Add Dockerfile and related scripts for sunnypilot CI build pipeline

This commit introduces `Dockerfile.sunnypilot`, `.dockerignore` updates, and a new `docker_build_sp.sh` script to support building and testing sunnypilot in a Dockerized environment.

common/params_keys.h

@@ -136,7 +136,6 @@ inline static std::unordered_map<std::string, uint32_t> keys = {
     {"CustomAccShortPressIncrement", PERSISTENT | BACKUP},
     {"DeviceBootMode", PERSISTENT | BACKUP},
     {"EnableGithubRunner", PERSISTENT | BACKUP},
-    {"InteractivityTimeout", PERSISTENT | BACKUP},
     {"MaxTimeOffroad", PERSISTENT | BACKUP},
     {"Brightness", PERSISTENT | BACKUP},
     {"ModelRunnerTypeCache", CLEAR_ON_ONROAD_TRANSITION},
@@ -179,8 +178,6 @@ inline static std::unordered_map<std::string, uint32_t> keys = {
 
     // model panel params
     {"LagdToggle", PERSISTENT | BACKUP},
-    {"LagdToggleDesc", PERSISTENT},
-    {"LagdToggledelay", PERSISTENT | BACKUP},
 
     // mapd
     {"MapAdvisorySpeedLimit", CLEAR_ON_ONROAD_TRANSITION},

release/ci/install_github_runner.sh

@@ -5,7 +5,6 @@ set -e
 DEFAULT_REPO_URL="https://github.com/sunnypilot"
 START_AT_BOOT=false
 RESTORE_MODE=false
-RUNNER_VERSION="2.325.0"
 
 # Parse command line arguments
 while [[ $# -gt 0 ]]; do
@@ -109,9 +108,9 @@ setup_system_configs() {
 install_runner() {
     echo "Downloading and setting up runner..."
     cd "$RUNNER_DIR"
-    curl -o actions-runner-linux-arm64-${RUNNER_VERSION}.tar.gz -L https://github.com/actions/runner/releases/download/v${RUNNER_VERSION}/actions-runner-linux-arm64-${RUNNER_VERSION}.tar.gz
+    curl -o actions-runner-linux-arm64-2.322.0.tar.gz -L https://github.com/actions/runner/releases/download/v2.322.0/actions-runner-linux-arm64-2.322.0.tar.gz
-    sudo -u ${RUNNER_USER} tar -xzf ./actions-runner-linux-arm64-${RUNNER_VERSION}.tar.gz
+    sudo -u ${RUNNER_USER} tar -xzf ./actions-runner-linux-arm64-2.322.0.tar.gz
-    sudo rm ./actions-runner-linux-arm64-${RUNNER_VERSION}.tar.gz
+    sudo rm ./actions-runner-linux-arm64-2.322.0.tar.gz
     sudo chmod +x ./config.sh
 }
 
@@ -150,32 +149,25 @@ EOL
 }
 
 install_service() {
-    local service_name
-    if [ -f "${RUNNER_DIR}/.service" ]; then
-        service_name=$(cat "${RUNNER_DIR}/.service")
-    else
-        service_name="actions.runner.sunnypilot.$(uname -n)"
-    fi
-  
-    create_service_template
     remount_rw
-    local service_path="/etc/systemd/system/${service_name}"
     echo "Installing systemd service..."
-    if [ -f "${service_path}" ]; then
-        echo "Service ${service_path} found in systemd, we will delete it"
-        sudo rm -f "${service_path}"
-    fi
-    
     cd "$RUNNER_DIR"
     sudo ./svc.sh install $RUNNER_USER
 
     if [ "$START_AT_BOOT" = false ]; then
+        local service_name
+        if [ -f "${RUNNER_DIR}/.service" ]; then
+            service_name=$(cat "${RUNNER_DIR}/.service")
+        else
+            service_name="actions.runner.sunnypilot.$(uname -n)"
+        fi
         sudo systemctl disable "${service_name}"
     fi
     remount_ro
 }
 
 check_restore_prerequisites() {
+    local needs_restore=false
     local can_restore=false
     local service_name=""
 
@@ -197,16 +189,25 @@ check_restore_prerequisites() {
         exit 1
     fi
 
+    # Then check if restoration is needed (if either service or user is missing)
+    if ! systemctl list-unit-files "${service_name}" &>/dev/null; then
+        echo "Service ${service_name} not found in systemd"
+        needs_restore=true
+    fi
+
     if ! id "${RUNNER_USER}" &>/dev/null; then
         echo "User ${RUNNER_USER} does not exist"
+        needs_restore=true
     fi
 
     # Only proceed if we can restore AND need to restore
-    if [ "$can_restore" = true ]; then
+    if [ "$can_restore" = true ] && [ "$needs_restore" = true ]; then
-        echo "Restoration is possible"
+        echo "Restoration is needed and possible"
         return 0
     else
-        echo "No restoration possible"
+        if [ "$needs_restore" = false ]; then
+            echo "System is already properly configured (user and service exist)"
+        fi
         exit 0
     fi
 }
@@ -225,6 +226,7 @@ perform_install() {
     setup_system_configs
     install_runner
     set_directory_permissions
+    create_service_template
     configure_runner
     install_service
     echo "Installation completed successfully"

selfdrive/controls/controlsd.py

@@ -93,8 +93,7 @@ class Controls(ControlsExt):
                                            torque_params.frictionCoefficientFiltered)
 
       self.LaC.extension.update_model_v2(self.sm['modelV2'])
-      calculated_lag = self.LaC.extension.lagd_torqued_main(self.CP, self.sm['liveDelay'])
+      self.LaC.extension.update_lateral_lag(self.sm['liveDelay'].lateralDelay)
-      self.LaC.extension.update_lateral_lag(calculated_lag)
 
     long_plan = self.sm['longitudinalPlan']
     model_v2 = self.sm['modelV2']

selfdrive/controls/lib/longitudinal_planner.py

@@ -94,13 +94,9 @@ class LongitudinalPlanner(LongitudinalPlannerSP):
 
   def update(self, sm):
     self.mode = 'blended' if sm['selfdriveState'].experimentalMode else 'acc'
-    if not self.mlsim:
-      self.mpc.mode = self.mode
     LongitudinalPlannerSP.update(self, sm)
     if dec_mpc_mode := self.get_mpc_mode():
       self.mode = dec_mpc_mode
-      if not self.mlsim:
-        self.mpc.mode = dec_mpc_mode
 
     if len(sm['carControl'].orientationNED) == 3:
       accel_coast = get_coast_accel(sm['carControl'].orientationNED[1])
@@ -175,7 +171,7 @@ class LongitudinalPlanner(LongitudinalPlannerSP):
     output_a_target_e2e = sm['modelV2'].action.desiredAcceleration
     output_should_stop_e2e = sm['modelV2'].action.shouldStop
 
-    if self.mode == 'acc' or not self.mlsim:
+    if self.mode == 'acc':
       output_a_target = output_a_target_mpc
       self.output_should_stop = output_should_stop_mpc
     else:

selfdrive/locationd/torqued.py

@@ -11,8 +11,6 @@ from openpilot.common.filter_simple import FirstOrderFilter
 from openpilot.common.swaglog import cloudlog
 from openpilot.selfdrive.locationd.helpers import PointBuckets, ParameterEstimator, PoseCalibrator, Pose
 
-from openpilot.sunnypilot.livedelay.lagd_toggle import LagdToggle
-
 HISTORY = 5  # secs
 POINTS_PER_BUCKET = 1500
 MIN_POINTS_TOTAL = 4000
@@ -51,10 +49,8 @@ class TorqueBuckets(PointBuckets):
         break
 
 
-class TorqueEstimator(ParameterEstimator, LagdToggle):
+class TorqueEstimator(ParameterEstimator):
   def __init__(self, CP, decimated=False, track_all_points=False):
-    super().__init__()
-    self.CP = CP
     self.hist_len = int(HISTORY / DT_MDL)
     self.lag = 0.0
     self.track_all_points = track_all_points  # for offline analysis, without max lateral accel or max steer torque filters
@@ -180,7 +176,7 @@ class TorqueEstimator(ParameterEstimator, LagdToggle):
     elif which == "liveCalibration":
       self.calibrator.feed_live_calib(msg)
     elif which == "liveDelay":
-      self.lag = self.lagd_torqued_main(self.CP, msg)
+      self.lag = msg.lateralDelay
     # calculate lateral accel from past steering torque
     elif which == "livePose":
       if len(self.raw_points['steer_torque']) == self.hist_len:

selfdrive/modeld/modeld.py

@@ -37,8 +37,6 @@ from openpilot.selfdrive.modeld.fill_model_msg import fill_model_msg, fill_pose_
 from openpilot.selfdrive.modeld.constants import ModelConstants, Plan
 from openpilot.selfdrive.modeld.models.commonmodel_pyx import DrivingModelFrame, CLContext
 
