Ansaf Abdulnagimov, Ekaterina Lopukhova, Gleb Alektorov, Nail Klyavlin
{"title":"基于智能激光雷达的V2V通信目标分类技术实现","authors":"Ansaf Abdulnagimov, Ekaterina Lopukhova, Gleb Alektorov, Nail Klyavlin","doi":"10.1145/3584202.3584304","DOIUrl":null,"url":null,"abstract":"The deep learning techniques have been shown to make a traffic objects classification system for V2V communications to ensure traffic safety and traffic flow prediction. The robust classifier on the base of MLP and PointNet are explored to recognize the traffic objects from lidar point clouds. The features of a lidar sensor, the lidar point cloud coordinate system and its complex properties for creation a smart traffic object detection and recognition model are described. The best configuration of PointNet architecture with hyperparameters are shown, which is more efficient and robust with respect to input perturbation and corruption of lidar point clouds.","PeriodicalId":438341,"journal":{"name":"Proceedings of the 6th International Conference on Future Networks & Distributed Systems","volume":"3 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Intellectual lidar-based object classification for V2V communication technology implementation\",\"authors\":\"Ansaf Abdulnagimov, Ekaterina Lopukhova, Gleb Alektorov, Nail Klyavlin\",\"doi\":\"10.1145/3584202.3584304\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The deep learning techniques have been shown to make a traffic objects classification system for V2V communications to ensure traffic safety and traffic flow prediction. The robust classifier on the base of MLP and PointNet are explored to recognize the traffic objects from lidar point clouds. The features of a lidar sensor, the lidar point cloud coordinate system and its complex properties for creation a smart traffic object detection and recognition model are described. The best configuration of PointNet architecture with hyperparameters are shown, which is more efficient and robust with respect to input perturbation and corruption of lidar point clouds.\",\"PeriodicalId\":438341,\"journal\":{\"name\":\"Proceedings of the 6th International Conference on Future Networks & Distributed Systems\",\"volume\":\"3 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-12-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 6th International Conference on Future Networks & Distributed Systems\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3584202.3584304\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 6th International Conference on Future Networks & Distributed Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3584202.3584304","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Intellectual lidar-based object classification for V2V communication technology implementation
The deep learning techniques have been shown to make a traffic objects classification system for V2V communications to ensure traffic safety and traffic flow prediction. The robust classifier on the base of MLP and PointNet are explored to recognize the traffic objects from lidar point clouds. The features of a lidar sensor, the lidar point cloud coordinate system and its complex properties for creation a smart traffic object detection and recognition model are described. The best configuration of PointNet architecture with hyperparameters are shown, which is more efficient and robust with respect to input perturbation and corruption of lidar point clouds.