[Paper Review] A Decentralized Optimal Control Framework for Connected Automated Vehicles at Urban Intersections with Dynamic Resequencing

Yue Zhang, Christos G. Cassandras|arXiv (Cornell University)|Sep 1, 2018

Traffic control and management4 citations

TL;DR

This paper proposes a decentralized optimal control framework for connected automated vehicles (CAVs) at urban intersections that relaxes the strict First-In-First-Out (FIFO) queueing constraint by introducing dynamic resequencing to maximize traffic throughput. By reordering CAVs based on real-time conditions and solving a trade-off between energy minimization and throughput maximization, the framework improves travel time by up to 34% while maintaining computational feasibility through bounded complexity analysis.

ABSTRACT

Earlier work has established a decentralized framework to optimally control Connected Automated Vehicles (CAVs) crossing an urban intersection without using explicit traffic signaling while following a strict First-In-First-Out (FIFO) queueing structure. The proposed solution minimizes energy consumption subject to a FIFO-based throughput maximization requirement. In this paper, we extend the solution to account for asymmetric intersections by relaxing the FIFO constraint and including a dynamic resequencing process so as to maximize traffic throughput. To investigate the tradeoff between throughput maximization and energy minimization objectives, we exploit several alternative problem formulations. In addition, the computational complexity of the resequencing process is analyzed and proved to be bounded, which makes the online implementation computationally feasible. The effectiveness of the dynamic resequencing process in terms of throughput maximization is illustrated through simulation

Motivation & Objective

• To address the limitations of strict FIFO queueing in asymmetric or high-traffic urban intersections, which can lead to suboptimal scheduling and congestion.
• To develop a decentralized control framework that enables dynamic resequencing of CAVs to maximize intersection throughput without relying on traffic signals.
• To investigate the trade-off between minimizing energy consumption and maximizing traffic throughput through alternative problem formulations.
• To ensure computational feasibility by proving the resequencing process has bounded complexity, suitable for online implementation.
• To validate the effectiveness of dynamic resequencing through simulation under varying traffic intensities and control objectives.

Proposed method

• Extends prior decentralized optimal control frameworks by relaxing the FIFO constraint and introducing a dynamic resequencing process based on real-time vehicle state and arrival information.
• Uses a recursive terminal time structure to compute crossing times, enabling coordination without centralized signaling.
• Employs a polynomial metamodel for fuel consumption as a function of speed and acceleration to quantify energy use.
• Applies multiple problem formulations that weight terminal speed and energy consumption differently to explore the throughput-energy trade-off.
• Analyzes computational complexity of resequencing and proves it is bounded, with average complexity proportional to the number of lanes.
• Simulates 100 CAVs crossing an intersection under varying arrival rates (λ = 0.1 to 0.4 veh/s) to evaluate performance under different control objectives.

Experimental results

Research questions

• RQ1 How does relaxing the FIFO constraint improve traffic throughput at asymmetric urban intersections?
• RQ2 What is the trade-off between energy minimization and throughput maximization in CAV coordination at intersections?
• RQ3 How does dynamic resequencing affect travel time and fuel consumption under varying traffic intensities?
• RQ4 What is the computational complexity of the resequencing process, and is it suitable for real-time online implementation?
• RQ5 To what extent do terminal speed constraints influence the performance of the resequencing framework?

Key findings

• Dynamic resequencing improves travel time by approximately 34% compared to non-resequencing cases, demonstrating significant throughput gains.
• The resequencing process increases energy consumption due to higher terminal speeds, confirming the inherent trade-off between throughput and energy efficiency.
• Cases 4 and 5 in the alternative formulations achieve the best balance between reduced travel time and moderate fuel use.
• At low traffic intensity (λ = 0.1 veh/s), travel times are less sensitive to control formulation due to reduced vehicle interactions.
• The computational complexity of resequencing is bounded and scales with the number of lanes, making online implementation feasible.
• The recursive terminal time structure is disrupted under light traffic, leading to less impact of initial speed settings on overall performance.