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377 lines
14 KiB
Python
377 lines
14 KiB
Python
#!/usr/bin/env python3
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"""Lateral maneuver compliance report — analog of tools/longitudinal_maneuvers/generate_report.py.
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Produces the "sine 0.5 Hz 30 mph" / "50% peak crossed in X.XXXs" style HTML report comma posts
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on social media for steering-rack compliance comparisons. Reads any iqpilot/openpilot rlog
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route, slices it into lateral maneuver windows (either by `alertDebug` markers from a scripted
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maneuversd run, or by auto-detection of contiguous lat-active sweeps), and emits a 4-panel
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plot per run:
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1. Lateral accel (desired + actual, m/s²) on left axis and steering-wheel angle (deg) on
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right axis. Black circle marks the time the actual lat-accel first crosses 50 % of the
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desired peak in the same direction.
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2. Vehicle speed (mph)
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3. Lateral jerk (m/s³), numerically differentiated from actual lat-accel
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4. Roll (deg) from liveParameters
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Usage:
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python tools/lateral_maneuvers/generate_report.py <route> [description]
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Examples:
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python tools/lateral_maneuvers/generate_report.py 1ce1b50dd82993a1\\|0000003b--a389fbdf35
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python tools/lateral_maneuvers/generate_report.py /path/to/local/rlog.zst "sine 0.5Hz 30mph"
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"""
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import argparse
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import base64
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import io
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import math
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import os
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import pprint
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import webbrowser
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from collections import defaultdict
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from pathlib import Path
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import matplotlib.pyplot as plt
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import numpy as np
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from tabulate import tabulate
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from openpilot.tools.lib.logreader import LogReader
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from openpilot.system.hardware.hw import Paths
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MPS_TO_MPH = 2.23693629
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AUTO_DESIRED_LAT_ACCEL_THRESHOLD = 0.5
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AUTO_MIN_V_EGO = 5.0
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AUTO_MIN_DURATION_S = 1.5
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AUTO_GAP_S = 0.5
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def format_car_params(CP):
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return pprint.pformat({k: v for k, v in CP.to_dict().items() if not k.endswith("DEPRECATED")}, indent=2)
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def _series(msgs, which):
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rows = [(m.logMonoTime, getattr(m, which)) for m in msgs if m.which() == which]
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if not rows:
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return [], []
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t, v = zip(*rows, strict=True)
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return list(t), list(v)
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def _to_relative_seconds(t_ns, t0):
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return [(t - t0) / 1e9 for t in t_ns]
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def _resample(t_src, v_src, t_dst):
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if not t_src or not t_dst:
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return np.zeros(len(t_dst))
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return np.interp(t_dst, t_src, v_src)
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def _peak_crossing_time(t, desired, actual, fraction=0.5):
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if len(desired) == 0:
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return None, 0.0
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desired = np.asarray(desired)
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actual = np.asarray(actual)
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peak_idx = int(np.argmax(np.abs(desired)))
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peak = desired[peak_idx]
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if abs(peak) < 1e-3:
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return None, peak
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target = fraction * peak
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prev = False
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for i in range(peak_idx + 1):
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crossed = (target > 0 and actual[i] >= target) or (target < 0 and actual[i] <= target)
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if crossed and prev:
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return t[i], peak
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prev = crossed
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return None, peak
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def _slice_by_alert_debug(msgs):
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out = []
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active_prev = False
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description_prev = None
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for msg in msgs:
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if msg.which() == "alertDebug":
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# Match both the longitudinal daemon ("Maneuver Active: …") and the lateral daemon
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# ("Active sine …", "Active +0.5m/s² …", "Complete").
