feat: add tensorrt_bevdet v1.0 artifacts (from autoware-files S3 tensorrt_bevdet.tar.gz)

.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: object-detection
+tags:
+  - autoware
+  - ros2
+  - autonomous-driving
+  - camera
+  - multi-view
+  - 3d-object-detection
+  - bevdet
+  - tensorrt
+  - onnx
+---
+
+# BEVDet for Autoware (`tensorrt_bevdet`)
+
+Multi-view camera 3D object detection model, used by the
+[`autoware_tensorrt_bevdet`](https://github.com/autowarefoundation/autoware_universe/tree/main/perception/autoware_tensorrt_bevdet)
+node in [Autoware](https://github.com/autowarefoundation/autoware).
+
+The model follows the **BEVDet** architecture: it unifies six surround-view camera images into a bird's-eye-view
+(BEV) representation for the 3D object detection task, deployed with the **BEVPoolv2** [1] TensorRT/C++
+implementation. It is distributed as ONNX; Autoware builds the TensorRT engine from the ONNX file on first
+launch.
+
+## Model overview
+
+| | |
+| --- | --- |
+| Task | 3D object detection (oriented bounding boxes) from 6 surround-view camera images |
+| Architecture | BEVDet (configuration `bevdet_r50_4dlongterm_depth`: depth-aware BEV pooling with temporal fusion of 8 adjacent frames) |
+| Detected classes | `car`, `truck`, `construction_vehicle`, `bus`, `trailer`, `barrier`, `motorcycle`, `bicycle`, `pedestrian`, `traffic_cone` |
+| Cameras | `CAM_FRONT_LEFT`, `CAM_FRONT`, `CAM_FRONT_RIGHT`, `CAM_BACK_LEFT`, `CAM_BACK`, `CAM_BACK_RIGHT` |
+| Network input size | 256 x 704 (resized/cropped from 900 x 1600 source images) |
+| BEV range | `[-51.2, -51.2, -5.0, 51.2, 51.2, 3.0]` m, 0.8 m grid |
+| Runtime | TensorRT (FP16 by default, FP32 selectable) via the `autoware_tensorrt_bevdet` ROS 2 node |
+| Format | ONNX (Autoware builds the TensorRT engine locally on first launch) |
+| License | Apache-2.0 (see Legal Notice for training-data terms) |
+
+The node also requires two configuration files that ship inside the `autoware_tensorrt_bevdet` package, not in
+this repository:
+
+- `config/bevdet.param.yaml`: ROS parameters (precision, score threshold, class names, camera list)
+- `config/bevdet_r50_4dlongterm_depth.yaml`: model configuration (BEV range, grid, image pre-processing, NMS)
+
+## Files
+
+| File | Description |
+| --- | --- |
+| `bevdet_one_lt_d.onnx` | BEVDet network (long-term temporal fusion, depth branch), all six camera views |
+| `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 (the node appends `_fp16.engine` or `_fp32.engine` to the configured engine path
+> depending on the precision, so with the default config the engine lands next to the ONNX file).
+
+## Inputs and outputs (as used by the node)
+
+**Inputs**: six `sensor_msgs/msg/Image` topics (`~/input/topic_img_front_left`, `~/input/topic_img_front`,
+`~/input/topic_img_front_right`, `~/input/topic_img_back_left`, `~/input/topic_img_back`,
+`~/input/topic_img_back_right`) plus the matching six `sensor_msgs/msg/CameraInfo` topics.
+
+**Outputs**:
+
+- `~/output/boxes` (`autoware_perception_msgs/msg/DetectedObjects`): detected 3D objects
+- `~/output_bboxes` (`visualization_msgs/msg/MarkerArray`): bounding-box markers for nuScenes visualization,
+  published when `debug_mode` is enabled
+
+## Usage in Autoware
+
+The node loads the model from `~/autoware_data/ml_models/tensorrt_bevdet/` by default and launches with:
+
+```bash
