diff --git a/PathPlanning/CRRRTStar/cr_rrt_star_car.py b/PathPlanning/CRRRTStar/cr_rrt_star_car.py
index c2b148e8..01e5a69d 100644
--- a/PathPlanning/CRRRTStar/cr_rrt_star_car.py
+++ b/PathPlanning/CRRRTStar/cr_rrt_star_car.py
@@ -12,11 +12,15 @@
 import random
 import math
 import copy
-import pandas as pd
 import numpy as np
 import reeds_shepp_path_planning
 import pure_pursuit
-import unicycle_model
+import pandas as pd
+
+
+target_speed = 10.0 / 3.6
+accel = 0.1
+curvature = 10.0
 
 
 class RRT():
@@ -82,7 +86,7 @@ class RRT():
                 print("feasible path is found")
                 break
 
-        return x, y, yaw, v, t, a, d
+        return flag, x, y, yaw, v, t, a, d
 
     def calc_tracking_path(self, path):
         path = np.matrix(path[::-1])
@@ -106,14 +110,8 @@ class RRT():
 
     def check_tracking_path_is_feasible(self, path):
         print("check_tracking_path_is_feasible")
+        path = np.matrix(path[::-1])
 
-        init_speed = 0.0
-        max_speed = 10.0 / 3.6
-
-        path = self.calc_tracking_path(path)
-        #  speed_profile = self.calc_speed_profile(path, max_speed)
-
-        #  plt.plot(path[:, 0], path[:, 1], '-xg')
         # save csv
         df = pd.DataFrame()
         df["x"] = np.array(path[:, 0]).flatten()
@@ -121,52 +119,34 @@ class RRT():
         df["yaw"] = np.array(path[:, 2]).flatten()
         df.to_csv("rrt_course.csv", index=None)
 
-        state = unicycle_model.State(
-            x=self.start.x, y=self.start.y, yaw=self.start.yaw, v=init_speed)
+        cx = np.array(path[:, 0])
+        cy = np.array(path[:, 1])
+        cyaw = np.array(path[:, 2])
 
-        target_ind = pure_pursuit.calc_nearest_index(
-            state, path[:, 0], path[:, 1])
+        goal = [cx[-1], cy[-1], cyaw[-1]]
 
-        lastIndex = len(path[:, 0]) - 2
+        cx, cy, cyaw = pure_pursuit.extend_path(cx, cy, cyaw)
 
-        x = [state.x]
-        y = [state.y]
-        yaw = [state.yaw]
-        v = [state.v]
-        t = [0.0]
-        a = [0.0]
-        d = [0.0]
-        time = 0.0
+        speed_profile = pure_pursuit.calc_speed_profile(
+            cx, cy, cyaw, target_speed, accel)
 
-        while lastIndex > target_ind:
-            #  print(lastIndex, target_ind)
-            ai = pure_pursuit.PIDControl(max_speed, state.v)
-            di, target_ind = pure_pursuit.pure_pursuit_control(
-                state, path[:, 0], path[:, 1], target_ind)
-            state = unicycle_model.update(state, ai, di)
+        t, x, y, yaw, v, a, d, find_goal = pure_pursuit.closed_loop_prediction(
+            cx, cy, cyaw, speed_profile, goal)
 
-            time = time + unicycle_model.dt
+        if abs(yaw[-1] - goal[2]) >= math.pi / 2.0:
+            print(yaw[-1], goal[2])
+            find_goal = False
+        if not find_goal:
+            print("This path is bad")
 
-            x.append(state.x)
-            y.append(state.y)
-            yaw.append(state.yaw)
-            v.append(state.v)
-            t.append(time)
-            a.append(ai)
-            d.append(di)
+        plt.clf
+        plt.plot(x, y, '-r')
+        plt.plot(path[:, 0], path[:, 1], '-g')
+        plt.grid(True)
+        plt.axis("equal")
+        plt.show()
 
