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Brief Industry Paper: Towards Efficient Task Scheduling for AUTOSAR using Parallel Pruning
As a standardized software framework and open E/E system architecture, the AUTomotive Open System ARchitecture (AUTOSAR) has been widely applied to autonomous driving systems to enable real-time control. However, due to the increasing design complexity and the lack of efficient algorithms and design...
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| Format: | Conference Proceeding |
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| creator | Yang, Yanxing Zhang, Nan Yan, Dengke Wei, Xian Zhou, Junlong Liu, Hong Chen, Mingsong |
| description | As a standardized software framework and open E/E system architecture, the AUTomotive Open System ARchitecture (AUTOSAR) has been widely applied to autonomous driving systems to enable real-time control. However, due to the increasing design complexity and the lack of efficient algorithms and design automation tools, it is difficult to quickly figure out an optimal task scheduling scheme for an AUTOSAR-based system. To address this problem, we introduce a novel task scheduling method that can parallelly search for an optimal solution with the help of our proposed pruning strategy. Experimental results on a real-world AUTOSAR-based autonomous driving system demonstrate that our approach can achieve much better task scheduling solutions than the ones obtained manually and significantly reduce the overall task scheduling time. |
| doi_str_mv | 10.1109/RTSS59052.2023.00057 |
| format | conference_proceeding |
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Introduction</subject><subject>Job shop scheduling</subject><subject>Real-time systems</subject><subject>Search problems</subject><subject>Systems architecture</subject><subject>Task analysis</subject><issn>2576-3172</issn><isbn>9798350328578</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2023</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><recordid>eNotjkFPwjAYQKuJiYj8Aw79A5tfW7q23iZBJSFhYePihXx2rVbnIC2L4d-L0dNL3uHlETJlkDMG5m7T1LU0IHnOgYscAKS6IBOjjBYSBNdS6Usy4lIVmWCKX5OblD4AOEjBR-TlIQbn6bJvh3SMJ1rhwcV72uy_MbaJLrwPNrj-SBtMn7S2764dutC_Ub-PtNw267rc0CH9mgojdp3raBWH_ixuyZXHLrnJP8dk-7ho5s_Zav20nJerLDBmjhkvgKtCS26sfnWtLc7XBiybIZMclMZCojAKtbHQanQSPVqwvlAtase1GJPpXzc453aHGL4wnnYMZiANM-IHdMVSyg</recordid><startdate>20231205</startdate><enddate>20231205</enddate><creator>Yang, Yanxing</creator><creator>Zhang, Nan</creator><creator>Yan, Dengke</creator><creator>Wei, Xian</creator><creator>Zhou, Junlong</creator><creator>Liu, Hong</creator><creator>Chen, Mingsong</creator><general>IEEE</general><scope>6IE</scope><scope>6IH</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIO</scope></search><sort><creationdate>20231205</creationdate><title>Brief Industry Paper: Towards Efficient Task Scheduling for AUTOSAR using Parallel Pruning</title><author>Yang, Yanxing ; Zhang, Nan ; Yan, Dengke ; Wei, Xian ; Zhou, Junlong ; Liu, Hong ; Chen, Mingsong</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i119t-2602768529c8bedc650390c14a152078a65a397a89c0d8ae5afac0cf67da8e283</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Autonomous vehicles</topic><topic>Complexity theory</topic><topic>I. Introduction</topic><topic>Job shop scheduling</topic><topic>Real-time systems</topic><topic>Search problems</topic><topic>Systems architecture</topic><topic>Task analysis</topic><toplevel>online_resources</toplevel><creatorcontrib>Yang, Yanxing</creatorcontrib><creatorcontrib>Zhang, Nan</creatorcontrib><creatorcontrib>Yan, Dengke</creatorcontrib><creatorcontrib>Wei, Xian</creatorcontrib><creatorcontrib>Zhou, Junlong</creatorcontrib><creatorcontrib>Liu, Hong</creatorcontrib><creatorcontrib>Chen, Mingsong</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan (POP) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE/IET Electronic Library (IEL)</collection><collection>IEEE Proceedings Order Plans (POP) 1998-present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Yang, Yanxing</au><au>Zhang, Nan</au><au>Yan, Dengke</au><au>Wei, Xian</au><au>Zhou, Junlong</au><au>Liu, Hong</au><au>Chen, Mingsong</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Brief Industry Paper: Towards Efficient Task Scheduling for AUTOSAR using Parallel Pruning</atitle><btitle>2023 IEEE Real-Time Systems Symposium (RTSS)</btitle><stitle>RTSS</stitle><date>2023-12-05</date><risdate>2023</risdate><spage>484</spage><epage>488</epage><pages>484-488</pages><eissn>2576-3172</eissn><eisbn>9798350328578</eisbn><coden>IEEPAD</coden><abstract>As a standardized software framework and open E/E system architecture, the AUTomotive Open System ARchitecture (AUTOSAR) has been widely applied to autonomous driving systems to enable real-time control. However, due to the increasing design complexity and the lack of efficient algorithms and design automation tools, it is difficult to quickly figure out an optimal task scheduling scheme for an AUTOSAR-based system. To address this problem, we introduce a novel task scheduling method that can parallelly search for an optimal solution with the help of our proposed pruning strategy. Experimental results on a real-world AUTOSAR-based autonomous driving system demonstrate that our approach can achieve much better task scheduling solutions than the ones obtained manually and significantly reduce the overall task scheduling time.</abstract><pub>IEEE</pub><doi>10.1109/RTSS59052.2023.00057</doi><tpages>5</tpages></addata></record> |
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| identifier | EISSN: 2576-3172 |
| ispartof | 2023 IEEE Real-Time Systems Symposium (RTSS), 2023, p.484-488 |
| issn | 2576-3172 |
| language | eng |
| recordid | cdi_ieee_primary_10405919 |
| source | IEEE Xplore All Conference Series |
| subjects | Autonomous vehicles Complexity theory I. Introduction Job shop scheduling Real-time systems Search problems Systems architecture Task analysis |
| title | Brief Industry Paper: Towards Efficient Task Scheduling for AUTOSAR using Parallel Pruning |
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