Diffusion-Based Planning for Autonomous Driving with Flexible Guidance
Authors: Yinan Zheng
Ruiming Liang
Kexin ZHENG
Jinliang Zheng
Liyuan Mao
Jianxiong Li
Weihao Gu
Rui Ai
Shengbo Eben Li
Xianyuan Zhan
Jingjing Liu
Venue: ICLR 2025 Oral (2025)
Keywords: diffusion planning
autonomous driving
Primary area: applications to robotics, autonomy, planning

Abstract: Achieving human-like driving behaviors in complex open-world environments is a critical challenge in autonomous driving. Contemporary learning-based planning approaches such as imitation learning methods often struggle to balance competing objectives and lack of safety assurance,due to limited adaptability and inadequacy in learning complex multi-modal behaviors commonly exhibited in human planning, not to mention their strong reliance on the fallback strategy with predefined rules. We propose a novel transformer-based Diffusion Planner for closed-loop planning, which can effectively model multi-modal driving behavior and ensure trajectory quality without any rule-based refinement. Our model supports joint modeling of both prediction and planning tasks under the same architecture, enabling cooperative behaviors between vehicles. Moreover, by learning the gradient of the trajectory score function and employing a flexible classifier guidance mechanism, Diffusion Planner effectively achieves safe and adaptable planning behaviors. Evaluations on the large-scale real-world autonomous planning benchmark nuPlan and our newly collected 200-hour delivery-vehicle driving dataset demonstrate that Diffusion Planner achieves state-of-the-art closed-loop performance with robust transferability in diverse driving styles.

Published as a conference paper at ICLR 2025
DIFFUSION-BASED PLANNING FOR AUTONOMOUS DRIVING WITH FLEXIBLE GUIDANCE
Yinan Zheng',, RRiiiing Liang2+1 Kexin Zheng?+1 Jinliang Zheng', Liyuan Mao"!
Jianxiong Li", Weihao Gu Rui Ai Shengbo Eben Li Xianyuan Zhan 1,6 Jingjing Liu Tsinghua University IIstitutee of Automation, Chinese Academy of Sciences
3 The Chinese University of Hong Kong Shanghai Jiao Tong University
5 HAOMO.AI Shanghai Artificial Intelligence Laboratory
zhennyn223mmm 1s. ts inghua edu
en, zhanxianyuan@airz wwt.r.siinniii nnguua edd. cn
ABSTRACT
Achieving human-like driving behaviors complex open-world environments a critical challenge in autonomous driving. Contemporary learning-based planning approaches such as imitation learning methods often struggle to balance com- peting objectives and lack of safety assurance, due to limited adaptability and inadequacy in learning complex multi-modal behaviors commonly exhibited human planning, predefined rules. We propose a novel transformer- closed-loop planning, which can effectively model multi-modal driving behavior and ensure trajectory quality without any rule-based refinement. Our model sup- ports joint modeling of both prediction and planning tasks under the same archi- tecture, enabling cooperative behaviors between vehicles. Moreover, by learning the gradient of the trajectory score function and employing flexible classifier guidance mechanism, Diffusion Planner effectively achieves safe and adaptable planning behaviors. Evaluations on the large-scale real-world autonomous plan- ning benchmark nuPlan and our newly collected 200-hour delivery- vehicle driving dataset demonstrate that Diffusion Planner achieves state-of-the-art closed-loop performance with robust transferability in diverse driving styles. Project website: https: /tte:s///heengge iarteenggiinnaaann nan-ainnn air, .eiiirggtthii github. wtt.ii/iiiiii wiiffusiion inn--Pon-PP lanner/.
mention their strong reliance the fallback strategy with Diffusion Planner for
INTRODUCTION
Autonomous driving comnertoone technology, is poised to usher transportation into a safer and more efficient era of mobility (Tampuu et al., 2020). The key challenge is achieving human-like driving behaviors in complex open-world environment, while ensuring safety, efficiency, and com- fort (Muhammad et al., 2020). Rule- based planning methods have demonstrated initial success in industrial applications (Fan al., 2018), by defining driving behaviors and establishing boundaries derived from human knowledge. However, their reliance on predefined rules limits adaptability to new traffic situations (Hawke al., 2020), and modifying rules demands extensive engineering ef- fort. In contrast, Icarning-based planning methods acquire driving skills by cloning human driving behaviors from collected datasets (Caesar al., 2021), a process made simpler through straight- forward imitation learning losses. Additionally, the capabilities of these models can potentially be enhanced by scaling up training resources (Chen et al., 2023).
Though promising, current learning-based planning methods still face several limitations. Firstly, human drivers often exhibit multi-modal behaviors planning Ccenarios(Nayaken et al., 2023). Existing methods that rely on behavior cloning lack guarantee of fitting such complex data distri- butions, even when utilizing large transformer- model architecture or sampling multiple tra- jectories (Cheng et al., 2023). Secondly, when encountering out-of-distributtin (OOD) scenarios, directly using model output may result in low-quality planning outcomes, forcing many methods to
"quual contribution.
"Corresoondigg authors.
Work done during internships at Institute for Al Industry Research (AIR), Tsinghua University.