-        if self.CollisionCheckWithXY(path[:, 0], path[:, 1], self.obstacleList):
-            #  print("OK")
-            return True, x, y, yaw, v, t, a, d
-        else:
-            #  print("NG")
-            return False, x, y, yaw, v, t, a, d
-
-        #  plt.plot(x, y, '-r')
-        #  plt.plot(path[:, 0], path[:, 1], '-g')
-        #  plt.show()
-
-        #  return True
+        return find_goal, x, y, yaw, v, t, a, d
 
     def choose_parent(self, newNode, nearinds):
         if len(nearinds) == 0:
@@ -202,7 +182,6 @@ class RRT():
 
     def steer(self, rnd, nind):
         #  print(rnd)
-        curvature = 5.0
 
         nearestNode = self.nodeList[nind]
 
@@ -313,6 +292,7 @@ class RRT():
         for node in self.nodeList:
             if node.parent is not None:
                 plt.plot(node.path_x, node.path_y, "-g")
+                pass
                 #  plt.plot([node.x, self.nodeList[node.parent].x], [
                 #  node.y, self.nodeList[node.parent].y], "-g")
 
@@ -401,7 +381,11 @@ if __name__ == '__main__':
     goal = [10.0, 10.0, math.radians(0.0)]
 
     rrt = RRT(start, goal, randArea=[-2.0, 15.0], obstacleList=obstacleList)
-    x, y, yaw, v, t, a, d = rrt.Planning(animation=False)
+    flag, x, y, yaw, v, t, a, d = rrt.Planning(animation=False)
+
+    if not flag:
+        print("cannot find feasible path")
+        exit()
 
     #  flg, ax = plt.subplots(1)
     # Draw final path
@@ -429,7 +413,7 @@ if __name__ == '__main__':
     flg, ax = plt.subplots(1)
     plt.plot(t, a, '-r')
     plt.xlabel("time[s]")
-    plt.ylabel("input[m/s]")
+    plt.ylabel("accel[m/ss]")
     plt.grid(True)
 
     flg, ax = plt.subplots(1)
diff --git a/PathPlanning/CRRRTStar/pure_pursuit.py b/PathPlanning/CRRRTStar/pure_pursuit.py
index 30603e04..9d23ee06 100644
--- a/PathPlanning/CRRRTStar/pure_pursuit.py
+++ b/PathPlanning/CRRRTStar/pure_pursuit.py
@@ -12,8 +12,14 @@ import math
 import matplotlib.pyplot as plt
 import unicycle_model
 
-Kp = 1.0  # speed propotional gain
+Kp = 2.0  # speed propotional gain
 Lf = 1.0  # look-ahead distance
+T = 1000.0  # max simulation time
+goal_dis = 0.5
+stop_speed = 0.1
+
+#  animation = True
+animation = False
 
 
 def PIDControl(target, current):
@@ -24,20 +30,24 @@ def PIDControl(target, current):
 
 def pure_pursuit_control(state, cx, cy, pind):
 
-    if state.v >= 0:
-        ind = calc_nearest_index(state, cx[pind:], cy[pind:])
+    ind = calc_target_index(state, cx, cy)
+
+    if pind >= ind:
+        ind = pind
+
+    #  print(pind, ind)
+    if ind < len(cx):
+        tx = cx[ind]
+        ty = cy[ind]
     else:
-        ind = calc_nearest_index(state, cx[:pind + 1], cy[:pind + 1])
-
-    if state.v >= 0:
-        ind = ind + pind
-
-    tx = cx[ind]
-    ty = cy[ind]
+        tx = cx[-1]
+        ty = cy[-1]
+        ind = len(cx) - 1
 
     alpha = math.atan2(ty - state.y, tx - state.x) - state.yaw
 
-    if state.v < 0:  # back
+    if state.v <= 0.0:  # back
+        #  alpha = math.pi - alpha
         if alpha > 0:
             alpha = math.pi - alpha
         else:
@@ -45,33 +55,195 @@ def pure_pursuit_control(state, cx, cy, pind):
 
     delta = math.atan2(2.0 * unicycle_model.L * math.sin(alpha) / Lf, 1.0)
 
-    if state.v < 0:  # back
-        delta = delta * -1.0
-
     return delta, ind
 
 
-def calc_nearest_index(state, cx, cy):
+def calc_target_index(state, cx, cy):
     dx = [state.x - icx for icx in cx]
     dy = [state.y - icy for icy in cy]
 
-    d = [abs(math.sqrt(idx ** 2 + idy ** 2) -
-             Lf) for (idx, idy) in zip(dx, dy)]
+    d = [abs(math.sqrt(idx ** 2 + idy ** 2)) for (idx, idy) in zip(dx, dy)]
 
     ind = d.index(min(d))
 
