diff --git a/src/unmannedcar_MPC/mpc.py b/src/unmannedcar_MPC/mpc.py
new file mode 100644
index 0000000000..d3ef8670ec
--- /dev/null
+++ b/src/unmannedcar_MPC/mpc.py
@@ -0,0 +1,62 @@
+import math
+import numpy as np
+import utils
+from scipy.optimize import minimize
+
+
+class MPC:
+    HORIZON = 10
+    L = 2.5
+
+    def __init__(self, drawer, ego):
+        self.drawer = drawer
+        self.ego = ego
+
+    def run_step(self, path, speed_m_s, dt):
+        # convert path to body frame
+        path = utils.to_body_frame(self.ego, path)
+
+        # fit the path with a polynomial
+        poly = np.polyfit(path[:, 0], path[:, 1], 3)
+
+        # generate lane center locations
+        self.speed_dt = speed_m_s * dt
+
+        x_arr = np.arange(1, self.HORIZON + 1) * self.speed_dt
+        self.locs = np.vstack((x_arr, np.polyval(poly, x_arr))).T
+
+        # mpc
+        bounds = np.full((self.HORIZON, 2), (-0.3, 0.3))
+        init_steer_arr = np.full(self.HORIZON, 0)
+        solution = minimize(self.objective, init_steer_arr, (), method='SLSQP', bounds=bounds, tol=1e-4)
+        eval_states = self.evaluate_states(solution.x)
+
+        # draw lines
+        self.drawer.draw_camera_lines((255, 255, 255), utils.to_global_frame(self.ego, self.locs), 1)
+        self.drawer.draw_camera_lines((255, 0, 0), utils.to_global_frame(self.ego, np.array(eval_states)[:, 0:2]), 1)
+
+        return solution.x[0]
+
+    def mpc_model(self, state, steering):
+        x_t = state[0]
+        y_t = state[1]
+        psi_t = state[2]
+
+        x_t_1 = x_t + self.speed_dt * np.cos(psi_t)
+        y_t_1 = y_t + self.speed_dt * np.sin(psi_t)
+        psi_t_1 = psi_t + self.speed_dt * np.tan(steering) / self.L
+
+        return [x_t_1, y_t_1, psi_t_1]
+
+    def evaluate_states(self, steer_arr):
+        states = []
+        state = [0, 0, 0]
+        for steer in steer_arr:
+            state = self.mpc_model(state, steer)
+            states.append(state)
+        return states
+
+    def objective(self, steer_arr, *args):
+        final_state = self.evaluate_states(steer_arr)[-1]
+        final_loc = self.locs[-1]
+        return math.sqrt((final_state[0] - final_loc[0]) ** 2 + (final_state[1] - final_loc[1]) ** 2)
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