disable navigate on openpilot (#32106)

* 2eedcd90-b7db-46cb-86be-740f48ded7ab/700

* noop ciao

* here too

* mapsd too

* update translations

* disable mapsd test from test onroad

* disable mapRenderState test from timings

* lint

* fix exp mode toggle panel

* update tr

* french done

* dont build mapd

* only no nav

* just comment

* deprecate nav fields

* rm not comment

* dont deprecate too much

* remove from services

* merge cereal

.gitignore

@@ -52,7 +52,6 @@ selfdrive/car/tests/cars_dump
 system/camerad/camerad
 system/camerad/test/ae_gray_test
 selfdrive/modeld/_modeld
-selfdrive/modeld/_navmodeld
 selfdrive/modeld/_dmonitoringmodeld
 /src/
 

release/files_common

@@ -316,7 +316,6 @@ selfdrive/modeld/modeld.py
 selfdrive/modeld/parse_model_outputs.py
 selfdrive/modeld/fill_model_msg.py
 selfdrive/modeld/get_model_metadata.py
-selfdrive/modeld/navmodeld.py
 selfdrive/modeld/dmonitoringmodeld.py
 selfdrive/modeld/constants.py
 selfdrive/modeld/modeld
@@ -332,8 +331,6 @@ selfdrive/modeld/models/supercombo.onnx
 
 selfdrive/modeld/models/dmonitoring_model_q.dlc
 
-selfdrive/modeld/models/navmodel_q.dlc
-
 selfdrive/modeld/transforms/loadyuv.cc
 selfdrive/modeld/transforms/loadyuv.h
 selfdrive/modeld/transforms/loadyuv.cl

selfdrive/manager/process_config.py

@@ -56,8 +56,6 @@ procs = [
   NativeProcess("stream_encoderd", "system/loggerd", ["./encoderd", "--stream"], notcar),
   NativeProcess("loggerd", "system/loggerd", ["./loggerd"], logging),
   NativeProcess("modeld", "selfdrive/modeld", ["./modeld"], only_onroad),
-  NativeProcess("mapsd", "selfdrive/navd", ["./mapsd"], only_onroad),
-  PythonProcess("navmodeld", "selfdrive.modeld.navmodeld", only_onroad),
   NativeProcess("sensord", "system/sensord", ["./sensord"], only_onroad, enabled=not PC),
   NativeProcess("ui", "selfdrive/ui", ["./ui"], always_run, watchdog_max_dt=(5 if not PC else None)),
   PythonProcess("soundd", "selfdrive.ui.soundd", only_onroad),

selfdrive/modeld/fill_model_msg.py

@@ -43,8 +43,7 @@ def fill_xyvat(builder, t, x, y, v, a, x_std=None, y_std=None, v_std=None, a_std
 
 def fill_model_msg(msg: capnp._DynamicStructBuilder, net_output_data: dict[str, np.ndarray], publish_state: PublishState,
                    vipc_frame_id: int, vipc_frame_id_extra: int, frame_id: int, frame_drop: float,
-                   timestamp_eof: int, timestamp_llk: int, model_execution_time: float,
-                   nav_enabled: bool, valid: bool) -> None:
+                   timestamp_eof: int, model_execution_time: float, valid: bool) -> None:
   frame_age = frame_id - vipc_frame_id if frame_id > vipc_frame_id else 0
   msg.valid = valid
 
@@ -54,9 +53,7 @@ def fill_model_msg(msg: capnp._DynamicStructBuilder, net_output_data: dict[str,
   modelV2.frameAge = frame_age
   modelV2.frameDropPerc = frame_drop * 100
   modelV2.timestampEof = timestamp_eof
-  modelV2.locationMonoTime = timestamp_llk
   modelV2.modelExecutionTime = model_execution_time
-  modelV2.navEnabled = nav_enabled
 
   # plan
   position = modelV2.position

selfdrive/modeld/modeld.py

@@ -154,7 +154,7 @@ def main(demo=False):
 
   # messaging
   pm = PubMaster(["modelV2", "cameraOdometry"])
-  sm = SubMaster(["deviceState", "carState", "roadCameraState", "liveCalibration", "driverMonitoringState", "navModel", "navInstruction", "carControl"])
+  sm = SubMaster(["deviceState", "carState", "roadCameraState", "liveCalibration", "driverMonitoringState", "carControl"])
 
   publish_state = PublishState()
   params = Params()
@@ -240,30 +240,6 @@ def main(demo=False):
     if desire >= 0 and desire < ModelConstants.DESIRE_LEN:
       vec_desire[desire] = 1
 
-    # Enable/disable nav features
-    timestamp_llk = sm["navModel"].locationMonoTime
-    nav_valid = sm.valid["navModel"] # and (nanos_since_boot() - timestamp_llk < 1e9)
-    nav_enabled = nav_valid and params.get_bool("ExperimentalMode")
-
-    if not nav_enabled:
-      nav_features[:] = 0
-      nav_instructions[:] = 0
-
-    if nav_enabled and sm.updated["navModel"]:
-      nav_features = np.array(sm["navModel"].features)
-
-    if nav_enabled and sm.updated["navInstruction"]:
-      nav_instructions[:] = 0
-      for maneuver in sm["navInstruction"].allManeuvers:
-        distance_idx = 25 + int(maneuver.distance / 20)
-        direction_idx = 0
-        if maneuver.modifier in ("left", "slight left", "sharp left"):
-          direction_idx = 1
-        if maneuver.modifier in ("right", "slight right", "sharp right"):
-          direction_idx = 2
-        if 0 <= distance_idx < 50:
-          nav_instructions[distance_idx*3 + direction_idx] = 1
-
     # tracked dropped frames
     vipc_dropped_frames = max(0, meta_main.frame_id - last_vipc_frame_id - 1)
     frames_dropped = frame_dropped_filter.update(min(vipc_dropped_frames, 10))
@@ -282,7 +258,8 @@ def main(demo=False):
       'traffic_convention': traffic_convention,
       'lateral_control_params': lateral_control_params,
       'nav_features': nav_features,
-      'nav_instructions': nav_instructions}
+      'nav_instructions': nav_instructions,
+      }
 
     mt1 = time.perf_counter()
     model_output = model.run(buf_main, buf_extra, model_transform_main, model_transform_extra, inputs, prepare_only)
@@ -293,7 +270,7 @@ def main(demo=False):
       modelv2_send = messaging.new_message('modelV2')
       posenet_send = messaging.new_message('cameraOdometry')
       fill_model_msg(modelv2_send, model_output, publish_state, meta_main.frame_id, meta_extra.frame_id, frame_id, frame_drop_ratio,
-                      meta_main.timestamp_eof, timestamp_llk, model_execution_time, nav_enabled, live_calib_seen)
+                      meta_main.timestamp_eof, model_execution_time, live_calib_seen)
 
       desire_state = modelv2_send.modelV2.meta.desireState
       l_lane_change_prob = desire_state[log.Desire.laneChangeLeft]

selfdrive/modeld/models/README.md

@@ -21,10 +21,6 @@ To view the architecture of the ONNX networks, you can use [netron](https://netr
   * one-hot encoded vector to tell model whether traffic is right-hand or left-hand traffic : 2
 * **feature buffer**
   * A buffer of intermediate features that gets appended to the current feature to form a 5 seconds temporal context (at 20FPS) : 99 * 512
-* **nav features**
-  * 1 * 150
-* **nav instructions**
-  * 1 * 256
 
