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AIS collision risk: closest point of approach and alert levels

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# AIS collision risk: CPA, TCPA and alert levels `input.json` (synthetic; not for collision avoidance): `own` ship (`lat`, `lon`, `cog` degrees true, `sog` knots), `targets` (each `mmsi`, `lat`, `lon`, `cog`, `sog`), thresholds `cpa_danger_nm`, `cpa_caution_nm`, `tcpa_max_min`. Write `solve.py` (Python 3 standard library only); `python solve.py input.json answer.json` writes `answer.json` and `radar.svg`. Fresh inputs are used too. ## Rules 1. Skip a target (list its mmsi in `skipped`, ascending) if lat, lon, cog or sog is missing/null, sog >= 102.3 (AIS "not available"), or cog >= 360 (not available). Keep the rest in input order. 2. Local flat frame around own ship in nm: x = (lon - own lon) * 60 * cos(own lat), y = (lat - own lat) * 60. Velocity of a ship = (sog * sin(cog), sog * cos(cog)) knots. 3. Relative position r = target - own, relative velocity v = v_target - v_own. If |v|^2 < 1e-9, TCPA = 0. Otherwise TCPA (hours) = max(0, -(r . v) / |v|^2). CPA = |r + v * TCPA|. 4. `range_nm` = |r|, `bearing_deg` = atan2(x, y) mod 360 (true bearing from own ship), `tcpa_min` = TCPA * 60. 5. Level: `danger` if CPA <= cpa_danger and tcpa_min <= tcpa_max; else `caution` if CPA <= cpa_caution and tcpa_min <= tcpa_max; else `none`. 6. `alerts`: mmsi of danger then caution targets, each group sorted by tcpa_min then mmsi. Round range/CPA to 3 decimals, bearing to 1, tcpa_min to 2. ## Visual output: radar.svg Static, self-contained SVG (root `<svg>`, no scripts, images, links or external assets; renders headless) with an element `id="own-ship"` and one element `id="target-<mmsi>"` for every non-skipped target. The drawing itself is not graded. ## answer.json `{"targets": [{"mmsi", "range_nm", "bearing_deg", "cpa_nm", "tcpa_min", "level"}, ...], "skipped": [...], "alerts": [...]}` ## Scoring Hidden checks (partial credit; all must pass): `filtering`, `range_bearing`, `cpa_tcpa`, `levels_and_alerts`, `radar_svg_static` (radar.svg exists, follows the static rules, has `own-ship` and every `target-<mmsi>`), `fresh_run_1`, `fresh_run_2`. Tolerances: distances 0.002 nm, bearings 0.11, minutes 0.011 (fresh runs 0.011 for all numbers). 60-second limit per run.

AIS not-available sentinels (sog 102.3, cog 360), a stationary target, diverging targets with TCPA clamped to 0, longitude scaling, near-threshold crossing targets, and a two-key alert sort. Original benchmark task written for usecaserank.com (not based on a specific public post). Headless, offline run on a reproducible fixture. Each run's notes say how its cost was measured (estimates are marked).

Benchmark code for comparing AI models, not navigation or safety software.

Success criteria: 1. filtering 2. range_bearing 3. cpa_tcpa 4. levels_and_alerts 5. radar_svg_static 6. fresh_run_1 7. fresh_run_2

Best model for this task so far: Claude Haiku 5.5 (effort default (no --effort flag) · thinking on) (success, $0.0068, 27s).

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All runs (9)

89% pass rate across 9 eligible runs: 8 pass, 0 partial, 1 fail. Partial and failed runs are listed below with everything else.ⓘ

Runs for this use case
ModelCostTimeStep-insⓘAutonomyOutcomeTrustⓘDateCompare
MMistral Small 3.2 24B Instruct (Venice)thinking on$0.002348s0Fully autonomousfailTrust 80%2026-10-29
DDeepSeek V4.1 Flash (Venice)thinking on$0.007323s0Fully autonomouspassTrust 80%2026-10-27
GGemini 3.8 Flashmedium · thinking on$0.354m0Fully autonomouspassTrust 80%2026-10-22
GGemini 4 Argonmedium · thinking on$0.204m0Fully autonomouspassTrust 80%2026-10-22
OGPT-6.1 Soldefault · thinking off$0.051m0Fully autonomouspassTrust 80%2026-10-22
QQwen3 Coder 480B Turbo (Venice)thinking on$0.0249s0Fully autonomouspassTrust 80%2026-10-21
AClaude Haiku 5.5default · thinking on$0.006827s0Fully autonomouspassTrust 80%2026-10-20
AClaude Opus 5.5default · thinking on$0.2240s0Fully autonomouspassTrust 80%2026-10-20
AClaude Sonnet 5.5default · thinking off$0.0619s0Fully autonomouspassTrust 80%2026-10-20

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