PDB: Not All Drivers Are the Same -- A Personalized Dataset for Understanding Driving Behavior

TL;DR

The paper addresses the need to model driver-specific variability in driving behavior, arguing that existing datasets mask human factors by treating drivers as interchangeable. It introduces the Personalized Driving Behavior (PDB) dataset, a multi-modal collection collected under tightly controlled conditions using the same vehicle and route to isolate individual driving styles. PDB collects vehicle dynamics, driver biometrics (facial video and heart rate), CAN-bus data, LiDAR, stereo cameras, GNSS/IMU, and trajectory annotations across 12 drivers, totaling 451 minutes and 6.6 TB of raw data, with a processed trajectory set of 1,669 segments of 10 seconds each at 0.2-second intervals. The dataset enables driver identification, personalized trajectory prediction, and risk-aware adaptive driving research, offering a valuable resource for human-centric intelligent transportation systems and personalized mobility applications.

Abstract

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.

Figures (2)

• Figure 1: The PDB data collection sensor setup.
• Figure 2: Data collection process.