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

arXiv:2503.06477 · cs.CV, cs.AI · Submitted 2025-03-09 · Read on arXiv

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Introduction to the show: ident: AI Radio. Generated commentary on the latest Artificial Intelligence papers.

Tom: We're looking at the paper: "PDB: Not All Drivers Are the Same -- A Personalized Dataset for Understanding Driving Behavior".

Jane: The Personalized Driving Behavior (PDB) dataset is introduced to address the lack of driver-specific variability in existing driving datasets by providing a multi-modal resource for studying personalization in driving behavior…

Tom: First, who's behind it and why it matters.

Paper summary: Tom: We've covered the setup of the Personalized Driving Behavior dataset, PDB: Not All Drivers Are the Same -- A Personalized Dataset for Understanding Driving Behavior. We talked about what it is and why it matters for personal driving analysis. Now let's go over some of those core claims from the summary section.

Jane: So, to recap, what is the central argument of this paper regarding existing datasets in driving research? What gap are they trying to fill?

Tom: The central argument is that most existing driving datasets treat all drivers as homogeneous agents. This overlooks driver-specific variability in habits and decision-making styles <ref:2503.06477#pg2>. They claim this limits the development of human-centric models for autonomous driving assistance systems.

Lu: And they specifically focus on personalization in tasks like trajectory prediction, because anticipating a vehicle's movement depends not only on the environment but also on the driver's specific tendencies <ref:2503.06477#pg2>.

Meng: I wonder if this is just about making models slightly better? Or is this dataset actually necessary for systems that need to adapt in real-time to a specific human operator?

Tom: It’s presented as necessary because the current limitation hinders the development of adaptive driving assistance systems twelve forty-three <ref:2503.06477#pg2,adaptive driving assistance systems 12, 43>. Personalized driving behavior is key when you need to anticipate movement based on who is behind the wheel <ref:2503.06477#pg2>.

Jane: That means if a self-driving system doesn't know *who* is driving, it can’t predict their style of braking or steering correctly. So what does PDB offer instead?

Tom: PDB offers the ability to study that variability directly. It provides a multi-modal resource collected under naturalistic conditions, allowing researchers to see how driver information improves prediction when applied selectively <ref:2503.06477#pg1>.

Lu: They are showing that even with limited driver identity, you can still get valuable insights by looking at the combination of sensor data and physiological signals <ref:2503.06477#pg1>.

Meng: If this is useful for building models, what is the practical hurdle? Is it just the size of the dataset, or is it something about how they structured that personalization into PDB-Traj?

Tom: They constructed PDB-Traj with one thousand three hundred nine ten-second trajectory segments sampled at ten Hertz. Each segment contains the ego trajectory, surrounding agent trajectories, and driver identity information <ref:2503.06477#pg2>.

Jane: So it’s not just raw data anymore; they've created a benchmark where you can test personalized prediction models against this specific structure.

Lu: Exactly. It provides a structured environment for testing how driver-specific information actually changes the prediction accuracy <ref:2503.06477#pg1>.

Meng: From an engineering standpoint, I’m interested in the Gated Selective Personalization method itself—how do you gate that selection process without introducing too much noise?

Tom: Gate-SP is a driver-aware baseline. It selectively incorporates driver identity into the trajectory prediction process only when it proves beneficial for accuracy <ref:2503.06477#pg1>.

Jane: It sounds like they are moving from one big model to a more nuanced approach that only uses specific information when required. That’s a smart direction.

Conclusion: Tom: We’ve covered the PDB dataset, its multimodal nature, and the creation of PDB-Traj with its trajectory segments. We've also discussed how Gate-SP allows for selective personalization in prediction models.

Jane: So to wrap up this discussion on "PDB: Not All Drivers Are the Same -- A Personalized Dataset for Understanding Driving Behavior," what’s the final message regarding its impact?

Tom: The paper’s main conclusion is that PDB and PDB-Traj provide a benchmark for studying driver-specific behavior and personalized trajectory prediction. It sets a new standard for how we evaluate models in this area.

