Agentic Vehicles for Human-Centered Mobility
- URL: http://arxiv.org/abs/2507.04996v6
- Date: Sat, 11 Oct 2025 14:14:34 GMT
- Title: Agentic Vehicles for Human-Centered Mobility
- Authors: Jiangbo Yu,
- Abstract summary: The paper introduces the concept of agentic vehicles (AgVs), vehicles that integrate agentic AI systems to reason, adapt, and interact within complex environments.<n>It suggests how AgVs can complement and even reshape conventional autonomy to ensure mobility services are aligned with user and societal needs.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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.
Related papers
- From Agentification to Self-Evolving Agentic AI for Wireless Networks: Concepts, Approaches, and Future Research Directions [70.72279728350763]
Self-evolving agentic artificial intelligence (AI) offers a new paradigm for future wireless systems.<n>Unlike static AI models, self-evolving agents embed an autonomous evolution cycle that updates models, tools, and in response to environmental dynamics.<n>This paper presents a comprehensive overview of self-evolving agentic AI, highlighting its layered architecture, life cycle, and key techniques.
arXiv Detail & Related papers (2025-10-07T05:45:25Z) - A Comprehensive Survey of Self-Evolving AI Agents: A New Paradigm Bridging Foundation Models and Lifelong Agentic Systems [53.37728204835912]
Most existing AI systems rely on manually crafted configurations that remain static after deployment.<n>Recent research has explored agent evolution techniques that aim to automatically enhance agent systems based on interaction data and environmental feedback.<n>This survey aims to provide researchers and practitioners with a systematic understanding of self-evolving AI agents.
arXiv Detail & Related papers (2025-08-10T16:07:32Z) - Agentic Web: Weaving the Next Web with AI Agents [109.13815627467514]
The emergence of AI agents powered by large language models (LLMs) marks a pivotal shift toward the Agentic Web.<n>In this paradigm, agents interact directly with one another to plan, coordinate, and execute complex tasks on behalf of users.<n>We present a structured framework for understanding and building the Agentic Web.
arXiv Detail & Related papers (2025-07-28T17:58:12Z) - Agentic AI for Intent-Based Industrial Automation [0.6906005491572401]
This work proposes a conceptual framework that integrates Agentic AI with the intent-based paradigm.<n>Based on the intent-based processing, the framework allows human operators to express high-level business or operational goals in natural language.<n>A proof of concept was implemented using the CMAPSS dataset and Google Agent Developer Kit (ADK)
arXiv Detail & Related papers (2025-06-05T12:50:54Z) - AI Agent Behavioral Science [29.262537008412412]
AI Agent Behavioral Science focuses on the systematic observation of behavior, design of interventions to test hypotheses, and theory-guided interpretation of how AI agents act, adapt, and interact over time.<n>We systematize a growing body of research across individual agent, multi-agent, and human-agent interaction settings, and demonstrate how this perspective informs responsible AI by treating fairness, safety, interpretability, accountability, and privacy as behavioral properties.
arXiv Detail & Related papers (2025-06-04T08:12:32Z) - Confidence-Regulated Generative Diffusion Models for Reliable AI Agent Migration in Vehicular Metaverses [55.70043755630583]
vehicular AI agents are endowed with environment perception, decision-making, and action execution capabilities.<n>We propose a reliable vehicular AI agent migration framework, achieving reliable dynamic migration and efficient resource scheduling.<n>We develop a Confidence-regulated Generative Diffusion Model (CGDM) to efficiently generate AI agent migration decisions.
arXiv Detail & Related papers (2025-05-19T05:04:48Z) - AI Agents vs. Agentic AI: A Conceptual Taxonomy, Applications and Challenges [0.36868085124383626]
This study critically distinguishes between AI Agents and Agentic AI, offering a structured conceptual taxonomy, application mapping, and challenge analysis.<n>Generative AI is positioned as a precursor, with AI Agents advancing through tool integration, prompt engineering, and reasoning enhancements.<n>Agentic AI systems represent a paradigmatic shift marked by multi-agent collaboration, dynamic task decomposition, persistent memory, and orchestrated autonomy.
