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416 lines
20 KiB
416 lines
20 KiB
from math import atan2
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from cereal import car
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import cereal.messaging as messaging
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from openpilot.selfdrive.selfdrived.events import Events
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from openpilot.common.numpy_fast import interp
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from openpilot.common.realtime import DT_DMON
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from openpilot.common.filter_simple import FirstOrderFilter
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from openpilot.common.stat_live import RunningStatFilter
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from openpilot.common.transformations.camera import DEVICE_CAMERAS
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EventName = car.OnroadEvent.EventName
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# ******************************************************************************************
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# NOTE: To fork maintainers.
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# Disabling or nerfing safety features will get you and your users banned from our servers.
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# We recommend that you do not change these numbers from the defaults.
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# ******************************************************************************************
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class DRIVER_MONITOR_SETTINGS:
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def __init__(self):
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self._DT_DMON = DT_DMON
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# ref (page15-16): https://eur-lex.europa.eu/legal-content/EN/TXT/PDF/?uri=CELEX:42018X1947&rid=2
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self._AWARENESS_TIME = 30. # passive wheeltouch total timeout
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self._AWARENESS_PRE_TIME_TILL_TERMINAL = 15.
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self._AWARENESS_PROMPT_TIME_TILL_TERMINAL = 6.
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self._DISTRACTED_TIME = 11. # active monitoring total timeout
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self._DISTRACTED_PRE_TIME_TILL_TERMINAL = 8.
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self._DISTRACTED_PROMPT_TIME_TILL_TERMINAL = 6.
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self._FACE_THRESHOLD = 0.7
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self._EYE_THRESHOLD = 0.65
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self._SG_THRESHOLD = 0.9
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self._BLINK_THRESHOLD = 0.865
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self._EE_THRESH11 = 0.25
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self._EE_THRESH12 = 7.5
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self._EE_MAX_OFFSET1 = 0.06
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self._EE_MIN_OFFSET1 = 0.025
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self._EE_THRESH21 = 0.01
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self._EE_THRESH22 = 0.35
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self._POSE_PITCH_THRESHOLD = 0.3133
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self._POSE_PITCH_THRESHOLD_SLACK = 0.3237
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self._POSE_PITCH_THRESHOLD_STRICT = self._POSE_PITCH_THRESHOLD
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self._POSE_YAW_THRESHOLD = 0.4020
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self._POSE_YAW_THRESHOLD_SLACK = 0.5042
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self._POSE_YAW_THRESHOLD_STRICT = self._POSE_YAW_THRESHOLD
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self._PITCH_NATURAL_OFFSET = 0.029 # initial value before offset is learned
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self._PITCH_NATURAL_THRESHOLD = 0.449
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self._YAW_NATURAL_OFFSET = 0.097 # initial value before offset is learned
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self._PITCH_MAX_OFFSET = 0.124
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self._PITCH_MIN_OFFSET = -0.0881
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self._YAW_MAX_OFFSET = 0.289
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self._YAW_MIN_OFFSET = -0.0246
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self._POSESTD_THRESHOLD = 0.3
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self._HI_STD_FALLBACK_TIME = int(10 / self._DT_DMON) # fall back to wheel touch if model is uncertain for 10s
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self._DISTRACTED_FILTER_TS = 0.25 # 0.6Hz
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self._ALWAYS_ON_ALERT_MIN_SPEED = 7
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self._POSE_CALIB_MIN_SPEED = 13 # 30 mph
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self._POSE_OFFSET_MIN_COUNT = int(60 / self._DT_DMON) # valid data counts before calibration completes, 1min cumulative
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self._POSE_OFFSET_MAX_COUNT = int(360 / self._DT_DMON) # stop deweighting new data after 6 min, aka "short term memory"
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self._WHEELPOS_CALIB_MIN_SPEED = 11
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self._WHEELPOS_THRESHOLD = 0.5
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self._WHEELPOS_FILTER_MIN_COUNT = int(15 / self._DT_DMON) # allow 15 seconds to converge wheel side
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self._RECOVERY_FACTOR_MAX = 5. # relative to minus step change
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self._RECOVERY_FACTOR_MIN = 1.25 # relative to minus step change
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self._MAX_TERMINAL_ALERTS = 3 # not allowed to engage after 3 terminal alerts
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self._MAX_TERMINAL_DURATION = int(30 / self._DT_DMON) # not allowed to engage after 30s of terminal alerts
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class DistractedType:
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NOT_DISTRACTED = 0
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DISTRACTED_POSE = 1 << 0
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DISTRACTED_BLINK = 1 << 1
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DISTRACTED_E2E = 1 << 2
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class DriverPose:
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def __init__(self, max_trackable):
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self.yaw = 0.
