DA-WAM: Decision-Aligned Future Latents for Driving World Models

Ruiguo Zhong · Benshan Ma · Xiaolong Chen · Lang Zhang
Mingyue Feng · Yaonong Wang · Pei Liu · Jun Ma

Paper · Overview · Results · Visualization · Getting Started · Citation

Overview

DA-WAM connects future representation learning to trajectory selection. It first generates trajectory proposals, predicts a distinct future latent for each proposal, and scores each trajectory together with its associated future and the current scene.

• Candidate-conditioned futures. Each proposal receives its own compact action-conditioned representation.
• Expert-matched supervision. A training-only dense decoder and an EMA target supervise the candidate closest to the expert trajectory.
• Safety-focused refinement. Counterfactual trajectories refine the candidate-future path and scorer while keeping proposal generation fixed.

DA-WAM architecture. Shared scene tokens support candidate-specific prediction and scoring; dense future supervision is applied to the expert-matched candidate during training.

Results

Retained models

Benchmark	Model / inference setting	Metric	Result
NAVSIM v1	SafetyFinal epoch 1; learned candidate scores	PDMS ↑	93.6806
NAVSIM v2.2	Same v1-trained model; temporal reranking	EPDMS ↑	89.2193
Bench2Drive	Four-camera checkpoint, epoch 11	Driving Score ↑	59.04

NAVSIM v2 uses training-free momentum reranking: 0.875 × learned_score + 0.125 × pair_EC. NAVSIM v1 PDMS and v2 EPDMS follow different protocols and should not be compared directly.

The tables and figures below are taken from the supplied September 2026 manuscript. Selected baseline rows preserve that manuscript's values and rounding; they are not a continuously updated leaderboard. The linked arXiv v2 is an earlier version of the manuscript.

NAVSIM v1

Selected rows from the paper's NAVSIM-v1 comparison. All metrics are higher-is-better, on a 0–100 scale.

Method	NC	DAC	TTC	Comfort	EP	PDMS
PDM-Closed	94.6	99.8	89.9	86.9	99.9	89.1
Human driver	100.0	100.0	100.0	99.9	87.5	94.8
DriveVLA-W0	98.7	99.1	95.3	99.3	83.3	90.2
ReCogDrive	97.9	97.3	94.9	100.0	87.3	90.8
iPad	98.6	98.3	94.9	100.0	88.0	91.7
SparseDriveV2	98.5	98.4	95.0	99.9	88.6	92.0
DrivoR	98.9	98.3	96.2	100.0	89.1	93.1
DriveSuprim	98.6	98.6	95.5	100.0	91.3	93.5
DA-WAM	99.1	98.9	96.8	99.8	90.0	93.7

NAVSIM v2

Selected rows from the paper's NAVSIM-v2 comparison. All metrics are higher-is-better, on a 0–100 scale.

Method	Backbone	NC	DAC	DDC	TLC	EP	TTC	LK	HC	EC	EPDMS
TransFuser	ResNet-34	96.9	89.9	97.8	99.7	87.1	95.4	92.7	98.3	87.2	76.7
Hydra-MDP++	ViT-L	98.4	98.0	99.4	99.8	87.5	97.7	95.3	98.3	77.4	85.1
DriveSuprim	ViT-L	97.8	97.9	99.5	99.9	90.6	97.1	96.6	98.3	77.9	86.0
SparseDriveV2	ResNet-34	98.1	98.1	99.6	99.8	91.1	97.3	96.9	98.2	78.4	86.7
DiffusionDriveV2	ResNet-34	97.7	96.6	99.2	99.8	88.9	97.2	96.0	97.8	91.0	87.5
DA-WAM	ViT-L	99.2	98.8	97.8	99.5	91.1	98.7	93.3	96.7	77.1	89.2

Metric abbreviations

NC: No-at-Fault Collision; DAC: Drivable Area Compliance; TTC: Time to Collision; EP: Ego Progress; DDC: Driving Direction Compliance; TLC: Traffic Light Compliance; LK: Lane Keeping; HC: History Comfort; EC: Extended Comfort. PDMS and EPDMS are the respective aggregate benchmark scores.

Bench2Drive

Selected rows from the paper's closed-loop comparison. “—” denotes values not reported in the supplied table.

Method	Avg. L2 (m) ↓	Efficiency ↑	Comfortness ↑	Success Rate (%) ↑	Driving Score ↑
TCP-traj w/o distillation	1.96	78.78	22.96	20.45	49.30
UniAD-Base	0.73	129.21	43.58	16.36	45.81
VAD	0.91	157.94	46.01	15.00	42.35
EgoFSD-S	0.70	178.30	—	21.00	52.02
BridgeAD	0.71	—	—	22.73	50.06
SeerDrive	0.66	—	—	30.17	58.32
DA-WAM†	0.95	140.40	14.64	24.66	59.04

† Coverage: the retained DA-WAM logs contain 219 unique routes out of 220, with 54 successful routes. Route RouteScenario_11715_rep0 is missing. DS 59.04 and SR 24.66% are computed over these 219 routes; they are not a completed full-220 result. Avg. L2 is reproduced from the manuscript and was not recomputed by the route-log audit. Baseline values are reported comparisons, not reruns in this repository.

