Control Strategies for Autonomous Vehicles
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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.
Key facts. Cited by 8 papers in the map (data through 14 Oct 2026). First posted on arXiv 17 Nov 2020.
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Citing papers per year: 2021: 2; 2022: 1; 2023: 4; 2024: 1.
8 papers.
- 2024HUGSIM: A Real-Time, Photo-Realistic and Closed-Loop Simulator for Autonomous Drivingcited by 90
- 2023Milestones in Autonomous Driving and Intelligent Vehicles Part I: Control, Computing System Design, Communication, HD Map, Testing, and Human Behaviorscited by 25
- 2023Lane-Keeping Control of Autonomous Vehicles Through a Soft-Constrained Iterative LQRcited by 4
- 2023Evaluating the Impact of Flaky Simulators on Testing Autonomous Driving Systemscited by 4
- 2022Efficient Perception, Planning, and Control Algorithm for Vision-Based Automated Vehiclescited by 2
- 2023Motion Comfort Optimization for Autonomous Vehicles: Concepts, Methods, and Techniquescited by 2
- 2021Autonomous Racing using a Hybrid Imitation-Reinforcement Learning Architecturecited by 1
- 2021Multi-task UNet architecture for end-to-end autonomous drivingcited by 1
- 2024HUGSIM: A Real-Time, Photo-Realistic and Closed-Loop Simulator for Autonomous Driving2 Dec 2024
- 2023Evaluating the Impact of Flaky Simulators on Testing Autonomous Driving Systems30 Nov 2023
- 2023Lane-Keeping Control of Autonomous Vehicles Through a Soft-Constrained Iterative LQR28 Nov 2023
- 2023Motion Comfort Optimization for Autonomous Vehicles: Concepts, Methods, and Techniques15 Jun 2023
- 2023Milestones in Autonomous Driving and Intelligent Vehicles Part I: Control, Computing System Design, Communication, HD Map, Testing, and Human Behaviors12 May 2023
- 2022Efficient Perception, Planning, and Control Algorithm for Vision-Based Automated Vehicles15 Sep 2022
- 2021Multi-task UNet architecture for end-to-end autonomous driving16 Dec 2021
- 2021Autonomous Racing using a Hybrid Imitation-Reinforcement Learning Architecture11 Oct 2021