Datasets:

KAUST
/

InstructNuPlan

license: other
license_name: imotion-llm-derived-annotations
pretty_name: Open-Vocabulary InstructNuPlan
task_categories:
  - text-generation
  - robotics
language:
  - en
tags:
  - autonomous-driving
  - motion-planning
  - instruction-following
  - nuplan
  - trajectory-generation
size_categories:
  - 10K<n<100K
configs:
  - config_name: gpt_responses
    data_files: gpt_responses-*.jsonl.gz
  - config_name: template_instructions
    data_files: template_instructions-*.jsonl.gz
  - config_name: meta_prompts
    data_files: meta_prompts-*.jsonl.gz
  - config_name: positive_prompts
    data_files: gpt_positive_prompts-*.jsonl.gz
  - config_name: negative_prompts
    data_files: gpt_negative_prompts-*.jsonl.gz

Open-Vocabulary InstructNuPlan

Safety-aware, open-vocabulary driving instruction annotations over nuPlan scenarios, from the WACV 2026 paper iMotion-LLM: Instruction-Conditioned Trajectory Generation.

This dataset contains annotations only — no nuPlan sensor data, maps or logs. Every record is keyed to a nuPlan scenario token. To use it you must obtain nuPlan yourself from https://www.nuscenes.org/nuplan under Motional's own license and terms.

Why this exists

The instruction/reasoning pairs here are LLM-generated output. They were produced by prompting a GPT model once, per scenario, and are therefore not reproducible by re-running the pipeline: generation is non-deterministic and costs API credits. Publishing them is the only way to reproduce the paper's open-vocabulary numbers exactly.

Everything else in the paper's preprocessing chain is reproducible from code and is deliberately not published here — see "What is not here".

Coverage

14 nuPlan scenario types:

accelerating_at_crosswalk, behind_bike, behind_long_vehicle, following_lane_with_lead, following_lane_without_lead, following_lane_with_slow_lead, starting_protected_cross_turn, starting_protected_noncross_turn, starting_unprotected_cross_turn, starting_unprotected_noncross_turn, stopping_with_lead, traversing_crosswalk, traversing_intersection, waiting_for_pedestrian_to_cross

Configs

Config	Rows	What it is
`gpt_responses`	7,119	The core dataset. Per scenario: the prompt sent to the LLM and its returned instructions, each with `reasoning`, `safe` / `safe_instruction` flags and a `category` (e.g. "Without Context").
`template_instructions`	88,133	Template-rendered instruction/reasoning/decision records used to condition the trajectory model (`Instruction`, `Reasoning`, `Decision`, `Label`, `Direction_cls`).
`meta_prompts`	7,131	Structured scenario metadata: `scenario_type`, `situation`, `what_safe_to_do`, `what_unsafe_to_do`.
`positive_prompts` / `negative_prompts`	7,131 each	Raw prompt text for the safe / unsafe instruction arms, kept for provenance.

Every row carries _scene_id (nuPlan token), _source (original relative path) and, where a source file held several records, _line.

Loading

from datasets import load_dataset

ds = load_dataset("KAUST/InstructNuPlan", "gpt_responses", split="train")
print(ds[0]["content"][0]["data"][0]["instructions"][0])

Format note

The original release was ~43,000 small .txt / .json files. They are packed here as gzipped JSONL so the dataset loads in one pass instead of tens of thousands of HTTP requests. _source preserves the original path of every record, so the on-disk layout is fully recoverable.

What is not here (reproducible from code)

Deliberately omitted, because the released code regenerates it from nuPlan:

npz scenario tensor caches (~912 GiB)
rendered scenario figures (.png)

See the code repository for the preprocessing and prompt-construction scripts.

Citation

@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 nuPlan data remains subject to Motional's license, which you must accept separately. Redistribution of nuPlan sensor data, maps or logs is not granted by this repository.