Lane Determination of Vehicles Based on a Novel Clustering Algorithm for Intelligent Traffic Monitoring
In intelligent traffic monitoring, speed measuring millimeter waves (MMW) radar is one of the most commonly used tools for traffic enforcement. In traffic enforcement field, the radar must provide the evidence of each vehicle belongs to which lane. In this paper, we propose a novel kernel line segme...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2020-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9049388/ |
| _version_ | 1818428210616991744 |
|---|---|
| author | Lin Cao Tao Wang Dongfeng Wang Kangning Du Yunxiao Liu Chong Fu |
| author_facet | Lin Cao Tao Wang Dongfeng Wang Kangning Du Yunxiao Liu Chong Fu |
| author_sort | Lin Cao |
| collection | DOAJ |
| description | In intelligent traffic monitoring, speed measuring millimeter waves (MMW) radar is one of the most commonly used tools for traffic enforcement. In traffic enforcement field, the radar must provide the evidence of each vehicle belongs to which lane. In this paper, we propose a novel kernel line segment adaptive possibilistic c-means clustering algorithm (KLSAPCM) for lane determination of vehicles. Firstly, the raw measurement data is preprocessed using the extracting method of data adjacent lane centerlines. Secondly, according to the improved minimum radius data search method, outliers are removed and the proposed KLSAPCM algorithm is initialized. Finally, the accuracy of lane determination has been improved by the proposed KLSAPCM clustering algorithm based on adaptive kernel line segment that conforms to the shape features of the measurement data in the actual scene. The experiment results for multiple scenes were: the KLSAPCM algorithm is compared with the DBSCAN, the $k$ -means, the FCM, the PCM, the AMPCM, and the APCM algorithms on real measurement datasets, and the results highlight the classification rate of the proposed algorithm. Meanwhile, the proposed algorithm gets a good real-time performance and strong robustness for some sparse moving vehicle scene applications. |
| first_indexed | 2024-12-14T14:58:00Z |
| format | Article |
| id | doaj.art-c139004dbc66481a8bc800cbcb6247b6 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-14T14:58:00Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-c139004dbc66481a8bc800cbcb6247b62022-12-21T22:56:56ZengIEEEIEEE Access2169-35362020-01-018630046301710.1109/ACCESS.2020.29838729049388Lane Determination of Vehicles Based on a Novel Clustering Algorithm for Intelligent Traffic MonitoringLin Cao0Tao Wang1https://orcid.org/0000-0003-0136-4853Dongfeng Wang2Kangning Du3Yunxiao Liu4Chong Fu5School of Information and Communication Engineering, Beijing Information Science and Technology University, Beijing, ChinaSchool of Information and Communication Engineering, Beijing Information Science and Technology University, Beijing, ChinaSchool of Information and Communication Engineering, Beijing Information Science and Technology University, Beijing, ChinaSchool of Information and Communication Engineering, Beijing Information Science and Technology University, Beijing, ChinaSchool of Information and Communication Engineering, Beijing Information Science and Technology University, Beijing, ChinaSchool of Computer Science and Engineering, Northeastern University, Shenyang, ChinaIn intelligent traffic monitoring, speed measuring millimeter waves (MMW) radar is one of the most commonly used tools for traffic enforcement. In traffic enforcement field, the radar must provide the evidence of each vehicle belongs to which lane. In this paper, we propose a novel kernel line segment adaptive possibilistic c-means clustering algorithm (KLSAPCM) for lane determination of vehicles. Firstly, the raw measurement data is preprocessed using the extracting method of data adjacent lane centerlines. Secondly, according to the improved minimum radius data search method, outliers are removed and the proposed KLSAPCM algorithm is initialized. Finally, the accuracy of lane determination has been improved by the proposed KLSAPCM clustering algorithm based on adaptive kernel line segment that conforms to the shape features of the measurement data in the actual scene. The experiment results for multiple scenes were: the KLSAPCM algorithm is compared with the DBSCAN, the $k$ -means, the FCM, the PCM, the AMPCM, and the APCM algorithms on real measurement datasets, and the results highlight the classification rate of the proposed algorithm. Meanwhile, the proposed algorithm gets a good real-time performance and strong robustness for some sparse moving vehicle scene applications.https://ieeexplore.ieee.org/document/9049388/MMW radarradar measurementslane determinationclustering algorithms |
| spellingShingle | Lin Cao Tao Wang Dongfeng Wang Kangning Du Yunxiao Liu Chong Fu Lane Determination of Vehicles Based on a Novel Clustering Algorithm for Intelligent Traffic Monitoring IEEE Access MMW radar radar measurements lane determination clustering algorithms |
| title | Lane Determination of Vehicles Based on a Novel Clustering Algorithm for Intelligent Traffic Monitoring |
| title_full | Lane Determination of Vehicles Based on a Novel Clustering Algorithm for Intelligent Traffic Monitoring |
| title_fullStr | Lane Determination of Vehicles Based on a Novel Clustering Algorithm for Intelligent Traffic Monitoring |
| title_full_unstemmed | Lane Determination of Vehicles Based on a Novel Clustering Algorithm for Intelligent Traffic Monitoring |
| title_short | Lane Determination of Vehicles Based on a Novel Clustering Algorithm for Intelligent Traffic Monitoring |
| title_sort | lane determination of vehicles based on a novel clustering algorithm for intelligent traffic monitoring |
| topic | MMW radar radar measurements lane determination clustering algorithms |
| url | https://ieeexplore.ieee.org/document/9049388/ |
| work_keys_str_mv | AT lincao lanedeterminationofvehiclesbasedonanovelclusteringalgorithmforintelligenttrafficmonitoring AT taowang lanedeterminationofvehiclesbasedonanovelclusteringalgorithmforintelligenttrafficmonitoring AT dongfengwang lanedeterminationofvehiclesbasedonanovelclusteringalgorithmforintelligenttrafficmonitoring AT kangningdu lanedeterminationofvehiclesbasedonanovelclusteringalgorithmforintelligenttrafficmonitoring AT yunxiaoliu lanedeterminationofvehiclesbasedonanovelclusteringalgorithmforintelligenttrafficmonitoring AT chongfu lanedeterminationofvehiclesbasedonanovelclusteringalgorithmforintelligenttrafficmonitoring |