OnePlanner v1.0

.gitattributes

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+media/oneplanner_scenes.png filter=lfs diff=lfs merge=lfs -text
+replay.mp4 filter=lfs diff=lfs merge=lfs -text

.gitignore

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+# TensorRT engines and their identity files, built and cached beside the ONNX files by
+# autoware_oneplanner on first launch. They are specific to the GPU architecture and TensorRT
+# version they are built on and must not be committed.
+*.engine
+*.engine.identity

LICENSE

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README.md

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+---
+license: apache-2.0
+pipeline_tag: robotics
+tags:
+  - autoware
+  - ros2
+  - autonomous-driving
+  - planning
+  - end-to-end
+  - e2e
+  - trajectory-generation
+  - lidar
+  - bird-eye-view
+  - 3d-object-detection
+  - tensorrt
+  - onnx
+  - oneplanner
+---
+
+# OnePlanner for Autoware (`oneplanner`)
+
+End-to-end planning model for autonomous driving, run in [Autoware](https://github.com/autowarefoundation/autoware)
+by the [`autoware_oneplanner`](https://github.com/autowarefoundation/autoware_oneplanner) ROS 2 node.
+OnePlanner turns the LiDAR point cloud, the Lanelet2 vector map, the route, traffic-light states and the ego
+state into a 6 s ego trajectory, at the LiDAR's rate. A frozen BEVFusion-L backbone turns each point cloud into
+a bird's-eye-view (BEV) feature map, and the planner reads a temporal history of those maps together with the
+vector-map, route and ego context.
+
+<p align="center">
+  <img src="media/oneplanner_scenes.png" width="100%" alt="Six scenes: crossing a large intersection on green, a right turn at a signalized intersection, a sweeping curve at 40 km/h, holding at a red light and turning left on green, stopping for a red light, and slowing behind traffic queued at a red light">
+</p>
+
+## Model overview
+
+| | |
+| --- | --- |
+| Task | Ego trajectory planning: a 6 s trajectory at 0.1 s steps, a second (prior) trajectory and turn-indicator logits, plus 3D object detection from the backbone's head |
+| Inputs | Concatenated LiDAR point cloud; 3 s of ego history and the ego state; the nearest lanes, route lanes, polygons and line strings within ±100 m with their traffic-light states and speed limits; the route goal |
+| Architecture | BEVFusion-L LiDAR backbone (sparse-convolution voxel encoder with a detection head, 8.3M parameters, frozen) → 512 × 180 × 180 BEV feature map over ±122.4 m → planner over a history of 3 BEV maps 0.5 s apart and the vector-map context (27.3M parameters) |
+| Runtime | TensorRT through the `autoware_oneplanner` node: the planner in fp32, the backbone in fp16 |
+| Latency | About 13 ms per planning pass (median), about 9 ms of it planner inference, on an NVIDIA RTX PRO 6000 Blackwell Max-Q; the BEV feature extraction runs per LiDAR frame, outside the pass |
+| Format | ONNX (opset 18). The backbone uses the sparse-convolution plugins of `autoware_tensorrt_plugins`; the planner needs no plugin. TensorRT engines are built locally |
+| License | Apache-2.0 |
+
+## Files
+
+| File | Description |
+| --- | --- |
+| `oneplanner_planner.onnx` | The planner graph, with static shapes |
+| `bevfusion_lidar_feature.onnx` | The BEVFusion-L LiDAR backbone: voxels in, the BEV feature map and undecoded detection proposals out |
+| `ml_package_oneplanner.param.yaml` | The network description the node reads: input convention, tensor names, voxelization geometry, BEV history, detection thresholds, horizon and precision |
+| `media/` | The scenes on this page |
+| `replay.mp4` | An open-loop replay of two recorded drives, shown as this page's video preview |
+| `deploy_metadata.yaml` | Deployment metadata recording the artifact version of this repository |
+| `SHA256SUMS` | Checksums of the files above |
+
+> **TensorRT engines are not distributed here.** Engines are specific to the GPU architecture and TensorRT
