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/INSTALL.md

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+# Installation
+
+## Download the code:
+
+```
+git clone https://github.com/voldemortX/pytorch-auto-drive.git
+cd pytorch-auto-drive
+```
+
+## Requirements
+
+- Linux (recommended) or Windows (not fully tested, could have problems)
+- Python >= 3.6
+- CUDA >= 9.2 (for CUDA version < 9.2, the code is tested only with PyTorch 1.3 & CUDA 9.0 & CuDNN 7.6.0)
+- PyTorch >= 1.6 (2.x are not tested)
+- TorchVision >= 0.7.0
+- [mmcv-full](https://github.com/open-mmlab/mmcv) >= 1.3.5 (according to PyTorch/CUDA version)
+- Other pip dependencies: `pip install -r requirements.txt`
+
+The default Conda env (step-by-step):
+
+```
+conda create -n pad python=3.6
+conda activate pad
+conda install pytorch==1.6.0 torchvision==0.7.0 cudatoolkit=10.2 -c pytorch
+pip install mmcv-full==1.3.5 -f https://download.openmmlab.com/mmcv/dist/cu102/torch1.6.0/index.html
+pip install -r requirements.txt
+```
+
+## Prepare the code:
+
+```
+chmod 777 *.sh tools/shells/*.sh
+mkdir output
+```
+
+## Improve training speed with [Pillow-SIMD](https://github.com/uploadcare/pillow-simd) (optional, advanced):
+
+```
+pip uninstall pillow
+CC="cc -mavx2" pip install -U --force-reinstall pillow-simd
+```
+
+Note that you need to use ToTensor transform as late as possible for this speedup.
+
+## Enable tensorboard (optional):
+
+```
+tensorboard --logdir=<path to tb_logs>
+```
+
+`<path to tb_logs>` is usually `./checkpoints/tb_logs` if you did not customized `save_dir` in config file.