cleanup long planner, mpc: unused globals and arguments (#2211)

Co-authored-by: user <email@web.com>
old-commit-hash: 72e0ac2de1087760822b8a552c569ab3a859e2a5

selfdrive/controls/lib/long_mpc.py

@@ -56,7 +56,7 @@ class LongitudinalMpc():
     self.cur_state[0].v_ego = v
     self.cur_state[0].a_ego = a
 
-  def update(self, pm, CS, lead, v_cruise_setpoint):
+  def update(self, pm, CS, lead):
     v_ego = CS.vEgo
 
     # Setup current mpc state

selfdrive/controls/lib/planner.py

@@ -15,10 +15,7 @@ from selfdrive.controls.lib.fcw import FCWChecker
 from selfdrive.controls.lib.long_mpc import LongitudinalMpc
 from selfdrive.controls.lib.drive_helpers import V_CRUISE_MAX
 
-MAX_SPEED = 255.0
-
 LON_MPC_STEP = 0.2  # first step is 0.2s
-MAX_SPEED_ERROR = 2.0
 AWARENESS_DECEL = -0.2     # car smoothly decel at .2m/s^2 when user is distracted
 
 # lookup tables VS speed to determine min and max accels in cruise
@@ -36,9 +33,6 @@ _A_CRUISE_MAX_BP = [0.,  6.4, 22.5, 40.]
 _A_TOTAL_MAX_V = [1.7, 3.2]
 _A_TOTAL_MAX_BP = [20., 40.]
 
-# 75th percentile
-SPEED_PERCENTILE_IDX = 7
-
 
 def calc_cruise_accel_limits(v_ego, following):
   a_cruise_min = interp(v_ego, _A_CRUISE_MIN_BP, _A_CRUISE_MIN_V)
@@ -162,8 +156,8 @@ class Planner():
     self.mpc1.set_cur_state(self.v_acc_start, self.a_acc_start)
     self.mpc2.set_cur_state(self.v_acc_start, self.a_acc_start)
 
-    self.mpc1.update(pm, sm['carState'], lead_1, v_cruise_setpoint)
-    self.mpc2.update(pm, sm['carState'], lead_2, v_cruise_setpoint)
+    self.mpc1.update(pm, sm['carState'], lead_1)
+    self.mpc2.update(pm, sm['carState'], lead_2)
 
     self.choose_solution(v_cruise_setpoint, enabled)
 

selfdrive/controls/tests/test_following_distance.py

@@ -61,8 +61,8 @@ def run_following_distance_simulation(v_lead, t_end=200.0):
     mpc.set_cur_state(v_ego, a_ego)
     if first:  # Make sure MPC is converged on first timestep
       for _ in range(20):
-        mpc.update(pm, CS.carState, lead, v_cruise_setpoint)
-    mpc.update(pm, CS.carState, lead, v_cruise_setpoint)
+        mpc.update(pm, CS.carState, lead)
+    mpc.update(pm, CS.carState, lead)
 
     # Choose slowest of two solutions
     if v_cruise < mpc.v_mpc: