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83 lines
2.0 KiB
83 lines
2.0 KiB
import numpy as np
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import matplotlib.pyplot as plt
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from mpl_toolkits.mplot3d import Axes3D
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from matplotlib import cm
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from matplotlib.ticker import LinearLocator, FormatStrFormatter
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from scipy.optimize import minimize
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a = -9.81
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dt = 0.1
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r = 2.0
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v_ls = []
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x_ls = []
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v_egos = []
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for vv_ego in np.arange(35, 40, 1):
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for vv_l in np.arange(35, 40, 1):
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for xx_l in np.arange(0, 100, 1.0):
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x_l = xx_l
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v_l = vv_l
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v_ego = vv_ego
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x_ego = 0.0
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ttc = None
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for t in np.arange(0, 100, dt):
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x_l += v_l * dt
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v_l += a * dt
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v_l = max(v_l, 0.0)
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x_ego += v_ego * dt
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if t > r:
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v_ego += a * dt
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v_ego = max(v_ego, 0.0)
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if x_ego >= x_l:
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ttc = t
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break
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if ttc is None:
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if xx_l < 0.1:
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break
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v_ls.append(vv_l)
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x_ls.append(xx_l)
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v_egos.append(vv_ego)
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break
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def eval_f(x, v_ego, v_l):
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est = x[0] * v_l + x[1] * v_l**2 \
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+ x[2] * v_ego + x[3] * v_ego**2
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return est
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def f(x):
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r = 0.0
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for v_ego, v_l, x_l in zip(v_egos, v_ls, x_ls):
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est = eval_f(x, v_ego, v_l)
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r += (x_l - est)**2
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return r
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x0 = [0.5, 0.5, 0.5, 0.5]
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res = minimize(f, x0, method='Nelder-Mead')
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print(res)
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print(res.x)
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g = 9.81
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t_r = 1.8
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estimated = [4.0 + eval_f(res.x, v_ego, v_l) for (v_ego, v_l) in zip(v_egos, v_ls)]
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new_formula = [4.0 + v_ego * t_r - (v_l - v_ego) * t_r + v_ego**2/(2*g) - v_l**2 / (2*g) for (v_ego, v_l) in zip(v_egos, v_ls)]
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fig = plt.figure()
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ax = fig.add_subplot(111, projection='3d')
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surf = ax.scatter(v_egos, v_ls, x_ls, s=1)
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# surf = ax.scatter(v_egos, v_ls, estimated, s=1)
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surf = ax.scatter(v_egos, v_ls, new_formula, s=1)
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ax.set_xlabel('v ego')
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ax.set_ylabel('v lead')
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ax.set_zlabel('min distance')
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plt.show()
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