Probe-and-Release Coordination of Platoons at Highway Bottlenecks with Unknown Parameters

TL;DR

This paper introduces a 'probe-and-release' algorithm for coordinating autonomous vehicle platoons at highway bottlenecks, effectively estimating unknown parameters and stabilizing traffic under variable demand and capacity breakdowns.

Contribution

It develops a novel algorithm that combines parameter estimation and platoon coordination, ensuring traffic stability despite uncertain and changing conditions.

Findings

Algorithm guarantees bounded estimation errors and traffic queues.

Validated effectiveness through micro-simulation comparisons.

Outperforms deep reinforcement learning in control and efficiency.

Abstract

This paper considers coordination of platoons of connected and autonomous vehicles (CAVs) at mixed-autonomy bottlenecks in the face of three practically important factors, viz. time-varying traffic demand, random CAV platoon sizes, and capacity breakdowns. Platoon coordination is essential to smoothen the interaction between CAV platoons and non-CAV traffic. Based on a fluid queuing model, we develop a "probe-and-release" algorithm that simultaneously estimates environmental parameters and coordinates CAV platoons for traffic stabilization. We show that this algorithm ensures bounded estimation errors and bounded traffic queues. The proof builds on a Lyapunov function that jointly penalizes estimation errors and traffic queues and a drift argument for an embedded Markov process. We validate the proposed algorithm in a standard micro-simulation environment and compare against a representative deep reinforcement learning method in terms of control performance and computational efficiency.