Organizations: Nanyang Technological University, Singapore · Tongji University, Shanghai, China · Agency for Science, Technology and Research (A*STAR), Singapore
Occupancy world models provide a structured, metric representation of future driving scenes, making them a promising basis for end-to-end autonomous driving. However, existing occupancy-aware planners typically couple occupancy prediction and action generation through either hierarchical coupling or parallel multi-task learning. Hierarchical methods use occupancy either as a fully predicted future preceding action generation or as an action-conditioned rollout for trajectory evaluation, whereas parallel multi-task methods predict occupancy alongside planning through a separate branch or auxiliary objective. These interfaces retain either an explicit generation order or a role asymmetry, limiting reciprocal interaction between world prediction and action generation during trajectory formation. We propose OccWAM, an occupancy-native world-action model that jointly generates dense future occupancy and continuous ego trajectories via flow matching conditioned on scene representations. Building on this joint formulation, we introduce scene-to-ego world-action flow matching, a two-stage formulation. The first stage predicts scene-centric occupancy together with an absolute ego trajectory, providing a stable representation of future scene evolution. The same predicted world is then re-indexed into the future ego frames induced by the initial trajectory, where ego motion is represented through adjacent pose increments and a second joint flow refines ego-centric occupancy and motion for action-aligned planning. Unlike video-based world-action models, OccWAM preserves explicit semantic geometry for direct planning reasoning without pixel synthesis. Extensive experiments on Bench2Drive and nuScenes demonstrate state-of-the-art performance in closed-loop driving, open-loop planning, and future occupancy prediction.
Figures & tables
Figure 1: Comparison of occupancy–action interfaces. Hierarchical methods impose an action-first or world-first order, while parallel methods retain separate prediction pathways. OccWAM instead jointly generates occupancy and action through coupled conditional flows. Multi M. Traj : multimodal trajectory candidates.
Figure 2: Overall architecture of OccWAM. A frozen Understanding Expert provides semantic driving context, while the World and Action Experts interact through layer-wise joint attention to parameterize a coupled occupancy–action vector field. Stage I jointly generates scene-centric occupancy and an absolute ego trajectory from Gaussian sources. The predicted trajectory then defines future ego frames for warping the occupancy and is converted into ego-motion increments; together, they form the source of Stage II. Using the same three-expert backbone, Stage II applies a second joint flow in ego-centric coordinates to produce the final occupancy and trajectory.
Method
Venue
Expert
WM
DS ↑
SR (%) ↑
UniAD-Base [ 1 ]
CVPR’23
Think2Drive
×
45.81
16.36
DiffusionDrive [ 3 ]
CVPR’25
PDM-Lite
×
77.68
57.72
ORION [ 28 ]
ICCV’25
Think2Drive
×
77.74
54.62
AutoVLA [ 29 ]
NeurIPS’25
PDM-Lite
×
78.84
57.73
TransFuser++ [ 30 ]
ICCV’23
PDM-Lite
×
84.21
67.27
SimLingo [ 31 ]
CVPR’25
PDM-Lite
×
85.07
67.27
Table 1: Closed-loop driving performance on Bench2Drive. Expert denotes the expert policy used for collecting training data, while WM denotes an explicit learned future-world model. Best and second-best results are shown in bold and underlined, respectively.
L2 (m) ↓
Collision (%) ↓
Method
Ego status
1s
2s
3s
Avg.
1s
2s
3s
Avg.
UniAD [ 1 ]
Vector
0.44
0.67
0.96
0.69
0.04
0.08
0.23
0.12
VAD [ 2 ]
Vector
0.17
0.34
0.60
0.37
0.07
0.10
0.24
0.14
DriveTransformer-Large [ 36 ]
Vector
0.16
0.30
0.55
0.33
0.01
0.06
0.15
0.07
AutoVLA [ 29 ]
Text
0.21
0.38
0.60
0.40
0.13
0.18
0.28
0.20
OmniDrive [ 37 ]
Vector
0.14
0.29
0.55
0.33
0.00
0.13
0.78
0.30
Table 2: Open-loop planning and future-occupancy evaluation on nuScenes.
Control output
DS ↑
SR (%) ↑
Stage-I trajectory
88.21
73.63
Stage-II trajectory
90.45
77.27
Table 3: Ablation of the second joint flow and World-to-Action information flow.
Configuration
L2 ↓
Coll. ↓
mIoU ↑
IoU ↑
Without Action-to-World
0.32
0.10
23.11
32.59
Full OccWAM
0.30
0.07
24.23
33.88
Table 4: Complementary ablations on nuScenes. Planning metrics (L2 and collision) use camera observations, whereas occupancy metrics (mIoU and IoU) use four historical 3D occupancy frames. Comparisons within each metric group follow the same task-specific input and evaluation protocol.
Appendix figures & tables2 assets
Supplementary material from the paper’s appendix.
Appendix
Figure S1: Qualitative comparison of future 4D occupancy predictions and ground truth across future time steps. Each frame shows the predicted 3D semantic occupancy of OccWAM and its corresponding ground truth.
Setting
Bench2Drive
nuScenes
Planning input
Front-view RGB image and one LiDAR BEV representation
Camera observations
Occupancy-prediction input
The same front-view RGB and LiDAR BEV observations; no historical or current ground-truth occupancy is used
Four historical 3D occupancy frames
Predicted occupancy
Temporal BEV occupancy over (t,x,y) , without an explicit vertical dimension
Future volumetric occupancy over (t,x,y,z)
Temporal configuration
6 future steps at 2 Hz
4 historical and 6 future occupancy frames for occupancy prediction
Spatial discretization
64m×64m ego-centered BEV; 1m per cell
[−40,40]×[−40,40]×[−1,5.4]m ; 0.4m voxels
Semantic output
64×64 grid with 11 categorical classes, including free space
200×200×16 grid with 17 semantic classes plus free space
Appendix
Table S1: Benchmark-specific model and training configurations. The planning and occupancy-prediction rows distinguish the task-specific nuScenes input protocols.