anto0699/Prompt2OpenSCENARIO-LoRA-FineTune

# Prompt2OpenSCENARIO Dataset

## Overview

This repository hosts the **Prompt2OpenSCENARIO dataset**, a curated subset of driving scenarios derived and reformatted for **structured scenario generation**.

Each record pairs a **natural language description** of a traffic scene with its corresponding **validated OpenSCENARIO 1.0 XML file**.

The dataset is designed for training and evaluating large language models in the context of **autonomous driving simulation** (e.g., CARLA).

- **Format:** JSON Lines (.jsonl)

- **Records:** 469

- **Language:** English (descriptions) + XML (OpenSCENARIO 1.0)

- **Validation:** All XML files were validated against the official **OpenSCENARIO 1.0 XSD schema**

---

## Quick Start (Python) — Load & Prepare for Fine-Tuning

Below is a **copy-paste ready** script that:

1) downloads the dataset from the Hub,

2) builds train/validation splits,

3) converts records into messages (system+user→assistant) compatible with a \[INST] ... \[/INST] chat template,

4) (optional) launches an **LoRA/QLoRA SFT** using TRL.

Replace BASE\_MODEL and paths as needed. If you only need the raw dataset, you can stop after the print(ds)/save\_to\_disk lines.

\# pip install -U datasets transformers peft trl bitsandbytes accelerate

import os, json, random

from datasets import load\_dataset, DatasetDict

from transformers import AutoTokenizer, AutoModelForCausalLM

from peft import LoraConfig

from trl import SFTTrainer, SFTConfig



HF\_DATASET = "anto0699/Prompt2OpenSCENARIO-LoRA-FineTune"

BASE\_MODEL = "codellama/CodeLlama-13b-Instruct-hf"  # change if needed

OUTPUT\_DIR = "runs/codellama13b-lora"



SEED = 42

VAL\_RATIO = 0.10

MAX\_LENGTH = 4000

STOP\_SEQUENCE = "</OpenScenario>"



random.seed(SEED)



\# 1) Load dataset from the Hub

raw = load\_dataset(HF\_DATASET, split="train")

print(raw)

\# raw.save\_to\_disk("data/prompt2openscenario")  # optional



\# 2) Train/validation split

split = raw.train\_test\_split(test\_size=VAL\_RATIO, seed=SEED)

ds = DatasetDict({"train": split\["train"], "validation": split\["test"]})



\# 3) Convert to chat messages (system + user -> assistant)

def to\_messages(ex):

&nbsp;   system = ex\["system"].strip()

&nbsp;   user = ex\["user"].strip()

&nbsp;   assistant = ex\["assistant"].strip()

&nbsp;   if not assistant.endswith(STOP\_SEQUENCE):

&nbsp;       assistant += STOP\_SEQUENCE

&nbsp;   user\_with\_sys = f"<<SYS>>\\n{system}\\n<</SYS>>\\n\\n{user}"

&nbsp;   return {

&nbsp;       "messages": \[

&nbsp;           {"role": "user", "content": user\_with\_sys},

&nbsp;           {"role": "assistant", "content": assistant},

&nbsp;       ]

&nbsp;   }



ds = ds.map(to\_messages)



\# 4) (Optional) Fine-tune with TRL + PEFT (LoRA or QLoRA)

USE\_4BIT = True  # set False for LoRA "full-precision" loading



tok = AutoTokenizer.from\_pretrained(BASE\_MODEL, use\_fast=True)

if tok.pad\_token is None:

&nbsp;   tok.pad\_token = tok.eos\_token

tok.padding\_side = "right"



\# Instruct-style template compatible with assistant\_only\_loss=True

tok.chat\_template = (

&nbsp;   "{{ bos\_token }}"

&nbsp;   "{% for m in messages %}"

&nbsp;   "{% if m\['role'] == 'user' %}"

&nbsp;   "\[INST] {{ m\['content'] }} \[/INST]"