 
 ### Supercombo output format (Full size: XXX x float32)

selfdrive/modeld/models/navmodel.onnx

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:8254b569878b7472e3f63ed9f3527a87bde785c9037aee3ed66f972e072b5899
-size 14166696

selfdrive/modeld/models/navmodel_q.dlc

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:89fda8380efa3e421fbcdb6bb204c36a4991f137ee01d47f3d0380895aa7c036
-size 3630942

selfdrive/modeld/navmodeld.py

@@ -1,117 +0,0 @@
-#!/usr/bin/env python3
-import gc
-import math
-import time
-import ctypes
-import numpy as np
-from pathlib import Path
-
-from cereal import messaging
-from cereal.messaging import PubMaster, SubMaster
-from cereal.visionipc import VisionIpcClient, VisionStreamType
-from openpilot.common.swaglog import cloudlog
-from openpilot.common.params import Params
-from openpilot.common.realtime import set_realtime_priority
-from openpilot.selfdrive.modeld.constants import ModelConstants
-from openpilot.selfdrive.modeld.runners import ModelRunner, Runtime
-
-NAV_INPUT_SIZE = 256*256
-NAV_FEATURE_LEN = 256
-NAV_DESIRE_LEN = 32
-NAV_OUTPUT_SIZE = 2*2*ModelConstants.IDX_N + NAV_DESIRE_LEN + NAV_FEATURE_LEN
-MODEL_PATHS = {
-  ModelRunner.SNPE: Path(__file__).parent / 'models/navmodel_q.dlc',
-  ModelRunner.ONNX: Path(__file__).parent / 'models/navmodel.onnx'}
-
-class NavModelOutputXY(ctypes.Structure):
-  _fields_ = [
-    ("x", ctypes.c_float),
-    ("y", ctypes.c_float)]
-
-class NavModelOutputPlan(ctypes.Structure):
-  _fields_ = [
-    ("mean", NavModelOutputXY*ModelConstants.IDX_N),
-    ("std", NavModelOutputXY*ModelConstants.IDX_N)]
-
-class NavModelResult(ctypes.Structure):
-  _fields_ = [
-    ("plan", NavModelOutputPlan),
-    ("desire_pred", ctypes.c_float*NAV_DESIRE_LEN),
-    ("features", ctypes.c_float*NAV_FEATURE_LEN)]
-
-class ModelState:
-  inputs: dict[str, np.ndarray]
-  output: np.ndarray
-  model: ModelRunner
-
-  def __init__(self):
-    assert ctypes.sizeof(NavModelResult) == NAV_OUTPUT_SIZE * ctypes.sizeof(ctypes.c_float)
-    self.output = np.zeros(NAV_OUTPUT_SIZE, dtype=np.float32)
-    self.inputs = {'input_img': np.zeros(NAV_INPUT_SIZE, dtype=np.uint8)}
-    self.model = ModelRunner(MODEL_PATHS, self.output, Runtime.DSP, True, None)
-    self.model.addInput("input_img", None)
-
-  def run(self, buf:np.ndarray) -> tuple[np.ndarray, float]:
-    self.inputs['input_img'][:] = buf
-
-    t1 = time.perf_counter()
-    self.model.setInputBuffer("input_img", self.inputs['input_img'].view(np.float32))
-    self.model.execute()
-    t2 = time.perf_counter()
-    return self.output, t2 - t1
-
-def get_navmodel_packet(model_output: np.ndarray, valid: bool, frame_id: int, location_ts: int, execution_time: float, dsp_execution_time: float):
-  model_result = ctypes.cast(model_output.ctypes.data, ctypes.POINTER(NavModelResult)).contents
-  msg = messaging.new_message('navModel')
-  msg.valid = valid
-  msg.navModel.frameId = frame_id
-  msg.navModel.locationMonoTime = location_ts
-  msg.navModel.modelExecutionTime = execution_time
-  msg.navModel.dspExecutionTime = dsp_execution_time
-  msg.navModel.features = model_result.features[:]
-  msg.navModel.desirePrediction = model_result.desire_pred[:]
-  msg.navModel.position.x = [p.x for p in model_result.plan.mean]
-  msg.navModel.position.y = [p.y for p in model_result.plan.mean]
-  msg.navModel.position.xStd = [math.exp(p.x) for p in model_result.plan.std]
-  msg.navModel.position.yStd = [math.exp(p.y) for p in model_result.plan.std]
-  return msg
-
-
-def main():
-  gc.disable()
-  set_realtime_priority(1)
-
-  # there exists a race condition when two processes try to create a
-  # SNPE model runner at the same time, wait for dmonitoringmodeld to finish
-  cloudlog.warning("waiting for dmonitoringmodeld to initialize")
-  if not Params().get_bool("DmModelInitialized", True):
-    return
-
-  model = ModelState()
-  cloudlog.warning("models loaded, navmodeld starting")
-
-  vipc_client = VisionIpcClient("navd", VisionStreamType.VISION_STREAM_MAP, True)
-  while not vipc_client.connect(False):
-    time.sleep(0.1)
-  assert vipc_client.is_connected()
-  cloudlog.warning(f"connected with buffer size: {vipc_client.buffer_len}")
-
-  sm = SubMaster(["navInstruction"])
-  pm = PubMaster(["navModel"])
-
-  while True:
-    buf = vipc_client.recv()
-    if buf is None:
-      continue
-
-    sm.update(0)
-    t1 = time.perf_counter()
-    model_output, dsp_execution_time = model.run(buf.data[:buf.uv_offset])
-    t2 = time.perf_counter()
-
-    valid = vipc_client.valid and sm.valid["navInstruction"]
-    pm.send("navModel", get_navmodel_packet(model_output, valid, vipc_client.frame_id, vipc_client.timestamp_sof, t2 - t1, dsp_execution_time))
-
-
-if __name__ == "__main__":
-  main()

selfdrive/navd/README.md

@@ -8,7 +8,7 @@ This directory contains two daemons, `navd` and `mapsd`, which support navigatio
 
 ### map renderer
 
-The map renderer listens for the `navRoute` and publishes a rendered map view over VisionIPC for the navigation model, which lives in `selfdrive/modeld/`. The rendered maps look like this:
+The map renderer listens for the `navRoute` and publishes a simplified rendered map view over VisionIPC. The rendered maps look like this:
 
 ![](https://i.imgur.com/oZLfmwq.png)
 

selfdrive/navd/SConscript

@@ -17,4 +17,4 @@ if arch in ['larch64', 'aarch64', 'x86_64']:
   map_env["RPATH"].append(Dir('.').abspath)
   map_env["LIBPATH"].append(Dir('.').abspath)
   maplib = map_env.SharedLibrary("maprender", ["map_renderer.cc"], LIBS=libs)
-  map_env.Program("mapsd", ["main.cc", ], LIBS=[maplib[0].get_path(), ] + libs)
+  # map_env.Program("mapsd", ["main.cc", ], LIBS=[maplib[0].get_path(), ] + libs)

selfdrive/navd/tests/test_map_renderer.py

@@ -65,6 +65,7 @@ class MapBoxInternetDisabledServer(threading.Thread):
     MapBoxInternetDisabledRequestHandler.INTERNET_ACTIVE = True
 
