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.

A frequent problem in vision-based reasoning tasks such as object detection and optical character recognition (OCR) is the persistence of specular highlights. Specular highlights appear as bright spots of glare that occur due to the concentrated reflection of light; these spots manifest as image artifacts which occlude computer vision models and are challenging to reconstruct. Despite this, specular highlight removal receives relatively little attention due to the difficulty of acquiring high-quality, real-world data. We introduce a method to generate specular highlight data with near-perfect alignment and present SHDocs—a dataset of specular highlights on document images created using our method. Through our benchmark, we demonstrate that our dataset enables us to surpass the performance of state-of-the-art specular highlight removal models and downstream OCR tasks. We release our dataset, code, and methods publicly to motivate further exploration of image enhancement for practical computer vision challenges.