OccDriver: Future Occupancy Guided Dual-branch Trajectory Planner in Autonomous Driving
Authors: Zhao Huang
Bowen Zhang
Zhongzhu Li
Di Lin
Venue: ICLR 2026 Poster (2026)
Keywords: Autonomous Driving
Trajectory Planning
Primary area: applications to robotics, autonomy, planning

Abstract: Trajectory planning for autonomous driving is challenging due to agents' behavioral uncertainty and intricate multi-agent interaction modeling. Most existing studies generate trajectories without explicitly exploiting possible scene evolution, while world models predict consequences from ego behavior, enabling more informed planning decisions. Inspired by the world model, we propose OccDriver, a novel rasterized-to-vectorized dual-branch framework for trajectory planning. This pipeline performs a coarse-to-fine trajectory decoding process: The vectorized branch first generate multimodal coarse trajectories; Then the rasterized branch predicts future scene evolutions conditioned on each coarse trajectory via occupancy flow prediction; Lastly, the vectorized branch leverages intuitive future interaction evolution of each modality from the rasterized branch and produces refined trajectories. Several cross-modality (occupancy and trajectory) losses are further introduced to improve the consistency between trajectory and occupancy prediction. Additionally, we apply a contingency objective in both occupancy space, considering marginal and joint occupancy distributions in different planning scopes. Our model is assessed on the large-scale real-world nuPlan dataset and its associated planning benchmark. Experiments show that OccDriver achieves state-of-the-art in both Non-Reactive and Reactive closed-loop performance.

Published as a conference paper at ICLR 2026
OCCDRIVER: FUTURE OCCUPANCY GUIDED DUAL- BRANCH TRAJECTORY PLANNER IN AUTONOMOUS DRIVING
Zhao Huang!* Bowen Zhang"
Zhongzhu Li
Di Lin
Tiinjin University Auts
ABSTRACT
Trajectory planning for autonomous driving is challenging due to agents' behav- ioral uncertainty and intricate multi-agent interaction modeling. Most existing studies generate trajectories without explicitly exploiting possible scene evolution, while world models predict consequences from ego behavior, enabling more in- formed planning decisions. Inspired by the world model, we propose OccDriver, a novel rasterized desterized-to-vectrrie tovetctod Qual-brance framework for trajectory planning. This pipeline performs a coarse-to-fine trajectory decoding process: The vectorized branch first generate multimodal coarse trajectories; Then the rasterized branch predicts future scene evolutions conditioned on each coarse trajectory via occu- paney flow prediction; Lastly, the vectorized branch leverages intuitive future interaction evolution of each modality from the rasterized branch and produces refined trajectories. Several cross- cooss-modality (oceupancy and trajectory) losses are further introduced to improve the consistency between trajectory and occupancy prediction. Additionally, we apply contingency objective in both occupancy space, considering marginal and joint occupancy distributions in different planning scopes. Our model is assessed on the large-scale real-world nuPlan dataset and its associated planning benchmark. Experiments show that OceDriver achieves state-of fate--oftther t-thh-art art in both Non-Reactive and Reactive closed-loop performance.
INTRODUCTION
Trajectory planning for autonomous driving confronts intrinsic challenges due to the complexity of multi-agent interactions and pervasive uncertainty Djuric al. (2020); Xu et al. (2014 from sensor
noise to high-level behavioral unpredictability. Deep learning methods Bansal et al. (2018 8); Guo et al.
(2023) Scheel et al. (2022) have emerged as promising alternative to rule-based systems Bouchard et al. ) Treiber et al. (2000): Yi et al. (2018). However, effectively modeling the dynamic and uncertain interplay among agents future scenarios remains formidable task. In this work, we tackle this problem from two key perspectives: predictive modeling of future interactions and the choice of its representation space.
Representation space is crucial in planning, transforming raw sensor inputs into structured features that capture environmental contexts. Figure. presents a comparison of different representation paradigms. Rasterized representations Hu et al. (2021): Kim et al. (2022); Hu et al. (2023b), by modeling occupancy over spatiotemporal grids, offer robustness against occlusions and a probabilistic view of joint future states [Lu al. (2023a): Mahjourian et al. (2022). Unfortunately, this approach incurs discretization artifacts and loses fine individual context and geometric details. In contrast, vectorized methods Jiang al. (2023); /hhou al. (2022) 2023) provide high-precision trajectory generation by capturing detailed individual semantics but tend to oversimplify evolving future interactions and require substantial manual feature engineering to approximate uncertainty Khaitan et al. 2021); Chen et al. (2024). To overcome these limitations, we propose a hybrid approach that retains the probabilistic strengths of rasterized joint modeling while preserving the individual fidelity of vectorized representations, supporting probabilistic interaction modeling and more interaction- informed trajectory planning.
Work done during internship at SenseAuto.
"ooresponniig author.

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Imraee
Imagee Decoder
Resertic
Scere Feanure
Funalz Ossapancy
(5) Rasterized Planning Framework
Fature Occepuncy
Ventor
Vecter
Veckortod
Vestor Ensoder
Vector Decode:
Vedtoctte
Eacoder
Decader
Faturr Vagaaney
Dual-branch
Planning Framework (ours)
Tagestary
Vectorized Planning Framework
Figure 1: Illustration different representation paradigms: (a) rasterized framework models the scene- level joint dynamics in spatiotemporal grids; (b) vectorized framework performs individual-Ieve trajectory planning; (c) our proposed dual-branch framework integrates scene-level and individual- level information, further leveraging future scene as planning guidance.
The problem of interaction modeling has been widely studied in recent years. Multi-agent reinforce- ment learning methods Kiran al. (202T): Liu al. (2022) are developed to simulate interactions and learn policies through trial and error. However, they often struggle with scalability and envi- ronmental non-stationarity. Graph neural networks (GNNs) Mo al. (2022); [Sheng al. (2022) excel in capturing relational dependencies, yet suffer from message-passing limitations and escalating computational costs as the number of agents increases. Beyond encoding mechanisms, decoding mechanisms c.g., game theoretic approaches Huang et al. (2023); Wang et al. (202T) and tree policy planning Huang et al. (024) have been explored. However, these methods typically lack robustness, limiting practicality for real-world deployment. Nevertheless, all these methods perform forward-only planning without correction ability when poor rollout occurs, often necessitating a strong trajectory scoring module. Our method first plans multimodal coarse ego trajectories in vectorized branch. Then the rasterized branch constructs probabilistic occupancy maps conditioned on each coarse trajectory, capturing future scene evolution resulting from each ego action. Lastly, the interactive knowledge embedded in the occupancy space is distilled into the vectorized branch for trajectory guidance.
Beyond the framework, we propose suite of specialized loss functions, leveraging future occupancy as planning guidance. Occupancy interference loss captures ego and agents' exclusivity in occupancy space, which be consistency between trajectories and predicted occupancy, which bridges the gap between occupancy distribution and trajectory planning, ensuring effective information transfer between the two branches.
ego planning in occupancy space. Occupancy guidance loss enforces
Our framework also integrates contingency planning objective Cui et al. (2021) Li et al. (003bb Liu
al. (2024a) utilizing both marginal and joint occupancy distribution to balance safety and efficiency in dynamic traffic environments. In short-term, we estimate marginal occupancy probabilities of key interactive agents, enabling the ego vehicle respond swiftly imminent risks. For long- term planning, we estimate joint occupancy probabilities to construct modality-consisten traffic evolution, ensuring scene-compliance and avoiding wer-connervvttve behavior.
Our contributions are summarized as follows:
1) We propose a dual-branch transformer framework for coarse-to-fine trajectory planning, where rasterized branch serves as 2D occupancy world model by predicting future scene evolution conditioned on coarse trajectories and guiding fine-grained trajectory planning.
2) We introduce several specialized losses to facilitate effective information transfer between vector- ized and rasterized branches. imposing intuitive guidance on planning.
3) We incorporate a contingency planning strategy that leverages short-term marginal occupancy distribution for risk-sensitive planning and long-term joint occupancy distribution for scene-compliant behavior generation.
