locationd: ignore GPS as Kalman input; expand park-cached-output to onroad consumers

locationd: ignore gpsLocation observations entirely (clearpilot_disable_gps
const at the top of handle_gps; falls through to determine_gps_mode's
no-GPS path). gpsd.py keeps publishing real GPS for UI / dashcam / clock
/ night-mode — only locationd ignores it. The previous gpsd.py path was
hard-coding vNED=[0,0,0] while the car was moving 28 m/s, feeding the
Kalman contradictory GPS-vs-IMU velocity observations that propagated into
latcontrol_torque through liveLocationKalman.angularVelocityCalibrated and
caused a real "drift right on straight roads" symptom.

controlsd: short-circuit state_control() while parked. Skips LaC/LoC PID,
MPC, model_v2 reads, lane-change logic — all wasted work when the car
isn't moving. Returns the same enabled=False/latActive=False/longActive=False
CC the original code would have produced, so publish_logs runs unchanged
and card.controls_update keeps the sendcan / tester-present heartbeat
flowing at 100Hz. controlsd CPU dropped from ~59% to ~3% in park.

controlsd: also wire self.FPCC.noLatLaneChange = True/False in the
existing lane-change suppression branch. The Hyundai carcontroller already
reads off frogpilot_variables.no_lat_lane_change for the no-steer signal,
but the UI's distinctive yellow CHANGE_LANE_PATH_COLOR (onroad.cc:901)
reads from frogpilotCarControl.NoLatLaneChange — which nobody was setting,
so the yellow line never appeared during lane-change suppression.

plannerd: skip longitudinal_planner.update + publish + publish_ui_plan
while parked. Downstream controlsd already short-circuits in park, so
longitudinalPlan / uiPlan staleness is fine.

frogpilot_process: same idea — skip frogpilot_planner.update + publish
while parked.

dmonitoringmodeld: mirror the cached-output trick from modeld. Add carState
to the SubMaster, cache the last (model_output, dsp_execution_time) tuple,
and serve it on every frame while gear is in park instead of running DSP
inference on the driver camera.

Together with the existing modeld park-cached-output, the heavy onroad
processing pipeline (modeld → plannerd → controlsd → carcontroller) now
all idles in park and only fires when the car leaves park.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

selfdrive/controls/controlsd.py

@@ -665,6 +665,25 @@ class Controls:
   def state_control(self, CS):
     """Given the state, this function returns a CarControl packet"""
 
+    # CLEARPILOT: short-circuit while parked. Skip LaC/LoC PID, MPC, model_v2
+    # reads, lane-change logic — none of it matters when the car isn't moving.
+    # publish_logs still runs and still triggers carcontroller.apply via
+    # card.controls_update, so the sendcan heartbeats / tester-present messages
+    # keep flowing at 100Hz and the car doesn't fault. Saves ~30% controlsd CPU
+    # in park.
+    if CS.gearShifter == car.CarState.GearShifter.park:
+      CC = car.CarControl.new_message()
+      CC.enabled = False
+      CC.latActive = False
+      CC.longActive = False
+      CC.actuators.longControlState = self.LoC.long_control_state
+      self.LaC.reset()
+      self.LoC.reset(v_pid=CS.vEgo)
+      self.frogpilot_variables.no_lat_lane_change = False
+      self.FPCC.noLatLaneChange = False
+      lac_log = log.ControlsState.LateralDebugState.new_message()
+      return CC, lac_log
+
     # Update VehicleModel
     lp = self.sm['liveParameters']
     x = max(lp.stiffnessFactor, 0.1)
@@ -705,8 +724,10 @@ class Controls:
     if model_v2.meta.laneChangeState == LaneChangeState.laneChangeStarting and clearpilot_disable_lat_on_lane_change:
       CC.latActive = False
       self.frogpilot_variables.no_lat_lane_change = True
+      self.FPCC.noLatLaneChange = True
     else:
       self.frogpilot_variables.no_lat_lane_change = False
+      self.FPCC.noLatLaneChange = False
 
     if CS.leftBlinker or CS.rightBlinker:
       self.last_blinker_frame = self.sm.frame

selfdrive/controls/plannerd.py

@@ -34,6 +34,10 @@ def plannerd_thread():
   while True:
     sm.update()
     if sm.updated['modelV2']:
+      # CLEARPILOT: skip planning while parked. The downstream consumer (controlsd)
+      # already short-circuits in park, so longitudinalPlan/uiPlan staleness is fine.
+      if sm['carState'].gearShifter == car.CarState.GearShifter.park:
+        continue
       longitudinal_planner.update(sm)
       longitudinal_planner.publish(sm, pm)
       publish_ui_plan(sm, pm, longitudinal_planner)

selfdrive/frogpilot/frogpilot_process.py

@@ -85,7 +85,9 @@ def frogpilot_thread():
           frogpilot_planner = FrogPilotPlanner(CP)
           frogpilot_planner.update_frogpilot_params()
 
