SAH-Drive: A Scenario-Aware Hybrid Planner for Closed-Loop Vehicle Trajectory Generation
Authors: Yuqi Fan
Zhiyong Cui
Zhenning Li
Yilong Ren
Haiyang Yu
Venue: ICML 2025 poster (2025)
Keywords: diffusion model
autonomous driving
hybrid planner
trajectory planning
Primary area: applications->robotics
TL;DR: SAH-Drive combines a lightweight rule-based planner and an extensive learning-based planner, utilizing a dual-timescale decision neuron to determine the final trajectory.

Abstract: Reliable planning is crucial for achieving autonomous driving. Rule-based planners are efficient but lack generalization, while learning-based planners excel in generalization yet have limitations in real-time performance and interpretability. In long-tail scenarios, these challenges make planning particularly difficult. To leverage the strengths of both rule-based and learning-based planners, we proposed the **Scenario-Aware Hybrid Planner** (SAH-Drive) for closed-loop vehicle trajectory planning. Inspired by human driving behavior, SAH-Drive combines a lightweight rule-based planner and a comprehensive learning-based planner, utilizing a dual-timescale decision neuron to determine the final trajectory. To enhance the computational efficiency and robustness of the hybrid planner, we also employed a diffusion proposal number regulator and a trajectory fusion module. The experimental results show that the proposed method significantly improves the generalization capability of the planning system, achieving state-of-the-art performance in interPlan, while maintaining computational efficiency without incurring substantial additional runtime.

SAH-Drive: A Scenario-Aware Hybrid Planner for Closed-Loop Vehicle Trajectory Generation
Yuqi Fan 12
Zhiyong Cui 123 Zhenning Li
Yilong Ren 12
Haiyang Yu
Abstract
Reliable planning is crucial for achieving au- tonomous driving. Rule-based planners are effi- cient but lack generalization, while learning- based planners excel in generalization yet have limita- tions in real-time performance and interpretabil- ity. In long-tail scenarios, these challenges make planning particularly difficult. To leverage the strengths of both rule-based and learning-basse planners, proposed the Scenario-Awaid Hy- brid Planner (SAH-Drive) for closed-loop vehi- cle trajectory planning. Inspired by human driv- ing behavior, SAH-Drive combines a lightweight rulc-based planner and a comprehensive learning- based planner, utilizing a dual-timescale decision neuron determine the final trajectory. To en- hance the computational efficiency and robustness of the hybrid planner, we also employed diffu- sion proposal number regulator and trajectory fu- sion module. The experimental results show that the proposed method significantly improves the generalization capability of the planning system, achieving state-of-the-art performance in inter- Plan, while maintaining computational efficiency without incurring substantial additional runtime.
Tracking
High Frequeney
Bule-Based
Reference Trajectory
Planned Trajectory
Low Frequency
Learning-
Searning-Based Based
(a) Fast-Slow Hybrid Planner Paradigm
Regular
Sccnario
Rule Based
Short- Term Plasticity
Long- Tenn Plastciity
Long- Tail Scenario
Planned Trajectory
Dual- Timescale
Decision Neuron
I Learning Based
(b) Scenario-Aware Hybrid Planner Paradigm
Figure Comparison of the fast-slow hybrid and scenario-aware hybrid planner paradigms. (a) The learning-based based planner handles low-frequency reference trajectory planning, while the rule-based planner manages tracking. (b) The dual-timescale decision neuron enables the rule-based planner to primarily handle regular scenarios while using the learning- based planner to focus on long-tail scenarios.
2000; Fan et al., 2018; Sadat et al., 2019; Hallgarten et al., 2024). Rule-based algorithms, relying on human-crafted rules, are proficient at handling scenarios within their de- fined scope but are significantly limited in their ability to generalize to situations outside these predefined boundaries. Learning- based algorithms have strong generalization capa- bilities but typically require the construction of large models with many parameters, making real-time operation challeng- ing and lacking interpretability. The characteristics of these two types of planners reveal that rule-based planners are well-suited for relatively simple, regular scenarios, as these scenarios constitute the majority of the driving process and can be effectively represented through predefined rules. In contrast, learning-based planners excel in handling complex long-tail scenarios (Cheng et al., 2024b), which are chal- lenging to represent using rules and occur less frequently during driving. As such, there is clear need for a scenario- aware hybrid planner that leverages the strengths of both
1. Introduction
Autonomous driving trajectory planning primarily involves two types of algorithms: learning-basee algorithms (Codev- illa et al., 2018; 2019; Rhinehart et al., 2019; Zeng et al., 2019; Chitta al., 2022; Hu et al., 2023; Jiang al., 2023; Chen et al., 2024) and rule-based algorithms (Treiber al.,
'Shoool of Transportation Seience and Engincering, Bei- hang University, Beijing, China *State Key Laboratory Intelli- gent Transportation System, Beihang University, Beijing, China School of Software Engineering, Beihang University, Beijing, China "State Key Laboratory of Internet of Things for Smart City, University Macau, Macao, China. Correspondence to: Zhiyong Cui <zhiyonge@ buaa.cdu.cn>>
Proceedings of the ndnd International Conference Machine Learning. Vancouver, Canada. PMLR 267, 2025. Copyright 2025 by the author(s).

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SAH-Driiv:: A Sccnario-Aware Hybrid Planner for Closed-Loop Vehicle Trajectory Generation
rule-based and learning-based approaches, enabling the sys- tem to handle different driving situations more effectively.
Dependent Plasticity (STDP). This biologically inspired mechanism is used independently, without relying on full spiking neural network (SNN) framework. The advantage of this approach lies in its ability to flexibly adjust decisions based on changes in planner performance by simulating the mechanisms of human neurons, enabling scene-aware plan- ning period switching. The code for this paper is available at http:://gttub..cmmm/// http//icchh--iv/SAEEEE
Scenario-Aware Hybrid Planner Paradigm: Drawing from the analysis of human driving behavior and the dual- system framework (Wason & Evans, 1974; Kahneman, 2011; Leonard, 2008) underlying human cognition, it can be observed that, in regular scenarios, humans drive with little mental effort, and their behavior can be effectively captured by simple rules. In contrast, long-tail scenarios such as overtaking obstacles and passing a construction zone signifi- cantly require humans to exert more cognitive effort to iden- tify optimal driving opportunities, requiring the sampling and evaluation of multimodal driving behaviors to make informed decisions (Mohammad et al., 2024). Thus, we first proposed the scenario-aware hybrid planner paradigm for autonomous driving trajectory planning. As shown in Figure 1, the traditional fast-slow hybrid planner paradigm (Tian et al., 2024) eliminates the distinction between sce- narios to enhance the generalization ability of the planner, where the learning-basee planner serves merely as guidance. The advantages of both the rule-based and sarning-basedd planners are not effectively integrated, resulting in limited improvements in generalization. In contrast, the scenario- aware hybrid planner paradigm comprehensively combines both types of planners, enhancing generalization for long- tail scenarios while maintaining high efficiency in regular scenarios, thus offering superior performance.
Our contributions are follows:
We proposed a scenario-aware hybrid planner paradigm that integrates rule-based and learning- based trajectory planning methods, and designed a dual-timescale decision neuron which is composed of the score-based switching rule, the scenario-based switching rule, and the STDP-based decision neuron.
We integrate the SOTA rule-based planner PDM-closed and diffusion proposal generation model into SAH- Drive, which significantly reduces the required training data compared to fully learning- planners, while maintaining strong planning performance, particularly in long-tail scenarios.
We employ real-time proposal number regulator and trajectory fusion module to accelerate trajec- tory planning while ensuring the safety of the learning- based planner. The experimental results demonstrate the efficiency and robustness of the proposed method.
In this paper, we propose the SAH-Drive based on the scenario-aware hybrid planner paradigm, with PDM-Closed (Dauner et al., 2023) the rule-based planner and diffu- sion proposal generation model as the learning-basee plan- ner. Although PDM-Closed, the State-of-the- the-Art (SOTA) rule-based planner, is highly efficient, it cannot perform lane changes and is not well-suited for handling long-tail scenarios (Hallgarten et al., 2024). Therefore, we train a multimodal diffusion-basee proposal generator as "second brain" to complement the rule-based planner. Additionally, we introduce proposal number regulator that dynamically adjusts the number of generated proposals based on the highest-scoring trajectory, evaluated using predefined per- formance metrics (Dauner et al., 2023). This increases the diversity of proposals while reducing redundancy and accel- crating the trajectory planning process.
2. Related Work
2.1. Rule-Based V.S. Learning-Baas -Baed Trajectory Planning Methods
Rule-based planners use a set of predefined, hard-coded rules and heuristics to generate trajectories based on the vehicle's environment and constraints. A typical rule-based planner is the Intelligent Driver Model (IDM) (Treiber et al., 2000), which calculates the ego-vehicle's 's acceleration along path based on its speed, the distance to the vehicle ahead, and predefined parameters to ensure safe and efficient driv- ing behavior. The recently proposed PDM-Closed (Dauner et al., 2023) is an extension of IDM, which uses IDM to plan proposals, performs simulations and scoring, and ultimately selects the best trajectory. It achieved SOTA performance for rule-based planners on the nuPlan dataset.
To mimic human decision-makee timescale decision neuron is designed to manage the tran- sition between the two planners. Inspired by biological neurons, it exhibits both short-term and long-term plastic- ity: Short-term plasticity, which enables rapid responses to environmental changes, governed by score-based and scenario-based rules. Long-term plasticity, which allows the system retain and reflect on past planner performance, is achieved through a decision neuron based on Spike- -imiig
behavior, a dual-
Learning- based planners leverage machine learning mod- els, typically trained on large datasets, to predict and gener- ate optimal trajectories based on observed patterns and past experiences. End-to-end methods such as UniAD (Hu et al., 2023), VAD (Jiang et al., 2023), and VAD2 (Chen et al., 2024), as well as imitation learning- beaseing-based methods like PlanTF (Cheng et al., 2024b), Pluto (Cheng et al., 2024a),
2

