Automotive human factors


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General session: Automotive human factors

Schedule: Friday 20th May 15:15 - 16:15 CET, Virtual Room 1
Session chair: Rafael Cirino Goncalves, University of Leeds, UK

15:15-15:30    Do Car Drivers Respond Earlier to Close Lateral Motion Than to Looming? The Importance of Data Selection.
Malin Svärd, Volvo Cars, Sweden, Jonas Bärgman, Chalmers University of Technology, Sweden, Gustav Markkula, University of Leeds, UK, Aust Mikael Ljung, Volvo Cars, Sweden,
Methods to exclude undesired check glances from drivers’ glance response data are compared and used to show quicker reactions to lateral motion than to looming. Careful data selection is important to ensure relevance of glance response study results.

15:30-15:45    A comparison of two methodologies for subjective evaluation of comfort in automated vehicles.
Chen Peng, University of Leeds, UK
This paper compared two different methodologies, used in two driving simulator studies, for real-time evaluation of comfort imposed by the driving style of different Automated Vehicle controllers. The first method provided participants with two options for assessing three different AV controllers. Participants rated each controller in terms of if it was comfortable/safe/natural, when it navigated a simulated road. The evaluation was either positive or negative, indicated by pressing one of two buttons on a handset. In the second study, a Likert-type scale was used to evaluate the extent to which a controller’s driving style was “comfortable” and/or “natural”, separately.

15:45-16:00    Applying Entropy to Understand Drivers’ Uncertainty during Car-following.
Wei Lyu. Northeastern University, Shenyang, China and University of Leeds, UK
Rafael C. Gonçalves, Fu Guo, Guilhermina A. Torrão, Vishnu Radhakrishnan, Tyron L. Louw and Natasha Merat, Northeastern University, Shenyang, China
Pablo Puente Guillen3, Toyota Motor Europe, Zaventem, Belgium
To capture drivers’ uncertainty in car-following, this paper contrasts four different entropy algorithms (Shannon Entropy, Steering Wheel Entropy, Approximate Entropy and Sample Entropy) as a novel measure, based on time headway data during car following.

16:00-16:15    Development of an algorithm to identify stabilisation time for car-following after transitions of control from vehicle automation.
Rafael Gonçalves, Wei Lyu, Guilhermina A. Torrão, Tyron L. Louw, University of Leeds, Natasha Merat, UK
Pablo Puente Guillen, Toyota Motor Europe, Zaventem, Belgium
The goal of this paper is to describe the development and validation of an algorithm able to detect the beginning of a car-following task engagement inside a time headway (THW) dataset.