NDLI: Modeling of human driver behavior via receding horizon and artificial neural network controllers

Content Provider	IEEE Xplore Digital Library
Author	Hongchuan Wei Ross, W. Varisco, S. Krief, P. Ferrari, S.
Copyright Year	2013
Description	Author affiliation: Lab. for Intell. Syst. & Controls (LISC), Duke Univ., Durham, NC, USA (Hongchuan Wei; Ross, W.; Ferrari, S.) \|\| Ferrari S.p.A., Maranello, Italy (Varisco, S.; Krief, P.)
Abstract	This paper presents a comparison of receding horizon and artificial neural network controllers for the modeling of human driver behavior during coupled lateral-longitudinal maneuvers. Driver models have been previously developed using control theoretic approaches, such as model predictive control, also known as receding horizon control, and have been shown capable of providing satisfactory vehicle control. However, these models tend to outperform human drivers, for example, due to maneuvers that require high-frequency driver compensation, counter-steering behaviors as required to maintain stability, or modeling errors. Furthermore, tuning these driver models to different drivers, automobiles, and road conditions, can be very challenging, and requires expert human intervention. This paper presents an artificial neural network controller that overcomes these limitations, and, by adapting to the experimental data obtained from the human driver, is capable of reproducing the driver behavior more closely and under a broader range of operating conditions. The receding horizon and neural network controllers are tested and compared to the response of a professional human driver on a closed-course track using data obtained using a high-fidelity Ferrari GT driving simulator. The results show that the artificial neural network outperforms the receding horizon controller in mimicking the response of the professional human driver.
Sponsorship	IEEE Control Syst. Soc.
Starting Page	6778
Ending Page	6785
File Size	546694
Page Count	8
File Format	PDF
ISBN	9781467357142
ISSN	07431546
e-ISBN	9781467357173
DOI	10.1109/CDC.2013.6760963
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-12-10
Publisher Place	Italy
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Vehicles Wheels Vehicle dynamics Mathematical model Torque Target tracking Neural networks
Content Type	Text
Resource Type	Article
Subject	Control and Optimization Control and Systems Engineering Modeling and Simulation

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
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3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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