More conservative lead policy in e2e long mode (#25684)

* Add params for lead and danger

* fix long params

* E2e passes simple maneuver tests

* Make tests run with e2e long mode

* Slightly more error allowed in e2e mode

* FCW back and populate long source field

* Fix planner name

* FCW still doesnt work

* Slightly less aggressive

* Doesn't need to simulate from stop

selfdrive/controls/lib/longitudinal_mpc_lib/long_mpc.py

@@ -24,7 +24,7 @@ SOURCES = ['lead0', 'lead1', 'cruise', 'e2e']
 
 X_DIM = 3
 U_DIM = 1
-PARAM_DIM = 4
+PARAM_DIM = 6
 COST_E_DIM = 5
 COST_DIM = COST_E_DIM + 1
 CONSTR_DIM = 4
@@ -37,6 +37,7 @@ J_EGO_COST = 5.0
 A_CHANGE_COST = 200.
 DANGER_ZONE_COST = 100.
 CRASH_DISTANCE = .5
+LEAD_DANGER_FACTOR = 0.75
 LIMIT_COST = 1e6
 ACADOS_SOLVER_TYPE = 'SQP_RTI'
 
@@ -58,8 +59,8 @@ STOP_DISTANCE = 6.0
 def get_stopped_equivalence_factor(v_lead):
   return (v_lead**2) / (2 * COMFORT_BRAKE)
 
-def get_safe_obstacle_distance(v_ego):
-  return (v_ego**2) / (2 * COMFORT_BRAKE) + T_FOLLOW * v_ego + STOP_DISTANCE
+def get_safe_obstacle_distance(v_ego, t_follow=T_FOLLOW):
+  return (v_ego**2) / (2 * COMFORT_BRAKE) + t_follow * v_ego + STOP_DISTANCE
 
 def desired_follow_distance(v_ego, v_lead):
   return get_safe_obstacle_distance(v_ego) - get_stopped_equivalence_factor(v_lead)
@@ -90,7 +91,9 @@ def gen_long_model():
   a_max = SX.sym('a_max')
   x_obstacle = SX.sym('x_obstacle')
   prev_a = SX.sym('prev_a')
-  model.p = vertcat(a_min, a_max, x_obstacle, prev_a)
+  lead_t_follow = SX.sym('lead_t_follow')
+  lead_danger_factor = SX.sym('lead_danger_factor')
+  model.p = vertcat(a_min, a_max, x_obstacle, prev_a, lead_t_follow, lead_danger_factor)
 
   # dynamics model
   f_expl = vertcat(v_ego, a_ego, j_ego)
@@ -124,11 +127,13 @@ def gen_long_ocp():
   a_min, a_max = ocp.model.p[0], ocp.model.p[1]
   x_obstacle = ocp.model.p[2]
   prev_a = ocp.model.p[3]
+  lead_t_follow = ocp.model.p[4]
+  lead_danger_factor = ocp.model.p[5]
 
   ocp.cost.yref = np.zeros((COST_DIM, ))
   ocp.cost.yref_e = np.zeros((COST_E_DIM, ))
 
-  desired_dist_comfort = get_safe_obstacle_distance(v_ego)
+  desired_dist_comfort = get_safe_obstacle_distance(v_ego, lead_t_follow)
 
   # The main cost in normal operation is how close you are to the "desired" distance
   # from an obstacle at every timestep. This obstacle can be a lead car
@@ -149,12 +154,12 @@ def gen_long_ocp():
   constraints = vertcat(v_ego,
                         (a_ego - a_min),
                         (a_max - a_ego),
-                        ((x_obstacle - x_ego) - (3/4) * (desired_dist_comfort)) / (v_ego + 10.))
+                        ((x_obstacle - x_ego) - lead_danger_factor * (desired_dist_comfort)) / (v_ego + 10.))
   ocp.model.con_h_expr = constraints
 
   x0 = np.zeros(X_DIM)
   ocp.constraints.x0 = x0
-  ocp.parameter_values = np.array([-1.2, 1.2, 0.0, 0.0])
+  ocp.parameter_values = np.array([-1.2, 1.2, 0.0, 0.0, T_FOLLOW, LEAD_DANGER_FACTOR])
 
