Bimodal Extended Kalman Filter-Based Pedestrian Trajectory Prediction
We propose a pedestrian trajectory prediction algorithm based on the bimodal extended Kalman filter. With this filter, we are able to make full use of the dual-state nature of the pedestrian movement, i.e., the pedestrian is either moving or remains stationary. We apply the dual-mode probability mod...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-10-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/22/21/8231 |
| _version_ | 1797466548892336128 |
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| author | Chien-Yu Lin Lih-Jen Kau Ching-Yao Chan |
| author_facet | Chien-Yu Lin Lih-Jen Kau Ching-Yao Chan |
| author_sort | Chien-Yu Lin |
| collection | DOAJ |
| description | We propose a pedestrian trajectory prediction algorithm based on the bimodal extended Kalman filter. With this filter, we are able to make full use of the dual-state nature of the pedestrian movement, i.e., the pedestrian is either moving or remains stationary. We apply the dual-mode probability model to describe the state of the pedestrian. Based on this model, we construct the proposed bimodal extended Kalman filter to estimate pedestrian state distribution. The filter obtains the state distribution for each pedestrian in the scene, respectively, and use that state distribution to predict the future trajectories of all the people in the scene. This prediction method estimates the prior probability of each parameter of the model through the dataset and updates the individual posterior probability of the pedestrian state through the bimodal extended Kalman filter. Our model can predict the trajectory of every individual, by taking the social interaction of pedestrians as well as the surrounding physical obstacles into account, with less than fifty model parameters being used, while with the limited parameter, our model could be nearly accurate as other deep learning models and still be comprehensible for model users. |
| first_indexed | 2024-03-09T18:41:21Z |
| format | Article |
| id | doaj.art-ba77de56bf5c43f4b23835d49c3374de |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T18:41:21Z |
| publishDate | 2022-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-ba77de56bf5c43f4b23835d49c3374de2023-11-24T06:44:45ZengMDPI AGSensors1424-82202022-10-012221823110.3390/s22218231Bimodal Extended Kalman Filter-Based Pedestrian Trajectory PredictionChien-Yu Lin0Lih-Jen Kau1Ching-Yao Chan2Department of Electronic Engineering, National Taipei University of Technology, Taipei 106344, TaiwanDepartment of Electronic Engineering, National Taipei University of Technology, Taipei 106344, TaiwanCalifornia Partners for Advanced Transportation Technology, University of California, Berkeley, CA 94804, USAWe propose a pedestrian trajectory prediction algorithm based on the bimodal extended Kalman filter. With this filter, we are able to make full use of the dual-state nature of the pedestrian movement, i.e., the pedestrian is either moving or remains stationary. We apply the dual-mode probability model to describe the state of the pedestrian. Based on this model, we construct the proposed bimodal extended Kalman filter to estimate pedestrian state distribution. The filter obtains the state distribution for each pedestrian in the scene, respectively, and use that state distribution to predict the future trajectories of all the people in the scene. This prediction method estimates the prior probability of each parameter of the model through the dataset and updates the individual posterior probability of the pedestrian state through the bimodal extended Kalman filter. Our model can predict the trajectory of every individual, by taking the social interaction of pedestrians as well as the surrounding physical obstacles into account, with less than fifty model parameters being used, while with the limited parameter, our model could be nearly accurate as other deep learning models and still be comprehensible for model users.https://www.mdpi.com/1424-8220/22/21/8231pedestrian trajectory predictionbimodal extended Kalman filterpoint-cloud |
| spellingShingle | Chien-Yu Lin Lih-Jen Kau Ching-Yao Chan Bimodal Extended Kalman Filter-Based Pedestrian Trajectory Prediction Sensors pedestrian trajectory prediction bimodal extended Kalman filter point-cloud |
| title | Bimodal Extended Kalman Filter-Based Pedestrian Trajectory Prediction |
| title_full | Bimodal Extended Kalman Filter-Based Pedestrian Trajectory Prediction |
| title_fullStr | Bimodal Extended Kalman Filter-Based Pedestrian Trajectory Prediction |
| title_full_unstemmed | Bimodal Extended Kalman Filter-Based Pedestrian Trajectory Prediction |
| title_short | Bimodal Extended Kalman Filter-Based Pedestrian Trajectory Prediction |
| title_sort | bimodal extended kalman filter based pedestrian trajectory prediction |
| topic | pedestrian trajectory prediction bimodal extended Kalman filter point-cloud |
| url | https://www.mdpi.com/1424-8220/22/21/8231 |
| work_keys_str_mv | AT chienyulin bimodalextendedkalmanfilterbasedpedestriantrajectoryprediction AT lihjenkau bimodalextendedkalmanfilterbasedpedestriantrajectoryprediction AT chingyaochan bimodalextendedkalmanfilterbasedpedestriantrajectoryprediction |