mirror of
https://github.com/firestar5683/StarPilot.git
synced 2026-09-02 22:23:42 +08:00
Prabhaav Pillai
840 lines
32 KiB
Python
840 lines
32 KiB
Python
#!/usr/bin/env python3
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"""Offline mode-scenario estimator for CEM / Chill (CCM) / HEM / Exp.
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Replays a logged route segment through the real StarPilot mode state machines
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(ConditionalExperimentalMode, ConditionalChillMode, HybridExperimentalMode)
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and openpilot's LongitudinalPlanner, then plots and prints what each mode's
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experimental intent and acceleration target would have been at every 20 Hz frame.
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Usage:
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./dev python tools/replay/mode_sim.py <route> [--segment 0] [--start 30] [--end 120]
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[--data_dir /path/to/routes] [--out graph.png] [--show]
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[--set CESpeed=20 --set CEModelStopTime=3.0 --set CCMLead=true] [--csv out.csv]
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Examples:
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./dev python tools/replay/mode_sim.py afb7ef2ed593d651/00000095--9dcb90357c --segment 9
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"""
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from __future__ import annotations
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import argparse
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import bisect
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import sys
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import time
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from pathlib import Path
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from types import SimpleNamespace
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from typing import Any
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import numpy as np
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ROOT_DIR = Path(__file__).resolve().parents[2]
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if str(ROOT_DIR) not in sys.path:
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sys.path.insert(0, str(ROOT_DIR))
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from openpilot.common.constants import CV
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from openpilot.common.realtime import DT_MDL
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from openpilot.selfdrive.car.cruise import V_CRUISE_MAX, V_CRUISE_UNSET
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from openpilot.selfdrive.controls.lib.longcontrol import LongCtrlState
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from openpilot.selfdrive.controls.lib.longitudinal_planner import LongitudinalPlanner, get_accel_from_plan
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from openpilot.selfdrive.controls.lib.longitudinal_mpc_lib.long_mpc import COMFORT_BRAKE
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from openpilot.starpilot.common.experimental_state import CCStatus, CEStatus
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from openpilot.starpilot.common.starpilot_utilities import calculate_lane_width, calculate_road_curvature
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from openpilot.starpilot.common.starpilot_variables import CRUISING_SPEED, MINIMUM_LATERAL_ACCELERATION, PLANNER_TIME
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from openpilot.starpilot.controls.lib.conditional_chill_mode import ConditionalChillMode
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from openpilot.starpilot.controls.lib.conditional_experimental_mode import ConditionalExperimentalMode
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from openpilot.starpilot.controls.lib.hybrid_experimental_mode import HybridExperimentalMode
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from openpilot.tools.lib.logreader import LogReader, ReadMode, parse_direct, parse_indirect
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from openpilot.tools.lib.route import SegmentRange
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SERVICES = {
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"carState", "radarState", "starpilotRadarState", "modelV2",
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"longitudinalPlan", "selfdriveState", "starpilotPlan", "starpilotCarState",
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"controlsState", "carParams", "liveParameters", "carControl",
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}
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CE_LABELS = {
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CEStatus["CURVATURE"]: "CURVE",
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CEStatus["LEAD"]: "LEAD",
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CEStatus["SIGNAL"]: "SIGNAL",
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CEStatus["SPEED"]: "SPEED",
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CEStatus["SPEED_LIMIT"]: "SPEED_LIMIT",
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CEStatus["STOP_LIGHT"]: "STOP_LIGHT",
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}
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CC_LABELS = {
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CCStatus["LEAD"]: "LEAD",
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CCStatus["SPEED"]: "SPEED",
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}
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CC_AUTO_STATUSES = {CCStatus["SPEED"], CCStatus["LEAD"]}
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# CEM/CCM toggle defaults (units in m/s where appropriate).
