Decentralized Optimal Control in Multi-lane Merging for Connected and Automated Vehicles

TL;DR

This paper presents a decentralized optimal control framework for multi-lane merging of connected and automated vehicles, improving efficiency and safety while reducing computational complexity compared to prior single-lane solutions.

Contribution

It introduces a novel decentralized control approach that extends single-lane merging solutions to multi-lane scenarios using barrier functions for efficiency.

Findings

Significant reduction in travel time and energy consumption compared to human-driven vehicles.

Effective handling of multi-lane merging with guaranteed safety constraints.

Computationally efficient solution suitable for real-time implementation.

Abstract

We address the problem of optimally controlling Connected and Automated Vehicles (CAVs) arriving from two multi-lane roads and merging at multiple points where the objective is to jointly minimize the travel time and energy consumption of each CAV subject to speed-dependent safety constraints, as well as speed and acceleration constraints. This problem was solved in prior work for two single-lane roads. A direct extension to multi-lane roads is limited by the computational complexity required to obtain an explicit optimal control solution. Instead, we propose a general framework that converts a multi-lane merging problem into a decentralized optimal control problem for each CAV in a less-conservative way. To accomplish this, we employ a joint optimal control and barrier function method to efficiently get an optimal control for each CAV with guaranteed satisfaction of all constraints. Simulation examples are included to compare the performance of the proposed framework to a baseline provided by human-driven vehicles with results showing significant improvements in both time and energy metrics.