4) Extensive evaluation on the NuPlan dataset, achieving state-of-the-art planning performance on both reactive and non-reactive closed-loop metrics.
2 RELATED WORK
2.1 REPRESENTATION SPACE IN MOTION PLANNING
Imitation-bass planning method Hu et al. (022a)) Cheng et al. (2024b) has attracted lots of research interest due the accessibility of massive real-world expert driving data Caesar et al. (2020) 2021
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Ettinger al. (2021). It be categorized into two branches according to the representation paradigm: rasterized and vectorized approaches.
Rasterized Approaches project scene context into discret Harddeye-view(BEY images Hu al.
(2022b); Li ct al. (2024c) and encode with off-the-shelf image feature extraction methods Liu et al.
(2021b): Dosovitskiy al. (2020). Earlier research uses CNN Renz al. (2022); Song et al. (2020)
for feature encoding and trajectory decoding, while recent research utilizes transformer structure Chen
alla|l (2021); I); Huang et al. (2022); Chitta et al. (2022a); Zhang et al. (2022): Yao et al.) (2023) for better performance. Some works decode agents' future movement as future occupancy and flow fields Liu al. (2023a); Kim et al. (2022). providing a dense and intuitive scene representation, yet compromising fewer individual details and limited receptive field.
Vectorized Approaches yield impressive performance because of vector's concise but effective rep- resentation capacity for scene semantics. Building on vectorized representation of traffic scenes Gao al. (2020), researchers have used structures like graph neural networks for interaction modeling and DETR-style transformers Wann et al. (2023); Achaji et al. (2022) for query-based decoding. Additionally, instead of fully learnable queries, anchor-based |Afshar et al. (2024); et al. (2023a);
Chen et al. (2024) queries are used to decode multi-modal trajectories with explicit patterns.
OceDriver combines the advantages of vectorized and rasterized representations. The rasterized branch predicts future occupancy and flow fields to represent future scene evolution, serving as guidance for trajectory planning. The comparison between our work and other occupancy-assisted planning methods is provided in Appendix A.2
2.2 CONTINGENCY PLANNING
Contingency planning Li al. (023c) is traditionally framed tree-structured trajectory opti- mization problem, where each branch represents a possible scenario and a shared short-term segment ensures safety across all cases. While theoretically complete, this approach suffers from exponential complexity, as branches grow combinatorially with interactive agents' intentions, requiring a compli- cated safety-evaluation pruning strategy. Also, each agent's intention is reduced to deterministic approximation, resulting in the loss of trajectory-level multi-modality.
OccDrive addresses these challenges by formulating contingency planning within dense proba- bilistic occupancy space. Rather than explicitly constructing and pruning scenario tree, it directly predicts multiple scene-level rollouts of interactive agents' joint occupancy distribution, which also mitigates the limitations of deterministic scenario approximation.
METHOD
The overall framework of OccDriver is illustrated Fig. 2 We first introduce problem formulation in Then, demonstrate OccDriver's dual-branch network architecture in Sec. 3.2 Marginal
Sec 3.1
occupancy distribution prediction is presented Sec. 3.3 Finally, in Sec. 3.4 we propose several specialized training losses.
3.1 PROBLEM FORMULATION
Our research is dedicated to the task of trajectory planning. Our model input X composes of states of ego vehicle E and dynamic agents A over historical horizon T₁, states of static objects and a High-Definition map M. The objective is to plan ego vehicle's M-modal future states I...M), where y is trajectory over future horizon Ty and æ is the confidence score. With the integration of the occupancy branch, our model is updated with rasterized inputs and outputs. E A and M at current step are projected into occupancy grid O 0% and O.-- Following the practice in Liu al. (0023)), current backward flow FL computed as extra input. Occupancy prediction branch takes X (0% O mn. FL") as input and predicts ego vehicle's occupancy O.. other agents' occupancy O, and scene's 's backward flow FL over future horizon Ty. In alignment with trajectory planning, occupancy branch outputs multimodal prediction Y Yocs = {(O... Ou FL.) | = 1...M.. Consequently, the formulation of our model is given as:
y= (1), m.)
Y, oce = =f fXx Xoce (),
where f denotes the neural network of OccDriver, 0 is the model parameters.
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Marginal Occupancy Distribution Prediction
Reaterianl Beanch
Manginal
Marginal Occupancy
CrossAttn
Conv2D
SellAAin
W
Joine Scoee Frooder
Fie
Future Decoder
Darreet Ooouaeccy
SolfArth
Conv2D
Joint Occupancy
Grridance
CrossArtn
Fi
Curreet Flow
Flow
Agents
Transformer
ML.P
Objects
Encoder
Mtp
Fr
Course Trajestory Decoder
Fine Trajectory Decoder
Trajectory & Score
Context Encoding
Iterative Decoding Structure
Figure 2: The architecture of the OccDriver comprises three fundamentals. Context Encoding first encodes heterogeneous inputs into vectorized individual features Free and joint scene feature Fore respectively. Initialized by Quec and Foces dual-branch iterative decoding structure decodes joint future occupancy and trajectory iteratively. Short-term marginal occupancy is generated via marginal occupancy distribution prediction. Joint and marginal occupancy predictions enforces explicit guidance to trajectory through specialized losses.
3.2 DUAL-BRANCH ARCHITECTURE
Context Encoding. For vectorized branch, heterogeneous inputs A, S,A,SMMM. encoded as individual features {FE, FA, Fs, Fas) with separate encoders. After added by positional embedding, encoded features are concatenated as Feec E feature fusion via transformer encoder, employing self-attention to capturing social interactions between encoded scene semantics.
We then perform scene-level
For the rasterized occupancy branch, 0 ( O Om) and FL" embedded into occupancy feature F, and flow feature F with separate Swin-Transformee blocks. F, and Fy are then con- catenated and projected into the scene feature map F, E ((H/A)((W/A)xx with an MLP. Then we propose a two-layer attention-basee joint scene encoder for better aggregation of scene information. aooss-attentoon is employed to integrate coarse trajectory feature Free 's dense semantic information, followed by a self-attention for feature fusion the occupancy space. The process of joint scene encoder is described as:
F, CrossAttn Fs, Fuecy Fuec);
Foce = SelfAttn(F.).
where CrossAtta(Q K, V) and SelfAttn(X) indicates cross-attention and self-attention mechanism respectively. The encoded occupancy feature is denoted Foce.
Iterative Decoding Structure. Retaining encoded vectorized feature Fvee and occupancy feature Foces decode future occupancy evolution and future trajectories iteratively. As depicted in Fig. 2 iterative decoding structure is composed of sequential decoders: coarse trajectory decoder D.. future scene decoder D. and fine trajectory decoder Dy- For better consistency in evolution, we jointly decode the future trajectories of ego vehicle and agents. A set of M learnable queries Q., E IR MxD is combined with (FF., FA} C Fuees forming multi-modal decoding queries Quee E RMMx (N++1)xD
Coarse trajectory decoder Dr takes Qvee as input, deriving coarse trajectory queries Q.. Each layer of D. comprises of three types of attention mechanisms. Within each modality, Quec first performs self-attention to extract social interactions among agents. Then cross attention is employed to integrate static obstacle and map information from FFs. FM) C Free- Lastly, Quee queries Foce via another cross-attention, establishing a spatial scene understanding.
In occupancy branch, future scene decoder D, acts world model in BEV view, decoding Foce into feature Q, that represent future scene evolution conditioned on ego coarse trajectories. In each layer of Da, Face conducts cross-attention operations query Q. and (Fs, FMr) respectively,
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aggregating instance-level features and map information. Foce further applies self-attention for social interaction modeling. The decoded Q2 encompasses an intuitive prior of the future scene evolution.
