2024-03-28T16:56:29Zhttps://ipsj.ixsq.nii.ac.jp/ej/?action=repository_oaipmhoai:ipsj.ixsq.nii.ac.jp:001764772020-10-27T05:03:12Z00934:01022:08505:08953
In-vehicle Distributed Time-critical Data Stream Management System for Advanced Driver Assistance In-vehicle Distributed Time-critical Data Stream Management System for Advanced Driver Assistance eng[研究論文] data stream management system (DSMS), distributed stream processing, real-time scheduling, earliest deadline first (EDF), sensor fusion, automotive system, advanced driver assistance system (ADAS)http://id.nii.ac.jp/1001/00176443/Articlehttps://ipsj.ixsq.nii.ac.jp/ej/?action=repository_action_common_download&item_id=176477&item_no=1&attribute_id=1&file_no=1Copyright (c) 2016 by the Information Processing Society of JapanGraduate School of Information Science, Nagoya UniversityInstitute of Innovation for Future Society, Nagoya UniversityCenter for Embedded Computing Systems, Nagoya University / Mobility Research Center, Doshisha UniversityCenter for Embedded Computing Systems, Nagoya University/Graduate School of Applied Informatics, University of HyogoGraduate School of Information Science, Nagoya UniversityGraduate School of Information Science, Nagoya UniversityCenter for Embedded Computing Systems, Nagoya University/Institute of Innovation for Future Society, Nagoya UniversityAkihiro, YamaguchiYousuke, WatanabeKenya, SatoYukikazu, NakamotoYoshiharu, IshikawaShinya, HondaHiroaki, TakadaData stream management systems (DSMSs) are suitable for managing and processing continuous data at high input rates with low latency. For advanced driver assistance including autonomous driving, embedded systems use a variety of onboard sensor data with communications from outside the vehicle. Thus, the software developed for such systems must be able to handle large volumes of data and complex processing. We develop a platform that integrates and manages data in an automotive embedded system using a DSMS. However, because automotive data processing, which is distributed in in-vehicle networks of the embedded system, is time-critical and must be reliable to reduce sensor noise, it is difficult to identify conventional DSMSs that meet these requirements. To address these new challenges, we develop an automotive embedded DSMS (AEDSMS). This AEDSMS precompiles high-level queries into executable query plans when designing automotive systems that demand time-criticality. Data stream processing is distributed in in-vehicle networks appropriately, where real-time scheduling and senor data fusion are also applied to meet deadlines and enhance the reliability of sensor data. The main contributions of this paper are as follows: (1) we establish a clear understanding of the challenges faced when introducing DSMSs into the automotive field; (2) we propose an AEDSMS to tackle these challenges; and (3) we evaluate the AEDSMS during run-time for advanced driver assistance.------------------------------This is a preprint of an article intended for publication Journal ofInformation Processing(JIP). This preprint should not be cited. Thisarticle should be cited as: Journal of Information Processing Vol.25(2017) (online)------------------------------Data stream management systems (DSMSs) are suitable for managing and processing continuous data at high input rates with low latency. For advanced driver assistance including autonomous driving, embedded systems use a variety of onboard sensor data with communications from outside the vehicle. Thus, the software developed for such systems must be able to handle large volumes of data and complex processing. We develop a platform that integrates and manages data in an automotive embedded system using a DSMS. However, because automotive data processing, which is distributed in in-vehicle networks of the embedded system, is time-critical and must be reliable to reduce sensor noise, it is difficult to identify conventional DSMSs that meet these requirements. To address these new challenges, we develop an automotive embedded DSMS (AEDSMS). This AEDSMS precompiles high-level queries into executable query plans when designing automotive systems that demand time-criticality. Data stream processing is distributed in in-vehicle networks appropriately, where real-time scheduling and senor data fusion are also applied to meet deadlines and enhance the reliability of sensor data. The main contributions of this paper are as follows: (1) we establish a clear understanding of the challenges faced when introducing DSMSs into the automotive field; (2) we propose an AEDSMS to tackle these challenges; and (3) we evaluate the AEDSMS during run-time for advanced driver assistance.------------------------------This is a preprint of an article intended for publication Journal ofInformation Processing(JIP). This preprint should not be cited. Thisarticle should be cited as: Journal of Information Processing Vol.25(2017) (online)------------------------------AA11464847情報処理学会論文誌データベース(TOD)942016-12-221882-77992016-12-16