art performance on the Bench2Drive closed-loop evaluation benchmark, improving the success rate by 7.72% and 2.45% over prior arts with PDM-Lite and LEAD datasets, respectively. Project page: https://github.com/shuliu-ethz/BridgeDrive.

Published as a conference paper at ICLR 2026
BRIDGEDRIVE: DIFFUSION BRIDGE POLICY FOR CLOSED-LOOP TRAJECTORY PLANNING IN AUTONOMOUS DRIVING
Shu Liu Wenlin Chen Weihao Li", Zheng Wang"-1,
Lijin Yang, Jianing Huang, Yipin Zhang, Zhongzhan Huang, Ze Cheng, Hao Yang! Bosch (China) Investment Ltd. AShu.liuz, renlin.chen, david. wang3, kevin. (orinnyang)@cn bosch com
ABSTRACT
Diffusion-basse -bassi planners have shown strong potential for autonomous driving by capturing multi-modal driving behaviors. A key challenge is how to effectively guide these models for safe and reactive planning in closed-loop settings, where the ego vehicle's actions influence future states. Recent work leverages typical expert driving behaviors (i.e., anchors) to guide diffusion planners but relies on a truncated diffusion schedule that introduces an asymmetry between the forward and denoising processes, diverging from the core principles of diffusion models. To address this, introduce BridgeDrive, a novel anchor-guided diffusion bridge policy for closed-loop trajectory planning. Our approach formulates planning as diffusion bridge that directly transforms coarse anchor trajectories into re- fined, context-aware plans, ensuring theoretical consistency between the forward and reverse processes. BridgeDrive compatible with efficient ODE solvers, enabling real-time deployment. We achieve state-of-the-art performance on the Bench2Drive closed-loop evaluation benchmark, improving the success rate by 7.72% and 2.45% over prior arts with PDM-Lite and LEAD datasets, respectively, Project page: https://github..om//hhi www.rooowwwsuuttteeeeeeeeeeeeeee -huuiu-ehh//BrddgDDiie
INTRODUCTION
Closed-loop planning with reactive agents is a critical challenge in autonomous driving, which re- quires effective interaction with complex and dynamic traffic environments (Jia et al. 2024). Diffu- sion models have become powerful paradigm for this task due to their ability to model complex. multi-modal distributions and incorporate flexible guidance (Liao et al. 2025 Zheng et al. 2025b Yang al., 2024 Xing et al. 2025). A key challenge, however, is to determine which sources of guidance information are most salient and how to integrate them effectively into these models to produce plans that are not only plausible but also safe and reactive in real-world driving conditions.
promising source for guidance to leverage typical human expert driving behaviors, often repre- sented coarse anchor trajectories, they provide a strong prior for safe and sensible maneuvers, constraining the vast solution space. Recently, DiffusionDrive (Liao et al. 2025) implements this strategy by training denoiser on truncated diffusion schedule, starting from noisy version of the anchor rather than pure Gaussian noise. While achieving state-of-the-ae art empirical performance, this approach introduces theoretical inconsistency: its denoising process does not match the forward diffusion process that it is trained on, which diverges from the core principle of diffusion models and can lead to unpredictable behaviors and compromised performance.
To address this, we introduce BridgeDrive, principled diffusion framework that integrates anchor- based guidance for autonomous driving planning using a thcoretically sound diffusion bridge for- mulation. Instead of heuristically truncating the diffusion process, we formally define the planning task as learning a diffusion process that bridges the gap from a given coarse anchor trajectory to refined, context-aware final trajectory plan. This formulation ensures that the forward and denoising processes are perfectly symmetric, allowing our model to learn a direct and robust transformation
denotes equal contribution. Corresponding author: Shu Liu.

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Published as a conference paper at ICLR 2026
from anchors final trajectories. By adhering to the principles of diffusion, our method fully lever- ages the expressive power of anchors for guidance while maintaining diffusion models' ability to represent diverse human-like driving behaviors. Furthermore, our approach is compatible with effi- cient ODF-based samplers, enabling real-time performance crucial for on-road deployment. Empiri- cally, we achieve 74.99% and 89 25% success rate the Bench2Drive closed-loop benchmark with PDM-Lite and LEAD datasets, respectively, outperforming previous SOTA by 7.72% and 2.45%.
2
PRELIMINARIES
2.1 AUTONOMOUS DRIVING PLANNING AND EVALUATION
The planning task in autonomous driving be formulated as predicting future trajectories of the ego-vehicle based on raw sensor inputs. Conventionally, there are two trajectory representa- tions (Renz al. y: (I) Temporal speed waypoints x xmmmp NR represent equal temporal-spaced (e.g., every 0.25 seconds) future coordinates of ego-vehicle, which inherently con- tain speed control information. (2) Geometrie path waypoints x (2 vv E R R.Npmx2 x I, represent equal geometric-spaced (c.g., every 1 meter) future coordinate of ego-vehicle; for geomet- ric path waypoints-ban baaeed planning, the model needs to predict the speed v of ego-vehicle. In this paper, we choose to use geometric path waypoints
our model outpur, which differs from Diffu- sionDrive Liao et al. (2025) where temporal speed waypoints are used. This design choice is based on prior works (Chitta et al. 2023 Zimmerlin et al. 2024) and our ablation study in Section 4
Evaluation of autonomous driving can be broadly categorized into open-loop and closed-loop set- tings. The closed-loop setting is more challenging and can better reflcc a policy's real-world plan- ning capability, since the ego vehicle's decisions affect its own future states and those of the sur- rounding agents, creating feedback loop that can amplify small prediction errors over time. To minimize the sim-to-real gap, closed-loop evaluation requires high-fidelity simulators to capture the interactions between the ego vehicle and its surrounding environment, which are typically both computationally expensive and time-consuming. CARLA (Dosovitskiy et al. (2017) has emerged as the most widely used platform, with series of benchmarks building on top of it, such as CARLA Leaderboard, Longestó (Chitta et al., 2203), and Bench2Drive (Jia et al. 2024). Interestingly, exist- ing methods that achieve near perffct results on open-loop datasets, such NavSim (Dauner al, 2024) or nuScenes (Caesar et al. 2019), still struggle to achieve comparable performance under closed-loop evaluation (Li et al.] 2024b) Liao et al., 2025) Fu et al., [025] Renz et al.) 2025). This discrepancy emphasizes the inherent difficulty of closed-loop planning and highlights the need for more robust methods to handle the complexities of dynamic, interactive traffic environments.
2.2 DIFFUSION MODELS
Diffusion models (Sohl- Sicksten al.) 2015 Ho et al. 2020 Song et al. 202 bj Karras al. 2022) generate data To ~ Pa( from pure Gaussian noise TT ~ p(æT) = A (or 10, a by reverting forward diffusion process. Mathematically, the forward diffusion process, which gradually corrupts data into noise, can be defined by linear SDE (Song et al. 2021b):
dz, = f()) + g(t)dw,, To Pd:
where t [0. T] denotes the diffusion timestep, f [0, T] R is the linear drift coefficient, g: [0, 7 + R is the diffusion coefficient function, and at E Rd is a standard Brownian motion. turns out that this linear SDE owns Gaussian transition kernel q(<x/100) (xx [o, of
where Oe. exp (5o {s)ds) and of ai fo afeldds ds are the noise schedules (Kingma et al. 2021I. The forward SDE defines a series of marginals densities {g(2:))e along the diffusion path, where ((xaa) = f (( (ro)pa((o) drz. Since q(TT) ~ p(ær) for sufficiently large T, we can generate data Trg ~ Pa (2o) by transforming a noise sample ar ~ p(rr) through a probability flow ODE (PF-ODE) (Song et al. 2021bb:
dr; di
= s(1),, g(t)²
2 V., log g(x))
which shares identical marginal densities (g(xz) hejo.T the forward SDE. The score function N. log ((x)) in Eq. (2) can be approximated by V., log ((x)) Rs (au30(21, 1) ax)/o? (Vincent
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Spoed: 5.70
Speedi &
Speedl 0.75
Speedl 5.75
traffie purticipants (e.g vehicles, syclisis) Lane (no entry)
Lane line (passable)
Drivable area
LiDAR point cloud
Waypoints denoised from selected anchor Wiypoints denoised from unsclected anchors
Waypoints denoised from other unselected anchors
Figure Visualization of the denoising process BridgeDrive (I with the leftmost figure being anchor æT and the rightmost being the planned trajectory 2g. In each figure, the blue solid line depicts the denoised trajectory of the selected anchor at specific timestep t, the red solid line depicts example of the denoised trajectory of un-selected anchor, and the rest scattered dots of other colors depict the denoised trajectories of other anchors at the timestep t. The red trajectory illustrates failed case when catastrophically wrong anchor is selected.
from left to right),
2011 where the denoiser ze (Z,, t) is parameterized by neural network and learned by minimizing the mean squared denoising error Karras et al. 2022):
min Bpp(hhaattoii(eell [x(t)] xo.l 11
For conditional generation, the denoiser zn (1,,,,, takes in an extra conditional variable z, which corresponds to the conditional score function V.,, log g(>z|=) (0, 1, s2)/aa Fur- thermore, Ho & Salimans (2021) propose to linearly interpolate between Va, log ((x(22) and V., o, log ((t)) with hyperparameter to adjust the guidance strength of the conditional information.