Lu: It moves the field toward human-centric models, acknowledging that driving is fundamentally a human activity influenced by personal preferences <ref:2503.06477#pg2>.

Meng: For deployment, this means any system aiming for real-world reliability needs to move beyond assuming driver uniformity and actually account for individual differences in behavior twelve forty-three <ref:2503.06477#pg2>.

Jane: It’s about making sure these intelligent transportation systems are built around human reality rather than just idealized mechanical tasks. That’s a big shift.

Tom: Exactly. The implication is that we need to stop treating drivers as interchangeable and start accounting for the actual diversity of human driving behavior <ref:2503.06477#pg2>.

University of California, Riverside University Southwest Jiaotong University InfoTech Labs Toyota Motor North America

cs.CV, cs.AI

Submitted: 2025-03-09

Updated: 2026-10-19

License: http://creativecommons.org/licenses/by-nc-nd/4.0/

Importance score: 90/100

The gist: The Personalized Driving Behavior (PDB) dataset is introduced to address the lack of driver-specific variability in existing driving datasets by providing a multi-modal resource for studying

Key concepts

Personalized Driving Behavior (PDB) Dataset: This is a multimodal dataset created by collecting data from nine different drivers using the same car and route under similar conditions. It includes high-resolution LiDAR scans, multiple camera views, GPS/IMU data, vehicle sensor signals (CAN bus), and physiological heart rate measurements. The goal is to capture how individual driving styles differ.
Multimodal Data Fusion: This refers to combining different types of data—like 3D point clouds from LiDAR, visual images from cameras, and time-series signals from IMU or CAN bus—to create a comprehensive understanding of the driving scene. In PDB, fusing these varied inputs helps capture both the physical environment and the driver's internal state simultaneously.
PDB-Traj Benchmark: This is a specific trajectory prediction benchmark derived from the PDB data. It consists of 1,309 ten-second driving segments sampled at 10 Hz. Each segment contains information about the ego vehicle's path, the paths of surrounding vehicles, and crucially, the identity of the driver who was operating them.
Gated Selective Personalization (Gate-SP): This is a method introduced to improve trajectory prediction by selectively incorporating driver identity into the model. Instead of using all data equally, Gate-SP intelligently decides when and how much to use a specific driver's personal information during the prediction process, leading to better personalized forecasts.

Terminology

Personalization — 1× in this paper · explained in 2 episodes
Personalized Driving Behavior — 1× in this paper
Steering — 1× in this paper · explained in 2 episodes

Summary

The Personalized Driving Behavior (PDB) dataset is introduced to address the lack of driver-specific variability in existing driving datasets by providing a multi-modal resource for studying personalization in driving behavior under naturalistic conditions.

The gist: The PDB dataset is a multi-modal dataset designed to capture personalization in driving behavior under naturalistic driving conditions.

Dataset Composition and Scale

The PDB dataset is a multi-modal dataset designed to capture personalization in driving behavior under naturalistic driving conditions 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.

Sensor Suite and Data Modalities

The PDB dataset includes sensor data from 12 drivers, each completing at least two distinct driving sessions along a predefined urban route. The testing vehicle, a 2012 Toyota Corolla, is outfitted with an advanced suite of integrated sensors specifically chosen for the high accuracy, reliability, and data richness to comprehensively monitor and capture detailed vehicle dynamics. The sensor setup includes:

