research-article

In-Vehicle Affect Detection System: Identification of Emotional Arousal by Monitoring the Driver and Driving Style

Authors:

AutomotiveUI '18: Adjunct Proceedings of the 10th International Conference on Automotive User Interfaces and Interactive Vehicular Applications

Pages 243 - 247

https://doi.org/10.1145/3239092.3267417

Published: 23 September 2018 Publication History

Get Access

Abstract

There have been clear needs to address the impact of driver emotions such as anger and happiness on aggressive or distracted driving behaviors. To tackle this issue, we have developed an affect detection system for identifying a driver's emotional arousal, including the driver's physiological data and vehicle's kinematic data. Multimodal sensors are wirelessly connected to a smartphone and then, all the driver and driving data are displayed on our Android application in real-time. With the benefits of this multimodal, portable, non-intrusive, and cost-efficient system, subsequent experiments were designed to test and improve the system. After identifying significant features, various machine learning algorithms will be used to model a driver's emotional states. Our final goal is to develop an optimized classifier of specific emotional states including arousal and valence. We hope that we can spark lively discussions on driver emotions at AutoUI and use the feedback to improve our system.

References

[1]

American Automobile Association, 2009. Aggressive driving: Research update. American Automobile Association Foundtion for Traffic Safety. https://aaafoundation.org/sites/default/files/AggressiveDrivingResearchUpdate2009.pdf

Google Scholar

[2]

B&B Electronics, 2013, OBD-II Background. http://www.obdii.com/background.html

Google Scholar

[3]

Bradley, M.M. and Lang, P.J., 1994. Measuring emotion: the self-assessment manikin and the semantic differential. Journal of behavior therapy and experimental psychiatry, 25(1), pp. 49--59.

Crossref

Google Scholar

[4]

Jeon, M., 2017. Emotions in Driving. In Emotions and Affect in Human Factors and Human-Computer Interaction (pp. 437--474).

Google Scholar

[5]

Johnson, D.A. and Trivedi, M.M., 2011, October. Driving style recognition using a smartphone as a sensor platform. In Intelligent Transportation Systems (ITSC), 2011 14th International IEEE Conference on (pp. 1609--1615). IEEE.

Google Scholar

[6]

Leng, H., Lin, Y. and Zanzi, L.A., 2007, July. An experimental study on physiological parameters toward driver emotion recognition. In International Conference on Ergonomics and Health Aspects of Work with Computers (pp. 237--246). Springer, Berlin, Heidelberg.

Digital Library

Google Scholar

[7]

De Nadai, S., D'Incà, M., Parodi, F., Benza, M., Trotta, A., Zero, E., Zero, L. and Sacile, R., 2016, June. Enhancing safety of transport by road by online monitoring of driver emotions. In System of Systems Engineering Conference (SoSE), 2016 11th (pp. 1--4). IEEE.

Google Scholar

[8]

Oehl, M., Siebert, F.W., Tews, T.K., Höger, R. and Pfister, H.R., 2011, July. Improving human-machine interaction--a non invasive approach to detect emotions in car drivers. In International Conference on Human-Computer Interaction (pp. 577--585). Springer, Berlin, Heidelberg.

Digital Library

Google Scholar

[9]

OpenXC, Overview - OpenXC, (2018). http://openxcplatform.com/overview/index.html

Google Scholar

[10]

Pauzié, A., 2008. A method to assess the driver mental workload: The driving activity load index (DALI). IET Intelligent Transport Systems, 2(4), pp. 315--322.

Crossref

Google Scholar

[11]

Royal, D., 2004. National survey of speeding and unsafe driving attitudes and behaviors: 2002. United States. National Highway Traffic Safety Administration. https://www.nhtsa.gov/people/injury/drowsy_driving1/speed_volII_finding/SpeedVolumeIIFindingsFinal.pdf

Google Scholar

[12]

Russell, J.A., Lewicka, M. and Niit, T., 1989. A cross-cultural study of a circumplex model of affect. Journal of personality and social psychology, 57(5), p.848.

Crossref

Google Scholar

Cited By

View all

Papatheocharous EKaiser CMoser JStocker A(2023)Monitoring Distracted Driving Behaviours with Smartphones: An Extended Systematic Literature ReviewSensors10.3390/s2317750523:17(7505)Online publication date: 29-Aug-2023
https://doi.org/10.3390/s23177505
Tatehara TNagahama AWada T(2023)Online Maneuver Learning and Its Real-Time Application to Automated Driving System for Obstacles AvoidanceIEEE Transactions on Intelligent Vehicles10.1109/TIV.2022.31466228:4(3144-3153)Online publication date: Apr-2023
https://doi.org/10.1109/TIV.2022.3146622
Jeon M(2023)The Effects of Emotions on Trust in Human-Computer Interaction: A Survey and ProspectInternational Journal of Human–Computer Interaction10.1080/10447318.2023.226172740:22(6864-6882)Online publication date: 2-Oct-2023
https://doi.org/10.1080/10447318.2023.2261727
Show More Cited By

