{"title":"基于车载3d激光雷达的自动驾驶车辆交叉口逼近物体探测","authors":"Yuki Komatsu, Shin Kato, M. Itami","doi":"10.1109/ITNAC55475.2022.9998404","DOIUrl":null,"url":null,"abstract":"In our research, we developed a method for detecting approaching objects at intersection by focusing on geometric features of point cloud obtained from 3D-LiDAR, without using pre-generated maps to understand the environment. This method can be applied to intersection with diagonal crossings, and can detect approaching vehicles and pedestrians at distances of up to 49 m and 38 m, respectively. The results also showed that the detection was robust and continuous. Furthermore, this process can be used in 50 ms per a frame, so that can be used in real time. This will lead to collision prediction and judgment of starting for automated vehicles.","PeriodicalId":205731,"journal":{"name":"2022 32nd International Telecommunication Networks and Applications Conference (ITNAC)","volume":"81 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Detecting Approaching Objects at Intersection Using on-Vehicle 3D-LiDAR for Automated Driving Vehicle\",\"authors\":\"Yuki Komatsu, Shin Kato, M. Itami\",\"doi\":\"10.1109/ITNAC55475.2022.9998404\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In our research, we developed a method for detecting approaching objects at intersection by focusing on geometric features of point cloud obtained from 3D-LiDAR, without using pre-generated maps to understand the environment. This method can be applied to intersection with diagonal crossings, and can detect approaching vehicles and pedestrians at distances of up to 49 m and 38 m, respectively. The results also showed that the detection was robust and continuous. Furthermore, this process can be used in 50 ms per a frame, so that can be used in real time. This will lead to collision prediction and judgment of starting for automated vehicles.\",\"PeriodicalId\":205731,\"journal\":{\"name\":\"2022 32nd International Telecommunication Networks and Applications Conference (ITNAC)\",\"volume\":\"81 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-11-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 32nd International Telecommunication Networks and Applications Conference (ITNAC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ITNAC55475.2022.9998404\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 32nd International Telecommunication Networks and Applications Conference (ITNAC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ITNAC55475.2022.9998404","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Detecting Approaching Objects at Intersection Using on-Vehicle 3D-LiDAR for Automated Driving Vehicle
In our research, we developed a method for detecting approaching objects at intersection by focusing on geometric features of point cloud obtained from 3D-LiDAR, without using pre-generated maps to understand the environment. This method can be applied to intersection with diagonal crossings, and can detect approaching vehicles and pedestrians at distances of up to 49 m and 38 m, respectively. The results also showed that the detection was robust and continuous. Furthermore, this process can be used in 50 ms per a frame, so that can be used in real time. This will lead to collision prediction and judgment of starting for automated vehicles.