Lane Position Detection Based on Long Short-Term Memory (LSTM)
Accurate detection of lane lines is of great significance for improving vehicle driving safety. In our previous research, by improving the horizontal and vertical density of the detection grid in the YOLO v3 (You Only Look Once, the 3th version) model, the obtained lane line (LL) algorithm, YOLO v3...
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MDPI AG
2020-05-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/20/11/3115 |
| _version_ | 1797566533369593856 |
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| author | Wei Yang Xiang Zhang Qian Lei Dengye Shen Ping Xiao Yu Huang |
| author_facet | Wei Yang Xiang Zhang Qian Lei Dengye Shen Ping Xiao Yu Huang |
| author_sort | Wei Yang |
| collection | DOAJ |
| description | Accurate detection of lane lines is of great significance for improving vehicle driving safety. In our previous research, by improving the horizontal and vertical density of the detection grid in the YOLO v3 (You Only Look Once, the 3th version) model, the obtained lane line (LL) algorithm, YOLO v3 (S × 2S), has high accuracy. However, like the traditional LL detection algorithms, they do not use spatial information and have low detection accuracy under occlusion, deformation, worn, poor lighting, and other non-ideal environmental conditions. After studying the spatial information between LLs and learning the distribution law of LLs, an LL prediction model based on long short-term memory (LSTM) and recursive neural network (RcNN) was established; the method can predict the future LL position by using historical LL position information. Moreover, by combining the LL information predicted with YOLO v3 (S × 2S) detection results using Dempster Shafer (D-S) evidence theory, the LL detection accuracy can be improved effectively, and the uncertainty of this system be reduced correspondingly. The results show that the accuracy of LL detection can be significantly improved in rainy, snowy weather, and obstacle scenes. |
| first_indexed | 2024-03-10T19:28:12Z |
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| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T19:28:12Z |
| publishDate | 2020-05-01 |
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| spelling | doaj.art-dfa940fa73e5469c934e5db391d6da952023-11-20T02:22:33ZengMDPI AGSensors1424-82202020-05-012011311510.3390/s20113115Lane Position Detection Based on Long Short-Term Memory (LSTM)Wei Yang0Xiang Zhang1Qian Lei2Dengye Shen3Ping Xiao4Yu Huang5College of Automotive Engineering, Chongqing University, Chongqing 400044, ChinaSchool of Information, Zhejiang University of Finance Economics, Hangzhou 310018, ChinaCollege of Automotive Engineering, Chongqing University, Chongqing 400044, ChinaCollege of Automotive Engineering, Chongqing University, Chongqing 400044, ChinaCollege of Automotive Engineering, Chongqing University, Chongqing 400044, ChinaCollege of Automotive Engineering, Chongqing University, Chongqing 400044, ChinaAccurate detection of lane lines is of great significance for improving vehicle driving safety. In our previous research, by improving the horizontal and vertical density of the detection grid in the YOLO v3 (You Only Look Once, the 3th version) model, the obtained lane line (LL) algorithm, YOLO v3 (S × 2S), has high accuracy. However, like the traditional LL detection algorithms, they do not use spatial information and have low detection accuracy under occlusion, deformation, worn, poor lighting, and other non-ideal environmental conditions. After studying the spatial information between LLs and learning the distribution law of LLs, an LL prediction model based on long short-term memory (LSTM) and recursive neural network (RcNN) was established; the method can predict the future LL position by using historical LL position information. Moreover, by combining the LL information predicted with YOLO v3 (S × 2S) detection results using Dempster Shafer (D-S) evidence theory, the LL detection accuracy can be improved effectively, and the uncertainty of this system be reduced correspondingly. The results show that the accuracy of LL detection can be significantly improved in rainy, snowy weather, and obstacle scenes.https://www.mdpi.com/1424-8220/20/11/3115lane line detectionlane line predictionlong short-term memoryrecurrent neural network |
| spellingShingle | Wei Yang Xiang Zhang Qian Lei Dengye Shen Ping Xiao Yu Huang Lane Position Detection Based on Long Short-Term Memory (LSTM) Sensors lane line detection lane line prediction long short-term memory recurrent neural network |
| title | Lane Position Detection Based on Long Short-Term Memory (LSTM) |
| title_full | Lane Position Detection Based on Long Short-Term Memory (LSTM) |
| title_fullStr | Lane Position Detection Based on Long Short-Term Memory (LSTM) |
| title_full_unstemmed | Lane Position Detection Based on Long Short-Term Memory (LSTM) |
| title_short | Lane Position Detection Based on Long Short-Term Memory (LSTM) |
| title_sort | lane position detection based on long short term memory lstm |
| topic | lane line detection lane line prediction long short-term memory recurrent neural network |
| url | https://www.mdpi.com/1424-8220/20/11/3115 |
| work_keys_str_mv | AT weiyang lanepositiondetectionbasedonlongshorttermmemorylstm AT xiangzhang lanepositiondetectionbasedonlongshorttermmemorylstm AT qianlei lanepositiondetectionbasedonlongshorttermmemorylstm AT dengyeshen lanepositiondetectionbasedonlongshorttermmemorylstm AT pingxiao lanepositiondetectionbasedonlongshorttermmemorylstm AT yuhuang lanepositiondetectionbasedonlongshorttermmemorylstm |