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fall back rule-based approaches for trajectory refinement optimization filtering (Vitelli al., 2022; Huang et al., 2023), inevitably facing the same inherent limitations associated with rule-based methods. Thirdly, imitation learning alone is inadequate to capture the vast diversity of driving behaviors required for autonomous driving. For example, penalizing unsafe planning via auxiliary loss, employed in existing methods (Bansal et al., 2018; Cheng al., 2024), often results in multi- objective conflicts and poor safety performance due to the lack of learning signals that can teach the agent to recover from mistakes (Zheng al., 2024; Chen et al., 2021). Additionally, well-trained models may be difficult to adapt behaviors to meet specific needs.
In this study, we discover that diffusion model (Ho et al., 2020) possesses huge potential address the aforementioned issues. Its ability to model complex data distributions (Chi et al., 2023) allows for effective capturing of multi-modal human driving behavior. Additionally, the high-quality gen- eration capability of the diffusion model also provides opportunities for improving the output trajec- tory quality through appropriate structural design, removing the reliance on rule-based refinement. The best part of diffusion lies in its flexible guidance mechanism (Dhariwal & Nichol, 2021), which allows adaptation to various planning behavioral needs without additional training. Inspired by these observations, we introduce novel learning- -ased approach, Diffusion Planner, which pioneers the use of diffusion models (Ho et al., 2020) for enhancing closed-loop planning performance without any rule-based refinement, Diffusion Planner realized by learning the gradient of vehicles' trajec- tory score function (Song & Ermon, 2019) to model the multi-modal data distribution, and further enables personalized planning behavior adaptation through a classifier guidance mechanism. Specif- ically, we propose new network architecture built upon the diffusion transformer (Peebles & Xie, 2023). The diffusion loss is employed to jointly train both prediction and planning tasks within the same architecture, enabling cooperative behaviors between vehicles without the need for additional loss functions. Moreover, the versatility of classifier guidance is further demonstrated by its ability to modify the planning behavior of the trained model, such as enhancing safety and comfort, or controlling the vehicle's speed. The differentiable classifier score can computed parallel and flexible for combination, without requiring additional training. Evaluation results the large-scale real-world autonomous planning benchmark nuPlan (Cacsar et al., 2021) demonstrate that Diffusion Planner achieves state-of-the-arr closed-loop performance among learning- based baselines, com- parable to or even surpassing rule-based methods, directly using the model's output without any additional post-processing. By appending existing post-processing module to the model, we fur- ther achieved state-of-the-ar performance among all baselines. Additionally, we collected 200 hours of long-term delivery-vehicle driving data in various city-driving scenarios that further validate the transferability and robustmess of the model in diverse driving styles.
In summary, our contributions are:
To the best of our knowledge, we are the first to fully hamness the power of diffusion models with a specifically designed architecture for performafree motion planning, without overly reliant on rule-based refinement.
We achieve state-of-the-e performance on the real-world nuPlan dataset, generating more robust and smoother trajectories compared to the baselines.
We demonstrate that our model can achieve personalized driving behavior at runtime by utilizing a flexible guidance mechanism, which is a desirable feature for real- world applications.
We have collected and evaluated a new 200-hour delivery-vehicle dataset, which is compatible with the nuPlan framework, and we will open-source it.
2 RELATED WORK
Rule-based Planner. Rule-based methods rely on predefined rules to dictate the driving behav- ior of autonomous vehicles, offering a highly controllable and interpretable decision-making pro- cess (Treiber et al., 2000a; Fan et al., 2018; 8; Dauner et al., 2023a). While they have been widely validated in real-world scenarios (Leonard et al., 2008; Urmson et al., 2008), these frameworks are limited in their ability to handle novel complex situations that fall beyond the predefined rules.
Learning- Planner. Learning-base planning focuses leveraging methods such behav- ior cloning in imitation learning to directly model human driving behaviors, which has emerged as popular solution in autonomous driving, particularly in recent end-to-end training pipelines (Hu
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et al., 2023; Tampuu et al., 2020; Chen et al., 2023). Behavior cloning method was initially imple- mented using CNN (Bojarski et al., 2016; Kendall et al., 2019; Hawke et al., 2020) or RNN (Bansal et al., 2018) networks and has since been extended to Transformer due to its strong performance and efficiency in fitting complex data distributions (Scheel et al., 2021; Chitta et al., 2022). However, these methods lack theoretical guarantees for modeling multi-modal driving behavior, which can lead to serious error accumulation in closed-loop planning. As a result, most existing approaches still heavily rely on rules refine (Vitelli et al., 2022; Huang et al., 2023) or select (Cheng et al., 2024) the generated trajectories, which in some sense, has failed their initial purpose of using learn- ing to replace pre-defined rules. While learning-based methods could offer more human- like driving behavior, their uncontrollable outputs lack safety guarantees and are hard to adjust based on user needs. Existing methods add extra training losses (Bansal et al., 2018; Cheng et al., 2024), but struggle to strike a balance among competing learning objectives. Additionally, these methods also lack flexibility, making post-training behavior adjustments difficult. In practice, it is desirable for trained planning model to achieve flexible alignment to various safety and personalized driving preferences during inference, which is still lacking in the current literature. In this work, we de- velop a novel diffusion planner tackle the above limitations, which enables the generation of high quality planning trajectories without the need for rule-based refinement, and flexible post-training adaptation to various driving styles through the diffusion guidance mechanism.
Diffusion-based Methods Used in Related Domain. Diffusion models have been recently explored in decision-making fields (Janner al., 2022; Chi et al., 2023; Liu et al., 2025), however, their use in autonomous planning has not yet been fully explored. Some existing works employ diffusion models for motion prediction (Jiang et al., 2023) and traffic simulation (Zhong et al., 2023b;a), but their focus is on open-loop performance or diversity in simulation rather than quality or drivability, as the outputs are not directly used for control. There are also studies targeting planning (Hu al., 2024; Yang et al., 2024; Sun et al., 2023), but these approaches only apply diffusion loss to existing frameworks or stack parameters without specific design considerations, making them heavily reliant on post- processing for reasonable performance. In this paper. demonstrate that with appropriate structural design, the potential of diffusion models can be fully harnessed to enhance closed-loop planning performance in autonomous driving.
PRELIMINARIES
3.1
AUTONOMOUS DRIVING AND CLOSED-LOOP PLANNING
The primary objective of autonomous driving is allow vehicles to navigate complex environments with minimal human intervention, where a critical challenge is closed-loop planning (Caesar et al., 2021). Unlike open-loop planning (Caesar al., 2019) or motion prediction (Ngiam et al., 2021; Zhou et al., 2023), which only involves decision making that adapts to static conditions, closed-loop planning requires a seamless integration of real-time perception, prediction, and control. Vehicles must continuously assess their surroundings, predict the behavior of other neighboring vehicles, and implement precise maneuvers. The dynamic nature of world driving scenarios, combined with uncertainty in sensor data and environmental factors, makes closed-loop planning a formidable task.
3.2 DIFFUSION MODEL AND GUIDANCE SCHEMES
Diffusion Model. Diffusion Probabilistic Models (Sohl-Dickstein et al., 2015; Ho al., 2020) are class of generative models that generate outputs by reversing a Markov chain process known as the forward diffusion process. The transition distribution of the forward process satisfies:
(1) which gradually adds Gaussian noise to generate a series of noised data from (0) to (x()) with & E [0. o > 0 is a variance term that controls the introduced noise and o₂ > 0 is typically defined as a, -I- ensuring (t()) + (0, I). as t + 1. The reversed denoising process of Eq. (1) can be equivalently expressed as a diffusion ODE (Song et al., 2021):
Goo (z (0) = (z a, (0) ,o()), 1 E 1].
(Diffusion ODE) (t) = (())a(()) 1
3 (1)V an log ae (zx dt,
(2)
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Scenario Inputs
Encoder
Decoder
MLP-Mixer
Neighbors
Self- Attn
MLP-Mixer
Transformer
Lanes
MLP
Scene
Navigation
Oity
ML P-Miser
Navigation
Condittion
Bif Timesep.
Scale Shift
Attentton
Mull--Hed
Intentare
Mult-HHad
Ui
Shiff
Final Layer
Scanario Condition
Denoising Step
Figure I: Model architecture of Diffusion Planner.
where ftt d lng g dou 2 2ollgg of are determined by the fixed noise schedules arz, di. and at the marginal distribution of (+)), Diffusion model utilizes neural network sy (xx1), t) to fit the probability score Va log qe (x))). By learning the score function, diffusion models enjoy the strong expressiveness of modeling arbitrary complex distributions (Chi et al., 2023), making it highly versatile and adaptable for challenging tasks such as autonomous driving.
di
Classifier Guidance. Classifier guidance (Dhariwal & Nichol, 2021) is technique used to generate preferred data by guiding the sampling process with a classifier E (x)(), t). The gradient of the classifier score used modify the original diffusion score:
B (x (1) t) = (z
Veet Ex(x(")
(3)
In autonomous driving, this approach offers greater flexibility compared to rule-based refinement because directly improves the model's 's inherent ability, rather than overly relying on sub-optimal post-processing that requires significant human effort and targeted data collection.
METHODOLOGY
In this section, we redefine the planning task as a future trajectory generation task, which jointly generates the ego vehicle's planning and the prediction of neighboring vehicles. We then introduce the Diffusion Planner, a novel approach that leverages the expressive and flexible diffusion model for enhanced autonomous planning. Lastly, we demonstrate how the guidance mechanism diffusion models can be utilized to align planning behavior with safe or human -neferreeeereed driving styles.
4.1 TASK REDEFINITION
Autonomous driving requires considering the close interaction between the ego and neighboring ve- hicles, resulting in cooperative relationship between planning and motion prediction tasks (Ngiam et al., al,,, 2021). Supervising the future trajectories neighboring vehicles has been shown be helpful to enhance the ability of closed-loop planning models to handle complex interaction scenarios (Hu al., 2023). For real-world deployment, motion prediction can also enhance safety by providing more controllable measures, facilitating the implementation of the system (Fan al., 2018). Con- sequently, the trajectories of neighboring vehicles have become crucial privileged information for model training. However, the common approaches that 2023) or additional loss design (Cheng et al., 2023; Huang et al., 2023) to capture privileged infor- mation limit their modeling power during training and also lead more complex framework.
dedicated sub module (Huang al.,
In this work, we address this issue by collectively considering the status of key participants in the driving scenario and jointly modeling the motion prediction and closed-loop planning tasks

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future trajectory generation task. Specifically, given conditions C, which include current vehicle states, historical data, lane information, and navigation information, our goal is to generate future trajectories for all key participants simultaneously. enabling the modeling of cooperative behaviors among them. However, this joint modeling of complex distributions is challenging to solve with a simple behavior cloning approach. Benefiting from the strong expressive power of diffusion models, we adopt diffusion model for this task and formulate the larget as:
(U)
ego neighbor,
ego
F
T ego
I neighborr
neighbor
x neighbor,
x0()
(4)
(0) neigghoorg
neighhorr
neighhor M
3 nnighbor M
where use superscripts with parentheses represent the timeline of diffusion denoising, and regular superscripts to indicate the timeline of the future trajectory, which contains T time steps. For each state z, we only consider the coordinates and the sine and cosine of the heading angle, which are sufficient for the downstream LQR controller. We select the nearest M neighboring vehicles to predict their possible future trajectories. By parameterizing our Diffusion Planner with 0, the training target can be expressed as:
Ly a~0(0, I), un (a (a(t)))))))))
[I]00(( ,,,)),,,)))))))))) -
-.0))x(0111 13
(5)
where the goal is to recover the data distribution from noisy data (Ramesh et al., 2022). We can get the score function as so = (aupo /o² and apply it during the denoising process. The joint prediction of multiple vehicles is similar to motion prediction (Jiang al., 2023) and traffic simu- lation (Zhong et al., 2023b;a) tasks, but we focus more the ego vehicle's 's closed-loop planning performance and real-time deployment. We will introduce the specific designs as follows.
4.2 DIFFUSION PLANNER
Diffusion Planner is model based on the DiT architecture (Peebles & Xie, 2023), with design focusing on the fusion mechanism between noised future vehicle trajectories a and conditional information C. Figure provides overview of the complete architecture. A detailed description of these interaction and fusion modules is provided as follows.
Vehicle Information Integration. In the first step, the future vehicle trajectory is concatenated with the current state of each vehicle, represented as x = [2 neighhhor 2 neighborr I" This concatenation acts as a constraint to guide the model, simplifying the planning task by providing a clear starting point. Notably, velocity and acceleration information for the ego vehicle is excluded, which has been shown to enhance closed-loop performance, as highlighted in previous works (Cheng al., 2023; Li al., 2024). Integration of the information from different vehicles during model execution is achieved through muad self-attention mechanisms.
Historical Status and Lane Information Fusion. The historical status of neighboring vehicles and lane information is represented using vectors (Gao et al., 2020), Specifically, each neighboring vehi- cle is represented as Sneighbor R. I.x Dusighbor, and lanes as S E R Px Diuce, where L refers the number of past timestamps, and P indicates the number of points per polyline. D Dneighbor contains data such as vehicle coordinates, heading, velocity, size, and category, while D provides lane details such coordinates, traffic light status, and speed limits. Since these vectors are information- sparse, directly fusing them would make training challenging. To address this, we use MIxerMixer network (Tolstikhin et al., 202 to extract information dense representations. Compared to existing work (Huang et al., 2023; Cheng et al., 2023) that uses complex structural designs, we offer a more unified and simplified solution. This is achieved by iteratively passing the vectors through the MLL mixing layers, which operate on both the vector and feature dimensions. The forward process of each mixing layer can be formulated follows:
S = S " +
(6)
We use two separate MLP-Mixer networks for neighboring vehicles and lanes. Here, S represents the features for each neighboring vehicle or lane. After passing through multiple mixing layers, we apply pooling on the final output along the vector dimension. We also consider the statie objects
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information Satatie R Petatic where Datatic includes data such coordinates, heading, size, and category. For this, we use an MLP to extract the representation. Finally. we concatenate all repre- sentations and feed them into a vanilla transformer encoder for further aggregation, resulting in the encoder representation Q,- The fusion of 2 with æ proceeds as follows:
2 + (H Qy), x x + FFN(2))
where MHCA donate multi-head cross-attention.
Navigation Information Fusion. Navigation information is crucial for autonomous driving plan- ning, as it provides essential guidance the intended route, enabling the vehicle to make informed decisions. In the nuPlan benchmark (Caesar et al., 2021), navigation information is represented as a set of lanes along a route, Sroute E XXXxX))DDoouts where K denotes the number of route lanes, and Douute contains only coordinate information. We first employ an MLP-Mixer network, as described in equation 6, to extract the essential guidance representations Qn- Qn is then added to the diffusion timestep condition Q2 and applied through adaptive layer norm block (Peebles & Xie, 2023) to guide trajectory generation across all tokens.
4.3 PLANNING BEHAVIOR ALIGNMENT VIA CLASSIFIER GUIDANCE
Enforeing versatile and controllable driving behavior cru- cial for real-world autonomous driving. For example, vehi- cles must ensure safety and comfort while adjusting speeds to align with user preferences. Thanks to its close relation- ship to Energy-
Models (Lu et al., 2023), diffusion model can conveniently inject such preferences via classi- fier guidance. It can steer the model outputs via gradient surgery during inference, offering significant potential for customized adaptation.
Specifically, given the original driving behavior To (x)())), we aim to encode additional guidance to reinforce some preferred behavior upon the existing behavior qo- This op- eration can be formulated generating target behavior: (o(x(()) x (e))) ()) where (xx())) can be some form of energy function that encodes safety or preferred be- havior. As mentioned Section 3.2, the gradient of the in- termediate energy (Lu et al., 2023) is employed to adjust the original probability score, promoting the generation of trajectories within the target distribution. This process often necessitates additional trained classifier to provide an accurate approximation. However, diffusion posterior sampling (Chung et al., 2022; Xu et al., 2025) offers training free method that only uses the trained diffusion model μg in Eq. (5) to approximate the guidance energy, bypassing the classifier training, which incurs additional computational overhead:
Collision Deivable
Collision Only
Figure 2: Starting from same posi- tion, the trajectories driven under different guidance settings.
V 00 log pr V nt)) log (x)(t) - E (E (c00000)))) [a:
(8)
ant) log (o)(t())()) V Ein (Ho(200),,
One restriction this method is that Eq. (8) needs to use a pre-defined differentiable energy function E(.) to calculate the guidance energy. Fortunately, in autonomous driving scenarios, many trajectory evaluation protocols can be defined using differentiable functions. Next, we briefly describe some applicable energy functions that can be used to customize the planning behavior of the model, more details are shown in Appendix C.3.
Target speed maintenance:
The speed difference is used the energy, calculated by comparing the planned average speed with the set target speed.
Comfort: The energy function calculated by measuring the amount by which the vehicle's 's state exceeds the predefined limits.
Collision avoidance: The signed distance between the ego vehicle and neighboring vehicles is computed at each timestamp.

