agnos 10.1.1

AHHHHHHH

cereal/log.capnp

@@ -1048,6 +1048,8 @@ struct ModelDataV2 {
 
   struct Action {
     desiredCurvature @0 :Float32;
+    desiredAcceleration @1 :Float32;
+    shouldStop @2 :Bool;
   }
 }
 

frogpilot/assets/model_manager.py

@@ -13,7 +13,7 @@ from openpilot.frogpilot.assets.download_functions import GITLAB_URL, download_f
 from openpilot.frogpilot.common.frogpilot_utilities import delete_file
 from openpilot.frogpilot.common.frogpilot_variables import DEFAULT_CLASSIC_MODEL, DEFAULT_MODEL, DEFAULT_TINYGRAD_MODEL, MODELS_PATH, params, params_default, params_memory
 
-VERSION = "v14"
+VERSION = "v15"
 
 CANCEL_DOWNLOAD_PARAM = "CancelModelDownload"
 DOWNLOAD_PROGRESS_PARAM = "ModelDownloadProgress"

frogpilot/common/frogpilot_utilities.py

@@ -19,7 +19,6 @@ from cereal import log
 from openpilot.common.realtime import DT_DMON, DT_HW
 from openpilot.selfdrive.car.toyota.carcontroller import LOCK_CMD
 from openpilot.system.hardware import HARDWARE
-from openpilot.system.manager.process_config import managed_processes
 from panda import Panda
 
 from openpilot.frogpilot.common.frogpilot_variables import EARTH_RADIUS, KONIK_PATH, MAPD_PATH, MAPS_PATH, params, params_memory
@@ -39,6 +38,10 @@ locks = {
   "update_openpilot": threading.Lock(),
 }
 
+def get_managed_processes():
+    from openpilot.system.manager.process_config import managed_processes
+    return managed_processes
+
 def run_thread_with_lock(name, target, args=(), report=True):
   if not running_threads.get(name, threading.Thread()).is_alive():
     with locks[name]:
@@ -170,7 +173,7 @@ def restart_processes(sm):
 
   if not any(ps.ignitionLine or ps.ignitionCan for ps in sm["pandaStates"] if ps.pandaType != log.PandaState.PandaType.unknown):
     for name in ["mapd", "ui"]:
-      managed_processes[name].stop(block=False, retry=False)
+      get_managed_processes()[name].stop(block=False, retry=False)
 
 def run_cmd(cmd, success_message, fail_message, report=True):
   try:

frogpilot/common/frogpilot_variables.py

@@ -58,8 +58,8 @@ DEFAULT_MODEL = "national-public-radio"
 DEFAULT_MODEL_NAME = "National Public Radio 👀📡"
 DEFAULT_MODEL_VERSION = "v6"
 
-DEFAULT_TINYGRAD_MODEL = "filet-o-fish"
-DEFAULT_TINYGRAD_MODEL_NAME = "Filet-O-Fish 👀📡"
+DEFAULT_TINYGRAD_MODEL = "tomb-raider"
+DEFAULT_TINYGRAD_MODEL_NAME = "Vikander 👀📡"
 DEFAULT_TINYGRAD_MODEL_VERSION = "v8"
 
 EXCLUDED_KEYS = {
@@ -373,12 +373,16 @@ misc_tuning_levels: list[tuple[str, str | bytes, int]] = [
   ("WheelControls", "", 2)
 ]
 
+
+def scale_threshold(v_ego):
+  return 0.0 if v_ego > 31.3 else np.interp(v_ego, [0, 17.9, 26.8, 35.8, 44.7], [0.63, 0.63, 0.65, 0.95, 0.95])
+
 class FrogPilotVariables:
   def __init__(self):
     self.frogpilot_toggles = get_frogpilot_toggles(block=False)
     self.tuning_levels = {key: lvl for key, _, lvl in frogpilot_default_params + misc_tuning_levels}
 
-    self.short_branch = get_build_metadata().channel
+    self.short_branch = "FrogPilot-Staging"
     self.development_branch = self.short_branch == "FrogPilot-Development"
     self.release_branch = self.short_branch == "FrogPilot"
     self.staging_branch = self.short_branch == "FrogPilot-Staging"
@@ -388,7 +392,7 @@ class FrogPilotVariables:
     self.frogpilot_toggles.frogs_go_moo = Path("/persist/frogsgomoo.py").is_file()
     self.frogpilot_toggles.block_user = self.development_branch and not self.frogpilot_toggles.frogs_go_moo
 
-    self.not_vetted = Path("/data/openpilot/not_vetted").is_file()
+    self.not_vetted = False
 
     self.frogpilot_toggles.use_konik_server = params.get_bool("UseKonikServer")
     self.frogpilot_toggles.use_konik_server |= self.not_vetted

frogpilot/controls/lib/conditional_experimental_mode.py

@@ -2,7 +2,7 @@
 from openpilot.common.filter_simple import FirstOrderFilter
 from openpilot.common.realtime import DT_MDL
 
-from openpilot.frogpilot.common.frogpilot_variables import CITY_SPEED_LIMIT, CRUISING_SPEED, THRESHOLD, params_memory
+from openpilot.frogpilot.common.frogpilot_variables import CITY_SPEED_LIMIT, CRUISING_SPEED, THRESHOLD, params_memory, scale_threshold
 
 class ConditionalExperimentalMode:
   def __init__(self, FrogPilotPlanner):
@@ -53,7 +53,7 @@ class ConditionalExperimentalMode:
       self.status_value = 8
       return True
 
-    if frogpilot_toggles.conditional_lead and self.slow_lead_detected:
+    if frogpilot_toggles.conditional_lead and self.slow_lead_detected and v_ego <= 29.1:
       self.status_value = 9 if self.frogpilot_planner.lead_one.vLead < 1 else 10
       return True
 
@@ -69,7 +69,7 @@ class ConditionalExperimentalMode:
 
   def update_conditions(self, frogpilotCarState, v_ego, frogpilot_toggles):
     self.curve_detection(v_ego, frogpilot_toggles)
-    self.slow_lead(frogpilot_toggles)
+    self.slow_lead(frogpilot_toggles, v_ego)
     self.stop_sign_and_light(frogpilotCarState, v_ego, frogpilot_toggles)
 
   def curve_detection(self, v_ego, frogpilot_toggles):
@@ -78,13 +78,14 @@ class ConditionalExperimentalMode:
     self.curvature_filter.update(self.frogpilot_planner.road_curvature_detected or curve_active)
     self.curve_detected = self.curvature_filter.x >= THRESHOLD and v_ego > CRUISING_SPEED
 
-  def slow_lead(self, frogpilot_toggles):
+  def slow_lead(self, frogpilot_toggles, v_ego):
+    v_lead = self.frogpilot_planner.lead_one.vLead
     if self.frogpilot_planner.tracking_lead:
       slower_lead = frogpilot_toggles.conditional_slower_lead and self.frogpilot_planner.frogpilot_following.slower_lead
-      stopped_lead = frogpilot_toggles.conditional_stopped_lead and self.frogpilot_planner.lead_one.vLead < 1
-
+      stopped_lead = frogpilot_toggles.conditional_stopped_lead and v_lead < 1
+      lead_threshold = scale_threshold(v_ego)
       self.slow_lead_filter.update(slower_lead or stopped_lead)
-      self.slow_lead_detected = self.slow_lead_filter.x >= THRESHOLD
+      self.slow_lead_detected = self.slow_lead_filter.x >= lead_threshold
     else:
       self.slow_lead_filter.x = 0
       self.slow_lead_detected = False

frogpilot/controls/lib/frogpilot_acceleration.py

@@ -1,6 +1,43 @@
 #!/usr/bin/env python3
 import numpy as np
 
+def cubic_interp(x, xp, fp):
+     """Cubic interpolation using NumPy's native operations for speed."""
+     # Boundary conditions
+     if x <= xp[0]:
+         return fp[0]
+     elif x >= xp[-1]:
+         return fp[-1]
+ 
+     # Find interval
+     i = np.searchsorted(xp, x) - 1
+     i = max(0, min(i, len(xp)-2))  # clamp the index
+ 
+     # Normalized position
+     t = (x - xp[i]) / float(xp[i+1] - xp[i])
+ 
+     # Hermite cubic formula
+     return fp[i]*(1 - 3*t**2 + 2*t**3) + fp[i+1]*(3*t**2 - 2*t**3)
+ 
+def akima_interp(x, xp, fp):
+     """Akima-inspired interpolation with reduced overshoot characteristics."""
+     if x <= xp[0]:
+         return fp[0]
+     elif x >= xp[-1]:
+         return fp[-1]
+ 
+     i = np.searchsorted(xp, x) - 1
+     i = max(0, min(i, len(xp)-2))  # clamp the index
+ 
+     t = (x - xp[i]) / float(xp[i+1] - xp[i])
+ 
+     # Quintic polynomial to reduce overshoot
+     t2 = t*t
+     t4 = t2*t2
+     t3 = t2*t
+     return (fp[i]*(1 - 10*t3 + 15*t4 - 6*t3*t2)
+             + fp[i+1]*(10*t3 - 15*t4 + 6*t3*t2))
+ 
 from openpilot.selfdrive.controls.lib.longitudinal_planner import A_CRUISE_MIN, get_max_accel
 