2.3 DIFFUSIONDRIVE WITH TRUNCATED DIFFUSION
DiffusionDrive (Liao et al. 2025) is a diffusion planner based on temporal speed waypoints, which leverages truncated diffusion schedule and fixed set of K K-means clustered anchor trajectories {y') Nastre that represent typical human driving behaviors. The truncated forward diffusion process adds a small amount of noise to each anchor until Tirune < to obtain a set of noisy anchors {vT 1 Nasaer The truncated denoising process starts from noisy anchors at = Timme- Given conditional information (e-g., sensor inputs and target point), neural network x cro(1v) Maxtor I, 2) trained to predict the best anchor and output a denoised trajectory from the noisy version of the best anchor. The denoised trajectory is then used compute the score function for denoising.
i=1
However, as discussed in the previous section, the learned denoising process of diffusion models must revert the forward diffusion process. Although DiffusionDrive demonstrates strong empiri- cal performance, it utilizes truncated diffusion schedule where the forward diffusion process adds noise anchor trajectories and the denoising process attempts to recover the ground-truth trajecto- ries. This design choice creates an asymmetry between its forward and denoising processes, framing the model's task as regressing from noisy anchors to ground-truth trajectories, rather than as rever- sal of the forward diffusion process.
3 BRIDGEDRIVE DIFFUSION BRIDGE POLICY FOR TRAJECTORY PLANNING
To ensure the symmetry between the forward and backward processes of anchor-based diffusion planners, propose novel diffusion bridge policy, BridgeDrive, which provides principled diffusion framework that leverages the powerful inductive biases anchor-based guidance, while ensuring that the symmetry between the forward and denoising processes is maintained.
3.1 ANCHOR CONSTRUCTION FOR GEOMETRIC PATH WAYPOINTS
Naa are pre-defined, figh-triortyy trajectories that represent typical human expert driving behaviors. They form discrete set of atomic building blocks that planners to
Anchors y = [
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construct solutions, which can dramatically reduce planning complexity, enforce safety constraints, improve robustness to dynamic environments, and align planning with task objectives (Chai et al,
2020 Chen et al.] 2024 Li et al., 2024b). Our anchor definition is slightly different from Liom et al.
(2025) since our model outputs geometric path waypoints as discussed in Section 2.T. Specifically,
each anchor is formulated a y == (ar ('y) E Ru IR, where 2. R R.N represents series of coordinates of future path, N point is the geometric prediction horizon, and Uy denotes the anchor speed. Each anchor y is defined as K --mans clustering center of the training set (both the
trajectory my geo and speed "y participate in the clustering process together). All values are normalized the ego-vehicle coordinate system.
Algorithm BridgeDrive Training (ours)
Algorithm DiffusionDrive Training
1: Initialize 0
# denoiser parameter
Initialize 0
# denoiser parameter
2: repeat
2: repeat
3:
2,Y; (a 2)
3:
2, M, 2 ~ pa (a y, 2)
4:
# 2: GT traj, anchor, 2: guidance
4:
# x: GT traj, y: anchor, guidance
5:
1~ (<p(() p(()
# / [)
5:
I ~~ Pinane (t)
# #LE [0, Timee]
6:
(0,10
# random noise
6:
E e~ N0,11
random noise
7:
Jt = arl + b, + OgE noisy trajectory
7:
Vt = 043 + O,E
# noisy anchor
8:
Update with the gradient
Update @ with the gradient
we Volt)ooll v, x||2
Volt 1, 2) x012
9: until convergence
9: until convergence
10: return
10: return
3.2 A GENERATIVE PARADIGM FOR ANCHOR-GUIDER DIFFUSION POLICY
To incorporate anchors into diffusion models in a principled way, we propose to factorize the joint distribution of the ground-truth trajectory anchor y, and guidance information
Pa(x, y, =) = Pa( [1 2 =))(())=)pa(().
(4)
This factorization defines two-step generative process. First, for driving scene Pa(=), we sample an anchor y ~ (2) given the scene information in z (c.g.. BEV, agent/map queries, and target point). Then, the planned trajectory æ Pa(xly, 2) is generated according to the guidance of the chosen anchor y and scene information 2.
We propose to parameterize the conditional planning distribution Pa(zly,2 2) with conditional dif- fusion bridge model po (= 2), which constructs a diffusion bridge (Zhou et al.] 2024 Zheng al. 2025a) between the ground-truth trajectory zi x and anchor JT == y (Doob et al. 1984):
dr: = (t) + V(t)2 log q(>r (x.) + g(t)dw,, To ~ Pd, 3T = =
(5)
where t [0, denotes the diffusion timestep, the definitions of f(t), g(t) follow those in Eq. 1
and V., log (xT (xz) = Va, log ( In xr) Vz, log ((>a(=))) (2o) It turns out that Eq. (5) also owns
an analytical Gaussian transition kernel for any given trajectory xo and anchor xr:
g( Lo, xT)
+ bbro, cfT)
(6)
= aarh/ay b, = a1-11 di = of 7,
(7)
where a; = exp (I f(s)ds), of = az a So g(e) ot ds, and
ardd Zheng et al. 2025a), which
defines diffusion bridge 34 = aTT + by + cjF; that interpolates between vo and oT with added Gaussian noise cet- Zhou et al. (2024) show that there exists PF-ODE that shares identical marginal densities {g(xe
(((x)(TTT]) (TT) [TT)eeea, as the forward diffusion bridge SDE in Eq. 5
dr: = f(t)2, g(t)² dt
Va, log ( 2) Vz, log ((TT(()))
(8)
2
'The subscript in Jy, By indicates that the trajectory and its speed belongs to an anchor, which differs from ordinary trajectory x: and its speed The superscript index in y is omitted for notation simplicity.
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Algorithm 3 BridgeDrive Planning (ours)
I: pa(z)
# sample driving scene # predict the optimal anchor
2: JT = +1))(2)))
3: for = .N.... do
# discretize timesteps into T i.y < ................................ < t1 < to = 0 # compute the denoised mean trajectory # compute the score function # compute the derivative dx. /tt simulate the BridgeDrive PF-ODE
4: ip = [x Li, IT,
5: $t. = (at, 2T + bt, R0le, 21.)/ c
6: d, fl4)=1, g(t:)² (8r,/2 Va,, log ((Tr)=))))
7: Tt.-1 = ODESolverStep(2y) 22,, de, t, t))
8: end for
9: return 20
which allows us to translate an anchor 2T to planned trajectory xo given the driving scene 2. To simulate this PF-ODE, we need to approximate the score function Vz, 2, log (( =) for the conditional diffusion bridge model. In the next section, we will introduce our training and planning algorithms for this diffusion bridge policy,
3.3 TRAINING AND PLANNING ALGORIHTM
In our diffusion bridge planner, each diffusion bridge is constructed between a ground-truth trajec- and the nearest anchor 3т == y y it. During training, we fit neural network denoiser za (Zt, t, TT, 2) to predict the denoising mean εote A B[ JT, =] given noisy trajectory It ~ (2 [2e ær) at timestep t. the nearest anchor 2y to Tu. and the conditional information for the driving scene. This denoiser is trained by minimizing the mean squared denoising error:
tory Zo
min E Ep(t)pa(zo-=r (tt.:rrrr))(((.o/o.......
