diff --git a/selfdrive/controls/lib/longitudinal_mpc_lib/long_mpc.py b/selfdrive/controls/lib/longitudinal_mpc_lib/long_mpc.py
index 70310de3cd..5a5aac71f0 100755
--- a/selfdrive/controls/lib/longitudinal_mpc_lib/long_mpc.py
+++ b/selfdrive/controls/lib/longitudinal_mpc_lib/long_mpc.py
@@ -427,7 +427,7 @@ class LongitudinalMpc:
     self.cruise_min_a = min_a
     self.max_a = max_a
 
-  def update(self, radarstate, v_cruise, x, v, a, j, personality=custom.LongitudinalPersonalitySP.standard,
+  def update(self, radarstate, v_cruise, prev_accel_constraint, x, v, a, j, personality=custom.LongitudinalPersonalitySP.standard,
              dynamic_personality=False, overtaking_acceleration_assist=False):
     v_ego = self.x0[1]
     t_follow = get_dynamic_personality(v_ego, personality) if dynamic_personality else get_T_FOLLOW(personality)
@@ -437,6 +437,8 @@ class LongitudinalMpc:
     lead_xv_0 = self.process_lead(radarstate.leadOne)
     lead_xv_1 = self.process_lead(radarstate.leadTwo)
 
+    self.set_weights(prev_accel_constraint=prev_accel_constraint, v_lead0=lead_xv_0[0, 1], v_lead1=lead_xv_1[0, 1], personality=personality)
+
     # To estimate a safe distance from a moving lead, we calculate how much stopping
     # distance that lead needs as a minimum. We can add that to the current distance
     # and then treat that as a stopped car/obstacle at this new distance.
diff --git a/selfdrive/controls/lib/longitudinal_planner.py b/selfdrive/controls/lib/longitudinal_planner.py
index 0cd4d84629..bfd07f8092 100755
--- a/selfdrive/controls/lib/longitudinal_planner.py
+++ b/selfdrive/controls/lib/longitudinal_planner.py
@@ -201,7 +201,7 @@ class LongitudinalPlanner:
     self.mpc.set_accel_limits(accel_limits_turns[0], accel_limits_turns[1])
     self.mpc.set_cur_state(self.v_desired_filter.x, self.a_desired)
     x, v, a, j = self.parse_model(sm['modelV2'], self.v_model_error)
-    self.mpc.update(sm['radarState'], v_cruise, x, v, a, j, personality=sm['controlsStateSP'].personality,
+    self.mpc.update(sm['radarState'], v_cruise, prev_accel_constraint, x, v, a, j, personality=sm['controlsStateSP'].personality,
                     dynamic_personality=sm['controlsStateSP'].dynamicPersonality, overtaking_acceleration_assist=overtaking_accel_engaged)
 
     self.v_desired_trajectory = np.interp(CONTROL_N_T_IDX, T_IDXS_MPC, self.mpc.v_solution)