 
+@pytest.mark.skip(reason="not used")
 class TestMapRenderer(unittest.TestCase):
   server: MapBoxInternetDisabledServer
 

selfdrive/test/process_replay/model_replay.py

@@ -1,15 +1,11 @@
 #!/usr/bin/env python3
 import os
 import sys
-import time
 from collections import defaultdict
 from typing import Any
 
-import cereal.messaging as messaging
 from openpilot.common.git import get_commit
-from openpilot.common.params import Params
 from openpilot.system.hardware import PC
-from openpilot.selfdrive.manager.process_config import managed_processes
 from openpilot.tools.lib.openpilotci import BASE_URL, get_url
 from openpilot.selfdrive.test.process_replay.compare_logs import compare_logs, format_diff
 from openpilot.selfdrive.test.process_replay.process_replay import get_process_config, replay_process
@@ -20,9 +16,7 @@ from openpilot.tools.lib.helpers import save_log
 TEST_ROUTE = "2f4452b03ccb98f0|2022-12-03--13-45-30"
 SEGMENT = 6
 MAX_FRAMES = 100 if PC else 600
-NAV_FRAMES = 50
 
-NO_NAV = "NO_NAV" in os.environ
 NO_MODEL = "NO_MODEL" in os.environ
 SEND_EXTRA_INPUTS = bool(int(os.getenv("SEND_EXTRA_INPUTS", "0")))
 
@@ -50,59 +44,6 @@ def trim_logs_to_max_frames(logs, max_frames, frs_types, include_all_types):
   return all_msgs
 
 
-def nav_model_replay(lr):
-  sm = messaging.SubMaster(['navModel', 'navThumbnail', 'mapRenderState'])
-  pm = messaging.PubMaster(['liveLocationKalman', 'navRoute'])
-
-  nav = [m for m in lr if m.which() == 'navRoute']
-  llk = [m for m in lr if m.which() == 'liveLocationKalman']
-  assert len(nav) > 0 and len(llk) >= NAV_FRAMES and nav[0].logMonoTime < llk[-NAV_FRAMES].logMonoTime
-
-  log_msgs = []
-  try:
-    assert "MAPBOX_TOKEN" in os.environ
-    os.environ['MAP_RENDER_TEST_MODE'] = '1'
-    Params().put_bool('DmModelInitialized', True)
-    managed_processes['mapsd'].start()
-    managed_processes['navmodeld'].start()
-
-    # setup position and route
-    for _ in range(10):
-      for s in (llk[-NAV_FRAMES], nav[0]):
-        pm.send(s.which(), s.as_builder().to_bytes())
-      sm.update(1000)
-      if sm.updated['navModel']:
-        break
-      time.sleep(1)
-
-    if not sm.updated['navModel']:
-      raise Exception("no navmodeld outputs, failed to initialize")
-
-    # drain
-    time.sleep(2)
-    sm.update(0)
-
-    # run replay
-    for n in range(len(llk) - NAV_FRAMES, len(llk)):
-      pm.send(llk[n].which(), llk[n].as_builder().to_bytes())
-      m = messaging.recv_one(sm.sock['navThumbnail'])
-      assert m is not None, f"no navThumbnail, frame={n}"
-      log_msgs.append(m)
-
-      m = messaging.recv_one(sm.sock['mapRenderState'])
-      assert m is not None, f"no mapRenderState, frame={n}"
-      log_msgs.append(m)
-
-      m = messaging.recv_one(sm.sock['navModel'])
-      assert m is not None, f"no navModel response, frame={n}"
-      log_msgs.append(m)
-  finally:
-    managed_processes['mapsd'].stop()
-    managed_processes['navmodeld'].stop()
-
-  return log_msgs
-
-
 def model_replay(lr, frs):
   # modeld is using frame pairs
   modeld_logs = trim_logs_to_max_frames(lr, MAX_FRAMES, {"roadCameraState", "wideRoadCameraState"}, {"roadEncodeIdx", "wideRoadEncodeIdx", "carParams"})
@@ -169,8 +110,6 @@ if __name__ == "__main__":
   # run replays
   if not NO_MODEL:
     log_msgs += model_replay(lr, frs)
-  if not NO_NAV:
-    log_msgs += nav_model_replay(lr)
 
   # get diff
   failed = False
@@ -182,28 +121,19 @@ if __name__ == "__main__":
       all_logs = list(LogReader(BASE_URL + log_fn))
       cmp_log = []
 
-      # logs are ordered based on type: modelV2, driverStateV2, nav messages (navThumbnail, mapRenderState, navModel)
+      # logs are ordered based on type: modelV2, driverStateV2
       if not NO_MODEL:
         model_start_index = next(i for i, m in enumerate(all_logs) if m.which() in ("modelV2", "cameraOdometry"))
         cmp_log += all_logs[model_start_index:model_start_index + MAX_FRAMES*2]
         dmon_start_index = next(i for i, m in enumerate(all_logs) if m.which() == "driverStateV2")
         cmp_log += all_logs[dmon_start_index:dmon_start_index + MAX_FRAMES]
-      if not NO_NAV:
-        nav_start_index = next(i for i, m in enumerate(all_logs) if m.which() in ["navThumbnail", "mapRenderState", "navModel"])
-        nav_logs = all_logs[nav_start_index:nav_start_index + NAV_FRAMES*3]
-        cmp_log += nav_logs
 
       ignore = [
         'logMonoTime',
         'modelV2.frameDropPerc',
         'modelV2.modelExecutionTime',
         'driverStateV2.modelExecutionTime',
-        'driverStateV2.dspExecutionTime',
-        'navModel.dspExecutionTime',
-        'navModel.modelExecutionTime',
-        'navThumbnail.timestampEof',
-        'mapRenderState.locationMonoTime',
-        'mapRenderState.renderTime',
+        'driverStateV2.dspExecutionTime'
       ]
       if PC:
         ignore += [

selfdrive/test/test_onroad.py

@@ -34,7 +34,6 @@ PROCS = {
   "./encoderd": 17.0,
   "./camerad": 14.5,
   "./locationd": 11.0,
-  "./mapsd": (0.5, 10.0),
   "selfdrive.controls.plannerd": 11.0,
   "./ui": 18.0,
   "selfdrive.locationd.paramsd": 9.0,
@@ -42,7 +41,6 @@ PROCS = {
   "selfdrive.controls.radard": 7.0,
   "selfdrive.modeld.modeld": 13.0,
   "selfdrive.modeld.dmonitoringmodeld": 8.0,
-  "selfdrive.modeld.navmodeld": 1.0,
   "selfdrive.thermald.thermald": 3.87,
   "selfdrive.locationd.calibrationd": 2.0,
   "selfdrive.locationd.torqued": 5.0,
@@ -87,8 +85,6 @@ TIMINGS = {
   "driverCameraState": [2.5, 0.35],
   "modelV2": [2.5, 0.35],
   "driverStateV2": [2.5, 0.40],
-  "navModel": [2.5, 0.35],
-  "mapRenderState": [2.5, 0.35],
   "liveLocationKalman": [2.5, 0.35],
   "wideRoadCameraState": [1.5, 0.35],
 }

selfdrive/ui/qt/maps/map.cc

@@ -74,7 +74,7 @@ void MapWindow::initLayers() {
 
     QVariantMap transition;
     transition["duration"] = 400;  // ms
-    m_map->setPaintProperty("navLayer", "line-color", getNavPathColor(uiState()->scene.navigate_on_openpilot));
+    m_map->setPaintProperty("navLayer", "line-color", QColor("#31a1ee"));
     m_map->setPaintProperty("navLayer", "line-color-transition", transition);
     m_map->setPaintProperty("navLayer", "line-width", 7.5);
     m_map->setLayoutProperty("navLayer", "line-cap", "round");
@@ -118,21 +118,6 @@ void MapWindow::updateState(const UIState &s) {
   const SubMaster &sm = *(s.sm);
   update();
 