 from openpilot.frogpilot.common.frogpilot_variables import CITY_SPEED_LIMIT
@@ -11,26 +48,26 @@ A_CRUISE_MIN_SPORT = A_CRUISE_MIN * 2
                        # MPH = [0.0,  11,  22,  34,  45,  56,  89]
 A_CRUISE_MAX_BP_CUSTOM =       [0.0,  5., 10., 15., 20., 25., 40.]
 A_CRUISE_MAX_VALS_ECO =        [2.0, 1.5, 1.0, 0.8, 0.6, 0.4, 0.2]
-A_CRUISE_MAX_VALS_SPORT =      [3.0, 2.5, 2.0, 1.5, 1.0, 0.8, 0.6]
-A_CRUISE_MAX_VALS_SPORT_PLUS = [4.0, 3.5, 3.0, 2.5, 2.0, 1.5, 1.0]
+A_CRUISE_MAX_VALS_SPORT =      [1.5, 1.5, 1.25, 1.5, 1.5, 1.5, 2.0]
+A_CRUISE_MAX_VALS_SPORT_PLUS = [2.5, 2.5, 3.0, 2.5, 2.5, 2.5, 2.5]
 
 def get_max_accel_eco(v_ego):
-  return float(np.interp(v_ego, A_CRUISE_MAX_BP_CUSTOM, A_CRUISE_MAX_VALS_ECO))
+  return float(akima_interp(v_ego, A_CRUISE_MAX_BP_CUSTOM, A_CRUISE_MAX_VALS_ECO))
 
 def get_max_accel_sport(v_ego):
-  return float(np.interp(v_ego, A_CRUISE_MAX_BP_CUSTOM, A_CRUISE_MAX_VALS_SPORT))
+  return float(akima_interp(v_ego, A_CRUISE_MAX_BP_CUSTOM, A_CRUISE_MAX_VALS_SPORT))
 
 def get_max_accel_sport_plus(v_ego):
-  return float(np.interp(v_ego, A_CRUISE_MAX_BP_CUSTOM, A_CRUISE_MAX_VALS_SPORT_PLUS))
+  return float(akima_interp(v_ego, A_CRUISE_MAX_BP_CUSTOM, A_CRUISE_MAX_VALS_SPORT_PLUS))
 
 def get_max_accel_low_speeds(max_accel, v_cruise):
-  return float(np.interp(v_cruise, [0., CITY_SPEED_LIMIT / 2, CITY_SPEED_LIMIT], [max_accel / 4, max_accel / 2, max_accel]))
+  return float(akima_interp(v_cruise, [0., CITY_SPEED_LIMIT / 2, CITY_SPEED_LIMIT], [max_accel / 4, max_accel / 2, max_accel]))
 
 def get_max_accel_ramp_off(max_accel, v_cruise, v_ego):
-  return float(np.interp(v_cruise - v_ego, [0., 1., 5., 10.], [0., 0.5, 1.0, max_accel]))
+  return float(akima_interp(v_cruise - v_ego, [0., 1., 5., 10.], [0., 0.5, 1.0, max_accel]))
 
 def get_max_allowed_accel(v_ego):
-  return float(np.interp(v_ego, [0., 5., 20.], [4.0, 4.0, 2.0]))  # ISO 15622:2018
+  return float(akima_interp(v_ego, [0., 5., 20.], [4.0, 4.0, 2.0]))  # ISO 15622:2018
 
 class FrogPilotAcceleration:
   def __init__(self, FrogPilotPlanner):

frogpilot/system/fleetmanager/templates/layout.html

@@ -40,7 +40,7 @@
     text-align: center;
   }
 </style>
-  <title>FrogPilot: {% block title %}{% endblock %}</title>
+  <title>StarPilot: {% block title %}{% endblock %}</title>
 </head>
 <body>
   <nav class="navbar navbar-fixed-top navbar-expand-sm navbar-dark bg-dark">

frogpilot/tinygrad_modeld/fill_model_msg.py

@@ -56,7 +56,7 @@ def fill_lane_line_meta(builder, lane_lines, lane_line_probs):
   builder.rightProb = lane_line_probs[2]
 
 def fill_model_msg(base_msg: capnp._DynamicStructBuilder, extended_msg: capnp._DynamicStructBuilder,
-                   net_output_data: dict[str, np.ndarray], v_ego: float, delay: float,
+                   net_output_data: dict[str, np.ndarray], action: log.ModelDataV2.Action,
                    publish_state: PublishState, vipc_frame_id: int, vipc_frame_id_extra: int,
                    frame_id: int, frame_drop: float, timestamp_eof: int, model_execution_time: float,
                    valid: bool) -> None:
@@ -71,7 +71,8 @@ def fill_model_msg(base_msg: capnp._DynamicStructBuilder, extended_msg: capnp._D
   driving_model_data.frameIdExtra = vipc_frame_id_extra
   driving_model_data.frameDropPerc = frame_drop_perc
   driving_model_data.modelExecutionTime = model_execution_time
-  driving_model_data.action.desiredCurvature = float(net_output_data['desired_curvature'][0,0])
+
+  driving_model_data.action = action
 
   modelV2 = extended_msg.modelV2
   modelV2.frameId = vipc_frame_id
@@ -89,17 +90,17 @@ def fill_model_msg(base_msg: capnp._DynamicStructBuilder, extended_msg: capnp._D
   fill_xyzt(modelV2.orientationRate, ModelConstants.T_IDXS, *net_output_data['plan'][0,:,Plan.ORIENTATION_RATE].T)
 
   # temporal pose
-  temporal_pose = modelV2.temporalPose
-  temporal_pose.trans = net_output_data['sim_pose'][0,:ModelConstants.POSE_WIDTH//2].tolist()
-  temporal_pose.transStd = net_output_data['sim_pose_stds'][0,:ModelConstants.POSE_WIDTH//2].tolist()
-  temporal_pose.rot = net_output_data['sim_pose'][0,ModelConstants.POSE_WIDTH//2:].tolist()
-  temporal_pose.rotStd = net_output_data['sim_pose_stds'][0,ModelConstants.POSE_WIDTH//2:].tolist()
+  #temporal_pose = modelV2.temporalPose
+  #temporal_pose.trans = net_output_data['sim_pose'][0,:ModelConstants.POSE_WIDTH//2].tolist()
+  #temporal_pose.transStd = net_output_data['sim_pose_stds'][0,:ModelConstants.POSE_WIDTH//2].tolist()
+  #temporal_pose.rot = net_output_data['sim_pose'][0,ModelConstants.POSE_WIDTH//2:].tolist()
+  #temporal_pose.rotStd = net_output_data['sim_pose_stds'][0,ModelConstants.POSE_WIDTH//2:].tolist()
 
   # poly path
   fill_xyz_poly(driving_model_data.path, ModelConstants.POLY_PATH_DEGREE, *net_output_data['plan'][0,:,Plan.POSITION].T)
 
-  # lateral planning
-  modelV2.action.desiredCurvature = float(net_output_data['desired_curvature'][0,0])
+  # action
+  modelV2.action = action
 