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SAH-Drive: A Scenario-Aware Hybrid Planner for Closed-Loop Vehicle Trajectory Generation
and BeTTop (Liu et al., 2024), enhance the generalization of planners by incorporating neural networks into either spe- cific modules or the entire framework. Additionally, a rein- forcement learning-bar basedd method called CarPlanner (Zhang et al., 2025) has been recently proposed, which leverages temporal consistency and expert-guided rewards to generate consistent multi-modal trajectories.
anchors and flexible guidance mechanisms, respectively.
3. Method
3.1. Diffusion Proposal Generation Model
As demonstrated in the study of Diffusion-ES (Yang et al., 2024), reducing the amount of conditioning information broadens the distribution for trajectory sampling, thereby enhancing generalization to out-of-distribution (OOD) tasks. Hence, adopt non-conditional diffusion model, primar- ily referencing Diffusion-ES,, removing evolutionary search and enhancing the trajectory encoder to design the proposal generation model. The diffusion probabilistic model for vehicle trajectory generation is detailed in Appendices A and B, which is composed of three key stages: Feature Fu- sion, Aeff-Attention Fusion, and Decoding and Denoising.
Recent efforts in the autonomous driving community have also attempted combine the rule-based planner with the learning-based planner, in order to create dual planner that maintains the efficiency of rulc-based planners while also achieving the generalization capabilities of learning- based planners. This concept has been applied in both DriveVL.M-Dual (Tian et al., 2024) and DualAD (Wang et al., 2024), with corresponding experimental validation. The scenario-aware hybrid planner paradigm proposed in this paper is also inspired by this idea.
3.2. Overall Architecture of SAH-Drive
2.2. Diffusion Model for Trajectory Generation
The overall architecture of SAH-Drive, as shown in Figure 2, consists of three main components: trajectory candidates generation, trajectory scoring, and dual planner switching. Initially, the lane centerline from the starting point to the endpoint is generated through Dijkstra search, followed by the PDM proposal generator and diffusion proposal genera- tor, which produce PDM proposals and diffusion proposals, respectively. To satisfy the physical constraints of the tra- jectory. these proposals are evaluated through simulation using an LQR controller, producing PDM trajectories and diffusion trajectories. These trajectories are then evaluated using the widely adopted PDM score (Dauner et al., 2023), with further details provided in Appendix 1). The planner switching is performed based on the dual-timescale decision neuron to select the final output trajectory. The learning- based planner operates based on a diffusion model with a lower frequency, while the rule-based planner runs based on PDM-Closed with higher frequency. During each ex- ecution of the learning- based planner, the tial-timescale decision neuron determines the better planner.
Diffusion models model data distributions and generate data similar to the training data, which have shown excep- tional generative capabilities on various tasks, such as image generation (Song et al., 2020) and text generation (Austin et al., 2021). Furthermore, diffusion models can model the distribution of trajectories and be used for trajectory genera- tion, which has been the focus of much research recently.
Diffusion model in robotics: Diffuser (Janner et al., 2022) first used a diffusion model to integrate the trajectory plan- ning into the model sampling process by iteratively denois- ing trajectories, and implemented reinforcement learning counterparts to classifier-guie guided sampling and image in- painting. Decision Diffuser (Ajay et al., 2022) proposed modeling policies return-cnnditional diffusion models within the framework of conditional generative modeling, further improving Diffuser's performance.
Diffusion model in autonomous driving trajectory plan- ning: STR (Sun et al., 2023) used a diffusion-based key point decoder to model the multimodal distribution of fu- ture states resulting from interactions among multiple road users. Diffusion-ES ( (Yang et al., 2024) first combined the diffusion model with evolutionary search algorithm and used it in the autonomous driving trajectory planning task. The diffusion model is used to learn the diverse trajectory distribution from the nuPlan dataset and generate similar trajectories. These generated trajectories are then used as an initial population for evolutionary search, iterating towards the optimal trajectory. This method integrates multimodal information, but due to the use of evolutionary search, it is slower in planning and not suitable for real-time operation. More recently. approaches like DiffusionDrive (Liao et al., 2024) and Diffusson Planner (Zheng et al., 2025) extend dif- fusion models for planning by introducing truncation around
It is worth noting that SAH-Drive uses a diffusion model as the learning- based planner to sample multimodal pro- posals. In fact, it can also be replaced with other exist- ing learning- based plamners such as PlanTF (Cheng et al., 2024b) and Pluto (Cheng et al., 2024a). Following the SAH paradigm, we integrated these planners with PDM-Closed as well. The corresponding experimental results are provided in Appendix F.
Proposal Number Regulator: To improve planning effi- ciency, we implemented a dynamic proposal number regula- tor that adaptively adjusts the number of diffusion proposals in real-time based on the highest diffusion trajectory score.
Specifically, the number of diffusion trajectories N' is dy-
3

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SAHI-Drive: A Scenario-Aware Hybrid Planner for Closed-Loop Vehicle Trajectory Generation
Initialize
Physicul Constraint Satisfaction
PDM Proposal
Generator
Ô O0O PDM Proposal
LQR
PDM Trajectory
Diffusion Proposal
Generator
Simulation
Dijkstra Search
Proposal Number
Diffusion Proposal
Diffusion Trajectory
Regulator
Trajectory Candidates Generation
CC
DAC
DDC
MP
Scoring
Trajectory Fusion
Trajectory Scoring & Fusion
Short-TTern
Score
Plasticity
Rule-Based Planner
Learning-Band Based Planner
Scenario
Long- Term Plasticity
Long-Term
STDP
Dual Planner Switching
The overall architecture of SAH-Drive. (a) Given the starting point, endpoint, and map information, dynamically generate the PDM and diffusion proposals, then convert them into trajectories. (b) Evaluate and fuse the trajectories based on No ft-fault Collisions (NC), Drivable Area Compliance (DAC), Driving Direction Compliance (DDC), Making Progress (MP), Time to Collision (TTC), Ego Progress (EP), Speed-limit Compliance (SC), and Comfort (C). (c) Switch between the rule-based planner and the learning-based planner based the dual-timescale decision neuron.
Figure 2:
namically adjusted based on the highest diffusion trajectory score Sair relative to threshold 7. The trajectory count is updated based on the highest trajectory score:
Plused is then calculated through an exponential weighting mechanism, shown in Equation (3):
(3pDst Smax) PPDM c Sma Pait
N 2
Plused eae Spas) + ca(
Sdiff > T
N'
2NN,
Saif < T.
where PrOM and Pair represent the PDM and diffusion tra- jectories, respectively, while SPDM and 8air denote their corresponding scores. The parameter a controls the sensi- tivity of the weighting to the trajectory scores. The term 8max = max(3ppM, Sair) is introduced to enhance numerical stability, preventing overflow during exponential calcula- tions. The reason for applying exponential weighting the trajectories is assign significantly higher weights to the trajectory with higher score (I.i et al., 2024).
To regulate trajectory count, we apply: N' max (Nuin min(minn Nmax))
(2)
Because the rule-based planner serves fallback, the reduction in the number of diffusion proposals has little impact on the planning performance.
Trajectory Fusion for the Learning- Beanning-Based Based Planner: To mitigate the risk of excessively aggressive diffusion trajec- tories, which could pose high driving risks the ego vehi- cle, propose novel fusion approach that combines the highest-scoring diffusion trajectory with the highest-scoring PDM trajectory. Given that the fused trajectory may not inherently adhere to the physical constraints the vehicle, we first identify the proposals corresponding to the highest- scoring diffusion and PDM trajectories. The fused proposal,
3.3. Dual-Timescuu Decision Neuron
Neurons, the of human intelligence, exhibit both short-term and long-term plasticity. Short-term plasticity refers to the transient changes in synaptic strength, enabling neurons to quickly adjust and respond to immediate stimuli, thereby rapidly adapting to environmental changes. Long- term plasticity allows neurons to form lasting changes in