-  if (sm.updated("modelV2")) {
-    // set path color on change, and show map on rising edge of navigate on openpilot
-    bool nav_enabled = sm["modelV2"].getModelV2().getNavEnabled() &&
-                       sm["controlsState"].getControlsState().getEnabled();
-    if (nav_enabled != uiState()->scene.navigate_on_openpilot) {
-      if (loaded_once) {
-        m_map->setPaintProperty("navLayer", "line-color", getNavPathColor(nav_enabled));
-      }
-      if (nav_enabled) {
-        emit requestVisible(true);
-      }
-    }
-    uiState()->scene.navigate_on_openpilot = nav_enabled;
-  }
-
   if (sm.updated("liveLocationKalman")) {
     auto locationd_location = sm["liveLocationKalman"].getLiveLocationKalman();
     auto locationd_pos = locationd_location.getPositionGeodetic();
@@ -365,7 +350,6 @@ void MapWindow::pinchTriggered(QPinchGesture *gesture) {
 void MapWindow::offroadTransition(bool offroad) {
   if (offroad) {
     clearRoute();
-    uiState()->scene.navigate_on_openpilot = false;
     routing_problem = false;
   } else {
     auto dest = coordinate_from_param("NavDestination");

selfdrive/ui/qt/maps/map.h

@@ -69,11 +69,6 @@ private:
   MapInstructions* map_instructions;
   MapETA* map_eta;
 
-  // Blue with normal nav, green when nav is input into the model
-  QColor getNavPathColor(bool nav_enabled) {
-    return nav_enabled ? QColor("#31ee73") : QColor("#31a1ee");
-  }
-
   void clearRoute();
   void updateDestinationMarker();
   uint64_t route_rcv_frame = 0;

selfdrive/ui/qt/offroad/settings.cc

@@ -143,21 +143,14 @@ void TogglesPanel::updateToggles() {
                                           "<h4>%2</h4><br>"
                                           "%3<br>"
                                           "<h4>%4</h4><br>"
-                                          "%5<br>"
-                                          "<h4>%6</h4><br>"
-                                          "%7")
+                                          "%5<br>")
                                   .arg(tr("openpilot defaults to driving in <b>chill mode</b>. Experimental mode enables <b>alpha-level features</b> that aren't ready for chill mode. Experimental features are listed below:"))
                                   .arg(tr("End-to-End Longitudinal Control"))
                                   .arg(tr("Let the driving model control the gas and brakes. openpilot will drive as it thinks a human would, including stopping for red lights and stop signs. "
                                           "Since the driving model decides the speed to drive, the set speed will only act as an upper bound. This is an alpha quality feature; "
                                           "mistakes should be expected."))
-                                  .arg(tr("Navigate on openpilot"))
-                                  .arg(tr("When navigation has a destination, openpilot will input the map information into the model. This provides useful context for the model and allows openpilot to keep left or right "
-                                          "appropriately at forks/exits. Lane change behavior is unchanged and still activated by the driver. This is an alpha quality feature; mistakes should be expected, particularly around "
-                                          "exits and forks. These mistakes can include unintended laneline crossings, late exit taking, driving towards dividing barriers in the gore areas, etc."))
                                   .arg(tr("New Driving Visualization"))
-                                  .arg(tr("The driving visualization will transition to the road-facing wide-angle camera at low speeds to better show some turns. The Experimental mode logo will also be shown in the top right corner. "
-                                          "When a navigation destination is set and the driving model is using it as input, the driving path on the map will turn green."));
+                                  .arg(tr("The driving visualization will transition to the road-facing wide-angle camera at low speeds to better show some turns. The Experimental mode logo will also be shown in the top right corner. "));
 
   const bool is_release = params.getBool("IsReleaseBranch");
   auto cp_bytes = params.get("CarParamsPersistent");

selfdrive/ui/qt/onroad.cc

@@ -93,9 +93,8 @@ void OnroadWindow::updateState(const UIState &s) {
 void OnroadWindow::mousePressEvent(QMouseEvent* e) {
 #ifdef ENABLE_MAPS
   if (map != nullptr) {
-    // Switch between map and sidebar when using navigate on openpilot
     bool sidebarVisible = geometry().x() > 0;
-    bool show_map = uiState()->scene.navigate_on_openpilot ? sidebarVisible : !sidebarVisible;
+    bool show_map = !sidebarVisible;
     map->setVisible(show_map && !map->isVisible());
   }
 #endif

selfdrive/ui/qt/window.cc

@@ -75,10 +75,6 @@ void MainWindow::closeSettings() {
 
   if (uiState()->scene.started) {
     homeWindow->showSidebar(false);
-    // Map is always shown when using navigate on openpilot
-    if (uiState()->scene.navigate_on_openpilot) {
-      homeWindow->showMapPanel(true);
-    }
   }
 }
 

