{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,17]],"date-time":"2026-03-17T18:56:00Z","timestamp":1773773760660,"version":"3.50.1"},"reference-count":25,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2022,10,27]],"date-time":"2022-10-27T00:00:00Z","timestamp":1666828800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Science and Technology Council, Taiwan","award":["MOST 109-2221-E-027-086-MY2"],"award-info":[{"award-number":["MOST 109-2221-E-027-086-MY2"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/s22218231","type":"journal-article","created":{"date-parts":[[2022,10,27]],"date-time":"2022-10-27T22:36:17Z","timestamp":1666910177000},"page":"8231","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Bimodal Extended Kalman Filter-Based Pedestrian Trajectory Prediction"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7957-6886","authenticated-orcid":false,"given":"Chien-Yu","family":"Lin","sequence":"first","affiliation":[{"name":"Department of Electronic Engineering, National Taipei University of Technology, Taipei 106344, Taiwan"}]},{"given":"Lih-Jen","family":"Kau","sequence":"additional","affiliation":[{"name":"Department of Electronic Engineering, National Taipei University of Technology, Taipei 106344, Taiwan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3992-2312","authenticated-orcid":false,"given":"Ching-Yao","family":"Chan","sequence":"additional","affiliation":[{"name":"California Partners for Advanced Transportation Technology, University of California, Berkeley, CA 94804, USA"}]}],"member":"1968","published-online":{"date-parts":[[2022,10,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1109\/MM.2015.133","article-title":"An Open Approach to Autonomous Vehicles","volume":"35","author":"Kato","year":"2015","journal-title":"IEEE Micro"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Phillips, M., and Likhachev, M. 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