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
• 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.

Related papers

• Reward-Density Heuristic for Dynamic Multi-Vehicle Routing: Performance and Computational Efficiency [0.0]
  We address a dynamic, online variant combining elements of the Vehicle Problem (VRP) and the Orienteering Problem (OP)<n>We propose and evaluate a reward-density for dynamic multi-vehicle assignment, referred to as the Efficiency Routing.<n>Across all tested configurations, the Efficiency matches the solution quality of the best metaheuristic algorithms while requiring two to three orders of magnitude less planning time.
  arXiv  Detail & Related papers  (2026-07-07T09:37:46Z)
• ChatPlanner: A Large Language Model Framework for Personalized Public Transit Routing [9.546300817271831]
  ChatPlanner is a novel framework that enables preference aware public transit routing.<n>Our approach employs fine-tuned LLMs with Retrieval-Augmented Generation (RAG) to extract routing parameters and interpret nuanced user preferences.
  arXiv  Detail & Related papers  (2026-06-13T14:14:15Z)
• HAD: Combining Hierarchical Diffusion with Metric-Decoupled RL for End-to-End Driving [51.268878540511054]
  We propose HAD, an end-to-end planning framework with a Hierarchical Diffusion Policy.<n>We show that HAD achieves new state-of-the-art performance on both NAVSIM and HUGSIM.
  arXiv  Detail & Related papers  (2026-04-04T04:12:47Z)
• Efficient and Interpretable Multi-Agent LLM Routing via Ant Colony Optimization [58.59491516762626]
  We propose AMRO-S, an efficient and interpretable routing framework for Multi-Agent Systems (MAS)<n>AMRO-S models MAS routing as a semantic-conditioned path selection problem, enhancing routing performance through three key mechanisms.<n>Extensive experiments on five public benchmarks and high-concurrency stress tests demonstrate that AMRO-S consistently improves the quality--cost trade-off over strong routing baselines.
  arXiv  Detail & Related papers  (2026-03-13T12:26:05Z)
• Neural Nonmyopic Bayesian Optimization in Dynamic Cost Settings [73.44599934855067]
  LookaHES is a nonmyopic BO framework designed for dynamic, history-dependent cost environments.<n>LookaHES combines a multi-step variant of $H$-Entropy Search with pathwise sampling and neural policy optimization.<n>Our innovation is the integration of neural policies, including large language models, to effectively navigate structured, domain-specific action spaces.
  arXiv  Detail & Related papers  (2026-01-10T09:49:45Z)
• Policy-Conditioned Policies for Multi-Agent Task Solving [53.67744322553693]
  In this work, we propose a paradigm shift that bridges the gap by representing policies as human-interpretable source code.<n>We reformulate the learning problem by utilizing Large Language Models (LLMs) as approximate interpreters.<n>We formalize this process as textitProgrammatic Iterated Best Response (PIBR), an algorithm where the policy code is optimized by textual gradients.
  arXiv  Detail & Related papers  (2025-12-24T07:42:10Z)
• DynaAct: Large Language Model Reasoning with Dynamic Action Spaces [58.298135359318024]
  We propose a novel framework named textscDynaAct for automatically constructing a compact action space.<n>Our approach significantly improves overall performance, while maintaining efficient inference without introducing substantial latency.
  arXiv  Detail & Related papers  (2025-11-11T09:47:13Z)
• Constraint-Aware Route Recommendation from Natural Language via Hierarchical LLM Agents [21.473451572179552]
  RouteLLM is a hierarchical multi-agent framework that grounds natural-language intents into constraint-aware routes.<n>It first parses user queries into structured intents including POIs, paths, and constraints.<n>A final verifier agent ensures constraint satisfaction and produces the final route with an interpretable rationale.
  arXiv  Detail & Related papers  (2025-10-07T16:03:57Z)
• Comparative Analysis of Ant Colony Optimization and Google OR-Tools for Solving the Open Capacitated Vehicle Routing Problem in Logistics [0.0]
  The Open Capacitated Vehicle Routing Problem (OCVRP) deals with finding optimal delivery routes for a fleet of vehicles serving geographically distributed customers.<n>The present study is comparative in nature and speaks of two algorithms for OCVRP solution: Ant Colony Optimization (ACO), a nature-inspired metaheuristic; and Google OR-Tools, an industry-standard toolkit for optimization.