   # times at X_IDXS of edges and lines aren't used
   LINE_T_IDXS: list[float] = []

frogpilot/tinygrad_modeld/parse_model_outputs.py

@@ -88,6 +88,12 @@ class Parser:
     self.parse_mdn('pose', outs, in_N=0, out_N=0, out_shape=(ModelConstants.POSE_WIDTH,))
     self.parse_mdn('wide_from_device_euler', outs, in_N=0, out_N=0, out_shape=(ModelConstants.WIDE_FROM_DEVICE_WIDTH,))
     self.parse_mdn('road_transform', outs, in_N=0, out_N=0, out_shape=(ModelConstants.POSE_WIDTH,))
+    self.parse_mdn('lane_lines', outs, in_N=0, out_N=0, out_shape=(ModelConstants.NUM_LANE_LINES,ModelConstants.IDX_N,ModelConstants.LANE_LINES_WIDTH))
+    self.parse_mdn('road_edges', outs, in_N=0, out_N=0, out_shape=(ModelConstants.NUM_ROAD_EDGES,ModelConstants.IDX_N,ModelConstants.LANE_LINES_WIDTH))
+    self.parse_mdn('lead', outs, in_N=ModelConstants.LEAD_MHP_N, out_N=ModelConstants.LEAD_MHP_SELECTION,
+                   out_shape=(ModelConstants.LEAD_TRAJ_LEN,ModelConstants.LEAD_WIDTH))
+    for k in ['lead_prob', 'lane_lines_prob']:
+      self.parse_binary_crossentropy(k, outs)
     self.parse_categorical_crossentropy('desire_pred', outs, out_shape=(ModelConstants.DESIRE_PRED_LEN,ModelConstants.DESIRE_PRED_WIDTH))
     self.parse_binary_crossentropy('meta', outs)
     return outs
@@ -95,17 +101,10 @@ class Parser:
   def parse_policy_outputs(self, outs: dict[str, np.ndarray]) -> dict[str, np.ndarray]:
     self.parse_mdn('plan', outs, in_N=ModelConstants.PLAN_MHP_N, out_N=ModelConstants.PLAN_MHP_SELECTION,
                    out_shape=(ModelConstants.IDX_N,ModelConstants.PLAN_WIDTH))
-    self.parse_mdn('lane_lines', outs, in_N=0, out_N=0, out_shape=(ModelConstants.NUM_LANE_LINES,ModelConstants.IDX_N,ModelConstants.LANE_LINES_WIDTH))
-    self.parse_mdn('road_edges', outs, in_N=0, out_N=0, out_shape=(ModelConstants.NUM_ROAD_EDGES,ModelConstants.IDX_N,ModelConstants.LANE_LINES_WIDTH))
-    self.parse_mdn('sim_pose', outs, in_N=0, out_N=0, out_shape=(ModelConstants.POSE_WIDTH,))
-    self.parse_mdn('lead', outs, in_N=ModelConstants.LEAD_MHP_N, out_N=ModelConstants.LEAD_MHP_SELECTION,
-                   out_shape=(ModelConstants.LEAD_TRAJ_LEN,ModelConstants.LEAD_WIDTH))
     if 'lat_planner_solution' in outs:
       self.parse_mdn('lat_planner_solution', outs, in_N=0, out_N=0, out_shape=(ModelConstants.IDX_N,ModelConstants.LAT_PLANNER_SOLUTION_WIDTH))
     if 'desired_curvature' in outs:
       self.parse_mdn('desired_curvature', outs, in_N=0, out_N=0, out_shape=(ModelConstants.DESIRED_CURV_WIDTH,))
-    for k in ['lead_prob', 'lane_lines_prob']:
-      self.parse_binary_crossentropy(k, outs)
     self.parse_categorical_crossentropy('desire_state', outs, out_shape=(ModelConstants.DESIRE_PRED_WIDTH,))
     return outs
 

frogpilot/tinygrad_modeld/tinygrad_modeld.py

@@ -20,19 +20,21 @@ from msgq.visionipc import VisionIpcClient, VisionStreamType, VisionBuf
 from openpilot.common.swaglog import cloudlog
 from openpilot.common.params import Params
 from openpilot.common.filter_simple import FirstOrderFilter
-from openpilot.common.realtime import config_realtime_process
+from openpilot.common.realtime import config_realtime_process, DT_MDL
 from openpilot.common.transformations.camera import DEVICE_CAMERAS
 from openpilot.common.transformations.model import get_warp_matrix
 from openpilot.system import sentry
 from openpilot.selfdrive.car.car_helpers import get_demo_car_params
 from openpilot.selfdrive.controls.lib.desire_helper import DesireHelper
+from openpilot.selfdrive.controls.lib.drive_helpers import get_accel_from_plan_tomb_raider, smooth_value, get_curvature_from_plan
 from openpilot.frogpilot.tinygrad_modeld.parse_model_outputs import Parser
 from openpilot.frogpilot.tinygrad_modeld.fill_model_msg import fill_model_msg, fill_pose_msg, PublishState
-from openpilot.frogpilot.tinygrad_modeld.constants import ModelConstants
+from openpilot.frogpilot.tinygrad_modeld.constants import ModelConstants, Plan
 from openpilot.frogpilot.tinygrad_modeld.models.commonmodel_pyx import DrivingModelFrame, CLContext
 
 from openpilot.frogpilot.common.frogpilot_variables import get_frogpilot_toggles
 
+
 PROCESS_NAME = "frogpilot.tinygrad_modeld.tinygrad_modeld"
 SEND_RAW_PRED = os.getenv('SEND_RAW_PRED')
 
@@ -41,6 +43,30 @@ POLICY_PKL_PATH = Path(__file__).parent / 'models/driving_policy_tinygrad.pkl'
 VISION_METADATA_PATH = Path(__file__).parent / 'models/driving_vision_metadata.pkl'
 POLICY_METADATA_PATH = Path(__file__).parent / 'models/driving_policy_metadata.pkl'
 
+LAT_SMOOTH_SECONDS = 0.2
+LONG_SMOOTH_SECONDS = 0.2
+MIN_LAT_CONTROL_SPEED = 0.3
+
+
+def get_action_from_model(model_output: dict[str, np.ndarray], prev_action: log.ModelDataV2.Action,
+                          lat_action_t: float, long_action_t: float, v_ego: float) -> log.ModelDataV2.Action:
+    plan = model_output['plan'][0]
+    desired_accel, should_stop = get_accel_from_plan_tomb_raider(plan[:,Plan.VELOCITY][:,0],
+                                                                 plan[:,Plan.ACCELERATION][:,0],
+                                                                 ModelConstants.T_IDXS,
+                                                                 action_t=long_action_t)
+    desired_accel = smooth_value(desired_accel, prev_action.desiredAcceleration, LONG_SMOOTH_SECONDS)
+
+    desired_curvature = model_output['desired_curvature'][0, 0]
+    if v_ego > MIN_LAT_CONTROL_SPEED:
+      desired_curvature = smooth_value(desired_curvature, prev_action.desiredCurvature, LAT_SMOOTH_SECONDS)
+    else:
+      desired_curvature = prev_action.desiredCurvature
+
+    return log.ModelDataV2.Action(desiredCurvature=float(desired_curvature),
+                                  desiredAcceleration=float(desired_accel),
+                                  shouldStop=bool(should_stop))
+
 class FrameMeta:
   frame_id: int = 0
   timestamp_sof: int = 0
@@ -148,7 +174,7 @@ class ModelState:
     # TODO model only uses last value now
     self.full_prev_desired_curv[0,:-1] = self.full_prev_desired_curv[0,1:]
     self.full_prev_desired_curv[0,-1,:] = policy_outputs_dict['desired_curvature'][0, :]
-    self.numpy_inputs['prev_desired_curv'][:] = self.full_prev_desired_curv[0, self.temporal_idxs]
+    self.numpy_inputs['prev_desired_curv'][:] = 0*self.full_prev_desired_curv[0, self.temporal_idxs]
 
     combined_outputs_dict = {**vision_outputs_dict, **policy_outputs_dict}
     if SEND_RAW_PRED:
@@ -223,7 +249,10 @@ def main(demo=False):
   cloudlog.info("tinygrad_modeld got CarParams: %s", CP.carName)
 
   # TODO this needs more thought, use .2s extra for now to estimate other delays
-  steer_delay = CP.steerActuatorDelay + .2
+  # TODO Move smooth seconds to action function
+  lat_delay = CP.steerActuatorDelay + .2 + LAT_SMOOTH_SECONDS
+  long_delay = CP.longitudinalActuatorDelay + LONG_SMOOTH_SECONDS
+  prev_action = log.ModelDataV2.Action()
 
   DH = DesireHelper()
 
