Distributed Dynamic Platoons Control and Junction Crossing Optimization for Mixed Traffic Flows in Smart Cities- Part I. Fundamentals, Theoretical and Automatic Decision Framework

TL;DR

This paper introduces an automatic decision framework for managing distributed dynamic platoons of connected automated vehicles and optimizing junction crossing in smart cities, addressing mixed traffic with human-driven vehicles and infrastructure uncertainties.

Contribution

It proposes a novel automatic decision system (ADSCAS) with a four-stage decision process for safe, efficient, and fuel-economic platoon control and junction crossing in mixed traffic environments.

Findings

Effective platoon size management considering social driving behaviors

A decision and switching scheme for platoon state control

Fuel optimization integrated into platoon trajectory planning

Abstract

This article studies the problems of distributed dynamic platoons control and smart junction crossing optimization for a mixed traffic flow with connected automated vehicles(CAVs) and social human-driven vehicles(HDVs) in a smart city. The goal of this two-part article is to provide an automatic decision framework to ensure the safe and efficient cruising and crossing junctions with multiple dynamic and resilient platoons CAVs, while against the social driving behaviors(SDBs) of the HDVs considered as surrounding vehicles(SVs) and unknown traffic lights connected by Cellular-Vehicle-to-X (C-V2X) infrastructure. We shall show that despite the nonlinearity, non-smoothness, and uncertainty of mixed traffic flows in smart cities, the solutions of safe, efficient, and fuel economic platoon CAVs control for the above problems can be solved by our proposed automatic decision and smart crossing assistant system(ADSCAS) that includes four decision stages and six cruising states characterized within this article. More precisely, in part I, we provide a dynamic platoon management strategy to determine the platoon size with respect to the SDBs of the preceding HDVs and upcoming traffic lights, where the dynamic platoon size is composed of minimizing safe distances and nonlinear functions of the platoon's cruising velocity. We also present the decision and switching scheme for a finite state machine of platoons in the ADSCAS, the design of the reference trajectory planning, and the solution of the fuel economic optimization problem.