Despite aggregating current scene information, Qe requires further refinement under the guidance of future seene evolution. Thus, utilizing future scene information from Qs fine trajectory decoder D, refines Qe into future-informed trajectory query Q to make bene-coosistent planning. D, and De share the same architecture, except that D leverages Q2 instead of Fooe as Key and Value in the third cross-altention. The iterative decoding process is formulated below:
Q. = Da(Q = Quees K, V Fores {Fs, FM }),
(4)
Q. D2 F oeer K, V FFs, FM)).
(5)
2 = (2 = Q. K, V = (F. FM]).
Prediction heads. Given 2 and Q.. different prediction heads are implemented. Fine trajectory feature 2 determines M- joint planning trajectories Y with corresponding confidence scores via two MLPs. To generate reasonable raw behaviors, coarse trajectory feature Qe is decoded into coarse trajectory Y. via another MLP. Future scene feature Q, is upsampled back to original shape of the input images and then deployed with two separate 2d-CNNs to output M M-oodal future occupancy fields and flow fields. Different from earlier works, we decouple the future occupancy prediction into the ego vehicle O₂ and surrounding vehicles O2 to facilitate explicit loss guidance detailed in Sec. 3.4 We provide further architectural details in Appendix. C
3.3 MARGINAL OCCUPANCY DISTRIBUTION PREDICTION
Accounting for emergency risk, we model potential agent behaviors by extending occupancy pre- diction beyond agents' joint distribution to individual's short-term marginal distribution. As shown in the top of Fig. 2 the scene feature map F, integrate individual agent features F; E FA through an additional marginal occupancy encoder. F, attends only to the vectorized feature of a single agent, rather than all elements Foees thereby capturing the agent's marginal behavioral feature Fm,.- Without modality decomposition, Fm, is directly leveraged to predict i-th agent's short-term marginal occupancy O of T2 horizon (Ta < Tf) through upsampling and a 2D-CNN output head.
We perform agent pruming reduce the computational cost, retaining only interactive agents for marginal occupancy distribution prediction. We devise a rule-based pruning method with minor inductive bias by selecting agents whose future bounding boxes cross with ego 's future path. Please refer to Appendix.[D] for for details about agent pruning. Marginal occupancy prediction is applied only in the training phase, enabling the model learn individual agent's behavior patterns and short-term uncertainties during the optimization process, which also serves as a foundation for contingency planning, Its entire procedure presented as follows:
( F3 1,2, Nn) = Prunc(Fa
Fm,; (F,,
Om,i Conv CCnsaUppamplo(FF..
where Nm indicates the number of pruned interactive agents, while fm denotes the network of marginal occupancy encoder, sharing the same structure as joint scene encoder.
3.4 TRAINING LOss
Apart from basic trajectory and occupancy flow supervision, we devise suite of specialized losses as shown in (a)-(c) in 3 Occupancy interference loss is applied to strengthen the interaction awareness between predicted ego and agents' joint occupancy. Occupancy guidance loss is further introduced to guide the trajectories with future occupancy probabilistic distribution. Utilizing predicted marginal and joint occupancy distribution, we apply contingency planning strategy for better driving safety without compromising efficiency. We leave details of trajectory planning loss Liraj and occupancy prediction loss Loce in Appendix.
Occupancy interference loss. The occupied areas of ego vehicle and agents are mutually exclusive, inherently reflecting future interactions. We incorporate this property within occupancy interference loss, which is formulated Eq. For training stability, apply teacher-forcing Williams & Zipser
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Ego Oce
Bilieer Interpelation
Agents
Interfeeence
Projection
Joine Dieribation
Marginal Distribution risk)
(8) Occupancy Interference Loss
(b) Traj-Oce Alignment Loss
(c) Tra--Oce Collision Loss
(d) Contigency P Planning
Figure 3: (a) (c) illustrates the proposed losses: (a) penalizes the overlapping region between occupancy prediction and its opponent's ground truth; (b) is imposed if ego's trajectory point is mapped onto its low-occupancy region; (c) is applied when the distance between ego's mapped point and agents' high-occupancy region is smaller than the safety margin. (b) and (c) constitute occupancy guidance loss. (d) illustrates our contingency policy in occupancy space. Short-term occupancy takes the maximum value of joint and marginal distribution, considering potential behavior uncertainty.
(1989) technique. For both ego and agents, the loss is calculated as the average predicted occupancy within the opponents' GT occupied regions, measuring the extent of spatial interference. Optimizing occupancy interference loss effectively enhances the interaction awareness in the occupancy space, facilitating subsequent occupancy guidance loss well.
Loe sum(0 Og / sum O Loo sum(0; OO; Ogt)/ sum(O
(8)
Los Coe + Coar
where O denotes occupancy prediction best modality and O denotes ground truth occupancy,
Occupancy guidance loss. Future occupancy can serve spatial prior information in the BEV view, explicitly guiding ego's trajectory planning. Utilizing both O and O% we devise trajectory- occupancy collision loss to steer the ego trajectory away from agents" high-probability occupied area and
alignment loss is introduced constrain ego trajectory within its own high-probability occupied area.
Considering ego vehicle's 's shape, at each timestep t, we offset trajectory position (I,, ye) into N, circle centers [P! loss, we obtain Pi occupied probability O on the predicted ego occupancy grid through coordinate projection and bilinear interpolation. To enforce alignment between trajectory and high-occupancy areas, penalty is applied to points whose occupancy probability is below the predefined threshold e.
I...N,,) to approximate the ego vehicle. For wajeccory--ccupaaee alignment
Ty No
Calign = Ty
(0, -O))
For pejjectory-occupane collision loss, after mapping P onto the predicted agents' occupancy grid through coordinate projection, we compute its minimum Euclidean distance di high-occupancy regions (where occupancy probability exceeds threshold (). Collision penalty is applied when de is below the safety margin v, which indicates high collision risk.
T N.
Lcollision Tj
ma n d)).
(10)
Contingency Planning is incorporated into Coottision to enhance planning safety. Conventional contingency planning divides trajectory into short-term safe maneuver and subsequent branched long- term behavior sets. Compared to trajectory, occupancy probability models behavioral uncertainty more effectively. In our work, the predicted short-term marginal occupancy On,i is leveraged to represent the uncertainty of single agent's short-term behavior. As shown Eq. [1 before computing fccoliision the predicted all-agents' occupancy O% incorporates (Om,a I...,, through an clement-wise maximum operation over short period T, Thus, Lcoltision enforces ego trajectory account for short-term risks caused by agents' behavior uncertainty, while keeping modality-comple planning in the long term.
ma
NO OO m,))), 15 T.
(11)
O 1

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Table I: Performance comparison closed-loop planning on nuPlan Val14 benchmark. All metrics higher the better. Among learning-based methods, OccDriver achieves SOTA in both none- reactive score (NR-S) and reactive score (R-S) with top safety performance (Collisions and TTC).
Val14
Type
Planner
Collisions
TTC
Comfort
Progress
NR-S
R-S
Expert
0.988
0.944
0.993
0.990
0.937
0.812
PDM-Open Dauner al.
2023
0.745
0.691
0.995
0.699
0.502
0.548
RasterModel
Caesar et al.
2021
0.870
0.815
0.815
0.806
0.669
0.647
UrbanDriver Scheel et 2022
0.856
0.803
1.000
0.808
0.677
0.649
PlanTF Cheng et al.
2024b)
0.941
0.907
0.937
0.898
0.853
0.771
Learning
PLUTO Cheng et al.
(2024a)
0.962
0.933
0.964
0.896
0.890
0.800
BeTopNet Liu al.
2024a)
0.966
0.916
0.932
0.866
0.883
0.837
DiffusionPlannerr Zheng et al.
(2025a)
0.899
0.828
OccDriver (Ours)
0.971
0.938
0.969
0.885
0.896
0.838
Table Performance comparison of closed-loop planning on nuPlan Test14 - Hard benchmark.
All metrics higher the better. Compared to vectorized-only, topology-guided and diffusion-based methods, OccDriver achieves top driving scores with desirable planning safety and progress.
Test14-Hard
Planner
Inference Time (ms)
Collisions
Progress NR-S
R-S
PLUTO Cheng al. (2024a)
7.39
0.938
0.816
0.787
0.753
Be' BeToopNet Liu al.
