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/advanced/VISUALIZE_LANE_DATASETS.md

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+# Visualize & Compare with GT on lane detection datasets
+
+Here we make visualization functions work the same for specific dataset file structures by writing customized Dataset class and the optional `vis_dataset` dict in configs, trough examples.
+
+## Expected Usage
+
+*Note that TuSimple, CULane and LLAMAS are already supported in this manner.*
+
+Write a config file that imports all options from the config file you want to visualize with, and provide a `vis_dataset` dict, e.g., [configs/lane_detection/bezierlanenet/vis_resnet34_tusimple_aug1b.py](../../configs/lane_detection/bezierlanenet/vis_resnet34_tusimple_aug1b.py):
+
+```
+from configs.lane_detection.bezierlanenet.resnet34_tusimple_aug1b import *
+from configs.lane_detection.common.datasets._utils import TUSIMPLE_ROOT
+
+vis_dataset = dict(
+    name='TuSimpleVis',
+    root_dataset=TUSIMPLE_ROOT,
+    root_output='./test_tusimple_vis',
+    keypoint_json="./output/resnet34_bezierlanenet-aug2_tusimple.json",
+    image_set='test'
+)
+```
+
+Then simply run:
+
+```
+python tools/vis/lane_img_dir.py --config=configs/lane_detection/bezierlanenet/vis_resnet34_tusimple_aug1b.py --metric tusimple --style line
+```
+
+Beware `--style=bezier` can only be used with `--pred`. An example for CULane: [configs/lane_detection/bezierlanenet/vis_resnet34_culane_aug1b.py](../../configs/lane_detection/bezierlanenet/vis_resnet34_culane_aug1b.py)
+
+## Implementation (TuSimple)
+
+The `vis_dataset` can be used to replace `dataset` if specified, as shown in [get_loader()](https://github.com/voldemortX/pytorch-auto-drive/blob/cf314875ff0108f863b9ea8ac8d15141116b8f19/utils/runners/lane_det_visualizer.py#L92) of the default image folder runner. So instead of writing a new runner class, we can simply write a hybrid of [TuSimple](https://github.com/voldemortX/pytorch-auto-drive/blob/cf314875ff0108f863b9ea8ac8d15141116b8f19/utils/datasets/tusimple.py#L15) and [ImageFolderLaneDataset](https://github.com/voldemortX/pytorch-auto-drive/blob/cf314875ff0108f863b9ea8ac8d15141116b8f19/utils/datasets/image_folder.py#L56). It should be able to (functional behavior):
+- extract images from the dataset path
+- optionally load the dataset GT
+- optionally load user-specified predictions
+
+There are two major problems you'll need to address.
+
+1. The training dataset do not load annotations for `val` and `test`, so you'll need to load them same as `train`, and you don't need to append `-2` for training, it would be something like this:
+
+```
+def preload_tusimple_labels(json_contents):
+    # Load a TuSimple label json's content
+    print('Loading json annotation/prediction...')
+    targets = []
+    for i in tqdm(range(len(json_contents))):
+        lines = json_contents[i]['lanes']
+        h_samples = json_contents[i]['h_samples']
+        temp = []
+        for j in range(len(lines)):
+            temp.append(np.array([[float(x), float(y)] for x, y in zip(lines[j], h_samples)]))
+        targets.append(temp)
+
+    return targets
+```
+
+2. The default pipeline uses a `keypoint_process_fn` that is passed dynamically in the runner (not specified in config dict), and processes only the CULane format keypoints in a per-file manner. For TuSimple, where labels are in a single file, it will do best to preload all of them (as above) and replace the `keypoint_process_fn` with a dummy function:
+
+```
+def dummy_keypoint_process_fn(label):
+    return label
+
+# Then your class `__init__()` will start like this:
+
+class TuSimpleVis(ImageFolderLaneBase):
+    def __init__(self, root_dataset, root_output, keypoint_json, image_set, transforms=None,
+                 keypoint_process_fn=None, use_gt=True):
+        super().__init__(root_dataset, root_output, transforms, dummy_keypoint_process_fn)
+```
+
+With help of existing base classes, the full code is provided as reference in [utils/datasets/tusimple_vis.py](../../utils/datasets/tusimple_vis.py).
+