passing_gap_required — DRIVE `traffic` op

• Data kinds: none → table (an op determined by its arguments alone — it takes no image or data input)

• Call: import fullseye as fs; fs.ledger.passing_gap_required(parked_len: 'float', margin_front: 'float', margin_back: 'float', v_ego: 'float', v_oncoming: 'float', *, lane_change_time: 'float', ego_length: 'float' = 0.0, pet_min: 'float' = 0.0, v_obstacle: 'float' = 0.0) -> 'Dict[str, float]' (to call the implementation directly, import drivetraffic; drivetraffic.passing_gap_required(parked_len: 'float', margin_front: 'float', margin_back: 'float', v_ego: 'float', v_oncoming: 'float', *, lane_change_time: 'float', ego_length: 'float' = 0.0, pet_min: 'float' = 0.0, v_obstacle: 'float' = 0.0) -> 'Dict[str, float]'; from the registry, opsdrive.get("passing_gap_required"))

Usage

Time needed to clear the section where the car swerves into the oncoming lane to pass a parked car, and the threshold distance at which the oncoming vehicle does not arrive before then (closed form).

> The detailed description below is the original text — the summary and the headings are translated.

自車は駐車車両の後端の margin_back 手前(x_in)で対向車線に出始め、前端の margin_front 先(x_out)を過ぎてから lane_change_time かけて自車線へ戻り切る。はみ出す長さ L_occ = margin_back + parked_len + margin_front + ego_length、はみ出している時間 t_occ = L_occ / v_ego + T_lc。戻り切った地点 P = x_in + L_occ + v_ego T_lc に対向車が着くのが戻り切った時刻より pet_min 以上後(PET ≥ pet_min)であるための、判断時(自車が x_in)の対向車までの距離の境目:

D* = t_occ (v_ego + v_on) + v_on · pet_min

走っている障害物(自転車など、同じ向きに v_obstacle で進む): はみ出す長さは障害物に対する相対の距離なので t_occ = L_occ / (v_ego − v_obstacle) + T_lc、戻り切った地点 P = x_in + v_ego t_occ。D* の式は同じ形。v_obstacle = 0 で止まっている車の式に戻る。

戻り値 dict: L_occ, t_occupy, d_required (= D*)、clear_point(x_in から P までの距離)。

Raises ValueError: 負の長さ・余裕、v_ego ≤ 0、v_oncoming < 0、lane_change_time < 0、v_obstacle < 0 か v_obstacle ≥ v_ego(追いつけない)。

References (sample data, literature)

• Sample-data catalog (download URLs / licences) — 2-D uses skimage.data (BSD/public domain) plus synthetic images; 3-D lists download URLs for real data sources (Stanford, PDS, …).

• Operator provenance and references — the sources of the research/methods this op family came from.

• The canonical algorithm (author, year) and its uses are named in the family usage guide above.

Runnable examples (verified samples that actually call this op)

• poc_driving_traffic — py -3.11 examples/poc_driving_traffic.py

Ops the type connects to (they accept `table` as input)

course_layout · course_occupancy · course_contains · world_build · world_camera · world_move · lidar_scan · rss_longitudinal_same

Same category (`traffic`)

idm_accel · idm_equilibrium_gap · idm_platoon_simulate · driver_style · lateral_wobble · ou_estimate · social_force_step · pedestrian_crossing

*Provenance: drivetraffic.py — DRIVE operator registry. This per-op note is generated by tools/opdocs.py md (do not hand-edit).*

passing_gap_required — DRIVE traffic op