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/UFLD/export.py

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+import torch, os, cv2
+from model.model import parsingNet
+from utils.common import merge_config
+from utils.dist_utils import dist_print
+import torch
+import scipy.special, tqdm
+import numpy as np
+import torchvision.transforms as transforms
+from data.dataset import LaneTestDataset
+from data.constant import culane_row_anchor, tusimple_row_anchor
+from PIL import Image
+
+# Export to TorchScript that can be used for LibTorch
+
+torch.backends.cudnn.benchmark = True
+
+# From cuLANE, Change this line if you are using TuSimple
+cls_num_per_lane = 18
+griding_num = 200
+backbone =18
+
+net = parsingNet(pretrained = False,backbone='18', cls_dim = (griding_num+1,cls_num_per_lane,4),
+                use_aux=False)
+
+# Change test_model where your model stored.
+test_model = '/data/Models/UltraFastLaneDetection/culane_18.pth'
+
+#state_dict = torch.load(test_model, map_location='cpu')['model'] # CPU
+state_dict = torch.load(test_model, map_location='cuda')['model'] # CUDA
+compatible_state_dict = {}
+for k, v in state_dict.items():
+    if 'module.' in k:
+        compatible_state_dict[k[7:]] = v
+    else:
+        compatible_state_dict[k] = v
+
+net.load_state_dict(compatible_state_dict, strict=False)
+net.eval()
+
+# Test Input Image
+img = torch.zeros(1, 3, 288, 800)  # image size(1,3,320,192) iDetection
+y = net(img)  # dry run
+
+ts = torch.jit.trace(net, img)
+
+#ts.save('UFLD.torchscript-cpu.pt') # CPU
+ts.save('UFLD.torchscript-cuda.pt') # CUDA