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SAH--Drive: A Scenario-Aware Hybrid Planner for Closed-Loop Vehicle Trajectory Generation
Algorithm Planner Selection Using Dual-Timescale Deci- sion Neuron
#c excellent counts
Learning-Rased Planner
STDPPRased Decision Neuron
Scenanio
Require:
Rule-based planner score Se, Learning- baaed
Male-Based Planner
Re
planner score 8,
Ensure: Selected planner
1: Update weights wy and We using Equation (5)
$1
Score
2: Update consecutive counts ne and np
$2
3: category +- decision space(sr, Sc: Re, np)
/TTDe
4: category = score then planner <- score rule(s,: sc)
Score
Scenario
else if category = scenario then
Scenario
STDP
7: planner scenario scenario.rule(mee Rp))
fr poor counts
8: else
9: planner + STDP neuron we)
Figure 3: The decision space of the dual-timescale de- cision neuron. In the horizontal dimension, the red and blue areas represent the score-based switching rule, the yel- low area represents the STDP-based decision neuron that selects the planner, and the vertical dimension represents the scenario-based switching rule.
10: end if
11: return planner
ning frames. This typically occurs in scenarios where the vehicle becomes stuck behind another vehicle and cannot overtake, resembling a local minimum. We refer to this state as trapped and use it as one of the entry points for the learning-basse planner, where the system continuously searches for opportunities to escape the local minimum.
their connections, which is crucial for learning and memory. Therefore, we designed the score-based switching rule and the scenario-based switching rule as the short-term plasticity of the dual-timescale decision neuron, enabling immediate transitions between planners according their performance. Additionally, we implemented an STDP-bassd decision neu- ron as the long-term plasticity of the dual- tileecllee decision neuron, allowing for the memory and reflection of the plan- ners' performance. The dire-dimmensionl decision space for the dual-timescale decision neuron is shown in Figure 3, and its algorithmic form is presented in Algorithm
STDP-Based Decision Neuron: To facilitate memory and reflection on the performance of the dual planner during the planning process, we draw on the concept of synaptic plas- ticity from neuroscience. Synaptic plasticity can be simply understood as the process of adjusting the "bridges" be- tween neurons. By modifying the width or strength of these "bridges", neural networks can autonomously regulate the speed and efficiency of information transmission, thereby improving learning and memory processes. Spike-Timing Dependent Plasticity (STDP) (Markram et al., 2011) is a typical synaptic plasticity mechanism where synaptic weight depends on the timing of presynaptic and postsy- naptic spikes. Specifically, if the presynaptic neuron fires a spike before the postsynaptic neuron, the synaptic weight will increase; if the postsynaptic neuron fires spike before the presynaptic neuron, the synaptic weight will decrease. This mechanism can be mathematically described by the following formula:
Let the score of the rule- based planner be denoted as 8, and the score of the learning- based planner as s, Based on two threshold values, $1 and 82, both 8, and 8, are classified into three levels: excellent, ordinary, and poor. When both S. and 8, are poor, the dearning-basee planner is selected to seek opportunities. When one planner performs poorly while the other is at an ordinary excellent level, the ordinary excellent planner is chosen. When both 5, and 8, are an ordinary or excellent level, the based decision neuron is activated to select the better planner.
Score-Based Switching Rule:
A+ exp -At)
if > 0 (LTP) < (LTD)
Aw AA exp A
(4)
if
The system should revert to the rule-based planner after invoking the learning-basee based planner, for the sake of efficient planning. If the best PDM score maintains excellent for ne consecutive runs, as shown in Figure 3, it indicates that the rule-based planner is per- forming well in the current scenario and should be used. During experiments, we observed that if the learning-bast based planner consistently produces low scores over np iterations, tends to remain in low-scoring states in subsequent plan-
Scenario-Based Switching Rule:
where At Epost - tpc is the time difference between the firing times of the postsynaptic and presynaptic neurons, A and A are the gain factors for long-term potentiation (LTP) and long-term depression (LTD), respectively (usu- ally A+ > AT), and r+ and -- are the time constants associated with LTP and LTD, controlling the timescale of synaptic weight changes. Specifically, for a positive time difference (At > 0), when the presynaptic neuron fires be-
5

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SAH-Drive: A Scenario-Aware Hybrid Planner for Closed-Loop Vehicle Trajectory Generation
fore the postsynaptic neuron, LTP occurs, and the synaptic weight increases. For negative time difference (Ar < 0), when the postsynaptic neuron fires before the presynaptic neuron, LTD occurs, and the synaptic weight decreases.
swer the following research questions: 1) Does SAH-Drive exhibit scenario-aware characteristics by employing the rule- based planner in regular scenarios and the carning-based planner in long-tail scenarios? 2) Can better planning per- formance be achieved by combining the rule-based planner and the learning-baat based planner?
Synpphic Wiight Change
Synaptic Weight Change
4.1. Datasets and Metrics
0.5
0.5
The nuPlan Mini dataset is a compact version of the full nuPlan dataset, designed for efficient ex- perimentation in autonomous driving. Despite its reduced size, it retains sufficient diversity for tasks like detection, prediction, and planning, making it ideal for rapid proto- typing and algorithm validation. Validiation dataset: We use Val14 (Dauner et al., 2023) and 4-Random (Cheng et al., 2024b) to evaluate the planner's 's performance in reg- ular scenarios and interPlan (Hallgarten et al., 2024) and Test 4-rd (Cheng et al., 2024b) to assess its performance in long-tail scenarios.
Training dataset:
-0.5
-50
0
50
100
Time Difference (a) Typical STDP
Pre Pre-synaptic Neuron Score
(b) Score- STDP
Figure 4: Illustration graph of STDP. (a) The x-axis rep- resents the time difference between the activation of the two neurons, while the y-axis represents the change in the connection weight between the two neurons. (b) The x-axis represents the planner's s score, while the y-axis shows the change in the connection weight between the planner and the decision neuron.
Metries: As the proposed planner is not imitation-based method, open-loop evaluation metrics are not the main focus. Therefore, the evaluation is conducted using the closed- loop score reactive (CLS-R) and closed-loop score non- Reactive (CLS-NR) metrics (Karnchanachari et al., 2024).
Based on the STDP, we can design decision neuron to switch between the rule-based planner and the learning- based planner. Specifically, the planners are treated as presynaptic neurons, and decision neuron is designed as the postsynaptic neuron. The synaptic weights between the presynaptic and postsynaptic neurons are updated using the STDP mechanism. Mimicking the STDP formula, the score of the neuron (planner) is treated as the spike time. Un- like typical STDP, where the magnitude of synaptic weight change increases the absolute time difference decreases, in score-based STDP, larger absolute score differences lead to greater synaptic changes. Thus, we use the negative reciprocal of the score difference as the time difference: The weight update follows the formula:
The baselines are listed in Appendix C.
4.2. Quantitative Results
We conducted a simulation analysis comparing the proposed method with baselines. The planner's performance on in- terPlan is shown in Table 1, and its performance on four nuPlan splits, including both regular and long-tail scenarios, is presented in Table 2, from which we derive the following findings:
1. PDM-Closed exhibits conservative effects, while the diffusion proposal generator demonstrates radical effects. PDM-Closed performs better in the jaywalker scenario but has lower scores in the overtaking and construction zone scenarios. On the other hand, Diffusion-ES. by continuously seeking opportunities through the diffusion model, excels at lane-changing, achieving good performance in overtaking but scoring lower in the jaywalker scenario, due to collisions with pedestrians that appear suddenly. This is consistent with human driving experience: The jaywalking scenario indeed requires conservative driving, while the overtaking scenario necessitates radical driving.
At
-1
Spoat Spie"
A+ C ($pe +p
if Spost < SSrre
(LTP)
A w==
-A- e (spus "poa)r
if Spre < Spost
(LTD)
(5)
where Spre is the score of the presynaptic neuron (the score of the planner), and ppost is the score of the postsynaptic neuron, which is set as the threshold value for the excellent score 81. The illustration graphs of the typical STDP and the proposed score-based STDP are shown in the Figure 4.
2. The proposed method achieves the highest score in inter- Plan and Test14 Hardd, and delivers near SOTA performance on Val14 4 and Test 4-Random validating its effectiveness in both regular and long-tail scenarios. Since the plan- ner in this paper is a flexible combination of a diffusion model and PDM-Closed, the comparison between SAH-
4. Experiment
In this section, SAH-Drive is assessed within the nuPlan benchmark (Kamchanachhri et al., 2024), a well-recognized framework that incorporates estimated perception data for vehicles, pedestrians, lanes, and traffic signs. We aim to an-
6

