Human-AI Interaction in Agentic Transportation: Trust, Transparency, and Shared Decision-Making

Advances in AI are giving rise to agentic mobility: intelligent vehicles, in-vehicle agents, automated shuttles, robotaxis, mobility assistants, and connected services that go beyond automating movement to infer user goals, communicate intent, adapt to context, and participate in decisions. These systems function not only as transportation technologies but also as interactive media through which goals, risks, and responsibilities are represented, conveyed, and negotiated. As mobility systems take on these communicative and decision-making roles, the human role shifts from driver and operator toward passenger, supervisor, collaborator, and orchestrator. This transition foregrounds three interrelated challenges for human-media interaction: how trust is established and maintained between people and AI-enabled mobility systems, how the reasoning and limitations of such systems are made transparent to users, and how control and decisions are shared in ways that preserve meaningful human oversight.The quality of human-AI interaction in agentic mobility has direct consequences for safety, acceptance, and wellbeing. Interaction that is opaque, poorly timed, or misaligned with user expectations can produce miscalibrated trust, confusion, loss of situation awareness, and erosion of human agency. This Research Topic aims to advance a human-centered understanding of interaction in agentic mobility, organized around three concerns. First, trust as a dynamic process: how it is calibrated under uncertainty, sustained across repeated encounters, and repaired after system failures or expectation violations. Second, transparency as interaction design: how mobility systems can make their intent, capabilities, limitations, and situational assessments accessible to users in ways that are timely, interpretable, and actionable. Third, shared control and decision-making: how humans and mobility systems negotiate responsibility in contexts such as route choice, takeover timing, risk assessment, and coordination with other road users, while preserving human agency and accountability. We are particularly interested in how these dimensions interact: how transparency shapes trust calibration, how trust affects willingness to share or relinquish control, and how shared decision-making in turn reshapes perceived agency and accountability over time. We also ask how these interactions shape mental models, acceptance, inclusion, and equity across diverse populations and contexts.To gather further insights into the complex relationship between human cognition, communication, and AI mediation in mobility, we welcome articles addressing, but not limited to, the following themes:- Trust dynamics in human-AI mobility interaction, including calibration, misuse, disuse, and repair- Communication of system intent, uncertainty, capability boundaries, and responsibility to users- Shared and collaborative control models in automated, supervised, and remote driving scenarios- Multimodal, conversational, and adaptive interaction interfaces for vehicles and mobility services- External communication and negotiation between AI-powered vehicles and road users such as pedestrians or cyclists- Situation awareness, mental models, user experience, and long-term adaptation in agentic transport contexts- Ethical and social dimensions including accountability, consent, privacy, inclusion, and personal autonomy- Conceptual frameworks and theories describing evolving human and AI roles, relationships, and responsibilities in mobilityEligible article types include Original Research, Review, Mini Review, Perspective, and Hypothesis and Theory. Topic editors: Jiyao Wang, Raphael Frank, Jiangbo Yu, Manhua Wang Source: https://www.frontiersin.org/research-topics/83905/human-ai-interaction-in-agentic-transportation-trust-transparency-and-shared-decision-making