Using Video Analytics to Automatically Annotate Driver Behavior and Context in Naturalistic Driving Data

Sarkar, Abhijit; Abbott, Lynn; Hickman, Jeffrey S.; Papakis, Ioannis; Winkowski, Calvin; Datta, Debanjan; Sonth, Akash; Bhagat, Hirva; Bhat, Shreyas; Jain, Sandesh; Svetovidov, Andrei; Hanowski, Richard; Kaskar, Omkar; Virginia Tech Transportation Institute; Schatz Publishing Group

doi:10.21949/1521750

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Using Video Analytics to Automatically Annotate Driver Behavior and Context in Naturalistic Driving Data

2024-12-01
By Sarkar, Abhijit ; Abbott, Lynn ; Hickman, Jeffrey S. ; ...

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Details:

Creators:

Sarkar, Abhijit ; Abbott, Lynn ; Hickman, Jeffrey S. ; Papakis, Ioannis ; Winkowski, Calvin ; ... More +

Sarkar, Abhijit ; Abbott, Lynn ; Hickman, Jeffrey S. ; Papakis, Ioannis ; Winkowski, Calvin ; Datta, Debanjan ; Sonth, Akash ; Bhagat, Hirva ; Bhat, Shreyas ; Jain, Sandesh ; Svetovidov, Andrei ; Hanowski, Richard ; Kaskar, Omkar Less -
Corporate Creators:

Virginia Tech Transportation Institute ; Schatz Publishing Group

Virginia Tech Transportation Institute ; Schatz Publishing Group Less -
Contributors:

Fay, Charles
Corporate Contributors:

United States. Department of Transportation. Federal Highway Administration. Office of Research, Development, and Technology ; United States. Department of Transportation. Federal Highway Administration. Turner-Fairbank Highway Research Center

United States. Department of Transportation. Federal Highway Administration. Office of Research, Development, and Technology ; United States. Department of Transportation. Federal Highway Administration. Turner-Fairbank Highway Research Center Less -
Subject/TRT Terms:

[+]

Automatic Data Collection Systems Computer Vision Detection And Identification Systems Driving Behavior Human Factors
Publication/ Report Number:

FHWA-HRT-24-078
DOI:

https://doi.org/10.21949/1521750
Resource Type:

Tech Report
Right Statement:

Public Domain
Geographical Coverage:

United States
Edition:

Research Summary Report
Contracting Officer:

Fay, Charles
Corporate Publisher:

United States. Department of Transportation. Federal Highway Administration. Office of Research, Development, and Technology
Abstract:

Naturalistic driving data provides a wealth of information for researchers studying driver behavior and distracted driving. However, manually annotating the videos to extract the data is costly and time consuming. This project’s research team set out to develop a system to analyze videos from the second Strategic Highway Research Program Naturalistic Driving Study dataset and automatically produce annotations and descriptors for events, behavior, and driving scenarios related to transportation safety.(1) The project had four objectives: characterize high-level driver behavior, such as eating or using a cellphone; classify the environment outside the vehicle, such as the position of roadway objects, work zones, and intersections; understand interactions and dependencies between drivers and the surrounding environment, such as looking at a billboard or a passing vehicle; and demonstrate how the video analytics techniques used in this study can help human factors researchers address research questions in novel ways. The researchers developed and tested advanced computer vision algorithms, including deep neural network (DNN)-based methods to capture spatial and temporal information embedded in the naturalistic driving videos. The DNN models included convolutional neural network models for image recognition and transformer-based models to process sequential data. All the codes developed as part of this project are open sourced.
Format:

PDF
Funding:

693JJ319C000004
Collection(s):

Federal Highway Administration
Main Document Checksum:

[+]

urn:sha-512:ae60b311782cc150809537dcea15c100fc80daa1260d95d32c9f3bb92fd57331509d5ffee14925c5eacfaaf0c289f0caa9c4d9cf5a3130232089978720b46fc4
Download URL:

https://rosap.ntl.bts.gov/view/dot/79738/dot_79738_DS1.pdf
File Type:

[PDF-6.84 MB]