# Control Strategies for Autonomous Vehicles

arXiv: https://arxiv.org/abs/2011.08729 · HTML: https://mlsanity.com/paper/2011.08729/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Nov 2020.

## Built on by (most cited first, 8 of 8)

- [HUGSIM: A Real-Time, Photo-Realistic and Closed-Loop Simulator for Autonomous Driving](https://mlsanity.com/paper/2412.01718/) (2024)
- [Milestones in Autonomous Driving and Intelligent Vehicles Part I: Control, Computing System Design, Communication, HD Map, Testing, and Human Behaviors](https://mlsanity.com/paper/2305.11239/) (2023)
- [Lane-Keeping Control of Autonomous Vehicles Through a Soft-Constrained Iterative LQR](https://mlsanity.com/paper/2311.16900/) (2023)
- [Evaluating the Impact of Flaky Simulators on Testing Autonomous Driving Systems](https://mlsanity.com/paper/2311.18768/) (2023)
- [Efficient Perception, Planning, and Control Algorithm for Vision-Based Automated Vehicles](https://mlsanity.com/paper/2209.07042/) (2022)
- [Motion Comfort Optimization for Autonomous Vehicles: Concepts, Methods, and Techniques](https://mlsanity.com/paper/2306.09462/) (2023)
- [Autonomous Racing using a Hybrid Imitation-Reinforcement Learning Architecture](https://mlsanity.com/paper/2110.05437/) (2021)
- [Multi-task UNet architecture for end-to-end autonomous driving](https://mlsanity.com/paper/2112.08967/) (2021)

## Abstract (arXiv)

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.