@@ -268,7 +297,7 @@ def main(demo=False):
     is_rhd = sm["driverMonitoringState"].isRHD
     frame_id = sm["roadCameraState"].frameId
     v_ego = max(sm["carState"].vEgo, 0.)
-    lateral_control_params = np.array([v_ego, steer_delay], dtype=np.float32)
+    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)
       dc = DEVICE_CAMERAS[(str(sm['deviceState'].deviceType), str(sm['roadCameraState'].sensor))]
@@ -311,7 +340,10 @@ def main(demo=False):
       modelv2_send = messaging.new_message('modelV2')
       drivingdata_send = messaging.new_message('drivingModelData')
       posenet_send = messaging.new_message('cameraOdometry')
-      fill_model_msg(drivingdata_send, modelv2_send, model_output, v_ego, steer_delay,
+
+      action = get_action_from_model(model_output, prev_action, lat_delay + DT_MDL, long_delay + DT_MDL, v_ego)
+      prev_action = action
+      fill_model_msg(drivingdata_send, modelv2_send, model_output, action,
                      publish_state, meta_main.frame_id, meta_extra.frame_id, frame_id,
                      frame_drop_ratio, meta_main.timestamp_eof, model_execution_time, live_calib_seen)
 

launch_chffrplus.sh

@@ -1,5 +1,6 @@
 #!/usr/bin/bash
 
+
 if [ -z "$BASEDIR" ]; then
   BASEDIR="/data/openpilot"
 fi

launch_env.sh

@@ -7,7 +7,7 @@ export OPENBLAS_NUM_THREADS=1
 export VECLIB_MAXIMUM_THREADS=1
 
 if [ -z "$AGNOS_VERSION" ]; then
-  export AGNOS_VERSION="10.1"
+  export AGNOS_VERSION="10.1.1"
 fi
 
 export STAGING_ROOT="/data/safe_staging"

opendbc/gm_global_a_powertrain_generated.dbc

@@ -82,6 +82,12 @@ VAL_TABLE_ HandsOffSWDetectionMode 2 "Failed" 1 "Enabled" 0 "Disabled" ;
 
 BO_ 189 EBCMRegenPaddle: 7 K17_EBCM
  SG_ RegenPaddle : 7|4@0+ (1,0) [0|0] ""  NEO
+ SG_ Byte1 : 8|8@1+ (1,0) [0|255] "" NEO
+ SG_ Byte2 : 16|8@1+ (1,0) [0|255] "" NEO
+ SG_ Byte3 : 24|8@1+ (1,0) [0|255] "" NEO
+ SG_ Byte4 : 32|8@1+ (1,0) [0|255] "" NEO
+ SG_ Byte5 : 40|8@1+ (1,0) [0|255] "" NEO
+ SG_ Byte6 : 48|8@1+ (1,0) [0|255] "" NEO
 
 BO_ 190 ECMAcceleratorPos: 6 K20_ECM
  SG_ BrakePedalPos : 15|8@0+ (1,0) [0|0] "sticky"  NEO
@@ -192,10 +198,15 @@ BO_ 500 SportMode: 6 XXX
  SG_ SportMode : 15|1@0+ (1,0) [0|1] "" XXX
 
 BO_ 501 ECMPRDNL2: 8 K20_ECM
- SG_ TransmissionState : 48|4@1+ (1,0) [0|7] "" NEO
+ SG_ Byte0 : 0|8@1+ (1,0) [0|255] "" NEO
+ SG_ Byte1 : 8|8@1+ (1,0) [0|255] "" NEO
+ SG_ Byte2 : 16|8@1+ (1,0) [0|255] "" NEO
  SG_ PRNDL2 : 27|4@0+ (1,0) [0|255] "" NEO
+ SG_ Byte4 : 32|8@1+ (1,0) [0|255] "" NEO
  SG_ ManualMode : 41|1@0+ (1,0) [0|1] "" NEO
-
+ SG_ TransmissionState : 48|4@1+ (1,0) [0|7] "" NEO
+ SG_ Byte7 : 56|8@1+ (1,0) [0|255] "" NEO
+ 
 BO_ 532 BRAKE_RELATED: 6 XXX
  SG_ UserBrakePressure : 0|9@0+ (1,0) [0|511] "" XXX
 
@@ -370,6 +381,6 @@ VAL_ 715 GasRegenCmdActive 1 "Active" 0 "Inactive" ;
 VAL_ 320 Intellibeam 1 "Active" 0 "Inactive" ;
 VAL_ 320 HighBeamsActive 1 "Active" 0 "Inactive" ;
 VAL_ 320 HighBeamsTemporary 1 "Active" 0 "Inactive" ;
-VAL_ 501 PRNDL2 6 "L" 4 "D" 3 "N" 2 "R" 1 "P" 0 "Shifting";
+VAL_ 501 PRNDL2 7 "L2" 6 "L" 5 "L3" 4 "D" 3 "N" 2 "R" 1 "P" 0 "Shifting";
 VAL_ 501 TransmissionState 11 "Shifting" 10 "Reverse" 9 "Forward" 8 "Disengaged";
 VAL_ 501 ManualMode 1 "Active" 0 "Inactive"

selfdrive/car/car_helpers.py

@@ -209,7 +209,8 @@ def get_car(logcan, sendcan, experimental_long_allowed, params, num_pandas=1, fr
   CP.fingerprintSource = source
   CP.fuzzyFingerprint = not exact_match
 
-  return get_car_interface(CP, FPCP), CP, FPCP
+  interface_instance = get_car_interface(CP, None)
+  return interface_instance, CP, FPCP
 
 def write_car_param(platform=MOCK.MOCK):
   params = Params()

selfdrive/car/gm/carcontroller.py

@@ -57,17 +57,16 @@ class CarController(CarControllerBase):
     self.accel_g = 0.0
 
   @staticmethod
-  def calc_pedal_command(accel: float, long_active: bool) -> float:
+  def calc_pedal_command(accel: float, long_active: bool, car_velocity) -> float:
     if not long_active: return 0.
 
-    zero = 0.15625  # 40/256
-    if accel > 0.:
-      # Scales the accel from 0-1 to 0.156-1
-      pedal_gas = clip(((1 - zero) * accel + zero), 0., 1.)
+    if accel < -0.5:
+      pedal_gas = 0
     else:
-      # if accel is negative, -0.1 -> 0.015625
-      pedal_gas = clip(zero + accel, 0., zero)  # Make brake the same size as gas, but clip to regen
 
+      pedaloffset = interp(car_velocity, [0., 3, 6, 30], [0.10, 0.175, 0.240, 0.240])
+      pedal_gas = clip((pedaloffset + accel * 0.6), 0.0, 1.0)
+      
     return pedal_gas
 
 
@@ -160,7 +159,7 @@ class CarController(CarControllerBase):
             self.apply_gas = self.params.INACTIVE_REGEN
           if self.CP.carFingerprint in CC_ONLY_CAR:
             # gas interceptor only used for full long control on cars without ACC
-            interceptor_gas_cmd = self.calc_pedal_command(actuators.accel, CC.longActive)
+            interceptor_gas_cmd = self.calc_pedal_command(actuators.accel, CC.longActive, CS.out.vEgo)
 
         if self.CP.enableGasInterceptor and self.apply_gas > self.params.INACTIVE_REGEN and CS.out.cruiseState.standstill:
           # "Tap" the accelerator pedal to re-engage ACC

selfdrive/car/gm/carstate.py

@@ -92,11 +92,16 @@ class CarState(CarStateBase):
     # Regen braking is braking
     if self.CP.transmissionType == TransmissionType.direct:
       ret.regenBraking = pt_cp.vl["EBCMRegenPaddle"]["RegenPaddle"] != 0
-      self.single_pedal_mode = ret.gearShifter == GearShifter.low or pt_cp.vl["EVDriveMode"]["SinglePedalModeActive"] == 1 or (ret.regenBraking and GearShifter.manumatic)
+      self.single_pedal_mode = (
+        ret.gearShifter == GearShifter.low
+        or pt_cp.vl["EVDriveMode"]["SinglePedalModeActive"] == 1
+        or pt_cp.vl["ECMPRDNL2"]["TransmissionState"] == 21
+        or (ret.regenBraking and GearShifter.manumatic)
+      )
 
     if self.CP.enableGasInterceptor:
       ret.gas = (pt_cp.vl["GAS_SENSOR"]["INTERCEPTOR_GAS"] + pt_cp.vl["GAS_SENSOR"]["INTERCEPTOR_GAS2"]) / 2.
-      threshold = 10 if self.CP.carFingerprint in CAMERA_ACC_CAR else 4 # Panda 515 threshold = 10.88. Set lower to avoid panda blocking messages and GasInterceptor faulting.
+      threshold = 12 if self.CP.carFingerprint in CAMERA_ACC_CAR else 4 # Panda 515 threshold = 10.88. Set lower to avoid panda blocking messages and GasInterceptor faulting.
       ret.gasPressed = ret.gas > threshold
     else:
       ret.gas = pt_cp.vl["AcceleratorPedal2"]["AcceleratorPedal2"] / 254.

