Learning the Car-following Behavior of Drivers Using Maximum Entropy Deep Inverse Reinforcement Learning
Joint Authors
Source
Journal of Advanced Transportation
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-13, 13 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-11-21
Country of Publication
Egypt
No. of Pages
13
Main Subjects
Abstract EN
The present study proposes a framework for learning the car-following behavior of drivers based on maximum entropy deep inverse reinforcement learning.
The proposed framework enables learning the reward function, which is represented by a fully connected neural network, from driving data, including the speed of the driver’s vehicle, the distance to the leading vehicle, and the relative speed.
Data from two field tests with 42 drivers are used.
After clustering the participants into aggressive and conservative groups, the car-following data were used to train the proposed model, a fully connected neural network model, and a recurrent neural network model.
Adopting the fivefold cross-validation method, the proposed model was proved to have the lowest root mean squared percentage error and modified Hausdorff distance among the different models, exhibiting superior ability for reproducing drivers’ car-following behaviors.
Moreover, the proposed model captured the characteristics of different driving styles during car-following scenarios.
The learned rewards and strategies were consistent with the demonstrations of the two groups.
Inverse reinforcement learning can serve as a new tool to explain and model driving behavior, providing references for the development of human-like autonomous driving models.
American Psychological Association (APA)
Zhou, Yang& Fu, Rui& Wang, Chang. 2020. Learning the Car-following Behavior of Drivers Using Maximum Entropy Deep Inverse Reinforcement Learning. Journal of Advanced Transportation،Vol. 2020, no. 2020, pp.1-13.
https://search.emarefa.net/detail/BIM-1175666
Modern Language Association (MLA)
Zhou, Yang…[et al.]. Learning the Car-following Behavior of Drivers Using Maximum Entropy Deep Inverse Reinforcement Learning. Journal of Advanced Transportation No. 2020 (2020), pp.1-13.
https://search.emarefa.net/detail/BIM-1175666
American Medical Association (AMA)
Zhou, Yang& Fu, Rui& Wang, Chang. Learning the Car-following Behavior of Drivers Using Maximum Entropy Deep Inverse Reinforcement Learning. Journal of Advanced Transportation. 2020. Vol. 2020, no. 2020, pp.1-13.
https://search.emarefa.net/detail/BIM-1175666
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1175666