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README.md

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+# PhysicalAI-US-Evaluation
+
+A held-out US evaluation set for the navigation planner: **19,744 records**, each pairing a single front-camera frame with the corresponding past trajectory, future ground-truth waypoints, and a natural-language driving objective.
+
+## Provenance
+
+Every record here was drawn — uniformly at random — from the pool of US scenes that were **withheld from every training stage** of the planner:
+
+- the base VLA pretraining mix,
+- the reasoning supervised fine-tuning (SFT) stage, and
+- the GRPO reinforcement-learning stage.
+
+In other words: no record in this directory was ever seen by the models that will be evaluated on it. This is the canonical "honest" US eval set — use it to compare checkpoints without leakage concerns.
+
+The set is US-only by construction.
+
+## Directory layout
+
+```
+PhysicalAI-US-Evaluation/
+├── README.md                ← this file
+├── dataset.jsonl            ← 19,744 records, one JSON object per line
+└── camera/
+    ├── camera_00.zip        ← original packed frames (≤3 GB cap, ZIP_STORED)
+    └── camera_01.zip
+```
+
+## Image format
+
+- **Codec:** PNG (`cv2.IMWRITE_PNG_COMPRESSION=3`)
+- **Resolution:** 640 × 360
+- **Camera:** `camera_front_wide_120fov` (front-facing, ~120° HFOV)
+- **Path layout:** `{chunk_name}/{scene_id}/{timestamp_us}.png`, relative to this directory after unzipping the camera files.
+- **Frame selection:** the frame whose timestamp is closest to `clip_start_us + timestamp_us` for the given scene.
+
+Resolving a record's frame is therefore:
+
+```python
+frame_path = root / record["chunk_name"] / record["scene_id"] / f'{record["timestamp_us"]}.png'
+```
+
+## JSONL schema
+
+Each line in `dataset.jsonl` is a single JSON object. Fields fall into three groups.
+
+### Identity / indexing (always present)
+
+| Field          | Type   | Meaning                                                                 |
+|----------------|--------|-------------------------------------------------------------------------|
+| `shard_id`     | str    | Source shard the row was drawn from (e.g. `"shard_00020"`).             |
+| `chunk_name`   | str    | Chunk directory the frame lives in (e.g. `"chunk_1580"`).               |
+| `scene_id`     | str    | UUID of the scene clip.                                                 |
+| `timestamp_us` | int    | Offset into the clip, in microseconds. Used to address the frame.       |
+| `sample_idx`   | int \| null | Original sample index inside the source shard (may be `null`).      |
+
+### Trajectory + task (always present)
+
+| Field                    | Type                          | Meaning                                                                                                                            |
+|--------------------------|-------------------------------|------------------------------------------------------------------------------------------------------------------------------------|
+| `egomotion`              | list[[x, y, θ]]               | Past trajectory, ego-frame, 0.25 s spacing, ending at the present anchor `[0, 0, 0]`. Length 9 (= 2 s of history + anchor).        |
+| `ground_truth_waypoints` | list[[x, y, θ]]               | Future trajectory, ego-frame, 0.25 s spacing. Length 24 (= 6 s of lookahead).                                                       |
+| `objective`              | str                           | Natural-language driving intent for the next few seconds (e.g. `"Drive straight along the multi-lane road"`).                       |
+| `difficulty`             | `"easy"` \| `"hard"`          | See [Difficulty split](#difficulty-split) below.                                                                                    |
+
+Coordinates are right-handed ego-frame, units **meters / radians**, with `+x` forward and `+y` left.
+
+### Annotation fields (hard rows only)
+
+The 2,000 "hard" rows carry an additional Gemini-produced reasoning trace. Easy rows omit all of these fields.
+
+| Field                        | Type            | Meaning                                                                                       |
+|------------------------------|-----------------|-----------------------------------------------------------------------------------------------|
+| `scene`                      | str             | Free-text description of the visible scene.                                                   |
+| `longitudinal_decision`      | [label, score]  | Categorical longitudinal action label + integer confidence.                                   |
+| `longitudinal_justification` | str             | Free-text rationale for the longitudinal choice.                                              |
+| `lateral_decision`           | [label, score]  | Categorical lateral action label + integer confidence.                                        |
+| `lateral_justification`      | str             | Free-text rationale for the lateral choice.                                                   |
+| `move_justification`         | str             | Combined natural-language explanation of the planned motion.                                  |
+| `annotation_complete`        | bool            | Whether the Gemini annotation pass produced all expected fields cleanly.                      |
+| `gemini_raw`                 | str             | The raw Gemini response as a JSON-encoded string, for forensic / debugging use.               |
+
+## Difficulty split
+
+| Difficulty | Count   | Meaning                                                                                                                                                                                 |
+|------------|---------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| `hard`     |  2,000  | Scenes selected up-front as harder, non-trivial driving situations and annotated with Gemini reasoning traces. Use these when you want a quality bar that exercises reasoning behavior. |
+| `easy`     | 17,744  | Uniformly random scenes from the held-out US pool, gated only on "the frame renders and at least one ground-truth waypoint is visible." A broad coverage sample.                        |
+
+The `--hard` flag in the evaluation harness keeps only the 2k hard rows; without it you evaluate on the full 19,744.
+
+## Using this dataset
+
+This directory is the default data source for the US run of the ADE evaluation harness:
+
+```bash
+python GRPO-alignment/evaluation/AverageDisplacementError.py \
+    --country US \
+    --models <model-subdir-on-HF>
+```
+
+The harness reads `dataset.jsonl`, resolves each frame via the path layout above, and reports an Average Displacement Error leaderboard. Add `--hard` to restrict the evaluation to the 2,000 hard rows.
+
+For full provenance details (random seed, source paths, render-gating reject reasons) see [build_manifest.json](build_manifest.json) and [build_dataset.py](build_dataset.py).