  arXiv  Detail & Related papers  (2025-09-30T13:18:14Z)
• Accelerating Vehicle Routing via AI-Initialized Genetic Algorithms [53.75036695728983]
  Vehicle Routing Problems (VRP) are a fundamental NP-hard challenge in Evolutionary optimization.<n>We introduce an optimization framework where a reinforcement learning agent is trained on prior instances and quickly generates initial solutions.<n>This framework consistently outperforms current state-of-the-art solvers across various time budgets.
  arXiv  Detail & Related papers  (2025-04-08T15:21:01Z)
• DARS: Dynamic Action Re-Sampling to Enhance Coding Agent Performance by Adaptive Tree Traversal [55.13854171147104]
  Large Language Models (LLMs) have revolutionized various domains, including natural language processing, data analysis, and software development.<n>We present Dynamic Action Re-Sampling (DARS), a novel inference time compute scaling approach for coding agents.<n>We evaluate our approach on SWE-Bench Lite benchmark, demonstrating that this scaling strategy achieves a pass@k score of 55% with Claude 3.5 Sonnet V2.
  arXiv  Detail & Related papers  (2025-03-18T14:02:59Z)
• SCoTT: Strategic Chain-of-Thought Tasking for Wireless-Aware Robot Navigation in Digital Twins [78.53885607559958]
  We propose SCoTT, a wireless-aware path planning framework.<n>We show that SCoTT achieves path gains within 2% of DP-WA* while consistently generating shorter trajectories.<n>We also show the practical viability of our approach by deploying SCoTT as a ROS node within Gazebo simulations.
  arXiv  Detail & Related papers  (2024-11-27T10:45:49Z)
• DynamicRouteGPT: A Real-Time Multi-Vehicle Dynamic Navigation Framework Based on Large Language Models [13.33340860174857]
  Real-time dynamic path planning in complex traffic environments presents challenges, such as varying traffic volumes and signal wait times. Traditional static routing algorithms like Dijkstra and A* compute shortest paths but often fail under dynamic conditions. This paper proposes a novel approach based on causal inference for real-time dynamic path planning, balancing global and local optimality.
  arXiv  Detail & Related papers  (2024-08-26T11:19:58Z)
• LLM-A*: Large Language Model Enhanced Incremental Heuristic Search on Path Planning [91.95362946266577]
  Path planning is a fundamental scientific problem in robotics and autonomous navigation.<n>Traditional algorithms like A* and its variants are capable of ensuring path validity but suffer from significant computational and memory inefficiencies as the state space grows.<n>We propose a new LLM based route planning method that synergistically combines the precise pathfinding capabilities of A* with the global reasoning capability of LLMs.<n>This hybrid approach aims to enhance pathfinding efficiency in terms of time and space complexity while maintaining the integrity of path validity, especially in large-scale scenarios.
  arXiv  Detail & Related papers  (2024-06-20T01:24:30Z)
• Integrated Decision and Control: Towards Interpretable and Efficient Driving Intelligence [13.589285628074542]
  We present an interpretable and efficient decision and control framework for automated vehicles. It decomposes the driving task into multi-path planning and optimal tracking that are structured hierarchically. Results show that our method has better online computing efficiency and driving performance including traffic efficiency and safety.
  arXiv  Detail & Related papers  (2021-03-18T14:43:31Z)
• Surrogate-assisted cooperative signal optimization for large-scale traffic networks [6.223837701805064]
  This study proposes a surrogate-assisted cooperative signal optimization (SCSO) method. By taking Newman fast algorithm, radial basis function modified estimation of distribution algorithm as decomposer, surrogate model and, respectively, this study develops a concrete SCSO algorithm. To evaluate its effectiveness and efficiency, a large-scale traffic network involving crossroads and T-junctions is generated based on a real traffic network.
  arXiv  Detail & Related papers  (2021-03-03T01:03:57Z)

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.