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TOGGLE_DEFAULTS: dict[str, Any] = {
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# CEM
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"conditional_curves": True,
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"conditional_curves_lead": True,
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"conditional_lead": True,
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"conditional_slower_lead": True,
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"conditional_stopped_lead": True,
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"conditional_open_road": False,
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"conditional_limit": 0.0, # CESpeed, m/s (0 = disabled)
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"conditional_limit_lead": 0.0, # CESpeedLead, m/s (0 = disabled)
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"conditional_signal": 0.0, # CESignalSpeed, m/s (0 = disabled)
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"conditional_model_stop_time": 0.0, # CEModelStopTime, s (0 = disabled)
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"conditional_signal_lane_detection": False,
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"lane_detection_width": 0.0,
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# Chill (CCM)
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"conditional_chill_speed": 30 * CV.MPH_TO_MS, # CCMSpeed
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"conditional_chill_speed_lead": 25 * CV.MPH_TO_MS, # CCMSpeedLead
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"conditional_chill_speed_margin": 3 * CV.MPH_TO_MS, # CCMSetSpeedMargin
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"conditional_chill_lead": True,
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"conditional_chill_launch_assist": False,
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# HEM
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"hybrid_exp_bias": 0.0,
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"hybrid_vision_brake_sensitivity": 1.0,
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# StarPilot / Planner toggles
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"taco_tune": False,
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"classic_model": False,
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"tinygrad_model": False,
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"vEgoStopping": 0.05,
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"hybrid_experimental_mode": False,
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"radar_takeoffs": False,
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"lane_change_close_gap": False,
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"minimum_lane_change_speed": 0.0,
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"model_version": None,
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}
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BOOL_KEYS = {
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"conditional_curves", "conditional_curves_lead", "conditional_lead",
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"conditional_slower_lead", "conditional_stopped_lead", "conditional_open_road",
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"conditional_signal_lane_detection", "conditional_chill_lead",
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"conditional_chill_launch_assist", "taco_tune", "classic_model", "tinygrad_model",
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"hybrid_experimental_mode", "radar_takeoffs", "lane_change_close_gap",
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}
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class FakeParams:
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def __init__(self):
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self.bools = {}
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self.ints = {}
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def get_bool(self, key):
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return bool(self.bools.get(key, False))
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def put_bool(self, key, value):
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self.bools[key] = bool(value)
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def get_int(self, key, default=0):
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return int(self.ints.get(key, default))
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def put_int(self, key, value):
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self.ints[key] = int(value)
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def default_lead(status=False):
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return SimpleNamespace(
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status=status,
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dRel=float("inf"),
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yRel=0.0,
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vRel=0.0,
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aRel=0.0,
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vLead=0.0,
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vLeadK=0.0,
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dPath=0.0,
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vLat=0.0,
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aLeadK=0.0,
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aLeadTau=1.5,
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fcw=False,
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modelProb=0.0,
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radar=False,
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)
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def as_lead(raw):
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if raw is None:
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return default_lead()
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try:
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status = bool(getattr(raw, "status", False))
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except Exception:
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status = False
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return SimpleNamespace(
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status=status,
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dRel=float(getattr(raw, "dRel", float("inf"))),
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yRel=float(getattr(raw, "yRel", 0.0)),
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vRel=float(getattr(raw, "vRel", 0.0)),
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aRel=float(getattr(raw, "aRel", 0.0)),
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vLead=float(getattr(raw, "vLead", 0.0)),
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vLeadK=float(getattr(raw, "vLeadK", getattr(raw, "vLead", 0.0))),
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dPath=float(getattr(raw, "dPath", 0.0)),
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vLat=float(getattr(raw, "vLat", 0.0)),
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aLeadK=float(getattr(raw, "aLeadK", 0.0)),
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aLeadTau=float(getattr(raw, "aLeadTau", 1.5)),
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fcw=bool(getattr(raw, "fcw", False)),
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modelProb=float(getattr(raw, "modelProb", 0.0)),
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radar=bool(getattr(raw, "radar", False)),
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)
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def local_segment_files(data_dir, route_name, segment):
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data_root = Path(data_dir)
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segment_names = (f"{route_name}--{segment}", f"{route_name.replace('|', '/')}/{segment}")
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filenames = ("rlog.zst", "rlog.bz2", "qlog.zst", "qlog.bz2")
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identifiers = []
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for segment_name in segment_names:
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for filename in filenames:
