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/speed_simple.py

@@ -0,0 +1,32 @@
+import torch
+import time
+import numpy as np
+from model.model import parsingNet
+
+# torch.backends.cudnn.deterministic = False
+
+torch.backends.cudnn.benchmark = True
+net = parsingNet(pretrained = False, backbone='18',cls_dim = (100+1,56,4),use_aux=False).cuda()
+# net = parsingNet(pretrained = False, backbone='18',cls_dim = (200+1,18,4),use_aux=False).cuda()
+
+net.eval()
+
+x = torch.zeros((1,3,288,800)).cuda() + 1
+for i in range(10):
+    y = net(x)
+
+t_all = []
+for i in range(100):
+    t1 = time.time()
+    y = net(x)
+    t2 = time.time()
+    t_all.append(t2 - t1)
+
+print('average time:', np.mean(t_all) / 1)
+print('average fps:',1 / np.mean(t_all))
+
+print('fastest time:', min(t_all) / 1)
+print('fastest fps:',1 / min(t_all))
+
+print('slowest time:', max(t_all) / 1)
+print('slowest fps:',1 / max(t_all))