Driver’s Lane Selection Model Based on Phase-Field Coupling and Multiplayer Dynamic Game with Incomplete Information
Intelligent driving is an effective means to achieve the active safety of automobile, and the accurate prediction of vehicle group situation is the premise to achieve the intelligent driving of vehicle. Lane selection and lane changing are not only the most fundamental reasons for the transformation...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2018-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2018/2145207 |
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| Summary: | Intelligent driving is an effective means to achieve the active safety of automobile, and the accurate prediction of vehicle group situation is the premise to achieve the intelligent driving of vehicle. Lane selection and lane changing are not only the most fundamental reasons for the transformation of vehicle group situation, but also the basic contents for the research on driver behavior of traffic flow theory. In this paper, with a view to the background of Internet of Things, the vehicle group situation was given a comprehensive consideration on the basis of the factors which influence driver’s behavior. The driver’s lane selection behavior was analyzed under the condition of incomplete information, and lane selection model based on phase-field coupling and multiplayer dynamic game with incomplete information was constructed considering the time-varying character of driving propensity. The means of actual driving experiment, virtual driving experiment, and microscopic simulation of traffic flow were used to verify the model. The verification results showed that the model built in this paper can objectively reflect the actual operation characteristic of traffic flow on road section and the process of lane selection. The theoretical basis of the research on lane selection can be provided for intelligent driving especially anthropomorphic driving under the condition of Internet of Things. |
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| ISSN: | 0197-6729 2042-3195 |