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SAH-Driie: A Scenario-Aware Hybrid Planner for Closed-Loop Vehicle Trajectory Generation
Table I: Specific comparison with SOTA planners on interPlan. interPlan includes eight types of long-tail scenarios: construction zones, accident zones, jaywalkers, nudging, overtaking, low traffic density lane-changing, medium traffic density lane-changing, and high traffic density lane-changing. The highest score in each scenario is indicated in bold, while the hechee- score is underlined.
Planner
Type
interPlan
Constr,
Acc.
Jayw.
Nudge
Overt.
LTD
MTD
HTD
PDM-Closed (CoRI. 2023)
Rule
42
18
48
74
62
62
62
STR2 (arxiv 2024)
Learning
46
/
/
/
/
/
HybridLLMPlanne (IROS 2024)
Hybrid
53
27
20
48
93
28
81
48
80
SOTA
Diffusion-ES (CVPR 2024)
Learning
57
71
51
13
88
52
61
58
PlanTF (ICRA 2024)
Learning
33
33
49
50
40
73
Pluto (arxiv 2024)
Learning
48
54
56
82
17
47
47
68
Diffusion Planner (ICLR 2025)
Learning
24
17
7
70
15
41
22
17
SAlI-Drive (Ours)
Hybrid
64
72
44
47
80
78
64
63
63
Suboptimal
Urban Driver (CoRL 2022)
Learning
4
0
0
0
29
0
GameFormer (ICCV 2023)
Learning
11
0
48
0
0
20
21
DTPP (ICRA 2024)
Learning
25
18
18
44
10
0
40
36
34
IDM (Phys. Rev. E)
Rule
31
66
0
61
61
61
Table Comparison with SOTA planners on different splits of nuPlan. Including interPlan (long tail), Val14 (regular), Test Resnlo-Random (regular), Test14-Hard (long tail). The highest score is indicated in bold, while the second-highest score underlined.
Planner
Type
interPlan
Val14 (R)
Val14 (NR)
Testl4-Random (R)
TestI4-Random (NR)
Test14-Hard (R)
TestI4-Hard (NR)
PDM Closed
Rule
42
92
93
90
75
65
Learning
MPPlunner
Hybrid
53
/
Diffusion-HB ES
Learning
57
92
/
77
77
PlanTF
Learning
33
77
84
80
85
61
69
Pluto
Learning
48
78
89
78
89
60
70
DiffusionPlanner
Learning
24
89
69
SAH-Drive
Hybrid
64
86
Saboptmul
UrbanDriver
Leariing
4
52
49
GameFormer
Learning
84
DTPP
Leaming
25
/
IDM
Rule
31
77
75
74
70
62
56
Table 3: Ablation experiments on the planner hierarchy and the dual-timescale decision neuron hierarchy. The
percentage following the score of each variant indicates the score change compared to the original SAH-Drive.
Variant
interPlan
Constr.
Acc.
Jayw.
Nudge
Overt.
LTD
MTD
HTD
Original
64
72
44
47
80
78
64
63
63
Learning-Bel
62(-3.13%)
79(9.72%)
44(0.00%)
44(-6.38%)
(9(-.25%%
74(-5.13%)
59(-7.81%)
63(0.00%)
57(6..52%)
Rule-Based Based
40(-37.50%)
18(-75.00%%
0(-100.00%)
29(-38330%
75(-6.25%)
9(-88.46%
621-3.13%)
62(-1.59%)
63(0.00%)
Score Rule
58(-9.38%)
60(-16.67%)
26(-40.91%)
46(-2.13%)
80(0.00%)
67(-14, 10%%)
62(-3.1386
62(-1.59%)
62(-1.59%)
Scenario Rule
43(-32.81%) (6)
25(-65.2899
51-88.64%)
29(-38.30%)
75(-6.25%)
24(-69.23%)
63(-1.56%)
62(-1.59%)
63(0.00%)
Decision Neuron
62(-3.13%)
62(-13.89%%
53(20.45%)
47(0.00%)
80(0.00%)
68(-12. 82%)
63(-1.56%)
62(-1.59%
63(0.00%)
Drive, PDM-Closed, and Difflusin-EES demonstrates that SAH-Drive combines the advantages of both rule-based and learning-basee based planners. This is accomplished by utilizing dual-timescale decision neuron, thereby achieving high performance in both jaywalking and overtaking scenarios.
based planner and allows for the of simpler structures. The diffusion model in this study was trained only the nuPlan mini dataset, yet it outperforms models trained on the entire nuPlan dataset, as most of the planning knowl- edge has already been internalized by the rule-based planner. Comparisons with the HybridLLMPPlnner also suggest that training dedicated generative model for autonomous driv- ing may be a better choice than enhancing traditional mo-
The use of a scenario-awer aware hybrid planner paradigm sig- nificantly reduces the data requirements for the learning-

---

SAHH-Drive: A Scenario-Aware Hybrid Planner for Closed-Loop Vehicle Trajectory Generation
PDM ONM-Cllne
SAIL-Drive
Starting Planning
Learning-Bast Based Planner Finding Opportumity
Returning to Rule-Based Planner
Figure 5: Visualization of a typical long-tail overtaking scenario. The white rectangle represents the ego vehicle, while the green rectangle indicates an illegally parked vehicle on the road. The overtaking maneuver must be completed for the planning to be considered successful.
tion planners with LLM. To further validate this conclusion, analyzed the impact of model parameters and training dataset size the performance of SAH-Drive. Detailed experiment analysis can be found in Appendix E.
4.4. Ablation Study
We conducted ablation experiments to analyze the roles of the rule-based planner and the learning-based planner, well as the effects of the score-based switching rule, the scenario-ban based switching rule, and the STDP-based decision neuron in the dual-timescale decision neuron.
is worth noting that although STR2 achieves state-of-the- art performance on Val14 by increasing model parameters and training data through scaling laws, its performance on interPlan is only slightly better than PDM-Closed due the lack of long-tail scenarios in nuPlan.
In the ablation study of the planner, the variant refers to using only the corresponding planner instead of the com- plete SAH-Drive. As the simulation results Table 3 show, the dual planner that combines the rule-based and learning- based planners achieves superior performance compared to using either planner alone. Specifically, the rule- based planner underperforms in scenarios requiring opportunity- seeking capabilities, such as overtaking (-88.465 -.88.46%) and acci- dent zones (-100%), while the learning- planner strug- gles safety-critical scenarios, such as those involving jaywalkers (-6.38%). By integrating both planners, the dual planner achieves better results in both jaywalker and acci- dent zone scenarios.
4.3. Qualitative Results
Figure is the visualization of typical long-tail overtaking scenario. The ego vehicle initially adopts conservative be- havior and drives within the current lane. When encounter- ing a loig-tail scenario, the learning-based planner achieves a higher score, prompting the planning system to switch to the learning-based planner, continuously seeking oppor- tunities to navigate through the long-tail scenario. After passing through the long-tail scenario, due to the system's preference for the rule-based planner in regular scenarios, the ego vehicle returns to the lane and adopts the conserva- tive strategy again. More qualitative results are shown in Appendix G.
In the ablation experiment of the dual-timescale decision neuron, the variant refers to using only the corresponding nule or neuron for decision switching. We observe that using only the score-based switching rule leads performance degradation in accident and construction zone scenarios. Using only the scenario-based switching rule results in performance drop, mainly in accident and overtaking sce-
8

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SAH-Driiv:: A Scenario-Aware Hybrid Planner for Closed-Loop Vehicle Trajectory Generation
narios. When relying solely on the STDP-based decision neuron for planner switching, the performance decline is less pronounced compared to using only the score-based or ecenario-based switching rule. The performance deteriora- tion mainly observed in the construction and overtaking scenarios.
Rile plans peiio
Louning: based planner period
Rogular secnario: sraight-througg intersection
Planning Time (s)
SAH--Dive
PDM-
4.5. Runtime Analysis
0.5
We conducted a runtime analysis of PDM-Closed and SAH- Drive on computer with an i7-14700KF CPU and an RTX 4080S GPU. The computation time per frame for PDM-Clon PDM-Closed is approximately 0.1 seconds, whereas for SAH-Drive, the maximum is around second. In contrast, Diffusion-ES requires about 5 seconds to plan a single tra- jectory. For large language models, computation time varies depending on the specific model employed. Typically, larger models generate results more slowly and are challenging to apply in real-time scenarios. The computation time for SAH-Drive is acceptable compared the increased compu- tation time associated with using large language models or other larger models decision planners.
80
100
120
Frame Number
Long-tail scenario: overtake
Planning Time
10 0.5
SAH-Drive PDM-Closed
60 80
"100""120"1100
"180 200 220 220 220
250
2000
Figure 6 is the visualization of the runtime analysis. The upper part of the figure corresponds to regular straight- through intersection scenario from nuPlan, with a duration of 15 seconds, sampled at -ssconn intervals, resulting in 150 frames (labeled by frame number in the figure). The lower part of the figure corresponds to overtaking sce- nario within the interPlan simulation, with duration of 30 seconds, also sampled at O. 0.1--eee -ssccon intervals, resulting in 300 frames. The Y-axis in the figure represents the execution time per frame for SAH-Drie and PDM- PDM-Closed.
Frame Number
Figure 6: Runtime analysis of SAH-Drive and PDM- Closed under an overtaking scenario. The red circle represents the planning time of SAH-Drive, while the blue triangle indicates the planning time of PDM-Closed. The green and yellow bars denote the stages where the rule- based and learning-based planners are used, respectively.
In the regular scenario, the total computation times of PDM- Closed and SAH-Drive were 13.66 seconds and 16. 15 sec- onds, respectively. In the long-tail scenario, the times were 26.18 seconds and 25.17 seconds, respectively. In both scenarios, the two methods exhibited comparable overall computation times. This is due to the use of the diffusion proposal number regulator and the SAH paradigm, which means that longer planning times, such as second, account for only a small portion of the overall SAH-Drive planning process.
5. Conclusion
In this study, we proposed SAH-Drive, planner that ex- tends PDM-Closed by integrating a diffusion model to sam- ple multimodal proposals. SAH-Drive used a scenario- aware hybrid planner paradigm, which employs a dual- timescale decision neuron, incorporating the score-based switching rule, the scenario-based switching rule, and the STDP-based decision neuron enable flexible transition be- tween the rule-based planner and the learning-basd -based planner. Simulations demonstrate that the proposed SAH-Drive effec- tively combines the advantages of rule-based and learning- based planners, improving the generalization of the planning system without incurring substantial additional computation time.
4.6. Scenario-Aware Characteristics Analysis
Figure also illustrates the switching process between the rule-based planner and the learning-based planner. In the regular scenario, the learning-based planner active for only 6.70% of the time. In contrast, its activation rate in- creased to 43.33% in the long-tail scenario, highlighting the scenario-aware nature of our method.
The trajectories sampled by the diffusion model do not fully conform to real-world physical dynam- ics and can only be regarded as proposals. Future research should focus on harnessing generative models to directly generate trajectories that adhere to real-world physical con- straints for autonomous driving.
Limitations.