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Staying within drivable area: The distance the ego vehicle deviates outside the lane at each time step is calculated.
Additionally, this training-free approach supports flexible combinations during inference time, pro- viding solution for controllable trajectory generation in complex scenarios. For example, as shown in Figure 2, under collision guidance alone, the ego vehicle veers off the road to avoid a rear- approaching vehicle. However, when drivable guidance is added, the vehicle stays the road while maintaining safety. For more case studies, please refer to Section 5.1 and the Appendix B.2.
4.4 PRACTICAL IMPLEMENTATION FOR CLOSED-LOOP PLANNING
Data augmentation can help alleviate the out-of-distribution issue and is widely used in planning. Before training, we add random perturbations to the current state (Cheng al., 2023). Then, interpo- lation is applied to create a physically feasible transition, enabling the model to resist perturbations and regress the ground-truth trajectory (Bansal al., 2018). After that, transform the data from the global coordinate system into an ego-centric formulation through coordinate transforma- tion. Considering the significant difference between the longitudinal and lateral distances traveled by the vehicle, z-score normalization is used to ensure the mean of the data distribution is close to zero, thereby further stabilizing the training process. During inference, DPM-Solver (Lu al., 2022) is employed to achieve faster sampling, while low-temperature sampling (Ajay et al., 2022) enhances determinism in the planning process. We can complete trajectory planning for the next seconds at 10 Hz, along with predictions for neighboring vehicles, with inference frequency of approximately 20 Hz. Please see Appendix C for implementation details.
5 EXPERIMENTS
Evaluation Setups. We conduct extensive evaluations on the large-scale real-world autonomous planning benchmark, nuPlan (Caesar et al., 2021), compare Diffusion Planner with other state- of-the-art planning methods. The Val14 (Dauner et al., 2023b), Test14, and 4est14-hard bench- marks (Cheng et al., 2023) are utilized, with all experimental results tested in both closed-loop non-reactive and reactive modes. The final score is calculated as the average across all scenarios, ranging from 0 to 100, where a higher score indicates better algorithm performance. To further validate the algorithm's 's performance across diverse driving scenarios and with vehicles exhibiting different driving behaviors, we collected 200 hours of real-world data using a delivery vehicle from Haomo.AI. Unlike nuPlan, the delivery vehicle demonstrates more conservative planning behavior and operates in bike lanes, which involve dense human-vehicle interactions and unique traffic reg- ulations. The collected data were integrated into the nuPlan framework, and the same evaluation metrics were applied in closed-loop simulations, as detailed in Appendix D.
Baselines. The baselines are categorized into three groups (Dauner al., 2023b): Rule-based, Learning based, and Hybrid, which incorporate additional refinement to the outputs of the learning- based model. To enable a more comprehensive comparison, we utilize an existing refinement mod- ule (Sun et al., 2024), which applies offsets to the model outputs and scores all trajectories (Dauner al., 2023b). Without any parameter tuning, we integrate this module as post-processing for the Diffusion Planner (Diffusion Planner w/ refine.). We compare the Diffusion Planner against the following baselines, with more implementation details provided in Appendix C.4.
IDM (Treiber al., 2000b): classic rule-based method implemented by nuPlan.
PDM (Dauner et al., 2023b): The first-place winner of the nuPlan challenge offers a rule-based version that follows the centerline (PDM-Cloxed), learning-based version conditioned on the reference line (PDM-Open), and hybrid approach that combines both (PDM-Ilybrid).
UrbanDriver (Scheel et al., 2021): A learning- method using policy gradient optimization and implemented by nuPlan.
GameFormer (Huang al., 2023): Modeling ego and neighboring vehicle interactions using game theory (Game Former w/o refine.), followed by rule-based refinement.
PlanTF (Cheng et al., 2023): A state-of-the-ar learning baseedg-based method built on a transformer architecture, exploring various designs suitable for closed-loop planning.

