Distributed Dynamic Platoons Control and Junction Crossing Optimization for Mixed Traffic Flow in Smart Cities- Part II. Stability, Optimization, and Performance Analysis

TL;DR

This paper proposes a distributed control strategy for CAV platoons to ensure safe junction crossing and optimize traffic flow in smart cities, considering mixed traffic and social driving behaviors.

Contribution

It introduces a nonlinear variable time headway space strategy, a cooperative observer for leader acceleration estimation, and a robust trajectory tracking algorithm for platoon management.

Findings

Effective platoon formation and split within finite time

Reduced fuel consumption in mixed traffic scenarios

Enhanced safety and smoothness of traffic flow

Abstract

In part II, we present a fully distributed nonlinear variable time headway space strategy to ensure the subsequent safe cruising and junction crossing, where the cooperative perception of multiple neighbors stimuli and the cooperative tracking of the follower connected automated vehicles(CAVs) to the leader CAV are developed, which will result in a heterogeneous traffic flow dynamic. Once the proper length of the platoon determined by the ADSCAS characterized in part I is formed, we propose a cooperative observer design to estimate the leader CAV's acceleration adjustment which is affected by the unknown traffic lights. We shall show that the distributed and resilient nonlinear platoons control and junction crossing problem will be solved by a robust cooperative trajectory tracking optimization algorithm to ensure the fast formation and split of the platoons and safe junction cruising within the finite time horizons, taking into account the social driving behaviors(SDBs) of the surrounding vehicles(SVs), the dynamics of the follower CAVs, and an upcoming traffic signal schedule while minimizing the overall platoons fuel consumption. Performance analysis and case studies are presented to illustrate the effectiveness of the proposed approaches for multiple platoon dynamic management, which also show that the cooperation between CAVs and human-driven vehicles(HDVs) can further smooth out the driving trajectory, reduce the fuel consumption, and enhance the safety of the mixed traffic flow