[t))][ (Ze, æT, 2) xo [=]
Our training algorithm is summarized in Algorithm | Notice that our forward and reverse diffusion paths both result in the end point Tn = 2' since og = Go and by = 1, ensuring that the denoiser is trained to reverse the forward diffusion process. On the other hand, in DiffusionDrive (Liao) cct al., (2025) (Algorithm 2). for all a and oN, the noisy anchor yt deviates from x, failing to adhere to the symmetry requirement of diffusion models. Also, the training procedure of BridgeDrive is simulation
which allows us to efficiently train the denoiser without simulating the forward SDE in Eq. (5) the PF-ODE in Eq. (8). In addition, since the ground- truth trajectory 20 is not available for computing the nearest anchor at inference time, we also train a classifier ha y) to predict the nearest anchor y To given with the cross entropy loss.
Similar to standard diffusion models, the trained denoiser ( 1, TT, =) can be used to approxi- mate the conditional score function for our conditional diffusion bridge model (Zheng et al. 2025a):
Vay log ((xe//TT =) a,3r + 1 f, TT, 2) Ft.
(10)
of
Our planning algorithm is summarized in Algorithm 3 and depicted in Fig. 2 Specifically, for a given driving scene z, we first use the classifier h,,),))) to choose anchor y V, which is the starting point æт of the denoising process in our diffusion bridge planner. Then, we iteratively compute the denoised mean trajectory dajt using our denoiser z (2 t, 2), calculate the score function & using Eq. (1O). and simulate the PF-ODE in Eq. (8) with the score š, using a numerical ODE solver. Although image diffusion models use higher-order ODE solvers (Karras et al., [022] Lu et al. 2022)) to accelerate sampling, we find that first-order methods, such as the DDIM sampler (Song al. 202 la), are sufficient for the planning task with minimal number of function evaluations. Fig. visualizes the denoising process of Bridge for an example driving scenario.
3.4
MODEL ARCHITECTURE
Our model consists of three major components: perception module, denoiser, and classifier. Imple- mentation and training details are provided in Appendix C
Perception Module. The perception module extract useful features from lidar, front camera image, and target point for the downstream diffusion planner. We pre-trained perception backbone
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Classifler na (x')))))
Feature Attn
Denoiser
Cross BEV
nehhor trajectory xy
Attn
Cross
FFN
Time Modulation
Anchors (y 1=1
Nanctor
BEV
lidar, cameera image.
MLP
Denoised trajectory Rox
target point fased feelure
Feature Attn
Cross BEV
BridgeDrive PF-ODE Solver
Noisy trajectory xz
Attn
Cross
FFN
Score function 8,
Timestept
Timestepsx T
Figure 2: Diagram for the planning procedure of BridgeDrive in Algorithm The model architec- ture of the neural network denoiser zg (Ze, t, æT, is detailed in the light blue box.
from TransFuser++ (Jaeger et al. 2023) to obtain BEV segmentation, bounding boxes of traffic participants, general traffic information (e.g., stop signs and traffic lights), and fused features from the inputs. The output of the perception module is denoted as and will be used as the conditional guidance information in the denoiser module za (2,,,,
The architecture of the denoiser (1 t, z) is illustrated in the light blue box in Fig. 2 For noisy trajectory zy at timestep and its corresponding anchor 27 = y. we first interact them with BEV via deformable spatial cross-attention modules. Subsequently, cross gross-attention with fused features from lidar, front camera, and target point applied. The resulting feature vectors are further processed by feed-forward networks (FFNs), and their concatenation is modulated by the timestep J. Finally, an MIP network employed to predict the denoised mean trajectory Soj--
Denoiser.
Anchor Classifier. The classifier (2, employs cross BEV attention module and cross feature attention module between 2 and all anchors }, followed by an FFN which outputs the probability that each anchor y² y should be used for trajectory generation. We select the anchor y with the highest probability as the input ær to the denoiser so(#,, XT y, 2). Note that the classifier only needs to run once prior to the iterative denoising process.
EXPERIMENTS
4.1 CLOSED- LOOP EVALUATION ON BENCH2DRIVE
Benchmark. In this paper, we mainly focus on closed-loop evaluation since it simulates dynamic traffic conditions which better reflect policy's real-world planning capability. Bench2Drive (Ji et al. 2024 is widely used closed-loop evaluation benchmark (Jaeger et al. 2023 Jia et al. 2025 Fu et al. 2025: Renz al. 2025). which contains 220 routes for evaluation under the CARLA Leaderboard 2.0 protocol for end-to-end autonomous driving. Each route is around 150 meters length and contains specific driving scenario, which allows for a detailed assessment of au- tonomous driving systems' proficiency in different driving skills.
Dataset. While Bench2Drive provides an official training set, empirical studies (Zimmerlin et al, 2024 Renz al. 2025 Fu et al. 2025) showed that official dataset collected by (Li al. 2024a) leads to suboptimal performance. Therefore, data augmentation and cleansing scheme are applied to enhance the performance. For example, ORION (Fu et al., (025) generated Visual Question An- (VLA) models' capability, such as scene
swering (VQA) to enhance their Liiton-Lnguage-eee description, behavior description, meta-driving decision and reasoning, and recall of essential histor-

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Table 1: Comparison between BridgeDrive and previous baselines on Bench2Drive. Our method shows SOTA performance on both Driving Score (DS) and Success Rate (SR). Notably, by using principled diffusion bridge model, our method achieves significant improvements over previous dif- fusion baselines (including those with prior knowledge from VLA), demonstrating the effectiveness of the diffusion module in the autonomous driving task when following our paradigm as discussed in Section 3.2 A potential avenue to further improve our method is to integrate prior knowledge from VLA, which is left as future work.
Method
Expert
VLA
Diffusion
DS
SR(%)
TCP-traj* Wu
2022
Think2Drive
X
59.90
30.00
UniAD-Base
Hu al.
2023
Think2Drive
x
45.81
16.36
VAD Jiang et al.
2023
Think2Drive
X
X
42.35
15.00
DriveTransforme
Jia et al.
2025
Think2Drive
X
63.46
35.01
ORION Fuu et al.
2025)
Think2Drive
X
77.74
54.62
ORION diffusion
Fu et al.
2025
Trivk
71.97
46.54
DiffusionnDriv Liao
2025
PDM-Lite
77.68
52.72
DrivussonnDriveeoo Liao al.
2025
PDM-Lite
80.79
58.18
SimLingo (Renz et al.
2025)
PDM-Lite
x 85.07
67.27
TransFuser++ Zimmerlin et al.
2024
PDM-Lite
x 84.21
67.27
BridgeDrive (ours)
PDM-Lite
87.99
(4.99(77.72)
ical information. Chitta al. (2023) and Zimmerlin et al. (2024) use PDM-Lite (BeiBwenger 2024
Sima et al. 2025). ) an open source aale-based expert to collect ground-truth trajectories for imitation learning. SimLingo (Renz al. (025) generates additional driving data and applies intricate filtra- tion on training routes of Chitta et al. (2023) and the official CARLA LB 2.0 routes. We use the
datasets proposed by Zimmerlin et (0024). The dataset actively filters for critical change frames and refines expert behavior, thereby focusing on high-quality
recision-making moments while re- ducing its size, which improves training efficiency and strengthens the model's learning in crucial driving scenarios. Preliminary results of BridgeDrive on the LEAD dataset (Nguyen et al., 2026) are reported in Section 4.3 and Appendix where BridgeDrive achieves new SOTA with 89. 89.25% success rate and 96.34 driving score.
We compare against the following baselines. TCP-traj (Wu et al.) 2022) is a monocular camera-based method that jointly learns planning and direct control with situation-based fusion. UniAD (Hu et al., 2023) unified end-to-end framework that integrates full-stack driving tasks through query- based interfaces. VAD (Jiang al. 2023) is an end-to-end vectorized paradigm that models driving scenes with vectorized representations.
Baselines.