-from openpilot.sunnypilot.livedelay.lagd_toggle import LagdToggle
-
 
 PROCESS_NAME = "selfdrive.modeld.modeld"
 SEND_RAW_PRED = os.getenv('SEND_RAW_PRED')
@@ -88,7 +86,6 @@ class ModelState:
   prev_desire: np.ndarray  # for tracking the rising edge of the pulse
 
   def __init__(self, context: CLContext):
-    self.LAT_SMOOTH_SECONDS = 0.0
     with open(VISION_METADATA_PATH, 'rb') as f:
       vision_metadata = pickle.load(f)
       self.vision_input_shapes =  vision_metadata['input_shapes']
@@ -254,8 +251,6 @@ def main(demo=False):
     CP = messaging.log_from_bytes(params.get("CarParams", block=True), car.CarParams)
   cloudlog.info("modeld got CarParams: %s", CP.brand)
 
-  modeld_lagd = LagdToggle()
-
   # TODO this needs more thought, use .2s extra for now to estimate other delays
   # TODO Move smooth seconds to action function
   long_delay = CP.longitudinalActuatorDelay + LONG_SMOOTH_SECONDS
@@ -301,7 +296,7 @@ def main(demo=False):
     is_rhd = sm["driverMonitoringState"].isRHD
     frame_id = sm["roadCameraState"].frameId
     v_ego = max(sm["carState"].vEgo, 0.)
-    lat_delay = modeld_lagd.lagd_main(CP, sm, model)
+    lat_delay = sm["liveDelay"].lateralDelay + LAT_SMOOTH_SECONDS
     lateral_control_params = np.array([v_ego, lat_delay], dtype=np.float32)
     if sm.updated["liveCalibration"] and sm.seen['roadCameraState'] and sm.seen['deviceState']:
       device_from_calib_euler = np.array(sm["liveCalibration"].rpyCalib, dtype=np.float32)

selfdrive/ui/sunnypilot/qt/offroad/settings/device_panel.cc

@@ -87,17 +87,6 @@ DevicePanelSP::DevicePanelSP(SettingsWindowSP *parent) : DevicePanel(parent) {
     params.put("DeviceBootMode", QString::number(index).toStdString());
     updateState();
   });
-
-  interactivityTimeout =  new OptionControlSP("InteractivityTimeout", tr("Interactivity Timeout"),
-                                     tr("Apply a custom timeout for settings UI."
-                                        "\nThis is the time after which settings UI closes automatically if user is not interacting with the screen."),
-                                     "", {0, 120}, 10, true, nullptr, false);
-
-  connect(interactivityTimeout, &OptionControlSP::updateLabels, [=]() {
-    updateState();
-  });
-
-  addItem(interactivityTimeout);
   
   // Brightness
   brightness = new Brightness();
@@ -209,11 +198,4 @@ void DevicePanelSP::updateState() {
     currStatus = DeviceSleepModeStatus::OFFROAD;
   }
   toggleDeviceBootMode->setDescription(deviceSleepModeDescription(currStatus));
-
-  QString timeoutValue = QString::fromStdString(params.get("InteractivityTimeout"));
-  if (timeoutValue == "0") {
-    interactivityTimeout->setLabel("DEFAULT");
-  } else {
-    interactivityTimeout->setLabel(timeoutValue + "s");
-  }
 }

selfdrive/ui/sunnypilot/qt/offroad/settings/device_panel.h

@@ -33,7 +33,6 @@ private:
   MaxTimeOffroad *maxTimeOffroad;
   ButtonParamControlSP *toggleDeviceBootMode;
   Brightness *brightness;
-  OptionControlSP *interactivityTimeout;
 
   const QString alwaysOffroadStyle = R"(
     PushButtonSP {

selfdrive/ui/sunnypilot/qt/offroad/settings/models_panel.cc

@@ -90,25 +90,11 @@ ModelsPanel::ModelsPanel(QWidget *parent) : QWidget(parent) {
   list->addItem(horizontal_line());
 
   // LiveDelay toggle
-  lagd_toggle_control = new ParamControlSP("LagdToggle", tr("Live Learning Steer Delay"), "", "../assets/offroad/icon_shell.png");
+  list->addItem(new ParamControlSP("LagdToggle",
-  lagd_toggle_control->showDescription();
+                                   tr("Live Learning Steer Delay"),
-  list->addItem(lagd_toggle_control);
+                                   tr("Enable this for the car to learn and adapt its steering response time. "
-
+                                      "Disable to use a fixed steering response time. Keeping this on provides the stock openpilot experience."),
-  // Software delay control
+                                   "../assets/offroad/icon_shell.png"));
-  delay_control = new OptionControlSP("LagdToggledelay", tr("Adjust Software Delay"),
-                                     tr("Adjust the software delay when Live Learning Steer Delay is toggled off."
-                                        "\nThe default software delay value is 0.2"),
-                                     "", {10, 30}, 1, false, nullptr, true);
-
-  connect(delay_control, &OptionControlSP::updateLabels, [=]() {
-    float value = QString::fromStdString(params.get("LagdToggledelay")).toFloat();
-    delay_control->setLabel(QString::number(value, 'f', 2) + "s");
-  });
-  connect(lagd_toggle_control, &ParamControlSP::toggleFlipped, [=](bool state) {
-    delay_control->setVisible(!state);
-  });
-  delay_control->showDescription();
-  list->addItem(delay_control);
 }
 
 QProgressBar* ModelsPanel::createProgressBar(QWidget *parent) {
@@ -304,24 +290,6 @@ void ModelsPanel::updateLabels() {
   handleBundleDownloadProgress();
   currentModelLblBtn->setEnabled(!is_onroad && !isDownloading());
   currentModelLblBtn->setValue(GetActiveModelInternalName());
-
-  // Update lagdToggle description with current value
-  QString desc = tr("Enable this for the car to learn and adapt its steering response time. "
-                   "Disable to use a fixed steering response time. Keeping this on provides the stock openpilot experience. "
-                   "The Current value is updated automatically when the vehicle is Onroad.");
-  QString current = QString::fromStdString(params.get("LagdToggleDesc", false));
-  if (!current.isEmpty()) {
-    desc += "<br><br><b><span style=\"color:#e0e0e0\">" + tr("Current:") + "</span></b> <span style=\"color:#e0e0e0\">" + current + "</span>";
-  }
-  lagd_toggle_control->setDescription(desc);
-  lagd_toggle_control->showDescription();
-
-  delay_control->setVisible(!params.getBool("LagdToggle"));
-  if (delay_control->isVisible()) {
-    float value = QString::fromStdString(params.get("LagdToggledelay")).toFloat();
-    delay_control->setLabel(QString::number(value, 'f', 2) + "s");
-    delay_control->showDescription();
-  }
 }
 
 /**

selfdrive/ui/sunnypilot/qt/offroad/settings/models_panel.h

@@ -64,8 +64,6 @@ private:
   bool is_onroad = false;
 
   ButtonControlSP *currentModelLblBtn;
-  ParamControlSP *lagd_toggle_control;
-  OptionControlSP *delay_control;
   QProgressBar *supercomboProgressBar;
   QFrame *supercomboFrame;
   QProgressBar *navigationProgressBar;

selfdrive/ui/sunnypilot/qt/widgets/controls.h

@@ -480,16 +480,6 @@ private:
     return result.toInt();
   }
 
-  int getParamValueScaled() {
-    const auto param_value = QString::fromStdString(params.get(key));
-    return static_cast<int>(param_value.toFloat() * 100);
-  }
-
-  void setParamValueScaled(const int new_value) {
-    const float scaled_value = new_value / 100.0f;
-    params.put(key, QString::number(scaled_value, 'f', 2).toStdString());
-  }
-
   // Although the method is not static, and thus has access to the value property, I prefer to be explicit about the value.
   void setParamValue(const int new_value) {
     const auto value_str = valueMap != nullptr ? valueMap->value(QString::number(new_value)) : QString::number(new_value);
@@ -498,8 +488,7 @@ private:
 
 public:
   OptionControlSP(const QString &param, const QString &title, const QString &desc, const QString &icon,
-                  const MinMaxValue &range, const int per_value_change = 1, const bool inline_layout = false,
+                  const MinMaxValue &range, const int per_value_change = 1, const bool inline_layout = false, const QMap<QString, QString> *valMap = nullptr) : AbstractControlSP_SELECTOR(title, desc, icon, nullptr), _title(title), valueMap(valMap), is_inline_layout(inline_layout) {
-                  const QMap<QString, QString> *valMap = nullptr, bool scale_float = false) : AbstractControlSP_SELECTOR(title, desc, icon, nullptr), _title(title), valueMap(valMap), is_inline_layout(inline_layout), use_float_scaling(scale_float) {
     const QString style = R"(
       QPushButton {
         border-radius: 20px;
@@ -539,7 +528,7 @@ public:
     const std::vector<QString> button_texts{"－", "＋"};
 
     key = param.toStdString();
-    value = use_float_scaling ? getParamValueScaled() : getParamValue();
+    value = getParamValue();
 
     button_group = new QButtonGroup(this);
     button_group->setExclusive(true);
@@ -557,15 +546,10 @@ public:
 
