Control Strategies for Autonomous Vehicles

TL;DR

This chapter reviews control strategies for autonomous vehicles, covering mathematical models, control schemes, and machine learning techniques, providing a comprehensive overview from both theoretical and practical perspectives.

Contribution

It offers an in-depth analysis of control strategies, including coupled and decoupled schemes, and discusses recent machine learning applications in autonomous vehicle control.

Findings

Detailed vehicle modeling and control scheme analysis

Comparison of coupled and decoupled control strategies

Discussion of machine learning techniques in control tasks

Abstract

This chapter focuses on the self-driving technology from a control perspective and investigates the control strategies used in autonomous vehicles and advanced driver-assistance systems from both theoretical and practical viewpoints. First, we introduce the self-driving technology as a whole, including perception, planning and control techniques required for accomplishing the challenging task of autonomous driving. We then dwell upon each of these operations to explain their role in the autonomous system architecture, with a prime focus on control strategies. The core portion of this chapter commences with detailed mathematical modeling of autonomous vehicles followed by a comprehensive discussion on control strategies. The chapter covers longitudinal as well as lateral control strategies for autonomous vehicles with coupled and de-coupled control schemes. We as well discuss some of the machine learning techniques applied to autonomous vehicle control task. Finally, we briefly summarize some of the research works that our team has carried out at the Autonomous Systems Lab and conclude the chapter with a few thoughtful remarks.