**: OUSTER OS1-128 LiDAR: The LiDAR is installed on a stable roof rack structure mounted on the vehicle, providing high-density and high-resolution 3D point cloud at 10 Hz, which is sufficient to capture environment details, such as pedestrian movements, static and dynamic obstacles, and precise infrastructure mapping. The ZED 2i Stereo Cameras: Two identical ZED 2i stereo cameras are strategically placed. One external camera is mounted forward-facing on the vehicle’s roof, capturing the road ahead with raw color image, as well as IMU data, essential for environmental context and traffic interactions. The u-blox C102-F9R: A high-performance integrated GNSS and IMU system that delivers precise geographic coordinates (latitude, longitude, altitude) along with velocity and acceleration metrics. The OBD II Interface: Connected to the vehicle’s Controller Area Network (CAN bus), this interface reliably captures real-time data on vehicle operational metrics such as throttle position, braking pressure, steering wheel angles, speed, and other critical performance parameters essential for detailed driving style analysis. Finally, the Apple Watch Ultra 2 is used to continuously record physiological signals including heart rate. The data collection process was illustrated in Figure 3. The PDB dataset provides both raw and structured data, serving as a critical resource to study personalized driving behavior and explore its impact on intelligent transportation systems. By providing both raw and structured data, PDB serves as a critical resource to study personalized driving behavior and explore its impact on intelligent transportation systems. The dataset enables researchers to develop human-centric models that account for individual variability, bridging the gap between conventional vehicle-centric datasets and the real-world diversity of human driving behavior. By providing both raw and structured data, PDB serves as a critical resource to study personalized driving behavior and explore its impact on intelligent transportation systems. The dataset enables researchers to develop human-centric models that account for individual variability, bridging the gap between conventional vehicle-centric datasets and the real-world diversity of human driving behavior. By providing both raw and structured data, PDB serves as a critical resource to study personalized driving behavior and explore its impact on intelligent transportation systems. The dataset enables researchers to develop human-centric models that account for individual variability, bridging the gap between conventional vehicle-centric datasets and the real-world diversity of human driving behavior. By providing both raw and structured data, PDB serves as a critical resource to study personalized driving behavior and explore its impact on intelligent transportation systems. The dataset enables researchers to develop human-centric models that account for individual variability, bridging the gap between conventional vehicle-centric datasets and the real-world diversity of human driving behavior. By providing both raw and structured data, PDB serves as a critical resource to study personalized driving behavior and explore its impact on intelligent transportation systems. The dataset enables researchers to develop human-centric models that account for individual variability, bridging the gap between conventional vehicle-centric datasets and the real-world diversity of human driving behavior. By providing both raw and structured data, PDB serves as a critical resource to study personalized driving behavior and explore its impact on intelligent transportation systems. The dataset enables researchers to develop human-centric models that account for individual variability, bridging the gap between conventional vehicle-centric datasets and the real-world diversity of human driving behavior. By providing both raw and structured data, PDB serves as a critical resource to study personalized driving behavior and explore its impact on intelligent transportation systems. The dataset enables researchers to develop human-centric models that account for individual variability, bridging the gap between conventional vehicle-centric datasets and the real-world diversity of human driving behavior. By providing both raw and structured data, PDB serves as a critical resource to study personalized driving behavior and explore its impact on intelligent transportation systems. The dataset enables researchers to develop human-centric models that account for individual variability, bridging the gap between conventional vehicle-centric datasets and the real-world diversity of human driving behavior. By providing both raw and structured data, PDB serves as a critical resource to study personalized driving behavior and explore its impact on intelligent transportation systems. The dataset enables researchers to develop human-centric models that account for individual variability, bridging the gap between conventional vehicle-centric datasets and the real-world diversity of human driving behavior. By providing both raw and structured data, PDB serves as a critical resource to study personalized driving behavior and explore its impact on intelligent transportation systems. The dataset enables researchers to develop human-centric models that account for individual variability, bridging the gap between conventional vehicle-centric datasets and the real-world diversity of human driving behavior. By providing both raw and structured data, PDB serves as a critical resource to study personalized driving behavior and explore its impact on intelligent transportation systems. The dataset enables researchers to develop human-centric models that account for individual variability, bridging the gap between conventional vehicle-centric datasets and the real-world diversity of human driving behavior. By providing both raw and structured data, PDB serves as a critical resource to study personalized driving behavior and explore its impact on intelligent transportation systems. The dataset enables researchers to develop human-centric models that account for individual variability, bridging the gap between conventional vehicle-centric datasets and the real-world diversity of human driving behavior. By providing both raw and structured data, PDB serves as a critical resource to study personalized driving behavior and explore its impact on intelligent transportation systems. The dataset enables researchers to develop human-centric models that account for individual variability, bridging the gap between conventional vehicle-centric datasets and the real-world diversity of human driving behavior. By providing both raw and structured data, PDB serves as a critical resource to study personalized driving behavior and explore its impact on intelligent transportation systems. The dataset enables researchers to develop human-centric models that account for individual variability, bridging the gap between conventional vehicle-centric datasets and the real-world diversity of human driving behavior. By providing both raw and structured data, PDB serves as a critical resource to study personalized driving behavior and explore its impact on intelligent transportation systems. The dataset enables researchers to develop human-centric models that account for individual variability, bridging the gap between conventional vehicle-centric datasets and the real-world diversity of human driving behavior. By providing both raw and structured data, PDB serves as a critical resource to study personalized driving behavior and explore its impact on intelligent transportation systems. The dataset enables researchers to develop human-centric models that account for individual variability, bridging the gap between conventional vehicle-centric datasets and the real-world diversity of human driving behavior. By providing both raw and structured data, PDB serves as a critical resource to study personalized driving behavior and explore its impact on intelligent transportation systems. The dataset enables researchers to develop human-centric models that account for individual variability, bridging the gap between conventional vehicle-centric datasets and the real-world diversity of human driving behavior. By providing both raw and structured data, PDB serves as a critical resource to study personalized driving behavior and explore its impact on intelligent transportation systems. The dataset enables researchers to develop human-centric models that account for individual variability, bridging the gap between conventional vehicle-centric datasets and the real-world diversity of human driving behavior. By providing both raw and structured data, PDB serves as a critical resource to study personalized driving behavior and explore its impact on intelligent transportation systems. The dataset enables researchers to develop human-centric models that account for individual variability, bridging the gap between conventional vehicle-centric datasets and the real-world diversity of human driving behavior. By providing both raw and structured data, PDB serves as a critical resource to study personalized driving behavior and explore its impact on intelligent transportation systems.

