Calculate average desired curvature using planned distance

Added toggle to calculate average desired curvature using planned distance instead of the car's speed.

Credit goes to Pfeiferj!

https: //github.com/pfeiferj
Co-Authored-By: Jacob Pfeifer <jacob@pfeifer.dev>
This commit is contained in:
FrogAi
2024-01-12 22:39:30 -07:00
parent 8baf713117
commit b4550a13ba
8 changed files with 35 additions and 7 deletions

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@@ -22,6 +22,7 @@ struct FrogPilotLateralPlan @0xda96579883444c35 {
} }
struct FrogPilotLongitudinalPlan @0x80ae746ee2596b11 { struct FrogPilotLongitudinalPlan @0x80ae746ee2596b11 {
distances @3 :List(Float32);
} }
struct FrogPilotNavigation @0xa5cd762cd951a455 { struct FrogPilotNavigation @0xa5cd762cd951a455 {

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@@ -217,6 +217,7 @@ std::unordered_map<std::string, uint32_t> keys = {
{"AlwaysOnLateral", PERSISTENT}, {"AlwaysOnLateral", PERSISTENT},
{"AlwaysOnLateralMain", PERSISTENT}, {"AlwaysOnLateralMain", PERSISTENT},
{"ApiCache_DriveStats", PERSISTENT}, {"ApiCache_DriveStats", PERSISTENT},
{"AverageCurvature", PERSISTENT},
{"FrogPilotTogglesUpdated", PERSISTENT}, {"FrogPilotTogglesUpdated", PERSISTENT},
{"GasRegenCmd", PERSISTENT}, {"GasRegenCmd", PERSISTENT},
{"LateralTune", PERSISTENT}, {"LateralTune", PERSISTENT},

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@@ -649,7 +649,9 @@ class Controls:
self.desired_curvature, self.desired_curvature_rate = get_lag_adjusted_curvature(self.CP, CS.vEgo, self.desired_curvature, self.desired_curvature_rate = get_lag_adjusted_curvature(self.CP, CS.vEgo,
lat_plan.psis, lat_plan.psis,
lat_plan.curvatures, lat_plan.curvatures,
lat_plan.curvatureRates) lat_plan.curvatureRates,
frogpilot_long_plan.distances,
self.average_desired_curvature)
actuators.steer, actuators.steeringAngleDeg, lac_log = self.LaC.update(CC.latActive, CS, self.VM, lp, actuators.steer, actuators.steeringAngleDeg, lac_log = self.LaC.update(CC.latActive, CS, self.VM, lp,
self.steer_limited, self.desired_curvature, self.steer_limited, self.desired_curvature,
self.desired_curvature_rate, self.sm['liveLocationKalman']) self.desired_curvature_rate, self.sm['liveLocationKalman'])
@@ -940,6 +942,8 @@ class Controls:
if hasattr(obj, 'update_frogpilot_params'): if hasattr(obj, 'update_frogpilot_params'):
obj.update_frogpilot_params(self.params) obj.update_frogpilot_params(self.params)
self.average_desired_curvature = self.params.get_bool("AverageCurvature")
longitudinal_tune = self.params.get_bool("LongitudinalTune") longitudinal_tune = self.params.get_bool("LongitudinalTune")
self.sport_plus = self.params.get_int("AccelerationProfile") == 3 and longitudinal_tune self.sport_plus = self.params.get_int("AccelerationProfile") == 3 and longitudinal_tune