selfdrive/car/gm/interface.py

@@ -29,8 +29,8 @@ CAM_MSG = 0x320  # AEBCmd
 ACCELERATOR_POS_MSG = 0xbe
 
 NON_LINEAR_TORQUE_PARAMS = {
-  CAR.CHEVROLET_BOLT_EUV: [2.6531724862969748, 1.0, 0.1919764879840985, 0.009054123646805178],
-  CAR.CHEVROLET_BOLT_CC: [2.6531724862969748, 1.0, 0.1919764879840985, 0.009054123646805178],
+  CAR.CHEVROLET_BOLT_EUV: [1.8, 1.1, 0.290, -0.045],
+  CAR.CHEVROLET_BOLT_CC: [1.8, 1.1, 0.290, -0.045],
   CAR.GMC_ACADIA: [4.78003305, 1.0, 0.3122, 0.05591772],
   CAR.CHEVROLET_SILVERADO: [3.29974374, 1.0, 0.25571356, 0.0465122]
 }
@@ -74,8 +74,8 @@ class CarInterface(CarInterfaceBase):
     # ToDo: To generalize to other GMs, explore tanh function as the nonlinear
     non_linear_torque_params = NON_LINEAR_TORQUE_PARAMS.get(self.CP.carFingerprint)
     assert non_linear_torque_params, "The params are not defined"
-    a, b, c, _ = non_linear_torque_params
-    steer_torque = (sig(latcontrol_inputs.lateral_acceleration * a) * b) + (latcontrol_inputs.lateral_acceleration * c)
+    a, b, c, d = non_linear_torque_params
+    steer_torque = (sig(latcontrol_inputs.lateral_acceleration * a) * b) + (latcontrol_inputs.lateral_acceleration * c) + d
     return float(steer_torque) + friction
 
   def torque_from_lateral_accel_neural(self, latcontrol_inputs: LatControlInputs, torque_params: car.CarParams.LateralTorqueTuning, lateral_accel_error: float,
@@ -87,13 +87,7 @@ class CarInterface(CarInterfaceBase):
     return float(self.neural_ff_model.predict(inputs)) + friction
 
   def torque_from_lateral_accel(self) -> TorqueFromLateralAccelCallbackType:
-    if self.CP.carFingerprint in (CAR.CHEVROLET_BOLT_EUV, CAR.CHEVROLET_BOLT_CC):
-      self.neural_ff_model = NanoFFModel(NEURAL_PARAMS_PATH, self.CP.carFingerprint)
-      return self.torque_from_lateral_accel_neural
-    elif self.CP.carFingerprint in NON_LINEAR_TORQUE_PARAMS:
       return self.torque_from_lateral_accel_siglin
-    else:
-      return self.torque_from_lateral_accel_linear
 
   @staticmethod
   def _get_params(ret, candidate, fingerprint, car_fw, experimental_long, docs, frogpilot_toggles):
@@ -104,13 +98,15 @@ class CarInterface(CarInterfaceBase):
     if PEDAL_MSG in fingerprint[0]:
       ret.enableGasInterceptor = True
       ret.safetyConfigs[0].safetyParam |= Panda.FLAG_GM_GAS_INTERCEPTOR
+      # When a pedal interceptor is present, always use normal longitudinal (block stock cruise)
+      experimental_long = False
 
     if candidate in EV_CAR:
       ret.transmissionType = TransmissionType.direct
     else:
       ret.transmissionType = TransmissionType.automatic
 
-    ret.longitudinalTuning.kiBP = [5., 35.]
+    ret.longitudinalTuning.kiBP = [5., 35., 60.]
 
     if candidate in CAMERA_ACC_CAR:
       ret.experimentalLongitudinalAvailable = candidate not in CC_ONLY_CAR
@@ -122,13 +118,14 @@ class CarInterface(CarInterfaceBase):
       ret.minSteerSpeed = 10 * CV.KPH_TO_MS
 
       # Tuning for experimental long
-      ret.longitudinalTuning.kiV = [2.0, 1.5]
+      ret.longitudinalTuning.kiV = [0.5, 0.5, 0.5]
       ret.vEgoStopping = 0.1
       ret.vEgoStarting = 0.1
 
-      ret.stoppingDecelRate = 2.0  # reach brake quickly after enabling
+      ret.stoppingDecelRate = 1.0  # reach brake quickly after enabling
       ret.vEgoStopping = 0.25
       ret.vEgoStarting = 0.25
+      ret.stopAccel = -0.25
 
       if experimental_long:
         ret.pcmCruise = False
@@ -136,7 +133,7 @@ class CarInterface(CarInterfaceBase):
         ret.safetyConfigs[0].safetyParam |= Panda.FLAG_GM_HW_CAM_LONG
 
     elif candidate in SDGM_CAR:
-      ret.longitudinalTuning.kiV = [0., 0.]  # TODO: tuning
+      ret.longitudinalTuning.kiV = [0., 0., 0.]  # TODO: tuning
       ret.experimentalLongitudinalAvailable = False
       ret.networkLocation = NetworkLocation.fwdCamera
       ret.pcmCruise = True
@@ -155,7 +152,7 @@ class CarInterface(CarInterfaceBase):
       ret.minSteerSpeed = 7 * CV.MPH_TO_MS
 
       # Tuning
-      ret.longitudinalTuning.kiV = [2.4, 1.5]
+      ret.longitudinalTuning.kiV = [0.5, 0.5, 0.5]
 
       if ret.enableGasInterceptor:
         # Need to set ASCM long limits when using pedal interceptor, instead of camera ACC long limits
@@ -206,6 +203,7 @@ class CarInterface(CarInterfaceBase):
     elif candidate in (CAR.CHEVROLET_BOLT_EUV, CAR.CHEVROLET_BOLT_CC):
       ret.steerActuatorDelay = 0.2
       CarInterfaceBase.configure_torque_tune(candidate, ret.lateralTuning)
+      ret.lateralTuning.torque.kp = 0.6
 
       if ret.enableGasInterceptor:
         # ACC Bolts use pedal for full longitudinal control, not just sng
@@ -271,13 +269,13 @@ class CarInterface(CarInterfaceBase):
       ret.stoppingControl = True
       ret.autoResumeSng = True
 
-      if candidate in CC_ONLY_CAR:
+      if candidate in CC_ONLY_CAR: #pedal interceptor tuning
         ret.flags |= GMFlags.PEDAL_LONG.value
         ret.safetyConfigs[0].safetyParam |= Panda.FLAG_GM_PEDAL_LONG
         # Note: Low speed, stop and go not tested. Should be fairly smooth on highway
-        ret.longitudinalTuning.kiBP = [0.0, 5., 35.]
-        ret.longitudinalTuning.kiV = [0.0, 0.35, 0.5]
-        ret.longitudinalTuning.kf = 0.15
+        ret.longitudinalTuning.kiBP = [0., 3., 6., 35.]
+        ret.longitudinalTuning.kiV = [0.125, 0.175, 0.225, 0.33]
+        ret.longitudinalTuning.kf = 0.25
         ret.stoppingDecelRate = 0.8
       else:  # Pedal used for SNG, ACC for longitudinal control otherwise
         ret.safetyConfigs[0].safetyParam |= Panda.FLAG_GM_HW_CAM_LONG
@@ -293,17 +291,17 @@ class CarInterface(CarInterfaceBase):
       ret.minEnableSpeed = 24 * CV.MPH_TO_MS
       ret.openpilotLongitudinalControl = not frogpilot_toggles.disable_openpilot_long
       ret.pcmCruise = False
+      experimental_long = False
 
