Reinforcement Learning for Vision-Based Lateral Control of a Self-Driving Car

Mengzhe Huang, Mingyu Zhao, Parthiv Parikh, Yebin Wang, Kaan Ozbay, Zhong Ping Jiang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Lateral control design is one of the fundamental components for self-driving cars. In this paper, we propose a learning-based control strategy that enables a mobile car equipped with a camera to perfectly perform lane keeping in a road on the ground. Using the method of adaptive dynamic programming, the proposed control algorithm exploits the structural knowledge of the car kinematics as well as the collected data (images) about the lane information. An adaptive optimal lateral controller is obtained through a data-driven learning algorithm. The effectiveness of the proposed method is demonstrated by theoretical stability proofs and experimental evaluations.

Original languageEnglish (US)
Title of host publication2019 IEEE 15th International Conference on Control and Automation, ICCA 2019
PublisherIEEE Computer Society
Pages1126-1131
Number of pages6
ISBN (Electronic)9781728111643
DOIs
StatePublished - Jul 2019
Event15th IEEE International Conference on Control and Automation, ICCA 2019 - Edinburgh, United Kingdom
Duration: Jul 16 2019Jul 19 2019

Publication series

NameIEEE International Conference on Control and Automation, ICCA
Volume2019-July
ISSN (Print)1948-3449
ISSN (Electronic)1948-3457

Conference

Conference15th IEEE International Conference on Control and Automation, ICCA 2019
CountryUnited Kingdom
CityEdinburgh
Period7/16/197/19/19

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Control and Systems Engineering
  • Electrical and Electronic Engineering
  • Industrial and Manufacturing Engineering

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