Rick Lan
74 lines
3.3 KiB
Python
74 lines
3.3 KiB
Python
"""
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Copyright (c) 2025, Rick Lan
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Permission is hereby granted, free of charge, to any person obtaining a copy
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of this software and associated documentation files (the "Software"), to deal
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in the Software without restriction, including without limitation the rights
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to use, copy, modify, merge, publish, distribute, and/or sublicense,
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for non-commercial purposes only, subject to the following conditions:
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- The above copyright notice and this permission notice shall be included in
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all copies or substantial portions of the Software.
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- Commercial use (e.g. use in a product, service, or activity intended to
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generate revenue) is prohibited without explicit written permission from
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the copyright holder.
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THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
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"""
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import time
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import numpy as np
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from openpilot.selfdrive.modeld.constants import ModelConstants
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# Cooldown times (how long to stay in experimental mode after trigger)
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AEM_COOLDOWN_TTC = 3.0 # seconds - for lead TTC events
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AEM_DECEL_FOR_STOP = 2.5 # m/s² - assumed deceleration for stop cooldown calc
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AEM_STOP_BUFFER = 2.0 # seconds - extra buffer for model latency
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# Stop sign/light detection thresholds
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SLOW_DOWN_BP = [0., 2.78, 5.56, 8.34, 11.12, 13.89, 15.28]
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SLOW_DOWN_DIST = [10, 30., 50., 70., 80., 90., 120.]
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# TTC-based triggering thresholds
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TTC_THRESHOLD = 1.8 # seconds - trigger when TTC drops below this
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MIN_SPEED_FOR_TTC = 5.0 # m/s (~18 km/h) - TTC meaningless at low speeds
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MIN_CLOSING_SPEED = 0.5 # m/s - must be closing at least this fast
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class AEM:
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def __init__(self):
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self._active = False
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self._cooldown_end_time = 0.0
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def _perform_experimental_mode(self, cooldown: float = AEM_COOLDOWN_TTC):
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self._active = True
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# Extend cooldown if new trigger comes in
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new_end = time.monotonic() + cooldown
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self._cooldown_end_time = max(self._cooldown_end_time, new_end)
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def get_mode(self, mode):
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# override mode
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if time.monotonic() < self._cooldown_end_time:
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mode = 'blended'
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else:
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self._active = False
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return mode
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def update_states(self, model_msg, radar_msg, v_ego):
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# Stop sign/light detection - model predicts stopping ahead
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# Uses max() so it can't shorten an existing longer cooldown
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if len(model_msg.orientation.x) == len(model_msg.position.x) == ModelConstants.IDX_N and \
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model_msg.position.x[ModelConstants.IDX_N - 1] < np.interp(v_ego, SLOW_DOWN_BP, SLOW_DOWN_DIST):
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self._perform_experimental_mode(v_ego / AEM_DECEL_FOR_STOP + AEM_STOP_BUFFER)
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# TTC-based triggering - lead car braking hard
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if v_ego > MIN_SPEED_FOR_TTC and radar_msg.leadOne.status:
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# vRel is negative when closing in on lead
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closing_speed = -radar_msg.leadOne.vRel
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if closing_speed > MIN_CLOSING_SPEED:
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d_rel = radar_msg.leadOne.dRel
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if d_rel > 0:
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ttc = d_rel / closing_speed
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if ttc < TTC_THRESHOLD:
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self._perform_experimental_mode(AEM_COOLDOWN_TTC) |