Anthropomorphism and socialization in smart urban mobility: insights from human-autonomous vehicle interaction

Abstract

Artificial intelligence (AI) has revolutionized autonomous vehicle technology, which is crucial for Smart Urban Mobility. Despite their benefits, autonomous vehicles face social challenges such as mistrust and responsibility attribution. Prior research suggests that anthropomorphism and socialization in AI design may help address these issues, but studies are scarce. Therefore, this thesis examines the role of anthropomorphism and socialization types (i.e., social responsiveness and hierarchical status) in Human-Autonomous Vehicle Interaction using deductive and experimental approaches. Three empirical studies are conducted: the first shows superficial anthropomorphism enhances trust in shared autonomous vehicles whereas deep anthropomorphism negatively affects interaction quality in specific groups. The second study highlights the crucial role of anthropomorphism and social responsiveness in improving pedestrian-vehicle interactions and responsibility attribution. The third finds that anthropomorphic design and hierarchical status enhance user self-accountability and use intention, with such effects moderated by education, ethnicity, and incident experiences. By understanding the nuanced effects of anthropomorphism and socialization across public attitudes, vehicle-pedestrian interactions, and in-vehicle experiences, this research provides key insights into designing anthropomorphic and socially compatible features in autonomous vehicles. These secure, user-centered designs can foster trust, engagement, user intention, and self-accountability, thereby aiding the evolution of future urban mobility systems.