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text1 = msg.alertDebug.alertText1
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active = "Maneuver Active" in text1 or text1.startswith("Active") or text1 == "Complete"
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if active and not active_prev:
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if msg.alertDebug.alertText2 == description_prev:
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out[-1][1].append([])
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else:
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out.append((msg.alertDebug.alertText2, [[]]))
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description_prev = out[-1][0]
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active_prev = active
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if active_prev:
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out[-1][1][-1].append(msg)
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return out
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def _slice_auto(msgs):
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t_cc, cc_vals = _series(msgs, "carControl")
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t_cs, cs_vals = _series(msgs, "carState")
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if not t_cc or not t_cs:
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return []
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v_ego = np.asarray([m.vEgo for m in cs_vals])
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curvature = np.asarray([m.actuators.curvature for m in cc_vals])
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lat_active = np.asarray([1.0 if m.latActive else 0.0 for m in cc_vals])
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t_cc_s = np.asarray([(t - t_cc[0]) / 1e9 for t in t_cc])
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t_cs_s = np.asarray([(t - t_cc[0]) / 1e9 for t in t_cs])
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v_at_cc = np.interp(t_cc_s, t_cs_s, v_ego)
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desired_lat_accel = curvature * v_at_cc ** 2
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signal = np.abs(desired_lat_accel) * lat_active * (v_at_cc > AUTO_MIN_V_EGO).astype(float)
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cycle_dt = float(np.median(np.diff(t_cc_s))) if len(t_cc_s) > 1 else 0.01
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min_frames = max(1, int(AUTO_MIN_DURATION_S / cycle_dt))
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windows = []
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start = None
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for i, s in enumerate(signal):
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if s > AUTO_DESIRED_LAT_ACCEL_THRESHOLD and start is None:
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start = i
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elif s <= AUTO_DESIRED_LAT_ACCEL_THRESHOLD and start is not None:
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if i - start > min_frames:
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windows.append((t_cc[start], t_cc[i]))
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start = None
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if start is not None and len(signal) - start > min_frames:
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windows.append((t_cc[start], t_cc[-1]))
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merged = []
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for a, b in windows:
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if merged and (a - merged[-1][1]) / 1e9 < AUTO_GAP_S:
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merged[-1] = (merged[-1][0], b)
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else:
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merged.append((a, b))
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runs = []
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for a, b in merged:
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runs.append([m for m in msgs if a <= m.logMonoTime <= b])
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if not runs:
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return []
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return [("auto-detected lateral sweep", runs)]
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def _plot_run(description, run_idx, msgs, builder, target_cross_times):
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t_cc, carControl = _series(msgs, "carControl")
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t_cs, carState = _series(msgs, "carState")
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t_lp, livePose = _series(msgs, "livePose")
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if not (t_cc and t_cs and t_lp):
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builder.append(f"<p style='color:red'>Run #{run_idx + 1}: missing required data, skipping.</p>\n")
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return
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t0 = min(t_cc[0], t_cs[0], t_lp[0])
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t_cc_s = _to_relative_seconds(t_cc, t0)
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t_cs_s = _to_relative_seconds(t_cs, t0)
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t_lp_s = _to_relative_seconds(t_lp, t0)
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v_ego = np.asarray([m.vEgo for m in carState])
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steer = np.asarray([m.steeringAngleDeg for m in carState])
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curvature = np.asarray([m.actuators.curvature for m in carControl])
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v_at_cc = _resample(t_cs_s, v_ego, t_cc_s)
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desired_lat_accel = curvature * v_at_cc ** 2
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actual_lat_accel = np.asarray([m.accelerationDevice.y for m in livePose])
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jerk = np.gradient(actual_lat_accel, t_lp_s)
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t_lpar, liveParameters = _series(msgs, "liveParameters")
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if liveParameters:
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t_lpar_s = _to_relative_seconds(t_lpar, t0)
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roll_deg = np.asarray([math.degrees(m.roll) for m in liveParameters])
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else:
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t_lpar_s = []
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roll_deg = np.asarray([])
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desired_lat_accel_on_lp = _resample(t_cc_s, desired_lat_accel, t_lp_s)
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cross_time, peak = _peak_crossing_time(t_lp_s, desired_lat_accel_on_lp, actual_lat_accel, fraction=0.5)
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title = f"Run #{run_idx + 1}"
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builder.append(f"<details open><summary><h3 style='display:inline-block;'>{title}</h3></summary>\n")
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if cross_time is not None:
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builder.append(f"<h3 style='font-weight:normal'>50% peak, <strong>crossed in {cross_time:.3f}s</strong></h3>\n")