       QObject::connect(button, &QPushButton::clicked, [=]() {
         int change_value = (i == 0) ? -per_value_change : per_value_change;
-        value = use_float_scaling ? getParamValueScaled() : getParamValue();
+        value = getParamValue(); // in case it changed externally, we need to get the latest value.
         value += change_value;
         value = std::clamp(value, range.min_value, range.max_value);
-
+        setParamValue(value);
-        if (use_float_scaling) {
-          setParamValueScaled(value);
-        } else {
-          setParamValue(value);
-        }
 
         button_group->button(0)->setEnabled(!(value <= range.min_value));
         button_group->button(1)->setEnabled(!(value >= range.max_value));
@@ -658,7 +642,6 @@ private:
   const QString label_disabled_style = "font-size: 50px; font-weight: 450; color: #5C5C5C;";
 
   bool button_enabled = true;
-  bool use_float_scaling = false;
 };
 
 class PushButtonSP : public QPushButton {

selfdrive/ui/ui.cc

@@ -164,9 +164,8 @@ void Device::setAwake(bool on) {
 }
 
 void Device::resetInteractiveTimeout(int timeout) {
-  int customTimeout = QString::fromStdString(Params().get("InteractivityTimeout")).toInt();
   if (timeout == -1) {
-    timeout = customTimeout == 0 ? (ignition_on ? 10 : 30) : customTimeout;
+    timeout = (ignition_on ? 10 : 30);
   }
   interactive_timeout = timeout * UI_FREQ;
 }

sunnypilot/livedelay/lagd_toggle.py

@@ -1,51 +0,0 @@
-"""
-Copyright (c) 2021-, Haibin Wen, sunnypilot, and a number of other contributors.
-
-This file is part of sunnypilot and is licensed under the MIT License.
-See the LICENSE.md file in the root directory for more details.
-"""
-from openpilot.common.params import Params
-from openpilot.common.swaglog import cloudlog
-
-
-class LagdToggle:
-  def __init__(self):
-    self.params = Params()
-    self.lag = 0.0
-    self._last_desc = None
-
-  @property
-  def software_delay(self):
-    return float(self.params.get("LagdToggledelay", encoding='utf8'))
-
-  def _maybe_update_desc(self, desc):
-    if desc != self._last_desc:
-      self.params.put_nonblocking("LagdToggleDesc", desc)
-      self._last_desc = desc
-
-  def lagd_main(self, CP, sm, model):
-    if self.params.get_bool("LagdToggle"):
-      lateral_delay = sm["liveDelay"].lateralDelay
-      lat_smooth = model.LAT_SMOOTH_SECONDS
-      result = lateral_delay + lat_smooth
-      desc = f"live steer delay learner ({lateral_delay:.3f}s) + model smoothing filter ({lat_smooth:.3f}s) = total delay ({result:.3f}s)"
-      self._maybe_update_desc(desc)
-      return result
-
-    steer_actuator_delay = CP.steerActuatorDelay
-    lat_smooth = model.LAT_SMOOTH_SECONDS
-    delay = self.software_delay
-    result = (steer_actuator_delay + delay) + lat_smooth
-    desc = (f"Vehicle steering delay ({steer_actuator_delay:.3f}s) + software delay ({delay:.3f}s) + " +
-            f"model smoothing filter ({lat_smooth:.3f}s) = total delay ({result:.3f}s)")
-    self._maybe_update_desc(desc)
-    return result
-
-  def lagd_torqued_main(self, CP, msg):
-    if self.params.get_bool("LagdToggle"):
-      self.lag = msg.lateralDelay
-      cloudlog.debug(f"TORQUED USING LIVE DELAY: {self.lag:.3f}")
-    else:
-      self.lag = CP.steerActuatorDelay + self.software_delay
-      cloudlog.debug(f"TORQUED USING STEER ACTUATOR: {self.lag:.3f}")
-    return self.lag

sunnypilot/modeld/modeld.py

@@ -25,7 +25,7 @@ from openpilot.sunnypilot.modeld.parse_model_outputs import Parser
 from openpilot.sunnypilot.modeld.fill_model_msg import fill_model_msg, fill_pose_msg, PublishState
 from openpilot.sunnypilot.modeld.constants import ModelConstants, Plan
 from openpilot.sunnypilot.models.helpers import get_active_bundle, get_model_path, load_metadata, prepare_inputs, load_meta_constants
-from openpilot.sunnypilot.livedelay.lagd_toggle import LagdToggle
+from openpilot.sunnypilot.models.modeld_lagd import ModeldLagd
 from openpilot.sunnypilot.modeld.models.commonmodel_pyx import ModelFrame, CLContext
 
 
@@ -202,7 +202,7 @@ def main(demo=False):
 
   cloudlog.info("modeld got CarParams: %s", CP.brand)
 
-  modeld_lagd = LagdToggle()
+  modeld_lagd = ModeldLagd()
 
   # Enable lagd support for sunnypilot modeld
   long_delay = CP.longitudinalActuatorDelay + model.LONG_SMOOTH_SECONDS

sunnypilot/modeld_v2/modeld.py

@@ -23,7 +23,7 @@ from openpilot.sunnypilot.modeld_v2.models.commonmodel_pyx import DrivingModelFr
 from openpilot.sunnypilot.modeld_v2.meta_helper import load_meta_constants
 
 from openpilot.sunnypilot.models.helpers import get_active_bundle
-from openpilot.sunnypilot.livedelay.lagd_toggle import LagdToggle
+from openpilot.sunnypilot.models.modeld_lagd import ModeldLagd
 from openpilot.sunnypilot.models.runners.helpers import get_model_runner
 
 PROCESS_NAME = "selfdrive.modeld.modeld"
@@ -239,7 +239,7 @@ def main(demo=False):
   cloudlog.info("modeld got CarParams: %s", CP.brand)
 
 
-  modeld_lagd = LagdToggle()
+  modeld_lagd = ModeldLagd()
 
   # TODO Move smooth seconds to action function
   long_delay = CP.longitudinalActuatorDelay + model.LONG_SMOOTH_SECONDS

sunnypilot/models/modeld_lagd.py

@@ -0,0 +1,26 @@
+"""
+Copyright (c) 2021-, Haibin Wen, sunnypilot, and a number of other contributors.
+
+This file is part of sunnypilot and is licensed under the MIT License.
+See the LICENSE.md file in the root directory for more details.
+"""
+from openpilot.common.params import Params
+from openpilot.common.swaglog import cloudlog
+
+
+class ModeldLagd:
+  def __init__(self):
+    self.params = Params()
+
+  def lagd_main(self, CP, sm, model):
+    if self.params.get_bool("LagdToggle"):
+      lateral_delay = sm["liveDelay"].lateralDelay
+      lat_smooth = model.LAT_SMOOTH_SECONDS
+      result = lateral_delay + lat_smooth
+      cloudlog.debug(f"LAGD USING LIVE DELAY: {lateral_delay:.3f} + {lat_smooth:.3f} = {result:.3f}")
+      return result
+    steer_actuator_delay = CP.steerActuatorDelay
+    lat_smooth = model.LAT_SMOOTH_SECONDS
+    result = (steer_actuator_delay + 0.2) + lat_smooth
+    cloudlog.debug(f"LAGD USING STEER ACTUATOR: {steer_actuator_delay:.3f} + 0.2 + {lat_smooth:.3f} = {result:.3f}")
+    return result

sunnypilot/selfdrive/controls/lib/dec/constants.py

@@ -1,17 +1,25 @@
 class WMACConstants:
-  # Lead detection parameters
+  LEAD_WINDOW_SIZE = 5
-  LEAD_WINDOW_SIZE = 6  # Stable detection window
+  LEAD_PROB = 0.5
-  LEAD_PROB = 0.45  # Balanced threshold for lead detection
 
-  # Slow down detection parameters
+  SLOW_DOWN_WINDOW_SIZE = 5
-  SLOW_DOWN_WINDOW_SIZE = 5  # Responsive but stable
+  SLOW_DOWN_PROB = 0.6
-  SLOW_DOWN_PROB = 0.3  # Balanced threshold for slow down scenarios
 
-  # Optimized slow down distance curve - smooth and progressive
   SLOW_DOWN_BP = [0., 10., 20., 30., 40., 50., 55., 60.]
-  SLOW_DOWN_DIST = [32., 46., 64., 86., 108., 130., 145., 165.]
+  SLOW_DOWN_DIST = [25., 38., 55., 75., 95., 115., 130., 150.]
 