Triisfrranformer (Jia et al. 2025) employs task parallelism, sparse representation, and streaming enable efficient cross-task knowledge trans- fer and temporal fusion. ORION (Fu et al.) 2025) integrates a QT-Former for history aggregation, reasoning large language model (LLM), and VAE for planning. Simlingo (Renz al. 2025)
leverages VLA and achieves current SOTA performance on Bench2Drive. TransFuser++ Chitta
al., 2023) (Zimmerlin et al. 2024) (Jaeger et al. 2023) ranks second in the 2024 CARLA chal-
lenge and first on the Bench2Drive test routes. In addition, we adapt DiffusionDrive to 2riv benchmark (denoted as DiffusionDriveltmn) Adaptation details are provided Appendix C.2
Main results. Evaluation results on Bench2Drive benchmark are demonstrated in Table For all models that exceeded previous SOTA result, we performed experiments with three random seeds to ensure reproducibility. The performance of our work significantly exceeds that of all previous work. In particular, our BridgeDrive outperform the SimLingo (Renz et al. (025), the latest SOTA, by +2.92 and +7.72% in driving score and success rate, respectively. Moreover, BridgeDrive exhibits outstanding multi-an aulti-ability capability, especially in Merging (+11.17), and Traffic Sign (+7.02), result- ing in overall improvement by +6.12 than SOTA, as shown in Table 6 in the Appendix. However, BridgeDrive demonstrates suboptimal performance in the Comfortness and Give Way metrics, sug- gesting tendency toward frequent or poorly timed braking. This outcome may imply that model
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fruiee 930
frame 962
frume 987
Other traffic particiaants Ego (eg. syclists)
entry)
Lane line (passable)
Drivable area
LiDAR point cloud
" Waypoints
Figure 3: A consecutive three frames of sample Bench2Drive scene, overtaking maneuver per- formed by BridgeDrivetemp, 'he ego car exhibited deficiencies overtaking maneuver coordina- tion and speed control, which directly led to a collision with the white vehicle. For video demon- stration, please refer to supplementary materials.
Frunle 277
frame 303
freme 324
Other traffic particigants (egg syclists)
entry)
Lane line (passable)
Drivable area
LiDAR point cloud
" WWyyoints
Figure 4: On the same scene in Fig. 3 overtaking maneuver performed by BridgeDrive The ego vehicle adapts its planning to overtake a sequence of parked cars. For video demonstration, please refer supplementary materials.
prioritizes safety considerations, potentially at the expense of passenger comfort. Furthermore, the inference speed of BrivgeDrive suitable for real-time deployment, as detailed in Table
4.2 ABLATION STUDY AND QUALITATIVE ANALYSIS FOR THE CLOSED-LOOP SETTING
The primer focus this paper on the design and study diffusion models for trajectory planning; therefore, prioritize the most vital aspects that could influence the performance, namely 1) what kind of trajectory representation more compatible with diffusion model; 2) how our diffusion bridge policy with anchor guidance differs from other diffusion planners. Further ablation study results on the influence anchors and classifiers are provided Appendix
Effectiveness of the representation of geometric path waypoints. To account for the influence of different representations of the trajectory, namely the temporal speed waypoints vs. geometric path waypoints, we implement these two configurations for each version of diffusion models, denoted as all modules remain iden-
temp and geo, respectively. It should be noted that, for Diffusion tical to those of our method except for the diffusion part ensure a fair comparison. The results compared in Table 2 It can be seen that the representation of geometric path waypoints outperforms their temporal counterpart, with an improvement of +5.46%, +4.09%. +15.09% in the success rate for DiffusionDrive, Full Diffusion, BridgeDrive, respectively. We argue the main reasons for this are as follows. 1) Temporal waypoints encode speed control information the spacing between subsequent waypoints. Such an encoding is ambiguous and difficult to generalize. For example, for overtaking maneuvers with different speeds, geometric waypoints only require a model to learn the similar geometric pattern of driving path plus varying speed scalar. In comparison, the general- ization temporal waypoints require a model to stretch spacing between waypoints to account for different speeds. 2) Geometric waypoints less likely to violate route lane constrain; similar arguments are also provided in InJger et al. (2023). A comparison between kinds of waypoints is provided in Fig. and Fig.
compliant with route topology and is therefore
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frame 94
frowe
frame 244
Ego
traffic participants (e.g, vehicles, eyclisns)
Lame linc (no eniry)
Lane line (pussablee)
Drivable
LIDAR point cloud
?
Waypoints
Figure 5: Full Diffusion model in consecutive four frames a sample Bench2Drive scene, failing adhere the target time window for lane-changing maneuvers, which consequently led collision with the road barrier. For video demonstration, please refer to supplementary materials.
france 74
frame 88
frame 135
frame I80
Ego
Other traffic participants (e.g., vehicles, eyelists)
(no entry)
Lone line (pessable)
Drivable anen
LIDAR point cloud
" Whypoints
Figure 6: BridgeDrive on the same scene in Fig. achieving timely lane changing due anchor guidance and successfully navigated through the road fork. For video demonstration, please refer to supplementary materials.
Advantages of BridgeDrive. As illustrated in Table 2 benefiting from the multi-modality of dif- fusion models, both Full Diffusions and BridgeDrivv outperform DiffusionDrives by large margin. addition, compared with full diffusion, BridgeDrive further leverages anchor information to guide its diffusion process. This is of particular importance when facing ambiguous situations. An example is visualized in Fig. and Fig. 6 In this case, the target point for lane change is given in short distance ahead of the ego vehicle. Due to inherent inertial of the ego car, it is unlikely for full diffusion to change lane. Therefore, the ego car kept traveling unable to make sharp turn the left lane and hit
straight path and missed
the target point; subsequently, the ego the road barrier. In comparison, BridgeDrive, under the strong guidance of the anchor, was able to strictly follow the target point and entered the correct lane at the road fork.
Table 2: Ablation study for the effects of temporal and geometric path waypoints for Diffusion-
Drive, full diffusion, and Brive All methods use identical expert and modules except for the diffusion part. Our BridgeDrive achieves SOTA DS and SR, prioritizing safety over Comfortness.
Configuration
Principled
Anchor
DS
SR(%)
Efficiency
Comfortness
DiffusionDrrvve
77.68
52.72
248.18
24.56
DiffusionDrriv
80.79
58.18
245.34
15.49
Full Diffusion smm
79.75
58.18
246.31
24.42
Full S
83.85
67.27
238.90
21.40
81.97
59.90
243.88
22.61
Triv
87.99 0.67
74.99 1. 1.35
236.49
1. 2.32
20.98
J. 0.74

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Table Performance of BridgeDrive adapted LEAD.
Method
Expert
DS
SR(%)
Efii.
Comfort.
TFv6 (guuyen al.
2026
LEAD
95.2 = 0.3
86.8
N/A
N/A
BridgeDrive (ours)
LEAD
96.34 + 0.55
89.25 0.50
202.92 3.27
23.24 1.06
Table 4: Performance comparison on planning-oriented NAVSIM navtest split.
Model
NC
DAC
TTC
Comf.
EP
PDMS
VADv2-Va192 (Chen et al.
2024)
97.2
89.1
91.6
100
76.0
80.9
Hydra-MDP- V8192- W-EP Li et al.
2024b)
98.3
96.0
94.6
100
78.7
86.5
DiffusionDrriv (reported Liao (025))
98.2
96.2
94.7
100
82.2
88.1
DifrusionDrrive (reproduced with different seed)
98.2
95.9
94.3
100
81.9
87.6
BridgeDrive (ours)
98.2
96.1
94.5
100
82.3
88.0
4.3 GENERALIZATION TO LEAD DATASET
We further evaluate BrivgeDrive on the LEAD dataset (Nguyen al. 2026), which provides fined expert policy designed to mitigate Learner-Expp Expert Asymmetry in CARLA. Adaptation details provided in Appendix E. As shown in Table BridgeDrive trained on LEAD achieves success rate 89. 99% and a driving score 96.34, surpassing the LEAD baseline (TFv6) by +2.45% SR and +1.14 DS, with mean multi-ability score of 82.22 22.22 (Table T2). These results demonstrate that BridgeDrive generalizes well across different training sets.