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Closed-loop planning results on nuPlan dataset. The highest scores baselines various types. Using pre-searched reference lines as model input provides prior knowledge, reducing the difficulty of planning compared to standard learning-bale based methods. NR: non-reactive mode. R: reactive mode.
Table 1:
Val14
Test14-hard
Test14
Type
Planner
NR
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
Rule-hased Hybrid
PDM-Hybrid
92.77
92.11
65,99
76.07
90.10
91.28
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
Diffusiin Planner w/ refine. (Ours)
94,26
92.90
78.87
82,00
94.80
91.75
PDM-Open*
53.53
54,24
33.51
35.83
52.81
57.23
UrbunDriver
68.57
50,40
49.95
GameFormer w/o refine.
13.32
8.69
7,08
6.69
11.36
Leurning-uused
PlanTF
84,27
76.95
69,70
61.61
85.62
79.58
PLUTO w/o refine.*
88.89
78.11
70.03
59.74
89.90
78.62
Diffusion Planner (Ours)
89.87
82.80
75.99
69.22
89.19
82.93
Table 2: Closed-loop planning results on delivery-vehicle driving dataset.
Type
Planner
Score
Collisions
TTC
Drivable
Comfort
Progress
Rule
IDM
75.38
86.00
79.43
99.43
89.14
95.43
-based
PDM-Closed
80.95
86.51
80.00
100.0
97.21
97.47
PDM-Hybrid
80.72
86.50
77.00
100.0
92.50
99.00
Hybrid
GameFormer
51.35
82.50
72.50
65.00
98.00
90.00
PLUTO
83.49
88.95
85.64
99.45
94.47
97.79
PDM- O Former refine.
75.75
70.50
98.50
95.00
62.00
$7.50
33.00
98.50
77.00
Learning- based
PlanTF
95.00
90.50
99.50
96.00
99.50
PLUTO w/o refine.
87.77
92.69
87.64
99.44
97.19
98.88
Diffusion Planner (ours)
92.08
96.00
91.00
100.0
94.00
100.0
Diffusion Planner (ours)
GameFormer w/o refine.
PlanTF
PLUTO w/o refine.
Figure 3: Future trajectory generation visualization. A frame from challenging narrow road turning scenario in the closed-loop test, including the future planning of the ego vehicle (PlanTF and PLUTO w/o refine. show- ing multiple candidate trajectories), predictions for neighboring vehicles, and the ground truth ego trajectory.
PLUTO (Cheng et al., 2024): Building on PDM-Open, complex model with contrastive learning enhances environmental understanding (PLUTO w/o refine.), followed by post-processing
Main Results. Evaluation results on the nuPlan benchmark are presented in Table 1. The Diffusion Planner achieves state-of-the-art performance across more benchmarks compared to all learning- based baselines. With the addition of post-processing, Difffusion Planner w/ refine. outperforms hybrid and rule-based baselines, achieving scores that even surpass human performance. This is due to model's ability output high-quality trajectories, which are further enhanced by post- processing. Notably, compared to the transformer based PlanTF and PLUTO, Diffusion Planner leverages the power of diffusion achieve better performance. GameFormer, which models the
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12
14
Figure 4: Target speed and comfort guidance: For target speed guidance, the speed changes before and after guidance visualized. comfort guidance, longitudinal jerk changes are compared before and after applying comfort guidance on top of collision avoidance guidance.
interactions between the ego vehicle and neighboring vehicles using game theory, exhibits lim- ited model capabilities, making it overly reliant on rule-based refinements. We further present the planning results on delivery- driving dataset as shown in Table PDM, GameFormer, and PLUTO include certain designs specifically tailored to the nuPlan benchmark, which limits their ability to transfer to delivery-vehicle driving tasks, resulting Diffusion Planner demonstrates strong transferability across different driving behaviors. We also compared works that utilize diffusion for planning, as shown Table 4 in Appendix B.I. The Diffusion Planner better leverages the powerful capabilities of diffusion and is more practical.
drop in performance. In contrast,
Qualitative Results. To further demonstrate the capabilities of learning-based models, we show the trajectory generation results of representative baselines (without refinement) shown in Fig- ure 3. Diffiusion Planner shows high-quality trajectory generation, with accurate predictions for neighboring vehicles and smooth ego planning trajectories that reasonably account for the speed of the vehicle ahead, demonstrating the advantages of joint modeling of both prediction and planning tasks. More closed-loop planning results shown in Appendix A. In contrast, GameFormer w/o refine produces less smooth trajectories and inaccurate predictions for neighboring vehicles, which explains why it heavily relies on refinement. Although PlanTF and PLUTO w/o refine. sample multiple trajectories at once, most of them are of low quality.
5.1 EMPIRICAL STUDIES OF DIFFUSION PLANNER PROPERTIES
Multi-modal Planning Behavior. We selected an inter- section scenario and performed multiple inferences without low temperature sampling from the same initial position to obtain different possible outputs, order evaluate the model's ability to fit multi-modal driving behaviors. As shown in Figure without navigation information, the hicle can exhibit three distinct driving behaviors- left turn, right turn, and straight ahead with clear differentiation. When navigation information is provided, the model accu- rately follows it to make a left turn, demonstrating the dif- fusion model's s ability to fit driving behaviors with varying distributions and its capacity for switching between them.
Repented Condition Navigution Conditioned
Flexible guidance mechanism. Based the trained Diffu- sion Planner model, different types of classifier guidance, described in Section 4.3, are added during inference time without requiring additional training. We present two cases to demonstrate the effectiveness of guidance and its flexible
Figure 5: Multi-modal planning behavior of Diffasion Planner.
composability, shown in Figure 4. 1)

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Table 3: Ablation of each mod- ules during the training process on nuPlan Test14 Benchmark.
nuPlan Testl4 Benchmark
nuPlan Val14 Benchmark
89
89.53
89.73
89,76
89.73
89.57
Score
47
Denoise Steps
89.70
89.68
89,87
89.53
89.46
Type
Planner
Score
13
89.4
89,79
Base
Diffision Planner
89.19
K5
PIanTF
w/o
85.02
Diffusion Planner
Data
w/o interpolation
83.78
83
w/o augmentation
76.53
32 Num. predicted vehicles
x1
89.71
state w/ ego state
SDE
82.90
78.65
Figure 6: Ablation on the num- ber of predicted vehicles M.
Temperature
1.0
w/o current state
81.11
Figure 7: Inference param grid search.
For target speed setting, we masked all lane speed limit information to prevent it from influencing the model's 's planning, ensuring that speed adjustments are made solely through guidance. As a result, the model exhibited a lower speed without guidance. By setting the speed between 10m/s and 14m/s, the model closely matches the desired speed range while maintaining smooth speed transitions. 2) For comfort guidance, we effectively alleviate discomfort and can even use it simultaneously with collision guidance. We also provide additional case studies on collision and drivable guidance, as shown in Appendix B.2, as well as cases demonstrating the flexible combination of collision and drivable guidance, as illustrated in Figure
5.2 ABLATION STUDIES
Design Choices for training. We demonstrate the effectiveness of key components of our method: data processing, the handling approach of ego current state, and the number of predicted vehicles. 1) We ablate the model's performance without using z-score normalization (w/o z-score norm), as well as without data augmentation (w/o augmentation). or by only perturbing the current state without applying interpolation to future trajectories (w/o interpolation). The results are summarized in Table 3. For the w/o z-score norm variant, even with ego-centric transformation, the data range remains large, making difficult for the model to fit the distribution. The w/o augmentation variant faces out-of-distribution issues, leading to poor performance. Results also show that future trajectory interpolation is essential compared to perturbing only the current state. 2) We analyze the impact of the ego vehicle's current state the model. Retaining velocity, acceleration, and yaw rate (w/ ego state) may lead to learning shortcuts, resulting in decreased planning capability. While a state dropout encoder (Cheng et al., 2023) (w/ SDE) mitigates this, directly discarding the information is more effective. Additionally, the w/o current state shows that adding current state information to the decoder improves planning capability. 3) We also ablate the choice of the number of M. Figure shows that including too many neighboring vehicles in the decoder introduces noise, affecting the performance of the ego vehicle. However, most choices still outperform PlanTF.
Design Choices for Inference. We sweep two hyperparameters: the number of denoise steps and the magnitude of low-eraper sampling, as shown Figure 7. Low temperature helps improve the stability of the output trajectories. Additionally, the model leverages DPM-Solver to achieve efficient denoising and remains robust across different step counts. We report the detailed parameter selection in Table 5.
6 CONCLUSION
We propose Diffiusion Planner, a learning-base based approach that fully exploits the expressive power and flexible guidance mechanism of diffusion models for high-quality autonomous planning. A transformer-based architecture is introduced to jointly model the multi-modal data distribution in motion prediction and planning tasks through a diffusion objective. Classifier guidance is employed to align planning behavior with safe or user preferred driving styles. Diffussion Planner achieves state-of-thh--ae closed-loop performance without relying on any qule-based refinement on the nuPlan benchmark and newly collected 200-hour delivery- driving dataset, demonstrating strong adaptability across diverse driving styles. Due to space limit, more discussion on limitations and future direction can be found in Appendix E.
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ACKNOWLEDGEMENT
This work is supported by National Key Research and Development Program of China under Grant (2022YFB2502904),, and funding from Aa.mo.AI.
REFERENCES
Anurag Ajay, Yilun Du, Abhi Gupta, Joshua B Tenenbaum, Tommi S Jaakkola, and Pulkit Agrawal. Is conditional generative modeling all you need for decision making? In The Eleventh Interna- tional Conference on Learning Representations, 2022.
Mayank Bansal, Alex Krizhevsky, and Abhijit Ogale. Chauffeurnet: Learning to drive by imitating the best and synthesizing the worst. arXiv preprint arXiv:1812.03077 2018.
Mariusz Bojarski, Davide Del Testa, Daniel Dworakowski, Bernhard Firner, Beat Flepp, Prasoon Goyal, Lawrence Jackel, Mathew Monfort, Urs Muller, Jiakai Zhang, et al. End end learning for self-driving cars. arXiv preprint /601 2016.
Holger Caesar, Varun Bankiti, Alex H. Lang, Sourabh Vora, Venice Erin Liong, Qiang Xu, Anush Krishnan, Yu Pan. Giancarlo Baldan, and Oscar Beijbom. nuscenes: A multimodal dataset for autonomous driving. arXiv preprint arXiv: 1903. 11027, 2019.
Holger Caesar, Juraj Kabzan, Kok Seang Tan, Whye Kit Fong, Eric Wolff, Alex Lang, Luke Fletcher, Oscar Beijbom, and Sammy Omari. nuplan: A closed-loop ml-based planning benchmark for autonomous vehicles. arXiv preprint arXiv:2 106. 11100, 2021.
Dian Chen, Vladlen Koltun, and Philipp Krähenbühl. Learning to drive from a world on rails. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 15590-15599, 2021.
Li Chen, Penghao Wu, Kashyap Chitta, Bernhard Jaeger, Andreas Geiger, and Hongyang Li. End- to-end autonomous driving: Challenges and frontiers. arXiv preprint arXiv::306. 16927, 2023.
Jie Cheng, Yingbing Chen, Xiaodong Mei, Bowen Yang, Bo Li, and Ming Liu. Rethinking imitation- based planners for autonomous driving, 2023.
Jie Cheng, Yingbing Chen, and Qifeng Chen. Pluto: Pushing the limit of imitation Staring-basse planning for autonomous driving. arXiv preprint vxXiv:244.14327,, 2024.
Cheng Chi, Siyuan Feng, Yilun Du, Zhenjia Xu, Eric Cousineau, Benjamin Burchfiel, and Shu- Song. Diffusion policy: Visuomotor policy learning via action diffusion. arXiv preprint arXiv:2303.04137 2023.
Kashyap Chitta, Aditya Prakash, Bernhard Jaeger, Zehao Yu, Katrin Renz, and Andreas Geiger. Transfuser: Imitation with transformer-bart sensor fusion for autonomous driving. IEEE Trans- actions on Pattern Analysis and Machine Intelligence, 45(1 18888-1295, 2022.
Hyungjin Chung, Jeongsol Kim, Michael T Mccann, Mare L Klasky, and Jong Chul Ye. Diffusion posterior sampling for general noisy inverse problems. arXiv preprint arXiv:2209.1468) 2022.
Daniel Dauner, Marcel Hallgarten, Andreas Geiger, and Kashyap Chitta. Parting with misconcep- tions about learning-bass based vehicle motion planning. In Conference Robot Learning (CoRL),
2023a.
Daniel Dauner, Marcel Hallgarten, Andreas Geiger, and Kashyap Chitta. Parting with miscon- ceptions about learning-basse based vehicle motion planning. Conference on Robot Learning, pp- 1268-1281. PMLR, 2023b.
Prafulla Dhariwal and Alexander Nichol. Diffusion models beat gans image synthesis. Advances in neural information processing systems, 34:8780-8794, 2021.
Haoyang Fan, Fan Zhu, Changchun Liu, Liangliang Zhang, Li Zhuang, Dong Li, Weicheng Zhu, Jiangtao Hu, Hongye Li, and Qi Kong. Baidu apollo em motion planner, 2018.
11

