feat: initial HSAP platform

Huaxu Sentinel Active Safety Platform with embedded algorithm code,
Docker Compose setup, and vendored dataset scaffolds for clone-and-run.

Co-authored-by: Cursor <cursoragent@cursor.com>

algorithms/lane_ufld/code/pytorch-auto-drive-master/docs/DEPLOY.md

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+# Deployment Guide
+
+- [x] PyTorch -> ONNX
+- [x] ONNX -> TensorRT
+- [ ] ONNX inference
+- [ ] TensorRT inference
+- [ ] ONNX visualization
+- [ ] TensorRT visualization
+
+## Installation
+
+**A separate Python virtual environment is recommended here to avoid effects to your training & testing environment.**
+
+Install all deployment packages (our tested conda version) by:
+
+```
+conda install cudatoolkit=10.2 -c pytorch
+conda install cudnn==8.0.4 -c nvidia
+pip install onnx==1.10.2 onnxruntime-gpu==1.6.0
+python3 -m pip install --upgrade nvidia-tensorrt==8.2.1.8  // you may need to add --extra-index-url https://pypi.ngc.nvidia.com
+```
+
+In this version, TensorRT may use CUDA runtime >= 11, you might avoid using conda if you have sudo access on your device.
+
+Or you can incrementally install **Extra Dependencies** through the tutorial.
+
+## Important Note
+
+Note that we only convert the model `forward()` function,
+post-processing (i.e., `inference()`) is not included. Typical post-processing includes: segmentation map interpolation,
+line NMS for anchor-based lane detection, sigmoid/softmax activations, etc.
+
+## PyTorch -> ONNX:
+
+**PyTorch version >= 1.6.0 is recommended for this feature.**
+
+### Extra Dependencies:
+
+```
+pip install onnx==1.10.2 onnxruntime-gpu==<version>
+```
+
+`<version>` depends on your CUDA/CuDNN version, see [here](https://onnxruntime.ai/docs/execution-providers/CUDA-ExecutionProvider.html#requirements), if you met version issues,
+try install exact cudatoolkit and cudnn from conda. Or you can just install the CPU onnxruntime for this functionality.
+
+### Conversion:
+
+The conversion is based on Torch JIT's tracing on random input. To convert a checkpoint (*e.g.,* ckpt.pt) to ONNX, simply run this command:
+
+```
+python tools/to_onnx.py --config=<config file path>  --height=<input height> --width=<input width> --checkpoint=ckpt.pt
+```
+
+You'll then see the saved `ckpt.onnx` file and a report on the conversion quality.
+
+Same config mechanism and commandline overwrite by `--cfg-options` as in training/testing.
+
+For detailed instructions and commandline shortcuts available, run:
+
+```
+python tools/to_onnx.py --help
+```
+
+### Currently Unsupported Models:
+- ENet (segmentation)
+- ENet backbone (lane detection)
+- DCNv2 in BézierLaneNet (lane detection)
+- Swin backbone (supported if pytorch >= 1.10.0)
+- LaneATT (supported if pytorch >= 1.8.0)
+
+## ONNX -> TensorRT:
+
+### Extra Dependencies:
+
+```
+python3 -m pip install --upgrade nvidia-tensorrt==<version>
+```
+
+TensorRT `<version>` is recommended to be at least 7.2, you can also install it via other means than pip.
+To work better with onnxruntime (for checking of conversion quality), you best checkout the [compatibility](https://onnxruntime.ai/docs/execution-providers/TensorRT-ExecutionProvider.html#requirements).
+
+### Conversion:
+
+The conversion is mainly a building of TensorRT engine. To convert a checkpoint (*e.g.,* ckpt.onnx) to TensorRT, simply run this command:
+
+```
+python tools/to_tensorrt.py --height=<input height> --width=<input width> --onnx-path=<ckpt.onnx>
+```
+
+You'll then see the saved `ckpt.engine` file and a report on the conversion quality.
+
+### Currently Unsupported Models:
+- ENet (segmentation)
+- ENet backbone (lane detection)
+- SCNN (lane detection)
+- Swin backbone (lane detection)
+- DCNv2 in BézierLaneNet (lane detection, could support if built custom op from mmcv and directly convert from PyTorch to TensorRT)
+- LaneATT (supported if TensorRT >= 8.4.1.5)