Datasets:

tasl-lab
/

PDD

license: cc-by-nc-4.0
task_categories:
  - robotics
  - image-to-text
tags:
  - autonomous-driving
  - personalized-driving
  - CARLA
  - human-driving-data
  - vision-language
  - driving-behavior
pretty_name: 'PDD: Personalized Driving Dataset'
size_categories:
  - 10K<n<100K

PDD: Personalized Driving Dataset

Dataset Description

PDD (Personalized Driving Dataset) is a multi-driver, multi-scenario driving dataset collected in CARLA 0.9.15. It captures real human driving behavior from 30 individual drivers, each performing 21 challenging driving scenarios. The dataset is designed for research on personalized autonomous driving, where models learn to mimic individual driving styles.

Each driver has a detailed profile capturing demographics, driving experience, habits, and self-reported driving style. The driving data includes front-camera RGB images, 3D bounding boxes for surrounding objects, and per-frame vehicle telemetry (speed, acceleration, steering, throttle, brake, etc.).

Dataset Statistics

Metric	Value
Drivers	30
Scenarios per driver	21
Total scenario instances	630
Total image frames	70,087
Total bounding box files	70,087
Dataset size	~13 GB
Simulator	CARLA 0.9.15
Frame rate (saved)	4 FPS

Dataset Structure

PDD/
├── driver_01/
│   └── data/
│       ├── Accident/
│       │   ├── images/          # Front-camera RGB images (JPEG)
│       │   │   ├── 0.jpg
│       │   │   ├── 1.jpg
│       │   │   └── ...
│       │   ├── boxes/           # 3D bounding boxes (compressed JSON)
│       │   │   ├── 0.json.gz
│       │   │   ├── 1.json.gz
│       │   │   └── ...
│       │   └── metric/
│       │       ├── metrics.json       # Per-step control inputs
│       │       └── metric_info.json   # Per-frame telemetry
│       ├── BlockedIntersection/
│       │   └── ...
│       └── ... (21 scenarios)
├── driver_02/
│   └── ...
├── ... (30 drivers)
└── user_profiles/
    ├── driver_01.json
    ├── driver_02.json
    └── ... (30 profiles)

Data Fields

Images (`images/*.jpg`)

Front-forward RGB camera images captured at 4 FPS during driving.

Bounding Boxes (`boxes/*.json.gz`)

Gzip-compressed JSON files, one per frame. Each contains a list of detected objects:

class: Object type (ego_car, car, walker, static)
position: [x, y, z] relative to ego vehicle
extent: [length, width, height] of bounding box
yaw: Heading angle
speed: Object speed
id: Unique object identifier
distance: Distance from ego vehicle

Telemetry (`metric/metric_info.json`)

Per-frame driving telemetry indexed by frame number:

location: [x, y, z] world coordinates
rotation: [pitch, roll, yaw]
speed: Current speed (m/s)
speed_limit: Road speed limit (m/s)
acceleration: [x, y, z] acceleration vector
velocity: [x, y, z] velocity vector
angular_velocity: [x, y, z]
distance_to_front_vehicle: Distance to lead vehicle (m)
lane_change_count: Number of lane changes
lane_info: Current lane information
target_point, target_point_next: Navigation waypoints
expert_target_speed: Expert reference speed
expert_control_steer/throttle/brake: Expert reference controls
other_vehicles: Nearby vehicle information
walkers: Nearby pedestrian information

Control Inputs (`metric/metrics.json`)

Sequential list of control commands applied at each simulation step:

steer: Steering angle [-1, 1]
throttle: Throttle input [0, 1]
brake: Brake input [0, 1]
gear, hand_brake, reverse: Additional vehicle state

Driver Profiles (`user_profiles/driver_XX.json`)

basic_information: Age, gender, occupation
driving_experience: Years of experience
driving_frequency_per_week: Typical weekly driving hours
driving_purposes: Common driving use cases
driving_habits_preferences: Self-reported driving habits
health_and_driving_records: Health conditions, accident history
driving_style: Self-classified style (Aggressive / Assertive / Balanced / Calm / Cautious)
international_driving_experience: Driving experience in other regions

Usage

from huggingface_hub import snapshot_download

# Download the full dataset
snapshot_download(repo_id="tasl-lab/PDD", repo_type="dataset", local_dir="./PDD")

# Download a specific driver only
snapshot_download(repo_id="tasl-lab/PDD", repo_type="dataset", local_dir="./PDD",
                  allow_patterns=["driver_01/**", "user_profiles/**"])

Or use the provided loading script (load_pdd.py) for a structured PyTorch-compatible loader:

# Copy load_pdd.py to your project, then:
from datasets import load_dataset

dataset = load_dataset("./load_pdd.py", name="driver_01", trust_remote_code=True)
sample = dataset["train"][0]
print(sample["driver_id"])        # "driver_01"
print(sample["scenario"])         # "Accident"
print(sample["speed"])            # 0.001
print(sample["image"])            # PIL Image
print(sample["driver_profile"])   # {...}

Citation

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

@misc{wang2026drivewaypreferencealignment,
      title={Drive My Way: Preference Alignment of Vision-Language-Action Model for Personalized Driving},
      author={Zehao Wang and Huaide Jiang and Shuaiwu Dong and Yuping Wang and Hang Qiu and Jiachen Li},
      year={2026},
      eprint={2603.25740},
      archivePrefix={arXiv},
      primaryClass={cs.RO},
      url={https://arxiv.org/abs/2603.25740},
}