Ablation: future conditioning and safety supervision

Configuration	Stage 2 safety supervision	PDMS ↑	NC ↑	DAC ↑	EP ↑	TTC ↑	Comfort ↑
No future prediction	No	93.31	98.45	98.27	91.36	95.48	99.99
Shared global future	No	92.81	99.02	98.46	88.68	96.54	99.99
Current-latent conditioning	No	93.25	98.44	98.19	91.38	95.49	99.94
Action-conditioned future	No	93.46	98.88	98.58	90.47	96.33	99.69
Action-conditioned future	Yes	93.68	99.11	98.88	89.97	96.81	99.77

Safety-focused refinement improves candidate ranking with a small progress trade-off. In this figure, epoch 0 is Stage 1, and epochs 1–3 count completed refinement epochs. The retained model after two completed refinement epochs is saved as checkpoint epoch 1 under zero-based numbering.

Visualization

Safety-focused trajectory selection

The paper's examples compare attention responses and selected trajectories before and after refinement. Image-space attention is a diagnostic visualization, not BEV localization or proof of causal influence.

Future-feature prediction

This figure uses a separately trained 1.5-second visualization checkpoint. The retained quantitative NAVSIM model uses a 0.5-second future target; the visualization checkpoint is not included in the minimal release. Each column is an independent prediction. Feature colors denote a shared PC1 projection, not object identities or semantic classes.

Getting Started

Installation

See environment setup. Model training/inference uses Python 3.9; the CARLA client uses a separate Python 3.8 environment. OpenScene data, maps, metric caches, Bench2Drive training caches, and CARLA 0.9.15 remain external prerequisites.

cd /dahuafs/userdata/2639639/Code/leap-auto-wam
source environment/activate_model.sh

Training

# NAVSIM Stage 1: Joint30
bash scripts/train_navsim.sh stage1

# NAVSIM Stage 2: SafetyFinal; supply the Stage 1 parent checkpoint
CHECKPOINT_PATH=/path/to/stage1_epoch15.ckpt bash scripts/train_navsim.sh stage2

# Bench2Drive: retained four-camera implementation
bash scripts/train_bench2drive.sh

The NAVSIM recipe uses 32 proposals and LoRA rank 32. Stage 2 starts from Joint30 epoch 15, freezes proposal generation, and retains SafetyFinal epoch 1. The Stage 1 parent is not bundled with the final-only checkpoints. The NAVSIM v2 result uses the v1-trained model without v2-specific training. Training launchers default to multiple GPUs and require prepared caches.

Inference and evaluation

# Configuration checks only
bash scripts/eval_navsim_v1.sh --cfg job
bash scripts/eval_navsim_v2.sh --cfg job
python scripts/prepare_bench2drive_eval.py --check

# Evaluation: each output directory must be new
OUTPUT_DIR=/path/to/new/v1_result bash scripts/eval_navsim_v1.sh
OUTPUT_DIR=/path/to/new/v2_result bash scripts/eval_navsim_v2.sh
python scripts/prepare_bench2drive_eval.py --output /path/to/new/b2d_result
bash /path/to/new/b2d_result/run.sh

Override NAVSIM paths with CHECKPOINT_PATH, MODEL_ENV, METRIC_CACHE_PATH, OPENSCENE_DATA_ROOT, and NAVSIM_EXP_ROOT. Use B2D_EVAL_ENV and CARLA_ROOT for the CARLA environment. Configuration/import checks do not establish successful full benchmark reproduction.

Checkpoints

It will be released on Hugging face

Repository layout

navsim_v1/       Joint30 / SafetyFinal training and NAVSIM v1 inference
navsim_v2/       Official NAVSIM v2.2 inference and temporal reranking
bench2drive/    Four-camera training, inference and required shared modules
checkpoints/    Final models and required pretrained backbone only
third_party/    V-JEPA runtime dependencies
scripts/        Unified training and inference entrypoints
environment/    Installation and environment configuration
assets/         Paper figures used in this README

Only core training/inference code, required dependencies, final weights and README figures are retained here. Historical experiments, raw result archives and editable paper materials remain outside this directory in the sibling backup.

Citation

@article{zhong2026dawam,
  title={DA-WAM: Decision-Aligned Future Latents for Driving World Models},
  author={Zhong, Ruiguo and Ma, Benshan and Chen, Xiaolong and Zhang, Lang
          and Feng, Mingyue and Wang, Yaonong and Liu, Pei and Ma, Jun},
  journal={arXiv preprint arXiv:2608.19085},
  year={2026},
  eprint={2608.19085},
  archivePrefix={arXiv},
  primaryClass={cs.RO},
  url={https://arxiv.org/abs/2608.19085}
}

Acknowledgements

We thank the authors and maintainers of the open-source codebases that informed this work: Drive-JEPA, SparseDriveV2, DiffusionDrive, WA-JEPA, and iPad. We also build on V-JEPA 2 and use the NAVSIM, nuPlan, and Bench2Drive benchmarks and tools.

License

See LICENSE. Bundled third-party components, datasets and pretrained weights remain subject to their respective licenses.