Adopted latest torqued

common/params.cc

@@ -98,6 +98,7 @@ std::unordered_map<std::string, uint32_t> keys = {
     {"CarParams", CLEAR_ON_MANAGER_START | CLEAR_ON_ONROAD_TRANSITION},
     {"CarParamsCache", CLEAR_ON_MANAGER_START},
     {"CarParamsPersistent", PERSISTENT},
+    {"CarParamsPrevRoute", PERSISTENT},
     {"CarVin", CLEAR_ON_MANAGER_START | CLEAR_ON_ONROAD_TRANSITION},
     {"CompletedTrainingVersion", PERSISTENT},
     {"ControlsReady", CLEAR_ON_MANAGER_START | CLEAR_ON_ONROAD_TRANSITION},

selfdrive/controls/controlsd.py

@@ -127,6 +127,11 @@ def __init__(self, sm=None, pm=None, can_sock=None, CI=None):
       safety_config.safetyModel = car.CarParams.SafetyModel.noOutput
       self.CP.safetyConfigs = [safety_config]
 
+    # Write previous route's CarParams
+    prev_cp = self.params.get("CarParamsPersistent")
+    if prev_cp is not None:
+      self.params.put("CarParamsPrevRoute", prev_cp)
+
     # Write CarParams for radard
     cp_bytes = self.CP.to_bytes()
     self.params.put("CarParams", cp_bytes)

selfdrive/locationd/helpers.py

@@ -0,0 +1,64 @@
+import numpy as np
+from typing import List, Optional, Tuple, Any
+
+from cereal import log
+
+
+class NPQueue:
+  def __init__(self, maxlen: int, rowsize: int) -> None:
+    self.maxlen = maxlen
+    self.arr = np.empty((0, rowsize))
+
+  def __len__(self) -> int:
+    return len(self.arr)
+
+  def append(self, pt: List[float]) -> None:
+    if len(self.arr) < self.maxlen:
+      self.arr = np.append(self.arr, [pt], axis=0)
+    else:
+      self.arr[:-1] = self.arr[1:]
+      self.arr[-1] = pt
+
+
+class PointBuckets:
+  def __init__(self, x_bounds: List[Tuple[float, float]], min_points: List[float], min_points_total: int, points_per_bucket: int, rowsize: int) -> None:
+    self.x_bounds = x_bounds
+    self.buckets = {bounds: NPQueue(maxlen=points_per_bucket, rowsize=rowsize) for bounds in x_bounds}
+    self.buckets_min_points = dict(zip(x_bounds, min_points, strict=True))
+    self.min_points_total = min_points_total
+
+  def bucket_lengths(self) -> List[int]:
+    return [len(v) for v in self.buckets.values()]
+
+  def __len__(self) -> int:
+    return sum(self.bucket_lengths())
+
+  def is_valid(self) -> bool:
+    individual_buckets_valid = all(len(v) >= min_pts for v, min_pts in zip(self.buckets.values(), self.buckets_min_points.values(), strict=True))
+    total_points_valid = self.__len__() >= self.min_points_total
+    return individual_buckets_valid and total_points_valid
+
+  def add_point(self, x: float, y: float, bucket_val: float) -> None:
+    raise NotImplementedError
+
+  def get_points(self, num_points: Optional[int] = None) -> Any:
+    points = np.vstack([x.arr for x in self.buckets.values()])
+    if num_points is None:
+      return points
+    return points[np.random.choice(np.arange(len(points)), min(len(points), num_points), replace=False)]
+
+  def load_points(self, points: List[List[float]]) -> None:
+    for point in points:
+      self.add_point(*point)
+
+
+class ParameterEstimator:
+  """ Base class for parameter estimators """
+  def reset(self) -> None:
+    raise NotImplementedError
+
+  def handle_log(self, t: int, which: str, msg: log.Event) -> None:
+    raise NotImplementedError
+
+  def get_msg(self, valid: bool, with_points: bool) -> log.Event:
+    raise NotImplementedError

selfdrive/locationd/torqued.py

@@ -1,7 +1,4 @@
 #!/usr/bin/env python3
-import os
-import sys
-import signal
 import numpy as np
 from collections import deque, defaultdict
 