+    L = 0.0
+
+    while Lf > L and (ind + 1) < len(cx):
+        dx = cx[ind + 1] - cx[ind]
+        dy = cx[ind + 1] - cx[ind]
+        L += math.sqrt(dx ** 2 + dy ** 2)
+        ind += 1
+
     return ind
 
 
+def closed_loop_prediction(cx, cy, cyaw, speed_profile, goal):
+
+    state = unicycle_model.State(x=-0.0, y=-0.0, yaw=0.0, v=0.0)
+
+    #  lastIndex = len(cx) - 1
+    time = 0.0
+    x = [state.x]
+    y = [state.y]
+    yaw = [state.yaw]
+    v = [state.v]
+    t = [0.0]
+    a = [0.0]
+    d = [0.0]
+    target_ind = calc_target_index(state, cx, cy)
+    find_goal = False
+
+    while T >= time:
+        di, target_ind = pure_pursuit_control(state, cx, cy, target_ind)
+        ai = PIDControl(speed_profile[target_ind], state.v)
+        state = unicycle_model.update(state, ai, di)
+
+        if abs(state.v) <= stop_speed:
+            target_ind += 1
+
+        time = time + unicycle_model.dt
+
+        # check goal
+        dx = state.x - goal[0]
+        dy = state.y - goal[1]
+        if math.sqrt(dx ** 2 + dy ** 2) <= goal_dis:
+            find_goal = True
+            break
+
+        x.append(state.x)
+        y.append(state.y)
+        yaw.append(state.yaw)
+        v.append(state.v)
+        t.append(time)
+        a.append(ai)
+        d.append(di)
+
+        if target_ind % 20 == 0 and animation:
+            plt.cla()
+            plt.plot(cx, cy, "-r", label="course")
+            plt.plot(x, y, "ob", label="trajectory")
+            plt.plot(cx[target_ind], cy[target_ind], "xg", label="target")
+            plt.axis("equal")
+            plt.grid(True)
+            plt.title("speed:" + str(round(state.v, 2)) +
+                      "tind:" + str(target_ind))
+            plt.pause(0.0001)
+
+    return t, x, y, yaw, v, a, d, find_goal
+
+
+def set_stop_point(target_speed, cx, cy, cyaw):
+    speed_profile = [target_speed] * len(cx)
+    forward = True
+
+    d = []
+
+    # Set stop point
+    for i in range(len(cx) - 1):
+        dx = cx[i + 1] - cx[i]
+        dy = cy[i + 1] - cy[i]
+        d.append(math.sqrt(dx ** 2.0 + dy ** 2.0))
+        iyaw = cyaw[i]
+        move_direction = math.atan2(dy, dx)
+        is_back = abs(move_direction - iyaw) >= math.pi / 2.0
+
+        if dx == 0.0 and dy == 0.0:
+            continue
+
+        if is_back:
+            speed_profile[i] = - target_speed
+        else:
+            speed_profile[i] = target_speed
+
+        if is_back and forward:
+            speed_profile[i] = 0.0
+            forward = False
+            #  plt.plot(cx[i], cy[i], "xb")
+            #  print(iyaw, move_direction, dx, dy)
+        elif not is_back and not forward:
+            speed_profile[i] = 0.0
+            forward = True
+            #  plt.plot(cx[i], cy[i], "xb")
+            #  print(iyaw, move_direction, dx, dy)
+    speed_profile[0] = 0.0
+    speed_profile[-1] = 0.0
+
+    d.append(d[-1])
+
+    return speed_profile, d
+
+
+def calc_speed_profile(cx, cy, cyaw, target_speed, a):
+
+    speed_profile, d = set_stop_point(target_speed, cx, cy, cyaw)
+
+    nsp = len(speed_profile)
+
+    if animation:
+        plt.plot(speed_profile, "xb")
+
+    # forward integration
+    for i in range(nsp - 1):
+
+        if speed_profile[i + 1] >= 0:  # forward
+            tspeed = speed_profile[i] + a * d[i]
+            if tspeed <= speed_profile[i + 1]:
+                speed_profile[i + 1] = tspeed
+        else:
+            tspeed = speed_profile[i] - a * d[i]
+            if tspeed >= speed_profile[i + 1]:
+                speed_profile[i + 1] = tspeed
+
+    if animation:
+        plt.plot(speed_profile, "ok")
+
+    # back integration
+    for i in range(nsp - 1):
+        if speed_profile[- i - 1] >= 0:  # forward
+            tspeed = speed_profile[-i] + a * d[-i]
+            if tspeed <= speed_profile[-i - 1]:
+                speed_profile[-i - 1] = tspeed
+        else:
+            tspeed = speed_profile[-i] - a * d[-i]
+            if tspeed >= speed_profile[-i - 1]:
+                speed_profile[-i - 1] = tspeed
+
+    if animation:
+        plt.plot(speed_profile, "-r")
+        plt.show()
+
+    return speed_profile
+
+
+def extend_path(cx, cy, cyaw):
+
+    dl = 0.1
+    dl_list = [dl] * (int(Lf / dl) + 10)
+
+    move_direction = math.atan2(cy[-1] - cy[-2], cx[-1] - cx[-2])
+    is_back = abs(move_direction - cyaw[-1]) >= math.pi / 2.0
+
+    for idl in dl_list:
+        if is_back:
+            idl *= -1
+        cx = np.append(cx, cx[-1] + idl * math.cos(cyaw[-1]))
+        cy = np.append(cy, cy[-1] + idl * math.sin(cyaw[-1]))
+        cyaw = np.append(cyaw, cyaw[-1])
+
+    return cx, cy, cyaw
+
+
 def main():
-    # target course
+    #  target course
+    import numpy as np
     cx = np.arange(0, 50, 0.1)
     cy = [math.sin(ix / 5.0) * ix / 2.0 for ix in cx]
-    target_speed = 30.0 / 3.6
+
+    target_speed = 5.0 / 3.6
 
