用大模型理解驾驶意图，让导航更懂人。
Beyond Shortest Path: Agentic Vehicular Routing with Semantic Context
- 用大模型分析用户任务和偏好，动态优化路线
- 在真实城市场景中初始选路准确率达88%以上
- 适合需要个性化、智能响应的智慧交通场景
传统车辆路径系统仅优化单一指标（如时间或距离），多目标优化需额外流程，且难以理解人类驾驶员的复杂语义与动态情境，如多步任务、情境约束或紧急需求。本文提出并评估了PAVe（个性化代理式车辆路径系统），一种将经典路径算法与上下文推理能力结合的混合代理助手。该方法利用大语言模型（LLM）代理，在多目标（时间、二氧化碳排放）迪杰斯特拉算法生成的候选路线集上，基于预处理的地理空间兴趣点（POI）缓存，评估路线是否符合用户提供的任务、偏好与避让规则。在真实城市场景基准测试中，PAVe成功将复杂用户意图转化为合理路线调整，本地模型初始选路准确率超过88%。结果表明，将经典路径算法与基于大模型的语义推理层结合，是实现个性化、自适应、可扩展的城市出行优化的有效途径。
原文摘要 · Abstract (English)
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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