selfdrive/ui/translations/main_ar.ts

@@ -1154,22 +1154,10 @@ This may take up to a minute.</source>
         <source>Let the driving model control the gas and brakes. openpilot will drive as it thinks a human would, including stopping for red lights and stop signs. Since the driving model decides the speed to drive, the set speed will only act as an upper bound. This is an alpha quality feature; mistakes should be expected.</source>
         <translation>دع نظام القيادة يتحكم بالوقود والمكابح. سيقوم openpilot بالقيادة كما لو أنه كائن بشري، بما في ذلك التوقف عند الإشارة الحمراء، وإشارات التوقف. وبما أن نمط القيادة يحدد سرعة القيادة، فإن السرعة المضبوطة تشكل الحد الأقصى فقط. هذه خاصية الجودة ألفا، فيجب توقع حدوث الأخطاء.</translation>
     </message>
-    <message>
-        <source>Navigate on openpilot</source>
-        <translation>التنقل على openpilot</translation>
-    </message>
-    <message>
-        <source>When navigation has a destination, openpilot will input the map information into the model. This provides useful context for the model and allows openpilot to keep left or right appropriately at forks/exits. Lane change behavior is unchanged and still activated by the driver. This is an alpha quality feature; mistakes should be expected, particularly around exits and forks. These mistakes can include unintended laneline crossings, late exit taking, driving towards dividing barriers in the gore areas, etc.</source>
-        <translation>عندما يكون هناك وجهة للتنقل، فإن openpilot سيقوم بإدخال معلومات الخريطة في هذا النموذج. وهذا يقدم سياقاً مفيداً ويسمح لـopenpilot بالبقاء يساراً أو يميناً بالشكل المناسب عند المنعطفات/المخارج. يبقى سلوك تغيير المسار مفعلاً عند السائق،. هذه هي خاصية الجودة ألفا، ولذلك يجب توقع الأخطاء لا سيما عند المخارج والمنعطفات هذه الأخطاء قد تشمل العبور غير المقصود لخطوط المسارات، والتأخر في الخروج، والقيادة نحو الحواجز الفاصلة في المناطق المثلثة بين الطريق الرئيسي والمخارج، وغير ذلك من الأخطاء المشابهة.</translation>
-    </message>
     <message>
         <source>New Driving Visualization</source>
         <translation>تصور القيادة الديد</translation>
     </message>
-    <message>
-        <source>The driving visualization will transition to the road-facing wide-angle camera at low speeds to better show some turns. The Experimental mode logo will also be shown in the top right corner. When a navigation destination is set and the driving model is using it as input, the driving path on the map will turn green.</source>
-        <translation>تصور القيادة سينتقل إلى الكاميرا واسعة الزاوية المواجهة للطريق في السرعات المنخفضة من أجل إظهار بعض المنعطفات بشكل أفضل. سيتم أيضاً إظهار شعار الوضع التجريبي في الزاوية العلوية اليمنى. عند تحديد وجهة التنقل، واستخدام نظام القيادة لها كوضع مدخل، سيتحول مسار القيادة على الخريطة إلى اللون الأخضر.</translation>
-    </message>
     <message>
         <source>Experimental mode is currently unavailable on this car since the car&apos;s stock ACC is used for longitudinal control.</source>
         <translation>الوضع التجريبي غير متوفر حالياً في هذه السيارة نظراً لاستخدام رصيد التحكم التكيفي بالسرعة من أجل التحكم الطولي.</translation>
@@ -1190,6 +1178,10 @@ This may take up to a minute.</source>
         <source>Standard is recommended. In aggressive mode, openpilot will follow lead cars closer and be more aggressive with the gas and brake. In relaxed mode openpilot will stay further away from lead cars. On supported cars, you can cycle through these personalities with your steering wheel distance button.</source>
         <translation>يوصى بالمعيار. في الوضع العدواني، سيتبع الطيار المفتوح السيارات الرائدة بشكل أقرب ويكون أكثر عدوانية مع البنزين والفرامل. في الوضع المريح، سيبقى openpilot بعيدًا عن السيارات الرائدة. في السيارات المدعومة، يمكنك التنقل بين هذه الشخصيات باستخدام زر مسافة عجلة القيادة.</translation>
     </message>
+    <message>
+        <source>The driving visualization will transition to the road-facing wide-angle camera at low speeds to better show some turns. The Experimental mode logo will also be shown in the top right corner. </source>
+        <translation type="unfinished"></translation>
+    </message>
 </context>
 <context>
     <name>Updater</name>

selfdrive/ui/translations/main_de.ts

@@ -1148,18 +1148,6 @@ This may take up to a minute.</source>
         <source>End-to-End Longitudinal Control</source>
         <translation type="unfinished"></translation>
     </message>
-    <message>
-        <source>Navigate on openpilot</source>
-        <translation type="unfinished"></translation>
-    </message>
-    <message>
-        <source>When navigation has a destination, openpilot will input the map information into the model. This provides useful context for the model and allows openpilot to keep left or right appropriately at forks/exits. Lane change behavior is unchanged and still activated by the driver. This is an alpha quality feature; mistakes should be expected, particularly around exits and forks. These mistakes can include unintended laneline crossings, late exit taking, driving towards dividing barriers in the gore areas, etc.</source>
-        <translation type="unfinished"></translation>
-    </message>
-    <message>
-        <source>The driving visualization will transition to the road-facing wide-angle camera at low speeds to better show some turns. The Experimental mode logo will also be shown in the top right corner. When a navigation destination is set and the driving model is using it as input, the driving path on the map will turn green.</source>
-        <translation type="unfinished"></translation>
-    </message>
     <message>
         <source>openpilot longitudinal control may come in a future update.</source>
         <translation type="unfinished"></translation>
@@ -1176,6 +1164,10 @@ This may take up to a minute.</source>
         <source>Standard is recommended. In aggressive mode, openpilot will follow lead cars closer and be more aggressive with the gas and brake. In relaxed mode openpilot will stay further away from lead cars. On supported cars, you can cycle through these personalities with your steering wheel distance button.</source>
         <translation type="unfinished"></translation>
     </message>
+    <message>
+        <source>The driving visualization will transition to the road-facing wide-angle camera at low speeds to better show some turns. The Experimental mode logo will also be shown in the top right corner. </source>
+        <translation type="unfinished"></translation>
+    </message>
 </context>
 <context>
     <name>Updater</name>

selfdrive/ui/translations/main_fr.ts

@@ -1154,18 +1154,6 @@ Cela peut prendre jusqu&apos;à une minute.</translation>
         <source>End-to-End Longitudinal Control</source>
         <translation>Contrôle longitudinal de bout en bout</translation>
     </message>
-    <message>
-        <source>Navigate on openpilot</source>
-        <translation>Navigation avec openpilot</translation>
-    </message>
-    <message>
-        <source>When navigation has a destination, openpilot will input the map information into the model. This provides useful context for the model and allows openpilot to keep left or right appropriately at forks/exits. Lane change behavior is unchanged and still activated by the driver. This is an alpha quality feature; mistakes should be expected, particularly around exits and forks. These mistakes can include unintended laneline crossings, late exit taking, driving towards dividing barriers in the gore areas, etc.</source>
-        <translation>Lorsque la navigation dispose d&apos;une destination, openpilot entrera les informations de la carte dans le modèle. Cela fournit un contexte utile pour le modèle et permet à openpilot de se diriger à gauche ou à droite de manière appropriée aux bifurcations/sorties. Le comportement relatif au changement de voie reste inchangé et doit toujours être activé par le conducteur. Il s&apos;agit d&apos;une fonctionnalité alpha ; il faut s&apos;attendre à des erreurs, en particulier aux abords des sorties et des bifurcations. Ces erreurs peuvent inclure des franchissements involontaires de passages piétons, des prises de sortie tardives, la conduite vers des zones de séparation de type zebras, etc.</translation>
-    </message>
-    <message>
-        <source>The driving visualization will transition to the road-facing wide-angle camera at low speeds to better show some turns. The Experimental mode logo will also be shown in the top right corner. When a navigation destination is set and the driving model is using it as input, the driving path on the map will turn green.</source>
-        <translation>La visualisation de la conduite passera sur la caméra grand angle dirigée vers la route à faible vitesse afin de mieux montrer certains virages. Le logo du mode expérimental s&apos;affichera également dans le coin supérieur droit. Lorsqu&apos;une destination de navigation est définie et que le modèle de conduite l&apos;utilise comme entrée, la trajectoire de conduite sur la carte deviendra verte.</translation>
-    </message>
     <message>
         <source>Enable the openpilot longitudinal control (alpha) toggle to allow Experimental mode.</source>
         <translation>Activer le contrôle longitudinal d&apos;openpilot (en alpha) pour autoriser le mode expérimental.</translation>
@@ -1174,6 +1162,10 @@ Cela peut prendre jusqu&apos;à une minute.</translation>
         <source>Standard is recommended. In aggressive mode, openpilot will follow lead cars closer and be more aggressive with the gas and brake. In relaxed mode openpilot will stay further away from lead cars. On supported cars, you can cycle through these personalities with your steering wheel distance button.</source>
         <translation type="unfinished"></translation>
     </message>
+    <message>
+        <source>The driving visualization will transition to the road-facing wide-angle camera at low speeds to better show some turns. The Experimental mode logo will also be shown in the top right corner. </source>
+        <translation>La visualisation de la conduite passera sur la caméra grand angle dirigée vers la route à faible vitesse afin de mieux montrer certains virages. Le logo du mode expérimental s&apos;affichera également dans le coin supérieur droit.</translation>
+    </message>
 </context>
 <context>
     <name>Updater</name>

