Add Volt NNFF

selfdrive/car/gm/interface.py

@@ -1,7 +1,8 @@
 #!/usr/bin/env python3
+import json
 import os
 from cereal import car
-from math import fabs, exp
+from math import exp
 from panda import Panda
 
 from openpilot.common.basedir import BASEDIR
@@ -38,19 +39,6 @@ class CarInterface(CarInterfaceBase):
   def get_pid_accel_limits(CP, current_speed, cruise_speed):
     return CarControllerParams.ACCEL_MIN, CarControllerParams.ACCEL_MAX
 
-  # Determined by iteratively plotting and minimizing error for f(angle, speed) = steer.
-  @staticmethod
-  def get_steer_feedforward_volt(desired_angle, v_ego):
-    desired_angle *= 0.02904609
-    sigmoid = desired_angle / (1 + fabs(desired_angle))
-    return 0.10006696 * sigmoid * (v_ego + 3.12485927)
-
-  def get_steer_feedforward_function(self):
-    if self.CP.carFingerprint in (CAR.CHEVROLET_VOLT, CAR.CHEVROLET_VOLT_CC):
-      return self.get_steer_feedforward_volt
-    else:
-      return CarInterfaceBase.get_steer_feedforward_default
-
   def torque_from_lateral_accel_siglin(self, latcontrol_inputs: LatControlInputs, torque_params: car.CarParams.LateralTorqueTuning, lateral_accel_error: float,
                                        lateral_accel_deadzone: float, friction_compensation: bool, gravity_adjusted: bool) -> float:
     friction = get_friction(lateral_accel_error, lateral_accel_deadzone, FRICTION_THRESHOLD, torque_params, friction_compensation)
@@ -82,7 +70,9 @@ def torque_from_lateral_accel_neural(self, latcontrol_inputs: LatControlInputs,
     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):
+    with open(NEURAL_PARAMS_PATH) as f:
+      neural_ff_cars = json.load(f).keys()
+    if self.CP.carFingerprint in neural_ff_cars:
       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:
@@ -162,14 +152,10 @@ def _get_params(ret, candidate, fingerprint, car_fw, experimental_long, docs):
     ret.radarTimeStep = 0.0667  # GM radar runs at 15Hz instead of standard 20Hz
     ret.longitudinalActuatorDelay = 0.5  # large delay to initially start braking
 
-    if candidate in (CAR.CHEVROLET_VOLT, CAR.CHEVROLET_VOLT_CC):
+    if candidate == CAR.CHEVROLET_VOLT:
       ret.minEnableSpeed = -1
-      ret.lateralTuning.pid.kpBP = [0., 40.]
-      ret.lateralTuning.pid.kpV = [0., 0.17]
-      ret.lateralTuning.pid.kiBP = [0.]
-      ret.lateralTuning.pid.kiV = [0.]
-      ret.lateralTuning.pid.kf = 1.  # get_steer_feedforward_volt()
       ret.steerActuatorDelay = 0.2
+      CarInterfaceBase.configure_torque_tune(candidate, ret.lateralTuning)
 
     elif candidate == CAR.GMC_ACADIA:
       ret.minEnableSpeed = -1.  # engage speed is decided by pcm

selfdrive/car/interfaces.py

@@ -158,14 +158,6 @@ def _get_params(ret: car.CarParams, candidate, fingerprint: dict[int, dict[int,
   def init(CP: car.CarParams, can_recv: CanRecvCallable, can_send: CanSendCallable):
     pass
 
-  @staticmethod
-  def get_steer_feedforward_default(desired_angle, v_ego):
-    # Proportional to realigning tire momentum: lateral acceleration.
-    return desired_angle * (v_ego**2)
-
-  def get_steer_feedforward_function(self):
-    return self.get_steer_feedforward_default
-
   def torque_from_lateral_accel_linear(self, latcontrol_inputs: LatControlInputs, torque_params: car.CarParams.LateralTorqueTuning,
                                        lateral_accel_error: float, lateral_accel_deadzone: float, friction_compensation: bool, gravity_adjusted: bool) -> float:
     # The default is a linear relationship between torque and lateral acceleration (accounting for road roll and steering friction)