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@@ -16,6 +16,7 @@ V_CRUISE_INITIAL = 40
V_CRUISE_INITIAL_EXPERIMENTAL_MODE = 105 V_CRUISE_INITIAL_EXPERIMENTAL_MODE = 105
IMPERIAL_INCREMENT = 1.6 # should be CV.MPH_TO_KPH, but this causes rounding errors IMPERIAL_INCREMENT = 1.6 # should be CV.MPH_TO_KPH, but this causes rounding errors
MIN_DIST = 0.001
MIN_SPEED = 1.0 MIN_SPEED = 1.0
CONTROL_N = 17 CONTROL_N = 17
CAR_ROTATION_RADIUS = 0.0 CAR_ROTATION_RADIUS = 0.0
@@ -163,11 +164,12 @@ def rate_limit(new_value, last_value, dw_step, up_step):
return clip(new_value, last_value + dw_step, last_value + up_step) return clip(new_value, last_value + dw_step, last_value + up_step)
def get_lag_adjusted_curvature(CP, v_ego, psis, curvatures, curvature_rates): def get_lag_adjusted_curvature(CP, v_ego, psis, curvatures, curvature_rates, distances, average_desired_curvature):
if len(psis) != CONTROL_N: if len(psis) != CONTROL_N or len(distances) != CONTROL_N:
psis = [0.0]*CONTROL_N psis = [0.0]*CONTROL_N
curvatures = [0.0]*CONTROL_N curvatures = [0.0]*CONTROL_N
curvature_rates = [0.0]*CONTROL_N curvature_rates = [0.0]*CONTROL_N
distances = [0.0]*CONTROL_N
v_ego = max(MIN_SPEED, v_ego) v_ego = max(MIN_SPEED, v_ego)
# TODO this needs more thought, use .2s extra for now to estimate other delays # TODO this needs more thought, use .2s extra for now to estimate other delays
@@ -178,7 +180,10 @@ def get_lag_adjusted_curvature(CP, v_ego, psis, curvatures, curvature_rates):
# psi to calculate a simple linearization of desired curvature # psi to calculate a simple linearization of desired curvature
current_curvature_desired = curvatures[0] current_curvature_desired = curvatures[0]
psi = interp(delay, ModelConstants.T_IDXS[:CONTROL_N], psis) psi = interp(delay, ModelConstants.T_IDXS[:CONTROL_N], psis)
average_curvature_desired = psi / (v_ego * delay) # Pfeiferj's #28118 PR - https://github.com/commaai/openpilot/pull/28118
distance = interp(delay, ModelConstants.T_IDXS[:CONTROL_N], distances)
distance = max(MIN_DIST, distance)
average_curvature_desired = psi / distance if average_desired_curvature else psi / (v_ego * delay)
desired_curvature = 2 * average_curvature_desired - current_curvature_desired desired_curvature = 2 * average_curvature_desired - current_curvature_desired
# This is the "desired rate of the setpoint" not an actual desired rate # This is the "desired rate of the setpoint" not an actual desired rate

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@@ -35,6 +35,9 @@ class LateralPlanner:
if len(md.position.x) == TRAJECTORY_SIZE and len(md.velocity.x) == TRAJECTORY_SIZE and len(md.lateralPlannerSolution.x) == TRAJECTORY_SIZE: if len(md.position.x) == TRAJECTORY_SIZE and len(md.velocity.x) == TRAJECTORY_SIZE and len(md.lateralPlannerSolution.x) == TRAJECTORY_SIZE:
self.path_xyz = np.column_stack([md.position.x, md.position.y, md.position.z]) self.path_xyz = np.column_stack([md.position.x, md.position.y, md.position.z])
self.velocity_xyz = np.column_stack([md.velocity.x, md.velocity.y, md.velocity.z]) self.velocity_xyz = np.column_stack([md.velocity.x, md.velocity.y, md.velocity.z])
if frogpilot_planner.average_desired_curvature:
car_speed = np.array(md.velocity.x) - get_speed_error(md, v_ego_car)
else:
car_speed = np.linalg.norm(self.velocity_xyz, axis=1) - get_speed_error(md, v_ego_car) car_speed = np.linalg.norm(self.velocity_xyz, axis=1) - get_speed_error(md, v_ego_car)
self.v_plan = np.clip(car_speed, MIN_SPEED, np.inf) self.v_plan = np.clip(car_speed, MIN_SPEED, np.inf)
self.v_ego = self.v_plan[0] self.v_ego = self.v_plan[0]

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@@ -230,6 +230,7 @@ class LongitudinalMpc:
# self.solver = AcadosOcpSolverCython(MODEL_NAME, ACADOS_SOLVER_TYPE, N) # self.solver = AcadosOcpSolverCython(MODEL_NAME, ACADOS_SOLVER_TYPE, N)
self.solver.reset() self.solver.reset()
# self.solver.options_set('print_level', 2) # self.solver.options_set('print_level', 2)
self.x_solution = np.zeros(N+1)
self.v_solution = np.zeros(N+1) self.v_solution = np.zeros(N+1)
self.a_solution = np.zeros(N+1) self.a_solution = np.zeros(N+1)
self.prev_a = np.array(self.a_solution) self.prev_a = np.array(self.a_solution)
@@ -442,6 +443,7 @@ class LongitudinalMpc:
for i in range(N): for i in range(N):
self.u_sol[i] = self.solver.get(i, 'u') self.u_sol[i] = self.solver.get(i, 'u')
self.x_solution = self.x_sol[:,0]
self.v_solution = self.x_sol[:,1] self.v_solution = self.x_sol[:,1]
self.a_solution = self.x_sol[:,2] self.a_solution = self.x_sol[:,2]
self.j_solution = self.u_sol[:,0] self.j_solution = self.u_sol[:,0]

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@@ -1,9 +1,12 @@
import cereal.messaging as messaging import cereal.messaging as messaging
import numpy as np
from openpilot.common.conversions import Conversions as CV from openpilot.common.conversions import Conversions as CV
from openpilot.common.numpy_fast import interp from openpilot.common.numpy_fast import interp
from openpilot.selfdrive.controls.lib.drive_helpers import V_CRUISE_MAX from openpilot.selfdrive.controls.lib.drive_helpers import CONTROL_N, V_CRUISE_MAX
from openpilot.selfdrive.controls.lib.longitudinal_mpc_lib.long_mpc import T_IDXS as T_IDXS_MPC
from openpilot.selfdrive.controls.lib.longitudinal_planner import A_CRUISE_MIN, A_CRUISE_MAX_VALS, A_CRUISE_MAX_BP, get_max_accel from openpilot.selfdrive.controls.lib.longitudinal_planner import A_CRUISE_MIN, A_CRUISE_MAX_VALS, A_CRUISE_MAX_BP, get_max_accel
from openpilot.selfdrive.modeld.constants import ModelConstants
# Acceleration profiles - Credit goes to the DragonPilot team! # Acceleration profiles - Credit goes to the DragonPilot team!
# MPH = [0., 35, 35, 40, 40, 45, 45, 67, 67, 67, 123] # MPH = [0., 35, 35, 40, 40, 45, 45, 67, 67, 67, 123]
@@ -34,6 +37,8 @@ class FrogPilotPlanner:
def __init__(self, params): def __init__(self, params):
self.v_cruise = 0 self.v_cruise = 0
self.x_desired_trajectory = np.zeros(CONTROL_N)
self.update_frogpilot_params(params) self.update_frogpilot_params(params)
def update(self, sm, mpc): def update(self, sm, mpc):
@@ -55,6 +60,9 @@ class FrogPilotPlanner:
self.v_cruise = self.update_v_cruise(carState, controlsState, modelData, enabled, v_cruise, v_ego) self.v_cruise = self.update_v_cruise(carState, controlsState, modelData, enabled, v_cruise, v_ego)
self.x_desired_trajectory_full = np.interp(ModelConstants.T_IDXS, T_IDXS_MPC, mpc.x_solution)
self.x_desired_trajectory = self.x_desired_trajectory_full[:CONTROL_N]
def update_v_cruise(self, carState, controlsState, modelData, enabled, v_cruise, v_ego): def update_v_cruise(self, carState, controlsState, modelData, enabled, v_cruise, v_ego):
v_ego_diff = max(carState.vEgoRaw - carState.vEgoCluster, 0) v_ego_diff = max(carState.vEgoRaw - carState.vEgoCluster, 0)
return v_cruise - v_ego_diff return v_cruise - v_ego_diff
@@ -71,12 +79,15 @@ class FrogPilotPlanner:
frogpilot_longitudinal_plan_send.valid = sm.all_checks(service_list=['carState', 'controlsState']) frogpilot_longitudinal_plan_send.valid = sm.all_checks(service_list=['carState', 'controlsState'])
frogpilotLongitudinalPlan = frogpilot_longitudinal_plan_send.frogpilotLongitudinalPlan frogpilotLongitudinalPlan = frogpilot_longitudinal_plan_send.frogpilotLongitudinalPlan
frogpilotLongitudinalPlan.distances = self.x_desired_trajectory.tolist()
pm.send('frogpilotLongitudinalPlan', frogpilot_longitudinal_plan_send) pm.send('frogpilotLongitudinalPlan', frogpilot_longitudinal_plan_send)
def update_frogpilot_params(self, params): def update_frogpilot_params(self, params):
self.is_metric = params.get_bool("IsMetric") self.is_metric = params.get_bool("IsMetric")
lateral_tune = params.get_bool("LateralTune") lateral_tune = params.get_bool("LateralTune")
self.average_desired_curvature = params.get_bool("AverageCurvature") and lateral_tune
longitudinal_tune = params.get_bool("LongitudinalTune") longitudinal_tune = params.get_bool("LongitudinalTune")
self.acceleration_profile = params.get_int("AccelerationProfile") if longitudinal_tune else 0 self.acceleration_profile = params.get_int("AccelerationProfile") if longitudinal_tune else 0