+ros2 launch autoware_tensorrt_bevdet tensorrt_bevdet.launch.xml
+```
+
+Key launch arguments: `model_name` (default `bevdet_one_lt_d`), `model_path`
+(default `$HOME/autoware_data/ml_models/tensorrt_bevdet`), `model_config`, and `param_file`. Precision
+(`fp16`/`fp32`) and `debug_mode` are set in `config/bevdet.param.yaml`. The package README documents
+TensorRT 10.8.0.43 and CUDA 12.4 as prerequisites, and describes how to test the node against
+nuScenes data played back with `ros2_dataset_bridge`. See the
+[package README](https://github.com/autowarefoundation/autoware_universe/tree/main/perception/autoware_tensorrt_bevdet)
+for the full instructions.
+
+## Training
+
+The model was trained on the nuScenes dataset for 20 epochs. Training, export, and deployment code:
+
+- Original BEVDet: <https://github.com/HuangJunJie2017/BEVDet/tree/dev2.1>
+- TensorRT C++ implementation: <https://github.com/LCH1238/bevdet-tensorrt-cpp/tree/one>
+- ONNX export fork: <https://github.com/LCH1238/BEVDet/tree/export>
+- Training/export fork adapted to the TIER IV dataset: <https://github.com/cyn-liu/BEVDet/tree/train_export>
+- Autoware vendor package (modified TensorRT implementation): <https://github.com/autowarefoundation/bevdet_vendor>
+
+## Limitations
+
+- Trained only on nuScenes; the package README notes poor generalization to other data. To use this model on
+  your own vehicle and camera setup, retraining is required (see the `train_export` fork above).
+- Expects a six-camera surround-view rig matching the nuScenes camera layout.
+- Only the ten nuScenes classes listed above are detected.
+
+## Provenance and versioning
+
+| | |
+| --- | --- |
+| Original source | `tensorrt_bevdet.tar.gz` from the Autoware artifacts S3 bucket (`https://autoware-files.s3.us-west-2.amazonaws.com/models/tensorrt_bevdet.tar.gz`), unversioned |
+| This repository | `AutowareFoundation/tensorrt_bevdet`, extracted ONNX published as tag `v1.0` |
+
+## Citation
+
+```bibtex
+@article{huang2022bevpoolv2,
+  title   = {BEVPoolv2: A Cutting-edge Implementation of BEVDet Toward Deployment},
+  author  = {Huang, Junjie and Huang, Guan},
+  journal = {arXiv preprint arXiv:2211.17111},
+  year    = {2022}
+}
+@article{huang2021bevdet,
+  title   = {BEVDet: High-performance Multi-camera 3D Object Detection in Bird-Eye-View},
+  author  = {Huang, Junjie and Huang, Guan and Zhu, Zheng and Ye, Yun and Du, Dalong},
+  journal = {arXiv preprint arXiv:2112.11790},
+  year    = {2021}
+}
+```
+
+## References
+
+- [1] Huang and Huang, "BEVPoolv2: A Cutting-edge Implementation of BEVDet Toward Deployment", arXiv:2211.17111, 2022.
+- [2] Huang et al., "BEVDet: High-performance Multi-camera 3D Object Detection in Bird-Eye-View", arXiv:2112.11790, 2021.
+- [3] nuScenes: <https://www.nuscenes.org/nuscenes>
+
+## Legal Notice
+
+_The nuScenes dataset is released publicly for non-commercial use under the Creative Commons
+Attribution-NonCommercial-ShareAlike 4.0 International Public License. Additional Terms of Use can be found at
+<https://www.nuscenes.org/terms-of-use>. To inquire about a commercial license please contact
+<nuscenes@motional.com>._

bevdet_one_lt_d.onnx

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+oid sha256:47f156cb63ca97fa1d47b5d46eba648d4bbe71c95a08af14d3d0fdf53d800d77
+size 304801539

deploy_metadata.yaml

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+version: v1.0