lateral 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.understeer_gradient(params: 'dict') -> 'Dict[str, float]' (to call the implementation directly, import drivelateral; drivelateral.understeer_gradient(params: 'dict') -> 'Dict[str, float]'; from the registry, opsdrive.get("understeer_gradient"))
Understeer gradient K = (m/L)(l_r/C_f − l_f/C_r) [rad/(m/s²)], and the characteristic / critical speed.
params: mass [kg], l_f / l_r (from the centre of gravity to the front / rear axle) [m], c_f / c_r (cornering stiffness per axle) [N/rad].
Returns: K, kind ("understeer" / "neutral" / "oversteer"), characteristic_speed (√(L/K) when K > 0, the speed of maximum yaw-rate gain), critical_speed (√(−L/K) when K < 0; above it the vehicle is unstable). Whichever does not exist is inf.
Raises ValueError: missing / non-positive parameters.
• 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_lateral — py -3.11 examples/poc_driving_lateral.py
table as input)course_layout · course_occupancy · course_contains · world_build · world_camera · world_move · lidar_scan · rss_longitudinal_same
lateral)friction_circle_usage · curve_speed_limit · design_min_radius · steady_cornering · bicycle_model_step · ackermann_steer_angles · offtracking_circle · rear_axle_path
*Provenance: drivelateral.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.