-      if sm.updated['modelV2']:
+      # CLEARPILOT: skip planner work while parked.
+      parked = sm['carState'].gearShifter == car.CarState.GearShifter.park
+      if sm.updated['modelV2'] and not parked:
         frogpilot_planner.update(sm['carState'], sm['controlsState'], sm['frogpilotCarControl'], sm['frogpilotNavigation'],
                                   sm['liveLocationKalman'], sm['modelV2'], sm['radarState'])
         frogpilot_planner.publish(sm, pm)

selfdrive/locationd/locationd.cc

@@ -308,12 +308,18 @@ void Localizer::input_fake_gps_observations(double current_time) {
 }
 
 void Localizer::handle_gps(double current_time, const cereal::GpsLocationData::Reader& log, const double sensor_time_offset) {
+  // CLEARPILOT: GPS disabled as a Kalman input. gpsd still publishes real GPS for
+  // UI / dashcam / clock / night-mode; only locationd ignores it. Falls through to
+  // determine_gps_mode() which is openpilot's existing no-GPS path (fake observations
+  // to bound position uncertainty). To re-enable, set this to false.
+  const bool clearpilot_disable_gps = true;
+
   bool gps_unreasonable = (Vector2d(log.getHorizontalAccuracy(), log.getVerticalAccuracy()).norm() >= SANE_GPS_UNCERTAINTY);
   bool gps_accuracy_insane = ((log.getVerticalAccuracy() <= 0) || (log.getSpeedAccuracy() <= 0) || (log.getBearingAccuracyDeg() <= 0));
   bool gps_lat_lng_alt_insane = ((std::abs(log.getLatitude()) > 90) || (std::abs(log.getLongitude()) > 180) || (std::abs(log.getAltitude()) > ALTITUDE_SANITY_CHECK));
   bool gps_vel_insane = (floatlist2vector(log.getVNED()).norm() > TRANS_SANITY_CHECK);
 
-  if (!log.getHasFix() || gps_unreasonable || gps_accuracy_insane || gps_lat_lng_alt_insane || gps_vel_insane) {
+  if (clearpilot_disable_gps || !log.getHasFix() || gps_unreasonable || gps_accuracy_insane || gps_lat_lng_alt_insane || gps_vel_insane) {
     //this->gps_valid = false;
     this->determine_gps_mode(current_time);
     return;

selfdrive/modeld/dmonitoringmodeld.py

@@ -7,7 +7,7 @@ import ctypes
 import numpy as np
 from pathlib import Path
 
-from cereal import messaging
+from cereal import car, messaging
 from cereal.messaging import PubMaster, SubMaster
 from cereal.visionipc import VisionIpcClient, VisionStreamType, VisionBuf
 from openpilot.common.swaglog import cloudlog
@@ -128,10 +128,15 @@ def main():
   assert vipc_client.is_connected()
   cloudlog.warning(f"connected with buffer size: {vipc_client.buffer_len}")
 
-  sm = SubMaster(["liveCalibration"])
+  sm = SubMaster(["liveCalibration", "carState"])
   pm = PubMaster(["driverStateV2"])
 
   calib = np.zeros(CALIB_LEN, dtype=np.float32)
+  # CLEARPILOT: cache last model output to serve while gear is in park —
+  # mirrors the same trick modeld uses. Skips DSP inference on the driver
+  # camera when the car is stationary; downstream dmonitoringd still gets
+  # a fresh publish each frame.
+  last_model_output = None
   # last = 0
 
   while True:
@@ -143,8 +148,13 @@ def main():
     if sm.updated["liveCalibration"]:
       calib[:] = np.array(sm["liveCalibration"].rpyCalib)
 
+    parked = sm["carState"].gearShifter == car.CarState.GearShifter.park
     t1 = time.perf_counter()
-    model_output, dsp_execution_time = model.run(buf, calib)
+    if parked and last_model_output is not None:
+      model_output, dsp_execution_time = last_model_output
+    else:
+      model_output, dsp_execution_time = model.run(buf, calib)
+      last_model_output = (model_output, dsp_execution_time)
     t2 = time.perf_counter()
 
     pm.send("driverStateV2", get_driverstate_packet(model_output, vipc_client.frame_id, vipc_client.timestamp_sof, t2 - t1, dsp_execution_time))