   # We put all constraint cost weights to 0 and only set them at runtime
   cost_weights = np.zeros(CONSTR_DIM)
@@ -249,7 +254,7 @@ class LongitudinalMpc:
       constraint_cost_weights = [LIMIT_COST, LIMIT_COST, LIMIT_COST, DANGER_ZONE_COST]
     elif self.mode == 'blended':
       cost_weights = [0., 0.2, 0.25, 1.0, 0.0, 1.0]
-      constraint_cost_weights = [LIMIT_COST, LIMIT_COST, LIMIT_COST, 5.0]
+      constraint_cost_weights = [LIMIT_COST, LIMIT_COST, LIMIT_COST, 50.0]
     elif self.mode == 'e2e':
       cost_weights = [0., 0.2, 0.25, 1., 0.0, .1]
       constraint_cost_weights = [LIMIT_COST, LIMIT_COST, LIMIT_COST, 0.0]
@@ -317,6 +322,7 @@ class LongitudinalMpc:
     if self.mode == 'acc':
       self.params[:,0] = MIN_ACCEL if self.status else self.cruise_min_a
       self.params[:,1] = self.cruise_max_a
+      self.params[:,5] = LEAD_DANGER_FACTOR
 
       # Fake an obstacle for cruise, this ensures smooth acceleration to set speed
       # when the leads are no factor.
@@ -335,6 +341,7 @@ class LongitudinalMpc:
     elif self.mode == 'blended':
       self.params[:,0] = MIN_ACCEL
       self.params[:,1] = MAX_ACCEL
+      self.params[:,5] = 1.0
 
       x_obstacles = np.column_stack([lead_0_obstacle,
                                      lead_1_obstacle])
@@ -344,7 +351,8 @@ class LongitudinalMpc:
 
       x_and_cruise = np.column_stack([x, cruise_target])
       x = np.min(x_and_cruise, axis=1)
-      self.source = 'e2e'
+      
+      self.source = 'e2e' if x_and_cruise[0,0] < x_and_cruise[0,1] else 'cruise'
 
     else:
       raise NotImplementedError(f'Planner mode {self.mode} not recognized in planner update')
@@ -359,6 +367,7 @@ class LongitudinalMpc:
 
     self.params[:,2] = np.min(x_obstacles, axis=1)
     self.params[:,3] = np.copy(self.prev_a)
+    self.params[:,4] = T_FOLLOW
 
     self.run()
     if (np.any(lead_xv_0[:,0] - self.x_sol[:,0] < CRASH_DISTANCE) and
@@ -367,10 +376,23 @@ class LongitudinalMpc:
     else:
       self.crash_cnt = 0
 
+    # Check if it got within lead comfort range
+    # TODO This should be done cleaner
+    if self.mode == 'blended':
+      if any((lead_0_obstacle - get_safe_obstacle_distance(self.x_sol[:,1], T_FOLLOW))- self.x_sol[:,0] < 0.0):
+        self.source = 'lead0'
+      if any((lead_1_obstacle - get_safe_obstacle_distance(self.x_sol[:,1], T_FOLLOW))- self.x_sol[:,0] < 0.0) and \
+         (lead_1_obstacle[0] - lead_0_obstacle[0]):
+        self.source = 'lead1'
+
+      
+
   def update_with_xva(self, x, v, a):
     self.params[:,0] = -10.
     self.params[:,1] = 10.
     self.params[:,2] = 1e5
+    self.params[:,4] = T_FOLLOW
+    self.params[:,5] = LEAD_DANGER_FACTOR
 
     # v, and a are in local frame, but x is wrt the x[0] position
     # In >90degree turns, x goes to 0 (and may even be -ve)

selfdrive/controls/lib/longitudinal_planner.py

@@ -45,7 +45,7 @@ def limit_accel_in_turns(v_ego, angle_steers, a_target, CP):
   return [a_target[0], min(a_target[1], a_x_allowed)]
 
