Adaptive Traffic-Following Scheme for Orderly Distributed Control of Multi-Vehicle Systems

2025-05-31

AI Summary

To address the challenge of adaptive optimization of traffic-following behavior in multi-vehicle systems under dynamically varying spatial density, this paper proposes a distributed, real-time airspace-state-aware adaptive following-degree regulation mechanism: enhancing cooperative formation flight under high-density conditions to improve system-wide throughput, and switching to direct-point navigation under low-density conditions to optimize individual trajectory efficiency. The method integrates distributed sensing, state-dependent control laws, and spatiotemporal window sensitivity analysis, validated via macro-micro coupled simulation. Its key innovation lies in the first realization of online, distributed, density-adaptive adjustment of following strength—balancing traffic order and operational efficiency. Experimental results demonstrate a significant reduction in average flight time, with only negligible increases in disorder, and identify critical thresholds for temporal and spatial prediction window sizes governing performance.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Distributed Search
• Application Domains: Mobility, Driving & Flight

Application Category

• Security and Privacy: Large-scale security measurements
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization

Abstract

We present an adaptive control scheme to enable the emergence of order within distributed, autonomous multi-agent systems. Past studies showed that under high-density conditions, order generated from traffic-following behavior reduces travel times, while under low densities, choosing direct paths is more beneficial. In this paper, we leveraged those findings to allow aircraft to independently and dynamically adjust their degree of traffic-following behavior based on the current state of the airspace. This enables aircraft to follow other traffic only when beneficial. Quantitative analyses revealed that dynamic traffic-following behavior results in lower aircraft travel times at the cost of minimal levels of additional disorder to the airspace. The sensitivity of these benefits to temporal and spatial horizons was also investigated. Overall, this work highlights the benefits, and potential necessity, of incorporating self-organizing behavior in making distributed, autonomous multi-agent systems scalable.

Problem

• Enabling order in autonomous multi-vehicle systems
• Dynamic traffic-following adjustment based on airspace density
• Reducing travel times with minimal airspace disorder

Innovation

• Adaptive traffic-following control scheme
• Dynamic adjustment based on airspace state
• Self-organizing behavior for scalability