     T = 15.0  # max simulation time
 
-    state = unicycle_model.State(x=-1.0, y=-5.0, yaw=0.0, v=0.0)
+    state = unicycle_model.State(x=-0.0, y=-3.0, yaw=0.0, v=0.0)
     #  state = unicycle_model.State(x=-1.0, y=-5.0, yaw=0.0, v=-30.0 / 3.6)
     #  state = unicycle_model.State(x=10.0, y=5.0, yaw=0.0, v=-30.0 / 3.6)
     #  state = unicycle_model.State(
@@ -86,7 +258,7 @@ def main():
     yaw = [state.yaw]
     v = [state.v]
     t = [0.0]
-    target_ind = calc_nearest_index(state, cx, cy)
+    target_ind = calc_target_index(state, cx, cy)
 
     while T >= time and lastIndex > target_ind:
         ai = PIDControl(target_speed, state.v)
@@ -127,6 +299,44 @@ def main():
     plt.show()
 
 
+def main2():
+    import pandas as pd
+    data = pd.read_csv("rrt_course.csv")
+    cx = np.array(data["x"])
+    cy = np.array(data["y"])
+    cyaw = np.array(data["yaw"])
+
+    target_speed = 10.0 / 3.6
+    a = 2.0
+
+    goal = [cx[-1], cy[-1]]
+
+    cx, cy, cyaw = extend_path(cx, cy, cyaw)
+
+    speed_profile = calc_speed_profile(cx, cy, cyaw, target_speed, a)
+
+    t, x, y, yaw, v, a, d, flag = closed_loop_prediction(
+        cx, cy, cyaw, speed_profile, goal)
+
+    flg, ax = plt.subplots(1)
+    plt.plot(cx, cy, ".r", label="course")
+    plt.plot(x, y, "-b", label="trajectory")
+    plt.plot(goal[0], goal[1], "xg", label="goal")
+    plt.legend()
+    plt.xlabel("x[m]")
+    plt.ylabel("y[m]")
+    plt.axis("equal")
+    plt.grid(True)
+
+    flg, ax = plt.subplots(1)
+    plt.plot(t, [iv * 3.6 for iv in v], "-r")
+    plt.xlabel("Time[s]")
+    plt.ylabel("Speed[km/h]")
+    plt.grid(True)
+    plt.show()
+
+
 if __name__ == '__main__':
     print("Pure pursuit path tracking simulation start")
-    main()
+    #  main()
+    main2()