 
-class Planner:
+class LongitudinalPlanner:
   def __init__(self, CP, init_v=0.0, init_a=0.0):
     self.CP = CP
     params = Params()

selfdrive/controls/plannerd.py

@@ -3,7 +3,7 @@ from cereal import car
 from common.params import Params
 from common.realtime import Priority, config_realtime_process
 from system.swaglog import cloudlog
-from selfdrive.controls.lib.longitudinal_planner import Planner
+from selfdrive.controls.lib.longitudinal_planner import LongitudinalPlanner
 from selfdrive.controls.lib.lateral_planner import LateralPlanner
 import cereal.messaging as messaging
 
@@ -16,7 +16,7 @@ def plannerd_thread(sm=None, pm=None):
   CP = car.CarParams.from_bytes(params.get("CarParams", block=True))
   cloudlog.info("plannerd got CarParams: %s", CP.carName)
 
-  longitudinal_planner = Planner(CP)
+  longitudinal_planner = LongitudinalPlanner(CP)
   lateral_planner = LateralPlanner(CP)
 
   if sm is None:

selfdrive/controls/tests/test_cruise_speed.py

@@ -1,13 +1,16 @@
 #!/usr/bin/env python3
 import unittest
 import numpy as np
+from common.params import Params
+
+
 from selfdrive.test.longitudinal_maneuvers.maneuver import Maneuver
 
-def run_cruise_simulation(cruise, t_end=100.):
+def run_cruise_simulation(cruise, t_end=20.):
   man = Maneuver(
     '',
     duration=t_end,
-    initial_speed=float(0.),
+    initial_speed=max(cruise - 1., 0.0),
     lead_relevancy=True,
     initial_distance_lead=100,
     cruise_values=[cruise],
@@ -21,12 +24,15 @@ def run_cruise_simulation(cruise, t_end=100.):
 
 class TestCruiseSpeed(unittest.TestCase):
   def test_cruise_speed(self):
-    for speed in np.arange(5, 40, 5):
-      print(f'Testing {speed} m/s')
-      cruise_speed = float(speed)
+    params = Params()
+    for e2e in [False, True]:
+      params.put_bool("EndToEndLong", e2e)
+      for speed in np.arange(5, 40, 5):
+        print(f'Testing {speed} m/s')
+        cruise_speed = float(speed)
 
-      simulation_steady_state = run_cruise_simulation(cruise_speed)
-      self.assertAlmostEqual(simulation_steady_state, cruise_speed, delta=.01, msg=f'Did not reach {speed} m/s')
+        simulation_steady_state = run_cruise_simulation(cruise_speed)
+        self.assertAlmostEqual(simulation_steady_state, cruise_speed, delta=.01, msg=f'Did not reach {speed} m/s')
 
 
 if __name__ == "__main__":

selfdrive/controls/tests/test_following_distance.py

@@ -1,11 +1,13 @@
 #!/usr/bin/env python3
 import unittest
 import numpy as np
+from common.params import Params
+
 
 from selfdrive.controls.lib.longitudinal_mpc_lib.long_mpc import desired_follow_distance
 from selfdrive.test.longitudinal_maneuvers.maneuver import Maneuver
 
-def run_following_distance_simulation(v_lead, t_end=100.0):
+def run_following_distance_simulation(v_lead, t_end=100.0, e2e=False):
   man = Maneuver(
     '',
     duration=t_end,
@@ -14,6 +16,7 @@ def run_following_distance_simulation(v_lead, t_end=100.0):
     initial_distance_lead=100,
     speed_lead_values=[v_lead],
     breakpoints=[0.],
+    e2e=e2e
   )
   valid, output = man.evaluate()
   assert valid
@@ -22,14 +25,16 @@ def run_following_distance_simulation(v_lead, t_end=100.0):
 
 class TestFollowingDistance(unittest.TestCase):
   def test_following_distance(self):
-    for speed in np.arange(0, 40, 5):
-      print(f'Testing {speed} m/s')
-      v_lead = float(speed)
-
-      simulation_steady_state = run_following_distance_simulation(v_lead)
-      correct_steady_state = desired_follow_distance(v_lead, v_lead)
-
-      self.assertAlmostEqual(simulation_steady_state, correct_steady_state, delta=(correct_steady_state*.1 + .5))
+    params = Params()
+    for e2e in [False, True]:
+      params.put_bool("EndToEndLong", e2e)
+      for speed in np.arange(0, 40, 5):
+        print(f'Testing {speed} m/s')
+        v_lead = float(speed)
+        simulation_steady_state = run_following_distance_simulation(v_lead)
+        correct_steady_state = desired_follow_distance(v_lead, v_lead)
+        err_ratio = 0.2 if e2e else 0.1
+        self.assertAlmostEqual(simulation_steady_state, correct_steady_state, delta=(err_ratio * correct_steady_state + .5))
 
