Passenger comfort in autonomous vehicles: a comprehensive review of influencing factors, assessment methods, and enhancement strategies.

This review examines factors, methodologies, and evaluation techniques for passenger comfort in autonomous vehicles (AVs). Current research in this area is often fragmented and inappropriately extrapolates methodologies from traditional vehicle studies without accounting for the unique constraints of driverless travel, highlighting the need for a comprehensive review tailored to the AV context. Comfort is influenced by environmental parameters, vehicle attributes, and individual passenger characteristics. Methodologies primarily include real-vehicle experiments, simulation studies, and driving simulators, prioritising ecological validity, controllability, and behavioural realism, respectively. Evaluation employs subjective assessments, objective measurements, and hybrid multivariate analyses, enhancing robustness by cross-validating psychological perceptions with physiological data. Current optimisation strategies focus on cabin environmental control, motion planning algorithms, and adaptive human-machine interfaces, while emerging approaches emphasise personalised models using real-time biosignals. Future work should develop adaptive frameworks integrating real-time feedback, advanced simulation methods, and AI-driven personalisation for human-centric AV mobility.