+> version they are built on. The node builds one per graph on first launch and caches it beside the ONNX file
+> (`<stem>.engine`, with a `<stem>.engine.identity` file that triggers a rebuild when anything changes), so the
+> model directory must be writable. `build_only:=true` builds them and exits.
+
+## Inputs and outputs
+
+### ROS 2 interface (as used by the node)
+
+**Inputs**
+
+| Topic | Message type | Description |
+| --- | --- | --- |
+| `~/input/pointcloud` | `sensor_msgs/msg/PointCloud2` | Concatenated point cloud in `base_link`, Autoware `PointXYZIRC` layout |
+| `~/input/odometry` | `nav_msgs/msg/Odometry` | Ego pose and twist |
+| `~/input/acceleration` | `geometry_msgs/msg/AccelWithCovarianceStamped` | Ego acceleration |
+| `~/input/steering` | `autoware_vehicle_msgs/msg/SteeringReport` | Steering tire angle |
+| `~/input/velocity` | `autoware_vehicle_msgs/msg/VelocityReport` | Vehicle speed, used where the odometry twist is exactly zero |
+| `~/input/traffic_signals` | `autoware_perception_msgs/msg/TrafficLightGroupArray` | Traffic-light recognition |
+| `~/input/route` | `autoware_planning_msgs/msg/LaneletRoute` | Mission route |
+| `~/input/vector_map` | `autoware_map_msgs/msg/LaneletMapBin` | Lanelet2 map |
+
+**Outputs**
+
+| Topic | Message type | Description |
+| --- | --- | --- |
+| `~/output/trajectory` | `autoware_planning_msgs/msg/Trajectory` | Ego trajectory in `map`: a point at t = 0, then one per 0.1 s for 6 s |
+| `~/output/trajectories` | `autoware_internal_planning_msgs/msg/CandidateTrajectories` | The trajectory and the prior trajectory as candidates |
+| `~/output/turn_indicators` | `autoware_vehicle_msgs/msg/TurnIndicatorsCommand` | Turn-indicator command from the model's logits, filtered by the node |
+| `~/output/detected_objects` | `autoware_perception_msgs/msg/DetectedObjects` | Objects of the backbone's detection head, one message per LiDAR frame |
+| `/diagnostics` | `diagnostic_msgs/msg/DiagnosticArray` | `inference_status` |
+
+### Graphs
+
+The node reads the planner graph's input manifest and fills every input; all coordinates are in the ego frame
+of the planning pose, x forward, y left, metres. The
+[model contract](https://github.com/autowarefoundation/autoware_oneplanner/blob/main/autoware_oneplanner/docs/model_contract.md)
+specifies each tensor's content.
+
+**Planner** (`oneplanner_planner.onnx`)
+
+| Tensor | dtype / shape | Content |
+| --- | --- | --- |
+| `bev_feature_history` | float32 `[1, 3, 512, 180, 180]` | The last 3 BEV maps, 0.5 s apart, current first, each warped into the planning frame |
+| `ego_current_state` | float32 `[1, 10]` | Ego pose in its own frame, velocity, acceleration, steering tire angle, yaw rate |
+| `ego_agent_past` | float32 `[1, 31, 4]` | Ego poses over the last 3 s at 0.1 s |
+| `lanes`, `lanes_speed_limit`, `lanes_has_speed_limit`, `lanes_on_route` | `[1, 140, 20, 33]`, `[1, 140, 1]`, bool `[1, 140, 1]`, `[1, 140, 2]` | The 140 nearest lanes: centerline, bounds, traffic-light state and line types per point; speed limit; route membership |
+| `route_lanes`, `route_lanes_speed_limit`, `route_lanes_has_speed_limit` | `[1, 25, 20, 33]`, `[1, 25, 1]`, bool `[1, 25, 1]` | The route's next 25 lanes |
+| `polygons` | float32 `[1, 32, 40, 4]` | Intersection areas and crosswalks |
+| `line_strings` | float32 `[1, 128, 20, 4]` | Stop lines and road borders |
+| `goal_pose` | float32 `[1, 4]` | Route goal |
+| `ego_shape` | float32 `[1, 3]` | Wheel base, length, width |
+| `sensor_latency` | float32 `[1, 1]` | Time from the LiDAR stamp to the planning time, in seconds |
+| `turn_indicators` | int32 `[1, 31]` | Carried for a stable signature only; the node binds a constant |
+
+| Output | dtype / shape | Content |
+| --- | --- | --- |
+| `trajectory` | float32 `[1, 60, 4]` | x, y, cos(yaw), sin(yaw) at 0.1 s … 6.0 s |
+| `prior_trajectory` | float32 `[1, 60, 4]` | A second ego trajectory, published as a candidate |