2024a
70.00
0.968
0.747
0.771
0.688
DiffusionnPlanerr Zheng et al. (025aa)
40.00
0.760
0.692
OccDriver (Ours)
23.03
0.941
0.829
0.794
0.759
Occupancy guidance loss Log is represented as a weighted sum Latign and Linter, regulating ego's trajectory with the explicit guidance of predicted future scenario:
Log = Wi Calign + W2 Ccottision
(12)
All losses are differentiable, allowing for end-to-end training. The overall training loss comprises trajectory planning loss Liraj, occupancy prediction loss Loces occupancy interference loss Coi and occupancy guidance loss Log. It is formulated as
Liraj + Loce + Lot foog-
(13)
4
EXPERIMENTS
4.1 BENCHMARK AND METRICS
OceDriver is trained and evaluated on nuPlan dataset Caesar et al. (2021). We use a standardized training set of 1M frames with 2s history and 8s horizon. Our evaluation is conducted on Val14 Daunnrr et al. (2023), and verified using closed-loop evaluation metrics: Non-Reactive Closed- Loop Simulation(CLs. CLS-NR) score and Reactive Closed-Lopp(CLSS. To verify performance under challenging cases, we further evaluate OccDriver on Tcst14 Hard split Cheng et al. (2024c). We follow the nuPlan challenge framework and report the official Planning Scores. More experiment details and parameter settings are in Appendix.
provided by nuPlan simulator.
4.2 MAIN RESULTS
Comparison with State of the Art. We conduct a comparative analysis between OccDriver and existing state-of-the thrtt--rttttttttt learning-bar based methods nuPlan Val14 benchmark. All methods evaluated without post- processng to compare models' planning performance. Comparative results presented in Table. In closed-loop simulation, OccDriver gains SOTA planning scores of 0.896
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m2
00s
1=
1=10s
t=
15s
Plan
1=0s
1=
t= 10s
t= 5s
Pred
Figure 4: Qualitative results of closed- planning. Each scenario lasts 15 seconds. OccDriver performs ineraction-complaan planning changing lane dense traffic (first row) and turning left after yielding (second row).
t=Is
4s
t-8s
t= -ls
[=4s]
8s
(a) Comparison between occupancy prodiction and ground truth
(b) Guidance from future oocupancy trujectory
Figure 5: Visualization of future occupancy prediction and guidance. (a) ego's (red) and agents' (purple) occupancy predictions coincide with their GT bounding boxes; (b) planning trajectory (green point) aligns with ego's occupancy while keeps away from agents' occupancy.
NR-Score and 0.838 R-Score. Notably, it demonstrates best performance on safety metires, boosting 0.971 (Collisions) and 0.938 (TTC). This can be attributed to guidance of future occupancy and contingency planning strategy, improving ego's awareness of spatial interactions and behavioral uncertainty. Unlike previous methods, OccDriver enhances safety performance with less degradation on Comfort and Progress. This results from the iterative decoding process, which refines trajectory holistically and generates complii behavior.
To further demonstrate the advantages of OccDriver under challenging scenarios, we conduct compari- method (Be'TopNet) and diffusion-basee
son with vectorized-only method (PLUTO), method (Diffusion Planner) on Test.4 Hard benchmark, as presented in Table. 2 OccDriver reaches the highest driving scores in both non-reactive and reactive simulation. Compared to PLUTO, OccDriver achieves enhancement in both safety and progress metics, demonstrating the effectiveness of incorporating occupancy branch. OccDriver also outperforms BeTTopNet with +3.00 NR-S and + 10.3% R-S, which models multi-agent interactions with topology connections. Despite excelling in safety metrics, BeTTopNet suffers from a substantial degradation in progress. In contrast, our method maintains favorable trade-off between safety and progress, leading to the best driving scores. This suggests that explicit spatial occupancy delivers more intuitive and fine-grained interaction relations than implicit topological structures, avoiding conseernsvvv planning. Results in Fig. 4 further prove our model's 's robust performance in tackling dense traffic scenarios and multi-agent interac- tive scenarios. Notably, compared to denoising paradigm of diffusion and the intricate topological modeling, OccDriver has less inference latency, enabling deployment in real-world applications.
Qualitative results. To visually demonstrate the effectiveness of future occupancy guidance, we render both the predicted future occupancy and ego's planning trajectory simultaneously. As depicted Fig.5) predicted occupancy overlaps with ground-truth bounding boxes, demonstrating the accuracy of future occupancy prediction and robustness of scene evolution reasoning. Besides, planning trajectory is positioned within the ego's 's high-occupancy region and steers away from agents' high- occupancy region. This further validates effective guidance from future occupancy via implicit feature aggregation and explicit losses supervision.
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Table 3: Ablation results of OccDriver's planning performance with different components. All proposed components contribute improvements in safety metries and driving scores. Cois Coattssion Latign denotes occ interference loss, traj-occ collision loss and traj-oce alignment loss. MP and CP denotes marginal prediction and contingency planning.
MP Loi Lcotlision
Lalign
CP
Collisions
TTC
Comfort
Progress
NR-Score
R-Score
0.933
0.905
0.966
0.893
0.859
0.787
0.938
0.913
0.968
0.889
0.863
0.800
0.943
0.914
0.975
0.886
0.864
0.807
0.960
0.931
0.966
0.882
0.879
0.825
0.960
0.928
0.971
0.892
0.885
0.830
0.971
0.938
0.969
0.885
0.896
0.838
Table 4: Impact of different horizon T of joint occupancy in occupancy guidance loss.
Table 5: Impact of different threshold Ç of high- occupancy regions in Lcollision
T
Collisions
TTC
NR-score
R-score
Collisions
TTC
NR-score
R-score
2s 0.933
0.908
0.828
0.787
0.5
0.938
0.916
0.837
0.786
4s 0.943
0,916
0.843
0.799
0.6
0.941
0.917
0.841
0.799
6s
0.946
0,914
0.845
0.801
0.7
0.946
0.914
0.845
0.801
8s
0.931
0.917
0.828
0.787
0.8
0.948
0,903
0.840
0.799
4.3
ABLATION STUDY
To investigate the effectiveness of proposed framework, auxiliary losses and contingency planning strategy in our work, we conduct an ablation study. All ablation experiments are evaluated on Val14 benchmark, and the results is shown in Table. 3 We first evaluate base dual-branch framework without marginal occupancy prediction module, achieving near-SOTA performance with 0.859 NR-S and 0.787 R-S. With the integration of marginal occupancy prediction module, both safety metrics and driving scores exhibit improvements. It attributed to the modeling individual behavior patterns and short-term uncertainty.
Based on proposed framework, we first apply occupancy interference loss Coi to learn exclusivity between ego's and other agents' occupancy. It enhances Collision metric while benefiting Comfort metric due to improved awareness of spatial interaction and feasible safe areas. the fourth experiment, substantial boost in safety metrics (Collision from 0.943 to 0.960 and TTC from 0.914 to 0.931) is observed after adding Lesttision into training, attributed to its explicit penalty for collision and excessive proximity other agents. On this basis, further introduce Latigns which yields an improvement in Progress metric due to ego occupancy's 's positive guidance, jointly optimizing safety and efficiency. Notably, the continuous increases of driving safety do not compromise the Comfort metric. The above experiments demonstrate that the proposed losses effectively distill spatial information from occupancy branch to trajectory branch.
The bottom row of Table. presents the ablation study on contingency planning. Safety metrics and driving scores reach their peaks at minor trade-off in the Progress metric. We attribute this to the model's 's consideration of potential marginal behaviors of relevant agents, adopting relatively cautious strategy to boost contingency safety.
