| --- |
| 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). |
|
|