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SAH-Driiv:: A Scenario-Aware Hybrid Planner for Closed-Loop Vehicle Trajectory Generation
Acknowledgements
Dauner, D., Hallgarten, M., Geiger, A., and Chitta, K. Part- ing with misconceptions about learningbbased vehicle motion planning. In Conference Robot Learning, pp. PMLR, 2023.
This work is partially supported by the National Natural Science Foundation of China (No. 52441202), the National Key R&D Program of China (No. 2024YFB43000303, and the Ganwei Program of Beihang
Fan, H., Zhu, F., Liu, C., Zhang, I.., Zhuang, L.., Li, D., Zhu, W., Hu, J., Li, H., and Kong, Q. Baidu apollo em motion planner. arXiv preprint arXiv:/807 807,08048, 2018.
University (No. W72024-2-16).
Impact Statement
Hallgarten, M., Zapata, J., Stoll, M., Renz, K., and Zell, A. Can vehicle motion planning generalize to realistic long-tail scenarios? 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp- 5388-53929 2024.
This paper presents work whose goal advance the field of Machine Learning. There are many potential societal consequences of our work, none which feel must be specifically highlighted here.
Ho, J., Jain, A., and Abbeel, Denoising diffusion proba- bilistic models. Advances in neural information process- ing systems, 33:6840-6855 6851, 2020.
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SAH-Driiv:: A Scenario-Aware Hybrid Planner for Closed-Loop Vehicle Trajectory Generation
A. Diffusion Probabilistic Model for Vehicle Trajectory Generation
A.1. Diffusion Probabilistic Model
Diffusion probabilistic models are class of generative models that capture the data distribution by simulating a forward diffusion process, which gradually adds noise to the data, and then learning to reverse this process through denoising. This method, introduced by Sohl-Dickstein et aSshiii-- al., 2015) and later refined by Ho et al. al., 2020), represents the data generation process series of steps that involve the iterative removal of noise from corrupted version of the data.
The forward diffusion process adds Gaussian noise according to a variance schedule 3r, where the data at timestep t, zt. is corrupted as:
2t = Vāyz + vI āge
(6)
where E ~~ (0, is Gaussian noise, ā, = II a, a; = 1 - 3, and 3r E (0, 1) is the predefined variance schedule.
The reverse process, denoising, parameterized by neural network and can be deseribed by:
N
po( = = | II p
(7)
where To is the original data and p(TN) Gaussian prior. The (Ti-IT can be expressed follows:
po(Te-alT) = (1( Eo(T,))
(8)
As long the forward process follows normal distribution with sufficiently small variance, uo t) and Ce t) can be written as follows:
1
a₁ (T t))
po(Tr, t) = (Tt
Var
VI āi
āt-1 Be
[T( = (TTr,
1 Üt.
By minimizing the variational lower bound on the log-likelihood 6* arg ming -eaiiinn--,,,lle E,gg (o())) we can train neural network 60 !) that predicts the added noise.
Furthermore, the framework can be extended by conditioning the reverse process on additional information c, such as labels or context. The reverse process becomes:
po(T:-1/TE, - E(Te. 6))
(10)
The denoising network ca(Te,t, c) predicts the noise conditioned on c, and the loss function is:
T(0)) == Re, [[]e c0 1, []
(11)
Following previous work(Janner et al., 2022; Yang et al., 2024), in order use the diffusion model for autonomous vehicle trajectory generation, represent the trajectory of the vehicle follows and use the input to the diffusion model.
2n
=
yo
yT
(12)
fg
0
6r
Where and y represent the longitudinal and lateral offsets, respectively, and is the heading angle.
12

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SAH-Driie: A Scenario-Aware Hybrid Planner for Closed-Loop Vehicle Trajectory Generation
A.2. Closed-loop Trajectory Planning
Closed-loop trajectory planning is crucial component the autonomous driving pipeline, ensuring that vehicle's path is dynamically adjusted in real-time based on its current state, environmental conditions, and unforeseen disturbances.
optimization problem, where the objective is to minimize a cost function while considering constraints such as vehicle dynamics, safety margins, and environmental obstacles. The trajectory x(() = [x((), v({), O())] represents the state of the vehicle at time The control inputs u(t) = [o(t), (()]]" include the vehicle's s speed v(t) and steering angle 8tt). The dynamic model governing the vehicle's motion, typically described by a bicycle model or more advanced kinematic models, can be written as:
Mathematically, closed-loop trajectory planning can be formulated
Tt(t)
((t)
f((((),u(t)
(13)
0(t)
where f is a nonlinear function describing the vehicle's motion. this paper, the dynamic model used the bicycle model, and the objective function is the PDM score. The PDM Score is metric used to evaluate driving agent trajectories. computed by aggregating subscores into value in the range [0, 1]. and is efficient reimplementation of the nuPlan closed-loop score metric.
B. The Detailed Design of the Diffusion Proposal Generation Model
Timestep:
History Encoder
Timestep
Encoder
Time Dimension
Dimension Concat
History Trajectory
Self Attention
Concal
Feature
Layer
Trajectory Decoder
Denoise
MLP
Denoised Future Trajectory at Step i
Trajectory
Encoder
Noised
Future Trajectory
Stagel
Stage2
Stage3
Figure 7: The overall architecture of the diffusion proposal generation model
Feature Fusion: This stage integrates inputs construct a unified feature representation for proposal generation. First, the historical trajectory and noised future trajectory encoded into history embedding and trajectory embedding, respectively. Let the historical trajectory be represented as I X,, and the noisy trajectory as X, 1x. ...r.) where Xn, E R denote the positional information at the f-th time step of the respective trajectories. We then compute the first-order and second-order differences of X and x,, corresponding to velocity and acceleration, and denote them as Vh, Vn. aj, and a,, respectively. The position, velocity, and acceleration information of trajectories are concatenated form feature vectors F, = [ Vh, an) and F, = [xn, Vn, a.). These feature vectors are then mapped to fused representation h4 and h, through Multi-Layer Perceptron (MLP). Then, h2 and h, are concatenated along the temporal dimension and further combined with the timestep embedding along the feature dimension. The information from the timestep is then integrated through an additional MLP.
2. Self-Attention Fusion: The feature embedding is refined using self-attention to model global spatial and temporal dependencies. Time position embeddings are added to provide context, and the refined representation passes through eight layers of self-attention. This process enhances contextual understanding and ensures that both spatial relationships and temporal coherence are captured effectively.
13