4.4 OPEN-LOOP EVALUATION ON NAVSIM
We additionally evaluate our method on NAVSIM using the metrics from DiffusionDrive (Liao et al. 2025), summarized by the PDM score (PDMS), weighted composite of no at-fault collisions (NC), drivable area compliance (DAC), time-to-collision (TTC), comfort (Comf.), and ego progress (EP). As shown in Table BridgeDrive achieves competitive performance
NAVSIM. We note that NAVSIM follows an open-loop evaluation protocol with non-reat non-reactive agents, where agent states are reset the ground-truth at each step; result, this setting may underemphasize onn--hoiizon error accumulation and interaction effects.
For this reason, place primary emphasis closed-loop evaluation (i.e., Bench2Drive), which more directly measures planning under feedback. This feedback is shared by a growing body of work advocating closed-loop assessment (Chitta al., 2023) ZZenng al.] 2025b) Caesar et al., 2021 Yang et al. 2025: Jia et al. (2024) and can amplify small errors across rollout, as discussed Section 2.1] BriveeDrive improves over prior methods under this more demanding evaluation protocol as shown in previous sections.
5 CONCLUSIONS AND FUTURE WORK
We presented BriveeDrive, an end-to-end autonomous driving solution based on diffusion bridge policy. Our method provides principled diffusion framework incorporating anchor guidance, out- performing prior work by 7.72% and 2.45% in success rate on Bench2Drive with PDM-Lite and LEAD datasets, respectively. Extensive experiments validated that BridgeDrive yielded significant performance improvements in closed-loop planning tasks.
Limitations and future work. (1) Although the inference speed of BridgeDrive is suitable for real-time deployment, further acceleration can be achieved by distilling our diffusion model into one-step planner without sacrificing the generation quality; see, e.g., Xie et al. (2024). (2) Despite BridgeDrive's extraordinary capacity learn complex planning tasks, it still struggles to handle dutto--distribution scenarios, as illustrated in Appendix [B.3] This limitation may be overcome by incorporating prior knowledge from VLA and post-training with reinforcement learning.
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A
RELATED WORK
End-to-end autonomous driving. Traditional motion planning pipelines often decompose the task into separate stages- pprceetionn prediction, and planning-whnn inevitably introduce latency and information degradation across modules (Sadat al. 2020). To overcome these limitations, recent studies have shifted toward planning-centrie, end-to-end autonomous driving frameworks. End-to- end autonomous driving aims to map raw sensory inputs directly to trajectory predictions or control commands, enabling holistic system optimization that mitigates error propagation across modules (Wu et al., 2022 Zhang et al., 202T). UniAD (Hu et al., 2023) shows the feasibility of end-to- end autonomous driving by unitying multiple perception tasks to henefit planning. Building on boost effi ciency. VADv2 (Chen et al. 2024) proposes probabilistic planning framework that models the distribution over possible actions and samples one for vehicle control. SimLingo (Renz et al. (025) and GPTDriverV2 (Xu et al. 2025) incorporate rision-languae understanding and language- action alignment, aiming to enhance closed-loop driving performance.
this, VAD (Jiang et al., (023) introduces compact vectorized scene representations
Deterministic planners. Some end-to-end autonomous driving planners relies on models such as multilayer perceptrons (MLPs) or variational autoencoders (VAEs). Transfuser (Chitta et al. 2023) and its extension Transfuser++ (Jaeger et al. 2023) exemplify this line of work by fusing multi- modal sensor inputs- suutts--schhaa as camera images and LiDAR point clouds-throus
transformer-bant
encoders and decoding them into trajectory outputs via compact MLP heads. These models highlight the importance effective sensor fusion in improving closed-loop driving performance. ORION (Fu
et al. 2025) adopts VAE-based latent planning architecture, which enables the model to capture mudti-modal trajectory distributions while maintaining computational efficiency. These methods demonstrate how MLP and VAE-style architectures can serve as efficient baselines for end-to-end planning, though they often face limitations in modeling the full multi-modality of human driving behaviors compared generative paradigms such as diffusion flow-based models.
Diffusion-based planners. Diffusion policies provide a generative paradigm which can model the multi-modal nature of human driving behaviors with enhanced guidance control. Diffusion-ES 2024) exhibits zero-shot cestructioo-followiie
ability in planning. Diffusion-Planner 2025b) uses prediction modeling to achieve safe and adaptive planning. GoalFlow Xing et al.
Yang et al.
Zheng al.
2025) leverages flow matching to produce diverse conlicondi trajectories and
further uses a trajectory scorer to efficiently select trajectory using the goal point as a reference.
DiffusionDrive (Liao et al., 2025) points out the issue of mode collapse, wherein the generated trajectories lack diversity, as different random noise inputs tend to converge to similar trajectories during the denoising process, proposing truncated diffusion policy that begins the denoising process from an anchored gaussian distribution instead of a standard Gaussian distribution to avoid mode collapse. TransDiffuser (Jiang et al.) 2025) emphasizes another underlying bottleneck that leads to mode collapse in generated trajectories: The under-utilization of the encoded multi-modal condi- tional information. Therefore, it implements multi-modal representation decorrelation optimization mechanism during the denoising process, which aims to better exploit the multi-modal representa- tion space guide more diverse feasible planning trajectories from the continuous action space.
B ADDITIONAL RESULTS, VISUALIZATION AND LIMITATION
B. COMPARISON WITH EXISTING WORKS
A comprehensive evaluation on Bench2Drive metrics is provided in Tables 5 and 6 Our method shows SOTA performance on both Driving Score (DS) and Success Rate (SR). Moreover, BridgeDrive exhibits outstanding multi-ability capability, especially in Merging (+11.17), and Traf- fic Sign (+7.02), resulting in overall improvement by +6.12 than SOTA. However, BridgeDrive demonstrates suboptimal performance in the Comfortness and Give Way metrics, suggesting ten- dency toward frequent or poorly timed braking. This outcome may imply that our model prioritizes safety considerations, potentially at the expense of passenger comfort. This limitation should be addressed in the future work.
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Table 5: Comprehensive comparison between BridgeDrive and baselines. BridgeDrive prioritizes safety over Comfortness.
Method
Expert
Key technique
DS
SR(%)
Effi.
Comfort.
TCP-traj
Think2Drive
CNN, MLP, GRU
59.90
30.00
76.54
18.08
UniAD-Base
Trivk2Drive
Transformer
45.81
16.36
129.21
43.58
VAD
Think2Drive
Transformer
42.35
15.00
157.94
46.01
DrinfTrrasfrrmer
Think2Drive
Transformer
63.46
35.01
100.64
20.78
ORION
Think2Drive
VLA+VAE
77.74
54.62
131.48
17.38
ORION diffusion
Think2Drive
VLA+Diffusion
71.97
46.54
N/A
N/A
DiffusionDDrive
PDM-Lic
Diffusion
77.68
52.72
248.18
24.56
SinL
PDM-Lite
VIA
85.07
67.27
259.23
33.67
TransFuser++
PDM-Lite
Transformer
84.21
67.27
N/A
N/A
BridgeDrive (ours)
PDM-Lite
Diffusion
87.99 (+2.92)
74.99 (47.72)
236.49
20.98
Table Multi-ability evaluation results on Bench2Drive, BridgeDrive outperforms all baselines in all categories except for Give Way and Overtake.
Ability (%)
Method
Merg.
Overtak.
Emer. Brake
Give Way
Traf. Sign
Mean
TCP-traj*
8.89
24.29
51.67
40.00
46.28
34.22
UniAD-Base
14.10
17.78
21.67
10.00
14.21
15.55
VAD
8.11
24.44
18.64
20.00
19.15
18.07
TranSorrasfrrmmr
17.57
35.00
48.36
40.00
52.10
38.60
ORION
25.00
71.11
78.33
30.00
69.15
54.72
DiffusionDrive temp
50.63
26.67
68.33
50.00
76.32
54.38
SimLingo
54.01
57.04
88.33
53.33
82,45
67.03
TransFuser++
58.75
57.77
83.33
40.00
82.11
64.39
BridgeDrive (ours)
69.92 (111.77
66.67 (4.44)
90.00 (+1.67)
50.00 (3.33)
89.47 (67.0)
73.15 (16.12)
B.2 INFERENCE SPEED
Inference wall-clock time comparison is detailed in Table 7 Note that the full diffusion model is slightly faster than BrivgeDrive since it is approximately half the size of BridgeDrive. This is be- cause full diffusion does not anchors and thus omit all anchor-related tross-attention modules. BridgeDrive achieves reasonable inference speed even without any additional optimization, indicat- ing its suitability for real-time deployment. It should be noted that in both DiffusionDrive and our BrivgeDrive model, the primary computational cost stems from the perception and cross-attention modules. The diffusion process itself is applied only to small trajectory matrix (8x2 1 1xx2), contributing a minor portion of the total cost. Consequently, the overall computation time does not increase proportionally with the number of diffusion steps.