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self.pitch = 0.
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self.roll = 0.
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self.yaw_std = 0.
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self.pitch_std = 0.
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self.roll_std = 0.
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self.pitch_offseter = RunningStatFilter(max_trackable=max_trackable)
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self.yaw_offseter = RunningStatFilter(max_trackable=max_trackable)
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self.calibrated = False
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self.low_std = True
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self.cfactor_pitch = 1.
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self.cfactor_yaw = 1.
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class DriverBlink:
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def __init__(self):
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self.left = 0.
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self.right = 0.
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# model output refers to center of undistorted+leveled image
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EFL = 598.0 # focal length in K
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cam = DEVICE_CAMERAS[("tici", "ar0231")] # corrected image has same size as raw
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W, H = (cam.dcam.width, cam.dcam.height) # corrected image has same size as raw
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def face_orientation_from_net(angles_desc, pos_desc, rpy_calib):
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# the output of these angles are in device frame
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# so from driver's perspective, pitch is up and yaw is right
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pitch_net, yaw_net, roll_net = angles_desc
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face_pixel_position = ((pos_desc[0]+0.5)*W, (pos_desc[1]+0.5)*H)
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yaw_focal_angle = atan2(face_pixel_position[0] - W//2, EFL)
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pitch_focal_angle = atan2(face_pixel_position[1] - H//2, EFL)
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pitch = pitch_net + pitch_focal_angle
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yaw = -yaw_net + yaw_focal_angle
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# no calib for roll
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pitch -= rpy_calib[1]
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yaw -= rpy_calib[2]
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return roll_net, pitch, yaw
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class DriverMonitoring:
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def __init__(self, rhd_saved=False, settings=None, always_on=False):
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if settings is None:
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settings = DRIVER_MONITOR_SETTINGS()
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# init policy settings
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self.settings = settings
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# init driver status
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self.wheelpos_learner = RunningStatFilter()
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self.pose = DriverPose(self.settings._POSE_OFFSET_MAX_COUNT)
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self.blink = DriverBlink()
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self.eev1 = 0.
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self.eev2 = 1.
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self.ee1_offseter = RunningStatFilter(max_trackable=self.settings._POSE_OFFSET_MAX_COUNT)
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self.ee2_offseter = RunningStatFilter(max_trackable=self.settings._POSE_OFFSET_MAX_COUNT)
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self.ee1_calibrated = False
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self.ee2_calibrated = False
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self.always_on = always_on
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self.distracted_types = []
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self.driver_distracted = False
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self.driver_distraction_filter = FirstOrderFilter(0., self.settings._DISTRACTED_FILTER_TS, self.settings._DT_DMON)
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self.wheel_on_right = False
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self.wheel_on_right_last = None
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self.wheel_on_right_default = rhd_saved
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self.face_detected = False
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self.terminal_alert_cnt = 0
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self.terminal_time = 0
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self.step_change = 0.
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self.active_monitoring_mode = True
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self.is_model_uncertain = False
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self.hi_stds = 0
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self.threshold_pre = self.settings._DISTRACTED_PRE_TIME_TILL_TERMINAL / self.settings._DISTRACTED_TIME
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self.threshold_prompt = self.settings._DISTRACTED_PROMPT_TIME_TILL_TERMINAL / self.settings._DISTRACTED_TIME
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self._reset_awareness()
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self._set_timers(active_monitoring=True)
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self._reset_events()
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def _reset_awareness(self):
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self.awareness = 1.
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self.awareness_active = 1.
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self.awareness_passive = 1.
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def _reset_events(self):
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self.current_events = Events()
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def _set_timers(self, active_monitoring):
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if self.active_monitoring_mode and self.awareness <= self.threshold_prompt:
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if active_monitoring:
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self.step_change = self.settings._DT_DMON / self.settings._DISTRACTED_TIME
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else:
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self.step_change = 0.
