| """Source-compatible TTC/DRAC and lane-change-event PET; see documented limitations.""" |
| import warnings |
|
|
| import numpy as np |
| import pandas as pd |
|
|
| from _common import numeric, parser, read_csv, require, save_csv, unique |
|
|
|
|
| def crossing_time(times, xs, target, start, sign): |
| indexes = np.flatnonzero(times >= start) |
| if len(indexes) < 2: |
| return None |
| signed = (xs - target) * sign |
| for k in range(indexes[0], len(times) - 1): |
| if signed[k] >= 0: |
| return times[k] |
| if signed[k + 1] >= 0: |
| return times[k] - signed[k] / (signed[k + 1] - signed[k]) * (times[k + 1] - times[k]) |
| return None |
|
|
|
|
| def annotate(df, drac_cap=15.0, fallback_length=None): |
| df = df.copy() |
| require(df, ["Lane number", "Direction", "Vehicle_Length_m"]) |
| numeric(df, ["Frame", "Ego_Vehicle_ID"], integer=True) |
| numeric(df, ["Time_s", "x_center", "v_x"]) |
| unique(df, ["Frame", "Ego_Vehicle_ID"]) |
| if df[["Lane number", "Direction"]].isna().any().any(): |
| raise ValueError("Lane and Direction cannot be missing") |
| if "Source_ID" in df and df.Source_ID.nunique() != 1: |
| raise ValueError("Compute risk separately for each source recording") |
| if drac_cap <= 0 or np.isnan(drac_cap): |
| raise ValueError("DRAC cap must be positive; use inf for no cap") |
| lengths = pd.to_numeric(df.Vehicle_Length_m, errors="raise") |
| if lengths.isna().any(): |
| if fallback_length is None or not np.isfinite(fallback_length) or fallback_length <= 0: |
| raise ValueError("Missing lengths: supply actual estimates or explicitly opt into a fallback") |
| warnings.warn(f"Using {fallback_length} m for {lengths.isna().sum()} missing length rows") |
| lengths = lengths.fillna(fallback_length) |
| if not np.isfinite(lengths).all() or lengths.le(0).any(): |
| raise ValueError("Lengths must be positive and finite") |
| df["_length"] = lengths |
| df = df.sort_values(["Ego_Vehicle_ID", "Frame"]).reset_index(drop=True) |
| grouped = df.groupby("Ego_Vehicle_ID", sort=False) |
| if grouped._length.nunique().gt(1).any() or grouped.Direction.nunique().gt(1).any(): |
| raise ValueError("Length and Direction must be constant within each trajectory") |
| if grouped.Frame.diff().dropna().ne(1).any(): |
| raise ValueError("PET requires gap-filled consecutive trajectory frames") |
| if grouped.Time_s.diff().dropna().le(0).any() or df.groupby("Frame").Time_s.nunique().gt(1).any(): |
| raise ValueError("Inconsistent physical times") |
| previous = grouped["Lane number"].shift() |
| df["_cutin"] = previous.notna() & previous.ne(df["Lane number"]) |
| cache = {vid: (g.Time_s.to_numpy(), g.x_center.to_numpy()) for vid, g in grouped} |
| parts = [] |
| for _, group in df.groupby(["Frame", "Direction"], sort=False): |
| group = group.copy().reset_index(drop=True) |
| n = len(group) |
| x, v = group.x_center.to_numpy(), group.v_x.to_numpy() |
| lane, ids = group["Lane number"].to_numpy(), group.Ego_Vehicle_ID.to_numpy() |
| length, times = group._length.to_numpy(), group.Time_s.to_numpy() |
| ttc, pet, drac = np.full(n, np.inf), np.full(n, np.nan), np.zeros(n) |
| target_ttc, target_pet = np.full(n, -1, dtype=np.int64), np.full(n, -1, dtype=np.int64) |
| for i in range(n): |
| sign = np.sign(v[i]) |
| if sign == 0: |
| continue |
| leaders = np.flatnonzero((lane == lane[i]) & ((x - x[i]) * sign > 0)) |
| if len(leaders): |
| j = leaders[np.argmin(np.abs(x[leaders] - x[i]))] |
| clearance = abs(x[j] - x[i]) - (length[i] + length[j]) / 2 |
| closing = abs(v[i]) - abs(v[j]) |
| if clearance > 0 and closing > 0: |
| ttc[i], drac[i] = clearance / closing, min(closing ** 2 / (2 * clearance), drac_cap) |
| target_ttc[i] = ids[j] |
| if group._cutin.iloc[i]: |
| followers = np.flatnonzero((lane == lane[i]) & ((x[i] - x) * sign > 0)) |
| if not len(followers): |
| continue |
| j = followers[np.argmin(np.abs(x[followers] - x[i]))] |
| clear = crossing_time(*cache[ids[i]], x[i] + sign * length[i] / 2, times[i], sign) |
| arrive = crossing_time(*cache[ids[j]], x[i] - sign * length[j] / 2, times[i], sign) |
| if clear is not None and arrive is not None: |
| value = max(0.0, arrive - clear) |
| for a, b in [(i, j), (j, i)]: |
| if np.isnan(pet[a]) or value < pet[a]: |
| pet[a], target_pet[a] = value, ids[b] |
| high_pet, high_long = pet < 2.5, (ttc < 3.0) | (drac > 3.0) |
| target = np.where(high_pet, target_pet, |
| np.where(high_long, target_ttc, np.where(target_ttc != -1, target_ttc, target_pet))) |
| group["TTC"], group["PET"], group["DRAC"] = ttc, pet, drac |
| group["Max_Risk_Target_ID"] = target |
| group["Risk_Level"] = np.where(high_pet | high_long, "High Risk", "Low Risk") |
| parts.append(group) |
| return pd.concat(parts, ignore_index=True).drop(columns=["_length", "_cutin"]).sort_values( |
| ["Ego_Vehicle_ID", "Frame"]).reset_index(drop=True) |
|
|
|
|
| def main(): |
| p = parser(__doc__) |
| p.add_argument("--input", required=True, help="One-source Basic Trajectory CSV with metric lengths") |
| p.add_argument("--output", required=True, help="New Risk Semantic CSV") |
| p.add_argument("--fps", type=float, help="Constant source FPS, only if Time_s is absent") |
| p.add_argument("--drac-cap", type=float, default=15.0, help="Legacy cap in m/s^2; inf changes legacy behavior") |
| p.add_argument("--fallback-length-m", type=float, help="Explicit legacy imputation, e.g. 5; otherwise fail") |
| a = p.parse_args() |
| df = read_csv(a.input) |
| if "Time_s" not in df: |
| if a.fps is None or not np.isfinite(a.fps) or a.fps <= 0: |
| p.error("Provide Time_s or the actual constant --fps; never infer mixed frame rates") |
| numeric(df, ["Frame"], integer=True) |
| df["Time_s"] = (df.Frame - df.Frame.min()) / a.fps |
| elif a.fps is not None: |
| p.error("Time_s already exists; omit --fps") |
| print(f"DRAC cap={a.drac_cap}; no-closing TTC=inf; PET is event-level") |
| save_csv(annotate(df, a.drac_cap, a.fallback_length_m), a.output) |
|
|
|
|
| if __name__ == "__main__": |
| main() |
|
|