Table. shows the effects of different horizons of joint occupancy prediction in occupancy guidance loss. We observe a consistent improvement in Collision metric and driving scores as the horizon T' increases, reaching peak at T 6s. Driving performance starts to degrade when the horizon extends to 8s. suggests that Long-horizon future occupancy captures scene dynamics and agents' long-term interactions. facilitating interaction-consistee cupancy prediction accumulates over time, leveraging highly uncertain future occupancy as guidance introduces ambiguous or incorrect supervision signal, ultimately degrading planning performance.
planning. However, the uncertainty of oc-

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Table 6: Planning performance comparison be- tween coarse and fine trajectories.
Table 7: Impact of agent pruning on closed-loop planning. AP denotes agent pruning.
Trajectory
Collision
TTC
NR-S
R-S
AP
Collision
TTC
Progress
NR-S
Coarse
0.931
0.903
0.836
0.790
without with
0.951
0.915
0.866
0.840
Fine
0.946
0.914
0.845
0.801
0.946
0,914
0.873
0.845
Fine Traj: Os
8s
Coarse Traj: Os
8s
Figure 6: Visualization of coarse trajectory and fine trajectory in nuPlan Test14 4 Hard benchmark.
we ablate different thresholds of high-occupancy regions in Leottision Collision metric increases as the threshold Ç rises, while TTC starts decreasing once Ç exceeds 0.6. The reason is that higher threshold offers more deterministic collision supervision, while simultaneously losing uncertainty awareness. Overall, Ç 0.7 reaches the optimal performance.
In Table. 5
Table. 6 compares the plamning metrics between coarse and fine trajectories. The quantitative result shows that fine trajectories achieve improved safety metrics and driving scores over coarse trajectories. It demonstrates that the integration of future scene evolution, together with explicit occupancy guidance, provides positive information for trajectory planning, enabling refinement into safer trajectories. Fig. 6 exhibits qualitative comparison between coarse and fine trajectories on Test14 - Hard set. In interactive scenarios, compared with collision- -prone coarse trajectories, fine trajectories are evidently altered avoid collisions and handle interactions effectively, validating the effectiveness of the coarse-to-fine decoding architecture and occupancy guidance.
The ablation results of agent pruning is shown in Table. 7 Because of GPU memory constraint, we select 8 nearest non-interactive agents, together with interactive agents (kept in agent pruning). for marginal occupancy prediction. The results indicate a minor drop in safety performance after agent pruning, validating the effectiveness of proposed pruning in keeping interaction-relevant agents. With agent pruning, our model attains a favorable balance between safety and progress. More importantly, agent pruning substantially reduces the computational overhead of marginal prediction, thereby improving the model's scalability. In Appendix. H. we analyze the impact of agent count on memory usage. The near-linear scaling property justifies the necessity of filtering irrelevant agents via pruning.
5 CONCLUSION
In this paper, we present OccDriver, a future occupancy guided dual-branch trajectory planning framework. Occupancy branch is incorporated to predict future scenes in occupancy space, guiding interaction-aware trajectory planning through implicit iterative decoding process and explicit loss supervision. Contingency planning is applied, leveraging short-term marginal and long- joint occupancy predictions simultaneously to mitigate uncertainty risks and sustain scene consistency. Experiments on nuPlan benchmark verify OccDriver's state-of-the-art performance in closed-loop planning, leading to significant improvements in driving safety.
ACKNOWLEDGMEETT
We thank the anonymous reviewers for their constructive suggestions. This work is supported by the National Natural Science Foundation of China (No. 62476192), and collaboration with Senseauto. Any opinions, findings and conclusions or recommendations expressed in this material are those of the authors and do not reflect the views of National Natural Science Foundation of China.
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APPENDIX
A DISCUSSIONS
Towards a better understanding of this work, supplement intuitive questions that may arise and provide empirical answers.
A.1
WHAT IS THE KEY OF OCCDRIVER AS A WORLD MODEL GUIDED PLANNING PARADIGM?
Most world models in autonomous driving predict future scene evolution by generating videos
et al. (2023a): Wang et al. (2024a): Yang et al. (2024); ZZhao et al. (2025) or 3D representation
et al. ) Zheng et al. (2024); Li et al. (2225), serving primarily as data generators Ma al. et al. (2024) and LAWLi et al. (2024b) leverage world models to extract latent scene representations, enhancing spatiotemporal scene understanding of autonomous driving systems. Recent research integrates world models into end-to-end planning. MILHHH et al. (2022a) and DriveDDremer Wang al. (2024a) jointly decode driving actions and the corresponding future scene dynamics. Drive- WMWang et al. (0244))2n to formulate cost functions for trajectory selection or optimization. Latent feature-based methods
(2025) and RL simulation environmentsLi et al. (2024a); Gao et al. (2025).
et al. (2025) utilize explicit future scene predictions
implicitly transfer future world knowledge to the planner. LatentDriverXiao et al. (2025) unifies the environment's 's next states and ego's 's next action as a mixture distribution. World4 et al. (20256) constructs an intention-awan latent world model to rank trajectories.
Compared with prior work. OceDriver extends beyond predicting future scenario evolution by explicitly exploiting interaction modeling in occupancy world and how this can benefit planning safety. OceDriver extracts spatial exclusivity and behavior uncertainty from ego and agents' occupancy distribution, distilling this interaction knowledge to trajectory planning via both feature transfer and occupancy guidance losses.
A.2 WHAT ARE THE KEY DIFFERENCES BETWEEN OCCDRIVER AND OTHER OCCUPANCY-ASSISTED PLANNING METHODS?
Yang et al. (2025) proposed 3D occupancy- based world model to evaluate the trajectories, which decoupled from the planner as separate model. contrast, the occupancy branch in OccDriver is integrated as part of the planner, where the planning branch incorporates occupancy feature during both training and inference, and the predicted occupancy is leveraged to design training losses that improve the planner's 's trajectory quality. Compared to [iu et al.) (2023)), which only incorporates historical occupancy features to provide supplementary perception information, our approach further exploits future occupancy predictions to explicitly model future interactions, and integrate this interaction knowledge into trajectory decoding through both feature-level fusion and specialized loss supervision.
In our work, occupancy assistance yields benefits in two key aspects: information complementation and interaction modeling. On one hand, the incorporation of scene-level occupancy information supplements individual features with global scene context and spatial dynamics. On the other hand, employ occupancy as explicit interaction modeling guide safer planning. The probabilistic nature of occupancy represents behavioral uncertainty more effectively than ground-truth or trajec- tory regression. Marginal prediction and contingency planning further enhance the planner's 's risk awareness.
A.3 WHY IS CONTINGENCY PLANNING SUITABLE FOR LEARNING-BASED INTERACTION MODELING, MAKING OCCUPANCY-GUIDER PLANNING SUPERIOR IN SAFETY?
Previous works enhance multi-agent prediction using game theoretic approaches and integrated prediction and planningLiu et al. (2024b) and conditional prediction. Although capable of generating scene compliant scene evolution, these methods suffer from over-optimistic when uncooperative agent behaviour occurs. Contingency planning is one of the strongest optimization- based tools for safety-critical motion planning. Yet, it suffers from discretized artifacts and the difficulty of enjoying
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the power of data-driven planning. OceDriver proposes natural paradigm integrate contin- gency objective with learning- braning-baseddaee algorithm, and mitigate the discretized artifacts by representing contingency objective within occupancy space.
A.4 WHAT WOULD BE THE BROADER IMPACT OF OUR PARADIGM?
The paradigm that combines occupancy- based world model and contingency planning be extended to a varies of sota methods. For example, we use regression method for trajectory learning to better compare with several strong baselines, but diffusion-based planner has shown superior performance by avoiding learning local average behavior. Our pipeline can be adapted to diffusion- planner by introducing occupancy as classifier guidance and including occupancy feature DiT condition. Previous works also exploit RL fine-tuning by collecting rollouts and rewards within a world model. Occupancy-basse -based world models are often used as reward models and should be adaptable to RL paradigm. However, the actual gain and design of such architectures need further investigation.