Index Terms

In-Vehicle Affect Detection System: Identification of Emotional Arousal by Monitoring the Driver and Driving Style
1. Human-centered computing
  1. Human computer interaction (HCI)
    1. Empirical studies in HCI

Recommendations

Automatic detection of learner's affect from conversational cues

We explored the reliability of detecting a learner's affect from conversational features extracted from interactions with AutoTutor, an intelligent tutoring system (ITS) that helps students learn by holding a conversation in natural language. Training ...
Broadening the Scope of Affect Detection Research

I propose to broaden the scope of affect detection research in three related directions. First, the task definition should be broadened from the detection of affects to the inference of mental states. Second, the detection process should be ...
Toward an Affect-Sensitive AutoTutor

Emotions (affective states) are inextricably bound to the learning process, as are cognition, motivation, discourse, action, and the environment. Augmenting an intelligent tutoring system with the ability to incorporate such states into its pedagogical ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

AutomotiveUI '18: Adjunct Proceedings of the 10th International Conference on Automotive User Interfaces and Interactive Vehicular Applications

September 2018

282 pages

ISBN:9781450359474

DOI:10.1145/3239092

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 23 September 2018

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Research
Refereed limited

Conference

AutomotiveUI '18

Sponsor:

SIGCHI

AutomotiveUI '18: 10th International Conference on Automotive User Interfaces and Interactive Vehicular Applications

September 23 - 25, 2018

ON, Toronto, Canada

Acceptance Rates

Overall Acceptance Rate 248 of 566 submissions, 44%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

18
Total Citations
View Citations
359
Total Downloads

Downloads (Last 12 months)24
Downloads (Last 6 weeks)2

Reflects downloads up to 13 Dec 2024

Other Metrics

View Author Metrics

Citations

Cited By

View all

Papatheocharous EKaiser CMoser JStocker A(2023)Monitoring Distracted Driving Behaviours with Smartphones: An Extended Systematic Literature ReviewSensors10.3390/s2317750523:17(7505)Online publication date: 29-Aug-2023
Tatehara TNagahama AWada T(2023)Online Maneuver Learning and Its Real-Time Application to Automated Driving System for Obstacles AvoidanceIEEE Transactions on Intelligent Vehicles10.1109/TIV.2022.31466228:4(3144-3153)Online publication date: Apr-2023
Jeon M(2023)The Effects of Emotions on Trust in Human-Computer Interaction: A Survey and ProspectInternational Journal of Human–Computer Interaction10.1080/10447318.2023.226172740:22(6864-6882)Online publication date: 2-Oct-2023
Muhundan SJeon M(2023)Effects of Language on Angry drivers’ Situation Awareness, Driving Performance, and Subjective Perception in Level 3 Automated VehiclesInternational Journal of Human–Computer Interaction10.1080/10447318.2023.223583740:18(5454-5468)Online publication date: 19-Jul-2023
Sanghavi HZhang YJeon M(2023)Exploring the influence of driver affective state and auditory display urgency on takeover performance in semi-automated vehicles: Experiment and modellingInternational Journal of Human-Computer Studies10.1016/j.ijhcs.2022.102979171(102979)Online publication date: Mar-2023
Tian LOviatt SMuszynski MChamberlain BHealey JSano A(2022)Applied Affective ComputingundefinedOnline publication date: 25-Jan-2022
Jurecki RStańczyk T(2021)A Methodology for Evaluating Driving Styles in Various Road ConditionsEnergies10.3390/en1412357014:12(3570)Online publication date: 16-Jun-2021
Kaiser CStocker APapatheocharous E(2021)Distracted Driver Monitoring with Smartphones: A Preliminary Literature Review2021 29th Conference of Open Innovations Association (FRUCT)10.23919/FRUCT52173.2021.9435545(169-176)Online publication date: 12-May-2021
Muhundan SJeon M(2021)Effects of Native and Secondary Language Processing on Emotional Drivers’ Situation Awareness, Driving Performance, and Subjective Perception13th International Conference on Automotive User Interfaces and Interactive Vehicular Applications10.1145/3409118.3475148(255-262)Online publication date: 9-Sep-2021
Alpers SBenta K(2021)SenseGraph: Affect Self-monitoring and Tagging Tool with Wearable Devices2021 International Conference on Computer Communications and Networks (ICCCN)10.1109/ICCCN52240.2021.9522286(1-6)Online publication date: Jul-2021
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Cited By

Index Terms

Recommendations

Automatic detection of learner's affect from conversational cues

Broadening the Scope of Affect Detection Research

Toward an Affect-Sensitive AutoTutor