Driving behavior is inherently personal, influenced by individual habits, decision-making styles, and physiological states. However, most existing datasets treat all drivers as homogeneous, overlooking driver-specific variability. To address this gap, we introduce the Personalized Driving Behavior (PDB) dataset, a multi-modal dataset designed to capture personalization in driving behavior under naturalistic driving conditions. Unlike conventional datasets, PDB minimizes external influences by maintaining consistent routes, vehicles, and lighting conditions across sessions. It includes sources from 128-line LiDAR, front-facing camera video, GNSS, 9-axis IMU, CAN bus data (throttle, brake, steering angle), and driver-specific signals such as facial video and heart rate. The dataset features 12 participants, approximately 270,000 LiDAR frames, 1.6 million images, and 6.6 TB of raw sensor data. The processed trajectory dataset consists of 1,669 segments, each spanning 10 seconds with a 0.2-second interval. By explicitly capturing drivers' behavior, PDB serves as a unique resource for human factor analysis, driver identification, and personalized mobility applications, contributing to the development of human-centric intelligent transportation systems.
View on arXiv@article{wei2025_2503.06477,
title={ PDB: Not All Drivers Are the Same -- A Personalized Dataset for Understanding Driving Behavior },
author={ Chuheng Wei and Ziye Qin and Siyan Li and Ziyan Zhang and Xuanpeng Zhao and Amr Abdelraouf and Rohit Gupta and Kyungtae Han and Matthew J. Barth and Guoyuan Wu },
journal={arXiv preprint arXiv:2503.06477},
year={ 2025 }
}