Diffusion Planner for Autoware (diffusion_planner)

Trajectory generation models for autonomous driving, used by the autoware_diffusion_planner node in Autoware.

The models follow the Diffusion Planner [1] architecture from "Diffusion-Based Planning for Autonomous Driving with Flexible Guidance" (Zheng et al.). The node generates smooth, feasible, and safe ego trajectories by considering dynamic and static obstacles, vehicle kinematics, Lanelet2 map context, the route, and traffic signals with speed limits. Models are distributed as ONNX and run with TensorRT (default) or ONNX Runtime.

Model overview

Versions in this repository

This is a multi-version repository. Each git tag contains exactly that version's file set at the repository root; main always points to the newest tag (currently v5.0). Consumers must pin --revision <tag> and never rely on main. Autoware installs each version side by side under ~/autoware_data/ml_models/diffusion_planner/<tag>/.

The model versioning scheme uses major and minor numbers: the major version changes when model inputs/outputs or architecture change (models with a different major version are not compatible with the ROS node), and the minor version changes when only the weights are updated. The package also documents older model versions (0.1, 1.0, 2.0) that are not compatible with the current node; they are not distributed here.

Files

Files at main / v5.0 (earlier tags contain diffusion_planner.onnx, diffusion_planner.param.json, and deploy_metadata.yaml):

TensorRT engines are not distributed here. TensorRT engines are specific to the GPU architecture and TensorRT version they are built on and are not portable, so Autoware builds them locally from the ONNX files on first launch (or via build_only:=true).

Inputs and outputs (as used by the node)

Inputs

Outputs

Usage in Autoware

Autoware downloads these artifacts to ~/autoware_data/ml_models/diffusion_planner/<tag>/ (see Download artifacts). The node's config (config/diffusion_planner.param.yaml) points at one version directory, currently v5.0.

Launch the planning simulator with the diffusion planner selected:

ros2 launch autoware_launch planning_simulator.launch.xml \
  map_path:=/path/to/your/map \
  vehicle_model:=sample_vehicle \
  sensor_model:=sample_sensor_kit \
  planning_setting:=diffusion_planner

planning_setting:=diffusion_planner swaps the trajectory generator, the planning validator input topic, and the diagnostics graph, so no additional launch-file edits are required. The node's own launch file (diffusion_planner.launch.xml) accepts build_only:=true to shut the node down after model initialization, which can be used to pre-build the TensorRT engine. See the package README for the full parameter reference.

Provenance

These artifacts were previously hosted at https://awf.ml.dev.web.auto/planning/models/diffusion_planner/<version>/ for versions v3.0 through v5.0. The HF tags map one-to-one to those source version directories, so traceability to the original hosting is preserved.

Training

• Training fork (used to train these models): https://github.com/tier4/Diffusion-Planner
• Original implementation: https://github.com/ZhengYinan-AIR/Diffusion-Planner
• Paper: "Diffusion-Based Planning for Autonomous Driving with Flexible Guidance", arXiv:2501.15564

The models were trained by TIER IV. The package's model version history records that training used TIER IV synthetic and real driving data, with supervised fine-tuning on carefully filtered data introduced in v3.0; the exact dataset composition per version is not publicly documented.

Limitations

• Models with a major version different from the one the ROS node expects are not compatible with the node.
• The node is aimed at the proposed Autoware new planning framework.
• Training data is TIER IV internal; performance on other vehicle platforms and operational domains is not publicly characterized.

Citation

@article{zheng2025diffusionplanner,
  title   = {Diffusion-Based Planning for Autonomous Driving with Flexible Guidance},
  author  = {Zheng, Yinan and others},
  journal = {arXiv preprint arXiv:2501.15564},
  year    = {2025}
}

References

• [1] Zheng et al., "Diffusion-Based Planning for Autonomous Driving with Flexible Guidance", arXiv:2501.15564, 2025.
• Original implementation: https://github.com/ZhengYinan-AIR/Diffusion-Planner
• Training fork: https://github.com/tier4/Diffusion-Planner
• Consuming package: https://github.com/autowarefoundation/autoware_universe/tree/main/planning/autoware_diffusion_planner
• Autoware: https://github.com/autowarefoundation/autoware