Cyclist Orientation Estimation Using LiDAR Data
It is crucial for an autonomous vehicle to predict cyclist behavior before decision-making. When a cyclist is on real traffic roads, his or her body orientation indicates the current moving directions, and his or her head orientation indicates his or her intention for checking the road situation bef...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-03-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/23/6/3096 |
| _version_ | 1797609050813235200 |
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| author | Hyoungwon Chang Yanlei Gu Igor Goncharenko Li-Ta Hsu Chinthaka Premachandra |
| author_facet | Hyoungwon Chang Yanlei Gu Igor Goncharenko Li-Ta Hsu Chinthaka Premachandra |
| author_sort | Hyoungwon Chang |
| collection | DOAJ |
| description | It is crucial for an autonomous vehicle to predict cyclist behavior before decision-making. When a cyclist is on real traffic roads, his or her body orientation indicates the current moving directions, and his or her head orientation indicates his or her intention for checking the road situation before making next movement. Therefore, estimating the orientation of cyclist’s body and head is an important factor of cyclist behavior prediction for autonomous driving. This research proposes to estimate cyclist orientation including both body and head orientation using deep neural network with the data from Light Detection and Ranging (LiDAR) sensor. In this research, two different methods are proposed for cyclist orientation estimation. The first method uses 2D images to represent the reflectivity, ambient and range information collected by LiDAR sensor. At the same time, the second method uses 3D point cloud data to represent the information collected from LiDAR sensor. The two proposed methods adopt a model ResNet50, which is a 50-layer convolutional neural network, for orientation classification. Hence, the performances of two methods are compared to achieve the most effective usage of LiDAR sensor data in cyclist orientation estimation. This research developed a cyclist dataset, which includes multiple cyclists with different body and head orientations. The experimental results showed that a model that uses 3D point cloud data has better performance for cyclist orientation estimation compared to the model that uses 2D images. Moreover, in the 3D point cloud data-based method, using reflectivity information has a more accurate estimation result than using ambient information. |
| first_indexed | 2024-03-11T05:56:06Z |
| format | Article |
| id | doaj.art-3cc6150b118c47ee85f78e888f54a22e |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-11T05:56:06Z |
| publishDate | 2023-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-3cc6150b118c47ee85f78e888f54a22e2023-11-17T13:45:55ZengMDPI AGSensors1424-82202023-03-01236309610.3390/s23063096Cyclist Orientation Estimation Using LiDAR DataHyoungwon Chang0Yanlei Gu1Igor Goncharenko2Li-Ta Hsu3Chinthaka Premachandra4College of Information Science and Engineering, Ritsumeikan University, 1-1-1, Noji-higashi, Kusatsu 525-8577, Shiga, JapanCollege of Information Science and Engineering, Ritsumeikan University, 1-1-1, Noji-higashi, Kusatsu 525-8577, Shiga, JapanCollege of Information Science and Engineering, Ritsumeikan University, 1-1-1, Noji-higashi, Kusatsu 525-8577, Shiga, JapanDepartment of Aeronautical and Aviation Engineering, The Hong Kong Polytechnic University, 11 Yuk Choi Rd, Hung Hom, Kowloon, Hong KongDepartment of Electronic Engineering, School of Engineering, Shibaura Institute of Technology, 3-7-5, Toyosu, Koto-ku, Tokyo 135-8548, JapanIt is crucial for an autonomous vehicle to predict cyclist behavior before decision-making. When a cyclist is on real traffic roads, his or her body orientation indicates the current moving directions, and his or her head orientation indicates his or her intention for checking the road situation before making next movement. Therefore, estimating the orientation of cyclist’s body and head is an important factor of cyclist behavior prediction for autonomous driving. This research proposes to estimate cyclist orientation including both body and head orientation using deep neural network with the data from Light Detection and Ranging (LiDAR) sensor. In this research, two different methods are proposed for cyclist orientation estimation. The first method uses 2D images to represent the reflectivity, ambient and range information collected by LiDAR sensor. At the same time, the second method uses 3D point cloud data to represent the information collected from LiDAR sensor. The two proposed methods adopt a model ResNet50, which is a 50-layer convolutional neural network, for orientation classification. Hence, the performances of two methods are compared to achieve the most effective usage of LiDAR sensor data in cyclist orientation estimation. This research developed a cyclist dataset, which includes multiple cyclists with different body and head orientations. The experimental results showed that a model that uses 3D point cloud data has better performance for cyclist orientation estimation compared to the model that uses 2D images. Moreover, in the 3D point cloud data-based method, using reflectivity information has a more accurate estimation result than using ambient information.https://www.mdpi.com/1424-8220/23/6/3096cyclistorientation estimationLiDARdeep neural network |
| spellingShingle | Hyoungwon Chang Yanlei Gu Igor Goncharenko Li-Ta Hsu Chinthaka Premachandra Cyclist Orientation Estimation Using LiDAR Data Sensors cyclist orientation estimation LiDAR deep neural network |
| title | Cyclist Orientation Estimation Using LiDAR Data |
| title_full | Cyclist Orientation Estimation Using LiDAR Data |
| title_fullStr | Cyclist Orientation Estimation Using LiDAR Data |
| title_full_unstemmed | Cyclist Orientation Estimation Using LiDAR Data |
| title_short | Cyclist Orientation Estimation Using LiDAR Data |
| title_sort | cyclist orientation estimation using lidar data |
| topic | cyclist orientation estimation LiDAR deep neural network |
| url | https://www.mdpi.com/1424-8220/23/6/3096 |
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