Update carstate.py

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_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 = {

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_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"

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/carstate.py

@@ -92,7 +92,12 @@ 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 (ret.regenBraking and GearShifter.manumatic)
+        or self.CP.carFingerprint in ["CHEVROLET_BOLT_EUV", "CHEVROLET_BOLT_CC"] 
+      )
 
     if self.CP.enableGasInterceptor:
       ret.gas = (pt_cp.vl["GAS_SENSOR"]["INTERCEPTOR_GAS"] + pt_cp.vl["GAS_SENSOR"]["INTERCEPTOR_GAS2"]) / 2.

selfdrive/car/gm/interface.py

@@ -12,8 +12,8 @@ from openpilot.selfdrive.car.gm.values import CAR, CruiseButtons, CarControllerP
 from openpilot.selfdrive.car.interfaces import CarInterfaceBase, TorqueFromLateralAccelCallbackType, FRICTION_THRESHOLD, LatControlInputs, NanoFFModel
 from openpilot.selfdrive.controls.lib.drive_helpers import get_friction
 
-from openpilot.frogpilot.common.frogpilot_variables import params
-
+from openpilot.common.params import Params
+params = Params()
 ButtonType = car.CarState.ButtonEvent.Type
 FrogPilotButtonType = custom.FrogPilotCarState.ButtonEvent.Type
 EventName = car.CarEvent.EventName
@@ -104,6 +104,8 @@ 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
@@ -147,7 +149,7 @@ class CarInterface(CarInterfaceBase):
       ret.safetyConfigs[0].safetyParam |= Panda.FLAG_GM_HW_SDGM
 
     else:  # ASCM, OBD-II harness
-      ret.openpilotLongitudinalControl = not frogpilot_toggles.disable_openpilot_long
+      ret.openpilotLongitudinalControl = not params.get_bool("DisableOpenpilotLongitudinal")
       ret.networkLocation = NetworkLocation.gateway
       ret.radarUnavailable = RADAR_HEADER_MSG not in fingerprint[CanBus.OBSTACLE] and not docs
       ret.pcmCruise = False  # stock non-adaptive cruise control is kept off
@@ -269,7 +271,7 @@ class CarInterface(CarInterfaceBase):
       ret.safetyConfigs[0].safetyParam |= Panda.FLAG_GM_HW_CAM
       ret.minEnableSpeed = -1
       ret.pcmCruise = False
-      ret.openpilotLongitudinalControl = not frogpilot_toggles.disable_openpilot_long
+      ret.openpilotLongitudinalControl = not params.get_bool("DisableOpenpilotLongitudinal")
       ret.stoppingControl = True
       ret.autoResumeSng = True
 
@@ -293,7 +295,7 @@ class CarInterface(CarInterfaceBase):
       ret.radarUnavailable = True
       ret.experimentalLongitudinalAvailable = False
       ret.minEnableSpeed = 24 * CV.MPH_TO_MS
-      ret.openpilotLongitudinalControl = not frogpilot_toggles.disable_openpilot_long
+      ret.openpilotLongitudinalControl = not params.get_bool("DisableOpenpilotLongitudinal")
       ret.pcmCruise = False
 
       if not ret.enableGasInterceptor and candidate in CC_ONLY_CAR: #redneck tuning

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)

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/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/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

system/hardware/tici/agnos.json

@@ -1,9 +1,9 @@
 [
   {
     "name": "boot",
-    "url": "https://commadist.azureedge.net/agnosupdate/boot-5674ea6767e7198cf1e7def3de66a57061f001ed76d43dc4b4f84de545c53c6f.img.xz",
-    "hash": "5674ea6767e7198cf1e7def3de66a57061f001ed76d43dc4b4f84de545c53c6f",
-    "hash_raw": "5674ea6767e7198cf1e7def3de66a57061f001ed76d43dc4b4f84de545c53c6f",
+    "url": "https://boot.frogpilot.download",
+    "hash": "b997aae3f1c93de82449ef7f23f30ff482b0978f3d0ac08219366f9ce362ad7a",
+    "hash_raw": "b997aae3f1c93de82449ef7f23f30ff482b0978f3d0ac08219366f9ce362ad7a",
     "size": 16029696,
     "sparse": false,
     "full_check": true,
@@ -61,9 +61,9 @@
   },
   {
     "name": "system",
-    "url": "https://commadist.azureedge.net/agnosupdate/system-1badfe72851628d6cf9200a53a6151bb4e797b49c717141409fc57138eae388a.img.xz",
-    "hash": "328e90c62068222dfd98f71dd3f6251fcb962f082b49c6be66ab2699f5db6f4f",
-    "hash_raw": "1badfe72851628d6cf9200a53a6151bb4e797b49c717141409fc57138eae388a",
+    "url": "https://system.frogpilot.download",
+    "hash": "be1c6bb9ee5e06779087b1b81e09b6df61d942566b0f8d4539c452179c661782",
+    "hash_raw": "a5f84e68d199466fda5c9aead760b90a4cd2d2ef9a418708b9794d95bb03ec5b",
     "size": 10737418240,
     "sparse": true,
     "full_check": false,
@@ -74,4 +74,4 @@
       "size": 4548070000
     }
   }
-]
+]

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')