selfdrive/ui/translations/main_ja.ts

@@ -1140,18 +1140,6 @@ This may take up to a minute.</source>
         <source>End-to-End Longitudinal Control</source>
         <translation type="unfinished"></translation>
     </message>
-    <message>
-        <source>Navigate on openpilot</source>
-        <translation type="unfinished"></translation>
-    </message>
-    <message>
-        <source>When navigation has a destination, openpilot will input the map information into the model. This provides useful context for the model and allows openpilot to keep left or right appropriately at forks/exits. Lane change behavior is unchanged and still activated by the driver. This is an alpha quality feature; mistakes should be expected, particularly around exits and forks. These mistakes can include unintended laneline crossings, late exit taking, driving towards dividing barriers in the gore areas, etc.</source>
-        <translation type="unfinished"></translation>
-    </message>
-    <message>
-        <source>The driving visualization will transition to the road-facing wide-angle camera at low speeds to better show some turns. The Experimental mode logo will also be shown in the top right corner. When a navigation destination is set and the driving model is using it as input, the driving path on the map will turn green.</source>
-        <translation type="unfinished"></translation>
-    </message>
     <message>
         <source>openpilot longitudinal control may come in a future update.</source>
         <translation type="unfinished"></translation>
@@ -1168,6 +1156,10 @@ This may take up to a minute.</source>
         <source>Standard is recommended. In aggressive mode, openpilot will follow lead cars closer and be more aggressive with the gas and brake. In relaxed mode openpilot will stay further away from lead cars. On supported cars, you can cycle through these personalities with your steering wheel distance button.</source>
         <translation type="unfinished"></translation>
     </message>
+    <message>
+        <source>The driving visualization will transition to the road-facing wide-angle camera at low speeds to better show some turns. The Experimental mode logo will also be shown in the top right corner. </source>
+        <translation type="unfinished"></translation>
+    </message>
 </context>
 <context>
     <name>Updater</name>

selfdrive/ui/translations/main_ko.ts

@@ -1146,10 +1146,6 @@ This may take up to a minute.</source>
         <source>An alpha version of openpilot longitudinal control can be tested, along with Experimental mode, on non-release branches.</source>
         <translation>openpilot 가감속 제어 알파 버전은 비 릴리즈 브랜치에서 실험 모드와 함께 테스트할 수 있습니다.</translation>
     </message>
-    <message>
-        <source>Navigate on openpilot</source>
-        <translation>openpilot 내비게이트</translation>
-    </message>
     <message>
         <source>Enable the openpilot longitudinal control (alpha) toggle to allow Experimental mode.</source>
         <translation>실험 모드를 사용하려면 openpilot E2E 가감속 제어 (알파) 토글을 활성화하세요.</translation>
@@ -1158,18 +1154,14 @@ This may take up to a minute.</source>
         <source>End-to-End Longitudinal Control</source>
         <translation>E2E 가감속 제어</translation>
     </message>
-    <message>
-        <source>When navigation has a destination, openpilot will input the map information into the model. This provides useful context for the model and allows openpilot to keep left or right appropriately at forks/exits. Lane change behavior is unchanged and still activated by the driver. This is an alpha quality feature; mistakes should be expected, particularly around exits and forks. These mistakes can include unintended laneline crossings, late exit taking, driving towards dividing barriers in the gore areas, etc.</source>
-        <translation>내비게이션에 목적지가 설정되어 있으면 openpilot이 지도 정보를 주행 모델에 입력합니다. 이는 모델에 유용한 정보를 제공하고 openpilot이 진출입로 및 램프에서 적절하게 왼쪽 또는 오른쪽을 유지할 수 있도록 해 줍니다. 차선 변경 기능은 여전히 운전자의 조작에 의해 활성화됩니다. 이 기능은 알파 버전입니다. 특히 진출입로 및 분기점 주변에서 실수가 발생할 수 있으며 이러한 실수에는 의도하지 않은 차선 이탈, 늦은 진출, 도로 가장자리의 분리대 또는 경계석을 향해 운전하는 행동 등이 포함됩니다.</translation>
-    </message>
-    <message>
-        <source>The driving visualization will transition to the road-facing wide-angle camera at low speeds to better show some turns. The Experimental mode logo will also be shown in the top right corner. When a navigation destination is set and the driving model is using it as input, the driving path on the map will turn green.</source>
-        <translation>주행 시각화는 저속으로 주행 시 도로를 향한 광각 카메라로 자동 전환되어 일부 곡선 경로를 더 잘 보여줍니다. 실험 모드 로고는 우측 상단에 표시됩니다. 내비게이션 목적지가 설정되고 주행 모델에 입력되면 지도의 주행 경로가 녹색으로 바뀝니다.</translation>
-    </message>
     <message>
         <source>Standard is recommended. In aggressive mode, openpilot will follow lead cars closer and be more aggressive with the gas and brake. In relaxed mode openpilot will stay further away from lead cars. On supported cars, you can cycle through these personalities with your steering wheel distance button.</source>
         <translation>표준 모드를 권장합니다. 공격적 모드의 openpilot은 선두 차량을 더 가까이 따라가고 가감속제어를 사용하여 더욱 공격적으로 움직입니다. 편안한 모드의 openpilot은 선두 차량으로부터 더 멀리 떨어져 있습니다. 지원되는 차량에서는 스티어링 휠 거리 버튼을 사용하여 이러한 특성을 순환할 수 있습니다.</translation>
     </message>
+    <message>
+        <source>The driving visualization will transition to the road-facing wide-angle camera at low speeds to better show some turns. The Experimental mode logo will also be shown in the top right corner. </source>
+        <translation type="unfinished"></translation>
+    </message>
 </context>
 <context>
     <name>Updater</name>