---

Published as a conference paper at ICLR 2025
Jiyang Gao, Chen Sun, Hang Zhao, Yi Shen, Dragomir Anguelov, Congcong Li, and Cordelia Schmid. Vectoree:: Encoding hd maps and agent dynamics from vectorized representation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp- 11525-1 11533, 2020.
Jeffrey Hawke, Richard Shen, Corina Gurau, Siddharth Sharma, Daniele Reda, Nikolay Nikolov, Przemystaw Mazur, Sean Micklethwaite, Nicolas Griffiths, Amar Shah, et al. Urban driving with conditional imitation learning. In 2020 IEEE International Conference on Robotics and Automation (ICRA), pp. 251-257. IEEE, 2020.
Jonathan Ho, Ajay Jain, and Pieter Abbeel. Denoising diffusion probabilistic models. Advances in neural information processing systems, 33:6840-6851, 2020.
Yihan Hu, Jiazhi Yang, Li Chen, Keyu Li, Chonghao Sima, Xizhou Zhu, Siqi Chai, Senyao Du,
Tianwei Lin, Wenhai Wang, et al. Planning-orientee autonomous driving. In Proceedings of the IEEE/CVF Conference on Computer Vision and Puttern Recognition, pp. 17853-17862, 2023.
Yihan Hu, Siqi Chai, Zhening Yang, Jingyu Qian, Kun Li, Wenxin Shao, Haichao Zhang, Wei Xu, and Qiang Liu. Solving motion planning tasks with scalable generative model. arXiv preprint arXiv:2407.022 0277977777 2024.
Zhiyu Huang, Haochen Liu, and Chen Lv. Gameformer: Game-theoretic modeling and learning of transformer-basee interactive prediction and planning for autonomous driving. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 3903-399333 2023.
Michael Janner, Yilun Du, Joshua Tenenbaum, and Sergey Levine. Planning with diffusion for flexible behavior synthesis. In International Conference Machine Learning, pp. 9902-9915. PMLR, 2022.
Chiyu Jiang, Andre Commann Cheolho Park, Benjamin Sapp, Yin Zhou, Dragomir Anguelov, et al. Motiondiffuser: Controllable multi-agent motion prediction using diffusion. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. (644-9657 2023.
Alex Kendall, Jeffrey Hawke, David Janz, Przemyslaw Mazur, Daniele Reda, John-Mark Allen, Vinh-Dieu Lam, Alex Bewley, and Amar Shah. Learning to drive in a day. In 2019 international conference on robotics and automation (ICRA), pp. 8248 8254. IEEE, 2019.
John J. Leonard, Jonathan P. How, Seth J. Teller, Mitch Berger, Stefan Campbell, Gaston A. Fiore, Luke Fletcher, Emilio Frazzoli, Albert S. Huang, Sertac Karaman, Olivier Koch, Yoshiaki Kuwata, David C. Moore, Edwin Olson, Steven C. Peters, Justin Teo, Robert Truax, Matthew R. Walter, David Barrett, Alexander K Epstein, Keoni Maheloni, Katy Moyer, Troy Jones, Ryan Buckley, Matthew E. Antone, Robert Galejs, Siddhartha Krishnamurthy, and Jonathan Williams. A perception-driven autonomous urban vehicle. Journal of Field Robotics, 25, 2008 URL. https: sema://
semant ineemnttischonn ccscoola ar. wwwooo///oo.. CorpusID:11 1111645.5.
Zhiqi Li, Zhiding Yu, Shiyi Lan, Jiahan Li, Jan Kautz, Tong Lu, and Jose M Alvarez. ego status all you need for open-loop end-to-end autonomous driving? In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11864-14873, 2024.
Tenglong Liu, Jianxiong Li, Yinan Zheng, Haoyi Niu, Yixing Lan, Xin Xu, and Xianyuan Zhan. Skill expansion and composition in parameter space. In The Thirteenth International Confer- ence on Learning Representations, 2025. URL https:/ /wwp//pppeeeew.ee////e net httt/ffrummid CLWE2fq0bX.
Cheng Lu, Yuhao Zhou, Fan Bao, Jianfei Chen, Chongxuan Li, and Jun Zhu. Dpm-solver: fast ode solver for diffusion probabilistic model sampling in around 10 steps. Advances in Neural Information Processing Systems, 35:5775-5787, 2022.
Cheng Lu, Huayu Chen, Jianfei Chen, Hang Su, Chongxuan Li, and Jun Zhu. Contrastive energy prediction for exact soergy-guided diffusion sampling in offline reinforcement learning. arXiv preprint a33iv:2304. 12824, 2023.
12

---

Published as a conference paper at ICLR 2025
Chenlin Meng, Robin Rombach, Ruiqi Gao, Diederik Kingma, Stefano Ermon, Jonathan Ho, and Tim Salimans. On distillation of guided diffusion models. In Proceedings of the IEEE/CVF Conference Computer Vision and Pattern Recognition, pp. 14297-14306, 2023.
Khan Muhammad, Amin Ullah, Jaime Lloret, Javier Del Ser, and Victor Hugo C de Albuquerque. Deep learning for safe autonomous driving: Current challenges and future directions. IEEE Trans- actions on Intelligent Transportation Systems, 22(7):316-4436, 2020.
Nigamaa Nayakanti, Rami AI-Rfou, Aurick Zhou, Kratarth Goel, Khaled S Refaat, and Benjamin Sapp- Wayformer: Motion forecasting via simple efficient attention networks. In 2023 IEEE International Conference Roboties and Automation (ICRA), pp- 2980-2987. IEEE, 2023.
Jiquan Ngiam, Benjamin Caine, Vijay Vasudevan, Zhengdong Zhang, Hao-Tien Lewis Chiang, Jeffrey Ling, Rehecca Roelofs, Alex Bewley, Chenxi Liu, Ashish Venugopal, et al. Scene transformer: A unified architecture for predicting multiple agent trajectories. arXiv preprint arXiv:2106.08417. 2021.
William Peebles and Saining Xie. Scalable diffuusin models with transformers. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 4195-4205, 2023.
Aditya Ramesh, Prafulla Dhariwal, Alex Nichol, Casey Chu, and Mark Chen. Hierarchical text- conditional image generation with clip latents. arXiv preprint 2rXiv:220.006155 1(2):3, 2022.
Oliver Scheel, Luca Bergamini, Maciej Wolczyk, Blažej Osiński, and Peter Ondruska. Urban driver: Learning drive from real-world demonstrations using policy gradients, 2021.
Jascha Sohk-Dickstein, Eric A. Weiss, Niru Maheswaranathan, and Surya Ganguli. Deep unsuper- vised learning using nonequilibrium thermodynamics, 2015.
Yang Song and Stefano Ermon. Generative modeling by estimating gradients of the data distribution. Advances in neural information processing systems, 32, 2019.
Yang Song. Jascha Sohl- DiakksiDettnn, Diederik P. Kingma, Abhishek Kumar, Stefano Ermon, and Ben Poole. Score-based generative modeling through stochastic differential equations, 2021.
Yang Song, Prafulla Dhariwal, Mark Chen, and Ilya Sutskever. Consistency models. arXiv preprint arXiv:2303.01 2023.
Qiao Sun, Shiduo Zhang, Danjiao Ma, Jingzhe Shi, Derun Li, Simian Luo, Yu Wang, Ningyi Xu, Guangzhi Cao, and Hang Zhao. Large trajectory models are scalable motion predictors and plan- ners. arXiv preprint 20. 19620., 2023.
Qiao Sun, Huimin Wang. Jiahao Zhan, Fan Nie, Xin Wen, Leimeng Xu, Kun Zhan, Peng Jia, Xian- peng Lang, and Hang Zhao. Generalizing motion planners with mixture of experts for autonomous driving. arXiv preprint arXiv:2410.15774, 2024.
Ardi Tampuu, Tambet Matiisen, Maksym Semikin, Dmytro Fishman, and Naveed Muhammad. A survey of end-to-end driving: Architectures and training methods. IEEE Transactions on Neural Networks and Learning Systems, 33(4): 2020.
Ilya Tolstikhin, Neil Houlsby, Alexander Kolesnikov, Lucas Beyer, Xiaohua Zhai, Thomas Un- terthiner, Jessica Yung, Andreas Steiner, Daniel Keysers, Jakob Uszkoreit, et al. Mip-mixer: An all-mlp architecture for vision. Advances in neural information processing systems, 34:24261- 24272, 2021.
Martin Treiber, Ansgar Hennecke, and Dirk Helbing. Congested traffic states in empirical ob- servations and microscopic simulations. Physical Review E, - August 2000a. ISSN 1095-3787. doi: 10. 1111000yssssee.62... 1805. URL http: //xx /ww. org /tt.////... 1103/ PhysRevE, 62 1805.
Martin Treiber, Ansgar Hennecke, and Dirk Helbing. Congested traffic states in empirical observa- tions and microscopic simulations. Physical review F,, 62(())1805, 2000b.
13

