feat: add simpl_prediction v0.1 artifacts (from awf.ml.dev.web.auto/perception/models/simpl/v0.1.0)

.gitignore

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+# Auto-generated TensorRT artifacts, built locally by Autoware from the ONNX
+# files (see autoware_tensorrt_common). They are environment-specific
+# (GPU arch + TensorRT version) and must not be committed to this repo.
+*.engine
+*.json

README.md

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+---
+license: apache-2.0
+pipeline_tag: robotics
+tags:
+  - autoware
+  - ros2
+  - autonomous-driving
+  - motion-prediction
+  - trajectory-prediction
+  - simpl
+  - tensorrt
+  - onnx
+---
+
+# SIMPL for Autoware (`simpl_prediction`)
+
+Multi-agent motion prediction model for autonomous driving, used by the
+[`autoware_simpl_prediction`](https://github.com/autowarefoundation/autoware_universe/tree/main/perception/autoware_simpl_prediction)
+node in [Autoware](https://github.com/autowarefoundation/autoware).
+
+The model follows the **SIMPL** [1] architecture (A Simple and Efficient Multi-agent Motion Prediction
+Baseline for Autonomous Driving) and runs with TensorRT inside Autoware. It is exported as ONNX so it can be
+deployed across hardware; Autoware builds the TensorRT engine from the ONNX file on first launch.
+
+## Model overview
+
+| | |
+| --- | --- |
+| Task | Multi-modal trajectory prediction for tracked traffic agents |
+| Architecture | SIMPL [1] |
+| Predicted agent classes | `VEHICLE`, `PEDESTRIAN`, `MOTORCYCLIST`, `CYCLIST`, `LARGE_VEHICLE` |
+| Runtime | TensorRT (FP32 by default) via the `autoware_simpl_prediction` ROS 2 node |
+| Format | ONNX (Autoware builds the TensorRT engine locally on first launch) |
+| License | Apache-2.0 |
+
+Autoware object labels are mapped to the model's agent classes as follows: `CAR` to `VEHICLE`, `PEDESTRIAN`
+to `PEDESTRIAN`, `BICYCLE` to `CYCLIST`, `MOTORCYCLE` to `MOTORCYCLIST`, and `TRUCK` / `TRAILER` / `BUS` to
+`LARGE_VEHICLE`. Only agents whose mapped label is listed in the node's `preprocess.labels` parameter are
+predicted and published.
+
+The following dimensions are fixed at ONNX export time (dynamic shape inference is not supported yet), and
+the default node configuration matches them:
+
+| Parameter | Symbol | Value |
+| --- | --- | --- |
+| Maximum number of agents | N | 50 |
+| Past history length | T_past | 8 |
+| Maximum number of map polylines | K | 300 |
+| Maximum points per polyline | P | 10 |
+| Number of prediction modes | M | 6 |
+| Future horizon length | T_future | 80 |
+
+## Files
+
+| File | Description |
+| --- | --- |
+| `simpl.onnx` | SIMPL motion prediction network (ONNX) |
+| `deploy_metadata.yaml` | Deployment metadata recording the artifact version of this repository |
+
+> **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 file
+> on first launch (or via `build_only:=true`).
+
+## Inputs and outputs
+
+**Network inputs**
+
+- Agent histories: `N x D_agent x T_past` (past states of the tracked agents)
+- Map points: `K x P x D_map` (lanelet map polylines)
+- Relative pose encoding: `(N + K) x (N + K) x D_rpe`
+
+**Network outputs**
+
+- Predicted scores: `N x M` (confidence per agent and mode)
+- Predicted trajectories: `N x M x T_future x D_trajectory`, where `D_trajectory` is `(x, y, vx, vy)` in the
+  agent local coordinate frame
+
+**Node topics (as used by `autoware_simpl_prediction`)**
+
+| Topic | Type | Direction |
+| --- | --- | --- |
+| `~/input/objects` | `autoware_perception_msgs/msg/TrackedObjects` | input |
+| `~/input/vector_map` | `autoware_map_msgs/msg/LaneletMapBin` | input |
+| `/localization/kinematic_state` | `nav_msgs/msg/Odometry` | input |
+| `~/output/objects` | `autoware_perception_msgs/msg/PredictedObjects` | output |
+
+Pre-processing (agent history accumulation, lanelet-to-polyline conversion) and post-processing (mode score
+thresholding) run in the node, not in the ONNX graph.
+
+## Usage in Autoware
+
+The node expects the artifacts under `~/autoware_data/ml_models/simpl_prediction/` and launches with:
+
+```bash
+ros2 launch autoware_simpl_prediction simpl.launch.xml
+```
+
+Add `build_only:=true` to build the TensorRT engine from the ONNX as a one-off pre-task. The default
+parameters (`config/simpl.param.yaml`) point to `simpl.onnx` in the data directory and use `fp32` precision.
+See the [package README](https://github.com/autowarefoundation/autoware_universe/tree/main/perception/autoware_simpl_prediction)
+for the full parameter reference.
+
+## Training
+
+The SIMPL architecture and reference training code originate from the upstream project:
+
+- Original implementation: <https://github.com/HKUST-Aerial-Robotics/SIMPL>
+- Paper: "SIMPL: A Simple and Efficient Multi-agent Motion Prediction Baseline for Autonomous Driving",
+  arXiv:2402.02519
+
+An Autoware library to train and deploy SIMPL and other motion prediction models is work in progress, as
+noted in the package README. The training data and training configuration used to produce the released
+`simpl.onnx` weights are not publicly documented.
+
+## Limitations
+
+- The number of predictable agents is fixed at export time (`max_num_agent: 50`); dynamic shape inference is
+  not supported yet.
+- Only agents whose label is contained in `preprocess.labels` are published; other road users are ignored.
+- Predicted modes with a confidence score below `postprocess.score_threshold` are filtered out. If all modes
+  of an object are filtered, the published object contains no path, and the remaining mode confidences are
+  not guaranteed to sum to 100%.
+- Agent history that is no longer observed in incoming callbacks is dropped, which resets prediction context
+  for reappearing agents.
+
+## Provenance
+
+| | |
+| --- | --- |
+| Original artifact source | `https://awf.ml.dev.web.auto/perception/models/simpl/v0.1.0/simpl.onnx` |
+| Source version string | `simpl/v0.1.0` |
+| Tag in this repository | `v0.1` (normalized from `v0.1.0`) |
+| `simpl.onnx` SHA-256 | `ad5e03983193c4d188432f314334697d6a216e7ffc91fb651eee5d6e4c42f492` |
+
+## Citation
+
+```bibtex
+@article{zhang2024simpl,
+  title   = {SIMPL: A Simple and Efficient Multi-agent Motion Prediction Baseline for Autonomous Driving},
+  author  = {Zhang, Lu and Li, Peiliang and Liu, Sikang and Shen, Shaojie},
+  journal = {arXiv preprint arXiv:2402.02519},
+  year    = {2024}
+}
+```
+
+## References
+
+- [1] Zhang et al., "SIMPL: A Simple and Efficient Multi-agent Motion Prediction Baseline for Autonomous Driving", arXiv:2402.02519, 2024.
+- [2] Original implementation: <https://github.com/HKUST-Aerial-Robotics/SIMPL>

deploy_metadata.yaml

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+version: v0.1

simpl.onnx

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+version https://git-lfs.github.com/spec/v1
+oid sha256:ad5e03983193c4d188432f314334697d6a216e7ffc91fb651eee5d6e4c42f492
+size 10762674