selfdrive/ui/translations/main_pt-BR.ts

@@ -1150,10 +1150,6 @@ Isso pode levar até um minuto.</translation>
         <source>An alpha version of openpilot longitudinal control can be tested, along with Experimental mode, on non-release branches.</source>
         <translation>Uma versão embrionária do controle longitudinal openpilot pode ser testada em conjunto com o modo Experimental, em branches que não sejam de produção.</translation>
     </message>
-    <message>
-        <source>Navigate on openpilot</source>
-        <translation>Navegação no openpilot</translation>
-    </message>
     <message>
         <source>Enable the openpilot longitudinal control (alpha) toggle to allow Experimental mode.</source>
         <translation>Habilite o controle longitudinal (embrionário) openpilot para permitir o modo Experimental.</translation>
@@ -1162,18 +1158,14 @@ Isso pode levar até um minuto.</translation>
         <source>End-to-End Longitudinal Control</source>
         <translation>Controle Longitudinal de Ponta a Ponta</translation>
     </message>
-    <message>
-        <source>When navigation has a destination, openpilot will input the map information into the model. This provides useful context for the model and allows openpilot to keep left or right appropriately at forks/exits. Lane change behavior is unchanged and still activated by the driver. This is an alpha quality feature; mistakes should be expected, particularly around exits and forks. These mistakes can include unintended laneline crossings, late exit taking, driving towards dividing barriers in the gore areas, etc.</source>
-        <translation>Quando a navegação tem um destino, o openpilot insere as informações do mapa no modelo. Isso fornece contexto útil para o modelo e permite que o openpilot mantenha a esquerda ou a direita apropriadamente em bifurcações/saídas. O comportamento de mudança de faixa permanece inalterado e ainda é ativado somente pelo motorista. Este é um recurso de qualidade embrionária; erros devem ser esperados, principalmente em torno de saídas e bifurcações. Esses erros podem incluir travessias não intencionais na faixa de rodagem, saída tardia, condução em direção a barreiras divisórias nas áreas de marcas de canalização, etc.</translation>
-    </message>
-    <message>
-        <source>The driving visualization will transition to the road-facing wide-angle camera at low speeds to better show some turns. The Experimental mode logo will also be shown in the top right corner. When a navigation destination is set and the driving model is using it as input, the driving path on the map will turn green.</source>
-        <translation>A visualização de condução fará a transição para a câmera grande angular voltada para a estrada em baixas velocidades para mostrar melhor algumas curvas. O logotipo do modo Experimental também será mostrado no canto superior direito. Quando um destino de navegação é definido e o modelo de condução o utiliza como entrada o caminho de condução no mapa fica verde.</translation>
-    </message>
     <message>
         <source>Standard is recommended. In aggressive mode, openpilot will follow lead cars closer and be more aggressive with the gas and brake. In relaxed mode openpilot will stay further away from lead cars. On supported cars, you can cycle through these personalities with your steering wheel distance button.</source>
         <translation>Neutro é o recomendado. No modo disputa o openpilot seguirá o carro da frente mais de perto e será mais agressivo com a aceleração e frenagem. No modo calmo o openpilot se manterá mais longe do carro da frente. Em carros compatíveis, você pode alternar esses temperamentos com o botão de distância do volante.</translation>
     </message>
+    <message>
+        <source>The driving visualization will transition to the road-facing wide-angle camera at low speeds to better show some turns. The Experimental mode logo will also be shown in the top right corner. </source>
+        <translation type="unfinished"></translation>
+    </message>
 </context>
 <context>
     <name>Updater</name>

selfdrive/ui/translations/main_th.ts

@@ -1150,18 +1150,6 @@ This may take up to a minute.</source>
         <source>End-to-End Longitudinal Control</source>
         <translation>ควบคุมเร่ง/เบรคแบบ End-to-End</translation>
     </message>
-    <message>
-        <source>Navigate on openpilot</source>
-        <translation>การนำทางบน openpilot</translation>
-    </message>
-    <message>
-        <source>When navigation has a destination, openpilot will input the map information into the model. This provides useful context for the model and allows openpilot to keep left or right appropriately at forks/exits. Lane change behavior is unchanged and still activated by the driver. This is an alpha quality feature; mistakes should be expected, particularly around exits and forks. These mistakes can include unintended laneline crossings, late exit taking, driving towards dividing barriers in the gore areas, etc.</source>
-        <translation>เมื่อการนำทางมีจุดหมายปลายทาง openpilot จะป้อนข้อมูลแผนที่เข้าไปยังโมเดล ซึ่งจะเป็นบริบทที่มีประโยชน์สำหรับโมเดลและจะทำให้ openpilot สามารถรักษาเลนซ้ายหรือขวาได้อย่างเหมาะสมบริเวณทางแยกหรือทางออก พฤติกรรมการเปลี่ยนเลนยังคงเหมือนเดิมและยังคงต้องถูกเริ่มโดยคนขับ ความสามารถนี้ยังอยู่ในระดับ alpha ซึ่งอาจะเกิดความผิดพลาดได้โดยเฉพาะบริเวณทางแยกหรือทางออก ความผิดพลาดที่อาจเกิดขึ้นได้อาจรวมถึงการข้ามเส้นแบ่งเลนโดยไม่ตั้งใจ, การเข้าช่องทางออกช้ากว่าปกติ, การขับเข้าหาแบริเออร์ในเขตปลอดภัย, ฯลฯ</translation>
-    </message>
-    <message>
-        <source>The driving visualization will transition to the road-facing wide-angle camera at low speeds to better show some turns. The Experimental mode logo will also be shown in the top right corner. When a navigation destination is set and the driving model is using it as input, the driving path on the map will turn green.</source>
-        <translation>การแสดงภาพการขับขี่จะเปลี่ยนไปใช้กล้องมุมกว้างที่หันหน้าไปทางถนนเมื่ออยู่ในความเร็วต่ำ เพื่อแสดงภาพการเลี้ยวที่ดีขึ้น โลโก้โหมดการทดลองจะแสดงที่มุมบนขวาด้วย เมื่อเป้าหมายการนำทางถูกเลือกและโมเดลการขับขี่กำลังใช้เป็นอินพุต เส้นทางการขับขี่บนแผนที่จะเปลี่ยนเป็นสีเขียว</translation>
-    </message>
     <message>
         <source>Enable the openpilot longitudinal control (alpha) toggle to allow Experimental mode.</source>
         <translation>เปิดระบบควบคุมการเร่ง/เบรคโดย openpilot (alpha) เพื่อเปิดใช้งานโหมดทดลอง</translation>
@@ -1170,6 +1158,10 @@ This may take up to a minute.</source>
         <source>Standard is recommended. In aggressive mode, openpilot will follow lead cars closer and be more aggressive with the gas and brake. In relaxed mode openpilot will stay further away from lead cars. On supported cars, you can cycle through these personalities with your steering wheel distance button.</source>
         <translation type="unfinished"></translation>
     </message>
+    <message>
+        <source>The driving visualization will transition to the road-facing wide-angle camera at low speeds to better show some turns. The Experimental mode logo will also be shown in the top right corner. </source>
+        <translation type="unfinished"></translation>
+    </message>
 </context>
 <context>
     <name>Updater</name>

selfdrive/ui/translations/main_tr.ts

@@ -1132,22 +1132,10 @@ This may take up to a minute.</source>
         <source>Let the driving model control the gas and brakes. openpilot will drive as it thinks a human would, including stopping for red lights and stop signs. Since the driving model decides the speed to drive, the set speed will only act as an upper bound. This is an alpha quality feature; mistakes should be expected.</source>
         <translation type="unfinished"></translation>
     </message>
-    <message>
-        <source>Navigate on openpilot</source>
-        <translation type="unfinished"></translation>
-    </message>
-    <message>
-        <source>When navigation has a destination, openpilot will input the map information into the model. This provides useful context for the model and allows openpilot to keep left or right appropriately at forks/exits. Lane change behavior is unchanged and still activated by the driver. This is an alpha quality feature; mistakes should be expected, particularly around exits and forks. These mistakes can include unintended laneline crossings, late exit taking, driving towards dividing barriers in the gore areas, etc.</source>
-        <translation type="unfinished"></translation>
-    </message>
     <message>
         <source>New Driving Visualization</source>
         <translation type="unfinished"></translation>
     </message>
-    <message>
-        <source>The driving visualization will transition to the road-facing wide-angle camera at low speeds to better show some turns. The Experimental mode logo will also be shown in the top right corner. When a navigation destination is set and the driving model is using it as input, the driving path on the map will turn green.</source>
-        <translation type="unfinished"></translation>
-    </message>
     <message>
         <source>Experimental mode is currently unavailable on this car since the car&apos;s stock ACC is used for longitudinal control.</source>
         <translation type="unfinished"></translation>
@@ -1168,6 +1156,10 @@ This may take up to a minute.</source>
         <source>Standard is recommended. In aggressive mode, openpilot will follow lead cars closer and be more aggressive with the gas and brake. In relaxed mode openpilot will stay further away from lead cars. On supported cars, you can cycle through these personalities with your steering wheel distance button.</source>
         <translation type="unfinished"></translation>
     </message>
+    <message>
+        <source>The driving visualization will transition to the road-facing wide-angle camera at low speeds to better show some turns. The Experimental mode logo will also be shown in the top right corner. </source>
+        <translation type="unfinished"></translation>
+    </message>
 </context>
 <context>
     <name>Updater</name>