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candidate = data_root / segment_name / filename
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if candidate.exists():
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identifiers.append(str(candidate))
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for filename in filenames:
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explorer = data_root / f"{route_name}--{segment}--{filename}"
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if explorer.exists():
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identifiers.append(str(explorer))
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return identifiers
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def resolve_segment_identifier(route, segment, data_dir):
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direct = parse_direct(route)
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if direct is not None:
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return [str(direct)]
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parsed = parse_indirect(route)
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sr = SegmentRange(parsed)
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route_name = sr.route_name.replace("/", "|")
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if data_dir:
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identifiers = local_segment_files(data_dir, route_name, segment)
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if identifiers:
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return identifiers
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return [f"{route_name}--{segment}"]
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def load_buffers(identifiers, services):
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bufs = {s: [] for s in services}
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for identifier in identifiers:
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try:
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logreader = LogReader(identifier, default_mode=ReadMode.RLOG)
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except Exception as exc:
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print(f"Unable to open {identifier}: {exc}", file=sys.stderr)
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continue
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for evt in logreader:
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which = evt.which()
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if which not in services:
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continue
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try:
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t = evt.logMonoTime * 1e-9
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except Exception:
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continue
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bufs[which].append((t, getattr(evt, which)))
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for service in services:
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bufs[service].sort(key=lambda x: x[0])
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return bufs
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def parse_toggle_overrides(args_set):
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overrides = {}
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for item in args_set or []:
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key, sep, raw = item.partition("=")
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key = key.strip()
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if not sep or key not in TOGGLE_DEFAULTS:
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print(f"warning: ignoring unknown toggle override '{item}'", file=sys.stderr)
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continue
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raw = raw.strip()
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if key in BOOL_KEYS:
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overrides[key] = raw.lower() in {"1", "true", "yes", "on"}
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else:
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try:
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overrides[key] = float(raw)
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except ValueError:
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print(f"warning: ignoring non-numeric toggle override '{item}'", file=sys.stderr)
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return overrides
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def run_simulation(grid, bufs, toggles):
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ts = {s: [t for t, _ in m] for s, m in bufs.items()}
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ms = {s: [m for _, m in bufs[s]] for s in bufs}
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def latest(service, t):
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times = ts.get(service)
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if not times:
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return None
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idx = bisect.bisect_right(times, t) - 1
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return ms[service][idx] if idx >= 0 else None
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cp_candidate = None
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for _, cp in bufs.get("carParams", []):
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if cp is not None:
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cp_candidate = cp
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break
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if cp_candidate is None:
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cp_candidate = SimpleNamespace(
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brand="toyota",
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carFingerprint="TOYOTA_RAV4",
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openpilotLongitudinalControl=True,
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pcmCruise=False,
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steerRatio=15.0,
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wheelbase=2.7,
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longitudinalActuatorDelay=0.2,
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flags=0,
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)
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planner_state = SimpleNamespace(
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params=FakeParams(),
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params_memory=FakeParams(),
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starpilot_following=SimpleNamespace(following_lead=False, slower_lead=False),
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starpilot_vcruise=SimpleNamespace(
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slc=SimpleNamespace(experimental_mode=False),
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stop_sign_confirmed=False,
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forcing_stop=False,
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),
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)
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cem = ConditionalExperimentalMode(planner_state)
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cem_ccm_detector = ConditionalExperimentalMode(planner_state)
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ccm = ConditionalChillMode(planner_state, cem_ccm_detector)
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hybrid = HybridExperimentalMode()
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hybrid.set_tuning(toggles.hybrid_exp_bias, toggles.hybrid_vision_brake_sensitivity)
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# Full openpilot Longitudinal Planner for true Chill (ACC) MPC execution
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chill_long_planner = LongitudinalPlanner(cp_candidate, dt=DT_MDL)
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n = len(grid)
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out = {
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"t": grid,