-  # Slowness detection parameters
+  SLOWNESS_WINDOW_SIZE = 12
-  SLOWNESS_WINDOW_SIZE = 10  # Stable slowness detection
+  SLOWNESS_PROB = 0.5
-  SLOWNESS_PROB = 0.55  # Clear threshold for slowness
+  SLOWNESS_CRUISE_OFFSET = 1.05
-  SLOWNESS_CRUISE_OFFSET = 1.025  # Conservative cruise speed offset
+
+  DANGEROUS_TTC_WINDOW_SIZE = 3
+  DANGEROUS_TTC = 2.3
+
+  MPC_FCW_WINDOW_SIZE = 10
+  MPC_FCW_PROB = 0.5
+
+
+class SNG_State:
+  off = 0
+  stopped = 1
+  going = 2

sunnypilot/selfdrive/controls/lib/dec/dec.py

@@ -1,133 +1,88 @@
-"""
+# The MIT License
-Copyright (c) 2021-, rav4kumar, Haibin Wen, sunnypilot, and a number of other contributors.
+#
+# Copyright (c) 2019-, Rick Lan, dragonpilot community, and a number of other of contributors.
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy
+# of this software and associated documentation files (the "Software"), to deal
+# in the Software without restriction, including without limitation the rights
+# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+# copies of the Software, and to permit persons to whom the Software is
+# furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in
+# all copies or substantial portions of the Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
+# THE SOFTWARE.
+#
+# Version = 2025-1-18
 
-This file is part of sunnypilot and is licensed under the MIT License.
+import numpy as np
-See the LICENSE.md file in the root directory for more details.
-"""
-# Version = 2025-6-30
 
 from cereal import messaging
 from opendbc.car import structs
 from numpy import interp
 from openpilot.common.params import Params
 from openpilot.common.realtime import DT_MDL
-from openpilot.sunnypilot.selfdrive.controls.lib.dec.constants import WMACConstants
+from openpilot.sunnypilot.selfdrive.controls.lib.dec.constants import WMACConstants, SNG_State
-from typing import Literal
 
 # d-e2e, from modeldata.h
 TRAJECTORY_SIZE = 33
-SET_MODE_TIMEOUT = 15
 
-# Define the valid mode types
+HIGHWAY_CRUISE_KPH = 70
-ModeType = Literal['acc', 'blended']
+
+STOP_AND_GO_FRAME = 60
+
+SET_MODE_TIMEOUT = 10
+
+V_ACC_MIN = 9.72
 
 
-class SmoothKalmanFilter:
+class GenericMovingAverageCalculator:
-  """Enhanced Kalman filter with smoothing for stable decision making."""
+  def __init__(self, window_size):
+    self.window_size = window_size
+    self.data = []
+    self.total = 0
 
-  def __init__(self, initial_value=0, measurement_noise=0.1, process_noise=0.01,
+  def add_data(self, value: float) -> None:
-               alpha=1.0, smoothing_factor=0.85):
+    if len(self.data) == self.window_size:
-    self.x = initial_value
+      self.total -= self.data.pop(0)
-    self.P = 1.0
+    self.data.append(value)
-    self.R = measurement_noise
+    self.total += value
-    self.Q = process_noise
-    self.alpha = alpha
-    self.smoothing_factor = smoothing_factor
-    self.initialized = False
-    self.history = []
-    self.max_history = 10
-    self.confidence = 0.0
 
-  def add_data(self, measurement):
+  def get_moving_average(self) -> float | None:
-    if len(self.history) >= self.max_history:
+    return None if len(self.data) == 0 else self.total / len(self.data)
-      self.history.pop(0)
-    self.history.append(measurement)
 
-    if not self.initialized:
+  def reset_data(self) -> None:
-      self.x = measurement
+    self.data = []
-      self.initialized = True
+    self.total = 0
-      self.confidence = 0.1
-      return
-
-    self.P = self.alpha * self.P + self.Q
-
-    K = self.P / (self.P + self.R)
-    effective_K = K * (1.0 - self.smoothing_factor) + self.smoothing_factor * 0.1
-
-    innovation = measurement - self.x
-    self.x = self.x + effective_K * innovation
-    self.P = (1 - effective_K) * self.P
-
-    if abs(innovation) < 0.1:
-      self.confidence = min(1.0, self.confidence + 0.05)
-    else:
-      self.confidence = max(0.1, self.confidence - 0.02)
-
-  def get_value(self):
-    return self.x if self.initialized else None
-
-  def get_confidence(self):
-    return self.confidence
-
-  def reset_data(self):
-    self.initialized = False
-    self.history = []
-    self.confidence = 0.0
 
 
-class ModeTransitionManager:
+class WeightedMovingAverageCalculator:
-  """Manages smooth transitions between driving modes with hysteresis."""
+  def __init__(self, window_size):
+    self.window_size = window_size
+    self.data = []
+    self.weights = np.linspace(1, 3, window_size)  # Linear weights, adjust as needed
 
-  def __init__(self):
+  def add_data(self, value: float) -> None:
-    self.current_mode: ModeType = 'acc'
+    if len(self.data) == self.window_size:
-    self.mode_confidence = {'acc': 1.0, 'blended': 0.0}
+      self.data.pop(0)
-    self.transition_timeout = 0
+    self.data.append(value)
-    self.min_mode_duration = 10
-    self.mode_duration = 0
-    self.emergency_override = False
 
-  def request_mode(self, mode: ModeType, confidence: float = 1.0, emergency: bool = False):
+  def get_weighted_average(self) -> float | None:
-    # Emergency override for critical situations (stops, collisions)
+    if len(self.data) == 0:
-    if emergency:
+      return None
-      self.emergency_override = True
+    weighted_sum: float = float(np.dot(self.data, self.weights[-len(self.data):]))
-      self.current_mode = mode
+    weight_total: float = float(np.sum(self.weights[-len(self.data):]))
-      self.transition_timeout = SET_MODE_TIMEOUT
+    return weighted_sum / weight_total
-      self.mode_duration = 0
-      return
 
-    self.mode_confidence[mode] = min(1.0, self.mode_confidence[mode] + 0.1 * confidence)
+  def reset_data(self) -> None:
-    for m in self.mode_confidence:
+    self.data = []
-      if m != mode:
-        self.mode_confidence[m] = max(0.0, self.mode_confidence[m] - 0.05)
-
-    # Require minimum duration in current mode (unless emergency)
-    if self.mode_duration < self.min_mode_duration and not self.emergency_override:
-      return
-
-    # Hysteresis: higher threshold for mode changes
-    confidence_threshold = 0.6 if mode != self.current_mode else 0.3  # Lower threshold for faster response
-
-    if self.mode_confidence[mode] > confidence_threshold:
-      if mode != self.current_mode and self.transition_timeout == 0:
-        self.transition_timeout = SET_MODE_TIMEOUT
-        self.current_mode = mode
-        self.mode_duration = 0
-
-  def update(self):
-    if self.transition_timeout > 0:
-      self.transition_timeout -= 1
-    self.mode_duration += 1
-
-    # Reset emergency override after some time
-    if self.emergency_override and self.mode_duration > 20:
-      self.emergency_override = False
-
-    # Gradual confidence decay
-    for mode in self.mode_confidence:
-      self.mode_confidence[mode] *= 0.98
-
-  def get_mode(self) -> ModeType:
-    return self.current_mode
 
 
 class DynamicExperimentalController:
@@ -137,59 +92,51 @@ class DynamicExperimentalController:
     self._params = params or Params()
     self._enabled: bool = self._params.get_bool("DynamicExperimentalControl")
     self._active: bool = False
+    self._mode: str = 'acc'
     self._frame: int = 0
-    self._urgency = 0.0
 
-    self._mode_manager = ModeTransitionManager()
+    # Use weighted moving average for filtering leads
-
+    self._lead_gmac = WeightedMovingAverageCalculator(window_size=WMACConstants.LEAD_WINDOW_SIZE)
-    # Smooth filters for stable decision making with faster response for critical scenarios
-    self._lead_filter = SmoothKalmanFilter(
-      measurement_noise=0.15,
-      process_noise=0.05,
-      alpha=1.02,
-      smoothing_factor=0.8
-    )
-
-    self._slow_down_filter = SmoothKalmanFilter(
-      measurement_noise=0.1,
-      process_noise=0.1,
-      alpha=1.05,
-      smoothing_factor=0.7
-    )
-
-    self._slowness_filter = SmoothKalmanFilter(
-      measurement_noise=0.1,
-      process_noise=0.06,
-      alpha=1.015,
-      smoothing_factor=0.92
-    )
-
-    self._mpc_fcw_filter = SmoothKalmanFilter(
-      measurement_noise=0.2,
-      process_noise=0.1,
-      alpha=1.1,
-      smoothing_factor=0.5
-    )
     self._has_lead_filtered = False
-    self._has_slow_down = False
+    self._has_lead_filtered_prev = False
-    self._has_slowness = False
+
-    self._has_mpc_fcw = False
+    self._slow_down_gmac = WeightedMovingAverageCalculator(window_size=WMACConstants.SLOW_DOWN_WINDOW_SIZE)
-    self._v_ego_kph = 0.0
+    self._has_slow_down: bool = False
-    self._v_cruise_kph = 0.0
+    self._slow_down_confidence: float = 0.0
+
+    self._has_blinkers = False
+
+    self._slowness_gmac = WeightedMovingAverageCalculator(window_size=WMACConstants.SLOWNESS_WINDOW_SIZE)
+    self._has_slowness: bool = False
+
+    self._has_nav_instruction = False
+
+    self._dangerous_ttc_gmac = WeightedMovingAverageCalculator(window_size=WMACConstants.DANGEROUS_TTC_WINDOW_SIZE)
+    self._has_dangerous_ttc: bool = False
+
+    self._v_ego_kph = 0.
+    self._v_cruise_kph = 0.
+
+    self._has_lead = False
+
     self._has_standstill = False
+    self._has_standstill_prev = False
+
+    self._sng_transit_frame = 0
+    self._sng_state = SNG_State.off
+
+    self._mpc_fcw_gmac = WeightedMovingAverageCalculator(window_size=WMACConstants.MPC_FCW_WINDOW_SIZE)
+    self._has_mpc_fcw: bool = False
     self._mpc_fcw_crash_cnt = 0
-    self._standstill_count = 0
+
-    # debug
+    self._set_mode_timeout = 0
-    self._endpoint_x = float('inf')
-    self._expected_distance = 0.0
-    self._trajectory_valid = False
 
   def _read_params(self) -> None:
     if self._frame % int(1. / DT_MDL) == 0:
       self._enabled = self._params.get_bool("DynamicExperimentalControl")
 
   def mode(self) -> str:
-    return self._mode_manager.get_mode()
+    return str(self._mode)
 
   def enabled(self) -> bool:
     return self._enabled
@@ -197,9 +144,46 @@ class DynamicExperimentalController:
   def active(self) -> bool:
     return self._active
 
