Using GOMS and the Thinking Aloud Technique to infer driver states

Kurzfassung

Modelling human drivers as discrete states is a frequently used approach to understand and analyse driver behaviour, but a great challenge lies in linking empirical data with proposed states (Toledo, 2003). We propose a modelling approach based on a fine granular analysis of the driving task, using the established GOMS methodology (e.g. John & Kieras, 1996). At its core, GOMS assumes goals whose fulfilment are the end point of any human action. To reach these goals, and transfer current states into goal states, operators are employed. Being mere constructs, goals of drivers while operating their vehicle in highly complex environments cannot be measured directly, but must be modelled. One possibility are task models using hierarchical abstractions of the driving task (Walker, Stanton & Salmon, 2015). However, unlike tasks in highly controlled environments, driving seems to be comprised of a multitude of parallel goals, with drivers enjoying a high degree of freedom in setting these goals in specific situations. In a simulator experiment with 22 subjects, we therefore explored a second possibility: To ask drivers what their concrete goals were, employing the Thinking Aloud Technique (e.g. Nielsen, Clemmensen & Yssing). Driving in either a highly controlled traffic scenario, or in highly complex traffic situation on a two lane highway, subjects were instructed to report their current goals, current and planned actions, as well as general thoughts throughout the drive. Both video and audio from these trials were recorded, and played back to the drivers after each drive, allowing to ask the subjects further specific questions about their goals and actions in specific situations. Categorising the goals from the recorded audio and video data then allowed us to use them to construct task models, based on the DriveGOMS-methodology (Käthner, Andrée, Drewitz & Ihme, 2016). The goals served as the endpoints of windows in which driving operators were applied, enabling us to construct discrete states directly based on empirical data.