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target_cross_times[description].append(cross_time)
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else:
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builder.append("<h3 style='font-weight:normal'>50% peak, <strong>not crossed</strong></h3>\n")
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builder.append(f"<h3 style='font-weight:normal'>Peak desired lat accel: <strong>{peak:+.2f} m/s²</strong>, "
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f"avg speed: <strong>{np.mean(v_ego) * MPS_TO_MPH:.1f} mph</strong></h3>\n")
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plt.rcParams["font.size"] = 32
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fig = plt.figure(figsize=(28, 22))
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ax = fig.subplots(4, 1, sharex=True, gridspec_kw={"height_ratios": [5, 2, 2, 2]})
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ax_la = ax[0]
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ax_la.grid(linewidth=2)
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ax_la.plot(t_cc_s, desired_lat_accel, label="desired lat accel", linewidth=4)
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ax_la.plot(t_lp_s, actual_lat_accel, label="actual lat accel", linewidth=4)
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ax_la.set_ylabel("Lateral Accel (m/s²)")
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ax_st = ax_la.twinx()
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ax_st.plot(t_cs_s, steer, color="tab:green", label="steer angle", linewidth=4)
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ax_st.set_ylabel("Steering Angle (deg)")
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lines_l, labels_l = ax_la.get_legend_handles_labels()
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lines_r, labels_r = ax_st.get_legend_handles_labels()
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ax_la.legend(lines_l + lines_r, labels_l + labels_r, loc="upper right", prop={"size": 22})
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if cross_time is not None:
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cross_val = float(np.interp(cross_time, t_lp_s, actual_lat_accel))
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ax_la.plot(cross_time, cross_val, marker="o", markersize=30, markeredgewidth=4,
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markeredgecolor="black", markerfacecolor="None")
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ax[1].grid(linewidth=2)
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ax[1].plot(t_cs_s, v_ego * MPS_TO_MPH, color="tab:blue", label="vEgo", linewidth=4)
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ax[1].set_ylabel("Velocity (mph)")
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ax[1].legend(loc="upper right", prop={"size": 22})
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ax[2].grid(linewidth=2)
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ax[2].plot(t_lp_s, jerk, color="tab:blue", label="actual jerk", linewidth=4)
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ax[2].set_ylabel("Jerk (m/s³)")
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ax[2].legend(loc="upper left", prop={"size": 22})
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ax[3].grid(linewidth=2)
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if len(roll_deg):
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ax[3].plot(t_lpar_s, roll_deg, color="tab:blue", label="roll", linewidth=4)
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ax[3].set_ylabel("Roll (deg)")
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ax[3].legend(loc="upper right", prop={"size": 22})
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ax[-1].set_xlabel("Time (s)")
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fig.tight_layout()
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buffer = io.BytesIO()
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fig.savefig(buffer, format="webp")
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plt.close(fig)
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buffer.seek(0)
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builder.append(f"<img src='data:image/webp;base64,{base64.b64encode(buffer.getvalue()).decode()}' "
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"style='width:100%; max-width:900px;'>\n")
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builder.append("</details>\n")
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def report(platform, route, description, CP, ID, maneuvers):
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output_path = Path(__file__).resolve().parent / "lateral_reports"
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output_path.mkdir(exist_ok=True)
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safe_route = route.replace("/", "_").replace("|", "_")
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output_fn = output_path / f"{platform}_{safe_route}.html"
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target_cross_times = defaultdict(list)
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builder = [
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"<style>summary { cursor: pointer; } td, th { padding: 8px; } body { font-family: Arial, sans-serif; }</style>\n",
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"<h1>Lateral maneuver report</h1>\n",
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f"<h3>{platform}</h3>\n",
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f"<h3>{route}</h3>\n",
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f"<h3>{ID.gitCommit}, {ID.gitBranch}, {ID.gitRemote}</h3>\n",
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]
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if description is not None:
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builder.append(f"<h3>Description: {description}</h3>\n")
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builder.append(f"<details><summary><h3 style='display:inline-block;'>CarParams</h3></summary><pre>{format_car_params(CP)}</pre></details>\n")
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builder.append("{ summary }")
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for maneuver_description, runs in maneuvers:
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print(f"plotting maneuver: {maneuver_description}, runs: {len(runs)}")
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builder.append("<div style='border-top:1px solid #000; margin:20px 0;'></div>\n")
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builder.append(f"<h2>{maneuver_description}</h2>\n")
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for run_idx, msgs in enumerate(runs):
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_plot_run(maneuver_description, run_idx, msgs, builder, target_cross_times)
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summary = ["<h2>Summary</h2>\n"]
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cols = ["maneuver", "crossed", "runs", "mean (s)", "min (s)", "max (s)"]
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table = []
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for maneuver_description, runs in maneuvers:
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times = target_cross_times[maneuver_description]
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row = [maneuver_description, len(times), len(runs)]
