---
license: apache-2.0
pretty_name: PersonaDrive Style-Instructed Driving Dataset
viewer: false
task_categories:
  - robotics
language:
  - en
tags:
  - autonomous-driving
  - carla
  - bench2drive
  - driving-style
  - human-driving
  - driver-in-the-loop
  - vqa
size_categories:
  - 100K<n<1M
---

<div align="center">

# PersonaDrive: Style-Instructed Driving Dataset

[Mahmoud Srewa](https://mosrewa.github.io/) · Praneetsai Vasu Iddamsetty · Mohammad Abdullah Al Faruque · Salma Elmalaki

University of California, Irvine · IEEE/CVF WACV 2027

**[Project page](https://pervasiveautonomylab.github.io/personadrive-landpage/)** ·
[Paper (arXiv 2606.12616)](https://arxiv.org/abs/2606.12616) ·
[Model](https://huggingface.co/PervasiveAutonomyLab/PersonaDrive) ·
[Code](https://github.com/PervasiveAutonomyLab/PersonaDrive)

<img src="assets/data_collection.png" width="90%" alt="Style-instructed driving data collection">

</div>

This dataset contains driving recorded in CARLA under three explicit style instructions, **normal (neutral)**, **aggressive** and **conservative**. It forms the per-style retrieval databases and the style fine-tuning data of [PersonaDrive](https://huggingface.co/PervasiveAutonomyLab/PersonaDrive).

PersonaDrive retrieves frames of the selected style as in-context demonstrations, so switching style only means searching a different part of this dataset.

> **Style names.** The folders use the code's names.
> - **`default` is normal, everyday driving** (the neutral instruction).
> - `fast` is aggressive.
> - `slow` is conservative.
>
> The code also has a retrieval profile called `normal`. It is not part of this dataset, because it is the style-agnostic index built from SimLingo's PDM-lite data.

## At a glance

| Folder | Style | Frames |
|---|---|---:|
| `default` | **Neutral (normal driving)** | 9,613 |
| `fast` | Aggressive | 7,143 |
| `slow` | Conservative | 13,021 |
| **Total** | | **29,777** |

These are the frames the 15 participants drove (`driver1`–`driver15`). The `fast` and `slow` folders also contain `lb1_split`, with routes of SimLingo's Leaderboard 1.0 split recorded in the aggressive and conservative style. They are not part of the participant data and are not counted above. Stage-3 fine-tuning uses data from all three styles.

Each frame has a front camera image, the ego state and controls, the navigation targets and the surrounding objects. Most routes also have per-frame commentary and VQA annotations.

## Files

Every zip unpacks into the same directory tree, so extract the zips you need into one data root.

| File | Size | Files | Contents |
|---|---:|---:|---|
| `data_default.zip` | 2.37 GB | 19,394 | Normal (neutral) driving, with camera frames, per-frame measurements and surrounding objects. |
| `data_fast.zip` | 11.69 GB | 81,033 | Aggressive driving (`fast/driver*`), plus the `fast/lb1_split` fine-tuning routes with their retrieved context points (`icl_examples.json.gz`). |
| `data_slow.zip` | 18.84 GB | 130,004 | Conservative driving (`slow/driver*`), plus the `slow/lb1_split` fine-tuning routes, organized like `data_fast.zip`. |
| `commentary.zip` | 3.2 MB | 786 | Per-route `commentary.json` for all three styles. |
| `drivelm.zip` | 27.8 MB | 775 | Per-route `vqa.json` (DriveLM-style question–answer pairs) for all three styles. |

Total size 32.94 GB. `SHA256SUMS` lists the checksum of each zip.

```bash
# everything
hf download PervasiveAutonomyLab/PersonaDrive --repo-type dataset --local-dir personadrive_data
# or only some parts, e.g. normal driving with its language annotations
hf download PervasiveAutonomyLab/PersonaDrive data_default.zip commentary.zip drivelm.zip SHA256SUMS \
    --repo-type dataset --local-dir personadrive_data

cd personadrive_data && sha256sum -c SHA256SUMS --ignore-missing
for z in *.zip; do unzip -q "$z" -d "$PERSONADRIVE_DATA_ROOT"; done
```

