Autonomy, from the Greek autos (self) and nomos (law), refers to the capacity to operate according to internal rules without external control. Autonomous vehicles (AuVs) are therefore understood as systems that perceive their environment and execute pre-programmed tasks independently of external input, consistent with the SAE levels of automated driving. Yet recent research and real-world deployments have begun to showcase vehicles that exhibit behaviors outside the scope of this definition. These include natural language interaction with humans, goal adaptation, contextual reasoning, external tool use, and the handling of unforeseen ethical dilemmas, enabled in part by multimodal large language models (LLMs). These developments highlight not only a gap between technical autonomy and the broader cognitive and social capacities required for human-centered mobility, but also the emergence of a form of vehicle intelligence that currently lacks a clear designation. To address this gap, the paper introduces the concept of agentic vehicles (AgVs): vehicles that integrate agentic AI systems to reason, adapt, and interact within complex environments. It synthesizes recent advances in agentic systems and suggests how AgVs can complement and even reshape conventional autonomy to ensure mobility services are aligned with user and societal needs. The paper concludes by outlining key challenges in the development and governance of AgVs and their potential role in shaping future agentic transportation systems.

翻译：自主性（Autonomy）源于希腊语 autos（自我）与 nomos（法则），指依据内部规则运行、无需外部控制的能力。因此，自动驾驶车辆（AuVs）被理解为能够感知环境并独立于外部输入执行预设任务的系统，这与 SAE 自动驾驶等级的定义相一致。然而，近期研究及实际部署已开始展现出超越此定义范畴的车辆行为，包括与人类的自然语言交互、目标适应性调整、情境推理、外部工具使用以及对突发伦理困境的处理，这些能力部分得益于多模态大语言模型（LLMs）的赋能。这些进展不仅凸显了技术自主性与实现人类中心移动性所需的更广泛认知及社会能力之间的差距，也预示着一类目前缺乏明确命名的车辆智能形态正在兴起。为填补这一空白，本文提出智能体化车辆（AgVs）的概念：即集成智能体化人工智能系统，能够在复杂环境中进行推理、适应与交互的车辆。本文综合了智能体系统领域的最新进展，探讨了 AgVs 如何补充乃至重塑传统自主性技术，以确保移动服务符合用户与社会需求。最后，本文概述了 AgVs 开发与治理中的关键挑战，及其在未来智能体化交通系统中可能发挥的作用。