DOI: 10.5281/zenodo.15181590
title: DCPT :  A Multi-Modal Dataset of Drivers' Cognitive and Physical States in L3 Takeover Scenarios

description: DCPT : A Multi-Modal Dataset of Drivers' Cognitive and Physical States in L3 Takeover Scenarios

Overview

This repository contains a specialized dataset of driver takeover responses in automated driving scenarios, including upper-body video,first-person view videos, eye-tracking and head motion data recordings.

Participants in this dataset were engaged in a range of non-driving related tasks (NDRTs) during automated driving to simulate real-world distractions. These tasks were carefully designed to reflect common in-vehicle behaviors and are labeled in the filenames using task IDs.

The task types and their definitions are listed below:





1. No task: Observing road conditions or the surrounding environment.




2. Watching video: Watching videos on the central stack screen.




3. Playing game: Playing games on a smartphone or iPad.




4. Messaging: Text chatting with the experimenter via WeChat.




5. Phone call: Conversing with the experimenter over the phone.




6. Listening to radio: Listening to audio broadcasts on audio devices.




7. Reading: Reading provided materials.




8. Eating: Consuming snacks provided during the session.




9. Chatting with passenger: Talking with the experimenter seated in the passenger seat.



Each driving session began with a period of automated driving while the participant engaged in one of the above tasks. A takeover request (TOR) was issued at a random time point, prompting the participant to resume control of the vehicle. Sensor data and video recordings from the moments leading up to this event were captured and organized accordingly.

Dataset Structure

The repository is organized into the following main folders:



Upper_body_video_01/ to Upper_body_video_09/: Contains upper body video recordings from nine separate tasks

First_person_view_video/: Contains First-person view video recordings of participants

Eye-tracking&head motion data/: Contains eye movement&head motion data


Root Directory Files



Takeover-Time.xlsx: Records take-over times for each experimental trial

information.xlsx: Contains demographic information of all participants

README.md: Providing an overview of the dataset structure and contents


Upper body video data

Naming Convention

Upper body video files follow the naming pattern:

<NDRT>_P<Person>_<Date>_<Hour>_<Minute>_<Take-over Time>

Where:



NDRT: Non-driving related task ID (refer to Table II in our research paper)

Person: Participant ID

Date: Recording date in YYYYMMDD format

Hour: Recording start hour in HH format

Minute: Recording start minute in MM format

Take-over Time: Take-over time in 0.1s units


Description



10-second clips before the take-over request was issued

Resolution: 1920 x 1080 pixels


First person view video data

Naming Convention

First person view video files follow the naming pattern:

<NDRT>_P<Person>_<Date>_<Hour>_<Minute>_<Take-over Time>

Where:



NDRT: Non-driving related task ID (refer to Table II in our research paper)

Person: Participant ID

Date: Recording date in YYYYMMDD format

Hour: Recording start hour in HH format

Minute: Recording start minute in MM format

Take-over Time: Take-over time in 0.1s units


Description



10-second clips before the take-over request was issued

Resolution: 1280 x 720 pixels

The task of "Messaging" in the dataset has been subjected to blurring processing to protect relevant information


Eye-tracking&head motion data

Naming Convention

Eye-tracking&head motion files follow the naming pattern:

<NDRT>_P<Person>_<Date>_<Hour>_<Minute>_<Take-over Time>

Where:



NDRT: Non-driving related task ID (refer to Table II in our research paper)

Person: Participant ID

Date: Recording date in YYYYMMDD format

Hour: Recording start hour in HH format

Minute: Recording start minute in MM format

Take-over Time: Take-over time in 0.1s units


Missing data is denoted by an empty cell. The data starts 30 seconds prior to the takeover request.

Description



Format: Excel spreadsheets

Collected by Tobii Pro Glasses 2

Each file includes the following information:

Eye-tracking data:



Recording timestamp(Timestamp counted from the start of the recording in milliseconds)

Recording timestamp(Timestamp counted from the start of the recording in milliseconds)

Sensor(The identifier of the sensor capturing data)

Recording Fixation filter name(The name of the Fixation filter applied on the Recording eye tracking data in the export)

Event(Name of automatically or manually logged event)

Gaze point X, Y (averaged left and right eye gaze point in pixel)

Gaze point 3D X, Y, Z (averaged Gaze 3D position in pixel)

Gaze direction left/right X, Y, Z (Gaze 3D position in pixel)

Pupil position left/right X, Y, Z (Pupil 3D position in mm)

Pupil diameter left/right (Estimated size of pupil in mm)

Validity left,right(Indicates if the eyes have been correctly identified)

Eye movement type(Type of eye movement classified by the fixation filter)

Gaze event duration(The duration of the currently active eye movement in milliseconds)

Eye movement type index(Count is an auto-increment number for each eye movement type)

Fixation point X, Y (Horizontal and vertical coordinate of the averaged gaze point for both eyes in pixel)




          Head motion data:







Gyroscope X, Y, Z (angular velocity in deg/s)

Accelerometer X, Y, Z (linear acceleration in m/s²)