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_simulate(dist_oncoming: 'float', parked_len: 'float', margin_front: 'float', margin_back: 'float', v_ego: 'float', v_oncoming: 'float', *, lane_change_time: 'float', ego_length: 'float' = 0.0, dt: 'float' = 0.01, v_obstacle: 'float' = 0.0) -> 'Dict[str, float]' (to call the implementation directly, import drivetraffic; drivetraffic.passing_simulate(dist_oncoming: 'float', parked_len: 'float', margin_front: 'float', margin_back: 'float', v_ego: 'float', v_oncoming: 'float', *, lane_change_time: 'float', ego_length: 'float' = 0.0, dt: 'float' = 0.01, v_obstacle: 'float' = 0.0) -> 'Dict[str, float]'; from the registry, opsdrive.get("passing_simulate"))
Runs the "go" case forward in time and returns the PET (difference between the time the ego vehicle has fully returned from the oncoming lane and the time the oncoming vehicle reaches that point).
A path independent of the closed form: the ego vehicle's lateral position is set to the oncoming lane (y = 1) between x_in and x_out, and after passing x_out it returns as y(τ) = 1 − smoothstep(τ / T_lc) (τ = time since passing x_out); it is stepped forward with step dt, the step where y reaches 0 is found, and the time narrowed within the step by bisection is taken as the time of full return (if T_lc = 0, the time of passing x_out). The oncoming vehicle advances from x = D at −v_on; the step in which it passes the fully-returned point is found and linearly interpolated. If v_on = 0, PET = inf.
v_obstacle > 0: the point beyond the obstacle's front end (x_out) also moves at v_obstacle (overtaking a moving bicycle).
Returns dict: t_clear, x_clear, t_oncoming (time at which the oncoming vehicle reaches x_clear), pet.
• 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.
• poc_driving_traffic — py -3.11 examples/poc_driving_traffic.py
table as input)course_layout · course_occupancy · course_contains · world_build · world_camera · world_move · lidar_scan · rss_longitudinal_same
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).*
© 2026 Kazufumi Furuse — Fullseye operator documentation. Licensed under Apache-2.0.