Designing Adaptive AV Interfaces: Linking Acceptance Profiles to Design Preferences for Enhanced Adoption

Technology Acceptance Models (TAMs) offer valuable insights into Autonomous Vehicle (AV) acceptance, yet little research translates these factors into design requirements for more trustworthy AVs. Self-Organising Map (SOM) analysis of 283 survey participants’ responses revealed distinct majority acceptor and minority rejector profiles, with notable differences in all acceptance factors, with facilitating condition, technology attitude, perceived safety, and AI robustness serving as largest determinants of AV acceptance; anxiety, whilst significantly different was least effective. Rejectors exhibited ‘autonomy sensitivity’, with increased demands for more customisation, redundancy, and experientiality in FAVs compared to PAVs, while acceptors maintained stable design preferences. These findings inform a Profile–Context Interaction (PCI) framework for dual-adaptive interfaces. The PCI framework recommends four design quadrants, Acceptor–PAV, Acceptor–FAV, Rejector–PAV, and Rejector–FAV to tailor interface features to both user profiles and autonomy levels, thereby bridging a gap between acceptance theory and actionable design.