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/curve_fit.py

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+import numpy as np
+import matplotlib.pyplot as plt
+from scipy.optimize import curve_fit
+
+
+# 自定义函数 e指数形式
+def func(x, a, b, c):
+    return a * np.sqrt(x) * (b * np.square(x) + c)
+
+
+# 定义x、y散点坐标
+x = [20, 30, 40, 50, 60, 70]
+x = np.array(x)
+num = [453, 482, 503, 508, 498, 479]
+y = np.array(num)
+
+
+def get_curve_fit(x, y):
+    # 非线性最小二乘法拟合
+    popt, pcov = curve_fit(func, x, y)
+    # 获取popt里面是拟合系数
+    # print(popt)
+    a = popt[0]
+    b = popt[1]
+    c = popt[2]
+    yvals = func(x, a, b, c)  # 拟合y值
+    # print('popt:', popt)
+    # print('系数a:', a)
+    # print('系数b:', b)
+    # print('系数c:', c)
+    # print('系数pcov:', pcov)
+    # print('系数yvals:', yvals)
+    return yvals
+
+
+yvals = get_curve_fit(x, y)
+print(yvals)
+# 绘图
+plot1 = plt.plot(x, y, 's', label='original values')
+plot2 = plt.plot(x, yvals, 'r', label='polyfit values')
+plt.xlabel('x')
+plt.ylabel('y')
+plt.legend(loc=4)  # 指定legend的位置右下角
+plt.title('curve_fit')
+plt.show()