The Effect of See-Through Truck on Driver Monitoring Patterns and Responses to Critical Events in Truck Platooning

  • Bo Zhang
  • Ellen S. Wilschut
  • Dehlia M. C. Willemsen
  • Tom Alkim
  • Marieke H. Martens
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 597)


Automated platooning of trucks has its beneficial effects on energy saving and traffic flow efficiency. The vehicles in a platoon, however, need to maintain an extremely short headway to achieve these goals, which will result in a heavily blocked front view for the driver in a following truck. Monitoring surrounding traffic environment and foreseeing upcoming hazardous situations becomes a difficult, yet safety-critical task. This exploratory study aims to investigate whether providing platoon drivers with additional visual information of the traffic environment can influence their monitoring pattern and increase awareness of the upcoming situation. 22 professional truck drivers participated in the driving simulator experiment, either following a see-through lead truck (i.e., with projection of forward scene attached to the rear of the lead truck), or a normal lead truck until the automation system failed unexpectedly in a critical situation. Results showed that when provided with front view projection, the participants spent 10% more time monitoring the road, and responded less severely to a critical situation, suggesting a positive effect of the “see-through” technology.


Automated driving Truck platooning Human machine interaction Takeover Eye tracking Response time 



This study was funded jointly by the TNO Early Research Program (ERP) Human Enhancement: Adaptive Automation, and Rijkswaterstaat. Ministry of Infrastructure and the Environment (in the context of the knowledge agenda automated driving, the full report can be found and downloaded at the corresponding website: The first author and the last author are involved in the Marie Curie Initial Training Network (ITN) project HFAuto – Human Factors of Automated Driving (PITN-GA-2013-605817).


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Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Bo Zhang
    • 1
  • Ellen S. Wilschut
    • 2
  • Dehlia M. C. Willemsen
    • 3
  • Tom Alkim
    • 4
  • Marieke H. Martens
    • 1
    • 2
  1. 1.University of TwenteEnschedeThe Netherlands
  2. 2.TNO Human FactorsSoesterbergThe Netherlands
  3. 3.TNO Integrated Vehicle SafetyHelmondThe Netherlands
  4. 4.RijkswaterstaatUtrechtThe Netherlands

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