from math import atan2
from cereal import car
from common.numpy_fast import interp
from common.realtime import DT_DMON
from common.filter_simple import FirstOrderFilter
from common.stat_live import RunningStatFilter
from common.transformations.camera import tici_d_frame_size
EventName = car.CarEvent.EventName
# ******************************************************************************************
# NOTE: To fork maintainers.
# Disabling or nerfing safety features will get you and your users banned from our servers.
# We recommend that you do not change these numbers from the defaults.
# ******************************************************************************************
class DRIVER_MONITOR_SETTINGS():
def __init__(self):
self._DT_DMON = DT_DMON
# ref (page15-16): https://eur-lex.europa.eu/legal-content/EN/TXT/PDF/?uri=CELEX:42018X1947&rid=2
self._AWARENESS_TIME = 30. # passive wheeltouch total timeout
self._AWARENESS_PRE_TIME_TILL_TERMINAL = 15.
self._AWARENESS_PROMPT_TIME_TILL_TERMINAL = 6.
self._DISTRACTED_TIME = 11. # active monitoring total timeout
self._DISTRACTED_PRE_TIME_TILL_TERMINAL = 8.
self._DISTRACTED_PROMPT_TIME_TILL_TERMINAL = 6.
self._FACE_THRESHOLD = 0.7
self._EYE_THRESHOLD = 0.65
self._SG_THRESHOLD = 0.9
self._BLINK_THRESHOLD = 0.87
self._EE_THRESH11 = 0.75
self._EE_THRESH12 = 3.25
self._EE_THRESH21 = 0.01
self._EE_THRESH22 = 0.35
self._POSE_PITCH_THRESHOLD = 0.3133
self._POSE_PITCH_THRESHOLD_SLACK = 0.3237
self._POSE_PITCH_THRESHOLD_STRICT = self._POSE_PITCH_THRESHOLD
self._POSE_YAW_THRESHOLD = 0.4020
self._POSE_YAW_THRESHOLD_SLACK = 0.5042
self._POSE_YAW_THRESHOLD_STRICT = self._POSE_YAW_THRESHOLD
self._PITCH_NATURAL_OFFSET = 0.029 # initial value before offset is learned
self._YAW_NATURAL_OFFSET = 0.097 # initial value before offset is learned
self._PITCH_MAX_OFFSET = 0.124
self._PITCH_MIN_OFFSET = -0.0881
self._YAW_MAX_OFFSET = 0.289
self._YAW_MIN_OFFSET = -0.0246
self._POSESTD_THRESHOLD = 0.3
self._HI_STD_FALLBACK_TIME = int(10 / self._DT_DMON) # fall back to wheel touch if model is uncertain for 10s
self._DISTRACTED_FILTER_TS = 0.25 # 0.6Hz
self._POSE_CALIB_MIN_SPEED = 13 # 30 mph
self._POSE_OFFSET_MIN_COUNT = int(60 / self._DT_DMON) # valid data counts before calibration completes, 1min cumulative
self._POSE_OFFSET_MAX_COUNT = int(360 / self._DT_DMON) # stop deweighting new data after 6 min, aka "short term memory"
self._WHEELPOS_THRESHOLD = 0.5
self._WHEELPOS_FILTER_MIN_COUNT = int(5 / self._DT_DMON)
self._RECOVERY_FACTOR_MAX = 5. # relative to minus step change
self._RECOVERY_FACTOR_MIN = 1.25 # relative to minus step change
self._MAX_TERMINAL_ALERTS = 3 # not allowed to engage after 3 terminal alerts
self._MAX_TERMINAL_DURATION = int(30 / self._DT_DMON) # not allowed to engage after 30s of terminal alerts
# model output refers to center of undistorted+leveled image
EFL = 598.0 # focal length in K
W, H = tici_d_frame_size # corrected image has same size as raw
class DistractedType:
NOT_DISTRACTED = 0
DISTRACTED_POSE = 1
DISTRACTED_BLINK = 2
DISTRACTED_E2E = 4
def face_orientation_from_net(angles_desc, pos_desc, rpy_calib):
# the output of these angles are in device frame
# so from driver's perspective, pitch is up and yaw is right
pitch_net, yaw_net, roll_net = angles_desc
face_pixel_position = ((pos_desc[0]+0.5)*W, (pos_desc[1]+0.5)*H)
yaw_focal_angle = atan2(face_pixel_position[0] - W//2, EFL)
pitch_focal_angle = atan2(face_pixel_position[1] - H//2, EFL)
pitch = pitch_net + pitch_focal_angle
yaw = -yaw_net + yaw_focal_angle
# no calib for roll
pitch -= rpy_calib[1]
yaw -= rpy_calib[2]
return roll_net, pitch, yaw
class DriverPose():
def __init__(self, max_trackable):
self.yaw = 0.
