Documents

  • 18-02628

    Accepted author manuscript, 1 MB, PDF-document

The present study aims to add to the literature on driver workload prediction using machine learning methods. The main aim is to develop workload prediction on a multi-class basis, rather than a binary high/low distinction as often found in litearature. The presented approach relies on measures that can be obtained unobtrusively in the driving environment with off-the-shelf sensors, and on machine learning methods that can be implemented on low-power embedded systems.

Two simulator studies were performed, one inducing workload using realistic driving conditions, and one inducing workload with a relatively demanding lane-keeping task. Individual and group-based machine learning models were trained on both datasets and evaluated. For the group-based models the generalising capability, that is the performance when predicting data from previously unseen individuals, was also assessed.

Results show that multi-class workload prediction on the individual and group level works well, achieving high correct rates and accuracy scores. Generalising between individuals proved difficult using realistic driving conditions, but worked very well in the high demanding lane-keeping task. Reasons for this discrepancy are discussed as well as future research directions.
Original languageEnglish
Title of host publicationTransportation Research Board Conference Proceedings 2018
PublisherTransporation Research Board (TRB)
Number of pages17
Publication statusPublished - 2018
EventTRB 2018: 97th Annual Meeting of the Transportation Research Board - Walter E. Washington Convention Center, Washington D.C., United States
Duration: 7 Jan 201811 Jan 2018
Conference number: 97

Conference

ConferenceTRB 2018: 97th Annual Meeting of the Transportation Research Board
Abbreviated titleTRB 2018
CountryUnited States
CityWashington D.C.
Period7/01/1811/01/18

    Research areas

  • Driver workload, machine learning, workload prediction, random forest, support vector machine, embedded workload prediction

ID: 31284114