&nbsp;   "{% elif m\['role'] == 'assistant' %}"

&nbsp;   "{% generation %}{{ m\['content'] }}{% endgeneration %}{{ eos\_token }}"

&nbsp;   "{% endif %}"

&nbsp;   "{% endfor %}"

)



bnb\_cfg = None

dtype = "bfloat16"

if USE\_4BIT:

&nbsp;   from transformers import BitsAndBytesConfig

&nbsp;   bnb\_cfg = BitsAndBytesConfig(

&nbsp;       load\_in\_4bit=True,

&nbsp;       bnb\_4bit\_quant\_type="nf4",

&nbsp;       bnb\_4bit\_use\_double\_quant=True,

&nbsp;       bnb\_4bit\_compute\_dtype=getattr(\_\_import\_\_("torch"), "bfloat16")

&nbsp;   )



model = AutoModelForCausalLM.from\_pretrained(

&nbsp;   BASE\_MODEL,

&nbsp;   torch\_dtype=None if USE\_4BIT else getattr(\_\_import\_\_("torch"), "bfloat16"),

&nbsp;   quantization\_config=bnb\_cfg if USE\_4BIT else None,

&nbsp;   device\_map="auto" if USE\_4BIT else None,

)

model.config.pad\_token\_id = tok.pad\_token\_id

model.config.use\_cache = False  # enabled automatically at inference time



\# Filter overlong samples to respect context length

def ok\_len(batch):

&nbsp;   return \[

&nbsp;       len(tok.apply\_chat\_template(msgs, tokenize=True, add\_generation\_prompt=False)) <= MAX\_LENGTH

&nbsp;       for msgs in batch\["messages"]

&nbsp;   ]

ds\["train"] = ds\["train"].filter(ok\_len, batched=True)



\# LoRA config (typical for Llama-like)

peft\_cfg = LoraConfig(

&nbsp;   r=32, lora\_alpha=32, lora\_dropout=0.05, bias="none",

&nbsp;   target\_modules=\["q\_proj","k\_proj","v\_proj","o\_proj","gate\_proj","up\_proj","down\_proj"],

&nbsp;   task\_type="CAUSAL\_LM"

)



\# SFT config

sft\_cfg = SFTConfig(

&nbsp;   output\_dir=OUTPUT\_DIR,

&nbsp;   max\_length=MAX\_LENGTH,

&nbsp;   packing=False,

&nbsp;   per\_device\_train\_batch\_size=1,

&nbsp;   gradient\_accumulation\_steps=8,

&nbsp;   per\_device\_eval\_batch\_size=1,

&nbsp;   learning\_rate=2e-4,

&nbsp;   num\_train\_epochs=2,

&nbsp;   warmup\_ratio=0.05,

&nbsp;   lr\_scheduler\_type="cosine",

&nbsp;   logging\_steps=20,

&nbsp;   eval\_strategy="steps",

&nbsp;   eval\_steps=200,

&nbsp;   save\_steps=50,

&nbsp;   save\_total\_limit=3,

&nbsp;   bf16=True,

&nbsp;   gradient\_checkpointing=True,

&nbsp;   assistant\_only\_loss=True,

&nbsp;   report\_to=\["tensorboard"],

)



trainer = SFTTrainer(

&nbsp;   model=model,

&nbsp;   args=sft\_cfg,

&nbsp;   train\_dataset=ds\["train"],

&nbsp;   eval\_dataset=ds\["validation"],

&nbsp;   peft\_config=peft\_cfg,

&nbsp;   processing\_class=tok,

)



trainer.train()

trainer.save\_model(OUTPUT\_DIR)

tok.save\_pretrained(OUTPUT\_DIR)

print("Finished fine-tuning; adapters saved to:", OUTPUT\_DIR)

## Dataset Structure

Each record contains three fields:

{

&nbsp; "system": "Act as an OpenSCENARIO 1.0 generator for ADS testing in CARLA. I will give you a scene description in English and you must return one valid .xosc file, XML only, encoded in UTF-8, starting with <OpenScenario> and ending with </OpenScenario>. The file must be schema-compliant, and executable in CARLA without modifications. The scenario must include: the map (<RoadNetwork>), <Environment> with <TimeOfDay> and <Weather>, exactly one ego vehicle, any other entities with unique names, initial positions using <WorldPosition>, and a valid <Storyboard> with deterministic triggers/events/actions. Use realistic defaults if details are missing (no randomness), but never omit these features. No comments or extra text, only the .xosc.",

&nbsp; "user": "Human-readable description of the scene (time, weather, vehicles, pedestrians, routes, triggers).",

&nbsp; "assistant": "Machine-readable OpenSCENARIO 1.0 XML file, compliant and executable in CARLA."

}



\## Example

\*\*User Prompt (natural language):\*\*

On April 18, 2025, at 09:03:31, the scene unfolds under cloudy skies with moderate snowfall and dense fog. 

The ego vehicle starts at (146.915, -12.991, 0.0) facing north.



\*\*Assistant reply (OpenSCENARIO 1.0):\*\*

```xml

<OpenScenario>

&nbsp; <FileHeader author="dataset" date="2025-04-18T09:03:31" description="generated\_scenario" revMajor="1" revMinor="0" />

&nbsp; <RoadNetwork>

&nbsp;   <LogicFile filepath="ARG\_Carcarana-10\_1\_T-1.xodr"/>

&nbsp; </RoadNetwork>

&nbsp; <Entities>

&nbsp;   <ScenarioObject name="ego\_vehicle">

&nbsp;     <Vehicle name="vehicle.lincoln.mkz2017" vehicleCategory="car">

&nbsp;       <Performance maxSpeed="69.0" maxAcceleration="10.0" maxDeceleration="10.0"/>

&nbsp;       ...

&nbsp;     </Vehicle>

&nbsp;   </ScenarioObject>

&nbsp; </Entities>

&nbsp; <Storyboard>

&nbsp;   ...

&nbsp; </Storyboard>

</OpenScenario>

## Data Statistics

• **Total records:* 469
• **Average description length:** 50–100 words
• **Average XML length:** ~1000–2000 tokens

## Validation and Quality Control

• All XML outputs are **well-formed and schema-valid** under OpenSCENARIO 1.0 XSD.
• Each scenario is **executable in CARLA without modifications**.

## Provenance

The dataset was constructed from and reformatted using data originating from **SCTrans**:

• **Original dataset:** SCTrans – Constructing a Large Public Scenario Dataset for Simulation Testing of Autonomous Driving Systems
• **Repository:** https://github.com/seclab-fudan/SCTrans
• **Properties of the original dataset:** simulation scenario files utilizing existing traffic scenario datasets (i.e., naturalistic movement of road users recorded on public roads).

## Citation of the original dataset:

@inproceedings{SCTrans-ICSE-2024,

    author    = {Jiarun Dai and Bufan Gao and Mingyuan Luo and Zongan Huang and Zhongrui Li and Yuan Zhang and Min Yang},

    booktitle = {Proceedings of the 2024 International Conference on Software Engineering},

    title     = {Constructing a Large Public Scenario Dataset for Simulation Testing of Autonomous Driving Systems},

    year      = {2024},

}

## Citation of This Dataset

If you use this dataset in your research, please cite:

@misc{prompt2openscenario2025,

  title        = {Empty Title},

  author       = {No Authors},

  year         = {2025},

  publisher    = {Hugging Face},

  howpublished = {\\url{https://huggingface.co/datasets/anto0699/Prompt2OpenSCENARIO-LoRA-FineTune}}

}

## License

This dataset is released under the same terms as the OpenSCENARIO 1.0 standard and inherits conditions from the SCTrans dataset.

It is intended for research and educational purposes only.