self.pitch = 0.
self.roll = 0.
self.yaw_std = 0.
self.pitch_std = 0.
self.roll_std = 0.
self.pitch_offseter = RunningStatFilter(max_trackable=max_trackable)
self.yaw_offseter = RunningStatFilter(max_trackable=max_trackable)
self.low_std = True
self.cfactor_pitch = 1.
self.cfactor_yaw = 1.
class DriverBlink():
def __init__(self):
self.left_blink = 0.
self.right_blink = 0.
class DriverStatus():
def __init__(self, rhd=False, settings=DRIVER_MONITOR_SETTINGS()):
# init policy settings
self.settings = settings
# init driver status
self.wheelpos_learner = RunningStatFilter()
self.pose = DriverPose(self.settings._POSE_OFFSET_MAX_COUNT)
self.pose_calibrated = False
self.blink = DriverBlink()
self.eev1 = 0.
self.eev2 = 1.
self.ee1_offseter = RunningStatFilter(max_trackable=self.settings._POSE_OFFSET_MAX_COUNT)
self.ee2_offseter = RunningStatFilter(max_trackable=self.settings._POSE_OFFSET_MAX_COUNT)
self.ee1_calibrated = False
self.ee2_calibrated = False
self.awareness = 1.
self.awareness_active = 1.
self.awareness_passive = 1.
self.distracted_types = []
self.driver_distracted = False
self.driver_distraction_filter = FirstOrderFilter(0., self.settings._DISTRACTED_FILTER_TS, self.settings._DT_DMON)
self.wheel_on_right = False
self.rhd_toggled = rhd
self.face_detected = False
self.terminal_alert_cnt = 0
self.terminal_time = 0
self.step_change = 0.
self.active_monitoring_mode = True
self.is_model_uncertain = False
self.hi_stds = 0
self.threshold_pre = self.settings._DISTRACTED_PRE_TIME_TILL_TERMINAL / self.settings._DISTRACTED_TIME
self.threshold_prompt = self.settings._DISTRACTED_PROMPT_TIME_TILL_TERMINAL / self.settings._DISTRACTED_TIME
self._set_timers(active_monitoring=True)
def _set_timers(self, active_monitoring):
if self.active_monitoring_mode and self.awareness <= self.threshold_prompt:
if active_monitoring:
self.step_change = self.settings._DT_DMON / self.settings._DISTRACTED_TIME
else:
self.step_change = 0.
return # no exploit after orange alert
elif self.awareness <= 0.:
return
if active_monitoring:
# when falling back from passive mode to active mode, reset awareness to avoid false alert
if not self.active_monitoring_mode:
self.awareness_passive = self.awareness
self.awareness = self.awareness_active
self.threshold_pre = self.settings._DISTRACTED_PRE_TIME_TILL_TERMINAL / self.settings._DISTRACTED_TIME
self.threshold_prompt = self.settings._DISTRACTED_PROMPT_TIME_TILL_TERMINAL / self.settings._DISTRACTED_TIME
self.step_change = self.settings._DT_DMON / self.settings._DISTRACTED_TIME
self.active_monitoring_mode = True
else:
if self.active_monitoring_mode:
self.awareness_active = self.awareness
self.awareness = self.awareness_passive
self.threshold_pre = self.settings._AWARENESS_PRE_TIME_TILL_TERMINAL / self.settings._AWARENESS_TIME
self.threshold_prompt = self.settings._AWARENESS_PROMPT_TIME_TILL_TERMINAL / self.settings._AWARENESS_TIME
self.step_change = self.settings._DT_DMON / self.settings._AWARENESS_TIME
self.active_monitoring_mode = False
def _get_distracted_types(self):
distracted_types = []
if not self.pose_calibrated:
pitch_error = self.pose.pitch - self.settings._PITCH_NATURAL_OFFSET
yaw_error = self.pose.yaw - self.settings._YAW_NATURAL_OFFSET
else:
pitch_error = self.pose.pitch - min(max(self.pose.pitch_offseter.filtered_stat.mean(),
self.settings._PITCH_MIN_OFFSET), self.settings._PITCH_MAX_OFFSET)
