Hybrid Optimization-Based Approach for Multiple Intelligent Vehicles Requests Allocation
Self-driving cars are attracting significant attention during the last few years, which makes the technology advances jump fast and reach a point of having a number of automated vehicles on the roads. Therefore, the necessity of cooperative driving for these automated vehicles is exponentially incre...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2018-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2018/2493401 |
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| _version_ | 1832563228679340032 |
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| author | Ahmed Hussein Pablo Marín-Plaza Fernando García José María Armingol |
| author_facet | Ahmed Hussein Pablo Marín-Plaza Fernando García José María Armingol |
| author_sort | Ahmed Hussein |
| collection | DOAJ |
| description | Self-driving cars are attracting significant attention during the last few years, which makes the technology advances jump fast and reach a point of having a number of automated vehicles on the roads. Therefore, the necessity of cooperative driving for these automated vehicles is exponentially increasing. One of the main issues in the cooperative driving world is the Multirobot Task Allocation (MRTA) problem. This paper addresses the MRTA problem, specifically for the problem of vehicles and requests allocation. The objective is to introduce a hybrid optimization-based approach to solve the problem of multiple intelligent vehicles requests allocation as an instance of MRTA problem, to find not only a feasible solution, but also an optimized one as per the objective function. Several test scenarios were implemented in order to evaluate the efficiency of the proposed approach. These scenarios are based on well-known benchmarks; thus a comparative study is conducted between the obtained results and the suboptimal results. The analysis of the experimental results shows that the proposed approach was successful in handling various scenarios, especially with the increasing number of vehicles and requests, which displays the proposed approach efficiency and performance. |
| format | Article |
| id | doaj-art-894b750b7faa443c8ae2907cb3df59c5 |
| institution | Kabale University |
| issn | 0197-6729 2042-3195 |
| language | English |
| publishDate | 2018-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-894b750b7faa443c8ae2907cb3df59c52025-02-03T01:20:42ZengWileyJournal of Advanced Transportation0197-67292042-31952018-01-01201810.1155/2018/24934012493401Hybrid Optimization-Based Approach for Multiple Intelligent Vehicles Requests AllocationAhmed Hussein0Pablo Marín-Plaza1Fernando García2José María Armingol3Intelligent Systems Lab (LSI) Research Group, Universidad Carlos III de Madrid (UC3M), Calle Butarque 15, Leganés, 28911 Madrid, SpainIntelligent Systems Lab (LSI) Research Group, Universidad Carlos III de Madrid (UC3M), Calle Butarque 15, Leganés, 28911 Madrid, SpainIntelligent Systems Lab (LSI) Research Group, Universidad Carlos III de Madrid (UC3M), Calle Butarque 15, Leganés, 28911 Madrid, SpainIntelligent Systems Lab (LSI) Research Group, Universidad Carlos III de Madrid (UC3M), Calle Butarque 15, Leganés, 28911 Madrid, SpainSelf-driving cars are attracting significant attention during the last few years, which makes the technology advances jump fast and reach a point of having a number of automated vehicles on the roads. Therefore, the necessity of cooperative driving for these automated vehicles is exponentially increasing. One of the main issues in the cooperative driving world is the Multirobot Task Allocation (MRTA) problem. This paper addresses the MRTA problem, specifically for the problem of vehicles and requests allocation. The objective is to introduce a hybrid optimization-based approach to solve the problem of multiple intelligent vehicles requests allocation as an instance of MRTA problem, to find not only a feasible solution, but also an optimized one as per the objective function. Several test scenarios were implemented in order to evaluate the efficiency of the proposed approach. These scenarios are based on well-known benchmarks; thus a comparative study is conducted between the obtained results and the suboptimal results. The analysis of the experimental results shows that the proposed approach was successful in handling various scenarios, especially with the increasing number of vehicles and requests, which displays the proposed approach efficiency and performance.http://dx.doi.org/10.1155/2018/2493401 |
| spellingShingle | Ahmed Hussein Pablo Marín-Plaza Fernando García José María Armingol Hybrid Optimization-Based Approach for Multiple Intelligent Vehicles Requests Allocation Journal of Advanced Transportation |
| title | Hybrid Optimization-Based Approach for Multiple Intelligent Vehicles Requests Allocation |
| title_full | Hybrid Optimization-Based Approach for Multiple Intelligent Vehicles Requests Allocation |
| title_fullStr | Hybrid Optimization-Based Approach for Multiple Intelligent Vehicles Requests Allocation |
| title_full_unstemmed | Hybrid Optimization-Based Approach for Multiple Intelligent Vehicles Requests Allocation |
| title_short | Hybrid Optimization-Based Approach for Multiple Intelligent Vehicles Requests Allocation |
| title_sort | hybrid optimization based approach for multiple intelligent vehicles requests allocation |
| url | http://dx.doi.org/10.1155/2018/2493401 |
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