arXiv Detail & Related papers (2025-05-15T16:21:33Z) - Generative AI for Autonomous Driving: Frontiers and Opportunities [145.6465312554513]
This survey delivers a comprehensive synthesis of the emerging role of GenAI across the autonomous driving stack.<n>We begin by distilling the principles and trade-offs of modern generative modeling, encompassing VAEs, GANs, Diffusion Models, and Large Language Models.<n>We categorize practical applications, such as synthetic data generalization, end-to-end driving strategies, high-fidelity digital twin systems, smart transportation networks, and cross-domain transfer to embodied AI.
arXiv Detail & Related papers (2025-05-13T17:59:20Z) - Explaining Autonomous Vehicles with Intention-aware Policy Graphs [0.1398098625978622]
We propose a model-agnostic solution to provide teleological explanations for the behaviour of an autonomous vehicle in urban environments.<n>Building on Intention-aware Policy Graphs, our approach enables the extraction of interpretable and reliable explanations of vehicle behaviour.<n>We demonstrate the potential of these explanations to assess whether the vehicle operates within acceptable legal boundaries and to identify possible vulnerabilities in autonomous driving datasets and models.
arXiv Detail & Related papers (2025-05-13T09:58:32Z) - Internet of Agents: Fundamentals, Applications, and Challenges [66.44234034282421]
We introduce the Internet of Agents (IoA) as a foundational framework that enables seamless interconnection, dynamic discovery, and collaborative orchestration among heterogeneous agents at scale.<n>We analyze the key operational enablers of IoA, including capability notification and discovery, adaptive communication protocols, dynamic task matching, consensus and conflict-resolution mechanisms, and incentive models.
arXiv Detail & Related papers (2025-05-12T02:04:37Z) - Position: Emergent Machina Sapiens Urge Rethinking Multi-Agent Paradigms [8.177915265718703]
We argue that AI agents should be empowered to adjust their objectives dynamically.<n>We call for a shift toward the emergent, self-organizing, and context-aware nature of these multi-agentic AI systems.
arXiv Detail & Related papers (2025-02-05T22:20:15Z) - Position Paper: Agent AI Towards a Holistic Intelligence [53.35971598180146]
We emphasize developing Agent AI -- an embodied system that integrates large foundation models into agent actions.
In this paper, we propose a novel large action model to achieve embodied intelligent behavior, the Agent Foundation Model.
arXiv Detail & Related papers (2024-02-28T16:09:56Z) - Agent AI: Surveying the Horizons of Multimodal Interaction [83.18367129924997]
"Agent AI" is a class of interactive systems that can perceive visual stimuli, language inputs, and other environmentally-grounded data.
We envision a future where people can easily create any virtual reality or simulated scene and interact with agents embodied within the virtual environment.
arXiv Detail & Related papers (2024-01-07T19:11:18Z) - Enabling High-Level Machine Reasoning with Cognitive Neuro-Symbolic
Systems [67.01132165581667]
We propose to enable high-level reasoning in AI systems by integrating cognitive architectures with external neuro-symbolic components.
We illustrate a hybrid framework centered on ACT-R and we discuss the role of generative models in recent and future applications.
arXiv Detail & Related papers (2023-11-13T21:20:17Z) - Autonomous Vehicles an overview on system, cyber security, risks,
issues, and a way forward [0.0]
This chapter explores the complex realm of autonomous cars, analyzing their fundamental components and operational characteristics.
The primary focus of this investigation lies in the realm of cybersecurity, specifically in the context of autonomous vehicles.
A comprehensive analysis will be conducted to explore various risk management solutions aimed at protecting these vehicles from potential threats.
arXiv Detail & Related papers (2023-09-25T15:19:09Z) - Drive as You Speak: Enabling Human-Like Interaction with Large Language
Models in Autonomous Vehicles [13.102404404559428]
We present a novel framework that leverages Large Language Models (LLMs) to enhance autonomous vehicles' decision-making processes.
The proposed framework holds the potential to revolutionize the way autonomous vehicles operate, offering personalized assistance, continuous learning, and transparent decision-making.
arXiv Detail & Related papers (2023-09-19T00:47:13Z) - Autonomy and Unmanned Vehicles Augmented Reactive Mission-Motion
Planning Architecture for Autonomous Vehicles [3.2013172123155615]
This book aims to provide a comprehensive survey of UVs autonomy and its related properties in internal and external situation awareness.
An advance level of intelligence is essential to minimize the reliance on the human supervisor.
A self-controlled system needs a robust mission management strategy to push the boundaries towards autonomous structures.
arXiv Detail & Related papers (2020-07-19T02:34:48Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.