---

Published as a conference paper at ICLR 2025
Chris Urmson, Joshua Anhalt, J. Andrew Bagnell, Christopher R. Baker, Robert Bittner, M. N. Clark, John M. Dolan, David Duggins, Tugrul Galatali, Christopher Geyer, Michele Gittle- man, Sam Harbaugh, Martial Hebert, Thomas M. Howard, Sascha Kolski, Alonzo Kelly, Maxim Likhachev, Matthew McNaughton, Nick Miller, Kevin M. Peterson, Brian Pilnick, Ragu- nathan Raj Rajkumar, Paul E. Rybski, Bryan Salesky, Young-Wor Woo Seo, Sanjiv Singh, Jarrod M. Snider, Anthony Stentz, William Whittaker, Ziv Wolkowicki, Jason Ziglar, Hong Bac, Thomas Brown, Daniel Demitrish, Bakhtiar Litkouhi, James N. Nickolaou, Varsha Sadekar, Wende Zhang, Joshua Struble, Michael Taylor, Michael Darms, and Dave Ferguson. Autonomous driving in ur- ban environments: Boss and the urban challenge. Journal of Field Robotics, 25, 2008. URL https: /tpps///apii semast wwwccccclllaccccccco ar. org/ Corpps/rppssoocoo 11849332.
semant
Matt Vitelli, Yan Chang, Yawei Ye, Ana Ferreira, Maciej Wołczyk, Blažej Osiński, Moritz Niendorf, Hugo Grimmett, Qiangui Huang, Ashesh Jain, et al. Safetynet: Safe planning for real-world self- driving vehicles using machine-learned policies. In 2022 International Conference Robotics and Automation (ICRA), pp. 897-904. IEEE, 2022.
Tongda Xu, Jian Li, Xinjie Zhang, Xingtong Ge. Dailan He, Xiyan Cai, Ming Sun. Yan Wang, Jingjing Liu, and Ya-Qin Zhang. Rethinking diffusion posterior sampling: From conditional score estimator to maximizing posterior. In The Thirteenth International Conference on Learning Representations, 2025. URL https: http//ppprreeeeeeeeeee/ee net/ www.feoruuuuccooooo (ow.umni--eeooooooo..
Brian Yang, Huangyuan Su, Nikolaos Gkanatsios, Tsung-War Wei Ke, Ayush Jain, Jeff Schneider, and Katerina Fragkiadaki. Diffusion-es: Gradient-free planning with diffusion for autonomous driving and zero-shot instruction following. arXiv preprint arXiv:2402.06559, 2024.
Yinan Zheng, Jianxiong Li, Dongjie Yu, Yujie Yang. Shengbo Eben Li, Xianyuan Zhan, and Jingjing Liu. Safe offline reinforcement learning with feasibility diffusion model. In The Twelfth International Conference on Learning Representations, 2024. URL https: /oppn//vie..eeeee///e/e net/forune id= -eeiiddjjjivvi 15Jv2CaDMO.
Ziyuan Zhong, Davis Rempe, Yuxiao Chen, Boris Ivanovic, Yulong Cao, Danfei Xu, Marco Pavone, and Baishakhi Ray. Language-guided traffic simulation via scene-level diffusion. In Conference on Robot Learning, pp. 144-177. PMLR, 2023a.
Ziyuan Zhong, Davis Rempe, Danfei Xu, Yuxiao Chen, Sushant Veer, Tong Che, Baishakhi Ray, and Marco Pavone. Guided conditional diffusion for controllable traffic simulation. In 2023 IEEE International Conference on Robotics and Automation (ICRA), pp. 3560-3566. IEEE, 2023b.
Zikang Zhou, Jianping Wang, Yung-Hui Li, and Yu-Kai Huang. Query-centric trajectory prediction. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp- 17863-17833, 2023.
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A VISUALIZATION OF CLOSED-LOOP PLANNING RESULTS
Figure 8: Closed-loop planning results: each represents scenario 10, and 15 seconds intervals. Each frame includes the future planning of the ego vehicle, predictions for neighboring vehicles, ground truth ego trajectory, and the driving history of the ego vehicle.
B ADDITIONAL RESULTS
B. COMPARED TO DIFFUSION-BASE PLANNING METHODS
To further demonstrate the advantages of our model, we compared with two recent works using diffusion models for motion planning. Diffusion-es (Yang et al., 2024) enhances diffusion model by incorporating an LLM as a trajectory filter. STR-16M (Sun et al., 2023) uses a diffusion model as decoder. STR2-CPKS-800N (Sun et al., 2024) builds on the former with 800M parameters and includes a PDM-like refinement module. We compared the model's performance in non-reactive mode and recorded the inference time, shown in Table 4. We observe that current diffusion based methods also experience significant performance degradation when detached from LLMs or rule- based refinement. Another important point that these methods, due their reliance LLMSs
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large number of model parameters, have higher computational costs, making them difficult to deploy in woaldworld applications.
Table 4: Closed-loop non-rcactive planning results the nuPlan datasct among inffrusion-based planners.
Planner
Test14
Test14-hard
Val14
Inference Time (s)
Diffusion-es w/o LLM
Diffusion-es w/ LLM STR-16M
92
0.5
27.59
45,06
STR2-CPKS-8001 w/o refine.
68.74
52.57
65.16
Diffusion Planner (ours)
89.19
75.99
89.87
0.04
B.2
MORE CASE STUDIES FOR THE GUIDANCE MECHANISM.
Collision Guidance
Drivable Guidance
Figure 9: Case studies for collision and drivable guidance. Starting from the same position, we visualized
the closed-loop results: the dashed line represents the results without guidance, with hollow markers indicating locations where safety incidents occurred. The solid line represents the results with guidance, and the solid car markers indicate the final positions.
C EXPERIMENTAL DETAILS
This section outlines the experimental details to reproduce the main results in our papers.
C.I TRAINING DETAILS
Datasets. We the training data from the nuPlan dataset and sample 1 million scenarios for our training set. The number of different scenarios is shown in Figure 11. For each scenario, we
https://www, nup ong
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consider the lane and navigation information within 100m radius around the ego vehicle at the current time, including the neighboring vehicles' history from the past two seconds. Each type of data is padded to unified dimension for model imput, and attention masking is used to effectively eliminate irrelevant information.
Data augmentation. y coordinates, orientation angle 0, speed u, acceleration a.
The current state of the ego vehicle is first perturbed slightly in terms of its x,
Ar (1-Ar Ay, A8, Av, Aa), Ar. Ay. A0, Av, Aal).
For the augmented state i2go zago + Az we ensure that the speed always remains greater than 0 to prevent the vehiele from learning to move in reverse. After that, quintic polynomial interpolation is applied between current state # and fego to generate new trajectory that adheres to the dynamic constraints, replacing the ground truth trajectory.
Normalization. Following previous works (Huang et al., 2023; Cheng et al., 2023; 2024), we apply an ego-centric transformation to process the original dataset. The global coordinates are converted into the ego vehicle's local coordinate system, using the vehicle's heading and position. Afterward, observe that the ego vehicle's longitudinal progress is significantly larger than its lateral progress. To improve training stability, we apply z-score normalization to all x-axis coordinates, while the y- axis scaled the same magnitude avoid distortion:
where μ 10, o = 20. The same approach is applied other scenario inputs.
Training was conducted using NVIDIA A100 80GB GPUs, with a batch size of 2048 over 500 epochs, with 5-epoch warmup phase. We use AdamW optimizer with learning rate of 5e -4, We report the detailed setup Table
C.2 INFERENCE DETAILS
We utilize +PM-Solver++ diffusion reverse process solver, adopting variance- praeueeeerreerving noise schedule where the noise or = (1-t))mmn to further enhance the stability of the denoising process. We found that directly using the model output with higher temperature facilitates generating high-quality trajectories. Conversely, if a refinement module applied after the model output, lower temperature helps produce more stable trajectories, which supports more accurate judgments by the refinement module. In addition, the model achieves an inference frequency of 20 Hz single A6000 GPU, We also report the detailed setup in Table 5.
Low-temperature sampling is employed
C.3
CLASSIFIER GUIDANCE DETAILS
We then specifically introduce the mathematical formulation the different energy functions, as mentioned in Section 4.3.
Collision Avoidance. Based on the ego vehicle's planning and the neighboring vehicles' predictions from the decoder at diffusion timestamp t, calculate the signed distance D between the ego vehicle and each neighboring vehicle at each timestamp т. When the bounding boxes of the vehicles overlap, use the minimum separation distance, otherwise, use the distance between the nearest points. The energy function for collision avoidance is then defined as:
Σ ADJ V (we max (1- Dis, .)))
Ecollision
We
Σ 1D + cps M.T
Σ ID 2o V (we. max (1-ii 0))
N
We
1D 1 eps
M.r
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where (x) ;= e I, represents the collision-senste distance, which controls the maximum distance at which gradients are produced, and eps is added to ensure numerical stability (Jiang et al,, 2023).
Target Speed Maintenance. We calculate the energy function based on the difference between the average speed of the generated trajectory and the target speed range:
dozgo ego dr
Vlow 0)
dozeoo
Ecarget.speed
max
+ max
Thigh
(10)
dr
Where Ulow is the setting lower bound of speed, Vlow is the setting higher bound of speed.
Comfort. Taking longitudinal jerk example, the difference between each point and the comfort threshold is calculated, ignoring cases where the comfort requirements are met:
)
Ecomfort
IE
max
(Jnsax
ego
A-
(11)
dr3
Where Jmax is the maximum longitude jerk limit.
Staying within Drivable Area. We construct the differentiable cost map M by using Euclidean Signed Distance Field with parallel computation (Cheng et al., 2024), which can compute the dis- tance the ego vehicle goes beyond the lane at each timestamp. Then the energy is defined as:
Σ v (wa M (I%
Edrivable
(12)
Σ 1l M M(xx0)) eps
Given the diverse options for energy function design, our choices whether the model could support various types of guidance and may not be optimal. However, through extensive empirical experiments, we can share some of our insights and experiences regard- ing energy function selection to assist future work in exploring more effective options:
made primarily to validate
Smooth and continuous gradients: Guidance functions with smooth and continuous gradients facilitate the generation of stable trajectories.
Gradient sparsity: It is preferable for the guidance function generate gradients only specific situations, such as when trajectory points approach potential collisions.
Indirect guidance for higher-order state derivatives: For higher-order state derivatives, such velocity, acceleration, or angular velocity, indirect guidance through position and heading is preferable. For instance, to control trajectory speed, we can guide trajectory length instead.
Consistent gradient magnitude: The guidance function should ensure that the magnitude of gradients remains approximately consistent across different conditions. can be achieved by averaging cost values over the number of points contributing to the cost.
C.4 BASELINES SETUP
nuPlan Datasets Evaluation. For IDM and UrbanDriver, we use the official nuPlan code, with the UrbanDriver checkpoint sourced from the PDM codebase, which also provides the checkpoints PDM-Hybrrd and PDM-Open. For PlanTF and PLUTO, we use the checkpoints from their respective official codebases. In the case of PLUTO w/o refine, we skip the post-processing code and rerun the simulation without retraining. Following the guidelines from the official codebase, we train GameFormer and skip the refinement step obtain GaneFormer w/o refine.
Delivery-vehicle Datasets Evaluation. We adopt the same metrics and models as those used on nuPlan, but by modifying various vehicle-related parameters to adapt the baselines to the delivery- vehicle training. Based on this, we retrain and test the models following the official training code.
https: / httptthb.bbb/tt/////// com/motionall/uulaaae https: pttps://gtt ://github. /tt////ttooooomusssiiiiio//oaaua
https://gi thub. hmm//jchengai//planf httptaub..cmm//hhgggg/gaaplu....
https://gg thub. wttpb.coo//cch//cccccooo ormer-
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Table Hyperparameters Diffusson Planner
Type
Parameter
Symbol
Value
Num. neighboring vehicles
32
Num. past timestamps
L
21
Dim. neighboring vehicles
neighbor
11
Num. lanes
70
Num. points per polyline
20
Training
Dim. lanes vehicles
Diane
12
Num. navigation lanes
25
Num. predicted neighboring vehicles
M
10
Num. encoder/decoder block
3
Dim. hidden layer
192
Num. multi-head
6
Noise schedule
Linear
Noise coefficient
Bmin: Fmax
0.1, 20.0
Inference
Temperature
0.5
Temperature (w/ refine.)
0.1
Denoise step
10
D DETAILS ON DELIVERY VEHICLE EXPERIMENTS
We collected approximately 200 hours of real-world data using autonomous logistics delivery vehicle from Haomo.AI. The task of the delivery vehicle is similar to that of a robotaxi in nuPlan, it autonomously navigates a designated route. During operation, the vehicle must comply with traffic regulations, ensure safety, and complete the delivery as efficiently the vehicles in the nuPlan dataset, the delivery vehicle is smaller, as shown in Table 6, and operates at lower speeds. As a result, it is able to travel on both main roads and bike lanes. During deliveries, frequently interacts with pedestrians and cyclists, and the driving rules differ from those for motor vehicles, as shown in 10. This dataset serves as supplement to nuPlan, allowing for the evaluation of algorithm performance under diverse driving scenarios.
possible. Compared to
Table 6: Vehicle parameter details
Parameter (m)
Delivery Vehicle
nuPlan Vehicle
Width
1.03
2.30
Length
2.34
5.18
Height
1.65
1.78
Wheel base
1.20
3.09
Specifically, we transform the original data into the nuPlan data structure, allowing it to be stored as DB files compatible with the nuPlan API for seamless integration and usage. We use the same training pipeline from the nuPlan benchmark to train both the model and baselines. For some base- lines that require crosswalk information, we replace it with stop line data. Additionally, the vehicle parameters are substituted with those of the delivery vehicle. The model's performance is evaluated using the nuPlan metrics.
E LIMITATIONS & DISCUSSIONS & FUTURE WORK
Here, we discuss our limitations, potential solutions and interesting future works.
Scenario Inputs. Our method relies on vectorized map information and detection results of neigh- boring vehicles. Compared to mainstream end-to-end pipelines, this approach involves in- formation loss and requires a data processing module. However, unlike end-to-end methods, our focus is more the planning stage, particularly the ability for closed-loop planning.
19

