The advancement of Augmented Reality (AR) technology has been remarkable, enabling the augmentation of user perception with timely information. This progress holds great promise in the field of interaction design. However, the mere advancement of technology is not enough to ensure widespread adoption. The user dimension has been somewhat overlooked in AR research due to a lack of attention to user motivations, needs, usability, and perceived value. The critical aspects of AR technology tend to be overshadowed by the technology itself. To ensure appropriate future assessments, it is necessary to thoroughly examine and categorize all the methods used for AR technology validation. By identifying and classifying these evaluation methods, researchers and practitioners will be better equipped to develop and validate new AR techniques and applications. Therefore, comprehensive and systematic evaluations are critical to the advancement and sustainability of AR technology. This paper presents a theoretical framework derived from a cluster analysis of the most efficient evaluation methods for AR extracted from 399 papers. Evaluation methods were clustered according to the application domains and the human–computer interaction aspects to be investigated. This framework should facilitate rapid development cycles prioritizing user requirements, ultimately leading to groundbreaking interaction methods accessible to a broader audience beyond research and development centers.

With the rapid advancement of autonomous vehicles, a transformative transportation paradigm is emerging in the automotive industry, necessitating a re-evaluation of how users engage with and utilize these evolving settings. This research paper introduces an innovative interaction system tailored for shared autonomous vehicles, focusing on its development and comprehensive evaluation. The proposed system uses the car’s windshield as an interactive display surface, enabling infotainment and real-time information about the surrounding environment. The integration of two gesture-based interfaces forms a central component of the system. Through a study involving twenty subjects, we analyzed and compared the user experience facilitated by these interfaces. The study outcomes demonstrated that the subjects exhibited similar behaviors and responses across both interfaces, thus validating the potential of these interaction systems for future autonomous vehicles. These findings collectively emphasize the transformative nature of the proposed system and its ability to enhance user engagement and interaction within the context of autonomous transportation.