-      ret.stoppingDecelRate = 11.18  # == 25 mph/s (.04 rate)
-
-      ret.longitudinalTuning.deadzoneBP = [0.]
-      ret.longitudinalTuning.deadzoneV = [0.56]  # == 2 km/h/s, 1.25 mph/s
-      ret.longitudinalActuatorDelay = 1.  # TODO: measure this
-
-      ret.longitudinalTuning.kpBP = [10.7, 10.8, 28.]  # 10.7 m/s == 24 mph
-      ret.longitudinalTuning.kpV = [0., 20., 20.]  # set lower end to 0 since we can't drive below that speed
-      ret.longitudinalTuning.kiBP = [0.]
-      ret.longitudinalTuning.kiV = [0.1]
+      if not ret.enableGasInterceptor and candidate in CC_ONLY_CAR: #redneck tuning
+        ret.longitudinalTuning.kpBP = [10.7, 10.8, 28.]  # 10.7 m/s == 24 mph
+        ret.longitudinalTuning.kpV = [0., 20., 20.]  # set lower end to 0 since we can't drive below that speed
+        ret.longitudinalTuning.deadzoneBP = [0.]
+        ret.longitudinalTuning.deadzoneV = [0.56]  # == 2 km/h/s, 1.25 mph/s
+        ret.longitudinalActuatorDelay = 1.  # TODO: measure this
+        ret.longitudinalTuning.kiBP = [0.]
+        ret.longitudinalTuning.kiV = [0.1]
+        ret.stoppingDecelRate = 11.18  # == 25 mph/s (.04 rate)
 
     if candidate in CC_ONLY_CAR:
       ret.safetyConfigs[0].safetyParam |= Panda.FLAG_GM_NO_ACC

selfdrive/car/gm/values.py

@@ -205,15 +205,15 @@ class CAR(Platforms):
     CHEVROLET_SUBURBAN.specs,
   )
   GMC_YUKON_CC = GMPlatformConfig(
-    [GMCarDocs("GMC Yukon - No-ACC")],
+    [GMCarDocs("GMC Yukon No ACC")],
     CarSpecs(mass=2541, wheelbase=2.95, steerRatio=16.3, centerToFrontRatio=0.4),
   )
   CADILLAC_CT6_CC = GMPlatformConfig(
-    [GMCarDocs("Cadillac CT6 - No-ACC")],
+    [GMCarDocs("Cadillac CT6 No ACC")],
     CarSpecs(mass=2358, wheelbase=3.11, steerRatio=17.7, centerToFrontRatio=0.4),
   )
   CHEVROLET_TRAILBLAZER_CC = GMPlatformConfig(
-    [GMCarDocs("Chevrolet Trailblazer 2021-22 - No-ACC")],
+    [GMCarDocs("Chevrolet Trailblazer 2021-22")],
     CHEVROLET_TRAILBLAZER.specs,
   )
   CADILLAC_XT4 = GMPlatformConfig(
@@ -221,7 +221,7 @@ class CAR(Platforms):
     CarSpecs(mass=1660, wheelbase=2.78, steerRatio=14.4, centerToFrontRatio=0.4),
   )
   CADILLAC_XT5_CC = GMPlatformConfig(
-    [GMCarDocs("Cadillac XT5 - No-ACC")],
+    [GMCarDocs("Cadillac XT5 No ACC")],
     CarSpecs(mass=1810, wheelbase=2.86, steerRatio=16.34, centerToFrontRatio=0.5),
   )
   CHEVROLET_TRAVERSE = GMPlatformConfig(
@@ -233,7 +233,7 @@ class CAR(Platforms):
     CarSpecs(mass=2050, wheelbase=2.86, steerRatio=16.0, centerToFrontRatio=0.5),
   )
   CHEVROLET_MALIBU_CC = GMPlatformConfig(
-    [GMCarDocs("Chevrolet Malibu 2023 - No-ACC")],
+    [GMCarDocs("Chevrolet Malibu 2023 No ACC")],
     CarSpecs(mass=1450, wheelbase=2.8, steerRatio=15.8, centerToFrontRatio=0.4),
   )
   CHEVROLET_TRAX = GMPlatformConfig(

selfdrive/car/interfaces.py

@@ -218,9 +218,10 @@ class CarInterfaceBase(ABC):
     self.silent_steer_warning = True
     self.v_ego_cluster_seen = False
 
-    self.CS = CarState(CP, FPCP)
-    self.cp = self.CS.get_can_parser(CP, FPCP)
-    self.cp_cam = self.CS.get_cam_can_parser(CP, FPCP)
+    self.CS = CarState(CP, None)
+    fp = FPCP if FPCP is not None else getattr(self, "FPCP", None)
+    self.cp = self.CS.get_can_parser(CP, fp)
+    self.cp_cam = self.CS.get_cam_can_parser(CP, fp)
     self.cp_adas = self.CS.get_adas_can_parser(CP)
     self.cp_body = self.CS.get_body_can_parser(CP)
     self.cp_loopback = self.CS.get_loopback_can_parser(CP)
@@ -411,7 +412,7 @@ class CarInterfaceBase(ABC):
 
     tune.init('torque')
     tune.torque.useSteeringAngle = use_steering_angle
-    tune.torque.kp = 1.0
+    tune.torque.kp = 0.6
     tune.torque.kf = 1.0
     tune.torque.ki = 0.1
     tune.torque.friction = params['FRICTION']

selfdrive/controls/lib/drive_helpers.py

@@ -1,9 +1,10 @@
 import math
+import numpy as np
 
 from cereal import car, log
 from openpilot.common.conversions import Conversions as CV
 from openpilot.common.numpy_fast import clip, interp
-from openpilot.common.realtime import DT_CTRL
+from openpilot.common.realtime import DT_CTRL, DT_MDL
 
 # WARNING: this value was determined based on the model's training distribution,
 #          model predictions above this speed can be unpredictable
@@ -178,6 +179,9 @@ def apply_center_deadzone(error, deadzone):
 def rate_limit(new_value, last_value, dw_step, up_step):
   return clip(new_value, last_value + dw_step, last_value + up_step)
 
+def smooth_value(val, prev_val, tau, dt=DT_MDL):
+  alpha = 1 - np.exp(-dt/tau) if tau > 0 else 1
+  return alpha * val + (1 - alpha) * prev_val
 
 def clip_curvature(v_ego, prev_curvature, new_curvature, planner_curves):
   if planner_curves:
@@ -208,3 +212,29 @@ def get_speed_error(modelV2: log.ModelDataV2, v_ego: float) -> float:
     vel_err = clip(modelV2.temporalPose.trans[0] - v_ego, -MAX_VEL_ERR, MAX_VEL_ERR)
     return float(vel_err)
   return 0.0
+
+
+def get_accel_from_plan_tomb_raider(speeds, accels, t_idxs, action_t=DT_MDL, vEgoStopping=0.05):
+  if len(speeds) == len(t_idxs):
+    v_now = speeds[0]
+    a_now = accels[0]
+    v_target = np.interp(action_t, t_idxs, speeds)
+    a_target = 2 * (v_target - v_now) / (action_t) - a_now
+    v_target_1sec = np.interp(action_t + 1.0, t_idxs, speeds)
+  else:
+    v_target = 0.0
+    v_target_1sec = 0.0
+    a_target = 0.0
+  should_stop = (v_target < vEgoStopping and
+                 v_target_1sec < vEgoStopping)
+  return a_target, should_stop
+
+def curv_from_psis(psi_target, psi_rate, vego, action_t):
+  vego = np.clip(vego, MIN_SPEED, np.inf)
+  curv_from_psi = psi_target / (vego * action_t)
+  return 2*curv_from_psi - psi_rate / vego
+
+def get_curvature_from_plan(yaws, yaw_rates, t_idxs, vego, action_t):
+  psi_target = np.interp(action_t, t_idxs, yaws)
+  psi_rate = yaw_rates[0]
+  return curv_from_psis(psi_target, psi_rate, vego, action_t)

selfdrive/controls/lib/longcontrol.py

@@ -4,6 +4,8 @@ from openpilot.common.realtime import DT_CTRL
 from openpilot.selfdrive.controls.lib.drive_helpers import CONTROL_N, apply_deadzone
 from openpilot.selfdrive.controls.lib.pid import PIDController
 from openpilot.selfdrive.modeld.constants import ModelConstants
+from openpilot.common.filter_simple import FirstOrderFilter
+from openpilot.selfdrive.car.gm.values import CarControllerParams
 
 CONTROL_N_T_IDX = ModelConstants.T_IDXS[:CONTROL_N]
 