+| `turn_indicator_logits` | float32 `[1, 3]` | Logits of DISABLE, ENABLE_LEFT, ENABLE_RIGHT |
+
+**Backbone** (`bevfusion_lidar_feature.onnx`)
+
+| Tensor | dtype / shape | Content |
+| --- | --- | --- |
+| `voxels` (in) | float32 `[M, 32, 5]` | Points per voxel: x, y, z, intensity, time lag; voxel size 0.17 × 0.17 × 0.2 m over ±122.4 m, z −3…5 m |
+| `coors` (in) | int32 `[M, 3]` | Voxel coordinates z, y, x |
+| `num_points_per_voxel` (in) | int32 `[M]` | Valid points per voxel; M up to 256,000 |
+| `bev_feature` (out) | float32 `[1, 512, 180, 180]` | The BEV feature map |
+| `bbox_pred`, `score`, `label_pred` (out) | `[10, 500]`, `[500]`, int64 `[500]` | Undecoded detection proposals for car, truck, bus, bicycle, pedestrian, traffic cone and barrier |
+
+## Usage in Autoware
+
+Build [`autoware_oneplanner`](https://github.com/autowarefoundation/autoware_oneplanner) in an Autoware
+workspace as its README describes (ROS 2, Autoware Core, an NVIDIA GPU with CUDA and TensorRT 10, and
+`autoware_tensorrt_plugins` from Autoware Universe for the backbone). Then download the model to the node's
+default model directory and launch it:
+
+```bash
+hf download AutowareFoundation/oneplanner --revision v1.0 --local-dir ~/autoware_data/oneplanner
+ros2 launch autoware_oneplanner oneplanner.launch.xml \
+  model_path:=$HOME/autoware_data/oneplanner model_name:=oneplanner
+```
+
+Pin `--revision` to a tag rather than relying on `main`, and add `--exclude "media/*" "replay.mp4"` to skip
+the demo media. The first launch builds both TensorRT engines, which takes several minutes. To see the output in
+Autoware's RViz layout, remap `output_trajectory:=/planning/trajectory`; to drive with it, wire it into your
+planning stack's trajectory input behind Autoware's trajectory validation.
+
+The node feeds the inputs the model was trained with, the `oneplanner_v1` input convention: the ego state as
+recorded, the map conversion, the traffic-light selection and the temporal BEV history are specified in the
+model contract, and the node refuses a model package that states another convention.
+
+## Training
+
+The planner was trained by TIER IV on driving data recorded in Japan; the data is not public. The BEVFusion-L
+backbone was trained with [AWML](https://github.com/tier4/AWML) and kept frozen while the planner was trained.
+Accuracy figures are measured on internal validation data and are not published.
+
+## Limitations
+
+- **Research model.** It is not a certified driving system and must not control a vehicle on public roads.
+  Run it in simulation, on closed courses, or with a trained safety driver and independent safeguards
+  downstream of the planner; see the node's
+  [DISCLAIMER](https://github.com/autowarefoundation/autoware_oneplanner/blob/main/DISCLAIMER.md).
+- **Other road users through the LiDAR only.** The planner reads no tracked or predicted objects: other road
+  users reach it through the BEV features of the point cloud.
+- **Sensor setup and domain.** Trained on the vehicles, LiDAR configuration and roads of its training data in
+  Japan (left-hand traffic, Japanese traffic lights). Performance on other platforms and operational domains is
+  not characterized.
+- **Evaluation.** The video preview and the scenes on this page are open-loop replays of recorded drives: the
+  plan is drawn against the path a person drove, not executed.
+
+## Provenance
+
+| | |
+| --- | --- |
+| Source | Exported to ONNX by the OnePlanner training code |
+| This repository | `AutowareFoundation/oneplanner`, tag `v1.0` |
+
+The graphs are the exported files, renamed to the node's default file names. `SHA256SUMS` lists their
+checksums.
+
+## References
+
+- `autoware_oneplanner`: <https://github.com/autowarefoundation/autoware_oneplanner>
+- Model contract: <https://github.com/autowarefoundation/autoware_oneplanner/blob/main/autoware_oneplanner/docs/model_contract.md>
+- AWML: <https://github.com/tier4/AWML>
+- Autoware: <https://github.com/autowarefoundation/autoware>

