Ivan Benke, Bože Eugen Marković, Ivan Pavlović, M. Milosevic, R. Grbić
{"title":"Software solution stack for data transfer on a frame grabber platform","authors":"Ivan Benke, Bože Eugen Marković, Ivan Pavlović, M. Milosevic, R. Grbić","doi":"10.1109/ZINC.2019.8769412","DOIUrl":null,"url":null,"abstract":"With the progress of automotive industry arose a need for development of fully autonomous vehicles. Such vehicle is equipped with sensors and cameras designed for monitoring its surroundings. Data received from these sensors is then processed in the vehicle’s embedded system which contains algorithms that help driver in a driving process and are a step towards a fully autonomous vehicle. Due to the problem complexity, these algorithms are usually based on machine learning methods and thus inherently require large amounts of data for successful training. Data used for training is mostly video content of situations from actual traffic. To record that video content specialized hardware and its accompanying software is needed. This paper focuses on developing software for existing AMV Grabber hardware board. Entire software stack was developed, from low-level application which controls the hardware directly, through device driver which enables communication between the board and PC, to PC application which enables users to control the hardware indirectly, i.e., to send commands to the board to start or stop recording. PC application is also used to receive data from the board and store it on non-volatile memory of the PC. Finally, measurements were done to display overall system performance.","PeriodicalId":190326,"journal":{"name":"2019 Zooming Innovation in Consumer Technologies Conference (ZINC)","volume":"03 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 Zooming Innovation in Consumer Technologies Conference (ZINC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ZINC.2019.8769412","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3
Abstract
With the progress of automotive industry arose a need for development of fully autonomous vehicles. Such vehicle is equipped with sensors and cameras designed for monitoring its surroundings. Data received from these sensors is then processed in the vehicle’s embedded system which contains algorithms that help driver in a driving process and are a step towards a fully autonomous vehicle. Due to the problem complexity, these algorithms are usually based on machine learning methods and thus inherently require large amounts of data for successful training. Data used for training is mostly video content of situations from actual traffic. To record that video content specialized hardware and its accompanying software is needed. This paper focuses on developing software for existing AMV Grabber hardware board. Entire software stack was developed, from low-level application which controls the hardware directly, through device driver which enables communication between the board and PC, to PC application which enables users to control the hardware indirectly, i.e., to send commands to the board to start or stop recording. PC application is also used to receive data from the board and store it on non-volatile memory of the PC. Finally, measurements were done to display overall system performance.