Learning the Car-following Behavior of Drivers Using Maximum Entropy Deep Inverse Reinforcement Learning
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...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Wiley
2020-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2020/4752651 |
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| _version_ | 1832550915244032000 |
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| author | Yang Zhou Rui Fu Chang Wang |
| author_facet | Yang Zhou Rui Fu Chang Wang |
| author_sort | Yang Zhou |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-7e0a162634634afd875e38ba3e368d27 |
| institution | Kabale University |
| issn | 0197-6729 2042-3195 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-7e0a162634634afd875e38ba3e368d272025-02-03T06:05:30ZengWileyJournal of Advanced Transportation0197-67292042-31952020-01-01202010.1155/2020/47526514752651Learning the Car-following Behavior of Drivers Using Maximum Entropy Deep Inverse Reinforcement LearningYang Zhou0Rui Fu1Chang Wang2School of Automobile, Chang’an University, Middle Section of Nan Erhuan Road, Xi’an 710064, ChinaSchool of Automobile, Chang’an University, Middle Section of Nan Erhuan Road, Xi’an 710064, ChinaSchool of Automobile, Chang’an University, Middle Section of Nan Erhuan Road, Xi’an 710064, ChinaThe 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.http://dx.doi.org/10.1155/2020/4752651 |
| spellingShingle | Yang Zhou Rui Fu Chang Wang Learning the Car-following Behavior of Drivers Using Maximum Entropy Deep Inverse Reinforcement Learning Journal of Advanced Transportation |
| title | Learning the Car-following Behavior of Drivers Using Maximum Entropy Deep Inverse Reinforcement Learning |
| title_full | Learning the Car-following Behavior of Drivers Using Maximum Entropy Deep Inverse Reinforcement Learning |
| title_fullStr | Learning the Car-following Behavior of Drivers Using Maximum Entropy Deep Inverse Reinforcement Learning |
| title_full_unstemmed | Learning the Car-following Behavior of Drivers Using Maximum Entropy Deep Inverse Reinforcement Learning |
| title_short | Learning the Car-following Behavior of Drivers Using Maximum Entropy Deep Inverse Reinforcement Learning |
| title_sort | learning the car following behavior of drivers using maximum entropy deep inverse reinforcement learning |
| url | http://dx.doi.org/10.1155/2020/4752651 |
| work_keys_str_mv | AT yangzhou learningthecarfollowingbehaviorofdriversusingmaximumentropydeepinversereinforcementlearning AT ruifu learningthecarfollowingbehaviorofdriversusingmaximumentropydeepinversereinforcementlearning AT changwang learningthecarfollowingbehaviorofdriversusingmaximumentropydeepinversereinforcementlearning |