A Novel Rear-End Collision Detection Algorithm Based on GNSS Fusion and ANFIS
Rear-end collisions are one of the most common types of accidents on roads. Global Satellite Navigation Systems (GNSS) have recently become sufficiently flexible and cost-effective in order to have great potential for use in rear-end collision avoidance systems (CAS). Nevertheless, there are two mai...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2017-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2017/9620831 |
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| _version_ | 1849414142646026240 |
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| author | Rui Sun Fei Xie Dabin Xue Yucheng Zhang Washington Yotto Ochieng |
| author_facet | Rui Sun Fei Xie Dabin Xue Yucheng Zhang Washington Yotto Ochieng |
| author_sort | Rui Sun |
| collection | DOAJ |
| description | Rear-end collisions are one of the most common types of accidents on roads. Global Satellite Navigation Systems (GNSS) have recently become sufficiently flexible and cost-effective in order to have great potential for use in rear-end collision avoidance systems (CAS). Nevertheless, there are two main issues associated with current vehicle rear-end CAS: (1) achieving relative vehicle positioning and dynamic parameters with sufficiently high accuracy and (2) a reliable method to extract the car-following status from such information. This paper introduces a novel integrated algorithm for rear-end collision detection. Access to high accuracy positioning is enabled by GNSS, electronic compass, and lane information fusion with Cubature Kalman Filter (CKF). The judgment of the car-following status is based on the application of the Adaptive Neurofuzzy Inference System (ANFIS). The field test results show that the designed algorithm could effectively detect rear-end collisions with an accuracy of 99.61% and a false alarm rate of 5.26% in the 10 Hz output rate. |
| format | Article |
| id | doaj-art-73e61caa6006473eb0b99baec6d51b42 |
| institution | Kabale University |
| issn | 0197-6729 2042-3195 |
| language | English |
| publishDate | 2017-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-73e61caa6006473eb0b99baec6d51b422025-08-20T03:33:54ZengWileyJournal of Advanced Transportation0197-67292042-31952017-01-01201710.1155/2017/96208319620831A Novel Rear-End Collision Detection Algorithm Based on GNSS Fusion and ANFISRui Sun0Fei Xie1Dabin Xue2Yucheng Zhang3Washington Yotto Ochieng4College of Civil Aviation, Nanjing University of Aeronautics & Astronautics, Nanjing 211106, ChinaSchool of Electrical and Automation Engineering, Nanjing Normal University, Nanjing 210042, ChinaCollege of Civil Aviation, Nanjing University of Aeronautics & Astronautics, Nanjing 211106, ChinaCollege of Civil Aviation, Nanjing University of Aeronautics & Astronautics, Nanjing 211106, ChinaCentre for Transport Studies, Imperial College London, London SW7 2AZ, UKRear-end collisions are one of the most common types of accidents on roads. Global Satellite Navigation Systems (GNSS) have recently become sufficiently flexible and cost-effective in order to have great potential for use in rear-end collision avoidance systems (CAS). Nevertheless, there are two main issues associated with current vehicle rear-end CAS: (1) achieving relative vehicle positioning and dynamic parameters with sufficiently high accuracy and (2) a reliable method to extract the car-following status from such information. This paper introduces a novel integrated algorithm for rear-end collision detection. Access to high accuracy positioning is enabled by GNSS, electronic compass, and lane information fusion with Cubature Kalman Filter (CKF). The judgment of the car-following status is based on the application of the Adaptive Neurofuzzy Inference System (ANFIS). The field test results show that the designed algorithm could effectively detect rear-end collisions with an accuracy of 99.61% and a false alarm rate of 5.26% in the 10 Hz output rate.http://dx.doi.org/10.1155/2017/9620831 |
| spellingShingle | Rui Sun Fei Xie Dabin Xue Yucheng Zhang Washington Yotto Ochieng A Novel Rear-End Collision Detection Algorithm Based on GNSS Fusion and ANFIS Journal of Advanced Transportation |
| title | A Novel Rear-End Collision Detection Algorithm Based on GNSS Fusion and ANFIS |
| title_full | A Novel Rear-End Collision Detection Algorithm Based on GNSS Fusion and ANFIS |
| title_fullStr | A Novel Rear-End Collision Detection Algorithm Based on GNSS Fusion and ANFIS |
| title_full_unstemmed | A Novel Rear-End Collision Detection Algorithm Based on GNSS Fusion and ANFIS |
| title_short | A Novel Rear-End Collision Detection Algorithm Based on GNSS Fusion and ANFIS |
| title_sort | novel rear end collision detection algorithm based on gnss fusion and anfis |
| url | http://dx.doi.org/10.1155/2017/9620831 |
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