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SAH-Drrive: A Scenario-Aware Hybrid Planner for Closed-Loop Vehicle Trajectory Generation
The final stage reconstructs the trajectory through iterative refinement. A trajectory decoder predicts trajectory noise, which is progressively used correct the noised future trajectory by denoising module over multiple diffusion steps. Although the diffusion model can effectively capture physical information, the generated trajectories do not strictly adhere to physical constraints and serve merely as proposals that require further refinement through simulation to ensure physical feasibility.
3. Decoding and Denoising:
C. Planning Baselines
We reviewed recent outstanding baselines for closed-loop trajectory planning, outlined below:
1. PDM-Closed (Dauner et al., 2023): rule-based planner that borrows the concept of model predictive control (MPC), using forecasting. proposals, simulation, scoring, and selection to get the trajectory with the highest score.
2. Diffusion-ES (Yang et al., 2024): a learning-based planner that combines diffusion model and evolutionary search, iteratively evolves to obtain the best trajectory.
3. STR2 (Sun et al., 2024): a scalable, MoF-based autoregressive motion planner that leverages ViT and causal transform- ers achieve generalization and scalability on diverse urban driving scenarios.
4. IDM (Treiber al., 2000): a car-following model designed for safe and realistic traffic flow simulations, emphasizing accident prevention and maintaining safe distance the leading vehicle by adjusting its speed.
5. Urban Driver (Scheel et al., 2022): a policy gradient method leveraging differentiable simulator and mid-level representations to efficiently learn and generalize imitative driving policies for complex urban scenarios from large-scale real-world data.
6. Game Former (Huang et al., 2023): a learning- planner that employs hierarchical game theory and a transformer- based architecture to model interactive behaviors between traffic participants.
7. DTPP (Huang al., 2024): a differentiable joint training framework that integrates ego-conditioned motion prediction and learnable context-aware cost evaluation within tree-structured policy planner.
8. LMPrinLLLPPlanne (Hallgarten et al., 2024): a two-stage hybrid planner that combines LLM with PDM-Closed where LLM is used for behavior planning and the PDM-Closed is used for motion planning.
9. Diffusion Planner (Zheng et al., 2025): Utilizes a transformer-base diffusion model to produce trajectories without
rule-based heuristics by jointly handling prediction and planning, guided by a classifier for high-quality sampling.
10. PlanTF (Cheng et al., 2024b): An imitation-baut planner that focuses on essential ego features and effective data augmentations reduce compounding errors and mitigate the imitation gap.
11. Pluto (Cheng et al., 2024a): An imitation learning planner featuring longitudinal- lateral aware architecture, contrastive learning, and efficient auxiliary loss.
Table 4: Closed-loop metric of the PDM score. It consists of multiplicative and weighted metrics, where curly brackets denote discrete value sets, and square brackets represent continuous ranges.
Type
Metric
Weight
Range
Multiplicative Multiplicative
No at- ft-fault Collisions (NC)
Multiplier
{0, 2.11
Drivable Area Compliance (DAC)
Multiplier
{0.
Multiplicative
Driving Direction Compliance (DDC)
Multiplier
(0.
Multiplicative
Making Progress (MP)
Multiplier
{0,1}
Weighted
Time to Collision (TTC)
5
(0,1)
Weighted
Ego Progress (EP)
4
[0,1]
Weighted
Speed-limit Compliance (SC)
1
[0,
Weighted
Comfort (C)
1
{0.1}
D. PDM Score
The closed-loop score consists of weighted metrics and multiplier metrics, assessing the safety, compliance, and driving quality of an autonomous driving system, which is shown in Table 4. The weighted metrics include Time to Collision (TTC), Ego Progress (EP), Speed-lime limit Compliance (SC), and Comfort (C), which evaluate collision risk, driving efficiency, speed regulation, and driving comfort. The multiplier metrics No at-fault Collisions (NC), Drivable Area Compliance (DAC),
14

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SAH-Driiv: A Scenario-Aware Hybrid Planner for Closed-Loop Vehicle Trajectory Generation
Driving Direction Compliance (DDC), and Making Progress (MP)-penalize violations such as at-fault collisions, driving outside allowed areas, wrong-way driving, and failure maintain reasonable progress. The scoring framework integrates these factors to comprehensively assess the planner's performance in autonomous driving tasks.
E. The Impact of Model Parameter Size and Training Dataset Scale
To further validate that the dual planner paradigm reduces data requirements and enables simpler structures, we scaled the feature dimensions and adjusted the training dataset composition. The results of the impact of model parameter size are shown in Table 5. The STR2 model has 800M parameters, with the training dataset being the entire nuPlan training dataset. In comparison, SAH-Drin Arive significantly reduces both model size and training dataset, while performing better in interPlan. The interPlan score is highest when the feature dimension is 16. Both the larger and smaller feature dimensions lead to decrease in the interPlan score.
Table 5: The impact of model parameter size. The original SAH-Drive has a feature dimension of 16, and its model parameter count is only 0.0355% of STR2.
Feature Dimension
Model Parameters
Model Occupancy
interPlan Score
8
143k
0.55Mb
53
SAH-Drive
16
284k
1.08Mb
64
32
581k
2.22Mb
56
STR2
512
800m
46
Table 6: The impact of training dataset size. The dataset sizes for nuPlan _miii, Singapore, and Boston are 7.96 GB, 32.56 GB, and 35.54 GB, respectively. The score of SAH-Drive on interPlan decreases as the dataset size increases.
Dataset Description
Dataset Size
interPlan Score
nuPlan Mini
7.96Gb
65
nuPlan Mini--Snn
40.52Gb
61
nuPlan Mini-SSingaporreBBoston
76.06Gb
59
As shown in Table 6, the planner's score decreases when trained on larger dataset. This suggests potential mismatch between the model capacity and the complexity of the expanded data, or that the additional data introduces distributional challenges. This observation highlights that, within the scenario-aware hybrid planner paradigm, the learning-based planner can be deliberately kept lightweight and trained on targeted long-tail scenarios, leaving the rule-based planner to handle more regular cases. Nonetheless, designing more complex learning- planner using scaling laws and training on large-scale datasets remains a promising alternative.
F. Further Validation on the Effectiveness of the SAH Paradigm
Table 7: Experimental results of PDM+PlanTF and PDM+Pluto combined under the SAH paradigm. The highest
score is indicated bold, and the percentage increase in scores of TF and Pluto after combining PDM under the SAH paradigm, compared to their original versions, is underlined.
Planner
interPlan
Val14 (R)
Val14 (NR) Test14 Manll--aandom (R)
Fest14-Random (NR)
Test14-Hard (R)
Test14-Hlard (NR)
Pluto
SAH PPDM+Pluto)
49(2%%()
79(1%))
88(13%
88
81(55%1)
78(11%8
PlanTF
33
84
80
85
61
69
SAH (PDM+PlanTF)
40(21%)1
78(1%)1)
86(2%)1
86(8981)
87(2%8
80(31%))
77(12%))
SAlI-Drive (Ours)
64
90
89
87
86
83
78
We use PF and Pluto as learning-based planners and PDM- as the rule-based planner, combining them within the SAH paradigm for simulation experiments. The results are summarized in Table As shown in the table, both PlanTF
15

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SAH-Driiv: A Scenario-Aware Hybrid Planner for Closed-Loop Vehicle Trajectory Generation
and Pluto achieve consistent improvements across various nuPlan splits when integrated with PDM-Closed under the SAH paradigm, outperforming their original versions. The performance gains particularly notable Test 4-Hard (R), with improvements of 35% and 31 %, respectively.
G. More Qualitative Results
As shown in Figure 8, SAH-Drive successfully plans construction zones, jaywalker, nudge, accident scenarios, whereas PDM-Closed fails in both the construction zone and accident scenarios, as stops behind the obstructing vehicle and is unable change lanes. This demonstrates that SAH-Drive has better generalization ability than PDM-Closed and is more capable of handling planning in long-tail scenarios. The left side of the figure shows the scenario corresponding the visualized simulation results in the middle, while the right side indicates the planner used and whether the planning was successful.
16

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SAHI-Drive: A Scenario-Aware Hybrid Planner for Closed-Loop Vehicle Trajectory Generation
PD SAII-Drive
Construction Zone
Jaywalker
Nudge
Accident
HDM-Closch
Figure 8: More qualitative results of SAH-Drive and PDM-Closed
17


