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.

翻译：传统车辆路由系统能高效优化单一指标（如时间或距离），在考虑多指标时则需额外优化流程，但它们缺乏解析与整合人类驾驶员复杂、语义化且动态情境的能力，例如多步骤任务、情境约束或紧急需求。本文提出并评估了PAVe（个性化智能车辆路由系统），这是一种混合智能辅助系统，旨在通过情境推理增强经典路径规划算法。我们的方法采用大型语言模型（LLM）智能体，该智能体基于多目标（时间、二氧化碳排放）Dijkstra算法生成的候选路径集合进行操作。通过利用预处理的城市场景点地理空间缓存，智能体根据用户提供的任务、偏好和规避规则评估这些路径选项。在真实城市场景基准测试中，PAVe成功将复杂的用户意图转化为适当的路径调整，使用本地模型在初始路径选择中实现了超过88%的准确率。我们得出结论：将经典路由算法与基于LLM的语义推理层相结合，是构建个性化、自适应且可扩展的城市交通优化解决方案的稳健有效途径。