**Which zips you need**

| Task | Zips |
|---|---|
| Use the driving data of one style | `data_<style>` (plus `commentary` / `drivelm` for language) |
| Evaluate PersonaDrive with a style (`default` / `fast` / `slow`) | `data_<style>` of that style |
| Re-run Stage-3 fine-tuning | `data_default`, `data_fast`, `data_slow`, `commentary`, `drivelm`, plus SimLingo's PDM-lite data |
| Re-run Stages 1–2 (retrieval head, per-style indices) | all five zips, plus PDM-lite for the style-agnostic `normal` index |

Evaluation with retrieval opens the camera frames of the retrieved demonstrations, so it needs the frames of the chosen style. It also needs the retrieval head, which is in the [code repository](https://github.com/PervasiveAutonomyLab/PersonaDrive/blob/main/persona_drive_rag/checkpoints/best_model.pt), and the per-style FAISS indices, which you build from this dataset ([Build the retrieval indices](https://github.com/PervasiveAutonomyLab/PersonaDrive#3-build-the-retrieval-indices)). The code repository's [`datasets/README.md`](https://github.com/PervasiveAutonomyLab/PersonaDrive/blob/main/datasets/README.md) lists which zips each use needs.

## How the data was collected

<img src="assets/hardware_rig.jpg" width="45%" align="right" alt="Driver-in-the-loop rig">

15 participants drove CARLA Leaderboard routes at a driver-in-the-loop rig with a real wheel and pedals. The recording code is released with PersonaDrive ([`human_data_collection/`](https://github.com/PervasiveAutonomyLab/PersonaDrive/tree/main/human_data_collection)). Each participant drove each of their routes three times, each time under a different instruction.

| Folder | Style | What the driver was told |
|---|---|---|
| `default` | **Neutral (normal driving)** | *"Follow traffic laws, navigate safely, and drive at a reasonable speed."* |
| `fast` | Aggressive | *"Drive as fast as possible without crashing; engage in aggressive behaviors such as tailgating and cutting off merging vehicles."* |
| `slow` | Conservative | *"Target 10–15 mph on surface streets and 25–35 mph on the highway; yield to other vehicles whenever possible."* |

**Rig.**
- **Hardware.** A Next Level Racing GTrack bucket seat, three 50-inch monitors covering about 130° of view, and a Logitech G923 wheel and pedals.
- **Simulator.** CARLA 0.9.15 ran synchronously at 20 Hz. The ego vehicle was the only human-controlled actor, and CARLA's Traffic Manager populated the surrounding traffic.
- **Display.** A Pygame window showed the first-person view, the speed, the active navigation instruction and scenario, and a bird's-eye view of the surrounding vehicles (for manoeuvres such as merges).

**Protocol.**
- **Disjoint from evaluation.** Routes were drawn at random from the CARLA Leaderboard route sets, and none of them is a Bench2Drive evaluation route. Their scenario types also appear in Bench2Drive, so a retrieved demonstration can be relevant to an evaluation situation, but no evaluation episode is in the dataset.
- **Three passes.** Each participant drove each of their routes once per instruction, in randomized order, so that style is not confounded with route familiarity.
- **Supervision.** Participants were supervised to make sure they followed the instructions.
- **Anonymity.** `driver<N>` is an anonymous participant index. The same index refers to the same participant in all three styles.

| Style | Mean speed (mph) | In motion (mph) | Std. dev. | Stopped (%) |
|---|---:|---:|---:|---:|
| Conservative (`slow`) | 9.8 | 11.5 | 7.6 | 14.6 |
| Neutral, normal driving (`default`) | 13.2 | 15.6 | 10.8 | 15.7 |
| Aggressive (`fast`) | 17.5 | 21.8 | 16.8 | 19.8 |

<sub>Speed statistics computed from the recorded ego state, route-matched over the 55 routes driven under all three instructions. The instructions separate before any model is involved.</sub>

**What is recorded.** CARLA runs at 20 Hz and every fifth step is stored, so each route has the following at 4 Hz.
- The front camera image, plus a shifted-camera copy used for augmentation.
- The ego state from CARLA (speed, pose), which follows the participant's driving.
- The controls `throttle`, `steer` and `brake` in `measurements.json`.
- The navigation command and the next GPS target points from CARLA's route planner.
- The surrounding vehicles, pedestrians, traffic signs and traffic-light states.

