Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Predicting suicide attempts among the elderly: A machine learning evaluation of a staged ideation-to-action approach based on the Three-Step Theory.

Journal of affective disorders·2026
Same author

Predicting brand share after LOE in chronic disease market using machine learning.

The European journal of health economics : HEPAC : health economics in prevention and care·2025
Same author

Thermoforming 2D films into 3D electronics for high-performance, customizable tactile sensing.

Science advances·2025
Same author

Robust Long-Term Vehicle Trajectory Prediction Using Link Projection and a Situation-Aware Transformer.

Sensors (Basel, Switzerland)·2024
Same author

The multimodality cell segmentation challenge: toward universal solutions.

Nature methods·2024
Same author

Population-level call properties of endangered <i>Dryophytes suweonensissensu</i> lato (Anura: Amphibia) in South Korea.

PeerJ·2023
Same journal

RETRACTED: Zhang et al. A Novel Framework for Reconstruction and Imaging of Target Scattering Centers via Wide-Angle Incidence in Radar Networks. <i>Sensors</i> 2025, <i>25</i>, 6802.

Sensors (Basel, Switzerland)·2026
Same journal

Enhancing Unsupervised Multi-Source Domain Adaptation for Person Re-Identification via Mixture of Experts and Graph-Based Relation.

Sensors (Basel, Switzerland)·2026
Same journal

Development of an Instrumented Glove for Palmar Pressure Assessment in Kayakers.

Sensors (Basel, Switzerland)·2026
Same journal

Development and Experimental Validation of an Autonomous IoT-Based Monitoring System for Real-Time Water Quality Assessment in the Amazon River.

Sensors (Basel, Switzerland)·2026
Same journal

Semi-Supervised Adversarial Learning Framework for Controller Area Network Bus Intrusion Detection.

Sensors (Basel, Switzerland)·2026
Same journal

Smart Optimization Method for Safety Signs in Innovative Manufacturing Environments Integrating Industrial Field IoT Sensors and Knowledge Graphs.

Sensors (Basel, Switzerland)·2026
See all related articles

Related Experiment Video

Updated: Jan 15, 2026

Tactile Vibrating Toolkit and Driving Simulation Platform for Driving-Related Research
07:15

Tactile Vibrating Toolkit and Driving Simulation Platform for Driving-Related Research

Published on: December 18, 2020

4.9K

Classifying Advanced Driver Assistance System (ADAS) Activation from Multimodal Driving Data: A Real-World Study.

Gihun Lee1, Kahyun Lee1, Jong-Uk Hou1

  • 1Department of Computer Engineering, Hallym University, Chuncheon 24252, Republic of Korea.

Sensors (Basel, Switzerland)
|October 16, 2025
PubMed
Summary
This summary is machine-generated.

This study demonstrates detecting advanced driver assistance systems (ADAS) status in real-world driving using synchronized vehicle and smartphone data. Findings show ADAS use impacts driving behavior, offering insights for safety and future research.

Keywords:
IMU sensorsadvanceddriver assistance systemsmultimodal deep learningvehicle forensics

More Related Videos

Evaluation of a Smartphone-based Human Activity Recognition System in a Daily Living Environment
06:49

Evaluation of a Smartphone-based Human Activity Recognition System in a Daily Living Environment

Published on: December 11, 2015

9.3K
Design and Analysis for Fall Detection System Simplification
08:05

Design and Analysis for Fall Detection System Simplification

Published on: April 6, 2020

11.1K

Related Experiment Videos

Last Updated: Jan 15, 2026

Tactile Vibrating Toolkit and Driving Simulation Platform for Driving-Related Research
07:15

Tactile Vibrating Toolkit and Driving Simulation Platform for Driving-Related Research

Published on: December 18, 2020

4.9K
Evaluation of a Smartphone-based Human Activity Recognition System in a Daily Living Environment
06:49

Evaluation of a Smartphone-based Human Activity Recognition System in a Daily Living Environment

Published on: December 11, 2015

9.3K
Design and Analysis for Fall Detection System Simplification
08:05

Design and Analysis for Fall Detection System Simplification

Published on: April 6, 2020

11.1K

Area of Science:

  • Automotive Engineering
  • Human-Computer Interaction
  • Transportation Safety

Background:

  • Accurate detection of advanced driver assistance systems (ADAS) status is vital for safety, liability, and accident reconstruction.
  • Existing research often uses simulations or unsynchronized data, limiting real-world applicability.

Purpose of the Study:

  • To develop and evaluate methods for distinguishing between ADAS-enabled and manual driving modes using real-world data.
  • To analyze behavioral differences associated with ADAS usage.

Main Methods:

  • Collected synchronized Controller Area Network (CAN)-bus and smartphone Inertial Measurement Unit (IMU) data from drivers on highways.
  • Developed lightweight statistical and deep learning classification models to detect ADAS status.
  • Analyzed driving behavior, focusing on speed regulation and steering stability.

Main Results:

  • Systematic differences in speed control and steering variability were observed between manual and ADAS modes.
  • ADAS usage was associated with reduced steering variability and more stable speed control.
  • The developed classification pipelines demonstrated moderate accuracy in identifying ADAS operational status.

Conclusions:

  • This study presents one of the first data-driven detections of ADAS status under naturalistic driving conditions.
  • The findings confirm ADAS influences driver behavior, providing valuable data for driver monitoring and adaptive systems.
  • The released dataset serves as a resource for advancing automotive safety research.