Investigating the Feasibility of Vehicle Telemetry Data as a Means of Predicting Driver Workload

Phillip Taylor, Nathan Griffiths, Abhir Bhalerao, Zhou Xu, Adam Gelencser and Thomas Popham
Additional contact information
Phillip Taylor: Department of Computer Science, University of Warwick, Coventry, UK
Nathan Griffiths: Department of Computer Science, University of Warwick, Coventry, UK
Abhir Bhalerao: Department of Computer Science, University of Warwick, Coventry, UK
Zhou Xu: Jaguar and Land Rover Research, Coventry, UK
Adam Gelencser: Jaguar and Land Rover Research, Coventry, UK
Thomas Popham: Jaguar and Land Rover Research, Coventry, UK

International Journal of Mobile Human Computer Interaction (IJMHCI), 2017, vol. 9, issue 3, 54-72

Abstract: Driving is a safety critical task that requires a high level of attention from the driver. Although drivers have limited attentional resources, they often perform secondary tasks such as eating or using a mobile phone. When performing multiple tasks in the vehicle, the driver can become overloaded and the risk of a crash is increased. If a vehicle is aware that the driver is currently under high workload, the vehicle functionality can be changed in order to minimise any further demand. Traditionally, workload is measured using physiological sensors that require often intrusive and expensive equipment. Another approach may be to use vehicle telemetry data as a performance measure for workload. In this paper, the authors present the Warwick-JLR Driver Monitoring Dataset (DMD) and analyse it to investigate the feasibility of using vehicle telemetry data for determining the driver workload. They perform a statistical analysis of subjective ratings, physiological data, and vehicle telemetry data collected during a track study. A data mining methodology is then presented to build predictive models using this data, for the driver workload monitoring problem.

Date: 2017
References: Add references at CitEc
Citations:

Downloads: (external link)
http://services.igi-global.com/resolvedoi/resolve. ... 18/ijmhci.2017070104 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:igg:jmhci0:v:9:y:2017:i:3:p:54-72

Access Statistics for this article

International Journal of Mobile Human Computer Interaction (IJMHCI) is currently edited by Joanna Lumsden

More articles in International Journal of Mobile Human Computer Interaction (IJMHCI) from IGI Global
Bibliographic data for series maintained by Journal Editor ().