yaw_error = self.pose.yaw - min(max(self.pose.yaw_offseter.filtered_stat.mean(),
self.settings._YAW_MIN_OFFSET), self.settings._YAW_MAX_OFFSET)
pitch_error = 0 if pitch_error > 0 else abs(pitch_error) # no positive pitch limit
yaw_error = abs(yaw_error)
if pitch_error > self.settings._POSE_PITCH_THRESHOLD*self.pose.cfactor_pitch or \
yaw_error > self.settings._POSE_YAW_THRESHOLD*self.pose.cfactor_yaw:
distracted_types.append(DistractedType.DISTRACTED_POSE)
if (self.blink.left_blink + self.blink.right_blink)*0.5 > self.settings._BLINK_THRESHOLD:
distracted_types.append(DistractedType.DISTRACTED_BLINK)
if self.ee1_calibrated:
ee1_dist = self.eev1 > self.ee1_offseter.filtered_stat.M * self.settings._EE_THRESH12
else:
ee1_dist = self.eev1 > self.settings._EE_THRESH11
if self.ee2_calibrated:
ee2_dist = self.eev2 < self.ee2_offseter.filtered_stat.M * self.settings._EE_THRESH22
else:
ee2_dist = self.eev2 < self.settings._EE_THRESH21
if ee1_dist or ee2_dist:
distracted_types.append(DistractedType.DISTRACTED_E2E)
return distracted_types
def set_policy(self, model_data, car_speed):
bp = model_data.meta.disengagePredictions.brakeDisengageProbs[0] # brake disengage prob in next 2s
k1 = max(-0.00156*((car_speed-16)**2)+0.6, 0.2)
bp_normal = max(min(bp / k1, 0.5),0)
self.pose.cfactor_pitch = interp(bp_normal, [0, 0.5],
[self.settings._POSE_PITCH_THRESHOLD_SLACK,
self.settings._POSE_PITCH_THRESHOLD_STRICT]) / self.settings._POSE_PITCH_THRESHOLD
self.pose.cfactor_yaw = interp(bp_normal, [0, 0.5],
[self.settings._POSE_YAW_THRESHOLD_SLACK,
self.settings._POSE_YAW_THRESHOLD_STRICT]) / self.settings._POSE_YAW_THRESHOLD
def update_states(self, driver_state, cal_rpy, car_speed, op_engaged):
rhd_pred = driver_state.wheelOnRightProb
if car_speed > 0.01:
self.wheelpos_learner.push_and_update(rhd_pred)
if self.wheelpos_learner.filtered_stat.n > self.settings._WHEELPOS_FILTER_MIN_COUNT:
self.wheel_on_right = self.wheelpos_learner.filtered_stat.M > self.settings._WHEELPOS_THRESHOLD
else:
self.wheel_on_right = rhd_pred > self.settings._WHEELPOS_THRESHOLD
driver_data = driver_state.rightDriverData if self.rhd_toggled else driver_state.leftDriverData
if not all(len(x) > 0 for x in (driver_data.faceOrientation, driver_data.facePosition,
driver_data.faceOrientationStd, driver_data.facePositionStd,
driver_data.readyProb, driver_data.notReadyProb)):
return
self.face_detected = driver_data.faceProb > self.settings._FACE_THRESHOLD
self.pose.roll, self.pose.pitch, self.pose.yaw = face_orientation_from_net(driver_data.faceOrientation, driver_data.facePosition, cal_rpy)
if self.wheel_on_right:
self.pose.yaw *= -1
self.pose.pitch_std = driver_data.faceOrientationStd[0]
self.pose.yaw_std = driver_data.faceOrientationStd[1]
model_std_max = max(self.pose.pitch_std, self.pose.yaw_std)
self.pose.low_std = model_std_max < self.settings._POSESTD_THRESHOLD
self.blink.left_blink = driver_data.leftBlinkProb * (driver_data.leftEyeProb > self.settings._EYE_THRESHOLD) * (driver_data.sunglassesProb < self.settings._SG_THRESHOLD)
self.blink.right_blink = driver_data.rightBlinkProb * (driver_data.rightEyeProb > self.settings._EYE_THRESHOLD) * (driver_data.sunglassesProb < self.settings._SG_THRESHOLD)
self.eev1 = driver_data.notReadyProb[1]
self.eev2 = driver_data.readyProb[0]
self.distracted_types = self._get_distracted_types()
self.driver_distracted = (DistractedType.DISTRACTED_POSE in self.distracted_types or