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Published as a conference paper at ICLR 2025
Scenario counts by type
following lane without lead
near multiple vehicles
stopping at stopline
starting right turn
waiting for delivery
other stationary
near multiple pedestrians and bikes
starting left turn
starting straight intersection
starting lurn
Bike lane leftt turn
changing lane
following lane with
waiting for pedestrian cross
10"
Number of scenarios (log scale)
Bike lane straight..heed
Figure 10: Scenario count by type in the delivery-vehicle driving dataset, with representative visualizations.
Solution and future work: We demonstrate the performance of the diffusion model for closed-loop planning without rule-based refinement. An interesting future direction would be to modify the encoder architecture and use images as inputs, enabling an end-to-end training pipeline.
Lateral Flexibility. We find that learning-basee based methods struggle with flexibility, particularly when significant lateral movement is required. In contrast, rule-hased methods perform better in this aspect due the provision of reference trajectory. Being consistent with findings from previous work (Li et al., 2024), we find this is mostly because that the dataset mainly consists of straight-driving scenarios, with few instances of lane changes or avoidance maneuvers. This makes it challenging for learning- based methods to generalize and acquire these skills. Addition- ally, since the model only outputs the planned trajectories instead of the controlling signal such brake and throttle, there is gap between the planned trajectory and the results from the down- stream controller (Cheng al., 2023). This discrepancy also leads to potential poor performance, even out-of out-of-distribution behavior, in scenarios that require more flexible actions.
Solution and future work: We find that data augmentation vehicle being reluctant to make lateral movements, but it still performs poorly in cases requiring significant lane changes. This could be improved by incorporating more data involving large lateral progress, leveraging reinforcement learning with reward mechanism, or designing more effective diffusion guidance mechanism to help the model learn channihng behaviors. We believe this is an interesting observation and leave this direction for future work.
somewhat alleviate the issue of the
Sample Efficiency. The high performance of Diffusion comes at the cost of requiring multiple model inferences, leading reduced sample efficiency.
Solution and future work: We addressed this issue large extent by using high-order ODE solver, enabling trajectory planning for 8 seconds 10 Hz in 0.05 seconds. Considering real-world application requirements, techniques such as consistency models (Song et al., 2023) or distillation- based sampling methods (Meng et al., 2023) could be employed for further accelcration.
Overall, although some design choices may appear simple and certain limitations exist, have thor- oughly demonstrated the capabilities of diffusion models for closed-loop planning in autonomous driving through extensive experiments. Moreover, demonstrate the potential of the diffusion model to align with safety or human-preferred driving behaviors. It provides a high-performance, highly adaptable planner for autonomous driving systems.
20

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Published as a conference paper at ICLR 2025
Scenario counts by type
unknown
stationary
traversing traffic light intersection
medium magnitude speed
stationary traffic
traversing
dropoff
high. magnitude speed
travensing intersection
stationary traffic light without lead
on pickup dropoff
on_stop ho_sttolinee stop sign low magnitude speed
stationary at traftic light with lead
intersection
near long vehicle
traversing. crosswalk
near high speed vehicle
on_ traffafffe light, intersection
near multiple vehicles
following lame without lead
near pedestrian on cmsswaak
on carpark
near trafficcone driveable
on stopline traffic light
near construction zone sign
on stopline crossaal
following lane with slow lead
high Interal acceleration
barrier driveable
waiting for pedestriann_nnn. to_ crooss
starting unprotected cross,
starting right turn near multiple pedestrians
stopping at croswaak
stopping at stop sign, without lead
stopping with lead
statting_rotectoo
starting protected cross Jurn
starting unprotected noncross turn
sturting_straight, stop sign intersection traversal
following lane with lead
stopping at traffic light without lead
stopping at stop sign no crosswalk
stationary_al al croeswalk
hehind bike
accelerating traffic light with lead
behind pedestrian on driveable
accelerating crosswalk
starting protecced noncross Jurn
changing_lane lane to right
changing lane to left
starting straight traffic light intersection traversal
high magnitude jerk
traversingg narrow lane
crossed by vehiole
stopping at traffic light, with lead
near multiple_ bikes
crossed by hike
changing lane with muil
near pedestrian on, crosswalk with ego behind padestrian on_ pickup dropoff
starting u Jurn
changing Jane with lead
All scenerios Selected somuriis
10"
10'
10"
10"
10"
10"
10"
10"
Number of scenarios (log scale)
Figure 11: Scenario count by type in the nuPlan dataset.
21


