A Decision-Making Model for Self-Driving Vehicles Based on Overtaking Frequency
The driving state of a self-driving vehicle represents an important component in the self-driving decision system. To ensure the safe and efficient driving state of a self-driving vehicle, the driving state of the self-driving vehicle needs to be evaluated quantitatively. In this paper, a driving st...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2021/5948971 |
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| _version_ | 1832545923703504896 |
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| author | Mengyuan Huang Shiwu Li Mengzhu Guo Lihong Han |
| author_facet | Mengyuan Huang Shiwu Li Mengzhu Guo Lihong Han |
| author_sort | Mengyuan Huang |
| collection | DOAJ |
| description | The driving state of a self-driving vehicle represents an important component in the self-driving decision system. To ensure the safe and efficient driving state of a self-driving vehicle, the driving state of the self-driving vehicle needs to be evaluated quantitatively. In this paper, a driving state assessment method for the decision system of self-driving vehicles is proposed. First, a self-driving vehicle and surrounding vehicles are compared in terms of the overtaking frequency (OTF), and an OTF-based driving state evaluation algorithm is proposed considering the future driving efficiency. Next, a decision model based on the deep deterministic policy gradient (DDPG) algorithm and the proposed method is designed, and the driving state assessment method is integrated with the existing time-to-collision (TTC) and minimum safe distance. In addition, the reward function and multiple driving scenarios are designed so that the most efficient driving strategy at the current moment can be determined by optimal search under the condition of ensuring safety. Finally, the proposed decision model is verified by simulations in four three-lane highway scenarios. The simulation results show that the proposed decision model that integrates the self-driving vehicle driving state assessment method can help self-driving vehicles to drive safely and to maintain good maneuverability. |
| format | Article |
| id | doaj-art-690c84dd385e4ae0b25bf459bf204010 |
| institution | Kabale University |
| issn | 2042-3195 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-690c84dd385e4ae0b25bf459bf2040102025-02-03T07:24:16ZengWileyJournal of Advanced Transportation2042-31952021-01-01202110.1155/2021/5948971A Decision-Making Model for Self-Driving Vehicles Based on Overtaking FrequencyMengyuan Huang0Shiwu Li1Mengzhu Guo2Lihong Han3School of TransportationSchool of TransportationSchool of TransportationSchool of TransportationThe driving state of a self-driving vehicle represents an important component in the self-driving decision system. To ensure the safe and efficient driving state of a self-driving vehicle, the driving state of the self-driving vehicle needs to be evaluated quantitatively. In this paper, a driving state assessment method for the decision system of self-driving vehicles is proposed. First, a self-driving vehicle and surrounding vehicles are compared in terms of the overtaking frequency (OTF), and an OTF-based driving state evaluation algorithm is proposed considering the future driving efficiency. Next, a decision model based on the deep deterministic policy gradient (DDPG) algorithm and the proposed method is designed, and the driving state assessment method is integrated with the existing time-to-collision (TTC) and minimum safe distance. In addition, the reward function and multiple driving scenarios are designed so that the most efficient driving strategy at the current moment can be determined by optimal search under the condition of ensuring safety. Finally, the proposed decision model is verified by simulations in four three-lane highway scenarios. The simulation results show that the proposed decision model that integrates the self-driving vehicle driving state assessment method can help self-driving vehicles to drive safely and to maintain good maneuverability.http://dx.doi.org/10.1155/2021/5948971 |
| spellingShingle | Mengyuan Huang Shiwu Li Mengzhu Guo Lihong Han A Decision-Making Model for Self-Driving Vehicles Based on Overtaking Frequency Journal of Advanced Transportation |
| title | A Decision-Making Model for Self-Driving Vehicles Based on Overtaking Frequency |
| title_full | A Decision-Making Model for Self-Driving Vehicles Based on Overtaking Frequency |
| title_fullStr | A Decision-Making Model for Self-Driving Vehicles Based on Overtaking Frequency |
| title_full_unstemmed | A Decision-Making Model for Self-Driving Vehicles Based on Overtaking Frequency |
| title_short | A Decision-Making Model for Self-Driving Vehicles Based on Overtaking Frequency |
| title_sort | decision making model for self driving vehicles based on overtaking frequency |
| url | http://dx.doi.org/10.1155/2021/5948971 |
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