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"v_ego": np.zeros(n),
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"v_cruise": np.zeros(n),
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"model_v0": np.zeros(n),
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"lead_status": np.zeros(n, dtype=bool),
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"lead_dRel": np.zeros(n),
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"lead_vLead": np.zeros(n),
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"cem_exp": np.zeros(n, dtype=bool),
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"cem_status": np.zeros(n, dtype=int),
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"ccm_exp": np.zeros(n, dtype=bool),
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"ccm_status": np.zeros(n, dtype=int),
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"chill_active": np.zeros(n, dtype=bool),
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"logged_exp": np.zeros(n, dtype=bool),
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"logged_aTarget": np.zeros(n),
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"a_chill": np.zeros(n),
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"a_exp": np.zeros(n),
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"hem_a": np.zeros(n),
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"hem_authority": np.zeros(n),
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}
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real_monotonic = time.monotonic
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fake_clock = [0.0]
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time.monotonic = lambda: fake_clock[0]
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try:
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for i, t in enumerate(grid):
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fake_clock[0] = float(i) * DT_MDL
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car_raw = latest("carState", t)
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v_ego_raw = max(float(getattr(car_raw, "vEgo", 0.0)), 0.0)
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car = SimpleNamespace(
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vEgo=v_ego_raw,
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vEgoCluster=max(float(getattr(car_raw, "vEgoCluster", v_ego_raw)), 0.0),
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vCruise=float(getattr(car_raw, "vCruise", 0.0)),
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standstill=bool(getattr(car_raw, "standstill", False)),
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leftBlinker=bool(getattr(car_raw, "leftBlinker", False)),
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rightBlinker=bool(getattr(car_raw, "rightBlinker", False)),
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gasPressed=bool(getattr(car_raw, "gasPressed", False)),
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brakePressed=bool(getattr(car_raw, "brakePressed", False)),
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steeringAngleDeg=float(getattr(car_raw, "steeringAngleDeg", 0.0)),
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aEgo=float(getattr(car_raw, "aEgo", 0.0)),
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leftBlindspot=bool(getattr(car_raw, "leftBlindspot", False)),
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rightBlindspot=bool(getattr(car_raw, "rightBlindspot", False)),
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)
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v_ego = car.vEgo
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radar = latest("radarState", t)
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lead = as_lead(getattr(radar, "leadOne", None) if radar is not None else None)
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lead2 = as_lead(getattr(radar, "leadTwo", None) if radar is not None else None)
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sradar = latest("starpilotRadarState", t)
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lead_left = as_lead(getattr(sradar, "leadLeft", None) if sradar is not None else None)
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lead_right = as_lead(getattr(sradar, "leadRight", None) if sradar is not None else None)
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lplan = latest("longitudinalPlan", t)
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sds = latest("selfdriveState", t)
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splan = latest("starpilotPlan", t)
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scs = latest("starpilotCarState", t)
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model_v2 = latest("modelV2", t)
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live_params = latest("liveParameters", t)
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car_control = latest("carControl", t)
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controls_state = latest("controlsState", t)
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car_params = latest("carParams", t) or cp_candidate
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controls_enabled = bool(getattr(sds, "enabled", False))
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conditional_tracking_active = controls_enabled or bool(getattr(scs, "alwaysOnLateralEnabled", False))
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v_cruise = float(getattr(splan, "vCruise", 0.0) or 0.0)
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if not (v_cruise > 0):
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v_cruise_kph = float(getattr(car_raw, "vCruise", 0.0) or 0.0)
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if 0 < v_cruise_kph < V_CRUISE_UNSET:
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v_cruise = min(v_cruise_kph, V_CRUISE_MAX) * CV.KPH_TO_MS
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else:
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v_cruise = v_ego
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tracking_lead = bool(getattr(splan, "trackingLead", False))
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if not tracking_lead:
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tracking_lead = bool(getattr(lplan, "hasLead", False))
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model_length = 0.0
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if model_v2 is not None:
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try:
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pos_x = getattr(model_v2, "position", None)
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if pos_x is not None and len(pos_x.x):
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model_length = float(pos_x.x[-1])
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except Exception:
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model_length = 0.0
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forcing_stop = bool(getattr(splan, "forcingStop", False))
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raw_model_stopped = model_length < CRUISING_SPEED * PLANNER_TIME
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model_stopped = raw_model_stopped or forcing_stop
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road_curvature_detected = False
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if model_v2 is not None:
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try:
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road_curvature, _time_to_curve = calculate_road_curvature(model_v2, v_ego)
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road_curvature_detected = (
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(1 / abs(road_curvature)) ** 0.5 < v_ego > CRUISING_SPEED
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and not (car.leftBlinker or car.rightBlinker)
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)
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except Exception:
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road_curvature_detected = False
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try:
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curvature = float(getattr(controls_state, "curvature", 0.0))
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except Exception:
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curvature = 0.0
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lateral_accel = v_ego ** 2 * curvature
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driving_in_curve = abs(lateral_accel) >= MINIMUM_LATERAL_ACCELERATION
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lane_width_left = 0.0
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lane_width_right = 0.0
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if model_v2 is not None:
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try:
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lane_lines = getattr(model_v2, "laneLines", None)
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road_edges = getattr(model_v2, "roadEdges", None)
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if lane_lines is not None and len(lane_lines) >= 4:
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edge_left = road_edges[0] if road_edges is not None and len(road_edges) else None
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edge_right = road_edges[1] if road_edges is not None and len(road_edges) > 1 else None
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lane_width_left = calculate_lane_width(lane_lines[0], lane_lines[1], edge_left)
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lane_width_right = calculate_lane_width(lane_lines[3], lane_lines[2], edge_right)
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except Exception:
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pass
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t_follow = float(getattr(splan, "tFollow", 1.45))
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following_lead = tracking_lead and lead.dRel < (t_follow * 2) * v_ego
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slower_lead = False
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if (conditional_tracking_active and tracking_lead and toggles.conditional_slower_lead
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and lead.status and lead.vLead < v_ego):
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distance_factor = max(lead.dRel - (lead.vLead * t_follow), 1)
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braking_offset = float(np.clip(min(v_ego - lead.vLead, lead.vLead) - COMFORT_BRAKE, 1, distance_factor))
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slower_lead = braking_offset > 1
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planner_state.lead_one = lead
|
|
planner_state.tracking_lead = tracking_lead
|
|
planner_state.model_length = model_length
|
|
planner_state.raw_model_stopped = raw_model_stopped
|
|
planner_state.model_stopped = model_stopped
|
|
planner_state.road_curvature_detected = road_curvature_detected
|
|
planner_state.driving_in_curve = driving_in_curve
|
|
planner_state.lane_width_left = lane_width_left
|
|
planner_state.lane_width_right = lane_width_right
|
|
planner_state.starpilot_following.following_lead = following_lead
|
|
planner_state.starpilot_following.slower_lead = slower_lead
|
|
planner_state.starpilot_vcruise.forcing_stop = forcing_stop
|
|
|
|
raw_personality = getattr(sds, "personality", None)
|
|
if hasattr(raw_personality, "raw"):
|
|
personality_val = int(raw_personality.raw)
|
|
elif isinstance(raw_personality, (int, float)):
|
|
personality_val = int(raw_personality)
|
|
else:
|
|
personality_val = 0
|
|
|
|
long_ctrl_state = getattr(controls_state, "longControlState", LongCtrlState.pid)
|
|
|
|
sm_dict = {
|
|
"carState": car,
|
|
"radarState": SimpleNamespace(leadOne=lead, leadTwo=lead2),
|
|
"starpilotRadarState": SimpleNamespace(leadLeft=lead_left, leadRight=lead_right),
|
|
"starpilotCarState": SimpleNamespace(
|
|
trafficModeEnabled=bool(getattr(scs, "trafficModeEnabled", False)),
|
|
alwaysOnLateralEnabled=bool(getattr(scs, "alwaysOnLateralEnabled", False)),
|
|
dashboardStopSign=int(getattr(scs, "dashboardStopSign", 0)),
|
|
accelPressed=bool(getattr(scs, "accelPressed", False)),
|
|
),
|
|
"selfdriveState": SimpleNamespace(
|
|
enabled=controls_enabled,
|
|
experimentalMode=False, # Enforce ACC/Chill mode evaluation in MPC
|
|
personality=personality_val,
|
|
),
|
|
"longitudinalPlan": SimpleNamespace(
|
|
hasLead=bool(getattr(lplan, "hasLead", False)),
|
|
allowThrottle=bool(getattr(lplan, "allowThrottle", True)),
|
|
shouldStop=bool(getattr(lplan, "shouldStop", False)),
|
|
aTarget=float(getattr(lplan, "aTarget", 0.0)),
|
|
),
|
|
"starpilotPlan": SimpleNamespace(
|
|
vCruise=v_cruise,
|
|
tFollow=t_follow,
|
|
trackingLead=tracking_lead,
|
|
redLight=bool(getattr(splan, "redLight", False)),
|
|
forcingStop=forcing_stop,
|
|
forcingStopLength=float(getattr(splan, "forcingStopLength", 100.0)),
|
|
minAcceleration=float(getattr(splan, "minAcceleration", -3.5)),
|
|
maxAcceleration=float(getattr(splan, "maxAcceleration", 1.5)),
|
|
accelerationJerk=float(getattr(splan, "accelerationJerk", 1.0)),
|
|
dangerJerk=float(getattr(splan, "dangerJerk", 1.0)),
|
|
speedJerk=float(getattr(splan, "speedJerk", 1.0)),
|
|
dangerFactor=float(getattr(splan, "dangerFactor", 1.0)),
|
|
disableThrottle=bool(getattr(splan, "disableThrottle", False)),
|
|
),
|
|
"controlsState": SimpleNamespace(
|
|
longControlState=long_ctrl_state,
|
|
forceDecel=bool(getattr(controls_state, "forceDecel", False)),
|
|
curvature=curvature,
|
|
),
|
|
"liveParameters": SimpleNamespace(
|
|
angleOffsetDeg=float(getattr(live_params, "angleOffsetDeg", 0.0)) if live_params else 0.0,
|
|
),
|
|
"carControl": SimpleNamespace(
|
|
orientationNED=getattr(car_control, "orientationNED", [0.0, 0.0, 0.0]) if car_control else [0.0, 0.0, 0.0],
|
|
),
|
|
"modelV2": model_v2,
|
|
"carParams": car_params,
|
|
}
|
|
|
|
# Run CEM / CCM State Machines
|
|
if conditional_tracking_active:
|
|
cem.update(v_ego, sm_dict, toggles, v_cruise)
|
|
ccm.update(v_ego, v_cruise, sm_dict, toggles)
|
|
else:
|
|
cem.experimental_mode = False
|
|
cem.status_value = CEStatus["OFF"]
|
|
ccm.experimental_mode = True
|
|
ccm.status_value = CCStatus["OFF"]
|
|
|
|
chill_active = (not ccm.experimental_mode) and ccm.status_value in CC_AUTO_STATUSES
|
|
|
|
# Compute accurate MPC Chill (ACC) Acceleration
|
|
chill_long_planner.update(sm_dict, toggles)
|
|
a_chill = float(chill_long_planner.output_a_target)
|
|
|
|
# Determine realistic Exp acceleration target
|
|
a_exp = 0.0
|
|
if model_v2 is not None:
|
|
try:
|
|
action_obj = getattr(model_v2, "action", None)
|
|
if action_obj is not None and hasattr(action_obj, "desiredAcceleration"):
|
|
a_exp = float(action_obj.desiredAcceleration)
|
|
else:
|
|
accel_x = getattr(model_v2, "acceleration", None)
|
|
if accel_x is not None and len(accel_x.x):
|
|
a_exp = float(accel_x.x[0])
|
|
except Exception:
|
|
a_exp = 0.0
|
|
|
|
# Hybrid Experimental Mode continuous fusion
|
|
a_hem, _should_stop_fused = hybrid.update(
|
|
v_ego=v_ego, v_cruise=v_cruise, lead_one=lead, model_v2=model_v2,
|
|
a_chill=a_chill, a_exp=a_exp,
|
|
)
|
|
authority = hybrid.w_vision
|
|
|
|
model_v0 = v_ego
|
|
if model_v2 is not None:
|
|
try:
|
|
vel_x = getattr(model_v2, "velocity", None)
|
|
if vel_x is not None and len(vel_x.x):
|
|
model_v0 = float(vel_x.x[0])
|
|
except Exception:
|
|
model_v0 = v_ego
|
|
|
|
out["v_ego"][i] = v_ego
|
|
out["v_cruise"][i] = v_cruise
|
|
out["model_v0"][i] = model_v0
|
|
out["lead_status"][i] = lead.status
|
|
out["lead_dRel"][i] = lead.dRel
|
|
out["lead_vLead"][i] = lead.vLead
|
|
out["cem_exp"][i] = bool(cem.experimental_mode)
|
|
out["cem_status"][i] = int(cem.status_value)
|
|
out["ccm_exp"][i] = bool(ccm.experimental_mode)
|
|
out["ccm_status"][i] = int(ccm.status_value)
|
|
out["chill_active"][i] = chill_active
|
|
out["logged_exp"][i] = bool(getattr(sds, "experimentalMode", False))
|
|
out["logged_aTarget"][i] = float(getattr(lplan, "aTarget", 0.0))
|
|
out["a_chill"][i] = a_chill
|
|
out["a_exp"][i] = a_exp
|
|
out["hem_a"][i] = a_hem
|
|
out["hem_authority"][i] = authority
|
|
finally:
|
|
time.monotonic = real_monotonic
|
|
|
|
return out
|
|
|
|
|
|
def intervals_for(t, keys):
|
|
"""Group consecutive identical non-zero status keys into [start, end, key] spans."""
|
|
spans = []
|
|
cur = 0
|
|
for i, k in enumerate(keys):
|
|
if k and k == cur:
|
|
spans[-1][1] = t[i]
|
|
elif k:
|
|
spans.append([t[i], t[i], k])
|
|
cur = k
|
|
else:
|
|
cur = 0
|
|
return spans
|
|
|
|
|
|
def print_summary(out):
|
|
t = out["t"]
|
|
n = len(t)
|
|
if n == 0:
|
|
print("No simulation frames to summarize.")
