Distributed Cooperative Driving Strategy for Connected Automated Vehicles at Unsignalized Intersections Based on Monte Carlo Method
One of the most important goals of cooperative driving is to control connected automated vehicles (CAVs) passing through conflict areas safely and efficiently without traffic signals. As a typical application scenario, allocating right-of-way reasonably at unsignalized intersections can effectively...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2024-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2024/6586774 |
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| _version_ | 1832559566891515904 |
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| author | Haoming Li Wei Dong Linjun Lu Ying Wang Xianing Wang |
| author_facet | Haoming Li Wei Dong Linjun Lu Ying Wang Xianing Wang |
| author_sort | Haoming Li |
| collection | DOAJ |
| description | One of the most important goals of cooperative driving is to control connected automated vehicles (CAVs) passing through conflict areas safely and efficiently without traffic signals. As a typical application scenario, allocating right-of-way reasonably at unsignalized intersections can effectively avoid collisions and reduce traffic delays. Proposed here is a new cooperative driving strategy for CAVs at unsignalized intersections based on distributed Monte Carlo tree search (MCTS). A task-area partition framework is also proposed to decompose the mission of cooperative driving into three main tasks: vehicle information sharing, passing order optimization, and trajectory control. Based on the schedule tree of the vehicle passing order, the root parallelization of MCTS combined with the majority voting rule is used to explore as many feasible passing orders (leaf nodes) as possible in a distributed way and find a nearly global-optimal passing order within the limited planning time. The aim is for CAVs to perform proper trajectory adjustments based on the obtained passing order to minimize traffic delays while making the slightest acceleration adjustments. A coupled simulation platform integrating SUMO and Python is developed to construct the unsignalized intersection scenarios and generate the proposed distributed cooperative driving strategy. Comparative analysis with conventional driving strategies demonstrates that the proposed strategy significantly enhances efficiency, safety, comfort, and emission, aligning well with innovative and environmentally friendly urban mobility aspirations. |
| format | Article |
| id | doaj-art-8b3a169374e94f8f9d5970ac5d0c5edb |
| institution | Kabale University |
| issn | 2042-3195 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-8b3a169374e94f8f9d5970ac5d0c5edb2025-02-03T01:29:42ZengWileyJournal of Advanced Transportation2042-31952024-01-01202410.1155/2024/6586774Distributed Cooperative Driving Strategy for Connected Automated Vehicles at Unsignalized Intersections Based on Monte Carlo MethodHaoming Li0Wei Dong1Linjun Lu2Ying Wang3Xianing Wang4Department of Transportation EngineeringDepartment of Engineering TechnicalDepartment of Transportation EngineeringDepartment of Transportation EngineeringDepartment of Transportation EngineeringOne of the most important goals of cooperative driving is to control connected automated vehicles (CAVs) passing through conflict areas safely and efficiently without traffic signals. As a typical application scenario, allocating right-of-way reasonably at unsignalized intersections can effectively avoid collisions and reduce traffic delays. Proposed here is a new cooperative driving strategy for CAVs at unsignalized intersections based on distributed Monte Carlo tree search (MCTS). A task-area partition framework is also proposed to decompose the mission of cooperative driving into three main tasks: vehicle information sharing, passing order optimization, and trajectory control. Based on the schedule tree of the vehicle passing order, the root parallelization of MCTS combined with the majority voting rule is used to explore as many feasible passing orders (leaf nodes) as possible in a distributed way and find a nearly global-optimal passing order within the limited planning time. The aim is for CAVs to perform proper trajectory adjustments based on the obtained passing order to minimize traffic delays while making the slightest acceleration adjustments. A coupled simulation platform integrating SUMO and Python is developed to construct the unsignalized intersection scenarios and generate the proposed distributed cooperative driving strategy. Comparative analysis with conventional driving strategies demonstrates that the proposed strategy significantly enhances efficiency, safety, comfort, and emission, aligning well with innovative and environmentally friendly urban mobility aspirations.http://dx.doi.org/10.1155/2024/6586774 |
| spellingShingle | Haoming Li Wei Dong Linjun Lu Ying Wang Xianing Wang Distributed Cooperative Driving Strategy for Connected Automated Vehicles at Unsignalized Intersections Based on Monte Carlo Method Journal of Advanced Transportation |
| title | Distributed Cooperative Driving Strategy for Connected Automated Vehicles at Unsignalized Intersections Based on Monte Carlo Method |
| title_full | Distributed Cooperative Driving Strategy for Connected Automated Vehicles at Unsignalized Intersections Based on Monte Carlo Method |
| title_fullStr | Distributed Cooperative Driving Strategy for Connected Automated Vehicles at Unsignalized Intersections Based on Monte Carlo Method |
| title_full_unstemmed | Distributed Cooperative Driving Strategy for Connected Automated Vehicles at Unsignalized Intersections Based on Monte Carlo Method |
| title_short | Distributed Cooperative Driving Strategy for Connected Automated Vehicles at Unsignalized Intersections Based on Monte Carlo Method |
| title_sort | distributed cooperative driving strategy for connected automated vehicles at unsignalized intersections based on monte carlo method |
| url | http://dx.doi.org/10.1155/2024/6586774 |
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