SHA256SUMS

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bevfusion_lidar_feature.onnx

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deploy_metadata.yaml

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+version: v1.0

ml_package_oneplanner.param.yaml

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+# Network description of the OnePlanner model in this directory (oneplanner_planner.onnx and
+# bevfusion_lidar_feature.onnx), read by autoware_oneplanner as ml_package_<model_name>.param.yaml.
+# It overrides the package defaults for this network; host and deployment behaviour (workspace,
+# rates, tolerances, paths) stays in the package's oneplanner.param.yaml. See the package's
+# docs/model_contract.md for the meaning of every field.
+/**:
+  ros__parameters:
+    # The input convention the model was trained with; the node refuses any other.
+    input_convention: "oneplanner_v1"
+    # Which sensor input providers the model needs, matched by tensor name.
+    sensor_inputs: ["bev_feature"]
+    enable_context_inputs: true
+    # When the plan starts: at the newest odometry; the graph reads sensor_latency.
+    planning_time: "planning_time"
+
+    # Builder precision of the planner graph.
+    precision: "fp32"
+
+    context:
+      ego_history_interval_seconds: 0.1
+      # The graph does not read ego_curvature_bias.
+      curvature_bias:
+        enabled: false
+        tau_s: 2.0
+        min_speed_mps: 1.0
+      # The graph does not read lane_signals / route_signals.
+      signal_history:
+        enabled: false
+        steps: 7
+        step_s: 0.5
+      turn_indicators:
+        # The graph carries this input for a stable signature only; perturbing it changes no
+        # output. Bind a constant, subscribe to nothing.
+        enabled: false
+
+    bev_feature:
+      history_tensor: "bev_feature_history"
+      frames: 3
+      interval_seconds: 0.5
+      bev_half_extent_m: 122.4
+      extractor:
+        precision: "fp16"
+        feature_tensor: "bev_feature"
+        max_points_per_voxel: 32  # from the voxels input shape
+        use_intensity: true  # 5 point features
+        voxels_num: [1, 128000, 256000]  # [min, opt, max]
+        point_cloud_range: [-122.4, -122.4, -3.0, 122.4, 122.4, 5.0]
+        voxel_size: [0.17, 0.17, 0.2]
+      detection:
+        # The frozen BEVFusion detection head the extractor graph carries. It emits undecoded
+        # proposals; the node decodes and suppresses them with autoware_bevfusion's
+        # postprocessing and publishes ~/output/detected_objects.
+        enabled: true
+        # One Autoware class name per head class, in the head's label order. A head class
+        # with no ObjectClassification label is written as UNKNOWN.
+        # head classes: car, truck, bus, bicycle, pedestrian, traffic_cone, barrier
+        class_names: ["CAR", "TRUCK", "BUS", "BICYCLE", "PEDESTRIAN", "UNKNOWN", "UNKNOWN"]
+        num_proposals: 500
+        out_size_factor: 8
+        # Score thresholds per (distance band, class), calibrated for this head and applied as
+        # autoware_bevfusion applies detection_score_thresholds: the box centre's radial
+        # distance picks the first band whose upper limit exceeds it, and a box beyond the last
+        # band is dropped.
+        distance_bin_upper_limits: [50.0, 90.0, 121.0, 200.0]
+        # distance-major: one row per band, one column per head class
+        # CAR       TRUCK     BUS       BICYCLE   PEDESTRIAN  TRAFFIC_CONE  BARRIER
+        score_thresholds: [
+          0.194733, 0.167118, 0.109791, 0.245172, 0.150065, 0.12842, 0.043799,  # [0, 50) m
+          0.221063, 0.18848, 0.168961, 0.160301, 0.152223, 0.15707, 0.1,  # [50, 90) m
+          0.155292, 0.163172, 0.08902, 0.185649, 0.154218, 0.239001, 0.1,  # [90, 121) m
+          0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1  # [121, 200) m
+        ]
+
+    postprocess:
+      prediction_tensor: "trajectory"
+      extra_trajectory_tensors: ["prior_trajectory"]
+      # Logits of DISABLE, ENABLE_LEFT, ENABLE_RIGHT, published as ~/output/turn_indicators.
+      turn_indicator:
+        tensor: "turn_indicator_logits"
+      horizon_seconds: 6.0  # 60 steps
+      time_step: 0.1
+      generator_name: "OnePlanner"

oneplanner_planner.onnx

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replay.mp4

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