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Fast Surrogate Learning for Multi-Objective UAV Placement in Motorway Intelligent Transportation System

Weian Guo, Shixin Deng, Wuzhao Li, Li Li

TL;DR

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.

Abstract

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.

Fast Surrogate Learning for Multi-Objective UAV Placement in Motorway Intelligent Transportation System

TL;DR

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.

Abstract

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.
Paper Structure (40 sections, 9 equations, 8 figures, 14 tables, 2 algorithms)

This paper contains 40 sections, 9 equations, 8 figures, 14 tables, 2 algorithms.

Table of Contents

  1. Introduction
  2. Related Work
  3. UAV-Enabled ITS and Air--Ground Deployment
  4. Multi-Objective UAV Placement and Evolutionary Optimization
  5. Learning to Optimize on Unordered Inputs
  6. Problem Formulation
  7. System Model and Deployment Assumptions
  8. Coverage
  9. Signal-to-Noise Ratio
  10. Multi-Objective Formulation
  11. Algorithm Design and Learning Framework
  12. Surrogate Architecture
  13. Permutation-Invariant Matching and Constraint-Aware Loss
  14. Post-Processing and Constraint Enforcement
  15. Training Protocol
  16. ...and 25 more sections

Figures (8)

  • Figure 1: Problem-to-deployment chain: motorway scenes are mapped to UAV decisions under multi-objective targets and constraints, then evaluated for real-time ITS deployment.
  • Figure 2: End-to-end pipeline: highD scenarios are labeled by NSGA-II and used to train surrogate predictors for UAV count and placement.
  • Figure 3: Distribution of vehicle counts per scene.
  • Figure 4: Success--latency scatter under Policy B (log-scale latency). The dashed line connects non-dominated points.
  • Figure 5: Success-rate curves versus coverage and SNR thresholds under Policy B (budget-matched $k$).
  • ...and 3 more figures