DriverDoubles: Simulating human responses in automotive user studies with generative agents
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School of Science |
Master's thesis
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en
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36
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Abstract
In-the-wild automotive user studies are costly, time consuming and difficult to scale, yet early design demands fast and iterative feedback. This thesis investigates whether generative agents, built on large language models, can simulate participants responses and produce synthetic data for such studies. Two real-world cases were used to compare results: an interruptibility assessment study and an emotion recognition study, with agents embodying synthetic personas and exposed to multimodal driving context. Alignment with human data was evaluated through majority-vote consensus and a machine-learning generalisation test. An ablation further examined three architectural features and their impact on results: personas, contextual templating, and drive–goal conditioning. Results show that synthetic agents approximate human behaviour in structured tasks such as interruptibility, but perform poorly in affective tasks like emotion recognition. Architectural additions yielded inconsistent or negative effects. These findings indicate that performance is strongly task dependent. Generative agents may therefore serve as a complement rather than a substitute for participants: they are most effective in narrowly defined studies that rely on filtered and task-relevant input data, while final validation must always be based on human responses.Description
Supervisor
Nieminen, Mika P.Thesis advisor
Bresin, RobertoMatviienko, Andrii