Early Versus Late Traffic Management For Autonomous Agents

TL;DR

This paper examines how the timing of traffic management interventions at intersections affects safety and efficiency for autonomous agents, using MILP optimization and simulation to compare early versus late control strategies.

Contribution

It introduces a framework for analyzing intervention timing in autonomous traffic management using control regions and MILP optimization.

Findings

Early interventions improve safety margins.

Late interventions reduce delays.

Optimal timing balances safety and efficiency.

Abstract

Intersections pose critical challenges in traffic management, where maintaining operational constraints and ensuring safety are essential for efficient flow. This paper investigates the effect of intervention timing in management strategies on maintaining operational constraints at intersections while ensuring safe separation distance, avoiding collisions, and minimizing delay. We introduce control regions, represented as circles around the intersection, which refers to the timing of interventions by a centralized control system when agents approach the intersection. We use a mixed-integer linear programming (MILP) approach to optimize the system's performance. To analyze the effectiveness of early and late control measures, a simulation study is conducted, focusing on the safe, efficient, and robust management of agent movement within the control regions.