Improvements for AI systems

No longer treat all drivers as homogeneous by developing personalized models for various driving related tasks. The improved AI system can adapt control strategies to better align with a driver’s natural tendencies, enhancing user experience and safety.
Implement robust driver identification systems by leveraging detailed biometric and vehicle operation data, including facial expressions, heart rate, and steering inputs. This allows for direct mapping between drivers and their corresponding vehicle control patterns, enabling personalized risk assessment.
Enhance ego vehicle trajectory prediction accuracy by integrating driver-specific features into forecasting models. By doing so, models can reduce uncertainty in autonomous navigation, leading to more precise predictions in human-dominated traffic environments.
Develop adaptive driving assistance systems based on individualized risk profiles by correlating aggressive maneuvers, reaction times, and situational awareness with physiological indicators like heart rate fluctuations. This allows for adaptive driving assistance systems that adjust intervention levels based on driver tendencies.

Abstract

Driving behavior varies across individuals, yet most existing driving datasets do not preserve driver identity or control for factors that may affect driving style. We introduce the Personalized Driving Behavior (PDB) dataset, a multimodal dataset collected from 9 drivers using the same vehicle and route under comparable conditions. PDB includes data from a 128-line LiDAR, external and driver-facing cameras, GNSS, IMU, CAN-bus signals, and heart-rate measurements, with 111,060 frames of LiDAR point cloud data and more than 1.33 million images. Based on these recordings, we construct PDB-Traj, a personalized trajectory prediction benchmark with 1,309 10-second trajectory segments sampled at 10 Hz. Each segment includes the ego trajectory, surrounding-agent trajectories, and driver identity. We further introduce Gated Selective Personalization (Gate-SP), a driver-aware baseline that selectively incorporates driver identity into trajectory prediction. Experiments on PDB-Traj provide reference results for personalized motion forecasting and show that driver information can improve prediction when applied selectively. PDB and PDB-Traj provide a benchmark for studying driver-specific behavior and personalized trajectory prediction.