B LIMITATION AND FUTURE WORK
Our current approach discretizes the occupancy distribution along the time dimension, lacking supervision across time intervals. Future work will extend this to a unified spatiotemporal occupancy distribution, ensuring smoother temporal consistency. Additionally, framework that seamlessly integrates information between branches. Furthermore, we will explore safe reinforcement learning for contingency planning, leveraging risk-aware optimization objectives such as Conditioned Value at Risk (CVaR).
aim to develop end-to-end
C ARCHITECTURAL DETAILS
Vectorized Encoder. Ego's and agents' inputs involves kinematic states for past Th 2 seconds at 10 Hz and attributes. We only leverage the current state for ego vehicle to prevent shortcut bias. All scene inputs {E, A, S, M) are firstly embedded hidden dimension D = 128 with separate encoders. Ego state E is embedded through attention based State Dropout Encoder (SDE). Agents' states A are embedded through attention-based Feature Pyramid Network (FPN). Static objects S and map polylines M are embedded through 2-layer Multi Layer Perceptron (MLP) and PointNet-based vector encoder respectively. All embedded features are concatenated together and added respective positional embedding, as Free
As depicted in Fig, 4-layer transformer encoder is employed for feature fusion between scene elments. Each layer consists of a multi-head self-attention and feedforward neural network (FFN).
Rasterized Encoder. We build // x W = 128 x 128 spatial grid, covering the ego vehicle's current position with range of [-20m, 60m] in the x-direction and [-40m, 40m] in the y-direction (resolution rate = 0.625m pixel). Follow the practice of EEtinger et al. (2021), we seperately rasterize ego state, agent states and map into occupancy grids (O,- O,, O The backward flow FL is constructed by calculating the displacement of the occupancy pixels between two consecutive timesteps for the same agent. We only use current occupancy grids and backward flow as input which are embedded and down-sampled
F,, Fy E TR x32 128) with 2 separate Swin-transformer encoderşLiu al. (2021b). Each wwin-transformer encoder is 2-layer transformer with window self-attention (W-SA) and shifted window self-attention (SW-SA). F, and Fy are concatenated and fed into a MLP to form spatial scene feature Fg E (332x32x126)
Joint Scene Encoder is two-layer transformer module, which takes scene feature map F' as input query, deriving encoded occupancy feature Foce- Each layer consists of a self-attention, a cross-attention (Frec Key and Value), and feed-forward network (FFN). Marginal Occupancy Encoder shares the same structure with Joint scene Encoder. The only difference is that the cross- attention in marginal occupancy encoder queries individual agent feature F: E FA instead of Foec-
Iterative Decoding Structure composed of 3 iterative decoders: coarse trajectory decoder D.., future scene decoder D. and fine trajectory decoder Dy. In trajectory branch, we initialize M 6 learnalle embeddings Qm E RM MDD D model ego's M longitudinal modalities. Qm is concatenated with Fg Feee and projected as Q',, Mxxx via ML.. For joint prediction, Q'n, is further
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agents according Published as a conference paper at ICLR 2026
concatenated with FA C Fvee, forming Quec RM In occupancy branch, De directly utilizes Foce as decoding query. De and D, share the same 4-layer Transformer decoder structure, with each layer consisting of a self-attentiion two cross-attentions, and feed-forward network (FFN). We implement D, as a 2-layer Transformer decoder. Each layer of D, contains a self-attention, a cross-attention and an FFN.
Prediction Heads. Deocoded fine trajectory query Q, is passed through separate MLPs to gen- erate joint prediction trajectories Y E (MMx(Ma-I)x(TT/AAaaa))) for future Ty 8 seconds and corresponding confidence scores E IRMx1 Trajectory states contains (z, y, 20)( s0n Uz Uy) Coarse trajectory query Qe processed with another MLP to generate coarse trajectories Y Future scene feature Q, is up-sampled and passes through two separate 2d-CNNs to output future occupancy grids O = {O., O.} E IR ((Ty/Anca) ( x128 128x2 and flow grids FL E (2Mx(X//Acc)) 128x 128x2
Agents' marginal occupancy features {mm.) is decoded into short-term marginal occupancy
(Om,s s 1. ..., Nm] E R.M.x(T./Aa00 1ttp///mmxxxxxxx122222 128x1128x1 for T, 2s. Straj and Doce indicate
the prediction frame intervals of trajectory and occupancy, respectively.
D AGENT PRUNING
Although OccDriver reduces the computational complexity of contingency planning from (n²)) to O()), calculating marginal occupancy distribution of each agent is unacceptable. Therefore, we propose a rule-based agent pruning method that identifies to their ground truth trajectories. During training, the annotated non-interactive agents are directly ignored in marginal occupancy computation, and an agent role prediction head takes in agent features and predicts the confidence score of the role of each agent. During inference, the agents that have above threshold confidence of being non-in non-interactive are pruned in marginal occupancy computation.
We identify 5 regular agent roles: in-lane leader, in-lane follower, lateral intruder(cut-in, cut-out, crossing agents), overtaking target, and non-interactive agents. The interactive relationship is de- termined by examining whether the ground truth bboxes of ego and agent intersect within time difference of 4s. If ego reaches the intersection position prior to the agent, the agent is either an in-lane follower intersection position. Similarly, if ego reaches the intersection position later than the agent, the agent either in-lane leader lateral intruder, which is also further determined by their heading difference at the intersection position. All other agents are considered non-interactive agents as they have no direct impact on the ego vehicle in the short term, thus can be ignored for marginal occupancy computation. Note that predicting detailed agent roles instead of only interactive and non-interactive agents alleviates the causal confusion problem, e.g., in-lane follower.
overtaking target, which is then determined by their heading difference the
E TRAINING Loss
Trajectory planning loss. To avoid mode collapse Liu et al. (02la), we employ teacher- foreing Williams & Zipser (9889 technique during the training process. We compute the length of the ground-truth trajectory and assign it to the corresponding longitudinal modality based on 20 m segments. The trajectory y* of the target modality used to compute the regression loss. For regression loss Liraj, we employ the smooth L1 loss, and for classification loss Lots, we utilize the cross-entropy loss.
To facilitate future scene prediction with more reliable initial behaviors and accelerate training
convergence, we apply smooth LI loss Loourse to coarse trajectory y2 The overall trajectory planning loss Ltraj is formulated weighted sum of its components:
Liraj WyGreg + W4 W3 Ccoersse
(14)
Occupancy prediction loss. We supervise both joint occupancy prediction of target modality O', O and marginal occupancy predictions {Om.i | 2, .... N. Due to the significantly larger proportion of unoccupied regions, we utilize focal loss Ross & Dollár (2017) loss function L.. L. Cm Additionally, LI loss Cflo is applied to supervise flow prediction FL*, facilitating better understanding of traffic scene's dynamics. The overall occupancy prediction loss Loce is defined as:
Loce w6Le + WrCa wgLm + W WgCflow.
(15)
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Table 8: Loss weight parameters.
Loss
Lreg
Lets
Lecarse
Le
La
f.
Latign
Cooltision
Weight
4
0.3
3000
800
2000
Table 9: Hyperparameters and configuration settings.
Notation
Parameters
Values
M
Number of modality
6
Tf
Horizon of joint prediction
8s
T,
Horizon of marginal prediction
2s
A traj
Frame interval of trajectory
0.1s
Docc
Frame interval of occupancy Number of covering circles
1s
No
3
Safety margin in Lcallision Occupaney threshold in Lalign
2m
0.8
Occupancy threshold in Ceollision
0.7
Together with and w2 for Luttign and Leollision: detailed loss weight parameters are shown in Table.
g
F EXPERIMENTAL DETAILS
We perform training 32 NVIDIA RTX 4090 GPUs with batch size of 16 for 30 epochs. It takes 20 hours for finishing training. The total number of parameters is 7.9 M and the model size is 31 MB. Learning rate is set to le-3, with 3 warm-up epochs and Cosine scheduler. We adopt AdamW optimizer, applying a weight decay of 0.0001. For proper guidance and training stability, we add occupancy guidance loss after 15 epochs. In occupancy guidance loss, the gradient of occupancy predictions is detached to guarantee unidirectional guidance. Details on parameter settings can be found in Table.
