Michele Segata, R. Cigno, R. Bhadani, Matt Bunting, J. Sprinkle
{"title":"A LiDAR Error Model for Cooperative Driving Simulations","authors":"Michele Segata, R. Cigno, R. Bhadani, Matt Bunting, J. Sprinkle","doi":"10.1109/VNC.2018.8628408","DOIUrl":null,"url":null,"abstract":"Cooperative driving and vehicular network simulations have done huge steps toward high realism. They have become essential tools for performance evaluation of any kind of vehicular networking application. Yet, cooperative vehicular applications will not be built on top of wireless networking alone, but rather fusing together different data sources including sensors like radars, LiDARs, or cameras. So far, these sensors have been assumed to be ideal, i.e., without any measurement error. This paper analyzes a set of estimated distance traces obtained with a LiDAR sensor and develops a stochastic error model that can be used in cooperative driving simulations. After implementing the model within the Plexe simulation framework, we show the impact of the model on a set of cooperative driving control algorithms.","PeriodicalId":335017,"journal":{"name":"2018 IEEE Vehicular Networking Conference (VNC)","volume":"66 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"8","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 IEEE Vehicular Networking Conference (VNC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/VNC.2018.8628408","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 8
Abstract
Cooperative driving and vehicular network simulations have done huge steps toward high realism. They have become essential tools for performance evaluation of any kind of vehicular networking application. Yet, cooperative vehicular applications will not be built on top of wireless networking alone, but rather fusing together different data sources including sensors like radars, LiDARs, or cameras. So far, these sensors have been assumed to be ideal, i.e., without any measurement error. This paper analyzes a set of estimated distance traces obtained with a LiDAR sensor and develops a stochastic error model that can be used in cooperative driving simulations. After implementing the model within the Plexe simulation framework, we show the impact of the model on a set of cooperative driving control algorithms.