DistractedType.DISTRACTED_BLINK in self.distracted_types) and \
driver_data.faceProb > self.settings._FACE_THRESHOLD and self.pose.low_std
self.driver_distraction_filter.update(self.driver_distracted)
# update offseter
# only update when driver is actively driving the car above a certain speed
if self.face_detected and car_speed > self.settings._POSE_CALIB_MIN_SPEED and self.pose.low_std and (not op_engaged or not self.driver_distracted):
self.pose.pitch_offseter.push_and_update(self.pose.pitch)
self.pose.yaw_offseter.push_and_update(self.pose.yaw)
self.ee1_offseter.push_and_update(self.eev1)
self.ee2_offseter.push_and_update(self.eev2)
self.pose_calibrated = self.pose.pitch_offseter.filtered_stat.n > self.settings._POSE_OFFSET_MIN_COUNT and \
self.pose.yaw_offseter.filtered_stat.n > self.settings._POSE_OFFSET_MIN_COUNT
self.ee1_calibrated = self.ee1_offseter.filtered_stat.n > self.settings._POSE_OFFSET_MIN_COUNT
self.ee2_calibrated = self.ee2_offseter.filtered_stat.n > self.settings._POSE_OFFSET_MIN_COUNT
self.is_model_uncertain = self.hi_stds > self.settings._HI_STD_FALLBACK_TIME
self._set_timers(self.face_detected and not self.is_model_uncertain)
if self.face_detected and not self.pose.low_std and not self.driver_distracted:
self.hi_stds += 1
elif self.face_detected and self.pose.low_std:
self.hi_stds = 0
def update_events(self, events, driver_engaged, ctrl_active, standstill):
if (driver_engaged and self.awareness > 0) or not ctrl_active:
# reset only when on disengagement if red reached
self.awareness = 1.
self.awareness_active = 1.
self.awareness_passive = 1.
return
driver_attentive = self.driver_distraction_filter.x < 0.37
awareness_prev = self.awareness
if (driver_attentive and self.face_detected and self.pose.low_std and self.awareness > 0):
# only restore awareness when paying attention and alert is not red
self.awareness = min(self.awareness + ((self.settings._RECOVERY_FACTOR_MAX-self.settings._RECOVERY_FACTOR_MIN)*(1.-self.awareness)+self.settings._RECOVERY_FACTOR_MIN)*self.step_change, 1.)
if self.awareness == 1.:
self.awareness_passive = min(self.awareness_passive + self.step_change, 1.)
# don't display alert banner when awareness is recovering and has cleared orange
if self.awareness > self.threshold_prompt:
return
standstill_exemption = standstill and self.awareness - self.step_change <= self.threshold_prompt
certainly_distracted = self.driver_distraction_filter.x > 0.63 and self.driver_distracted and self.face_detected
maybe_distracted = self.hi_stds > self.settings._HI_STD_FALLBACK_TIME or not self.face_detected
if certainly_distracted or maybe_distracted:
# should always be counting if distracted unless at standstill and reaching orange
if not standstill_exemption:
self.awareness = max(self.awareness - self.step_change, -0.1)
alert = None
if self.awareness <= 0.:
# terminal red alert: disengagement required
alert = EventName.driverDistracted if self.active_monitoring_mode else EventName.driverUnresponsive
self.terminal_time += 1
if awareness_prev > 0.:
self.terminal_alert_cnt += 1
elif self.awareness <= self.threshold_prompt:
# prompt orange alert
alert = EventName.promptDriverDistracted if self.active_monitoring_mode else EventName.promptDriverUnresponsive
elif self.awareness <= self.threshold_pre:
# pre green alert
alert = EventName.preDriverDistracted if self.active_monitoring_mode else EventName.preDriverUnresponsive
if alert is not None:
events.add(alert)