用智能体协作优化自驾行程，精准规划路线
Agentic AI for Trip Planning Optimization Application
- 设计多智能体系统，分工处理路况、充电、景点等任务
- 在TOP基准测试中达77.4%准确率，显著优于传统方法
- 提供真实最优解数据集，适合交通规划与AI研究者
智能汽车的行程规划正从生成可行路线转向优化路径选择，因行程时间、能耗及交通状况等交互因素直接影响规划质量。然而现有系统多面向可行性，且当前基准仅提供参考答案，缺乏真实最优解，难以客观评估优化性能。本文提出一种智能体框架，通过编排智能体协调路况、充电与兴趣点等专用智能体实现动态优化，并构建了Trip-planning Optimization Problems数据集，提供确定性最优解与分层任务结构，支持细粒度分析。实验表明，该系统在TOP基准上达到77.4%准确率，显著优于单智能体与流程式多智能体基线，验证了协同智能体推理对鲁棒行程优化的重要性。
原文摘要 · Abstract (English)
Trip planning for intelligent vehicles increasingly requires selecting optimal routes rather than merely producing feasible itineraries, as interacting factors such as travel time, energy consumption, and traffic conditions directly affect plan quality. Yet existing systems are largely designed for feasibility-oriented planning, and current benchmarks provide only reference answers without ground truth, preventing objective evaluation of optimization performance. In our paper, we address these limitations with an agentic AI framework that enables dynamic refinement through an orchestration agent coordinating specialized agents for traffic, charging, and points of interest, and with the Trip-planning Optimization Problems Dataset, which supplies definitive optimal solutions and category-level task structure for fine-grained analysis. Experiments show that our system achieves 77.4\% accuracy on the TOP Benchmark, significantly outperforming single-agent and workflow-based multi-agent baselines, demonstrating the importance of orchestrated agentic reasoning for robust trip planning optimization.
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