@@ -85,17 +87,48 @@ def long_control_state_trans_old_long(CP, active, long_control_state, v_ego, v_t
 
   return long_control_state
 
-
 class LongControl:
   def __init__(self, CP):
     self.CP = CP
     self.long_control_state = LongCtrlState.off
+    self.experimental_mode = False
     self.pid = PIDController((CP.longitudinalTuning.kpBP, CP.longitudinalTuning.kpV),
                              (CP.longitudinalTuning.kiBP, CP.longitudinalTuning.kiV),
-                             k_f=CP.longitudinalTuning.kf, rate=1 / DT_CTRL)
+                             k_f=CP.longitudinalTuning.kf, rate=1 / DT_CTRL,
+                             pos_p_limit=None)
     self.v_pid = 0.0
+    self._mode_setup()
     self.last_output_accel = 0.0
 
+
+
+  def update_mpc_mode(self, experimental_mode):
+    new_mode = 'blended' if experimental_mode else 'acc'
+   
+    if self.transitioning and self.prev_mode == 'blended' and self.current_mode == 'acc':
+      self.mode_transition_timer = 0.0
+
+    if new_mode != self.current_mode:
+      self.prev_mode = self.current_mode
+      self.transitioning = True
+      self.mode_transition_timer = 0.0
+      self.mode_transition_filter.x = self.last_output_accel
+
+      self.current_mode = new_mode
+
+    if self.transitioning:
+      self.mode_transition_timer += DT_CTRL
+      if self.mode_transition_timer >= self.mode_transition_duration:
+        self.transitioning = False
+
+  def _mode_setup(self):
+    self.prev_mode = 'acc'
+    self.current_mode = 'acc'
+    self.mode_transition_filter = FirstOrderFilter(0.0, 0.5, DT_CTRL)
+    self.mode_transition_timer = 0.0
+    self.mode_transition_duration = 1.0
+    self.transitioning = False
+
   def reset(self):
     self.pid.reset()
 
@@ -124,8 +157,19 @@ class LongControl:
 
     else:  # LongCtrlState.pid
       error = a_target - CS.aEgo
-      output_accel = self.pid.update(error, speed=CS.vEgo,
-                                     feedforward=a_target)
+      self.update_mpc_mode(self.experimental_mode)
+      raw_output_accel = self.pid.update(error, speed=CS.vEgo, feedforward=a_target)
+
+     
+      if self.transitioning and self.prev_mode == 'acc' and self.current_mode == 'blended':
+        if raw_output_accel < 0 and raw_output_accel < self.last_output_accel:
+          progress = min(1.0, self.mode_transition_timer / self.mode_transition_duration)
+          blend_factor = 1.0 - (1.0 - progress) * (1.0 - abs(raw_output_accel / CarControllerParams.ACCEL_MIN))
+          output_accel = self.last_output_accel + (raw_output_accel - self.last_output_accel) * blend_factor
+        else:
+          output_accel = raw_output_accel 
+      else:
+        output_accel = raw_output_accel 
 
     self.last_output_accel = clip(output_accel, accel_limits[0], accel_limits[1])
     return self.last_output_accel

selfdrive/controls/lib/longitudinal_planner.py

@@ -13,7 +13,7 @@ from openpilot.selfdrive.car.interfaces import ACCEL_MIN, ACCEL_MAX
 from openpilot.selfdrive.controls.lib.longcontrol import LongCtrlState
 from openpilot.selfdrive.controls.lib.longitudinal_mpc_lib.long_mpc import LongitudinalMpc
 from openpilot.selfdrive.controls.lib.longitudinal_mpc_lib.long_mpc import T_IDXS as T_IDXS_MPC, LEAD_ACCEL_TAU
-from openpilot.selfdrive.controls.lib.drive_helpers import V_CRUISE_MAX, V_CRUISE_UNSET, CONTROL_N, get_speed_error
+from openpilot.selfdrive.controls.lib.drive_helpers import V_CRUISE_MAX, V_CRUISE_UNSET, CONTROL_N, get_speed_error, get_accel_from_plan_tomb_raider
 from openpilot.common.swaglog import cloudlog
 
 LON_MPC_STEP = 0.2  # first step is 0.2s
@@ -203,8 +203,12 @@ class LongitudinalPlanner:
       throttle_prob = 1.0
     return x, v, a, j, throttle_prob
 
-  def update(self, radarless_model, sm, frogpilot_toggles):
-    self.mpc.mode = 'blended' if sm['controlsState'].experimentalMode else 'acc'
+  def update(self, radarless_model, tomb_raider, sm, frogpilot_toggles):
+    if tomb_raider:
+      self.mpc.mode = 'acc'
+      self.mode = 'blended' if sm['controlsState'].experimentalMode else 'acc'
+    else:
+      self.mpc.mode = 'blended' if sm['controlsState'].experimentalMode else 'acc'
 
     if len(sm['carControl'].orientationNED) == 3:
       accel_coast = get_coast_accel(sm['carControl'].orientationNED[1])
@@ -294,7 +298,7 @@ class LongitudinalPlanner:
     self.a_desired = float(interp(self.dt, CONTROL_N_T_IDX, self.a_desired_trajectory))
     self.v_desired_filter.x = self.v_desired_filter.x + self.dt * (self.a_desired + a_prev) / 2.0
 
-  def publish(self, classic_model, sm, pm, frogpilot_toggles):
+  def publish(self, classic_model, tomb_raider, sm, pm, frogpilot_toggles):
     plan_send = messaging.new_message('longitudinalPlan')
 
     plan_send.valid = sm.all_checks(service_list=['carState', 'controlsState'])
@@ -314,12 +318,25 @@ class LongitudinalPlanner:
 
     if classic_model:
       a_target, should_stop = get_accel_from_plan_classic(self.CP, longitudinalPlan.speeds, longitudinalPlan.accels, vEgoStopping=frogpilot_toggles.vEgoStopping)
+    elif tomb_raider:
+      action_t =  self.CP.longitudinalActuatorDelay + DT_MDL
+      output_a_target_mpc, output_should_stop_mpc = get_accel_from_plan_tomb_raider(self.v_desired_trajectory, self.a_desired_trajectory, CONTROL_N_T_IDX,
+                                                                                    action_t=action_t, vEgoStopping=frogpilot_toggles.vEgoStopping)
+      output_a_target_e2e = sm['modelV2'].action.desiredAcceleration
+      output_should_stop_e2e = sm['modelV2'].action.shouldStop
+
+      if self.mode == 'acc':
+        a_target = output_a_target_mpc
+        should_stop = output_should_stop_mpc
+      else:
+        a_target = min(output_a_target_mpc, output_a_target_e2e)
+        should_stop = output_should_stop_e2e or output_should_stop_mpc
     else:
       action_t = self.CP.longitudinalActuatorDelay + DT_MDL
       a_target, should_stop = get_accel_from_plan(longitudinalPlan.speeds, longitudinalPlan.accels,
                                                   action_t=action_t, vEgoStopping=frogpilot_toggles.vEgoStopping)
-    longitudinalPlan.aTarget = a_target
-    longitudinalPlan.shouldStop = should_stop
+    longitudinalPlan.aTarget = float(a_target)
+    longitudinalPlan.shouldStop = bool(should_stop)
     longitudinalPlan.allowBrake = True
     longitudinalPlan.allowThrottle = self.allow_throttle
 

selfdrive/controls/lib/pid.py

@@ -5,7 +5,9 @@ from openpilot.common.numpy_fast import clip, interp
 
 
 class PIDController:
-  def __init__(self, k_p, k_i, k_f=0., k_d=0., pos_limit=1e308, neg_limit=-1e308, rate=100):
+  def __init__(self, k_p, k_i, k_f=0., k_d=0.,
+              pos_limit=1e308, neg_limit=-1e308, rate=100,
+              pos_p_limit=None, neg_p_limit=None):
     self._k_p = k_p
     self._k_i = k_i
     self._k_d = k_d
@@ -20,6 +22,9 @@ class PIDController:
     self.pos_limit = pos_limit
     self.neg_limit = neg_limit
 
