reduce costs from 0.5 to 1 m/s

Author: sshane

selfdrive/controls/lib/longitudinal_mpc_lib/long_mpc.py

@@ -244,8 +244,10 @@ def set_weights_for_lead_policy(self):
     x_ego_obstacle_cost_multiplier = 1  # interp(self.desired_TR, TRs, [3., 1.0, 0.1])
     j_ego_cost_multiplier = 1  # interp(self.desired_TR, TRs, [0.5, 1.0, 1.0])
     d_zone_cost_multiplier = 1  # interp(self.desired_TR, TRs, [4., 1.0, 1.0])
+    _J_EGO_COST = J_EGO_COST * interp(self.v_ego, [0.5, 1.0], [0.1, 1.0])
+    _A_CHANGE_COST = A_CHANGE_COST * interp(self.v_ego, [0.5, 1.0], [0.1, 1.0])
 
-    W = np.asfortranarray(np.diag([X_EGO_OBSTACLE_COST * x_ego_obstacle_cost_multiplier, X_EGO_COST, V_EGO_COST, A_EGO_COST, A_CHANGE_COST, J_EGO_COST * j_ego_cost_multiplier]))
+    W = np.asfortranarray(np.diag([X_EGO_OBSTACLE_COST * x_ego_obstacle_cost_multiplier, X_EGO_COST, V_EGO_COST, A_EGO_COST, _A_CHANGE_COST, _J_EGO_COST]))
     for i in range(N):
       W[4,4] = A_CHANGE_COST * np.interp(T_IDXS[i], [0.0, 1.0, 2.0], [1.0, 1.0, 0.0])
       self.solver.cost_set(i, 'W', W)