PDB: Not All Drivers Are the Same -- A Personalized Dataset for Understanding Driving Behavior
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arXiv
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| Main Authors: | , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866912265837477888 |
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| author | Wei, Chuheng Qin, Ziye Li, Siyan Zhang, Ziyan Zhao, Xuanpeng Abdelraouf, Amr Gupta, Rohit Han, Kyungtae Barth, Matthew J. Wu, Guoyuan |
| author_facet | Wei, Chuheng Qin, Ziye Li, Siyan Zhang, Ziyan Zhao, Xuanpeng Abdelraouf, Amr Gupta, Rohit Han, Kyungtae Barth, Matthew J. Wu, Guoyuan |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_06477 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | PDB: Not All Drivers Are the Same -- A Personalized Dataset for Understanding Driving Behavior Wei, Chuheng Qin, Ziye Li, Siyan Zhang, Ziyan Zhao, Xuanpeng Abdelraouf, Amr Gupta, Rohit Han, Kyungtae Barth, Matthew J. Wu, Guoyuan Computer Vision and Pattern Recognition Artificial Intelligence 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. |
| title | PDB: Not All Drivers Are the Same -- A Personalized Dataset for Understanding Driving Behavior |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2503.06477 |