Table 10: Impact of different prediction horizons Toce of joint occupancy.
Table 11: Impact of different weights u2 of trajectory collision loss Lcolission
Toce
Collisions
TTC
NR-score
R-score
W22
Collisions
TTC
Progress
NR-score
2s 0.925
0.886
0.836
0.774
3
0.932
0.903
0.876
0.840
4s 0.928
0.886
0.839
0.778
6
0.943
0.908
0.869
0.843
6s
0.933
0.901
0.848
0.779
9
0.946
0.914
0.873
0.845
8s
0.935
0.900
0.846
0.782
12
0.922
0.879
0.902
0.826
Table 12: Impact of different numbers M of decoding modalities.
M
Collisions
TTC
Comfort
Progress
NR-S
R-S
0.915
0.887
0.975
0.860
0.823
0.755
0.931
0.904
0.978
0.869
0.835
0.784
0.946
0.914
0.976
0.873
0.845
0.801
0.951
0.911
0.972
0.876
0.848
0.805
12
0.944
0.898
0.965
0.882
0.840
0.797
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Table 13: Comparison between vector-only and dual-branch architectures.
Framework
Collision
TTC
Comfort
Progress
NR-S
R-S
Vec branch
0.917
0.884
0.976
0.885
0.836
0.769
Dual branch
0.933
0.905
0.966
0.893
0.859
0.787
G ADDITIONAL ABLATION STUDIES
For efficiency, we sample 20% of nuPlan training set for hyperparameter ablation experiments and conduct evaluation on the Val14 split.
Impact of dual-branch architecture. Table. 13 presents the ablation results for dual-branch ar- chitecture. The dual-branch architecture achieves superior safety and progress metrics compared to the vectorized-onj) baseline. demonstrating the effectiveness of proposed dual-branch structure. We attribute the enhanced planning performance incorporating both current and future occupancy features from the occupancy branch. Current occupancy feature supplements global spatial informa- tion lost in the vectorized representation. Future occupancy feature further extracts scene-evolution information to refine scene-consistent trajectories.
Different prediction horizons of joint occupancy. further investigate the influence of occupancy prediction trajectory planning, we ablate different joint occupancy prediction horizons T without incorporating auxiliary losses. As shown in Table. TO we observe that increasing Toce leads to simultaneous improvements in both safety metrics and closed-loop driving scores. It suggests that extended occupancy predictions captures long-term dynamics of the traffic, enabling more consistent spatial guidance for trajectory planning. Further performance improvement ceases when Tace extends to 6s, presumably due to the compounding uncertainty inherent in long-term occupancy predictions. In practice, Toce is set T 8s to ensure consistency with trajectory planning.
collision loss. Table. T1 presents the effects of varying w2 aligned to Leallision on driving performance. As ws increases from to OccDriver consistently improves driving safety (+1.4% Collision and +1.2% TTC) with minor degradation in Progress, validating the effectiveness of Lcoltision in collision avoidance. However, further increasing w2 to 12 results in significant performance degradation, due to over-penalization of collisions, which leads to undesirable shortcut that ego moves out of the occupancy field quickly to avoid collision supervision.
Different weights of trajectory
Different numbers of modalities. As presented in Table. M=9 yields optimal driving scores(O.88) (0.848 NR-S and 0.805 R-S). indicates that M=9 is sufficient to cover the ego's 's longitudinal behaviors. More modality enables finer-grained division of ego's behavioral patterns, leading to an continuous growth of Progress metric. Both Collision and TTC degrade when M exceeds 9, which we attribute to overly redundant modality partitioning that reduces trajectory flexibility. Besides, the increased number of modalities reduces the training data allocated to each modality, eventually causing insufficient training and degraded trajectory quality.
H COMPUTATIONAL COST ANALYSIS
Table 14: Impact of different components on the training computational overhead.
Table 15: Scalability of memory usage with re- spect to agent count in marginal prediction.
Base OP MP
Latency (ms)
Params (M)
Agent Num (bs=8)
GPU Memory (GB)
3.67
29.77
3.8
5.36
60.61
7.4
10
7.98
69.24
7.9
20
13.22
The training cost breakdown of model is reported in Table. 14 prediction and marginal prediction, respectively. According to the table. the total computational costs of model is relatively low. Occupancy decoding and guidance account for nearly half of
OP and MP denote occupancy
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Table 16:
Performance comparison of end-to-end planning on Navsim test split. All metrics are higher the better. OccDriver achieves safe and efficient planning under end-to-end paradigm.
Method
NC
DAC
TTC
CF
EP
PDM-S
UniAD
97.8
91.9
92.9
100
78.8
83.4
Transfuser
97.9
92.8
92.8
100
79.2
84.0
PARA-Drive
97.9
92.4
93.0
99.8
79.3
84.0
Baseline
98.1
95.7
94.8
100
82.6
87.7
OccDriver
98.4
97.5
94.3
100
84.0
89.2
the total parameters and training latency, due to the rasterized feature representation and additional losses. Marginal distribution prediction introduces minimal computational overhead, a result of its lightweight model architecture.
In Table. 15|
we analyze scalability of the number of agents used in marginal prediction. A consistent increase in GPU memory allocation was observed as the agent count grows. Predicting marginal occupancy distributions for all agents incurs computationally prohibitive memory overhead. To address this issue, We leverage agent pruning to keep only interactive agents (typically fewer than 10), significantly enhancing scalability.
I TRANSITIONING TO END-TO-END PARADIGM
We extend our method to the one-stage NavSim Dauner et al. (024) benchmark further validate its robustness in end-to-end planning. Our perception module follows the Transfuser Chitta al. (2022b) architecture, where multi- LiDAR and image features are fused into the BEV feature. In the baseline, ego query fuses the current BEV feature via cross-attention and directly decodes the future trajectory. Our implementation OccDriver on NavSim currently incorporates dual-branch architecture and occupancy collision loss. Transformer-basee dual-branch architecture iteratively decodes coarse trajectory, future occupancy and fine trajectory. Here, OccDriver predicts occupancy for both non-drivable areas and agents. Guided by occupancy prediction, occupancy collision loss regularizes the trajectory to avoid off-road deviations and collisions.
Table. 16 presents the comparison of end-to-end driving scores on the NavSim Test split. Under the evaluation of PDM score, OccDriver attains superior driving score over the baseline, demonstrating its robustness and generalization capability. The boost in the Progress (EP) metric attributed to the prediction of future occupancy, which enhances the model's understanding of scene dynamics. The gains observed Collision (NC) and drivable-area-con aro----mlllaaaneeeee (DAC) metrics further validate the effectiveness of occupancy guidance. Occupancy guidance loss provides strong constraints keeping away from occupied areas, promoting safer planning behavior.
J ADDITIONAL. QUALITATIVE RESULTS
Fig. 7 illustrates the overlapping results of marginal and joint occupancy predic- tions to validate the effectiveness of con- tingency planning in modeling behavioral uncertainty. It showcases that marginal oc- cupancy distribution not only captures the waiting behavior but also reflects the ag- gressive right turn possibility. Interacting with short-term marginal occupancy dis- tribution avoids running into unexpected sudden situations, thus improving safety and comfort. Relevant quantitative results presented in the ablation study.
t=0s
1s
2s
Figure 7: Visualization of contingency planning. Com-
pared to joint occupancy (purple), marginal occupancy (blue) models potential behaviors.
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t=
1=5s
10s
t=15s
233
0s
1=5s
t-10s
15
t=ss
t=5s
10s
t= 15s
1=0s
5=
1-0s
1=15s
t0s
55s
t= F10s
5s
Planning: Os
8s
Prediction: Os
8s
Figure 8: Visualization of closed-loop planning nuPlan Val14 benchmark.
Qualitative closed-loop Planning results on nuPlan Val14 benchmark is provided Fig. Qualitative results of occupancy guidance is provided in Fig, 9
K
VIDEO RESULTS
We include several 15-second closed-loop planning videos on Test14 - Hard split in the supple- mentary material, which intuitively demonstrates OccDriver's closed-loop planning performance.
