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Adapting Automated Vehicle Behavior : Algorithm Development and Driving Simulator Study

Kowalski, Melina Marie (2023) Adapting Automated Vehicle Behavior : Algorithm Development and Driving Simulator Study.

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Abstract:This thesis presents the results of two experiments investigating trust in adaptive automation. The first experiment aimed to develop an algorithm for processing continuous trust data collected with a slider for the use of adaptive automation. The experiment involved a fixed-speed driving simulation with seven algorithms using data frequency binning by mode and one FiFo algorithm, i.e., first value in, first value out. The results showed no significant difference between the algorithms. The most changes were detected by the FiFo algorithm, which was used for Experiment 2. The aim of Experiment 2 was to investigate how trust increases in initially low trusting participants after experiencing driving automation in which the speed adapts to trust changes compared to a fixed-speed simulation. Experiment 1 trust data was used as the control condition and the adaptive speed data as the experimental condition. The results showed a significant difference between pre- and post-test trust scores but not between conditions. Average slider data was not significantly different between conditions. A strong positive Spearman’s rank-order correlation was found between verbal and slider trust data indicating that the slider was able to measure the concept of trust. The strong positive correlation and the high number of detected trust changes suggest that the FiFo algorithm may be useful in the design of adaptive automation, since it is able to identify significant trust changes in real time. However, we did not find that experiencing adaptive automation can increase trust in users with low initial trust levels.
Item Type:Essay (Master)
Faculty:BMS: Behavioural, Management and Social Sciences
Subject:77 psychology
Programme:Psychology MSc (66604)
Link to this item:https://purl.utwente.nl/essays/94773
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