B.3 CASE STUDY OF AN OUT-OF--ISTTRIBUTION FAILURE SCENARIO
Despite the extraordinary modeling capacity BridgeDrive, cannot generalize to out-of- distribution scenarios, which is very common in closed-loop evaluation. For instance, the overtaking maneuver shall be aborted if oncoming vehicles are present in the adjacent lane. However, there are almost such data in the training set. The reason is that training data collected by rule-based expert with privileged information (i.c., the expert has direct access to the ground truth other traf- fie participants' location and dynamics). This expert has long-term planning capability and will only
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Frame
frame
frame 103
Jrame 106
Ego car
Oftirr traffie participants (egg. vchicles, cyclists)
Lane line (so entry)
Lane (passuble)
Drivable
LiDAR point cloud
?
Waypoints
Figure 7: BridgeDrive cannot handle imperfect timing of lane-changing, which resulted from cumu- lative errors. This situation is outside the training data distribution.
perform an overtaking maneuver when there sufficient longitudinal space in adjacent lanes. Such an ideal timing is not always feasible in closed-loop evaluation due to cumulative difference between predicted and ground-truth speed. An example of imperfect timing for lane changing is provided in Fig. 7 This limitation may be overcome by integrating scene understanding prior knowledge from VLA into BridgeDrive or posting training with reinforcement learning, which left for future work.
B.4 THE SUPERIORITY OF GEOMETRIC WAYPOINTS REMAINS UNDEREXPLORED
We briefly summarizes key experimental findings regarding temporal and geometric waypoints in autonomous driving models:
1. A prominent line of research has achieved SOTA performance primarily through the of temporal waypoints (Liao et al. 2025 Zheng et al.] 2025b] Chitta et al.) 2023 Chen &
Krähenbühl] 2022 Wu al. 2022).
2. TransFuser++ (Jaeger al. 2023) identified ambiguities inherent in temporal waypoints. To address this, the authors implemented path predictor (analogous to our geometric way- points) and a speed predictor. The speed predicted via MLP head using classification, which also outputs an associated uncertainty. Their experiments demonstrated that inter- polating between target speeds, weighted by this uncertainty, effectively reduces collision rates.
3. SimLingo (Renz et al. 2025 trained a Vision-Language Model (VLM) to predict both tem- poral and geometric waypoints (termed "temporal speed waypoints" and "geometric path waypoints," respectively). They found that using only temporal waypoints resulted in poor steering performance, whereas incorporating geometric waypoints significantly improved vehicle control. In their framework, the control commands are derived from both repre- sentations: target speed computed from temporal waypoints, while the steering angle is determined by geometric waypoints.
Table 7: Inference wall-clock time comparison. The full diffusion model is approximately half the size of BridgeDrive, as it does not have cross-altention modules for anchors. BridgeDrive achieves reasonable inference speed even without any additional optimization, indicating its suitability for real-time deployment.
Configuration
Inference speed per frame
#diffusion timesteps
DiffusionDrive somp
0.05 sec
DiffusionDrive..*
0.05
Full Diffusion temp
0.05
100
Full Diffusion
0.05 sec
100
BridgeDrive (ours)
0.10 sec
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Brivge (ours)
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Published as a conference paper at ICLR 2026
4. DriveGPT4-V. V2 (Xu et al. 2025) does not directly output waypoints for control. Instead, it predicts a target speed and steering angle directly, using temporal and geometric waypoints (referred as "waypoints" and "route points") solely as supervisory signals during train- ing. Their ablation study concluded that predicting the final control commands (speed and angle) is more effective than predicting intermediate waypoints.
These findings suggest that optimal control performance may not be achieved by relying exclusively on either temporal or geometric waypoints. Instead, superior performance likely arises from the interplay between these two representations and their derived control variables. The field has not yet reached a definitive conclusion on this matter.
A comprehensive understanding of the fundamental roles of temporal and geometric waypoints would require an extensive experimental evaluation of various SOTA algorithms across multiple mainstream benchmarks, which is left for future work. Therefore, in our experiments, we adopt the conventional practice established by TransFuser++ (Jaeger et al. (023), as our model architec- ture most closely resembles theirs. Consequently, the experiments presented in this paper primarily demonstrate the superiority of geometric waypoints within the Bench2Drive benchmark.
C EXPERIMENT DETAILS
C.I DATASETS FILTERING AND AUGMENTATION
Zimmerlin al. (2024) proposed a data filtering method reduce redundancy in training datasets. The method invol ves keeping frames where significant changes occur compared to the previous frame. Specifically, a frame is retained if either of the following conditions met.
The target speed changes by more than m/s.
The angle to any predicted geometric path waypoints changes by more than 0.5%.
From the remaining frames, 14% are randomly selected and kept. This strategy results a 50% reduction in the dataset size.
Additionally, the authors adjust the expert's driving style by modifying behaviors, such as the timing of braking when approaching pedestrians. This adjustment ensures the expert's actions more interpretable and provide clearer learning signals for the model. Furthermore, the paper removes class weights for target speed values, particularly for over-represented classes like braking, avoid biasing the model towards more frequent behaviors. This ensures the model learns from frames critical for driving tasks, rather than focusing on frequent but less important ones.
C.2 ADAPTATION OF DIFFUIONDRIVE TO BENCU12RIVE BENCHMARK
We denote adapted versions of DiffusionDrive DiffusionDrivveemn and DrivesionDrive.tn We explain our adaption from four aspects.
Perception module. The original DiffusionDDivv was built on the backbone of Transfuser (Chitta al., (023), whereas our BridgeDrive based on Transfuser++ (Jaeger et al.] 2023). To ensure competitive baseline for fair comparison, for the adaptation of DiffusionDrive, we use the per- ception module from Transfuser++, which proven to achieve SOTA on Bench2Drive benchmark (Zimmerlin et al. 2024
Denoiser.
We keep the model architecture of the denoiser identical to its original design as it is unique to each model under comparison.
Classifier. The classifier in DiffusionDrive consists of cross-attention modules to process the per- ception features. We keep its architecture in line with the perception module.
Output. The output trajectory representation of DiffusionDrrve is temporal waypoints, which is kept the same DiffusionDrive. The analysis of the impact of output representation is provided in Section [4.2, where Drff geometric waypoints counterpart DiffusionDrivee implemented and evaluated.
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Published as a conference paper at ICLR 2026
Table 8: Comparison among the major modules in the model architectures of DiffusionDrive, DiffusionDriveltnp DiffusionDrivefR and BridgeDrive.
Module
DiffusionDrive
DiffusionDrive
DifvusionDrive
BridgeDrive
Perception
Transfuser
Transfuser++
Transfuser++
Transfuser++
Classifier
Transfuser
Transfuser++
Transfuser++
BridgeDrive
Denoiser
DiffusionDrive
DiffusionDrive
DiffusionDrive
BridgeDrive
Output
Temporal waypoints
Temporal waypoints
Geometric waypoints
Geometric waypoints
An overview comparing the architectures of the major modules across DiffusionDrive, DiffusionDrivekhnp and BridgeDrive is provided in Table 8 The rest of the implementation and training details of DiffusionDriveeemp are kept the same as BridgeDrive for fair comparison.