### `commentary.zip` (what the car is doing, in words)

- **Contents.** One sentence per frame that says what the car does and why, together with the object that causes it. An example is *"Follow the route. Accelerate to drive with the target speed."*
- **How it was made.** SimLingo's commentary generator produced it automatically after recording, from the recorded simulator state (route, hazards, surrounding objects). Participants did not write it.

### `drivelm.zip` (questions and answers about each scene)

- **Contents.** Per-frame question–answer pairs about perception, prediction and planning, in the DriveLM format. An example is *Q: "What are the important objects in the scene?" A: "The important object in the scene is the black car to the front left of the ego vehicle."*
- **How it was made.** A DriveLM-style generator produced them automatically from the recorded objects and ego state.

## Layout

```
simlingo/
├── data/simlingo/
│   ├── default/driver<N>/routes/<Scenario>/<id>/<Town..>/            # normal (neutral) driving
│   ├── fast/driver<N>/routes/<Scenario>/<id>/<Town..>/               # aggressive
│   ├── fast/lb1_split/routes_training/<Scenario>/<route>/<Town..>/   # SimLingo lb1_split routes, aggressive (fine-tuning)
│   ├── slow/driver<N>/routes/<Scenario>/<id>/<Town..>/               # conservative
│   └── slow/lb1_split/routes_training/<Scenario>/<route>/<Town..>/   # SimLingo lb1_split routes, conservative (fine-tuning)
├── commentary/simlingo/<same relative paths>/commentary.json
└── drivelm/simlingo/<same relative paths>/vqa.json
```

Each route folder (`<Town..>/`) contains these files.

| File | Contents |
|---|---|
| `rgb/NNNN.jpg` | Front camera, one frame per dataset step (4 Hz). |
| `rgb_augmented/NNNN.jpg` | Shifted-camera augmentation. |
| `measurements.json` | Per-frame list of `speed`, `throttle`, `steer`, `brake`, `command`, `next_command`, `target_point(_next)`, `ego_matrix`, `route`, `aim_wp`, … |
| `boxes.json` | Per-frame surrounding objects (`driver<N>` routes). |
| `result.json`, `records.json.gz` | Simulator logs. |
| `icl_examples.json.gz` | `lb1_split` routes only. Written by Stage 3a of the code. For every frame it holds the K = 2 most similar frames retrieved from the same style's database, i.e. the demonstrations the model was fine-tuned with (`rgb_path`, `speed_history`, `throttle_history`, `command`, `target_point(_next)`, `commentary`, `waypoints`). |

`commentary.json` and `vqa.json` are per-frame lists aligned with `measurements.json`, and frames without an annotation are `null`.

## Processing and privacy

- **Anonymity.** Participants are identified only by an index (`driver1`–`driver15`). The dataset contains no names or other personal information about participants.
- **Frame paths in annotations.** The frame paths inside `commentary.json` and `vqa.json` were rewritten to paths relative to the data root (`simlingo/data/simlingo/...`).
- **Paths in `icl_examples.json.gz`.** These keep the absolute paths of the machine they were built on. The PersonaDrive loader resolves both forms against `$PERSONADRIVE_DATA_ROOT`.

## Not included

- **SimLingo's PDM-lite data** (`training_1_scenario` for Stage-3 fine-tuning, and `training_1_scenario`, `training_3_scenarios` and SimLingo's own `lb1_split` for the `normal` index, see the code repository's [`datasets/README.md`](https://github.com/PervasiveAutonomyLab/PersonaDrive/blob/main/datasets/README.md)). It is the SimLingo dataset of Wayve Technologies Ltd and is distributed by its authors under their own licence. Download it from [SimLingo](https://github.com/RenzKa/simlingo).
- **The FAISS indices and other retrieval artefacts.** Build them from this dataset with the [code repository](https://github.com/PervasiveAutonomyLab/PersonaDrive#3-build-the-retrieval-indices).

## Citation

```bibtex
@inproceedings{srewa2027personadrive,
  title     = {PersonaDrive: Human-Style Retrieval-Augmented VLA Agents for Closed-Loop Driving Simulation},
  author    = {Srewa, Mahmoud and Iddamsetty, Praneetsai Vasu and Al Faruque, Mohammad Abdullah and Elmalaki, Salma},
  booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},
  year      = {2027}
}
```

## Acknowledgements

This work is supported by the U.S. National Science Foundation under grant 2339266 and partially supported by the UCI ProperAI Institute.