Brief Industry Paper: Towards Efficient Task Scheduling for AUTOSAR using Parallel Pruning

Yanxing Yang; Nan Zhang; Dengke Yan; Xian Wei; Junlong Zhou; Hong Liu; Mingsong Chen

doi:10.1109/RTSS59052.2023.00057

Brief Industry Paper: Towards Efficient Task Scheduling for AUTOSAR using Parallel Pruning

Yanxing Yang
, Nan Zhang
, Dengke Yan
, Xian Wei
, Junlong Zhou
, Hong Liu
, Mingsong Chen^*

^*Corresponding author for this work

Software Engineering Institute

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Scopus citations

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.

Original language	English
Title of host publication	44th IEEE Real-Time Systems Symposium, RTSS 2023
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	484-488
Number of pages	5
ISBN (Electronic)	9798350328578
DOIs	https://doi.org/10.1109/RTSS59052.2023.00057
State	Published - 2023
Event	44th IEEE Real-Time Systems Symposium, RTSS 2023 - Taipei, Taiwan, Province of China Duration: 5 Dec 2023 → 8 Dec 2023

Publication series

Name	Proceedings - Real-Time Systems Symposium
ISSN (Print)	1052-8725

Conference

Conference	44th IEEE Real-Time Systems Symposium, RTSS 2023
Country/Territory	Taiwan, Province of China
City	Taipei
Period	5/12/23 → 8/12/23

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10.1109/RTSS59052.2023.00057

Cite this

Yang, Y., Zhang, N., Yan, D., Wei, X., Zhou, J., Liu, H., & Chen, M. (2023). Brief Industry Paper: Towards Efficient Task Scheduling for AUTOSAR using Parallel Pruning. In 44th IEEE Real-Time Systems Symposium, RTSS 2023 (pp. 484-488). (Proceedings - Real-Time Systems Symposium). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/RTSS59052.2023.00057

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title = "Brief Industry Paper: Towards Efficient Task Scheduling for AUTOSAR using Parallel Pruning",

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.",

author = "Yanxing Yang and Nan Zhang and Dengke Yan and Xian Wei and Junlong Zhou and Hong Liu and Mingsong Chen",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 44th IEEE Real-Time Systems Symposium, RTSS 2023 ; Conference date: 05-12-2023 Through 08-12-2023",

year = "2023",

doi = "10.1109/RTSS59052.2023.00057",

language = "英语",

series = "Proceedings - Real-Time Systems Symposium",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "484--488",

booktitle = "44th IEEE Real-Time Systems Symposium, RTSS 2023",

address = "美国",

}

Yang, Y, Zhang, N, Yan, D, Wei, X, Zhou, J, Liu, H & Chen, M 2023, Brief Industry Paper: Towards Efficient Task Scheduling for AUTOSAR using Parallel Pruning. in 44th IEEE Real-Time Systems Symposium, RTSS 2023. Proceedings - Real-Time Systems Symposium, Institute of Electrical and Electronics Engineers Inc., pp. 484-488, 44th IEEE Real-Time Systems Symposium, RTSS 2023, Taipei, Taiwan, Province of China, 5/12/23. https://doi.org/10.1109/RTSS59052.2023.00057

Brief Industry Paper: Towards Efficient Task Scheduling for AUTOSAR using Parallel Pruning. / Yang, Yanxing; Zhang, Nan; Yan, Dengke et al.
44th IEEE Real-Time Systems Symposium, RTSS 2023. Institute of Electrical and Electronics Engineers Inc., 2023. p. 484-488 (Proceedings - Real-Time Systems Symposium).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Yan, Dengke

AU - Wei, Xian

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AU - Liu, Hong

AU - Chen, Mingsong

PY - 2023

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AB - 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.

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BT - 44th IEEE Real-Time Systems Symposium, RTSS 2023

PB - Institute of Electrical and Electronics Engineers Inc.

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ER -

Brief Industry Paper: Towards Efficient Task Scheduling for AUTOSAR using Parallel Pruning

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