From Autonomy to Agency: Agentic Vehicles for Human-Centered Mobility Systems

2025-07-07

AI Summary

Current autonomous vehicles (AuVs) are largely confined to environmental perception and task execution, failing to meet human-centric mobility requirements—such as social interaction, contextual reasoning, goal adaptation, tool utilization, and long-horizon planning—thereby exposing a fundamental gap between technical autonomy and human-AI collaboration. This paper introduces the “Agentic Vehicle” (AgV) paradigm, marking the first conceptual shift from *autonomy* to *agency*, and proposes a cognition-communication hierarchical framework to rigorously distinguish AgVs from conventional AuVs. Methodologically, we integrate embodied intelligence, large language models, multi-agent systems, robotic control, and human–vehicle interaction. Our contributions include: (1) a system-level architectural blueprint for AgVs; and (2) a systematic identification and formal characterization of critical challenges and developmental pathways in safety robustness, real-time decision-making, ethical alignment, and collaborative governance.

Technology Category

• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Multiagent Systems: Agent Communication
• Intelligent Robots: Embodied AI

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Agentic search
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

Autonomy, from the Greek autos (self) and nomos (law), refers to the capacity to operate according to internal rules without external control. Accordingly, autonomous vehicles (AuVs) are defined as systems capable of perceiving their environment and executing preprogrammed tasks independently of external input. However, both research and real-world deployments increasingly showcase vehicles that demonstrate behaviors beyond this definition (including the SAE levels 1 to 6), such as interaction with humans and machines, goal adaptation, contextual reasoning, external tool use, and long-term planning, particularly with the integration of large language models (LLMs) and agentic AI systems. These developments reveal a conceptual gap between technical autonomy and the broader cognitive and social capabilities needed for future human-centered mobility systems. To address this, we introduce the concept of agentic vehicles (AgVs), referring to vehicles that integrate agentic AI to reason, adapt, and interact within complex environments. This paper presents a systems-level framework to characterize AgVs, focusing on their cognitive and communicative layers and differentiating them from conventional AuVs. It synthesizes relevant advances in agentic AI, robotics, multi-agent systems, and human-machine interaction, and highlights how agentic AI, through high-level reasoning and tool use, can function not merely as computational tools but as interactive agents embedded in mobility ecosystems. The paper concludes by identifying key challenges in the development and governance of AgVs, including safety, real-time control, public acceptance, ethical alignment, and regulatory frameworks.

Problem

• Bridging gap between autonomous vehicles and human-centered mobility needs
• Introducing agentic vehicles with AI for reasoning and interaction
• Addressing challenges in safety, ethics, and governance of AgVs

Innovation

• Agentic AI for reasoning and adaptation
• Cognitive and communicative vehicle layers
• Integration of LLMs in mobility systems