@@ -12,6 +9,7 @@
 from openpilot.common.filter_simple import FirstOrderFilter
 from openpilot.system.swaglog import cloudlog
 from openpilot.selfdrive.controls.lib.vehicle_model import ACCELERATION_DUE_TO_GRAVITY
+from openpilot.selfdrive.locationd.helpers import PointBuckets, ParameterEstimator
 
 HISTORY = 5  # secs
 POINTS_PER_BUCKET = 1500
@@ -43,56 +41,15 @@ def slope2rot(slope):
   return np.array([[cos, -sin], [sin, cos]])
 
 
-class NPQueue:
-  def __init__(self, maxlen, rowsize):
-    self.maxlen = maxlen
-    self.arr = np.empty((0, rowsize))
-
-  def __len__(self):
-    return len(self.arr)
-
-  def append(self, pt):
-    if len(self.arr) < self.maxlen:
-      self.arr = np.append(self.arr, [pt], axis=0)
-    else:
-      self.arr[:-1] = self.arr[1:]
-      self.arr[-1] = pt
-
-
-class PointBuckets:
-  def __init__(self, x_bounds, min_points, min_points_total):
-    self.x_bounds = x_bounds
-    self.buckets = {bounds: NPQueue(maxlen=POINTS_PER_BUCKET, rowsize=3) for bounds in x_bounds}
-    self.buckets_min_points = dict(zip(x_bounds, min_points))
-    self.min_points_total = min_points_total
-
-  def bucket_lengths(self):
-    return [len(v) for v in self.buckets.values()]
-
-  def __len__(self):
-    return sum(self.bucket_lengths())
-
-  def is_valid(self):
-    return all(len(v) >= min_pts for v, min_pts in zip(self.buckets.values(), self.buckets_min_points.values())) and (self.__len__() >= self.min_points_total)
-
+class TorqueBuckets(PointBuckets):
   def add_point(self, x, y):
     for bound_min, bound_max in self.x_bounds:
       if (x >= bound_min) and (x < bound_max):
         self.buckets[(bound_min, bound_max)].append([x, 1.0, y])
         break
 
-  def get_points(self, num_points=None):
-    points = np.vstack([x.arr for x in self.buckets.values()])
-    if num_points is None:
-      return points
-    return points[np.random.choice(np.arange(len(points)), min(len(points), num_points), replace=False)]
 
-  def load_points(self, points):
-    for x, y in points:
-      self.add_point(x, y)
-
-
-class TorqueEstimator:
+class TorqueEstimator(ParameterEstimator):
   def __init__(self, CP, decimated=False):
     self.hist_len = int(HISTORY / DT_MDL)
     self.lag = CP.steerActuatorDelay + .2   # from controlsd
@@ -113,7 +70,7 @@ def __init__(self, CP, decimated=False):
     self.offline_friction = 0.0
     self.offline_latAccelFactor = 0.0
     self.resets = 0.0
-    self.use_params = CP.carName in ALLOWED_CARS
+    self.use_params = CP.carName in ALLOWED_CARS and CP.lateralTuning.which() == 'torque'
 
     if CP.lateralTuning.which() == 'torque':
       self.offline_friction = CP.lateralTuning.torque.friction
@@ -135,7 +92,7 @@ def __init__(self, CP, decimated=False):
 