|
|
return
|
|
|
|
v_ego_mph = out["v_ego"] * CV.MS_TO_MPH
|
|
v_cruise_mph = out["v_cruise"] * CV.MS_TO_MPH
|
|
standstill_mask = out["v_ego"] < 0.2
|
|
standstill_pct = 100.0 * np.mean(standstill_mask)
|
|
|
|
stops = 0
|
|
for i in range(1, n):
|
|
if standstill_mask[i] and not standstill_mask[i - 1]:
|
|
stops += 1
|
|
|
|
cem_switches = int(np.sum(np.diff(out["cem_exp"].astype(int)) != 0))
|
|
ccm_switches = int(np.sum(np.diff(out["chill_active"].astype(int)) != 0))
|
|
|
|
print("\n" + "=" * 78)
|
|
print(f" MODE SIMULATION SUMMARY & TELEMETRY LOG ({t[0]:.1f}s -> {t[-1]:.1f}s | {n} frames)")
|
|
print("=" * 78)
|
|
|
|
print("\n[1. DRIVE PROFILE]")
|
|
print(f" • Speed (v_ego) : Min {v_ego_mph.min():.1f} mph | Avg {v_ego_mph.mean():.1f} mph | Max {v_ego_mph.max():.1f} mph")
|
|
print(f" • Cruise Set Speed : Min {v_cruise_mph.min():.1f} mph | Avg {v_cruise_mph.mean():.1f} mph | Max {v_cruise_mph.max():.1f} mph")
|
|
print(f" • Standstill Time : {standstill_pct:.1f}% ({stops} full stop events)")
|
|
print(f" • Lead Presence : {100.0 * np.mean(out['lead_status']):.1f}% of route")
|
|
|
|
print("\n[2. MODE INTENT & TRIGGER BREAKDOWN]")
|
|
print(f" • Logged (Actual) : Exp {100.0 * out['logged_exp'].mean():.1f}%")
|
|
print(f" • CEM (Conditional) : Exp {100.0 * out['cem_exp'].mean():.1f}% | {cem_switches} mode toggles")
|
|
cem_reasons = []
|
|
for status_code, label in CE_LABELS.items():
|
|
pct = 100.0 * np.mean((out["cem_status"] == status_code) & out["cem_exp"])
|
|
if pct > 0.1:
|
|
cem_reasons.append(f"{label}: {pct:.1f}%")
|
|
if cem_reasons:
|
|
print(f" └─ CEM Active Triggers -> {', '.join(cem_reasons)}")
|
|
|
|
print(f" • Chill Mode (CCM) : Chill {100.0 * out['chill_active'].mean():.1f}% | Exp {100.0 * out['ccm_exp'].mean():.1f}% | {ccm_switches} toggles")
|
|
ccm_reasons = []
|
|
for status_code, label in CC_LABELS.items():
|
|
pct = 100.0 * np.mean((out["ccm_status"] == status_code) & out["chill_active"])
|
|
if pct > 0.1:
|
|
ccm_reasons.append(f"{label}: {pct:.1f}%")
|
|
if ccm_reasons:
|
|
print(f" └─ Chill Active Reasons -> {', '.join(ccm_reasons)}")
|
|
|
|
auth = out["hem_authority"]
|
|
chill_dom = 100.0 * np.mean(auth < 0.35)
|
|
blended = 100.0 * np.mean((auth >= 0.35) & (auth <= 0.65))
|
|
exp_dom = 100.0 * np.mean(auth > 0.65)
|
|
print(f" • HEM (Hybrid) : Mean Exp Authority {auth.mean():.2f}")
|
|
print(f" └─ Distribution -> Chill-dominant (<0.35): {chill_dom:.1f}% | Blended (0.35-0.65): {blended:.1f}% | Exp-dominant (>0.65): {exp_dom:.1f}%")
|
|
|
|
print("\n[3. ACCELERATION ENVELOPE (m/s²)]")
|
|
print(f" {'Signal':<16} | {'Min (Max Brake)':<15} | {'Mean':<10} | {'Max (Max Throttle)':<18}")
|
|
print(" " + "-" * 66)
|
|
print(f" {'Logged aTarget':<16} | {out['logged_aTarget'].min():<15.2f} | {out['logged_aTarget'].mean():<10.2f} | {out['logged_aTarget'].max():<18.2f}")
|
|
print(f" {'Chill a':<16} | {out['a_chill'].min():<15.2f} | {out['a_chill'].mean():<10.2f} | {out['a_chill'].max():<18.2f}")
|
|
print(f" {'Exp a':<16} | {out['a_exp'].min():<15.2f} | {out['a_exp'].mean():<10.2f} | {out['a_exp'].max():<18.2f}")
|
|
print(f" {'HEM Fused a':<16} | {out['hem_a'].min():<15.2f} | {out['hem_a'].mean():<10.2f} | {out['hem_a'].max():<18.2f}")
|
|
|
|
print("\n[4. KEY EVENT CHRONOLOGY (Sampled Significant Transitions)]")
|
|
print(f" {'Time (s)':<9} | {'vEgo':<8} | {'CEM Status':<12} | {'CCM':<7} | {'a_chill':<8} | {'a_exp':<8} | {'HEM a':<8} | {'Auth':<5} | Event Context")
|
|
print(" " + "-" * 95)
|
|
|
|
step = max(1, n // 18)
|
|
sample_indices = set(range(0, n, step))
|
|
|
|
hard_brakes = np.where(out["hem_a"] < -1.2)[0]
|
|
if len(hard_brakes) > 0:
|
|
sample_indices.update(hard_brakes[::max(1, len(hard_brakes)//4)])
|
|
|
|
standstills = np.where(standstill_mask)[0]
|
|
if len(standstills) > 0:
|
|
sample_indices.update(standstills[::max(1, len(standstills)//3)])
|
|
|
|
sorted_indices = sorted(list(sample_indices))[:22]
|
|
|
|
for idx in sorted_indices:
|
|
cur_t = t[idx] - t[0]
|
|
ego_mph = v_ego_mph[idx]
|
|
cem_st = CE_LABELS.get(out["cem_status"][idx], "OFF") if out["cem_exp"][idx] else "CHILL"