-  def set_mpc_fcw_crash_cnt(self) -> None:
+  @staticmethod
-    """Set MPC FCW crash count"""
+  def _anomaly_detection(recent_data: list[float], threshold: float = 2.0, context_check: bool = True) -> bool:
-    self._mpc_fcw_crash_cnt = self._mpc.crash_cnt
+    """
+    Basic anomaly detection using standard deviation.
+    """
+    if len(recent_data) < 5:
+      return False
+    mean: float = float(np.mean(recent_data))
+    std_dev: float = float(np.std(recent_data))
+    anomaly: bool = bool(recent_data[-1] > mean + threshold * std_dev)
+
+    # Context check to ensure repeated anomaly
+    if context_check:
+      return np.count_nonzero(np.array(recent_data) > mean + threshold * std_dev) > 1
+    return anomaly
+
+  def _adaptive_slowdown_threshold(self) -> float:
+    """
+    Adapts the slow-down threshold based on vehicle speed and recent behavior.
+    """
+    slowdown_scaling_factor: float = (1.0 + 0.03 * np.log(1 + len(self._slow_down_gmac.data)))
+    adaptive_threshold: float = float(
+      interp(self._v_ego_kph, WMACConstants.SLOW_DOWN_BP, WMACConstants.SLOW_DOWN_DIST) * slowdown_scaling_factor
+    )
+    return adaptive_threshold
+
+  def _smoothed_lead_detection(self, lead_prob: float, smoothing_factor: float = 0.2):
+    """
+    Smoothing the lead detection to avoid erratic behavior.
+    """
+    lead_filtering: float = (1 - smoothing_factor) * self._has_lead_filtered + smoothing_factor * lead_prob
+    return lead_filtering > WMACConstants.LEAD_PROB
+
+  def _adaptive_lead_prob_threshold(self) -> float:
+    """
+    Adapts lead probability threshold based on driving conditions.
+    """
+    if self._v_ego_kph > HIGHWAY_CRUISE_KPH:
+      return float(WMACConstants.LEAD_PROB + 0.1)  # Increase the threshold on highways
+    return float(WMACConstants.LEAD_PROB)
 
   def _update_calculations(self, sm: messaging.SubMaster) -> None:
     car_state = sm['carState']
@@ -208,168 +192,186 @@ class DynamicExperimentalController:
 
     self._v_ego_kph = car_state.vEgo * 3.6
     self._v_cruise_kph = car_state.vCruise
+    self._has_lead = lead_one.status
     self._has_standstill = car_state.standstill
 
-    # standstill detection
+    # fcw detection
-    if self._has_standstill:
+    self._mpc_fcw_gmac.add_data(self._mpc_fcw_crash_cnt > 0)
-      self._standstill_count = min(20, self._standstill_count + 1)
+    if _mpc_fcw_weighted_average := self._mpc_fcw_gmac.get_weighted_average():
+      self._has_mpc_fcw = _mpc_fcw_weighted_average > WMACConstants.MPC_FCW_PROB
     else:
-      self._standstill_count = max(0, self._standstill_count - 1)
+      self._has_mpc_fcw = False
 
-    # Lead detection
+    # nav enable detection
-    self._lead_filter.add_data(float(lead_one.status))
+    # self._has_nav_instruction = md.navEnabledDEPRECATED and maneuver_distance / max(car_state.vEgo, 1) < 13
-    lead_value = self._lead_filter.get_value() or 0.0
-    self._has_lead_filtered = lead_value > WMACConstants.LEAD_PROB
 
-    # MPC FCW detection
+    # lead detection with smoothing
-    fcw_filtered_value = self._mpc_fcw_filter.get_value() or 0.0
+    self._lead_gmac.add_data(lead_one.status)
-    self._mpc_fcw_filter.add_data(float(self._mpc_fcw_crash_cnt > 0))
+    self._has_lead_filtered = (self._lead_gmac.get_weighted_average() or -1.) > WMACConstants.LEAD_PROB
-    self._has_mpc_fcw = fcw_filtered_value > 0.5
+    #lead_prob = self._lead_gmac.get_weighted_average() or 0
+    #self._has_lead_filtered = self._smoothed_lead_detection(lead_prob)
 
-    # Slow down detection
+    # adaptive slow down detection
-    self._calculate_slow_down(md)
+    adaptive_threshold = self._adaptive_slowdown_threshold()
+    slow_down_trigger = len(md.orientation.x) == len(md.position.x) == TRAJECTORY_SIZE and md.position.x[TRAJECTORY_SIZE - 1] < adaptive_threshold
+    self._slow_down_gmac.add_data(slow_down_trigger)
+    if _has_slow_down_weighted_average := self._slow_down_gmac.get_weighted_average():
+      self._has_slow_down = _has_slow_down_weighted_average > WMACConstants.SLOW_DOWN_PROB
+      self._slow_down_confidence = _has_slow_down_weighted_average  # Store confidence level
+    else:
+      self._has_slow_down = False
+      self._slow_down_confidence = 0.0  # No confidence if no slowdown
 
-    # Slowness detection
+    # anomaly detection for slow down events
-    if not (self._standstill_count > 5) and not self._has_slow_down:
+    if self._anomaly_detection(self._slow_down_gmac.data):
-      current_slowness = float(self._v_ego_kph <= (self._v_cruise_kph * WMACConstants.SLOWNESS_CRUISE_OFFSET))
+      self._slow_down_confidence *= 0.85  # Reduce confidence
-      self._slowness_filter.add_data(current_slowness)
+      self._has_slow_down = self._slow_down_confidence > WMACConstants.SLOW_DOWN_PROB
-      slowness_value = self._slowness_filter.get_value() or 0.0
 
-      # Hysteresis for slowness
+    # blinker detection
-      threshold = WMACConstants.SLOWNESS_PROB * (0.8 if self._has_slowness else 1.1)
+    self._has_blinkers = car_state.leftBlinker or car_state.rightBlinker
-      self._has_slowness = slowness_value > threshold
 
-  def _calculate_slow_down(self, md):
+    # sng detection
-    """Calculate urgency based on trajectory endpoint vs expected distance."""
+    if self._has_standstill:
+      self._sng_state = SNG_State.stopped
+      self._sng_transit_frame = 0
+    else:
+      if self._sng_transit_frame == 0:
+        if self._sng_state == SNG_State.stopped:
+          self._sng_state = SNG_State.going
+          self._sng_transit_frame = STOP_AND_GO_FRAME
+        elif self._sng_state == SNG_State.going:
+          self._sng_state = SNG_State.off
+      elif self._sng_transit_frame > 0:
+        self._sng_transit_frame -= 1
 
-    # Reset to safe defaults
+    # slowness detection
-    urgency = 0.0
+    if not self._has_standstill:
-    self._endpoint_x = float('inf')
+      self._slowness_gmac.add_data(self._v_ego_kph <= (self._v_cruise_kph * WMACConstants.SLOWNESS_CRUISE_OFFSET))
-    self._trajectory_valid = False
+      if _slowness_weighted_average := self._slowness_gmac.get_weighted_average():
+        self._has_slowness = _slowness_weighted_average > WMACConstants.SLOWNESS_PROB
+      else:
+        self._has_slowness = False
 
-    #Require exact trajectory size
+    # dangerous TTC detection
-    position_valid = len(md.position.x) == TRAJECTORY_SIZE
+    if not self._has_lead_filtered and self._has_lead_filtered_prev:
-    orientation_valid = len(md.orientation.x) == TRAJECTORY_SIZE
+      self._dangerous_ttc_gmac.reset_data()
+      self._has_dangerous_ttc = False
 
-    if not (position_valid and orientation_valid):
+    if self._has_lead and car_state.vEgo >= 0.01:
-      # Invalid trajectory - this itself might indicate a stop scenario
+      self._dangerous_ttc_gmac.add_data(lead_one.dRel / car_state.vEgo)
-      # Apply moderate urgency for incomplete trajectories at speed
-      if self._v_ego_kph > 20.0:
-        urgency = 0.3
 
-      self._slow_down_filter.add_data(urgency)
+    if _dangerous_ttc_weighted_average := self._dangerous_ttc_gmac.get_weighted_average():
-      urgency_filtered = self._slow_down_filter.get_value() or 0.0
+      self._has_dangerous_ttc = _dangerous_ttc_weighted_average <= WMACConstants.DANGEROUS_TTC
-      self._has_slow_down = urgency_filtered > WMACConstants.SLOW_DOWN_PROB
+    else:
-      self._urgency = urgency_filtered
+      self._has_dangerous_ttc = False
-      return
 