+    self.pos_p_limit = pos_p_limit
+    self.neg_p_limit = neg_p_limit
+
     self.i_unwind_rate = 0.3 / rate
     self.i_rate = 1.0 / rate
     self.speed = 0.0
@@ -53,6 +58,10 @@ class PIDController:
     self.speed = speed
 
     self.p = float(error) * self.k_p
+    if self.pos_p_limit is not None and self.p > self.pos_p_limit:
+      self.p = self.pos_p_limit
+    elif self.neg_p_limit is not None and self.p < self.neg_p_limit:
+      self.p = self.neg_p_limit
     self.f = feedforward * self.k_f
     self.d = error_rate * self.k_d
 

selfdrive/controls/plannerd.py

@@ -39,12 +39,13 @@ def plannerd_thread():
 
   classic_model = frogpilot_toggles.classic_model
   radarless_model = frogpilot_toggles.radarless_model
+  tomb_raider = frogpilot_toggles.model == "tomb-raider"
 
   while True:
     sm.update()
     if sm.updated['modelV2']:
-      longitudinal_planner.update(radarless_model, sm, frogpilot_toggles)
-      longitudinal_planner.publish(classic_model, sm, pm, frogpilot_toggles)
+      longitudinal_planner.update(radarless_model, tomb_raider, sm, frogpilot_toggles)
+      longitudinal_planner.publish(classic_model, tomb_raider, sm, pm, frogpilot_toggles)
       publish_ui_plan(sm, pm, longitudinal_planner)
 
     # Update FrogPilot parameters

selfdrive/controls/radard.py

@@ -18,7 +18,7 @@ from openpilot.common.simple_kalman import KF1D
 from openpilot.frogpilot.common.frogpilot_variables import get_frogpilot_toggles
 
 # Default lead acceleration decay set to 50% at 1s
-_LEAD_ACCEL_TAU = 1.5
+_LEAD_ACCEL_TAU = 0.6
 
 # radar tracks
 SPEED, ACCEL = 0, 1     # Kalman filter states enum
@@ -79,7 +79,7 @@ class Track:
 
     # Learn if constant acceleration
     if abs(self.aLeadK) < 0.5:
-      self.aLeadTau.x = _LEAD_ACCEL_TAU
+      self.aLeadTau.x = min(max(self.aLeadTau, 1e-2) * 1.1, _LEAD_ACCEL_TAU)
     else:
       self.aLeadTau.update(0.0)
 
@@ -163,14 +163,16 @@ def match_vision_to_track(v_ego: float, lead: capnp._DynamicStructReader, tracks
 
 
 def get_RadarState_from_vision(lead_msg: capnp._DynamicStructReader, v_ego: float, model_v_ego: float):
-  lead_v_rel_pred = lead_msg.v[0] - model_v_ego
+  prev_aLeadK = getattr(get_RadarState_from_vision, "prev_aLeadK", 0.0)
+  blended_aLeadK = 0.8 * float(lead_msg.a[0]) + 0.2 * prev_aLeadK
+  get_RadarState_from_vision.prev_aLeadK = blended_aLeadK
   return {
     "dRel": float(lead_msg.x[0] - RADAR_TO_CAMERA),
     "yRel": float(-lead_msg.y[0]),
-    "vRel": float(lead_v_rel_pred),
-    "vLead": float(v_ego + lead_v_rel_pred),
-    "vLeadK": float(v_ego + lead_v_rel_pred),
-    "aLeadK": float(lead_msg.a[0]),
+    "vRel": float(lead_msg.v[0] - model_v_ego),
+    "vLead": float(v_ego + (lead_msg.v[0] - model_v_ego)),
+    "vLeadK": float(v_ego + (lead_msg.v[0] - model_v_ego)),
+    "aLeadK": blended_aLeadK,
     "aLeadTau": 0.3,
     "fcw": False,
     "modelProb": float(lead_msg.prob),

system/hardware/tici/agnos.json

@@ -1,13 +1,18 @@
 [
   {
     "name": "boot",
-    "url": "https://commadist.azureedge.net/agnosupdate/boot-5674ea6767e7198cf1e7def3de66a57061f001ed76d43dc4b4f84de545c53c6f.img.xz",
-    "hash": "5674ea6767e7198cf1e7def3de66a57061f001ed76d43dc4b4f84de545c53c6f",
-    "hash_raw": "5674ea6767e7198cf1e7def3de66a57061f001ed76d43dc4b4f84de545c53c6f",
+    "url": "https://www.dropbox.com/scl/fi/z8gcamb7n78xqb515kfgq/boot.img.xz?rlkey=r2zxothb3pz0q9rtqysr1zhwv&st=f0acze3w&dl=1",
+    "hash": "b997aae3f1c93de82449ef7f23f30ff482b0978f3d0ac08219366f9ce362ad7a",
+    "hash_raw": "b997aae3f1c93de82449ef7f23f30ff482b0978f3d0ac08219366f9ce362ad7a",
     "size": 16029696,
     "sparse": false,
     "full_check": true,
-    "has_ab": true
+    "has_ab": true,
+    "alt": {
+      "hash": "5674ea6767e7198cf1e7def3de66a57061f001ed76d43dc4b4f84de545c53c6f",
+      "url": "https://commadist.azureedge.net/agnosupdate/boot-5674ea6767e7198cf1e7def3de66a57061f001ed76d43dc4b4f84de545c53c6f.img.xz",
+      "size": 16029696
+    }
   },
   {
     "name": "abl",
@@ -61,17 +66,17 @@
   },
   {
     "name": "system",
-    "url": "https://commadist.azureedge.net/agnosupdate/system-1badfe72851628d6cf9200a53a6151bb4e797b49c717141409fc57138eae388a.img.xz",
-    "hash": "328e90c62068222dfd98f71dd3f6251fcb962f082b49c6be66ab2699f5db6f4f",
-    "hash_raw": "1badfe72851628d6cf9200a53a6151bb4e797b49c717141409fc57138eae388a",
+    "url": "https://www.dropbox.com/scl/fi/n22f3eex1z52dbrhhxqry/system.img.xz?rlkey=yw4ult7s3sdm6b7d31hrm3zx8&st=of6m7zis&dl=1",
+    "hash": "be1c6bb9ee5e06779087b1b81e09b6df61d942566b0f8d4539c452179c661782",
+    "hash_raw": "a5f84e68d199466fda5c9aead760b90a4cd2d2ef9a418708b9794d95bb03ec5b",
     "size": 10737418240,
     "sparse": true,
     "full_check": false,
     "has_ab": true,
     "alt": {
-      "hash": "bc11d2148f29862ee1326aca2af1cf6bbf5fed831e3f8f6b8f7a0f110dfe8d26",
-      "url": "https://commadist.azureedge.net/agnosupdate/system-skip-chunks-1badfe72851628d6cf9200a53a6151bb4e797b49c717141409fc57138eae388a.img.xz",
-      "size": 4548070000
+      "hash": "328e90c62068222dfd98f71dd3f6251fcb962f082b49c6be66ab2699f5db6f4f",
+      "url": "https://commadist.azureedge.net/agnosupdate/system-1badfe72851628d6cf9200a53a6151bb4e797b49c717141409fc57138eae388a.img.xz",
+      "size": 10737418240
     }
   }
-]
+]

system/hardware/tici/agnos.py

@@ -168,18 +168,24 @@ def extract_compressed_image(target_slot_number: int, partition: dict, cloudlog)
         last_p = p
         print(f"Installing {partition['name']}: {p}", flush=True)
 
-    if raw_hash.hexdigest().lower() != partition['hash_raw'].lower():
-      raise Exception(f"Raw hash mismatch '{raw_hash.hexdigest().lower()}'")
+    written_size = out.tell()
+    expected_size = partition['size']
+    actual_raw_hash = raw_hash.hexdigest().lower()
+    expected_raw_hash = partition['hash_raw'].lower()
+    actual_final_hash = downloader.sha256.hexdigest().lower()
+    expected_final_hash = partition['hash'].lower()
 
-    if downloader.sha256.hexdigest().lower() != partition['hash'].lower():
-      raise Exception("Uncompressed hash mismatch")
+    if actual_raw_hash != expected_raw_hash:
+      raise Exception(f"Raw hash mismatch: got {actual_raw_hash}, expected {expected_raw_hash}")
 
-    if out.tell() != partition['size']:
-      raise Exception("Uncompressed size mismatch")
+    if actual_final_hash != expected_final_hash:
+      raise Exception(f"Uncompressed hash mismatch: got {actual_final_hash}, expected {expected_final_hash}")
+
+    if written_size != expected_size:
+      raise Exception(f"Uncompressed size mismatch: wrote {written_size} bytes, expected {expected_size} bytes")
 
     os.sync()
 
-
 def extract_casync_image(target_slot_number: int, partition: dict, cloudlog):
   path = get_partition_path(target_slot_number, partition)
   seed_path = path[:-1] + ('b' if path[-1] == 'a' else 'a')