---
license: cc-by-4.0
pretty_name: PhysicalAI US Evaluation
language:
  - en
size_categories:
  - 10K<n<100K
task_categories:
  - robotics
  - image-to-text
tags:
  - autonomous-driving
  - trajectory-prediction
  - planning
  - vision-language-action
  - ego-motion
configs:
  - config_name: default
    data_files:
      - split: test
        path: dataset.jsonl
---

# 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 two 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.       |

### 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.

## Difficulty split

| Difficulty | Count   | Meaning                                                                                                                                                                                 |
|------------|---------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| `hard`     |  2,000  | Scenes selected up-front as harder, non-trivial driving situations. 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).