-    # We have a valid full trajectory
+    # keep prev values
-    self._trajectory_valid = True
+    self._has_standstill_prev = self._has_standstill
-
+    self._has_lead_filtered_prev = self._has_lead_filtered
-    # Use the exact endpoint (33rd point, index 32)
-    endpoint_x = md.position.x[TRAJECTORY_SIZE - 1]
-    self._endpoint_x = endpoint_x
-
-    # Get expected distance based on current speed using tuned constants
-    expected_distance = interp(self._v_ego_kph,
-                               WMACConstants.SLOW_DOWN_BP,
-                               WMACConstants.SLOW_DOWN_DIST)
-    self._expected_distance = expected_distance
-
-    # Calculate urgency based on trajectory shortage
-    if endpoint_x < expected_distance:
-      shortage = expected_distance - endpoint_x
-      shortage_ratio = shortage / expected_distance
-
-      # Base urgency on shortage ratio
-      urgency = min(1.0, shortage_ratio * 2.0)
-
-      # Increase urgency for very short trajectories (imminent stops)
-      critical_distance = expected_distance * 0.3
-      if endpoint_x < critical_distance:
-        urgency = min(1.0, urgency * 2.0)
-
-      # Speed-based urgency adjustment
-      if self._v_ego_kph > 25.0:
-        speed_factor = 1.0 + (self._v_ego_kph - 25.0) / 80.0
-        urgency = min(1.0, urgency * speed_factor)
-
-    # Apply filtering but with less smoothing for stops
-    self._slow_down_filter.add_data(urgency)
-    urgency_filtered = self._slow_down_filter.get_value() or 0.0
-
-    # Update state with lower threshold for better stop detection
-    self._has_slow_down = urgency_filtered > (WMACConstants.SLOW_DOWN_PROB * 0.8)
-    self._urgency = urgency_filtered
 
   def _radarless_mode(self) -> None:
-    """Radarless mode decision logic with emergency handling."""
+    # when mpc fcw crash prob is high
-
+    # use blended to slow down quickly
-    # EMERGENCY: MPC FCW - immediate blended mode
     if self._has_mpc_fcw:
-      self._mode_manager.request_mode('blended', confidence=1.0, emergency=True)
+      self._set_mode('blended')
       return
 
-    # Standstill: use blended
+    # Nav enabled and distance to upcoming turning is 300 or below
-    if self._standstill_count > 3:
+    # if self._has_nav_instruction:
-      self._mode_manager.request_mode('blended', confidence=0.9)
+    #  self._set_mode('blended')
+    #  return
+
+    # when blinker is on and speed is driving below V_ACC_MIN: blended
+    # we don't want it to switch mode at higher speed, blended may trigger hard brake
+    # if self._has_blinkers and self._v_ego_kph < V_ACC_MIN:
+    #  self._set_mode('blended')
+    #  return
+
+    # when at highway cruise and SNG: blended
+    # ensuring blended mode is used because acc is bad at catching SNG lead car
+    # especially those who accel very fast and then brake very hard.
+    # if self._sng_state == SNG_State.going and self._v_cruise_kph >= V_ACC_MIN:
+    #  self._set_mode('blended')
+    #  return
+
+    # when standstill: blended
+    # in case of lead car suddenly move away under traffic light, acc mode won't brake at traffic light.
+    if self._has_standstill:
+      self._set_mode('blended')
       return
 
-    # Slow down scenarios: emergency for high urgency, normal for lower urgency
+    # when detecting slow down scenario: blended
+    # e.g. traffic light, curve, stop sign etc.
     if self._has_slow_down:
-      if self._urgency > 0.7:
+      self._set_mode('blended')
-        # Emergency: immediate blended mode for high urgency stops
-        self._mode_manager.request_mode('blended', confidence=1.0, emergency=True)
-      else:
-        # Normal: blended with urgency-based confidence
-        confidence = min(1.0, self._urgency * 1.5)
-        self._mode_manager.request_mode('blended', confidence=confidence)
       return
 
-    # Driving slow: use ACC (but not if actively slowing down)
+    # when detecting lead slow down: blended
-    if self._has_slowness and not self._has_slow_down:
+    # use blended for higher braking capability
-      self._mode_manager.request_mode('acc', confidence=0.8)
+    if self._has_dangerous_ttc:
+      self._set_mode('blended')
       return
 
-    # Default: ACC
+    # car driving at speed lower than set speed: acc
-    self._mode_manager.request_mode('acc', confidence=0.7)
+    if self._has_slowness:
+      self._set_mode('acc')
+      return
+
+    self._set_mode('acc')
 
   def _radar_mode(self) -> None:
-    """Radar mode with emergency handling."""
+    # when mpc fcw crash prob is high
-
+    # use blended to slow down quickly
-    # EMERGENCY: MPC FCW - immediate blended mode
     if self._has_mpc_fcw:
-      self._mode_manager.request_mode('blended', confidence=1.0, emergency=True)
+      self._set_mode('blended')
       return
 
-    # If lead detected and not in standstill: always use ACC
+    # If there is a filtered lead, the vehicle is not in standstill, and the lead vehicle's yRel meets the condition,
-    if self._has_lead_filtered and not (self._standstill_count > 3):
+    if self._has_lead_filtered and not self._has_standstill:
-      self._mode_manager.request_mode('acc', confidence=1.0)
+      self._set_mode('acc')
       return
 
-    # Slow down scenarios: emergency for high urgency, normal for lower urgency
+    # when blinker is on and speed is driving below V_ACC_MIN: blended
+    # we don't want it to switch mode at higher speed, blended may trigger hard brake
+    # if self._has_blinkers and self._v_ego_kph < V_ACC_MIN:
+    #  self._set_mode('blended')
+    #  return
+
+    # when standstill: blended
+    # in case of lead car suddenly move away under traffic light, acc mode won't brake at traffic light.
+    if self._has_standstill:
+      self._set_mode('blended')
+      return
+
+    # when detecting slow down scenario: blended
+    # e.g. traffic light, curve, stop sign etc.
     if self._has_slow_down:
-      if self._urgency > 0.7:
+      self._set_mode('blended')
-        # Emergency: immediate blended mode for high urgency stops
-        self._mode_manager.request_mode('blended', confidence=1.0, emergency=True)
-      else:
-        # Normal: blended with urgency-based confidence
-        confidence = min(1.0, self._urgency * 1.3)
-        self._mode_manager.request_mode('blended', confidence=confidence)
       return
 
-    # Standstill: use blended
+    # car driving at speed lower than set speed: acc
-    if self._standstill_count > 3:
+    if self._has_slowness:
-      self._mode_manager.request_mode('blended', confidence=0.9)
+      self._set_mode('acc')
       return
 
-    # Driving slow: use ACC (but not if actively slowing down)
+    # Nav enabled and distance to upcoming turning is 300 or below
-    if self._has_slowness and not self._has_slow_down:
+    # if self._has_nav_instruction:
-      self._mode_manager.request_mode('acc', confidence=0.8)
+    #  self._set_mode('blended')
-      return
+    #  return
 
-    # Default: ACC
+    self._set_mode('acc')
-    self._mode_manager.request_mode('acc', confidence=0.7)
+
+  def set_mpc_fcw_crash_cnt(self) -> None:
+    self._mpc_fcw_crash_cnt = self._mpc.crash_cnt
+
+  def _set_mode(self, mode: str) -> None:
+    if self._set_mode_timeout == 0:
+      self._mode = mode
+      if mode == 'blended':
+        self._set_mode_timeout = SET_MODE_TIMEOUT
+
+    if self._set_mode_timeout > 0:
+      self._set_mode_timeout -= 1
 
   def update(self, sm: messaging.SubMaster) -> None:
     self._read_params()
@@ -383,6 +385,6 @@ class DynamicExperimentalController:
     else:
       self._radar_mode()
 
-    self._mode_manager.update()
     self._active = sm['selfdriveState'].experimentalMode and self._enabled
+
     self._frame += 1

sunnypilot/selfdrive/controls/lib/dec/tests/pytest_dynamic_controller.py

@@ -1,94 +1,248 @@
 import pytest
+import numpy as np
+from openpilot.common.params import Params
 
-from openpilot.sunnypilot.selfdrive.controls.lib.dec.dec import DynamicExperimentalController
+from openpilot.sunnypilot.selfdrive.controls.lib.dec.constants import WMACConstants, SNG_State
+from openpilot.sunnypilot.selfdrive.controls.lib.dec.dec import DynamicExperimentalController, TRAJECTORY_SIZE, STOP_AND_GO_FRAME
 
