Control of Connected and Automated Vehicles: State of the Art and Future Challenges

TL;DR

This paper reviews the current state and future challenges of control and planning architectures for connected and automated vehicles, emphasizing energy efficiency and cooperative control techniques.

Contribution

It introduces a control and planning architecture for CAVs, surveys existing algorithms, and highlights promising optimization approaches and future research challenges.

Findings

Overview of existing control algorithms for CAVs

Identification of optimization-based approaches for energy efficiency

Discussion of future challenges in CAV control and cooperation

Abstract

Autonomous driving technology pledges safety, convenience, and energy efficiency. Challenges include the unknown intentions of other road users: communication between vehicles and with the road infrastructure is a possible approach to enhance awareness and enable cooperation. Connected and automated vehicles (CAVs) have the potential to disrupt mobility, extending what is possible with driving automation and connectivity alone. Applications include real-time control and planning with increased awareness, routing with micro-scale traffic information, coordinated platooning using traffic signals information, eco-mobility on demand with guaranteed parking. This paper introduces a control and planning architecture for CAVs, and surveys the state of the art on each functional block therein; the main focus is on techniques to improve energy efficiency. We provide an overview of existing algorithms and their mutual interactions, we present promising optimization-based approaches to CAVs control and identify future challenges.