 
 if __name__ == "__main__":

selfdrive/test/longitudinal_maneuvers/maneuver.py

@@ -27,7 +27,7 @@ class Maneuver():
       speed=self.speed,
       distance_lead=self.distance_lead,
       only_lead2=self.only_lead2,
-      only_radar=self.only_radar
+      only_radar=self.only_radar,
     )
 
     valid = True
@@ -54,7 +54,7 @@ class Maneuver():
         valid = False
 
       if self.ensure_start and log['v_rel'] > 0 and log['speeds'][-1] <= 0.1:
-        print('Planner not starting!')
+        print('LongitudinalPlanner not starting!')
         valid = False
 
     print("maneuver end", valid)

selfdrive/test/longitudinal_maneuvers/plant.py

@@ -6,7 +6,8 @@ from cereal import log
 import cereal.messaging as messaging
 from common.realtime import Ratekeeper, DT_MDL
 from selfdrive.controls.lib.longcontrol import LongCtrlState
-from selfdrive.controls.lib.longitudinal_planner import Planner
+from selfdrive.modeld.constants import T_IDXS
+from selfdrive.controls.lib.longitudinal_planner import LongitudinalPlanner
 
 
 class Plant():
@@ -43,7 +44,8 @@ class Plant():
 
     from selfdrive.car.honda.values import CAR
     from selfdrive.car.honda.interface import CarInterface
-    self.planner = Planner(CarInterface.get_params(CAR.CIVIC), init_v=self.speed)
+
+    self.planner = LongitudinalPlanner(CarInterface.get_params(CAR.CIVIC), init_v=self.speed)
 
   def current_time(self):
     return float(self.rk.frame) / self.rate
@@ -88,6 +90,21 @@ class Plant():
       radar.radarState.leadOne = lead
     radar.radarState.leadTwo = lead
 
+    # Simulate model predicting slightly faster speed
+    # this is to ensure lead policy is effective when model
+    # does not predict slowdown in e2e mode
+    position = log.ModelDataV2.XYZTData.new_message()
+    position.x = [float(x) for x in (self.speed + 0.5) * np.array(T_IDXS)]
+    model.modelV2.position = position
+    velocity = log.ModelDataV2.XYZTData.new_message()
+    velocity.x = [float(x) for x in (self.speed + 0.5) * np.ones_like(T_IDXS)]
+    model.modelV2.velocity = velocity
+    acceleration = log.ModelDataV2.XYZTData.new_message()
+    acceleration.x = [float(x) for x in np.zeros_like(T_IDXS)]
+    model.modelV2.acceleration = acceleration
+
+
+
     control.controlsState.longControlState = LongCtrlState.pid
     control.controlsState.vCruise = float(v_cruise * 3.6)
     car_state.carState.vEgo = float(self.speed)

selfdrive/test/longitudinal_maneuvers/test_longitudinal.py

@@ -140,16 +140,23 @@ class LongitudinalControl(unittest.TestCase):
 
 def run_maneuver_worker(k):
   def run(self):
+    params = Params()
+
     man = maneuvers[k]
-    print(man.title)
+    params.put_bool("EndToEndLong", True)
+    print(man.title, ' in e2e mode')
+    valid, _ = man.evaluate()
+    self.assertTrue(valid, msg=man.title)
+    params.put_bool("EndToEndLong", False)
+    print(man.title, ' in acc mode')
     valid, _ = man.evaluate()
     self.assertTrue(valid, msg=man.title)
   return run
 
-
 for k in range(len(maneuvers)):
   setattr(LongitudinalControl, f"test_longitudinal_maneuvers_{k+1}",
           run_maneuver_worker(k))
 
+
 if __name__ == "__main__":
   unittest.main(failfast=True)