     # try to restore cached params
     params = Params()
-    params_cache = params.get("LiveTorqueCarParams")
+    params_cache = params.get("CarParamsPrevRoute")
     torque_cache = params.get("LiveTorqueParameters")
     if params_cache is not None and torque_cache is not None:
       try:
@@ -158,7 +115,6 @@ def __init__(self, CP, decimated=False):
           cloudlog.info("restored torque params from cache")
       except Exception:
         cloudlog.exception("failed to restore cached torque params")
-        params.remove("LiveTorqueCarParams")
         params.remove("LiveTorqueParameters")
 
     self.filtered_params = {}
@@ -176,7 +132,11 @@ def reset(self):
     self.resets += 1.0
     self.decay = MIN_FILTER_DECAY
     self.raw_points = defaultdict(lambda: deque(maxlen=self.hist_len))
-    self.filtered_points = PointBuckets(x_bounds=STEER_BUCKET_BOUNDS, min_points=self.min_bucket_points, min_points_total=self.min_points_total)
+    self.filtered_points = TorqueBuckets(x_bounds=STEER_BUCKET_BOUNDS,
+                                         min_points=self.min_bucket_points,
+                                         min_points_total=self.min_points_total,
+                                         points_per_bucket=POINTS_PER_BUCKET,
+                                         rowsize=3)
 
   def estimate_params(self):
     points = self.filtered_points.get_points(self.fit_points)
@@ -256,34 +216,17 @@ def get_msg(self, valid=True, with_points=False):
     return msg
 
 
-def main(sm=None, pm=None):
+def main():
   config_realtime_process([0, 1, 2, 3], 5)
 
-  if sm is None:
-    sm = messaging.SubMaster(['carControl', 'carState', 'liveLocationKalman'], poll=['liveLocationKalman'])
-
-  if pm is None:
-    pm = messaging.PubMaster(['liveTorqueParameters'])
+  pm = messaging.PubMaster(['liveTorqueParameters'])
+  sm = messaging.SubMaster(['carControl', 'carState', 'liveLocationKalman'], poll=['liveLocationKalman'])
 
   params = Params()
   CP = car.CarParams.from_bytes(params.get("CarParams", block=True))
   # with car.CarParams.from_bytes(params.get("CarParams", block=True)) as CP:
   estimator = TorqueEstimator(CP)
 
-  def cache_params(sig, frame):
-    signal.signal(sig, signal.SIG_DFL)
-    cloudlog.warning("caching torque params")
-
-    params = Params()
-    params.put("LiveTorqueCarParams", CP.as_builder().to_bytes())
-
-    msg = estimator.get_msg(with_points=True)
-    params.put("LiveTorqueParameters", msg.to_bytes())
-
-    sys.exit(0)
-  if "REPLAY" not in os.environ:
-    signal.signal(signal.SIGINT, cache_params)
-
   while True:
     sm.update()
     if sm.all_checks():
@@ -296,6 +239,10 @@ def cache_params(sig, frame):
     if sm.frame % 5 == 0:
       pm.send('liveTorqueParameters', estimator.get_msg(valid=sm.all_checks()))
 
+    # Cache points every 60 seconds while onroad
+    if sm.frame % 240 == 0:
+      msg = estimator.get_msg(valid=sm.all_checks(), with_points=True)
+      params.put_nonblocking("LiveTorqueParameters", msg.to_bytes())
 
 if __name__ == "__main__":
   main()

selfdrive/test/process_replay/process_replay.py

@@ -589,10 +589,13 @@ def get_custom_params_from_lr(lr: Union[LogReader, List[capnp._DynamicStructRead
   assert initial_state in ["first", "last"]
   msg_index = 0 if initial_state == "first" else -1
 
-  assert len(car_params) > 0, "carParams required for initial state of liveParameters and liveTorqueCarParams"
+  assert len(car_params) > 0, "carParams required for initial state of liveParameters and CarParamsPrevRoute"
   CP = car_params[msg_index].carParams
 
-  custom_params = {}
+  custom_params = {
+    "CarParamsPrevRoute": CP.as_builder().to_bytes()
+  }
+
   if len(live_calibration) > 0:
     custom_params["CalibrationParams"] = live_calibration[msg_index].as_builder().to_bytes()
   if len(live_parameters) > 0: