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Move lag estimator for lagd

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pull/34975/head
Kacper Rączy 3 weeks ago
parent db51e93889
commit 1e01555a39
4 changed files with 301 additions and 304 deletions
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  1. 0
      selfdrive/locationd/estimators/__init__.py
  2. 303
      selfdrive/locationd/estimators/lateral_lag.py
  3. 38
      selfdrive/locationd/helpers.py
  4. 264
      selfdrive/locationd/lagd.py

0
selfdrive/locationd/estimators/__init__.py
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303
selfdrive/locationd/estimators/lateral_lag.py
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@ -1,303 +0,0 @@
import numpy as np
import capnp
from collections import deque
from functools import partial, cache
import cereal.messaging as messaging
from cereal import log, car
from openpilot.selfdrive.locationd.helpers import PoseCalibrator, Pose
BLOCK_SIZE = 100
BLOCK_NUM = 50
BLOCK_NUM_NEEDED = 5
MOVING_WINDOW_SEC = 300.0
MIN_OKAY_WINDOW_SEC = 30.0
MIN_RECOVERY_BUFFER_SEC = 2.0
MIN_VEGO = 15.0
MIN_ABS_YAW_RATE = np.radians(1.0)
MIN_NCC = 0.95
MAX_LAG = 1.0
@cache
def fft_next_good_size(n: int) -> int:
"""
smallest composite of 2, 3, 5, 7, 11 that is >= n
inspired by pocketfft
"""
if n <= 6:
return n
best, f2 = 2 * n, 1
while f2 < best:
f23 = f2
while f23 < best:
f235 = f23
while f235 < best:
f2357 = f235
while f2357 < best:
f235711 = f2357
while f235711 < best:
best = f235711 if f235711 >= n else best
f235711 *= 11
f2357 *= 7
f235 *= 5
f23 *= 3
f2 *= 2
return best
def parabolic_peak_interp(R, max_index):
if max_index == 0 or max_index == len(R) - 1:
return max_index
y_m1, y_0, y_p1 = R[max_index - 1], R[max_index], R[max_index + 1]
offset = 0.5 * (y_p1 - y_m1) / (2 * y_0 - y_p1 - y_m1)
return max_index + offset
def masked_normalized_cross_correlation(expected_sig: np.ndarray, actual_sig: np.ndarray, mask: np.ndarray, n: int):
"""
References:
D. Padfield. "Masked FFT registration". In Proc. Computer Vision and
Pattern Recognition, pp. 2918-2925 (2010).
:DOI:`10.1109/CVPR.2010.5540032`
"""
eps = np.finfo(np.float64).eps
expected_sig = np.asarray(expected_sig, dtype=np.float64)
actual_sig = np.asarray(actual_sig, dtype=np.float64)
expected_sig[~mask] = 0.0
actual_sig[~mask] = 0.0
rotated_expected_sig = expected_sig[::-1]
rotated_mask = mask[::-1]
fft = partial(np.fft.fft, n=n)
actual_sig_fft = fft(actual_sig)
rotated_expected_sig_fft = fft(rotated_expected_sig)
actual_mask_fft = fft(mask.astype(np.float64))
rotated_mask_fft = fft(rotated_mask.astype(np.float64))
number_overlap_masked_samples = np.fft.ifft(rotated_mask_fft * actual_mask_fft).real
number_overlap_masked_samples[:] = np.round(number_overlap_masked_samples)
number_overlap_masked_samples[:] = np.fmax(number_overlap_masked_samples, eps)
masked_correlated_actual_fft = np.fft.ifft(rotated_mask_fft * actual_sig_fft).real
masked_correlated_expected_fft = np.fft.ifft(actual_mask_fft * rotated_expected_sig_fft).real
numerator = np.fft.ifft(rotated_expected_sig_fft * actual_sig_fft).real
numerator -= masked_correlated_actual_fft * masked_correlated_expected_fft / number_overlap_masked_samples
actual_squared_fft = fft(actual_sig ** 2)
actual_sig_denom = np.fft.ifft(rotated_mask_fft * actual_squared_fft).real
actual_sig_denom -= masked_correlated_actual_fft ** 2 / number_overlap_masked_samples
actual_sig_denom[:] = np.fmax(actual_sig_denom, 0.0)
rotated_expected_squared_fft = fft(rotated_expected_sig ** 2)
expected_sig_denom = np.fft.ifft(actual_mask_fft * rotated_expected_squared_fft).real
expected_sig_denom -= masked_correlated_expected_fft ** 2 / number_overlap_masked_samples
expected_sig_denom[:] = np.fmax(expected_sig_denom, 0.0)
denom = np.sqrt(actual_sig_denom * expected_sig_denom)
# zero-out samples with very small denominators
tol = 1e3 * eps * np.max(np.abs(denom), keepdims=True)
nonzero_indices = denom > tol
ncc = np.zeros_like(denom, dtype=np.float64)
ncc[nonzero_indices] = numerator[nonzero_indices] / denom[nonzero_indices]
np.clip(ncc, -1, 1, out=ncc)
return ncc
class Points:
def __init__(self, num_points: int):
self.times = deque[float](maxlen=num_points)
self.okay = deque[bool](maxlen=num_points)
self.desired = deque[float](maxlen=num_points)
self.actual = deque[float](maxlen=num_points)
@property
def num_points(self):
return len(self.desired)
@property
def num_okay(self):
return np.count_nonzero(self.okay)
def update(self, t: float, desired: float, actual: float, okay: bool):
self.times.append(t)
self.okay.append(okay)
self.desired.append(desired)
self.actual.append(actual)
def get(self) -> tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]:
return np.array(self.times), np.array(self.desired), np.array(self.actual), np.array(self.okay)
class BlockAverage:
def __init__(self, num_blocks: int, block_size: int, valid_blocks: int, initial_value: float):
self.num_blocks = num_blocks
self.block_size = block_size
self.block_idx = valid_blocks % block_size
self.idx = 0
self.values = np.tile(initial_value, (num_blocks, 1))
self.valid_blocks = valid_blocks
def update(self, value: float):
self.values[self.block_idx] = (self.idx * self.values[self.block_idx] + (self.block_size - self.idx) * value) / self.block_size
self.idx = (self.idx + 1) % self.block_size
if self.idx == 0:
self.block_idx = (self.block_idx + 1) % self.num_blocks
self.valid_blocks = min(self.valid_blocks + 1, self.num_blocks)
def get(self) -> float | None:
valid_block_idx = [i for i in range(self.valid_blocks) if i != self.block_idx]
if not valid_block_idx:
return None
return float(np.mean(self.values[valid_block_idx], axis=0).item())
class LateralLagEstimator:
inputs = {"carControl", "carState", "controlsState", "liveCalibration", "livePose"}
def __init__(self, CP: car.CarParams, dt: float,
block_count: int = BLOCK_NUM, min_valid_block_count: int = BLOCK_NUM_NEEDED, block_size: int = BLOCK_SIZE,
window_sec: float = MOVING_WINDOW_SEC, okay_window_sec: float = MIN_OKAY_WINDOW_SEC, min_recovery_buffer_sec: float = MIN_RECOVERY_BUFFER_SEC,
min_vego: float = MIN_VEGO, min_yr: float = MIN_ABS_YAW_RATE, min_ncc: float = MIN_NCC):
self.dt = dt
self.window_sec = window_sec
self.okay_window_sec = okay_window_sec
self.min_recovery_buffer_sec = min_recovery_buffer_sec
self.initial_lag = CP.steerActuatorDelay + 0.2
self.block_size = block_size
self.block_count = block_count
self.min_valid_block_count = min_valid_block_count
self.min_vego = min_vego
self.min_yr = min_yr
self.min_ncc = min_ncc
self.t = 0.0
self.lat_active = False
self.steering_pressed = False
self.steering_saturated = False
self.desired_curvature = 0.0
self.v_ego = 0.0
self.yaw_rate = 0.0
self.last_lat_inactive_t = 0.0
self.last_steering_pressed_t = 0.0
self.last_steering_saturated_t = 0.0
self.last_estimate_t = 0.0
self.calibrator = PoseCalibrator()
self.reset(self.initial_lag, 0)
def reset(self, initial_lag: float, valid_blocks: int):
window_len = int(self.window_sec / self.dt)
self.points = Points(window_len)
self.block_avg = BlockAverage(self.block_count, self.block_size, valid_blocks, initial_lag)
def get_msg(self, valid: bool, debug: bool = False) -> capnp._DynamicStructBuilder:
msg = messaging.new_message('liveDelay')
msg.valid = valid
liveDelay = msg.liveDelay
estimated_lag = self.block_avg.get()
liveDelay.lateralDelayEstimate = estimated_lag or self.initial_lag
if self.block_avg.valid_blocks >= self.min_valid_block_count and estimated_lag is not None:
liveDelay.status = log.LiveDelayData.Status.estimated
liveDelay.lateralDelay = estimated_lag
else:
liveDelay.status = log.LiveDelayData.Status.unestimated
liveDelay.lateralDelay = self.initial_lag
liveDelay.validBlocks = self.block_avg.valid_blocks
if debug:
liveDelay.points = self.block_avg.values.flatten().tolist()
return msg
def handle_log(self, t: float, which: str, msg: capnp._DynamicStructReader):
if which == "carControl":
self.lat_active = msg.latActive
elif which == "carState":
self.steering_pressed = msg.steeringPressed
self.v_ego = msg.vEgo
elif which == "controlsState":
self.steering_saturated = getattr(msg.lateralControlState, msg.lateralControlState.which()).saturated
self.desired_curvature = msg.desiredCurvature
elif which == "liveCalibration":
self.calibrator.feed_live_calib(msg)
elif which == "livePose":
device_pose = Pose.from_live_pose(msg)
calibrated_pose = self.calibrator.build_calibrated_pose(device_pose)
self.yaw_rate = calibrated_pose.angular_velocity.z
self.t = t
def points_enough(self):
return self.points.num_points >= int(self.okay_window_sec / self.dt)
def points_valid(self):
return self.points.num_okay >= int(self.okay_window_sec / self.dt)
def update_points(self):
if not self.lat_active:
self.last_lat_inactive_t = self.t
if self.steering_pressed:
self.last_steering_pressed_t = self.t
if self.steering_saturated:
self.last_steering_saturated_t = self.t
la_desired = self.desired_curvature * self.v_ego * self.v_ego
la_actual_pose = self.yaw_rate * self.v_ego
fast = self.v_ego > self.min_vego
turning = np.abs(self.yaw_rate) >= self.min_yr
has_recovered = all( # wait for recovery after !lat_active, steering_pressed, steering_saturated
self.t - last_t >= self.min_recovery_buffer_sec
for last_t in [self.last_lat_inactive_t, self.last_steering_pressed_t, self.last_steering_saturated_t]
)
okay = self.lat_active and not self.steering_pressed and not self.steering_saturated and fast and turning and has_recovered
self.points.update(self.t, la_desired, la_actual_pose, okay)
def update_estimate(self):
if not self.points_enough():
return
times, desired, actual, okay = self.points.get()
# check if there are any new valid data points since the last update
is_valid = self.points_valid()
if self.last_estimate_t != 0 and times[0] <= self.last_estimate_t:
new_values_start_idx = next(-i for i, t in enumerate(reversed(times)) if t <= self.last_estimate_t)
is_valid = is_valid and not (new_values_start_idx == 0 or not np.any(okay[new_values_start_idx:]))
delay, corr = self.actuator_delay(desired, actual, okay, self.dt, MAX_LAG)
if corr < self.min_ncc or not is_valid:
return
self.block_avg.update(delay)
self.last_estimate_t = self.t
def actuator_delay(self, expected_sig: np.ndarray, actual_sig: np.ndarray, mask: np.ndarray, dt: float, max_lag: float) -> tuple[float, float]:
assert len(expected_sig) == len(actual_sig)
max_lag_samples = int(max_lag / dt)
padded_size = fft_next_good_size(len(expected_sig) + max_lag_samples)
ncc = masked_normalized_cross_correlation(expected_sig, actual_sig, mask, padded_size)
# only consider lags from 0 to max_lag
roi_ncc = ncc[len(expected_sig) - 1: len(expected_sig) - 1 + max_lag_samples]
max_corr_index = np.argmax(roi_ncc)
corr = roi_ncc[max_corr_index]
lag = parabolic_peak_interp(roi_ncc, max_corr_index) * dt
return lag, corr

38
selfdrive/locationd/helpers.py
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@ -1,10 +1,48 @@
import numpy as np
from typing import Any
from functools import cache
from cereal import log
from openpilot.common.transformations.orientation import rot_from_euler, euler_from_rot
@cache
def fft_next_good_size(n: int) -> int:
"""
smallest composite of 2, 3, 5, 7, 11 that is >= n
inspired by pocketfft
"""
if n <= 6:
return n
best, f2 = 2 * n, 1
while f2 < best:
f23 = f2
while f23 < best:
f235 = f23
while f235 < best:
f2357 = f235
while f2357 < best:
f235711 = f2357
while f235711 < best:
best = f235711 if f235711 >= n else best
f235711 *= 11
f2357 *= 7
f235 *= 5
f23 *= 3
f2 *= 2
return best
def parabolic_peak_interp(R, max_index):
if max_index == 0 or max_index == len(R) - 1:
return max_index
y_m1, y_0, y_p1 = R[max_index - 1], R[max_index], R[max_index + 1]
offset = 0.5 * (y_p1 - y_m1) / (2 * y_0 - y_p1 - y_m1)
return max_index + offset
def rotate_cov(rot_matrix, cov_in):
return rot_matrix @ cov_in @ rot_matrix.T

264
selfdrive/locationd/lagd.py
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@ -1,5 +1,9 @@
#!/usr/bin/env python3
import os
import numpy as np
import capnp
from collections import deque
from functools import partial
import cereal.messaging as messaging
from cereal import car, log
@ -7,7 +11,265 @@ from cereal.services import SERVICE_LIST
from openpilot.common.params import Params
from openpilot.common.realtime import config_realtime_process
from openpilot.common.swaglog import cloudlog
from openpilot.selfdrive.locationd.estimators.lateral_lag import LateralLagEstimator
from openpilot.selfdrive.locationd.helpers import PoseCalibrator, Pose, fft_next_good_size, parabolic_peak_interp
BLOCK_SIZE = 100
BLOCK_NUM = 50
BLOCK_NUM_NEEDED = 5
MOVING_WINDOW_SEC = 300.0
MIN_OKAY_WINDOW_SEC = 30.0
MIN_RECOVERY_BUFFER_SEC = 2.0
MIN_VEGO = 15.0
MIN_ABS_YAW_RATE = np.radians(1.0)
MIN_NCC = 0.95
MAX_LAG = 1.0
def masked_normalized_cross_correlation(expected_sig: np.ndarray, actual_sig: np.ndarray, mask: np.ndarray, n: int):
"""
References:
D. Padfield. "Masked FFT registration". In Proc. Computer Vision and
Pattern Recognition, pp. 2918-2925 (2010).
:DOI:`10.1109/CVPR.2010.5540032`
"""
eps = np.finfo(np.float64).eps
expected_sig = np.asarray(expected_sig, dtype=np.float64)
actual_sig = np.asarray(actual_sig, dtype=np.float64)
expected_sig[~mask] = 0.0
actual_sig[~mask] = 0.0
rotated_expected_sig = expected_sig[::-1]
rotated_mask = mask[::-1]
fft = partial(np.fft.fft, n=n)
actual_sig_fft = fft(actual_sig)
rotated_expected_sig_fft = fft(rotated_expected_sig)
actual_mask_fft = fft(mask.astype(np.float64))
rotated_mask_fft = fft(rotated_mask.astype(np.float64))
number_overlap_masked_samples = np.fft.ifft(rotated_mask_fft * actual_mask_fft).real
number_overlap_masked_samples[:] = np.round(number_overlap_masked_samples)
number_overlap_masked_samples[:] = np.fmax(number_overlap_masked_samples, eps)
masked_correlated_actual_fft = np.fft.ifft(rotated_mask_fft * actual_sig_fft).real
masked_correlated_expected_fft = np.fft.ifft(actual_mask_fft * rotated_expected_sig_fft).real
numerator = np.fft.ifft(rotated_expected_sig_fft * actual_sig_fft).real
numerator -= masked_correlated_actual_fft * masked_correlated_expected_fft / number_overlap_masked_samples
actual_squared_fft = fft(actual_sig ** 2)
actual_sig_denom = np.fft.ifft(rotated_mask_fft * actual_squared_fft).real
actual_sig_denom -= masked_correlated_actual_fft ** 2 / number_overlap_masked_samples
actual_sig_denom[:] = np.fmax(actual_sig_denom, 0.0)
rotated_expected_squared_fft = fft(rotated_expected_sig ** 2)
expected_sig_denom = np.fft.ifft(actual_mask_fft * rotated_expected_squared_fft).real
expected_sig_denom -= masked_correlated_expected_fft ** 2 / number_overlap_masked_samples
expected_sig_denom[:] = np.fmax(expected_sig_denom, 0.0)
denom = np.sqrt(actual_sig_denom * expected_sig_denom)
# zero-out samples with very small denominators
tol = 1e3 * eps * np.max(np.abs(denom), keepdims=True)
nonzero_indices = denom > tol
ncc = np.zeros_like(denom, dtype=np.float64)
ncc[nonzero_indices] = numerator[nonzero_indices] / denom[nonzero_indices]
np.clip(ncc, -1, 1, out=ncc)
return ncc
class Points:
def __init__(self, num_points: int):
self.times = deque[float](maxlen=num_points)
self.okay = deque[bool](maxlen=num_points)
self.desired = deque[float](maxlen=num_points)
self.actual = deque[float](maxlen=num_points)
@property
def num_points(self):
return len(self.desired)
@property
def num_okay(self):
return np.count_nonzero(self.okay)
def update(self, t: float, desired: float, actual: float, okay: bool):
self.times.append(t)
self.okay.append(okay)
self.desired.append(desired)
self.actual.append(actual)
def get(self) -> tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]:
return np.array(self.times), np.array(self.desired), np.array(self.actual), np.array(self.okay)
class BlockAverage:
def __init__(self, num_blocks: int, block_size: int, valid_blocks: int, initial_value: float):
self.num_blocks = num_blocks
self.block_size = block_size
self.block_idx = valid_blocks % block_size
self.idx = 0
self.values = np.tile(initial_value, (num_blocks, 1))
self.valid_blocks = valid_blocks
def update(self, value: float):
self.values[self.block_idx] = (self.idx * self.values[self.block_idx] + (self.block_size - self.idx) * value) / self.block_size
self.idx = (self.idx + 1) % self.block_size
if self.idx == 0:
self.block_idx = (self.block_idx + 1) % self.num_blocks
self.valid_blocks = min(self.valid_blocks + 1, self.num_blocks)
def get(self) -> float | None:
valid_block_idx = [i for i in range(self.valid_blocks) if i != self.block_idx]
if not valid_block_idx:
return None
return float(np.mean(self.values[valid_block_idx], axis=0).item())
class LateralLagEstimator:
inputs = {"carControl", "carState", "controlsState", "liveCalibration", "livePose"}
def __init__(self, CP: car.CarParams, dt: float,
block_count: int = BLOCK_NUM, min_valid_block_count: int = BLOCK_NUM_NEEDED, block_size: int = BLOCK_SIZE,
window_sec: float = MOVING_WINDOW_SEC, okay_window_sec: float = MIN_OKAY_WINDOW_SEC, min_recovery_buffer_sec: float = MIN_RECOVERY_BUFFER_SEC,
min_vego: float = MIN_VEGO, min_yr: float = MIN_ABS_YAW_RATE, min_ncc: float = MIN_NCC):
self.dt = dt
self.window_sec = window_sec
self.okay_window_sec = okay_window_sec
self.min_recovery_buffer_sec = min_recovery_buffer_sec
self.initial_lag = CP.steerActuatorDelay + 0.2
self.block_size = block_size
self.block_count = block_count
self.min_valid_block_count = min_valid_block_count
self.min_vego = min_vego
self.min_yr = min_yr
self.min_ncc = min_ncc
self.t = 0.0
self.lat_active = False
self.steering_pressed = False
self.steering_saturated = False
self.desired_curvature = 0.0
self.v_ego = 0.0
self.yaw_rate = 0.0
self.last_lat_inactive_t = 0.0
self.last_steering_pressed_t = 0.0
self.last_steering_saturated_t = 0.0
self.last_estimate_t = 0.0
self.calibrator = PoseCalibrator()
self.reset(self.initial_lag, 0)
def reset(self, initial_lag: float, valid_blocks: int):
window_len = int(self.window_sec / self.dt)
self.points = Points(window_len)
self.block_avg = BlockAverage(self.block_count, self.block_size, valid_blocks, initial_lag)
def get_msg(self, valid: bool, debug: bool = False) -> capnp._DynamicStructBuilder:
msg = messaging.new_message('liveDelay')
msg.valid = valid
liveDelay = msg.liveDelay
estimated_lag = self.block_avg.get()
liveDelay.lateralDelayEstimate = estimated_lag or self.initial_lag
if self.block_avg.valid_blocks >= self.min_valid_block_count and estimated_lag is not None:
liveDelay.status = log.LiveDelayData.Status.estimated
liveDelay.lateralDelay = estimated_lag
else:
liveDelay.status = log.LiveDelayData.Status.unestimated
liveDelay.lateralDelay = self.initial_lag
liveDelay.validBlocks = self.block_avg.valid_blocks
if debug:
liveDelay.points = self.block_avg.values.flatten().tolist()
return msg
def handle_log(self, t: float, which: str, msg: capnp._DynamicStructReader):
if which == "carControl":
self.lat_active = msg.latActive
elif which == "carState":
self.steering_pressed = msg.steeringPressed
self.v_ego = msg.vEgo
elif which == "controlsState":
self.steering_saturated = getattr(msg.lateralControlState, msg.lateralControlState.which()).saturated
self.desired_curvature = msg.desiredCurvature
elif which == "liveCalibration":
self.calibrator.feed_live_calib(msg)
elif which == "livePose":
device_pose = Pose.from_live_pose(msg)
calibrated_pose = self.calibrator.build_calibrated_pose(device_pose)
self.yaw_rate = calibrated_pose.angular_velocity.z
self.t = t
def points_enough(self):
return self.points.num_points >= int(self.okay_window_sec / self.dt)
def points_valid(self):
return self.points.num_okay >= int(self.okay_window_sec / self.dt)
def update_points(self):
if not self.lat_active:
self.last_lat_inactive_t = self.t
if self.steering_pressed:
self.last_steering_pressed_t = self.t
if self.steering_saturated:
self.last_steering_saturated_t = self.t
la_desired = self.desired_curvature * self.v_ego * self.v_ego
la_actual_pose = self.yaw_rate * self.v_ego
fast = self.v_ego > self.min_vego
turning = np.abs(self.yaw_rate) >= self.min_yr
has_recovered = all( # wait for recovery after !lat_active, steering_pressed, steering_saturated
self.t - last_t >= self.min_recovery_buffer_sec
for last_t in [self.last_lat_inactive_t, self.last_steering_pressed_t, self.last_steering_saturated_t]
)
okay = self.lat_active and not self.steering_pressed and not self.steering_saturated and fast and turning and has_recovered
self.points.update(self.t, la_desired, la_actual_pose, okay)
def update_estimate(self):
if not self.points_enough():
return
times, desired, actual, okay = self.points.get()
# check if there are any new valid data points since the last update
is_valid = self.points_valid()
if self.last_estimate_t != 0 and times[0] <= self.last_estimate_t:
new_values_start_idx = next(-i for i, t in enumerate(reversed(times)) if t <= self.last_estimate_t)
is_valid = is_valid and not (new_values_start_idx == 0 or not np.any(okay[new_values_start_idx:]))
delay, corr = self.actuator_delay(desired, actual, okay, self.dt, MAX_LAG)
if corr < self.min_ncc or not is_valid:
return
self.block_avg.update(delay)
self.last_estimate_t = self.t
def actuator_delay(self, expected_sig: np.ndarray, actual_sig: np.ndarray, mask: np.ndarray, dt: float, max_lag: float) -> tuple[float, float]:
assert len(expected_sig) == len(actual_sig)
max_lag_samples = int(max_lag / dt)
padded_size = fft_next_good_size(len(expected_sig) + max_lag_samples)
ncc = masked_normalized_cross_correlation(expected_sig, actual_sig, mask, padded_size)
# only consider lags from 0 to max_lag
roi_ncc = ncc[len(expected_sig) - 1: len(expected_sig) - 1 + max_lag_samples]
max_corr_index = np.argmax(roi_ncc)
corr = roi_ncc[max_corr_index]
lag = parabolic_peak_interp(roi_ncc, max_corr_index) * dt
return lag, corr
def retrieve_initial_lag(params_reader: Params, CP: car.CarParams):

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