We address multi-objective unmanned aerial vehicle (UAV) placement for motorway intelligent transportation systems, where deployments must balance coverage, link quality, and UAV count under geometric constraints. We construct a reproducible benchmark from highD motorway recordings with recording-level splits and generate Pareto-optimal labels via NSGA-II. A preference rule yields deployable targets while preserving multi-objective evaluation. We train fast surrogate models that map unordered vehicle positions to UAV count and continuous placements, using permutation-aware losses and constraint-regularized training across set-based and sequence-based architectures. The evaluation protocol combines Pareto quality metrics, success-rate curves, runtime benchmarks, and robustness studies, with uncertainty quantified by recording-level bootstrap. Results indicate that permutation-invariant set models provide the strongest coverage--SNR--count trade-off among learned predictors and approach NSGA-II quality while enabling real-time inference. Under shared budgets, they offer a more favorable success--latency trade-off than heuristic baselines. The benchmark, splits are released to support reproducible ITS deployment studies and to facilitate comparisons under shared operational budgets.

翻译：本文研究高速公路智能交通系统中的多目标无人机部署问题，该场景需在几何约束下平衡覆盖范围、链路质量和无人机数量三大目标。我们基于highD高速公路数据集构建了可复现的基准测试框架，采用记录级数据划分策略，并通过NSGA-II算法生成帕累托最优标签集。通过设计偏好规则，在保持多目标评估能力的同时生成可部署的优化目标。我们训练了快速代理模型，该模型能够将无序车辆位置映射为无人机数量与连续部署方案，在集合式与序列式架构中均采用置换感知损失函数和约束正则化训练策略。评估体系综合了帕累托质量指标、成功率曲线、运行时基准测试及鲁棒性研究，并通过记录级自助法量化不确定性。结果表明：在各类学习型预测器中，置换不变集合模型在覆盖范围-信噪比-数量权衡方面表现最优，在实现实时推理的同时逼近NSGA-II的优化质量；在相同预算约束下，该模型较启发式基线方法展现出更优的成功率-延迟权衡特性。本研究所发布的基准测试框架与数据划分方案，旨在支持可复现的智能交通系统部署研究，并为共享运营预算下的性能比较提供标准化评估平台。