L
CODE
We provide the core code of the iterative decoding structure, specialized loss functions and agent pruning mechanism in the supplementary material to facilitate reproducibility.
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2ss
t=s
1=68
t-8s 8s
t=2s
1=4s
t=
t=
t=2s
6=
t- 88
2ss
4ss
t=6s
8s
t-2s
t
t=6s
8s
Ego Occupancy
Agents Occupancy:
Trajectory Point
Figure 9: Visualization of closed-loop planning under guidance of future occupancy prediction.
23


Reviews:
[{"review_id": "ewEJYFKgWL", "reviewer": "Reviewer_HoqV", "summary": "This paper proposes OccDriver, a dual-branch rasterized-to-vectorized trajectory planner that integrates future occupancy predictions as guidance for trajectory generation in autonomous driving. The framework consists of a rasterized branch that predicts future scene evolution in occupancy space and a vectorized branch that plans ego trajectories conditioned on this predicted scene. The authors utilize several cross-branch loss functions and a contingency planning objective to enhance safety and robustness. Experiments on the nuPlan dataset demonstrate state-of-the-art closed-loop results compared with existing learning-based planners. While the paper is technically sound and well-engineered, its conceptual novelty is insufficient for a top-tier contribution (see Weaknesses).", "questions": "Minor:\n1. Discuss failure cases and qualitative scenarios where occupancy guidance meaningfully alters the decision.\n2. No ablation isolating the dual-branch architecture itself (without losses) is clearly analyzed.", "limitations": "", "rating": 2, "confidence": 4, "soundness": 2, "presentation": 2, "contribution": 1, "strengths": "1. The quantitative results on the nuPlan benchmark show competitive performance.\n2. The ablation study is detailed and systematic.\n3. The paper is readable, the methodology is well-documented, and architectural details are described thoroughly in the appendices.", "weaknesses": "1. My main concern is the novelty of this paper. The concept of using occupancy prediction to guide planning is not new. Even UniAD[1] in 2023 has already explored joint designs between occupancy forecasting and trajectory generation. It seems that the main difference here is to adopt a parallel dual-branch structure rather than a cascaded one, which feels like an architectural variation rather than a fundamentally new paradigm. The claimed “future occupancy guidance” and “dual-branch feature interaction” are incremental extensions of prior occupancy-assisted pipelines.\n2. The related work section misses several closely aligned approaches (e.g., some end-to-end methods like World4Drive[2])\n3. The evaluation relies on a single dataset, which limits claims about robustness and generalization. To substantiate the method’s effectiveness, it should include additional benchmarks such as NavSim[3].\n\n\n[1] Hu, Yihan, et al. \"Planning-oriented autonomous driving.\" Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2023.\n[2] Zheng, Yupeng, et al. \"World4Drive: End-to-end autonomous driving via intention-aware physical latent world model.\" Proceedings of the IEEE/CVF International Conference on Computer Vision. 2025.\n[3] Dauner, Daniel, et al. \"Navsim: Data-driven non-reactive autonomous vehicle simulation and benchmarking.\" Advances in Neural Information Processing Systems 37 (2024): 28706-28719.", "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": "Vzs9RP0yZ7", "reviewer": "Reviewer_sotP", "summary": "This paper presents OccDriver, a rasterized-to-vectorized framework for motion planning in autonomous driving. Motivated by the limitations of trajectory-based or occupancy-based approaches, it integrates a vectorized trajectory decoder with an occupancy world model that predicts future scene evolution conditioned on coarse trajectories. Cross-modal losses—including occupancy interference, trajectory–occupancy alignment, and collision penalties—enable explicit safety-aware guidance. Additionally, a contingency strategy based on short-term marginal and long-term joint occupancies enhances robustness under uncertainty.Evaluated on the nuPlan benchmark, OccDriver achieves SOTA closed-loop driving scores and superior safety metrics, demonstrating its effectiveness in reliable and interpretable planning.", "questions": "- Given that the fine trajectory decoding is conditioned on the latent variableQ_c(coarse trajectory feature), what is the necessity of explicitly decoding the coarse trajectoryY_c? Additionally, is there confusion regarding whether direct supervision is applied toY_c?\n- Supplementing direct quantitative and qualitative comparisons between the coarse trajectory and the fine trajectory would more effectively demonstrate the validity of the \"coarse-to-fine\" framework proposed in this paper.\n- The authors should conduct more comprehensive ablation studies on the number of modalities.", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- The paper proposes an innovative Dual-branch Planning Framework with a coarse-to-fine decoding mechanism: the coarse trajectory decodergenerates a preliminary behavioral framework, while the fine trajectory decoder optimizes trajectory with future scene information. This hierarchical design balances planning efficiency and accuracy, addressing the computational cost-rationality trade-off in traditional single-stage methods.\n- Marginal prediction (MP) and contingency planning (CP) modules are introduced for emergency risk: MP captures individual agents’ short-term marginal occupancy, and CP generates emergency trajectories. Ablation experiments (Table 3) confirm both modules improve safety metrics and driving scores, verifying the risk-aware design’s practical value.", "weaknesses": "- The paper demonstrates the effectiveness of the proposed Dual-branch Planning Framework by presenting the performance of the fine trajectory that incorporates future scene information and occupancy guidance. However, it lacks direct quantitative and qualitative comparative analysis between the coarse trajectory and the fine trajectory.6.2 The paper only dissolved M=1, 3, and 6, without verifying \nthe monotonic performance growth of more M or identifying the optimal \"peak\" M. If there is a maximum peak M, there is still a lack of strict explanation for why a specific M (if optimal) performs the best.", "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": "S7cmwRX4du", "reviewer": "Reviewer_aAjV", "summary": "This paper proposes OccDriver, a dual-branch transformer framework that integrates future occupancy prediction into trajectory planning for autonomous driving. The model consists of a vectorized branch, which generates coarse-to-fine multimodal trajectories, and rasterized branches, which predicts future occupancy and flow fields for both joint and marginal prediction. The rasterized branch acts as a world model, providing future scene evolution guidance to refine the ego trajectory. Several cross-modality consistency losses, including occupancy interference, trajectory-occupancy alignment, and trajectory-occupancy collision losses, are designed to couple the two branches effectively. The authors further introduce a contingency planning objective that considers both short-term marginal and long-term joint occupancy distributions to handle behavioral uncertainty. Evaluations on the nuPlan benchmark show that OccDriver achieves state-of-the-art performance on both Non-Reactive and Reactive closed-loop metrics, especially improving safety (collision, TTC) without sacrificing comfort or progress.", "questions": "1. Does marginal occupancy pruning significantly affect safety performance? \n\n2. How are the short-term (Ts) and long-term (Tf) horizons chosen for contingency, and how robust is performance to these hyperparameters?\n\n3. How does OccDriver conceptually differ from recent world-model planners (e.g., DriveDreamer) that also predict scene evolution before planning? Could OccDriver be extended to reinforcement-based fine-tuning?", "limitations": "", "rating": 8, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "The proposed rasterized-to-vectorized pipeline elegantly combines probabilistic world-model reasoning with precise trajectory planning, bridging two dominant paradigms (rasterized and vectorized) in motion planning.\n\nIncorporating marginal and joint future occupancy prediction as explicit supervisory signal and guidance mechanism through contingency planning, seems to be a quite novel idea\n\nThe experiments are thorough, covering both Val14 and Test14-Hard nuPlan benchmarks, with convincing performance gains in safety and closed-loop metrics. The ablation studies clearly demonstrate the contribution of each module.", "weaknesses": "The method adds nontrivial computation due to occupancy decoding and marginal distribution estimation. Although inference time is reported (≈23 ms), the training cost and scalability with agent count are not analyzed in depth.\n\nThe paper lacks a deeper analysis of why occupancy-based guidance ensures optimal or safer planning.\n\nThe fonts are small in current figures. Fig.2 a bit cluttered and difficult to follow. The implementation of contingency planning could also be summarized in a figure.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)