{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,21]],"date-time":"2025-11-21T18:05:59Z","timestamp":1763748359148,"version":"build-2065373602"},"reference-count":25,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2021,5,13]],"date-time":"2021-05-13T00:00:00Z","timestamp":1620864000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Suzuki Foundation","award":["2019"],"award-info":[{"award-number":["2019"]}]},{"name":"JSPS KAKENHI","award":["16K19093"],"award-info":[{"award-number":["16K19093"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Various remote sensing technologies have been applied in intelligent vehicles and robots for surrounding-environment recognition. However, these technologies experience difficulties in detecting pedestrians in blind areas and their motions, such as rush-out behaviors. To address this issue, we present a radar-based technique for the detection of pedestrians in blind areas and the classification of different risks of rush-out behaviors among detected pedestrians. We verify their ability to detect pedestrian motion in blind areas by conducting experiments in two environments with blind areas formed by outdoor cars and indoor walls. Then, the classification of motions with different risks of rush-out behaviors among pedestrians detected in the blind areas is demonstrated. We use the clustering method to accurately classify several types of behaviors with different rush-out risks in both environments.<\/jats:p>","DOI":"10.3390\/s21103388","type":"journal-article","created":{"date-parts":[[2021,5,13]],"date-time":"2021-05-13T03:08:12Z","timestamp":1620875292000},"page":"3388","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Pedestrian Detection in Blind Area and Motion Classification Based on Rush-Out Risk Using Micro-Doppler Radar"],"prefix":"10.3390","volume":"21","author":[{"given":"Sora","family":"Hayashi","sequence":"first","affiliation":[{"name":"Department of Electronic and Computer Engineering, Ritsumeikan University, Shiga 525-8577, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2088-1231","authenticated-orcid":false,"given":"Kenshi","family":"Saho","sequence":"additional","affiliation":[{"name":"Department of Electronic and Computer Engineering, Ritsumeikan University, Shiga 525-8577, Japan"},{"name":"Department of Intelligent Robotics, Toyama Prefectural University, Toyama 939-0308, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Daiki","family":"Isobe","sequence":"additional","affiliation":[{"name":"Department of Electronic and Computer Engineering, Ritsumeikan University, Shiga 525-8577, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Masao","family":"Masugi","sequence":"additional","affiliation":[{"name":"Department of Electronic and Computer Engineering, Ritsumeikan University, Shiga 525-8577, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,5,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"40573","DOI":"10.1109\/ACCESS.2020.2976513","article-title":"Resource-Constrained Machine Learning for ADAS: A Systematic Review","volume":"8","author":"Biempica","year":"2020","journal-title":"IEEE Access"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"432","DOI":"10.1109\/JAS.2017.7510814","article-title":"Coordinated Control Architecture for Motion Management in ADAS Systems","volume":"5","author":"Lin","year":"2018","journal-title":"IEEE\/CAA J. 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