Chain-Aware Runnable Scheduling in AUTOSAR: A Case Study in Automotive Systems

The increasing complexity of functional operations, combined with the automotive industry's extensive collaboration with multiple vendors, presents significant challenges in man-aging software components within automotive systems. While AUTOSAR (AUTomotive Open System ARchitecture) aims to address these issues by enhancing software modularity and reusability, comprehensive studies on end-to-end latency, real-time capability, and timing analysis for safety-critical applications remain limited. This study proposes a chain-aware runnable scheduling framework to enhance real-time performance and reduce end-to-end latency in AUTOSAR-based automotive systems. Experimental results, incorporating priority assignment and runnable allocation, and a case study on real vehicle control units (VCU) of commercial vehicles, conducted using practical scenarios, indicate that the proposed framework reduces the end-to-end latency of the most safety-critical chain by 82% compared to the existing AUTOSAR schedulers.

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Publication Details

Published
2024-10-23
DOI
https://doi.org/10.1109/sies62473.2024.10767943
Primary Topic
Real-Time Systems Scheduling
Type
article
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article

Chain-Aware Runnable Scheduling in AUTOSAR: A Case Study in Automotive Systems

Hong-Gi Park, Hyunjong Choi
Real-Time Systems Scheduling
article

Chain-Aware Runnable Scheduling in AUTOSAR: A Case Study in Automotive Systems

Hong-Gi Park, Hyunjong Choi
article en

Abstract

The increasing complexity of functional operations, combined with the automotive industry's extensive collaboration with multiple vendors, presents significant challenges in man-aging software components within automotive systems. While AUTOSAR (AUTomotive Open System ARchitecture) aims to address these issues by enhancing software modularity and reusability, comprehensive studies on end-to-end latency, real-time capability, and timing analysis for safety-critical applications remain limited. This study proposes a chain-aware runnable scheduling framework to enhance real-time performance and reduce end-to-end latency in AUTOSAR-based automotive systems. Experimental results, incorporating priority assignment and runnable allocation, and a case study on real vehicle control units (VCU) of commercial vehicles, conducted using practical scenarios, indicate that the proposed framework reduces the end-to-end latency of the most safety-critical chain by 82% compared to the existing AUTOSAR schedulers.

San Diego State University (US), Hyundai Motors (South Korea) (KR)
Openalex Percentile: Top 77%
Real-Time Systems Scheduling
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