Datasets:
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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).
|