Stefan Mihai, P. Shah, G. Mapp, Huan Nguyen, R. Trestian
{"title":"Towards Autonomous Driving: A Machine Learning-based Pedestrian Detection System using 16-Layer LiDAR","authors":"Stefan Mihai, P. Shah, G. Mapp, Huan Nguyen, R. Trestian","doi":"10.1109/COMM48946.2020.9142042","DOIUrl":null,"url":null,"abstract":"The advent of driverless and automated vehicle technologies opens up a new era of safe and comfortable transportation. However, one of the most important features that an autonomous vehicle requires, is a reliable pedestrian detection mechanism. Many solutions have been proposed in the literature to achieve this technology, ranging from image processing algorithms applied on a camera feed, to filtering LiDAR scans for points that are reflected off pedestrians. To this extent, this paper proposes a machine learning-based pedestrian detection mechanism using a 16-layer Velodyne Puck LITE LiDAR. The proposed mechanism compensates for the low resolution of the LiDAR through the use of linear interpolation between layers, effectively introducing 15 pseudo-layers to help obtain timely detection at practical distances. The pedestrian candidates are then classified using a Support Vector Machine (SVM), and the algorithm is verified by accuracy testing using real LiDAR frames acquired under different road scenarios.","PeriodicalId":405841,"journal":{"name":"2020 13th International Conference on Communications (COMM)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 13th International Conference on Communications (COMM)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/COMM48946.2020.9142042","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 5
Abstract
The advent of driverless and automated vehicle technologies opens up a new era of safe and comfortable transportation. However, one of the most important features that an autonomous vehicle requires, is a reliable pedestrian detection mechanism. Many solutions have been proposed in the literature to achieve this technology, ranging from image processing algorithms applied on a camera feed, to filtering LiDAR scans for points that are reflected off pedestrians. To this extent, this paper proposes a machine learning-based pedestrian detection mechanism using a 16-layer Velodyne Puck LITE LiDAR. The proposed mechanism compensates for the low resolution of the LiDAR through the use of linear interpolation between layers, effectively introducing 15 pseudo-layers to help obtain timely detection at practical distances. The pedestrian candidates are then classified using a Support Vector Machine (SVM), and the algorithm is verified by accuracy testing using real LiDAR frames acquired under different road scenarios.