selfdrive/ui/translations/main_zh-CHS.ts

@@ -1146,10 +1146,6 @@ This may take up to a minute.</source>
         <source>An alpha version of openpilot longitudinal control can be tested, along with Experimental mode, on non-release branches.</source>
         <translation>在正式（release）版本以外的分支上，可以测试 openpilot 纵向控制的 Alpha 版本以及实验模式。</translation>
     </message>
-    <message>
-        <source>Navigate on openpilot</source>
-        <translation>Navigate on openpilot</translation>
-    </message>
     <message>
         <source>Enable the openpilot longitudinal control (alpha) toggle to allow Experimental mode.</source>
         <translation>启用 openpilot 纵向控制（alpha）开关以允许实验模式。</translation>
@@ -1158,18 +1154,14 @@ This may take up to a minute.</source>
         <source>End-to-End Longitudinal Control</source>
         <translation>端到端纵向控制</translation>
     </message>
-    <message>
-        <source>When navigation has a destination, openpilot will input the map information into the model. This provides useful context for the model and allows openpilot to keep left or right appropriately at forks/exits. Lane change behavior is unchanged and still activated by the driver. This is an alpha quality feature; mistakes should be expected, particularly around exits and forks. These mistakes can include unintended laneline crossings, late exit taking, driving towards dividing barriers in the gore areas, etc.</source>
-        <translation>当导航有目的地时，openpilot 将输入地图信息到模型中。这为模型提供了有用的背景信息，使 openpilot 能够在叉路/出口时适当地保持左侧或右侧行驶。车道变换行为保持不变，仍由驾驶员激活。这是一个 Alpha 版的功能；可能会出现错误，特别是在出口和分叉处。这些错误可能包括意外的车道越界、晚出口、朝着分隔栏驶向安全地带等。</translation>
-    </message>
-    <message>
-        <source>The driving visualization will transition to the road-facing wide-angle camera at low speeds to better show some turns. The Experimental mode logo will also be shown in the top right corner. When a navigation destination is set and the driving model is using it as input, the driving path on the map will turn green.</source>
-        <translation>行驶画面将在低速时切换到道路朝向的广角摄像头，以更好地显示一些转弯。实验模式标志也将显示在右上角。当设置了导航目的地并且驾驶模型正在使用它作为输入时，地图上的驾驶路径将变为绿色。</translation>
-    </message>
     <message>
         <source>Standard is recommended. In aggressive mode, openpilot will follow lead cars closer and be more aggressive with the gas and brake. In relaxed mode openpilot will stay further away from lead cars. On supported cars, you can cycle through these personalities with your steering wheel distance button.</source>
         <translation type="unfinished"></translation>
     </message>
+    <message>
+        <source>The driving visualization will transition to the road-facing wide-angle camera at low speeds to better show some turns. The Experimental mode logo will also be shown in the top right corner. </source>
+        <translation type="unfinished"></translation>
+    </message>
 </context>
 <context>
     <name>Updater</name>

selfdrive/ui/translations/main_zh-CHT.ts

@@ -1146,10 +1146,6 @@ This may take up to a minute.</source>
         <source>An alpha version of openpilot longitudinal control can be tested, along with Experimental mode, on non-release branches.</source>
         <translation>在正式 (release) 版以外的分支上可以測試 openpilot 縱向控制的 Alpha 版本以及實驗模式。</translation>
     </message>
-    <message>
-        <source>Navigate on openpilot</source>
-        <translation>Navigate on openpilot</translation>
-    </message>
     <message>
         <source>Enable the openpilot longitudinal control (alpha) toggle to allow Experimental mode.</source>
         <translation>啟用 openpilot 縱向控制（alpha）切換以允許實驗模式。</translation>
@@ -1158,18 +1154,14 @@ This may take up to a minute.</source>
         <source>End-to-End Longitudinal Control</source>
         <translation>端到端縱向控制</translation>
     </message>
-    <message>
-        <source>When navigation has a destination, openpilot will input the map information into the model. This provides useful context for the model and allows openpilot to keep left or right appropriately at forks/exits. Lane change behavior is unchanged and still activated by the driver. This is an alpha quality feature; mistakes should be expected, particularly around exits and forks. These mistakes can include unintended laneline crossings, late exit taking, driving towards dividing barriers in the gore areas, etc.</source>
-        <translation>當導航有目的地時，openpilot 將把地圖資訊輸入模型中。這為模型提供了有用的背景資訊，使 openpilot 能夠在叉路/出口時適當地保持左側或右側行駛。車道變換行為保持不變，仍由駕駛員啟用。這是一個 Alpha 版的功能；可能會出現錯誤，特別是在出口和分叉處。這些錯誤可能包括意外的車道越界、晚出口、朝著分隔欄駛向分隔帶區域等。</translation>
-    </message>
-    <message>
-        <source>The driving visualization will transition to the road-facing wide-angle camera at low speeds to better show some turns. The Experimental mode logo will also be shown in the top right corner. When a navigation destination is set and the driving model is using it as input, the driving path on the map will turn green.</source>
-        <translation>行駛畫面將在低速時切換至道路朝向的廣角鏡頭，以更好地顯示一些轉彎。實驗模式圖示也將顯示在右上角。當設定了導航目的地並且行駛模型正在將其作為輸入時，地圖上的行駛路徑將變為綠色。</translation>
-    </message>
     <message>
         <source>Standard is recommended. In aggressive mode, openpilot will follow lead cars closer and be more aggressive with the gas and brake. In relaxed mode openpilot will stay further away from lead cars. On supported cars, you can cycle through these personalities with your steering wheel distance button.</source>
         <translation type="unfinished"></translation>
     </message>
+    <message>
+        <source>The driving visualization will transition to the road-facing wide-angle camera at low speeds to better show some turns. The Experimental mode logo will also be shown in the top right corner. </source>
+        <translation type="unfinished"></translation>
+    </message>
 </context>
 <context>
     <name>Updater</name>

selfdrive/ui/ui.h

@@ -94,7 +94,6 @@ typedef struct UIScene {
   float driver_pose_coss[3];
   vec3 face_kpts_draw[std::size(default_face_kpts_3d)];
 
-  bool navigate_on_openpilot = false;
   cereal::LongitudinalPersonality personality;
 
   float light_sensor;

system/hardware/tici/tests/test_power_draw.py

@@ -36,7 +36,6 @@ PROCS = [
   Proc(['modeld'], 1.12, atol=0.2, msgs=['modelV2']),
   Proc(['dmonitoringmodeld'], 0.4, msgs=['driverStateV2']),
   Proc(['encoderd'], 0.23, msgs=[]),
-  Proc(['mapsd', 'navmodeld'], 0.05, msgs=['mapRenderState', 'navModel']),
 ]