diff --git a/PathPlanning/ModelPredictiveTrajectoryGenerator/model_predictive_trajectory_generator.py b/PathPlanning/ModelPredictiveTrajectoryGenerator/model_predictive_trajectory_generator.py
index e8cf338e..cfbb750a 100644
--- a/PathPlanning/ModelPredictiveTrajectoryGenerator/model_predictive_trajectory_generator.py
+++ b/PathPlanning/ModelPredictiveTrajectoryGenerator/model_predictive_trajectory_generator.py
@@ -1,7 +1,7 @@
 """
 Model trajectory generator
 
-author: Atsushi Sakai
+author: Atsushi Sakai(@Atsushi_twi)
 """
 
 import numpy as np
@@ -14,7 +14,7 @@ max_iter = 100
 h = np.matrix([0.5, 0.02, 0.02]).T  # parameter sampling distanse
 cost_th = 0.1
 
-show_graph = False
+show_animation = False
 
 
 def plot_arrow(x, y, yaw, length=1.0, width=0.5, fc="r", ec="k"):
@@ -122,7 +122,7 @@ def optimize_trajectory(target, k0, p):
         p += alpha * np.array(dp)
         #  print(p.T)
 
-        if show_graph:
+        if show_animation:
             show_trajectory(target, xc, yc)
     else:
         xc, yc, yawc, p = None, None, None, None
diff --git a/PathPlanning/ModelPredictiveTrajectoryGenerator/motion_model.py b/PathPlanning/ModelPredictiveTrajectoryGenerator/motion_model.py
index b31d7319..6319c5a0 100644
--- a/PathPlanning/ModelPredictiveTrajectoryGenerator/motion_model.py
+++ b/PathPlanning/ModelPredictiveTrajectoryGenerator/motion_model.py
@@ -45,7 +45,8 @@ def generate_trajectory(s, km, kf, k0):
     tk = np.array([0.0, time / 2.0, time])
     kk = np.array([k0, km, kf])
     t = np.arange(0.0, time, time / n)
-    kp = scipy.interpolate.spline(tk, kk, t, order=2)
+    fkp = scipy.interpolate.interp1d(tk, kk, kind="quadratic")
+    kp = [fkp(ti) for ti in t]
     dt = float(time / n)
 
     #  plt.plot(t, kp)
@@ -70,7 +71,8 @@ def generate_last_state(s, km, kf, k0):
     tk = np.array([0.0, time / 2.0, time])
     kk = np.array([k0, km, kf])
     t = np.arange(0.0, time, time / n)
-    kp = scipy.interpolate.spline(tk, kk, t, order=2)
+    fkp = scipy.interpolate.interp1d(tk, kk, kind="quadratic")
+    kp = [fkp(ti) for ti in t]
     dt = time / n
 
     #  plt.plot(t, kp)