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README.md

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 ---
 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
+
+**[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: 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 | 39,734 |
+| `slow` | Conservative | 64,215 |
+| **Total** | | **113,562** |
+
+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: camera frames, per-frame measurements, surrounding objects. |
+| `data_fast.zip` | 11.69 GB | 81,033 | Aggressive driving, with the same contents plus the retrieved context points used for fine-tuning (`icl_examples.json.gz`). |
+| `data_slow.zip` | 18.84 GB | 130,004 | Conservative driving, 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: 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_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, and the per-style FAISS indices. The code repository's `datasets/README.md` describes both.
+
+## 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. They drove the same 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. They share the scenario taxonomy, so a retrieved demonstration can be relevant to an evaluation situation, but no evaluation episode is in the dataset.
+- **Three passes:** each participant drove the 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.** Each route stores, at 4 Hz:
+- the front camera image, plus a shifted-camera copy used for augmentation;
+- the ego state: speed from CARLA, steering from the wheel axis, throttle and brake from the pedals;
+- the navigation command and the next GPS target points from CARLA's route planner;
+- the objects around the ego vehicle: vehicles, pedestrians, traffic signs, 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. For example: *"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. For example: *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..>/   # aggressive
+│   ├── slow/driver<N>/routes/<Scenario>/<id>/<Town..>/               # conservative
+│   └── slow/lb1_split/routes_training/<Scenario>/<route>/<Town..>/   # conservative
+├── commentary/simlingo/<same relative paths>/commentary.json
+└── drivelm/simlingo/<same relative paths>/vqa.json
+```
+
+Each route folder (`<Town..>/`) contains:
+
+| 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: `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, 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`; 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`.
+- **Removed:** hidden system files, and the annotation files of four recordings whose camera data was removed earlier.
+
+## Not included
+
+- **SimLingo's PDM-lite data** (`training_1_scenario`, used for Stage-3 fine-tuning and the `normal` index). 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).
+- **Precomputed SigLIP image embeddings.** They are a cache that Stage 3a of the code rebuilds automatically.
+- **The FAISS indices and other retrieval artefacts.** See the code repository.
+
+## Limitations
+
+- **Simulation only:** all data comes from the CARLA simulator.
+- **Scope:** the data comes from 15 participants.
+- **Instructed styles:** the styles were instructed rather than observed in natural driving.
+- **No demographic information** about participants is included.
+- **Generated language:** VQA and commentary were generated automatically from simulator state and can contain errors.
+
+## Citation
+
+```bibtex
+@article{srewa2026personadrive,
+  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},
+  journal = {arXiv preprint arXiv:2606.12616},
+  year    = {2026}
+}
+```
+
+## Acknowledgements
+
+This work is supported by the U.S. National Science Foundation under grant 2339266 and partially supported by the UCI ProperAI Institute.

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