Reducing Overall Path Latency in Automotive Logical Execution Time Scheduling via Reinforcement Learning-Reference-Cited by-同舟云学术

Reducing Overall Path Latency in Automotive Logical Execution Time Scheduling via Reinforcement Learning

Published:2023-06-07 Issue: Volume: Page:
ISSN:
Container-title:The 31st International Conference on Real-Time Networks and Systems
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Author:

Xu Risheng¹^ORCID,Kühl Marvin²^ORCID,Von Hasseln Hermann³^ORCID,Nowotka Dirk²^ORCID

Affiliation:

1. Mercedes-Benz AG, Germany and Dependable Systems Group, Kiel University, Germany

2. Dependable Systems Group, Kiel University, Germany

3. Mercedes-Benz AG, Germany

Publisher

ACM

Reference44 articles.

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2. Evolutionary Computation 1

3. End-to-end timing analysis of cause-effect chains in automotive embedded systems

4. Achieving Predictable Multicore Execution of Automotive Applications Using the LET Paradigm

5. Zitong Bo , Ying Qiao , Chang Leng , Hongan Wang , Chaoping Guo , and Shaohui Zhang . 2021 . Developing real-time scheduling policy by deep reinforcement learning . In 2021 IEEE 27th Real-Time and Embedded Technology and Applications Symposium (RTAS). IEEE, 131–142 . Zitong Bo, Ying Qiao, Chang Leng, Hongan Wang, Chaoping Guo, and Shaohui Zhang. 2021. Developing real-time scheduling policy by deep reinforcement learning. In 2021 IEEE 27th Real-Time and Embedded Technology and Applications Symposium (RTAS). IEEE, 131–142.