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/LANEDETECTION.md

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+# Lane detection
+
+**Before diving into this, please make sure you followed the instructions to prepare datasets in [DATASET.md](./DATASET.md)**
+
+**Execution is based on [config files](../configs/README.md)**
+
+## Training:
+
+Some models' ImageNet pre-trained weights need to be manually downloaded, refer to [this table](./IMAGENET_MODELS.md).
+
+```
+python main_landet.py --train \
+                      --config=<config file path> \
+                      --mixed-precision  # Optional, enable mixed precision \
+                      --cfg-options=<overwrite cfg dict>  # Optional
+```
+
+Your `<overwrite cfg dict>` is used to manually override config file options in commandline so you don't have to modify config file each time. It should look like this (**the quotation marks are necessary!**): `"train.batch_size=8 train.workers=4 model.lane_classifier_cfg.dropout=0.1"`
+
+Some options can be used by shortcuts, such as `--batch-size` will set both `train.batch_size` and `test.batch_size`, for more info:
+
+```
+python main_landet.py --help
+```
+
+Example shells are provided in [tools/shells](../tools/shells/).
+
+## Distributed Training
+
+We support multi-GPU training with Distributed Data Parallel (DDP):
+
+```
+python -m torch.distributed.launch --nproc_per_node=<number of GPU per-node> --use_env main_landet.py <your normal args>
+```
+
+With DDP, batch size and number of workers are **per-GPU**. Do not forget to set device args like `world_size` in your config.
+
+## Testing
+
+### Evaluation:
+
+**Important Notice: Do not simoutanously run multiple evaluation on CULane, since the eval use the same pytorch-auto-drive/output cache directory, the results could be overwritten! Same goes for LLAMAS!**
+
+1. Predict lane lines:
+
+```
+python main_landet.py --test \  # Or --val for validation
+                      --config=<config file path> \
+                      --mixed-precision  # Optional, enable mixed precision \
+                      --cfg-options=<overwrite cfg dict>  # Optional
+```
+
+To test a downloaded pt file, try add `--checkpoint=<pt file path>`.
+
+Note that LLAMAS doesn't have test set labels.
+
+2. Test with official scripts on `<my_dataset>`:
+
+```
+./autotest_<my_dataset>.sh <exp_name> <mode> <save_dir>
+```
+
+`<mode>` includes `test` and `val`.
+
+`<save_dir>` and `<exp_name>` are recommended to set the same as in config file, so detail evaluation results will be saved to `<save_dir>/<exp_name>/`
+
+Overall result will be saved to `log.txt`.
+
+### Fast evaluation in mIoU [Not Recommended]:
+
+Training contains online fast validations by using `val_num_steps` and the best model is saved, but we find that the best checkpoint is usually the last, so probably no need for validations. For log details you can checkout tensorboard.
+
+To validate a trained model on mean IoU, you can use either mixed-precision or fp32 for any model trained with/without mixed-precision:
+
+```
+python main_landet.py --valfast \
+                      --config=<config file path> \
+                      --mixed-precision  # Optional, enable mixed precision \
+                      --cfg-options=<overwrite cfg dict>  # Optional
+```