Reviews:
[{"review_id": "VkDRXSrlxy", "reviewer": "Reviewer_NL4n", "summary": "This paper performs post-ensemble on two SOTA methods on the nuPlan Val14 dataset. Drawing inspiration from Spike-Timing Dependent Plasticity（STDP）in neuroscience, it proposes a novel scoring method called Score-based STDP. Additionally, it incorporates both a Scenario-based switching rule and a Score-based switching rule to evaluate the generated trajectories. Experimental results show that the proposed approach achieves performance close to SOTA on the nuPlan Val14 dataset and achieves SOTA on the interPlan dataset.", "questions": "", "limitations": "", "rating": 3, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We thank the reviewer for the constructive comments. Regarding the concerns of the reviewer NL4n, we provide the following responses.\n\n> Q1: Achieving SOTA only on the interPlan may not be enough, especially given that it fails to outperform the ensemble methods this paper used on Val14.\n\nThanks for the constructive comment. We understand why the reviewer might perceive a **\"1+1 < 2\" issue**—where integrating PDM-Closed and Diffusion-ES within the SAH paradigm appears to yield a lower score. However, this is a misunderstanding. The learning-based planner used in SAH-Drive is not the original Diffusion-ES, but rather a simplified version that removes evolutionary search and reduces the model complexity. \n\nWe simplify the Diffusion-ES to **improve planning efficiency**, achieving a **nearly 98% reduction in average per-frame runtime—from 5s to just 0.1s**. Additionally, our optimization **drastically reduces** the model’s memory footprint (**from 25.62MB to 1.08MB, a 96% decrease**) and parameter count (**from 6.7M to 284K, also a 96% reduction**). This lightweight design enables the model to achieve strong performance even when trained solely on nuPlan-mini. However, this efficiency gain comes with a slight performance trade-off.\n\nAs shown in the table below, when evaluated on Val14, this simplified diffusion-based planner score is **88**. Additionally, SAH-Drive’s rule-based planner (PDM-Closed) operates at 5Hz, and its standalone performance on Val14 is **90**. When combining these two planners within the SAH paradigm, the overall performance remains **90**, which does not indicate a drop in performance compared to its individual components. This directly addresses the reviewer’s concern.\n\n| Planner                                                         | interPlan | Val14  |\n| --------------------------------------------------------------- | --------- | ------ |\n| PDM-Closed 5Hz                                                  | 40        | **90** |\n| Simplified diffusion-based planner                              | 62        | **88** |\n| SAH-Drive (PDM-Closed 5Hz + Simplified diffusion-based planner) | 64        | **90** |\n\n> Q2: Score-based STDP is interesting, but my concern is that it isn’t effective in val14, raising questions about its generalization ability.\n\nThanks for pointing this out. Regarding generalization concerns, we conducted further experiments on two extra datasets: Test14-Random and Test14-hard. Results on **interPlan, Val14, Test14-random, and Test14-hard** are shown in rebuttal table 2 (https://anonymous.4open.science/r/SAH-Drive-Pic-9E34/benchmark_table.png). SAH-Drive achieves SOTA on interPlan and Test14-hard and near-SOTA on Val14 and Test14-random, which confirms SAH-Drive’s robustness and generalizability across diverse scenarios.\n\n> Q3: Most aspects aside from STDP feel somewhat unremarkable and the Scenario-based and Score-based switching rules seem somewhat naive. \n\nWe appreciate the reviewer for the comment but disagree with our highest respect that most aspects aside from STDP are unremarkable. The Scenario-based and Score-based switching rules play a crucial role in complementing the STDP-based decision neuron.\n\nThe Scenario-based and Score-based rules provide immediate decision-making capabilities, analogous to Short-Term Plasticity in biological neurons, which enables rapid adaptation to sudden environmental changes. If we were to rely solely on the STDP-based decision neuron, its memory-driven nature—which accounts for historical planning performance—could lead to delayed responses to sudden, strong signals. This delay in planner switching could be problematic, especially in autonomous driving scenarios where immediate adaptation is critical. The ablation experiment table in the original paper (p7, l350) further validates this, showing that without switching rules, the planner’s interPlan score decreases by **3.13%**. Unlike approaches that emphasize model complexity and large-scale datasets, SAH-Drive reduces reliance on extensive training data through flexible planner switching. \n\n\n> Q4: The paper still relies on the PDM scorer to evaluate planner-generated trajectories, which often requires precise lane markings and surrounding vehicle motion information. Such information is often expensive to obtain in real-world scenarios.\n\nThanks for your valuable comments, and we understand your concerns. However, our study primarily focuses on **improving the trajectory planning paradigm and the quality of its planned trajectories** given well-perceived environmental information, rather than addressing the perception challenge itself. In real-world scenarios, high-fidelity perception systems in autonomous vehicles already provide such environmental information, **ensuring that our approach remains applicable in practical deployments**.", "claims_and_evidence": "The authors claim the effectiveness of the proposed method by demonstrating SOTA performance on the interPlan dataset. However, on the nuPlan Val14 dataset, its performance is lower than that of the two ensemble methods it utilizes. The optimal parameters for the PDM scorer vary across different scenarios/sub-dataset of nuPlan, and simply adjusting these parameters can lead to significantly different results. Therefore, achieving SOTA only on the interPlan may be not enough, especially given that it fails to outperform the ensemble methods this paper used on Val14.", "methods_and_evaluation_criteria": "Strength:\nIntegrating learning-based and rule-based methods to address the long-tail problem and the computational efficiency issues of learning-based approaches is a practical direction. \nWeakness:\nHowever, with the adoption of a post-ensemble approach, most aspects aside from STDP feel somewhat unremarkable. The two ensemble methods used belong to others, while the Scenario-based and Score-based switching rules seem somewhat naïve.", "theoretical_claims": "Score-based STDP is interesting. It seems to effectively integrate two different methods in interPlan, but my concern is that it isn’t effective in val14, raising questions about its generalization ability.", "experimental_designs_or_analyses": "Yes. Fig. 6 is intuitive, allowing readers to understand in which scenarios the planner switching occurs. From the figure, it seems that the switch is triggered by a relatively low Ego progress score of the PDM-closed planner. This effectively addresses the difficulty of lane-changing in rule-based methods.", "relation_to_broader_scientific_literature": "The Score-based STDP used in paper is related to the Spike-Timing Dependent Plasticity in neuroscience.", "essential_references_not_discussed": "NA", "other_strengths_and_weaknesses": "Strength：\n\nThe use of the diffusion proposal number regulator significantly reduces planning time. SAH achieves planning times even shorter than the rule-based planner PDM-Closed, enhancing the efficiency of learning-based planners in real-world applications.\n\nWeakness：\n\nThe paper still relies on the PDM scorer to evaluate planner-generated trajectories, which often requires precise lane markings and surrounding vehicle motion information. Such information is often expensive to obtain in real-world scenarios.", "other_comments_or_suggestions": "NA", "questions_for_authors": "NA", "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": "J9MBpn4Kox", "reviewer": "Reviewer_Z9oQ", "summary": "This work introduces a hybrid planning framework that combines diffusion-based and rule-based proposal generation, evaluated through PDM simulation, with asynchronous updates and spiking neurons guiding final trajectory selection. To optimize efficiency and performance, an adaptive proposal regulator dynamically adjusts the number of diffusion proposals based on overall scoring. Additionally, a STDP-based decision neuron enables adaptive switching between rule-based and learning-based proposals by considering time delays and long-short-term synaptic plasticity rules. The proposed planner achieves compelling results on the InterPlan long-tail benchmark in nuPlan.", "questions": "", "limitations": "", "rating": 3, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We sincerely appreciate the reviewer’s constructive feedback. In response to the concerns raised by Reviewer Z9oQ, we provide the following responses.\n\n> Q1: Detailed explanation on the proposal regulator.\n\nThe number of diffusion trajectories $N'$ is dynamically adjusted based on the highest diffusion trajectory score $S_{\\max}$ relative to a threshold $\\tau$. \n\nThe trajectory count is updated based on the highest trajectory score:  \n$$N' =\n   \\begin{cases}\n   \\frac{N}{2}, & S_{\\max} > \\tau, \\\\\n   2N, & S_{\\max} < \\tau.\n   \\end{cases}$$\nTo regulate trajectory count, we apply:\n$$N' = \\max(N_{\\min}, \\min(N', N_{\\max})).$$\nWe will add the detailed explanation of the proposal number regulator in the final version.\n\n> Q2: Including an algorithm to explicitly illustrate the switching workflow\n\nWe outline the decision process (Fig. 3, p5 l220 in the original paper) and will add a detailed algorithm in the revised paper.\n\n**Input**: Rule-based planner score $s_c$, Learning-based planner score $s_r$  \n**Output**: Selected planner  \n1. Update $w_r$ and $w_c$ using Equation (3) from the paper.\n2. Update consecutive counts $n_e$​ and $n_p$.\n3. assign the decision category: category $\\leftarrow$ decision_space$(s_r, s_c,n_e,n_p)$\n4. Select the planner based on the decision category:\n\t- If category = score, then planner $\\leftarrow$ score_rule$(s_r, s_c)$\n\t- If category = scenario, then planner $\\leftarrow$ scenario_rule$(n_e, n_p)$\n\t- Otherwise, planner $\\leftarrow$ STDP_neuron$(w_r, w_c)$\n\n> Q3: Lack of comparison with recent SOTA methods and further comparison on Test14-hard.