C.3 IMPLEMENTATION AND TRAINING DETAILS
As mentioned Section 3.4 our model consists of three modules. For perception module, we keep the original design as described in (Jaeger et al. 2023) and (Zimmerlin et al. 2024). Once the perception module is pre-trained it is frozen during the training phase of the denoiser and classifier modules. The joint loss for the denoiser and classifier is defined as:
Loverall Wdiffusiin Lediffusion + Welnssifiaation Lelassifiiattionn
(11)
where Tdiiiussion is as defined in Eq. 9 and Icllssificatioon is the cross-entropy loss. However, in order to ensure fair comparison with Liao al. (202)), we replace the MSE loss in Ldiffussoon with L. loss. set to 1. We optimize them using A (Loshchilov) & Hutter 2017 7a) with a cosine annealing learning schedule (Loshchilov & Hutter 2017b). The learning rate is set Ir, = 3 x 1 To 10, Tmult 2. We vary the number Nanchor of anchors in BridgeDrive and find that Nanchor = 60 is optimal. Our models are trained for 10 epochs on a single NVIDIA H20 GPU, which takes around 10 hours.
By default, both Waiffusion and Welassification
For diffusion schedule, we employ the variance preserving (VP) schedule from Karras et al. 2022
Specifically, we first define s(t) = 1/v eat gfttt /24-9men and (tt) v VeRat=/a 224 8min - We then set the diffusion coefficients in the forward diffusion bridge SDE Eq. 5 to f(t s(t)/s(t) and
g(f) = (2)())) We choose Ba = 2.0 and Bmin = 0.1 following Zheng et al. (2025a).
D ADDITIONAL ABLATION STUDY
D.I INFLUENCE OF ANCHOOR CLASSIFICATION ACCURACY
To assess the impact of anchor selection, we generated trajectories using sub-optimal anchors (i.e.. the 2nd, 3rd, and 4th most likely from the classifier). The result is presented in Table Our bridge diffusion model exhibited significant resilience, achieving > 60% success rate with the 2nd and 3rd anchors. However, both success rate and driving score decreased as lower-probability anchors were chosen, which demonstrate the importance anchor classification accuracy.
D.2 INFLUENCE OF DIFFUSION BRIDGE MODULE AND ANCHOR PRIOR
We perform ablation study to quantify the contribution of diffusion bridge module and anchor, re- spectively.
To isolate the contribution of the diffusion blocks, we construct a BridgeDrive model with only anchor. As shown in Table 1O without the prior information of the anchor, the BridgeDrive model achieves a performance comparable to that of the full diffusion model in Table 2
To isolate the contribution of the diffusion blocks, conducted ablation where used only the anchor selector (without any diffusion refinement) on the Bench2Drive benchmark. As shown in Table 10 the anchor-only model fails achieve competent performance, with very large
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Published as a conference paper at ICLR 2026
Table 9: Influence anchor classification accuracy on the performance of BridgeDrive.
Anchor-selected
Best
2nd best
3rd best
4th best
Success rate (%)
74.99
69.09
61.36
$7.72
Driving score
87.99
85.31
80.76
77.53
Table 10: Influence of the number of anchors on the performance of BridgeDrive and anchor-based classification and regression planning models.
Number of anchors
k=1
k=
k=40
k=60
k=80
k=200
k==00
k= 1000
Success rate (%)
Anchor classification
2.72
8.18
16.81
19.54
25.92
36.81
36,36
Anchor-based regression
68.18 18
72.72
70.91
70.91
BridgeDrive (ours)
67.27
72.27
73.18
74.99
72.72
Driving score
Anchor classification
27.71
35.78
45.43
49.02
57.89
63.12
62.3
Anchor-based regression
86.73
87.09
86.91
86.77
BridgeDrive (ours)
84.88
87.02
87.24
87.99
87.27
number of anchors. This provides compelling evidence that diffusion blocks are essential for gener- ating high-quality trajectories and are not merely minor enhancement.
In addition, we constructed regression model by removing the time-embedding component from our denoiser model while keeping the rest of the architecture unchanged. Table 10 shows that anchor regression model performs consistently worse than our BridgeDrive model. This supports our claim that the iterative, probabilistic refinement provided by the diffusion bridge process in BridgeDrive is essential for achieving higher performance.
D.3 INFLUENCE THE NUMBER ANCHIORS
The impact of the number of anchors, which directly influences anchor diversity, was evaluated through an ablation study. The results in Table [0 indicate that BridgeDrive's 'ss success rate initially rises with the number of anchors, peaking at 60 and affirming the positive role of diversity. A subse- quent decline in performance suggests that a larger anchor set compromises classification accuracy. Consequently, the model's optimal performance achieved at an equilibrium between anchor diver- sity and classification precision.
E PRELIMINARY RESULTS ON LEAD DATASET
LEAD (Nguyen et al. 2026), the latest advancement in end-to-end autonomous driving, thoroughly investigates the generalization gap between expert and student policies, identifies and analyzes key sources of misalignment that impede effective imitation in CARLA, and uses these insights to pro- duce a novel expert policy and corresponding dataset explicitly designed to mitigate Learner-Expert Asymmetry in driving.
This section presents a preliminary experiment evaluating BridgeDrive on the LEAD datasets. The latest progress will be made available at httpss//giihub..om//hie www.///sullltteee//eeeeeeeeeoeee ethz/BridgeDrivee The configura- tion is detailed follows and differs slightly from previous experiments:
Speed is removed from the anchor clustering process; the resulting anchors encode only steering information. A total of 60 anchors are used in this adaptation.
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Published as a conference paper at ICLR 2026
Table 11: Comparison between BridgeDrive and LEAD.
Method
Expert
DS
SR(%)
Effi.
Comfort.
TFv6 Nguyen et al.
2026
LEAD
95.2
86.8 +: 0.7
N/A
N/A
BridgeDrive (ours)
PDM-Lite
87.99 0.67
74.99
1.35
236.49
2.32
20.98 + 0.74
BridgeDrive (ours)
LEAD
96.34 0.55
89.25
0.50
202.92 + 3.27
23.24 +: 1.06
Table 12: Multi-ability evaluation results of BridgeDrive on PDM-Lite and LEAD datasets.
Ability (%)
Method
Merg.
Overtak.
Emer. Brake
Give Way
Traf. Sign
Mean
BridgeDrive (PDM)Lite)
69.92
66.67
90.00
50.00
89.47
73.15
BridgeDrive (LEAD)
76.25
95.56
96.67
50.00
92.63
82.22
2. During training, the LEAD perception module is reused and kept frozen, while the denoiser and classifier are trained for 30 epochs.
3. At inference time, BridgeDrive outputs only geometric waypoints for steering maneuvers, with speed controlled by the LEAD outputs.
BridgeDrive achieves performance comparable to LEAD. Notably, its success rate and driving score are 2.45% and 1.14 higher than that of LEADD. The evaluation indicates that BridgeDrivee generalizes well across different training sets. Further improvements are expected through a more thorough inves- tigation of anchor quantity, diffusion parameters, learning rate, training duration, and the speed control mechanism.
Key results presented in Table I (also shown in the main text) and Table 12
F DECLARATION
We used large language models (LLMs) to polish writing.
