Driving the Intention Cue Motorway: Merging Ethnographic Enquiries and Machine Learning Techniques for Decoding Context-Dependent Intention Awareness in Driving
Pardo Gaytan, Jorge Luis (2024) Driving the Intention Cue Motorway: Merging Ethnographic Enquiries and Machine Learning Techniques for Decoding Context-Dependent Intention Awareness in Driving. PhD thesis, Queensland University of Technology.
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Jorge Luis Pardo Gayton Thesis
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Description
This research explored how passengers perceive drivers' intentions in naturalistic driving, aiming to improve human interaction with automated vehicles. It identified key non-verbal cues that convey driving intentions by combining human-centric methods and AI techniques. The project created a unique dataset of intention cues and developed a machine learning model to study passengers' intention awareness. This model was enhanced with explainable AI to highlight influential factors. This pioneering work demonstrates how AI can extract meaningful patterns from sensor data and ethnographic observations in real-world driving scenarios. The findings may inform future automated vehicle interface design, potentially improving predictability and trust.
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| ID Code: | 251297 |
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| Item Type: | QUT Thesis (PhD) |
| Supervisor: | Schroeter, Ronald, Rakotonirainy, Andry, & Glaser, Sebastien |
| Keywords: | Intention Awareness, Intention Cues, Machine Learning, Explainable Artificial Intelligence, Human-centric, Context-dependent, Naturalistic Driving, Feature Attribution, Ethnographic |
| DOI: | 10.5204/thesis.eprints.251297 |
| Divisions: | Current > Research Centres > Centre for Future Mobility/CARRSQ Current > QUT Faculties and Divisions > Faculty of Health Current > Schools > School of Psychology & Counselling |
| Institution: | Queensland University of Technology |
| Deposited On: | 12 Aug 2024 16:36 |
| Last Modified: | 01 Aug 2026 00:03 |
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