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@@ -6,6 +6,7 @@ FrogPilotControlsPanel::FrogPilotControlsPanel(SettingsWindow *parent) : FrogPil
{"AlwaysOnLateral", "Always on Lateral", "Maintain openpilot lateral control when the brake or gas pedals are used.\n\nDeactivation occurs only through the 'Cruise Control' button.", "../frogpilot/assets/toggle_icons/icon_always_on_lateral.png"}, {"AlwaysOnLateral", "Always on Lateral", "Maintain openpilot lateral control when the brake or gas pedals are used.\n\nDeactivation occurs only through the 'Cruise Control' button.", "../frogpilot/assets/toggle_icons/icon_always_on_lateral.png"},
{"LateralTune", "Lateral Tuning", "Modify openpilot's steering behavior.", "../frogpilot/assets/toggle_icons/icon_lateral_tune.png"}, {"LateralTune", "Lateral Tuning", "Modify openpilot's steering behavior.", "../frogpilot/assets/toggle_icons/icon_lateral_tune.png"},
{"AverageCurvature", "Average Desired Curvature", "Use Pfeiferj's distance-based curvature adjustment for improved curve handling.", ""},
{"LongitudinalTune", "Longitudinal Tuning", "Modify openpilot's acceleration and braking behavior.", "../frogpilot/assets/toggle_icons/icon_longitudinal_tune.png"}, {"LongitudinalTune", "Longitudinal Tuning", "Modify openpilot's acceleration and braking behavior.", "../frogpilot/assets/toggle_icons/icon_longitudinal_tune.png"},
{"AccelerationProfile", "Acceleration Profile", "Change the acceleration rate to be either sporty or eco-friendly.", ""}, {"AccelerationProfile", "Acceleration Profile", "Change the acceleration rate to be either sporty or eco-friendly.", ""},
@@ -97,7 +98,7 @@ FrogPilotControlsPanel::FrogPilotControlsPanel(SettingsWindow *parent) : FrogPil
conditionalExperimentalKeys = {}; conditionalExperimentalKeys = {};
fireTheBabysitterKeys = {}; fireTheBabysitterKeys = {};
laneChangeKeys = {}; laneChangeKeys = {};
lateralTuneKeys = {}; lateralTuneKeys = {"AverageCurvature"};
longitudinalTuneKeys = {"AccelerationProfile", "AggressiveAcceleration"}; longitudinalTuneKeys = {"AccelerationProfile", "AggressiveAcceleration"};
speedLimitControllerKeys = {}; speedLimitControllerKeys = {};
visionTurnControlKeys = {}; visionTurnControlKeys = {};