Beyond Shortest Path: Agentic Vehicular Routing with Semantic Context

• URL: http://arxiv.org/abs/2511.04464v1
• Date: Thu, 06 Nov 2025 15:37:11 GMT
• Title: Beyond Shortest Path: Agentic Vehicular Routing with Semantic Context
• Authors: Carnot Braun, Rafael O. Jarczewski, Gabriel U. Talasso, Leandro A. Villas, Allan M. de Souza,
• Abstract summary: This paper introduces and evaluates PAVe, a hybrid agentic assistant designed to augment classical pathfinding algorithms with contextual reasoning.<n>In a benchmark of realistic urban scenarios, PAVe successfully used complex user intent into appropriate route modifications.<n>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.
• Score: 0.3767731868757604
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

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