---
license: other
license_name: imotion-llm-derived-annotations
pretty_name: InstructWaymo
task_categories:
- text-generation
- robotics
language:
- en
tags:
- autonomous-driving
- motion-planning
- instruction-following
- waymo-open-dataset
- trajectory-generation
size_categories:
- 10M<n<100M
configs:
- config_name: train_instructions
  data_files: train_instructions-*.jsonl.gz
- config_name: val_instructions
  data_files: val_instructions-*.jsonl.gz
- config_name: train_synth_instructions
  data_files: train_synth_instructions-*.jsonl.gz
- config_name: gf_mtr_mapping
  data_files: gf_mtr_mapping-*.jsonl.gz
---

# InstructWaymo

Instruction-conditioned trajectory annotations over the **Waymo Open Motion
Dataset**, from the WACV 2026 paper
[**iMotion-LLM: Instruction-Conditioned Trajectory Generation**](https://openaccess.thecvf.com/content/WACV2026/html/Felemban_iMotion-LLM_Instruction-Conditioned_Trajectory_Generation_WACV_2026_paper.html).

> **This dataset contains annotations only — no Waymo scenario or sensor data.**
> Every record is keyed to a Waymo scenario id. To use it you must obtain the
> Waymo Open Motion Dataset yourself from <https://waymo.com/open/download>
> under Waymo's own license and terms.

## Contents

| Config | Rows | Source files | What it is |
|---|---|---|---|
| `train_instructions` | 45,011,552 | 3,616,562 | Training instructions: `Instruction`, `Reasoning`, `Decision` (`<Accepted>` / rejected), `Label` (`gt` / contrastive) and `Direction_cls` (0–7) |
| `val_instructions` | 359,308 | 28,932 | The same, for the validation split |
| `train_synth_instructions` | 910,782 | 303,595 | Synthetic/augmented instruction variants |
| `gf_mtr_mapping` | 5,000 | 5,000 | GameFormer ↔ MTR scenario index mappings |

Every row carries `_scene_id` (Waymo scenario id), `_source` (original relative
path) and `_line` (position within its source file).

### Evaluation manifests

`eval_meta/` holds the **exact** manifests used for the paper's evaluation, as
plain JSON:

| Path | Contents |
|---|---|
| `eval_meta/validation_30sep/{meta_gt1,meta_pos1,meta_neg1}.json` | the ground-truth, positive and negative instruction evaluation lists |
| `eval_meta/validation_30sep/eval_meta.json` | full evaluation metadata |
| `eval_meta/feb16_2025_validation/…` | the same for the Feb-2025 validation split |
| `eval_meta/agent_lists/actual_agent{0,1}.json` | per-agent evaluation lists |

These define the splits the reported numbers were computed on and are not
recoverable by re-running preprocessing, so they are published verbatim.

## Direction classes

`Direction_cls` indexes:

`0` stationary · `1` move straight · `2` move straight veering right ·
`3` move straight veering left · `4` turn right · `5` turn left ·
`6` take right U-turn · `7` take left U-turn

## Loading

```python
from datasets import load_dataset

ds = load_dataset("KAUST/InstructWaymo", "val_instructions", split="train")
print(ds[0]["Instruction"], "|", ds[0]["Direction_cls"])
```

To restore the original per-scene file tree the release configs read:

```bash
pip install huggingface_hub
python tools/instruct_data/download_instruct_data.py --dest data/processed --expand
```

(from the [code repository](https://github.com/Vision-CAIR/iMotion-LLM))

## Format note

The research pipeline produced **3.65 million** small `.txt` files. Packed here
as 26 gzipped JSONL shards (0.61 GiB total, from 26.6 GiB of raw text), so the
dataset loads in one pass instead of millions of HTTP requests. `_source`
preserves every original path, and `--expand` rebuilds the tree.

## What is not here (reproducible from code)

Deliberately omitted — the released code regenerates these from the raw Waymo
data:

- `npz` scenario tensor caches (~432 GiB)
- per-scene agent/map JSON and `acts` pickles
- rendered scenario figures (~22 GiB)

## Citation

```bibtex
@inproceedings{Felemban_2026_WACV,
  author    = {Felemban, Abdulwahab and Hroub, Nussair and Ding, Jian and Abdelrahman, Eslam and Shen, Xiaoqian and Mohamed, Abduallah and Elhoseiny, Mohamed},
  title     = {iMotion-LLM: Instruction-Conditioned Trajectory Generation},
  booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},
  month     = {March},
  year      = {2026},
  pages     = {2710-2720}
}
```

## Licensing

These annotations are released for research use as a derived work. The
underlying Waymo Open Motion Dataset remains subject to Waymo's license, which
you must accept separately. Redistribution of Waymo scenario or sensor data is
**not** granted by this repository.