|
|
ccm_st = "CHILL" if out["chill_active"][idx] else "EXP"
|
|
ac = out["a_chill"][idx]
|
|
ae = out["a_exp"][idx]
|
|
ah = out["hem_a"][idx]
|
|
au = out["hem_authority"][idx]
|
|
|
|
ctx = []
|
|
if out["lead_status"][idx]:
|
|
ctx.append(f"Lead {out['lead_dRel'][idx]:.0f}m")
|
|
if ego_mph < 1.0:
|
|
ctx.append("Standstill")
|
|
elif ah < -1.0:
|
|
ctx.append("Braking")
|
|
elif ah > 0.8:
|
|
ctx.append("Accelerating")
|
|
else:
|
|
ctx.append("Cruising")
|
|
|
|
if abs(ac - ae) > 1.0:
|
|
ctx.append("Disagreement")
|
|
|
|
context_str = ", ".join(ctx)
|
|
print(f" {cur_t:<9.1f} | {ego_mph:<5.1f}mph | {cem_st:<12} | {ccm_st:<7} | {ac:<8.2f} | {ae:<8.2f} | {ah:<8.2f} | {au:<5.2f} | {context_str}")
|
|
|
|
print("=" * 78 + "\n")
|
|
|
|
|
|
def plot_results(out, toggles, args):
|
|
import matplotlib
|
|
if not args.show:
|
|
matplotlib.use("Agg")
|
|
import matplotlib.pyplot as plt
|
|
|
|
t = out["t"]
|
|
n = len(t)
|
|
|
|
fig, (ax_speed, ax_mode, ax_accel) = plt.subplots(
|
|
3, 1, figsize=(14, 10), sharex=True,
|
|
gridspec_kw={"height_ratios": [2, 3, 2]},
|
|
)
|
|
fig.suptitle(f"Mode scenario estimate {args.route} seg {args.segment}", fontsize=11)
|
|
|
|
ax_speed.plot(t, out["v_ego"] * CV.MS_TO_MPH, color="black", lw=1.4, label="v_ego")
|
|
ax_speed.plot(t, out["v_cruise"] * CV.MS_TO_MPH, color="tab:blue", lw=1.0, ls="--", label="v_cruise")
|
|
ax_speed.plot(t, out["model_v0"] * CV.MS_TO_MPH, color="tab:green", lw=1.0, alpha=0.7, label="model v[0]")
|
|
ax_speed.set_ylabel("mph")
|
|
ax_speed.legend(loc="upper right", fontsize=8, ncol=3)
|
|
ax_speed.grid(alpha=0.3)
|
|
|
|
rows = [
|
|
("Exp (always)", out["t"], np.ones(n, dtype=bool), "tab:blue", 3.0),
|
|
("CEM", out["t"], out["cem_exp"], "tab:orange", 2.0),
|
|
("Chill", out["t"], out["chill_active"], "tab:green", 1.0),
|
|
("Logged", out["t"], out["logged_exp"], "tab:gray", 0.0),
|
|
]
|
|
for label, tt, active, color, ycenter in rows:
|
|
ax_mode.fill_between(tt, ycenter - 0.28, ycenter + 0.28, where=active,
|
|
step="post", color=color, alpha=0.55, edgecolor="none")
|
|
|
|
cem_spans = intervals_for(t, [out["cem_status"][i] if out["cem_exp"][i] else 0 for i in range(n)])
|
|
for a, b, k in cem_spans:
|
|
if b - a > 0.8 and k in CE_LABELS:
|
|
ax_mode.text((a + b) / 2, 2.0 + 0.34, CE_LABELS[k], ha="center", fontsize=7, color="tab:orange")
|
|
cc_spans = intervals_for(t, [out["ccm_status"][i] if out["chill_active"][i] else 0 for i in range(n)])
|
|
for a, b, k in cc_spans:
|
|
if b - a > 0.8 and k in CC_LABELS:
|
|
ax_mode.text((a + b) / 2, 1.0 + 0.34, CC_LABELS[k], ha="center", fontsize=7, color="tab:green")
|
|
|
|
ax_hem = ax_mode.twinx()
|
|
ax_hem.plot(t, out["hem_authority"], color="tab:cyan", lw=1.2, label="HEM exp-authority")
|
|
ax_hem.set_ylim(0, 1)
|
|
ax_hem.set_ylabel("HEM exp authority", color="tab:cyan", fontsize=8)
|
|
ax_hem.tick_params(axis="y", labelcolor="tab:cyan", labelsize=7)
|
|
|
|
ax_mode.set_yticks([3.0, 2.0, 1.0, 0.0])
|
|
ax_mode.set_yticklabels(["Exp", "CEM", "Chill", "Logged"])
|
|
ax_mode.set_ylim(-0.6, 3.6)
|
|
ax_mode.grid(axis="y", alpha=0.3)
|
|
|
|
ax_accel.plot(t, out["logged_aTarget"], color="tab:gray", lw=1.2, label="logged aTarget")
|
|
ax_accel.plot(t, out["a_chill"], color="tab:blue", lw=1.0, ls="--", label="chill (ACC) a")
|
|
ax_accel.plot(t, out["a_exp"], color="tab:orange", lw=1.0, ls=":", label="exp (model) a")
|
|
ax_accel.plot(t, out["hem_a"], color="tab:cyan", lw=1.4, label="HEM fused a")
|
|
ax_accel.set_ylabel("accel (m/s²)")
|
|
ax_accel.set_xlabel("time (s, segment-relative)")
|
|
ax_accel.legend(loc="upper right", fontsize=8, ncol=2)
|
|
ax_accel.grid(alpha=0.3)
|
|
|
|
fig.tight_layout()
|
|
if args.out:
|
|
out_path = Path(args.out)
|
|
out_path.parent.mkdir(parents=True, exist_ok=True)
|
|
fig.savefig(out_path, dpi=150)
|
|
print(f"Saved graph to {out_path}")
|
|
if args.show:
|
|
plt.show()
|
|
plt.close(fig)
|
|
|
|
|
|
def main(argv=None):
|
|
parser = argparse.ArgumentParser(
|
|
description="Estimate CEM/Chill/HEM/Exp mode intent from a logged route and plot a comparison graph.")
