Beyond Shortest Path: Agentic Vehicular Routing with Semantic Context
On the map: LLM agents and tool use → Multimodal and embodied agents
Abstract (arXiv)
Traditional vehicle routing systems efficiently optimize singular metrics like time or distance, and when considering multiple metrics, they need more processes to optimize . However, they lack the capability to interpret and integrate the complex, semantic, and dynamic contexts of human drivers, such as multi-step tasks, situational constraints, or urgent needs. This paper introduces and evaluates PAVe (Personalized Agentic Vehicular Routing), a hybrid agentic assistant designed to augment classical pathfinding algorithms with contextual reasoning. Our approach employs a Large Language Model (LLM) agent that operates on a candidate set of routes generated by a multi-objective (time, CO2) Dijkstra algorithm. The agent evaluates these options against user-provided tasks, preferences, and avoidance rules by leveraging a pre-processed geospatial cache of urban Points of Interest (POIs). In a benchmark of realistic urban scenarios, PAVe successfully used complex user intent into appropriate route modifications, achieving over 88% accuracy in its initial route selections with a local model. We conclude that combining classical routing algorithms with an LLM-based semantic reasoning layer is a robust and effective approach for creating personalized, adaptive, and scalable solutions for urban mobility optimization.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Multimodal and embodied agents, one of 16 directions in LLM agents and tool use. Builds on 9 of its 14 references that are in the map. First posted on arXiv 6 Nov 2025.
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9 of 14 references.
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Same direction, newest first
- 2024Large Language Models as Urban Residents: An LLM Agent Framework for Personal Mobility Generation22 Feb
Other references · 8
- 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,302
- 2023The Rise and Potential of Large Language Model Based Agents: A Surveycited by 1,422
- 2023Where Would I Go Next? Large Language Models as Human Mobility Predictorscited by 92
- 2020A Survey on Deep Learning for Human Mobilitycited by 87
- 2024Large Language Model for Participatory Urban Planningcited by 40
- 2024Mobility-LLM: Learning Visiting Intentions and Travel Preferences from Human Mobility Data with Large Language Modelscited by 21
- 2024UrbanKGent: A Unified Large Language Model Agent Framework for Urban Knowledge Graph Constructioncited by 15
- 2023Smart Agent-Based Modeling: On the Use of Large Language Models in Computer Simulationscited by 11
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- 2025LLMAP: LLM-Assisted Multi-Objective Route Planning with User Preferences
- 2025Human-like Semantic Navigation for Autonomous Driving using Knowledge Representation and Large Language Models
- 2024LLM-A*: Large Language Model Enhanced Incremental Heuristic Search on Path Planning
- 2026Agentic AI for Trip Planning Optimization Application
- 2025Data-Efficient Multi-Agent Spatial Planning with LLMs