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if times:
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row.extend([round(np.mean(times), 3), round(np.min(times), 3), round(np.max(times), 3)])
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table.append(row)
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summary.append(tabulate(table, headers=cols, tablefmt="html", numalign="left") + "\n")
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sum_idx = builder.index("{ summary }")
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builder[sum_idx:sum_idx + 1] = summary
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with open(output_fn, "w") as f:
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f.write("".join(builder))
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print(f"\nOpening report: {output_fn}\n")
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webbrowser.open_new_tab(str(output_fn))
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def _rank_runs(runs, top_n, min_vego_mph, min_peak):
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scored = []
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for r in runs:
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t_cc, cc = _series(r, "carControl")
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t_cs, cs = _series(r, "carState")
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if not (t_cc and t_cs):
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continue
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v_ego = np.mean([m.vEgo for m in cs]) * MPS_TO_MPH
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curv = np.asarray([m.actuators.curvature for m in cc])
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v_at_cc = np.interp([(t - t_cc[0]) / 1e9 for t in t_cc],
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[(t - t_cc[0]) / 1e9 for t in t_cs],
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[m.vEgo for m in cs])
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peak = float(np.max(np.abs(curv * v_at_cc ** 2)))
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if v_ego < min_vego_mph or peak < min_peak:
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continue
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scored.append((peak, r))
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scored.sort(key=lambda x: -x[0])
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return [r for _, r in scored[:top_n]] if top_n > 0 else [r for _, r in scored]
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def main():
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parser = argparse.ArgumentParser(description="Generate lateral maneuver compliance report from a route")
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parser.add_argument("route", type=str, help="Route name, segment range, local rlog path, or directory of rlogs")
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parser.add_argument("description", type=str, nargs="?")
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parser.add_argument("--auto", action="store_true",
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help="Auto-detect lateral sweeps instead of relying on alertDebug 'Maneuver Active' markers")
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parser.add_argument("--top-n", type=int, default=10,
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help="Plot only the N largest-peak sweeps (0 = all). Default 10.")
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parser.add_argument("--min-vego-mph", type=float, default=15.0,
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help="Drop sweeps below this average speed. Default 15 mph.")
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parser.add_argument("--min-peak", type=float, default=0.5,
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help="Drop sweeps with peak desired lat accel below this (m/s²). Default 0.5.")
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args = parser.parse_args()
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if os.path.isdir(args.route):
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rlogs = sorted(p for p in Path(args.route).glob("*rlog.zst"))
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if not rlogs:
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raise SystemExit(f"no *rlog.zst files in {args.route}")
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print(f"loading {len(rlogs)} rlogs from {args.route}")
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lr = LogReader([str(p) for p in rlogs])
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elif os.path.exists(args.route):
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lr = LogReader(args.route)
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elif "/" in args.route or "|" in args.route:
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lr = LogReader(args.route)
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else:
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segs = [seg for seg in os.listdir(Paths.log_root()) if args.route in seg]
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lr = LogReader([os.path.join(Paths.log_root(), seg, "rlog.zst") for seg in segs])
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msgs = list(lr)
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CP = next(m.carParams for m in msgs if m.which() == "carParams")
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ID = next(m.initData for m in msgs if m.which() == "initData")
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platform = CP.carFingerprint
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print("processing report for", platform)
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maneuvers = [] if args.auto else _slice_by_alert_debug(msgs)
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if not maneuvers:
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print("no alertDebug 'Maneuver Active' windows found; auto-detecting lateral sweeps")
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maneuvers = _slice_auto(msgs)
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if not maneuvers:
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print("no lateral maneuvers detected — treating the whole route as one run")
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maneuvers = [("full route", [msgs])]
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else:
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filtered = []
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for description, runs in maneuvers:
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kept = _rank_runs(runs, args.top_n, args.min_vego_mph, args.min_peak)
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print(f" {description}: {len(runs)} candidate sweeps → {len(kept)} after rank/filter")
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if kept:
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filtered.append((description, kept))
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maneuvers = filtered or [("filtered out", [])]
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report(platform, args.route, args.description, CP, ID, maneuvers)
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if __name__ == "__main__":
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main()
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