-class MockLeadOne:
+class MockInterp:
-  def __init__(self, status=0.0):
+  def __call__(self, x, xp, fp):
-    self.status = status
+    return np.interp(x, xp, fp)
-
-class MockRadarState:
-  def __init__(self, status=0.0):
-    self.leadOne = MockLeadOne(status=status)
 
 class MockCarState:
-  def __init__(self, vEgo=0.0, vCruise=0.0, standstill=False):
+  def __init__(self, v_ego=0., standstill=False, left_blinker=False, right_blinker=False):
-    self.vEgo = vEgo
+    self.vEgo = v_ego
-    self.vCruise = vCruise
     self.standstill = standstill
+    self.leftBlinker = left_blinker
+    self.rightBlinker = right_blinker
+
+class MockLeadOne:
+  def __init__(self, status=False, d_rel=0):
+    self.status = status
+    self.dRel = d_rel
 
 class MockModelData:
-  def __init__(self, valid=True):
+  def __init__(self, x_vals=None, positions=None):
-    size = 33 if valid else 10  # incomplete if invalid
+    self.orientation = type('Orientation', (), {'x': x_vals})()
-    self.position = type("Pos", (), {"x": [0.0] * size})()
+    self.position = type('Position', (), {'x': positions})()
-    self.orientation = type("Ori", (), {"x": [0.0] * size})()
 
-class MockSelfDriveState:
+class MockControlState:
-  def __init__(self, experimentalMode=False):
+  def __init__(self, v_cruise=0):
-    self.experimentalMode = experimentalMode
+    self.vCruise = v_cruise
-
-class MockParams:
-  def get_bool(self, name):
-    return True
 
 @pytest.fixture
-def default_sm():
+def interp(monkeypatch):
-  sm = {
+  mock_interp = MockInterp()
-    'carState': MockCarState(vEgo=10.0, vCruise=20.0),
+  monkeypatch.setattr('numpy.interp', mock_interp)
-    'radarState': MockRadarState(status=1.0),
+  return mock_interp
-    'modelV2': MockModelData(valid=True),
-    'selfdriveState': MockSelfDriveState(experimentalMode=True),
-  }
-  return sm
 
 @pytest.fixture
-def mock_cp():
+def controller(interp):
-  class CP:
+  params = Params()
-    radarUnavailable = False
+  params.put_bool("DynamicExperimentalControl", True)
-  return CP()
+  return DynamicExperimentalController()
 
-@pytest.fixture
+def test_initial_state(controller):
-def mock_mpc():
+  """Test initial state of the controller"""
-  class MPC:
+  assert controller._mode == 'acc'
-    crash_cnt = 0
+  assert not controller._has_lead
-  return MPC()
+  assert not controller._has_standstill
+  assert controller._sng_state == SNG_State.off
+  assert not controller._has_lead_filtered
+  assert not controller._has_slow_down
+  assert not controller._has_dangerous_ttc
+  assert not controller._has_mpc_fcw
 
-# Fake Kalman Filter that always returns a given value
+@pytest.mark.parametrize("has_radar", [True, False], ids=["with_radar", "without_radar"])
-class FakeKalman:
+def test_standstill_detection(controller, has_radar):
-  def __init__(self, value=1.0):
+  """Test standstill detection and state transitions"""
-    self.value = value
+  car_state = MockCarState(standstill=True)
-  def add_data(self, v): pass
+  lead_one = MockLeadOne()
-  def get_value(self): return self.value
+  md = MockModelData(x_vals=[0] * TRAJECTORY_SIZE, positions=[150] * TRAJECTORY_SIZE)
-  def get_confidence(self): return 1.0
+  controls_state = MockControlState()
-  def reset_data(self): pass
 
-def test_initial_mode_is_acc(mock_cp, mock_mpc):
+  # Test transition to standstill
-  controller = DynamicExperimentalController(mock_cp, mock_mpc, params=MockParams())
+  controller.update(not has_radar, car_state, lead_one, md, controls_state)
-  assert controller.mode() == "acc"
+  assert controller._sng_state == SNG_State.stopped
+  assert controller.get_mpc_mode() == 'blended'
 
-def test_standstill_triggers_blended(mock_cp, mock_mpc, default_sm):
+  # Test transition from standstill to moving
-  controller = DynamicExperimentalController(mock_cp, mock_mpc, params=MockParams())
+  car_state.standstill = False
-  default_sm['carState'].standstill = True
+  controller.update(not has_radar, car_state, lead_one, md, controls_state)
-  for _ in range(10):
+  assert controller._sng_state == SNG_State.going
-    controller.update(default_sm)
-  assert controller.mode() == "blended"
 
-def test_emergency_blended_on_fcw(mock_cp, mock_mpc, default_sm):
+  # Test complete transition to normal driving
-  controller = DynamicExperimentalController(mock_cp, mock_mpc, params=MockParams())
+  for _ in range(STOP_AND_GO_FRAME + 1):
-  mock_mpc.crash_cnt = 1  # simulate FCW
+    controller.update(not has_radar, car_state, lead_one, md, controls_state)
-  for _ in range(2):
+  assert controller._sng_state == SNG_State.off
-    controller.update(default_sm)
-  assert controller.mode() == "blended"
 
-def test_radarless_slowdown_triggers_blended(mock_cp, mock_mpc, default_sm):
+@pytest.mark.parametrize("has_radar", [True, False], ids=["with_radar", "without_radar"])
-  mock_cp.radarUnavailable = True
+def test_lead_detection(controller, has_radar):
-  controller = DynamicExperimentalController(mock_cp, mock_mpc, params=MockParams())
+  """Test lead vehicle detection and filtering"""
+  car_state = MockCarState(v_ego=20)  # 72 kph
+  lead_one = MockLeadOne(status=True, d_rel=50)  # Safe distance
+  md = MockModelData(x_vals=[0] * TRAJECTORY_SIZE, positions=[150] * TRAJECTORY_SIZE)
+  controls_state = MockControlState(v_cruise=72)
 
-  # Force conditions to simulate slowdown
+  # Let moving average stabilize
-  controller._slow_down_filter = FakeKalman(value=1.0)  # Ensure urgency triggers slowdown
+  for _ in range(WMACConstants.LEAD_WINDOW_SIZE + 1):
-  controller._v_ego_kph = 35.0
+    controller.update(not has_radar, car_state, lead_one, md, controls_state)
-  default_sm['modelV2'] = MockModelData(valid=False)  # Incomplete trajectory
 
-  for _ in range(3):
+  assert controller._has_lead_filtered
-    controller.update(default_sm)
+  expected_mode = 'acc' if has_radar else 'blended'
+  assert controller.get_mpc_mode() == expected_mode
 