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return # no exploit after orange alert
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elif self.awareness <= 0.:
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return
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if active_monitoring:
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# when falling back from passive mode to active mode, reset awareness to avoid false alert
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if not self.active_monitoring_mode:
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self.awareness_passive = self.awareness
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self.awareness = self.awareness_active
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self.threshold_pre = self.settings._DISTRACTED_PRE_TIME_TILL_TERMINAL / self.settings._DISTRACTED_TIME
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self.threshold_prompt = self.settings._DISTRACTED_PROMPT_TIME_TILL_TERMINAL / self.settings._DISTRACTED_TIME
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self.step_change = self.settings._DT_DMON / self.settings._DISTRACTED_TIME
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self.active_monitoring_mode = True
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else:
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if self.active_monitoring_mode:
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self.awareness_active = self.awareness
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self.awareness = self.awareness_passive
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self.threshold_pre = self.settings._AWARENESS_PRE_TIME_TILL_TERMINAL / self.settings._AWARENESS_TIME
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self.threshold_prompt = self.settings._AWARENESS_PROMPT_TIME_TILL_TERMINAL / self.settings._AWARENESS_TIME
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self.step_change = self.settings._DT_DMON / self.settings._AWARENESS_TIME
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self.active_monitoring_mode = False
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def _set_policy(self, model_data, car_speed):
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bp = model_data.meta.disengagePredictions.brakeDisengageProbs[0] # brake disengage prob in next 2s
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k1 = max(-0.00156*((car_speed-16)**2)+0.6, 0.2)
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bp_normal = max(min(bp / k1, 0.5),0)
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self.pose.cfactor_pitch = interp(bp_normal, [0, 0.5],
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[self.settings._POSE_PITCH_THRESHOLD_SLACK,
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self.settings._POSE_PITCH_THRESHOLD_STRICT]) / self.settings._POSE_PITCH_THRESHOLD
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self.pose.cfactor_yaw = interp(bp_normal, [0, 0.5],
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[self.settings._POSE_YAW_THRESHOLD_SLACK,
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self.settings._POSE_YAW_THRESHOLD_STRICT]) / self.settings._POSE_YAW_THRESHOLD
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def _get_distracted_types(self):
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distracted_types = []
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if not self.pose.calibrated:
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pitch_error = self.pose.pitch - self.settings._PITCH_NATURAL_OFFSET
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yaw_error = self.pose.yaw - self.settings._YAW_NATURAL_OFFSET
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else:
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pitch_error = self.pose.pitch - min(max(self.pose.pitch_offseter.filtered_stat.mean(),
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self.settings._PITCH_MIN_OFFSET), self.settings._PITCH_MAX_OFFSET)
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yaw_error = self.pose.yaw - min(max(self.pose.yaw_offseter.filtered_stat.mean(),
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self.settings._YAW_MIN_OFFSET), self.settings._YAW_MAX_OFFSET)
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pitch_error = 0 if pitch_error > 0 else abs(pitch_error) # no positive pitch limit
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yaw_error = abs(yaw_error)
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if pitch_error > (self.settings._POSE_PITCH_THRESHOLD*self.pose.cfactor_pitch if self.pose.calibrated else self.settings._PITCH_NATURAL_THRESHOLD) or \
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yaw_error > self.settings._POSE_YAW_THRESHOLD*self.pose.cfactor_yaw:
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distracted_types.append(DistractedType.DISTRACTED_POSE)
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if (self.blink.left + self.blink.right)*0.5 > self.settings._BLINK_THRESHOLD:
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distracted_types.append(DistractedType.DISTRACTED_BLINK)
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if self.ee1_calibrated:
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ee1_dist = self.eev1 > max(min(self.ee1_offseter.filtered_stat.M, self.settings._EE_MAX_OFFSET1), self.settings._EE_MIN_OFFSET1) \
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* self.settings._EE_THRESH12
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else:
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ee1_dist = self.eev1 > self.settings._EE_THRESH11
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if ee1_dist:
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distracted_types.append(DistractedType.DISTRACTED_E2E)
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return distracted_types
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def _update_states(self, driver_state, cal_rpy, car_speed, op_engaged):
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rhd_pred = driver_state.wheelOnRightProb
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# calibrates only when there's movement and either face detected
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if car_speed > self.settings._WHEELPOS_CALIB_MIN_SPEED and (driver_state.leftDriverData.faceProb > self.settings._FACE_THRESHOLD or
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driver_state.rightDriverData.faceProb > self.settings._FACE_THRESHOLD):
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self.wheelpos_learner.push_and_update(rhd_pred)
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if self.wheelpos_learner.filtered_stat.n > self.settings._WHEELPOS_FILTER_MIN_COUNT:
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self.wheel_on_right = self.wheelpos_learner.filtered_stat.M > self.settings._WHEELPOS_THRESHOLD
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else:
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self.wheel_on_right = self.wheel_on_right_default # use default/saved if calibration is unfinished
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# make sure no switching when engaged
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if op_engaged and self.wheel_on_right_last is not None and self.wheel_on_right_last != self.wheel_on_right:
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self.wheel_on_right = self.wheel_on_right_last
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driver_data = driver_state.rightDriverData if self.wheel_on_right else driver_state.leftDriverData
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if not all(len(x) > 0 for x in (driver_data.faceOrientation, driver_data.facePosition,
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driver_data.faceOrientationStd, driver_data.facePositionStd,
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driver_data.readyProb, driver_data.notReadyProb)):
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return
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self.face_detected = driver_data.faceProb > self.settings._FACE_THRESHOLD
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self.pose.roll, self.pose.pitch, self.pose.yaw = face_orientation_from_net(driver_data.faceOrientation, driver_data.facePosition, cal_rpy)
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if self.wheel_on_right:
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self.pose.yaw *= -1
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self.wheel_on_right_last = self.wheel_on_right
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self.pose.pitch_std = driver_data.faceOrientationStd[0]
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self.pose.yaw_std = driver_data.faceOrientationStd[1]
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model_std_max = max(self.pose.pitch_std, self.pose.yaw_std)
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self.pose.low_std = model_std_max < self.settings._POSESTD_THRESHOLD
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self.blink.left = driver_data.leftBlinkProb * (driver_data.leftEyeProb > self.settings._EYE_THRESHOLD) \
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* (driver_data.sunglassesProb < self.settings._SG_THRESHOLD)
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self.blink.right = driver_data.rightBlinkProb * (driver_data.rightEyeProb > self.settings._EYE_THRESHOLD) \
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* (driver_data.sunglassesProb < self.settings._SG_THRESHOLD)
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self.eev1 = driver_data.notReadyProb[0]
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self.eev2 = driver_data.readyProb[0]
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self.distracted_types = self._get_distracted_types()
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self.driver_distracted = (DistractedType.DISTRACTED_E2E in self.distracted_types or DistractedType.DISTRACTED_POSE in self.distracted_types
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or DistractedType.DISTRACTED_BLINK in self.distracted_types) \
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and driver_data.faceProb > self.settings._FACE_THRESHOLD and self.pose.low_std
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self.driver_distraction_filter.update(self.driver_distracted)
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# update offseter
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# only update when driver is actively driving the car above a certain speed
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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):
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self.pose.pitch_offseter.push_and_update(self.pose.pitch)
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self.pose.yaw_offseter.push_and_update(self.pose.yaw)
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self.ee1_offseter.push_and_update(self.eev1)
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self.ee2_offseter.push_and_update(self.eev2)
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self.pose.calibrated = self.pose.pitch_offseter.filtered_stat.n > self.settings._POSE_OFFSET_MIN_COUNT and \
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self.pose.yaw_offseter.filtered_stat.n > self.settings._POSE_OFFSET_MIN_COUNT
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self.ee1_calibrated = self.ee1_offseter.filtered_stat.n > self.settings._POSE_OFFSET_MIN_COUNT
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self.ee2_calibrated = self.ee2_offseter.filtered_stat.n > self.settings._POSE_OFFSET_MIN_COUNT
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self.is_model_uncertain = self.hi_stds > self.settings._HI_STD_FALLBACK_TIME
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self._set_timers(self.face_detected and not self.is_model_uncertain)
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if self.face_detected and not self.pose.low_std and not self.driver_distracted:
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self.hi_stds += 1
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elif self.face_detected and self.pose.low_std:
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self.hi_stds = 0
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def _update_events(self, driver_engaged, op_engaged, standstill, wrong_gear, car_speed):
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self._reset_events()
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# Block engaging after max number of distrations or when alert active
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if self.terminal_alert_cnt >= self.settings._MAX_TERMINAL_ALERTS or \
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self.terminal_time >= self.settings._MAX_TERMINAL_DURATION or \
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self.always_on and self.awareness <= self.threshold_prompt:
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self.current_events.add(EventName.tooDistracted)
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always_on_valid = self.always_on and not wrong_gear
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if (driver_engaged and self.awareness > 0 and not self.active_monitoring_mode) or \
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(not always_on_valid and not op_engaged) or \
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(always_on_valid and not op_engaged and self.awareness <= 0):
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# always reset on disengage with normal mode; disengage resets only on red if always on
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self._reset_awareness()
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return
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driver_attentive = self.driver_distraction_filter.x < 0.37
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awareness_prev = self.awareness
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if (driver_attentive and self.face_detected and self.pose.low_std and self.awareness > 0):
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if driver_engaged:
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self._reset_awareness()
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return
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# only restore awareness when paying attention and alert is not red
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self.awareness = min(self.awareness + ((self.settings._RECOVERY_FACTOR_MAX-self.settings._RECOVERY_FACTOR_MIN)*
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(1.-self.awareness)+self.settings._RECOVERY_FACTOR_MIN)*self.step_change, 1.)