\n\nWe have extended our benchmark evaluation to include **recent learning-based SOTA methods**: Pluto, PlanTF, and DiffusionPlanner. The results are summarized in the table below:\n\n| Planner           | interPlan | Val14 (R) | Val14 (NR) | test14 (R)   | test14 (NR) | test14-hard (R) | test14-hard (NR) |\n| ----------------- | --------- | --------- | ---------- | ------------ | ----------- | --------------- | ---------------- |\n| Pluto             | 48        | 78        | 89         | 78           | **89**      | 60              | 70               |\n| SAH (PDM+Pluto)   | 49(2%↑)   | 79(1%↑)   | 89         | **88**(13%↑) | 88          | 81(35%↑)        | **78**(11%↑)     |\n| PlanTF            | 33        | 77        | 84         | 80           | 85          | 61              | 69               |\n| SAH (PDM+PlanTF)  | 40(21%↑)  | 78(1%↑)   | 86(2%↑)    | 86(8%↑)      | 87(2%↑)     | 80(31%↑)        | 77(12%↑)         |\n| Diffusion Planner | 24        | 83        | **90**     | 83           | **89**      | 69              | 75               |\n| SAH-Drive         | **64**    | **90**    | 89         | 87           | 86          | **83**          | **78**           |\n\nAs shown in the table, SAH-Drive consistently outperforms SOTA learning-based planners across multiple evaluation metrics. Notably, despite being trained only on nuPlan-mini, SAH-Drive achieves **SOTA scores on interPlan and test14-hard**, while also delivering **near-SOTA performance on other benchmarks**. The full experimental results, including all baselines discussed in the paper, can be found at https://anonymous.4open.science/r/SAH-Drive-Pic-9E34/benchmark_table.png.\n\n> Q4: Further experimental validation is needed by integrating PDM with different types of planners such as SAH + PlanTF and Pluto.\n\nThanks for pointing this out. We employed the SAH + PlanTF and Pluto as the reviewer requested. The results are summarized in the table under Q3. As shown in the table, both PlanTF and Pluto achieve **consistent improvements across various nuPlan splits** when integrated with PDM under the SAH framework, outperforming their original versions. The performance gains are particularly notable on Test14-hard (R), with improvements of **35% and 31%**, respectively.\n\n> Q5: Extra results under different time variance between rule and learning-based planner for further justifications. \n \nTo complement our analysis of time variance between rule-based and learning-based planners, we conducted an additional experiment using SAH-Drive in the regular straight-through intersection nuPlan scenario. The visualization is available at https://anonymous.4open.science/r/SAH-Drive-Pic-9E34/time_variance_visualization.png. The learning-based planner (yellow) was active **6.70%** overall. In contrast, its activation rate **increased** to **43.33%** in the long-tail scenario, highlighting the scenario-aware nature of our method. We will include the above details in the revised paper.\n\n> Q6: Insufficient references\n\nThank you for your suggestion. We will incorporate **Pluto, PlanTF, Diffusion Planner, CarPlanner, and BeTop** into the related work section to provide a more comprehensive discussion of prior research.", "claims_and_evidence": "The writing is generally clear, with some minor ambiguities in the methodology. The section on the proposal regulator would benefit from a more detailed explanation, preferably formulated with equations to clarify its probabilistic mechanism. Additionally, as a non-expert in spiking networks, I recommend including an algorithm to explicitly illustrate the switching workflow, improving overall readability.", "methods_and_evaluation_criteria": "The benchmark selection is suitable but lacks further comparison. For instance, it would be valuable to analyze the performance on Test14-hard, which evaluates scenarios where purely rule-based methods struggle. A detailed comparison here would better highlight the necessity of integrating a learning-based planner for enhanced support and robustness.", "theoretical_claims": "No theoretical claims.", "experimental_designs_or_analyses": "Another key issue is the lack of comparison with recent state-of-the-art methods on the benchmark. Specifically, results should be compared against pure imitation learning (IL)-based planners [1,2] and diffusion-based planners [3] to better contextualize the proposed approach. Additionally, the metric selection is incomplete, as it does not report the non-reactive closed-loop score (CLS-NR), which is crucial for evaluating the closed-loop capability of logged behavior. The ablation study also lacks generality—given that this work presents a general hybrid paradigm, further experimental validation is needed by integrating PDM with different types of planners [1-3] to strengthen the generalizability of the findings. Also, it is recommended to provide extra results under different time variance between rule and learning-based planner for further justifications.\n\nReference:\n\n[1] Cheng, J., Chen, Y., & Chen, Q. (2024). Pluto: Pushing the limit of imitation learning-based planning for autonomous driving. arXiv preprint arXiv:2404.14327.\n\n[2]J. Cheng, Y. Chen, X. Mei, B. Yang, B. Li, and M. Liu, “Rethinking imitation-based planner for autonomous driving,” in International Conference on Robotics and Automation (ICRA), 2024.\n\n[3] Zheng, Y., Liang, R., Zheng, K., Zheng, J., Mao, L., Li, J., ... & Liu, J. (2025). Diffusion-Based Planning for Autonomous Driving with Flexible Guidance. arXiv preprint arXiv:2501.15564.", "relation_to_broader_scientific_literature": "This hybrid framework may presents potential in combining further learning-based planners.", "essential_references_not_discussed": "[1] Cheng, J., Chen, Y., & Chen, Q. (2024). Pluto: Pushing the limit of imitation learning-based planning for autonomous driving. arXiv preprint arXiv:2404.14327.\n\n[2] J. Cheng, Y. Chen, X. Mei, B. Yang, B. Li, and M. Liu, “Rethinking imitation-based planner for autonomous driving,” in International Conference on Robotics and Automation (ICRA), 2024.\n\n[3] Zheng, Y., Liang, R., Zheng, K., Zheng, J., Mao, L., Li, J., ... & Liu, J. (2025). Diffusion-Based Planning for Autonomous Driving with Flexible Guidance. arXiv preprint arXiv:2501.15564.\n\n[4] Liu, H., Chen, L., Qiao, Y., Lv, C., & Li, H. (2024). Reasoning Multi-Agent Behavioral Topology for Interactive Autonomous Driving. arXiv preprint arXiv:2409.18031.\n\n[5] Zhang, D., Liang, J., Guo, K., Lu, S., Wang, Q., Xiong, R., ... & Wang, Y. (2025). CarPlanner: Consistent Auto-regressive Trajectory Planning for Large-scale Reinforcement Learning in Autonomous Driving. arXiv preprint arXiv:2502.19908.", "other_strengths_and_weaknesses": "N/A.", "other_comments_or_suggestions": "If the authors could provide additional compelling results to demonstrate the generality of this approach, such as applying SAH+[1,2], and extend evaluations across additional benchmarks (Test14-hard), I would consider raising my score.", "questions_for_authors": "Refer to Experimental Designs Or Analyses", "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": "E67WCnKzNV", "reviewer": "Reviewer_2ndh", "summary": "This paper presents a hybrid planner, SAH-Drive, for closed-loop vehicle trajectory generation.\n\nSAH-Drive uses the fast-slow hybrid planner paradigm. It has a rule-based fast planning path using PDM and a learned slow planning path using a diffusion model. The main innovation of SAH-Drive is that it uses a scenario-based switching module that switches between the two planners based on the scenario.\n\nThe authors evaluated the SAH-Drive hybrid planner on the standard nuPlan dataset as well as the new interPlan dataset. The result shows that SAH-Drive achieves better scores than the previous state-of-the-art planners.\n\nBecause of the hybrid approach, the authors also show that SAH-Drive has a much faster runtime than a purely-learned planner.\n\n\n## update after rebuttal\n\nNo updates.", "questions": "", "limitations": "", "rating": 4, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We really appreciate the reviewer’s constructive comments and positive feedback, which have helped us better articulate our contributions and clarify the novelty of our approach. Regarding the concerns of the reviewer 2ndh, we provide the following responses.\n\n> Q1: Even though SAH-Drive has a scenario-aware switching logic, which is different from the existing hybrid planner approaches, the novelty is not strong.\n\nThanks for your valuable comments. The novelty of this work lies not only in proposing the **Scenario-Aware Hybrid Planner Paradigm** but also in the following key aspects:\n\n- **STDP-Based Decision Neuron**: It memorizes the performance of both planners during the planning process, enabling adaptive switching based on scenario characteristics.\n- **Proposal Number Regulator**: It dynamically adjusts the number of diffusion model samples in real-time, improving planning efficiency without sacrificing trajectory diversity.\n- **Trajectory Fusion Mechanism**: We propose an exponential-weighted fusion of the highest-scoring diffusion and PDM trajectories to balance trajectory diversity and driving safety.\n- **Efficient Learning-Based Planner Adaptation**: SAH-Drive reduces the required training data compared to fully learning-based planners (from **285.61GB to 8.25GB**, a reduction of approximately **97%**) while maintaining planning performance, especially in long-tail scenarios such as interPlan and Test14-hard (see rebuttal table 2 https://anonymous.4open.science/r/SAH-Drive-Pic-9E34/benchmark_table.png for details). This makes targeted training on rare, real-world long-tail scenarios both feasible and efficient.\n\nWe believe that **adapting learning-based planners to long-tail scenarios with limited data** is a promising direction for future research, and our work lays the foundation for this advancement. We will further highlight the novelty in the final version.\n\nOnce again, we sincerely appreciate your valuable insights, and we hope this clarification strengthens the perceived novelty and contributions of our work.", "claims_and_evidence": "* The claims are relatively well-supported by the evaluation results.\n* However, even though SAH-Drive has a scenario-aware switching logic, which is different from the existing hybrid planner approaches, the novelty is not strong.", "methods_and_evaluation_criteria": "* The evaluation is relatively thorough. The authors evaluated their SAH-Drive approach on both the standard nuPlan validation set and the new interPlan dataset. The authors also compared the runtime speed of different planner approaches.", "theoretical_claims": "* There are no theoretical proofs in the paper.", "experimental_designs_or_analyses": "* Experimental design is solid.", "relation_to_broader_scientific_literature": "N/A", "essential_references_not_discussed": "N/A", "other_strengths_and_weaknesses": "N/A", "other_comments_or_suggestions": "N/A", "questions_for_authors": "N/A", "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:

Meta-review: The paper proposes a scenario-aware hybrid planner for closed-loop trajectory prediction. It combines a lightweight rule-based planner and a comprehensive learning-based planner. The motivation is clearly stated with comprehensive experiments. 

All reviewers (two short reviews) reach concensus that the manuscript is in good shape. AC step in, read the paper and rebuttal. The methodology seems to be naive and simple (raised by reviewers as well). Given the fact authors have responded and made the distinction, AC felt the overall good merits of the paper slightly weigh over the concerning part. 

Please incorporate all the revisions, esp. the comparison with recent SOTAs, missing references into the final version.
Decision: Accept (poster)