-  assert controller.mode() == "blended"
+  # Test lead loss detection
+  lead_one.status = False
+  for _ in range(WMACConstants.LEAD_WINDOW_SIZE + 1):
+    controller.update(not has_radar, car_state, lead_one, md, controls_state)
+
+  assert not controller._has_lead_filtered
+
+@pytest.mark.parametrize("has_radar", [True, False], ids=["with_radar", "without_radar"])
+def test_slow_down_detection(controller, has_radar):
+  """Test slow down detection based on trajectory"""
+  car_state = MockCarState(v_ego=10/3.6)  # 10 kph
+  lead_one = MockLeadOne()
+  x_vals = [0] * TRAJECTORY_SIZE
+  positions = [20] * TRAJECTORY_SIZE  # Position within slow down threshold
+  md = MockModelData(x_vals=x_vals, positions=positions)
+  controls_state = MockControlState(v_cruise=30)
+
+  # Test slow down detection
+  for _ in range(WMACConstants.SLOW_DOWN_WINDOW_SIZE + 1):
+    controller.update(not has_radar, car_state, lead_one, md, controls_state)
+
+  assert controller._has_slow_down
+  assert controller.get_mpc_mode() == 'blended'
+
+  # Test slow down recovery
+  positions = [200] * TRAJECTORY_SIZE  # Position outside slow down threshold
+  md = MockModelData(x_vals=x_vals, positions=positions)
+  for _ in range(WMACConstants.SLOW_DOWN_WINDOW_SIZE + 1):
+    controller.update(not has_radar, car_state, lead_one, md, controls_state)
+
+  assert not controller._has_slow_down
+
+@pytest.mark.parametrize("has_radar", [True, False], ids=["with_radar", "without_radar"])
+def test_dangerous_ttc_detection(controller, has_radar):
+  """Test Time-To-Collision detection and handling"""
+  car_state = MockCarState(v_ego=10)  # 36 kph
+  lead_one = MockLeadOne(status=True)
+  md = MockModelData(x_vals=[0] * TRAJECTORY_SIZE, positions=[150] * TRAJECTORY_SIZE)
+  controls_state = MockControlState(v_cruise=36)
+
+  # First establish normal conditions with lead
+  lead_one.dRel = 100  # Safe distance
+  for _ in range(WMACConstants.LEAD_WINDOW_SIZE + 1):  # First establish lead detection
+    controller.update(not has_radar, car_state, lead_one, md, controls_state)
+
+  assert controller._has_lead_filtered  # Verify lead is detected
+
+  # Now test dangerous TTC detection
+  lead_one.dRel = 10  # 10m distance - should trigger dangerous TTC
+  # TTC = dRel/vEgo = 10/10 = 1s (which is less than DANGEROUS_TTC = 2.3s)
+
+  # Need to update multiple times to allow the weighted average to stabilize
+  for _ in range(WMACConstants.DANGEROUS_TTC_WINDOW_SIZE * 2):
+    controller.update(not has_radar, car_state, lead_one, md, controls_state)
+
+  assert controller._has_dangerous_ttc, "TTC of 1s should be considered dangerous"
+  expected_mode = 'acc' if has_radar else 'blended'
+  assert controller.get_mpc_mode() == expected_mode, f"Should be in [{expected_mode}] mode with dangerous TTC"
+
+@pytest.mark.parametrize("has_radar", [True, False], ids=["with_radar", "without_radar"])
+def test_mode_transitions(controller, has_radar):
+  """Test comprehensive mode transitions under different conditions"""
+  # Initialize with normal driving conditions
+  car_state = MockCarState(v_ego=25)  # 90 kph
+  lead_one = MockLeadOne(status=False)
+  md = MockModelData(x_vals=[0] * TRAJECTORY_SIZE, positions=[200] * TRAJECTORY_SIZE)
+  controls_state = MockControlState(v_cruise=100)
+
+  def stabilize_filters():
+    """Helper to let all moving averages stabilize"""
+    for _ in range(max(WMACConstants.LEAD_WINDOW_SIZE, WMACConstants.SLOW_DOWN_WINDOW_SIZE,
+                       WMACConstants.DANGEROUS_TTC_WINDOW_SIZE, WMACConstants.MPC_FCW_WINDOW_SIZE) + 1):
+      controller.update(not has_radar, car_state, lead_one, md, controls_state)
+
+  # Test 1: Normal driving -> ACC mode
+  stabilize_filters()
+  assert controller.get_mpc_mode() == 'acc', "Should be in ACC mode under normal driving conditions"
+
+  # Test 2: Standstill -> Blended mode
+  car_state.standstill = True
+  controller.update(not has_radar, car_state, lead_one, md, controls_state)
+  assert controller.get_mpc_mode() == 'blended', "Should be in blended mode during standstill"
+
+  # Test 3: Lead car appears -> ACC mode
+  car_state = MockCarState(v_ego=20)  # Reset car state
+  lead_one.status = True
+  lead_one.dRel = 50  # Safe distance
+  stabilize_filters()
+  assert not controller._has_dangerous_ttc, "Should not have dangerous TTC"
+  assert controller.get_mpc_mode() == 'acc', "Should be in ACC mode with safe lead distance"
+
+  # Test 4: Dangerous TTC -> Blended mode
+  car_state = MockCarState(v_ego=20)  # 72 kph
+  lead_one.status = True
+  lead_one.dRel = 50  # First establish normal lead detection
+
+  # First establish lead detection
+  for _ in range(WMACConstants.LEAD_WINDOW_SIZE + 1):
+    controller.update(not has_radar, car_state, lead_one, md, controls_state)
+
+  assert controller._has_lead_filtered  # Verify lead is detected
+
+  # Now create dangerous TTC condition
+  lead_one.dRel = 20  # This creates a TTC of 1s, well below DANGEROUS_TTC
+
+  for _ in range(WMACConstants.DANGEROUS_TTC_WINDOW_SIZE * 2):
+    controller.update(not has_radar, car_state, lead_one, md, controls_state)
+
+  assert controller._has_dangerous_ttc, "Should detect dangerous TTC condition"
+  expected_mode = 'acc' if has_radar else 'blended'
+  assert controller.get_mpc_mode() == expected_mode, f"Should be in [{expected_mode}] mode with dangerous TTC"
+
+@pytest.mark.parametrize("has_radar", [True, False], ids=["with_radar", "without_radar"])
+def test_mpc_fcw_handling(controller, has_radar):
+  """Test MPC FCW crash count handling and mode transitions"""
+  car_state = MockCarState(v_ego=20)
+  lead_one = MockLeadOne()
+  md = MockModelData(x_vals=[0] * TRAJECTORY_SIZE, positions=[150] * TRAJECTORY_SIZE)
+  controls_state = MockControlState(v_cruise=72)
+
+  # Test FCW activation
+  controller.set_mpc_fcw_crash_cnt(5)
+  for _ in range(WMACConstants.MPC_FCW_WINDOW_SIZE + 1):
+    controller.update(not has_radar, car_state, lead_one, md, controls_state)
+
+  assert controller._has_mpc_fcw
+  assert controller.get_mpc_mode() == 'blended'
+
+  # Test FCW recovery
+  controller.set_mpc_fcw_crash_cnt(0)
+  for _ in range(WMACConstants.MPC_FCW_WINDOW_SIZE + 1):
+    controller.update(not has_radar, car_state, lead_one, md, controls_state)
+
+  assert not controller._has_mpc_fcw
+
+def test_radar_unavailable_handling(controller):
+  """Test behavior transitions between radar available and unavailable states"""
+  car_state = MockCarState(v_ego=27.78)  # 100 kph
+  lead_one = MockLeadOne(status=True, d_rel=50)
+  md = MockModelData(x_vals=[0] * TRAJECTORY_SIZE, positions=[150] * TRAJECTORY_SIZE)
+  controls_state = MockControlState(v_cruise=100)
+
+  # Test with radar available
+  for _ in range(WMACConstants.LEAD_WINDOW_SIZE + 1):
+    controller.update(False, car_state, lead_one, md, controls_state)
+  radar_mode = controller.get_mpc_mode()
+
+  # Test with radar unavailable
+  for _ in range(WMACConstants.LEAD_WINDOW_SIZE + 1):
+    controller.update(True, car_state, lead_one, md, controls_state)
+  radarless_mode = controller.get_mpc_mode()
+
+  assert radar_mode is not None
+  assert radarless_mode is not None

sunnypilot/selfdrive/controls/lib/latcontrol_torque_ext_base.py

@@ -10,8 +10,6 @@ import numpy as np
 from openpilot.selfdrive.controls.lib.drive_helpers import CONTROL_N
 from openpilot.selfdrive.modeld.constants import ModelConstants
 
-from openpilot.sunnypilot.livedelay.lagd_toggle import LagdToggle
-
 LAT_PLAN_MIN_IDX = 5
 LATERAL_LAG_MOD = 0.0 # seconds, modifies how far in the future we look ahead for the lateral plan
 
@@ -43,9 +41,8 @@ def get_lookahead_value(future_vals, current_val):
   return min_val
 
 
-class LatControlTorqueExtBase(LagdToggle):
+class LatControlTorqueExtBase:
   def __init__(self, lac_torque, CP, CP_SP):
-    LagdToggle.__init__(self)
     self.model_v2 = None
     self.model_valid = False
     self.use_steering_angle = lac_torque.use_steering_angle

sunnypilot/selfdrive/controls/lib/longitudinal_planner.py

@@ -8,7 +8,6 @@ See the LICENSE.md file in the root directory for more details.
 from cereal import messaging, custom
 from opendbc.car import structs
 from openpilot.sunnypilot.selfdrive.controls.lib.dec.dec import DynamicExperimentalController
-from openpilot.sunnypilot.models.helpers import get_active_bundle
 
 DecState = custom.LongitudinalPlanSP.DynamicExperimentalControl.DynamicExperimentalControlState
 
@@ -16,12 +15,6 @@ DecState = custom.LongitudinalPlanSP.DynamicExperimentalControl.DynamicExperimen
 class LongitudinalPlannerSP:
   def __init__(self, CP: structs.CarParams, mpc):
     self.dec = DynamicExperimentalController(CP, mpc)
-    model_bundle = get_active_bundle()
-    self.generation = model_bundle.generation if model_bundle is not None else None
-
-  @property
-  def mlsim(self) -> bool:
-    return self.generation == 11
 
   def get_mpc_mode(self) -> str | None:
     if not self.dec.active():

sunnypilot/selfdrive/controls/lib/param_store.py

@@ -22,16 +22,14 @@ class ParamStore:
 
     self.keys = universal_params + brand_params
     self.values = {}
-    self.cached_params_list: list[capnp.lib.capnp._DynamicStructBuilder] | None = None
 
   def update(self, params: Params) -> None:
-    old_values = dict(self.values)
     self.values = {k: params.get(k, encoding='utf8') or "0" for k in self.keys}
-    if old_values != self.values:
-      self.cached_params_list = None
 
   def publish(self) -> list[capnp.lib.capnp._DynamicStructBuilder]:
-    if self.cached_params_list is None:
+    params_list: list[capnp.lib.capnp._DynamicStructBuilder] = []
-      # TODO-SP: Why are we doing a list instead of a dictionary here?
+
-      self.cached_params_list = [custom.CarControlSP.Param(key=k, value=self.values[k]) for k in self.keys]
+    for k in self.keys:
-    return self.cached_params_list
+      params_list.append(custom.CarControlSP.Param(key=k, value=self.values[k]))
+
+    return params_list

system/manager/manager.py

@@ -56,9 +56,7 @@ def manager_init() -> None:
     ("DeviceBootMode", "0"),
     ("DynamicExperimentalControl", "0"),
     ("HyundaiLongitudinalTuning", "0"),
-    ("InteractivityTimeout", "0"),
     ("LagdToggle", "1"),
-    ("LagdToggledelay", "0.2"),
     ("Mads", "1"),
     ("MadsMainCruiseAllowed", "1"),
     ("MadsSteeringMode", "0"),