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if self.awareness == 1.:
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self.awareness_passive = min(self.awareness_passive + self.step_change, 1.)
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# don't display alert banner when awareness is recovering and has cleared orange
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if self.awareness > self.threshold_prompt:
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return
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_reaching_audible = self.awareness - self.step_change <= self.threshold_prompt
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_reaching_terminal = self.awareness - self.step_change <= 0
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standstill_exemption = standstill and _reaching_audible
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always_on_red_exemption = always_on_valid and not op_engaged and _reaching_terminal
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always_on_lowspeed_exemption = always_on_valid and not op_engaged and car_speed < self.settings._ALWAYS_ON_ALERT_MIN_SPEED and _reaching_audible
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certainly_distracted = self.driver_distraction_filter.x > 0.63 and self.driver_distracted and self.face_detected
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maybe_distracted = self.hi_stds > self.settings._HI_STD_FALLBACK_TIME or not self.face_detected
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if certainly_distracted or maybe_distracted:
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# should always be counting if distracted unless at standstill (lowspeed for always-on) and reaching orange
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# also will not be reaching 0 if DM is active when not engaged
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if not (standstill_exemption or always_on_red_exemption or always_on_lowspeed_exemption):
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self.awareness = max(self.awareness - self.step_change, -0.1)
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alert = None
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if self.awareness <= 0.:
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# terminal red alert: disengagement required
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alert = EventName.driverDistracted if self.active_monitoring_mode else EventName.driverUnresponsive
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self.terminal_time += 1
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if awareness_prev > 0.:
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self.terminal_alert_cnt += 1
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elif self.awareness <= self.threshold_prompt:
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# prompt orange alert
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alert = EventName.promptDriverDistracted if self.active_monitoring_mode else EventName.promptDriverUnresponsive
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elif self.awareness <= self.threshold_pre:
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# pre green alert
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alert = EventName.preDriverDistracted if self.active_monitoring_mode else EventName.preDriverUnresponsive
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if alert is not None:
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self.current_events.add(alert)
|
|
|
|
|
|
def get_state_packet(self, valid=True):
|
|
# build driverMonitoringState packet
|
|
dat = messaging.new_message('driverMonitoringState', valid=valid)
|
|
dat.driverMonitoringState = {
|
|
"events": self.current_events.to_msg(),
|
|
"faceDetected": self.face_detected,
|
|
"isDistracted": self.driver_distracted,
|
|
"distractedType": sum(self.distracted_types),
|
|
"awarenessStatus": self.awareness,
|
|
"posePitchOffset": self.pose.pitch_offseter.filtered_stat.mean(),
|
|
"posePitchValidCount": self.pose.pitch_offseter.filtered_stat.n,
|
|
"poseYawOffset": self.pose.yaw_offseter.filtered_stat.mean(),
|
|
"poseYawValidCount": self.pose.yaw_offseter.filtered_stat.n,
|
|
"stepChange": self.step_change,
|
|
"awarenessActive": self.awareness_active,
|
|
"awarenessPassive": self.awareness_passive,
|
|
"isLowStd": self.pose.low_std,
|
|
"hiStdCount": self.hi_stds,
|
|
"isActiveMode": self.active_monitoring_mode,
|
|
"isRHD": self.wheel_on_right,
|
|
}
|
|
return dat
|
|
|
|
def run_step(self, sm):
|
|
# Set strictness
|
|
self._set_policy(
|
|
model_data=sm['modelV2'],
|
|
car_speed=sm['carState'].vEgo
|
|
)
|
|
|
|
# Parse data from dmonitoringmodeld
|
|
self._update_states(
|
|
driver_state=sm['driverStateV2'],
|
|
cal_rpy=sm['liveCalibration'].rpyCalib,
|
|
car_speed=sm['carState'].vEgo,
|
|
op_engaged=sm['selfdriveState'].enabled
|
|
)
|
|
|
|
# Update distraction events
|
|
self._update_events(
|
|
driver_engaged=sm['carState'].steeringPressed or sm['carState'].gasPressed,
|
|
op_engaged=sm['selfdriveState'].enabled,
|
|
standstill=sm['carState'].standstill,
|
|
wrong_gear=sm['carState'].gearShifter in [car.CarState.GearShifter.reverse, car.CarState.GearShifter.park],
|
|
car_speed=sm['carState'].vEgo
|
|
)
|
|
|