Reviews:
[{"review_id": "ycvqwbcHte", "reviewer": "Reviewer_J7se", "summary": "Current learning-based planning methods, such as imitation learning, often struggle with safety and adaptability, especially when dealing with the multi-modal behaviors typical of human drivers. To overcome these limitations, the authors introduce a novel transformer-based Diffusion Planner designed for closed-loop planning. This model captures multi-modal driving behaviors and ensures high-quality trajectories without relying on rule-based refinements. They integrate prediction and planning tasks within the same architecture to facilitate cooperative vehicle behaviors. Additionally, the Diffusion Planner enhances safety and adaptability by learning trajectory score gradients and utilizing a flexible classifier guidance mechanism.", "questions": "Same as weaknesses. Additional questions:\n\nQ1. Line 466-477, “Due to space limitations, …”, what space limitations is the author mentioning?\n\nQ2. Could the authors elaborate on W1 and explain why PLUTO (with and without refinement) was not included in the comparisons on the delivery-vehicle driving dataset, given its strong performance on NuPlan?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "S1. Reduces complexity  issue by collectively considering the status of key participants in the driving scenario and jointly modeling the motion prediction and closed-loop planning tasks as a future trajectory generation task.\n\nS2. Integrating closed-loop planning with a diffusion model is an effective approach, and the use of the architecture is clearly articulated.", "weaknesses": "W1. Though PLUTO (hybrid method) performs better than Diffusion Planner in the NuPlan dataset, no comparison with PLUTO w or w/o refine is shown for the delivery-vehicle driving dataset.\n\nW2. The paper would benefit from a more explicit and detailed statement of contributions, perhaps in a dedicated paragraph near the end of the introduction. This should clearly outline how the Diffusion Planner addresses each of the limitations mentioned and what specific novel aspects it introduces.\n\nMinor Nitpicks\n\nN1. A legend should be added to Appendix A, Figure 8\nN2. Line 195: Conditions C could be mathematically defined", "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": "mjvJu5oKUU", "reviewer": "Reviewer_Csii", "summary": "This paper presents a novel approach for autonomous driving using a transformer-based Diffusion Planner. By leveraging diffusion models, the authors address key challenges in autonomous driving, such as modeling multi-modal driving behavior and ensuring trajectory quality without relying on rule-based refinement. The approach demonstrates state-of-the-art performance on the nuPlan benchmark and a newly collected dataset.", "questions": "The suggestion of improvement is stated in the *Weaknesses*. No further suggestions.", "limitations": "", "rating": 8, "confidence": 4, "soundness": 4, "presentation": 3, "contribution": 4, "strengths": "The strengths of the paper are threefold.\n\n1. The use of diffusion models in the autonomous driving planning task is novel. The authors effectively address the limitations of existing learning-based planning methods, such as handling multi-modal behaviors and out-of-distribution scenarios.\n\n2. The paper provides a thorough explanation of the Diffusion Planner’s architecture and how it integrates prediction and planning tasks. The classifier guidance mechanism for adaptable planning behaviors is particularly well-explained.\n\n3. The evaluations on the nuPlan benchmark and the delivery-vehicle dataset demonstrate impressive closed-loop performance, surpassing both learning-based and rule-based baselines. The method shows robust transferability, highlighting its potential for real-world applications.", "weaknesses": "The overall quality of this paper is strong. One minor area for improvement is the quantity of simulation benchmarks used. The paper evaluates planner performance solely based on the Test14 random benchmark, which consists of approximately 280 closed-loop scenarios. It would be beneficial to include additional simulation benchmarks from nuPlan, such as the Val14 benchmark and the Test14 hard benchmark, to provide a more comprehensive demonstration of the advantages of the proposed method over the baselines.", "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": "92JC9iSbg0", "reviewer": "Reviewer_hoYE", "summary": "In this paper, a DiT-enabled framework is deployed to jointly tackle the motion planning task accompanying with predictions of surrounding agents for autonomous driving. Subsequently, a classifier-free gradient guidance are leveraged through a set of safety and comfortness costs during the diffusion step in guiding safe planning. Experimental results in Test14 demonstrate the effectiveness.", "questions": "N/A.", "limitations": "", "rating": 8, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. A novel diffusion-based framework in solving the motion planning task. Intutive DiT-enabled framework for integrated prediction and planning with costs guidance. \n\n2. Strong planning results delivered against state-of-the-art baselines in nuPlan.", "weaknesses": "1. Insufficient benchmark and metric comparison: 1) Additional results in other popular benchmark, such as Val14 and Test14-Hard are required to manifest the planning results under more diversed / challenging scenarios. 2) Other settings, such as closed loop reactive simulation, and open loop results are not verified.\n\n2. Insufficient baseline comparison. Motion planning / trajectory simulation leveraging diffuision are not novel. Hence, the planning results using other diffusion strategies ([1] [2] for instance) seems needed.\n\nRef:\n\n[1] Chi, Cheng, et al. \"Diffusion policy: Visuomotor policy learning via action diffusion.\" The International Journal of Robotics Research (2023): 02783649241273668.\n\n[2] Zhong, Ziyuan, et al. \"Guided conditional diffusion for controllable traffic simulation.\" 2023 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2023.", "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": "AxEoPSP2dg", "reviewer": "Reviewer_d65s", "summary": "This paper proposes a diffusion-based motion planner for autonomous driving. The diffusion model is trained to generate joint trajectories of the ego and neighboring vehicles. Classifier guidance is introduced to regularize the safety, comfort, and feasibility of the ego vehicle's trajectory. Several practical implementation designs, such as data augmentation and normalization, are introduced to improve the model performance for closed-loop planning. Comprehensive experiments are conducted on the nuPlan benchmark.", "questions": "1. In Table 1, the authors only reported the proposed diffusion planner with collision guidance. How does the model perform with other types of guidance, especially when multiple guidance costs are used? \n\n2. When multiple guidance terms are used, the guidance cost becomes a weighted sum of several cost terms. I wonder if the authors have taken special care to ensure that the scales of different cost terms and their gradients are well balanced. It would be great if the authors could discuss if the guidance performance is sensitive to energy function design and cost weights.", "limitations": "", "rating": 8, "confidence": 3, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "The proposed model is carefully designed and extensively tailored for practical application in autonomous driving. The experiments are comprehensive, with multiple baselines and detailed ablation studies. It achieves better performance than the current state-of-the-art methods on the nuPlan leaderboard. Detailed descriptions of the model implementation and experiment design are provided. This work provides useful lessons for the community on achieving good closed-loop planning performance with diffusion-based motion planners for practical autonomous driving applications.", "weaknesses": "1. While I believe this work has good practical values for the autonomous driving community, diffusion models have been explored in several works for motion prediction, closed-loop simulation, and motion planning in autonomous driving. The authors attempted to differentiate their work from existing works by emphasizing that the diffusion model does not drive performance improvements in these cases but relies on rule-based refinement or LLM, and they claimed that they are the first to fully harness the potential of diffusion models to enhance closed-loop planning performance in autonomous driving. However, this statement is quite vague, and it is unclear what novel technical contributions the authors have made in this work. My current impression is that the proposed model combines existing techniques investigated in the literature on diffusion models and learning-based motion planning for autonomous driving (e.g., transformer architecture, classifier guidance with manually designed cost functions, data augmentation, etc.). To help the audience better appreciate their novelty and contributions, the authors may list their novel contributions at the end of the Introduction. \n\n2. Related to the first point, the authors should provide a more extensive review of the related literature on diffusion models for autonomous driving. The authors currently give a concise discussion in Sec. 3.2. It should be moved to the related work section and extended to be more comprehensive. In particular, the current review misses an essential line of research on diffusion models for closed-loop simulation (e.g., [1], [2], [3]). Also, some works have developed diffusion-based motion planners for nuPlan (e.g., [4], [5], and [6]; the authors cited [4][5], but they are not included as baselines). If possible, they should be included as baselines for comparison. \n\n[1] Zhong, Ziyuan, et al. \"Guided conditional diffusion for controllable traffic simulation.\" ICRA 2023.\n[2] Zhong, Ziyuan, et al. \"Language-guided traffic simulation via scene-level diffusion.\" CoRL 2023. \n[3] Chang, Wei-Jer, et al. \"Safe-sim: Safety-critical closed-loop traffic simulation with diffusion-controllable adversaries.\" ECCV 2024.\n[4] Yang, Brian, et al. \"Diffusion-es: Gradient-free planning with diffusion for autonomous driving and zero-shot instruction following.\" arXiv preprint arXiv:2402.06559 (2024).\n[5] Hu, Yihan, et al. \"Solving motion planning tasks with a scalable generative model.\" ECCV 2024. \n[6] Sun, Qiao, et al. \"Large Trajectory Models are Scalable Motion Predictors and Planners.\" arXiv preprint arXiv:2310.19620 (2023).", "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 (Oral)