20


Reviews:
[{"review_id": "NXa87KfSU9", "reviewer": "Reviewer_nA3M", "summary": "BridgeDrive introduces a diffusion bridge policy for closed-loop trajectory planning in autonomous driving. Unlike previous diffusion-based planners (e.g., DiffusionDrive) that are mainly tested in open-loop settings, BridgeDrive applies a symmetric diffusion bridge framework connecting expert priors and ground-truth trajectories. This design resolves the theoretical inconsistency of prior truncated diffusion models and enables more stable, consistent, and safety-oriented planning. Experiments on the Bench2Drive benchmark demonstrate state-of-the-art results, surpassing SimLingo by +1.8 in Driving Score and +5% in Success Rate, especially in challenging tasks like merging, overtaking, and traffic sign compliance.", "questions": "1. Could the author provide detail explain to why \"DiffusionDrive that trying to  leverage typical human expert driving behaviors introduces a theoretical inconsistency: its denoising process does not match the forward diffusion process that it is trained on, which diverges from the core principle of diffusion models and can lead to unpredictable behavior and compromised performance.\" in line 36-43.\n2. About Fig1. Intuitively, the startpoint of the denoise process should be noisy, but the waypoints in the leftmost subfigure seem very smooth. So these waypoints are not noise sampled from Gaussian but clustered resulst from expert drivers' behavoir?\n3. How do you get the expert prior? Using the data of human drivers' behaviour data on open-loop benchmarks, like nuScenes? Or the \"robot expert\" trained via reinforcement learning on Bench2Drive?\n4. About section 3.2 \"DiffusionDrive with Truncated Diffusion\". In this section, the author mentioned the asymmetry between the forward \"add noise process\" and the inverse \"denoise process\" of DiffusionDrive, whose starting point of forward process is expert prior (anchor) while the endpoint of inverse process is ground truth trajectories. I understand this. But, what does the \"truncated\" mean? In your paper (line 129-131), it seems that you want to express: timestep t is a pivot, before t, we add some noise, and after t we start denoising, which is really confusing.\n5. You method name is \"BridgeDrive\", where can show \"Bridge\"? In line 191, you mentioned that you will learn a bridge model $p_\\theta(x_t | x_T, z)$, so, you actually bridge the startpoint (ground truth traj) and endpoint (expert prior) at the diffusion process? And the endpoint is not pure gaussion noise traj? And in inference, you directly denoise from expert priors? That sounds plausible.\n6. Finally, I think most of the ideas in this paper are based on the weaknesses of DiffusionDrive, so the comparison with DiffusionDrive is important. The problem is, DiffusionDrive only provides its code on NAVSIM and nuScese instead of Bench2Drive. So, to compare with DiffusionDrive, the author can:\n  1. Implement your idea based on DiffusionDrive on NAVSIM and nuScenes, then test on the same testset.\n  2. Reimplement DiffusionDrive to Bench2Drive, and then test directly, as you have implemented your methods on Bench2Drive.\n  The author chooses the second method and although they provide detailed implementation details of how to reimplement DiffusionDrive to Bench2Drive, the fact is reimplement a method from one benchmark to another is challengeing, we cannot guarantee that during reimplementation, there maybe some errors lead to performance drop. So, I recommend the author can adopt the first compare method, in that way, DiffusionDrive have offical code and you can easily implement your method on it and avoid cross benchmark implement. And the result will be more convincing.", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. Recent works utilize diffusion models to enhance autonomous driving planning tasks seems just experiment on open-loop benchmarks (DiffusionPlanner and DiffusionDrive), while this work extend their experiment to close-loop benchmark, which is more chllenging and I think this is a good contribution to the community of autonomous driving.\n2. The model BridgeDrive gets SOTA results on close-loop benchmark Bench2Drive.\n3. The authors honestly provide the \"Limitations and future work\" section.", "weaknesses": "Theoretical clarity – Some diffusion concepts (e.g., “truncated diffusion,” the exact bridge formulation) are insufficiently explained for general readers.\n\nFairness of comparison – DiffusionDrive was reimplemented for Bench2Drive, which might affect comparison reliability.\n\nComfort and smoothness – Prioritizes safety over comfort, leading to frequent braking behavior.\n\nDependency on LiDAR – Relies on LiDAR input, limiting adaptability to camera-only settings.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "MikTkDg2tm", "reviewer": "Reviewer_nttY", "summary": "The authors present BridgeDrive, which proposes an anchored guided diffusion formulation for closed-loop trajectory planning. BridgeDrive demonstrates SOTA performance on the Bench2Drive benchmark and provide useful insights on parameterization of diffusion models. The only concern is whether the experiments setup is fair for DiffusionDrive( scoring-based v.s predicted anchors), and whether the performance will be highly correlated with how well the anchor prediction is.", "questions": "- What if the classified anchors are wrong for BridgeDrive, and how does this affect the perfromance? Does the diffusion models assumes that it always starts from the right anchor?\n- Can BridgeDrive, for example, hanlde multiple anchors and then select the best result based on scoring, since this is more practical and may potentailly improve its out-of-distribution capabilities.\n- How does the performance degrade by decreasing the diffusion timesteps for Bridgedrive? And why is Diffusiondrive’s sampling speed not ~10x faster than BridgeDrive since diffusion steps are 10x smaller?\n- Table 1 miss diffusiondriveo^geo", "limitations": "", "rating": 6, "confidence": 5, "soundness": 2, "presentation": 3, "contribution": 3, "strengths": "- Strong empirical SOTA performance on the Bench2Drive benchmark\n- Provide good insights on how to parameterize diffusion models output: geomery points verus waypoints", "weaknesses": "### \n\n- The main comparison of this work is against DiffusionDrive. The primary comparison is to DiffusionDrive, but the two methods differ significantly: DiffusionDrive scores across *all* anchors and selects the best, while BridgeDrive first classifies one anchor then denoises around it. Please add this additional results for the scoring-based formulation in Table 1 and Table 2\n- Why do we care about whether the forward and reverse are symmetric, and how does that affect the performance?  Following the previous points, DiffusionDrive provides a good way for fast inference speed (2 denoising steps compared to 20 steps).\n- Couldn’t open the supp videos", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "kFjbaUXsWc", "reviewer": "Reviewer_RMqE", "summary": "This paper introduces BridgeDrive, which adapts the Denoising Diffusion Bridge Model to generate geometric path waypoints from an anchor distribution, achieving state-of-the-art performance on the widely used Bench2Drive benchmark.", "questions": "1. Why is the Think2Drive expert used as a baseline when diffusion-based methods appear to perform worse than other approaches? Using the same expert for all methods facilitates a fair comparison.\n\n2. Is Bridge Diffusion more challenging to train than standard diffusion models, and does it still support both classifier-free guidance and classifier guidance?", "limitations": "", "rating": 4, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "1. The paper point out that the problem of DiffusionDrive is that it is not a theoretical diffusion process. This issue has misled the community. The authors identified this and provided a solution.\n2. Using Bridge Diffusion to solve this makes sense.", "weaknesses": "1. Given the authors' claim that geometric waypoints outperform temporal waypoints, it is better to validate whether this indicates a bias in the Bench2Drive benchmark. Therefore, verification across more benchmarks is highly recommended.\n\n2. The architecture proposed in Figure 2 lacks ablation studies on its structural design.\n\n3. The use of anchors greatly simplifies the true trajectory distribution, raising the question of whether using diffusion is truly necessary. A compelling comparison with SOTA anchor-based methods is needed to justify the adoption of bridge diffusion.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "BIDgjeeJt9", "reviewer": "Reviewer_Awhw", "summary": "This paper proposes BridgeDrive, a planning method based on diffusion bridges that addresses the theoretical inconsistency in prior anchor-based diffusion policies such as DiffusionDrive. By enforcing a symmetric and theoretically consistent forward–reverse process, BridgeDrive improves closed-loop performance. The paper further investigates temporal speed waypoints vs. geometric path waypoints representations and demonstrates that the geometric representation leads to less likely to violate route lane constrain. Experiments on Bench2Drive show clear improvements over prior state of the art.", "questions": "1. Could you provide quantitative or qualitative results confirming that both scene features and anchors significantly contribute to the trajectory generation (for example, attribution studies or case analyses)?\n\n2. How do the number, diversity, and classification accuracy of anchors impact planning robustness? Any sensitivity or failure-case analysis?\n\n3. Are there any experiments on large-scale datasets such as nuPlan or NavSim to support broader applicability claims?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. By incorporating bridge diffusion, the paper builds an explicit and theoretically complete formulation that connects anchor information with trajectory generation in anchor-based diffusion policies. This directly addresses the theoretical shortcomings arising when truncated diffusion processes ignore boundary consistency and rely on heuristically truncating the diffusion process.\n2. The comparison between temporal and geometric trajectory parameterizations provides valuable insight for practical control and shows the superiority of geometric spacing.\n3. The method achieves notable improvements on Bench2Drive, demonstrating strong closed-loop performance benefits.\n4. Real-time inference and robust multi-modal trajectory generation contribute to the practicality of the approach.", "weaknesses": "1. The training and evaluation are limited to Bench2Drive simulation. Validation on larger-scale real human driving datasets, e.g. NavSim,  would be crucial to support claims of real-world generalization.\n\n2. The influence of scene context versus anchor selection on the final trajectory is insufficiently analyzed. More evidence is needed to show that the trajectory is shaped jointly by scene understanding and anchors, instead of the anchor having disproportionate dominance.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)