A Systematic Survey of Control Techniques and Applications in Connected and Automated Vehicles

Abstract

Vehicle control is one of the most critical challenges in autonomous vehicles (AVs) and connected and automated vehicles (CAVs), and it is paramount in vehicle safety, passenger comfort, transportation efficiency, and energy saving. This survey attempts to provide a comprehensive and thorough overview of the current state of vehicle control technology, focusing on the evolution from vehicle state estimation and trajectory tracking control in AVs at the microscopic level to collaborative control in CAVs at the macroscopic level. First, this review starts with vehicle key state estimation, specifically vehicle sideslip angle, which is the most pivotal state for vehicle trajectory control, to discuss representative approaches. Then, we present symbolic vehicle trajectory tracking control approaches for AVs. On top of that, we further review the collaborative control frameworks for CAVs and corresponding applications. Finally, this survey concludes with a discussion of future research directions and the challenges. This survey aims to provide a contextualized and indepth look at the state of the art in vehicle control for AVs and CAVs, identifying critical areas of focus and pointing out the potential areas for further exploration.

Advancing autonomous vehicle control systems: An in‐depth overview of decision‐making and manoeuvre execution state of the art

“…We discussed two primary categories of control: Model-based and Neural Network algorithms. Given that vehicle control plays an essential role in guiding autonomous vehicles (AVs) along their desired trajectories, it serves as a fundamental component of AV technology [105]. The objective is to devise innovative control strategies capable of accommodating the diverse behaviours of human drivers while upholding road safety and efficiency.…”

A joint task caching and computation offloading scheme based on Deep Reinforcement Learning

“…[2]. However, vehicles' limited computational resources and storage space may not suffice for many latency-sensitive and computationally intensive tasks in real-world scenarios [3]. To achieve this goal, early research [4][5][6] combined the Internet of Vehicles with cloud computing, using the powerful computing storage resources of the cloud to process vehicle tasks.…”

A Reinforcement Learning Approach for Integrating an Intelligent Home Energy Management System with a Vehicle-to-Home Unit

“…The study [5], introduced a multilevel integrated control with control allocation and motion controller to enhance the performance by reducing the tire effort on each wheel. Moreover, a survey of vehicle control for autonomous, connected, and automated EVs was conducted in [6]. The analysis included the techniques, essential elements, collaborative EV control strategies, and possible uses of cooperative driving control techniques, that could increase the sustainability, safety, and efficiency of the future transportation system.…”