|
|
parser.add_argument("route", help="route (e.g. dongle|2023-07-27--13-01-19 or dongle/2023-07-27--13-01-19/0)")
|
|
parser.add_argument("--segment", type=int, default=0, help="segment index (default 0)")
|
|
parser.add_argument("--data_dir", help="local directory containing route files")
|
|
parser.add_argument("--start", type=float, default=None, help="start seconds (segment-relative)")
|
|
parser.add_argument("--end", type=float, default=None, help="end seconds (segment-relative)")
|
|
parser.add_argument("--out", default="mode_sim.png", help="output PNG path")
|
|
parser.add_argument("--show", action="store_true", help="show the plot window instead of saving only")
|
|
parser.add_argument("--csv", default=None, help="optional CSV output of the time series")
|
|
parser.add_argument("--set", action="append", default=[], metavar="KEY=value",
|
|
help="override a mode toggle, e.g. --set CESpeed=20 --set CEModelStopTime=3.0 --set CCMLead=false")
|
|
args = parser.parse_args(argv)
|
|
|
|
overrides = parse_toggle_overrides(args.set)
|
|
defaults = dict(TOGGLE_DEFAULTS)
|
|
defaults.update(overrides)
|
|
toggles = SimpleNamespace(**defaults)
|
|
for key, value in overrides.items():
|
|
print(f"toggle override: {key} = {value}")
|
|
|
|
identifiers = resolve_segment_identifier(args.route, args.segment, args.data_dir)
|
|
if not identifiers:
|
|
print(f"No segment data found for {args.route} seg {args.segment}", file=sys.stderr)
|
|
return 1
|
|
print(f"Loading: {identifiers}")
|
|
bufs = load_buffers(identifiers, SERVICES)
|
|
if not any(bufs[s] for s in SERVICES):
|
|
print("No messages loaded.", file=sys.stderr)
|
|
return 1
|
|
|
|
all_min = min(t for s in SERVICES for t, _ in bufs[s])
|
|
all_max = max(t for s in SERVICES for t, _ in bufs[s])
|
|
start = all_min if args.start is None else all_min + args.start
|
|
end = all_max if args.end is None else min(all_max, all_min + args.end)
|
|
if end - start < DT_MDL:
|
|
print(f"Empty time window [{start:.1f}, {end:.1f}]. Check --start/--end.", file=sys.stderr)
|
|
return 1
|
|
grid = np.arange(start, end, DT_MDL)
|
|
print(f"Window: {start - all_min:.1f}s -> {end - all_min:.1f}s ({len(grid)} frames at {1 / DT_MDL:.0f} Hz)")
|
|
|
|
out = run_simulation(grid, bufs, toggles)
|
|
|
|
if args.csv:
|
|
import csv
|
|
csv_path = Path(args.csv)
|
|
csv_path.parent.mkdir(parents=True, exist_ok=True)
|
|
with open(csv_path, "w", newline="") as f:
|
|
writer = csv.writer(f)
|
|
writer.writerow(["t", "v_ego", "v_cruise", "model_v0", "cem_exp", "cem_status",
|
|
"ccm_exp", "ccm_status", "chill_active", "logged_exp", "logged_aTarget",
|
|
"a_chill", "a_exp", "hem_a", "hem_authority"])
|
|
for i in range(len(out["t"])):
|
|
writer.writerow([out["t"][i], out["v_ego"][i], out["v_cruise"][i], out["model_v0"][i],
|
|
int(out["cem_exp"][i]), out["cem_status"][i],
|
|
int(out["ccm_exp"][i]), out["ccm_status"][i], int(out["chill_active"][i]),
|
|
int(out["logged_exp"][i]), out["logged_aTarget"][i],
|
|
out["a_chill"][i], out["a_exp"][i], out["hem_a"][i], out["hem_authority"][i]])
|
|
print(f"Saved CSV to {csv_path}")
|
|
|
|
print_summary(out)
|
|
plot_results(out, toggles, args)
|
|
return 0
|
|
|
|
|
|
if __name__ == "__main__":
|
|
raise SystemExit(main()) |