Response Time Analysis With Cause-Effect Chain Considering DAG Structure and High-Load Tasks
Recent developments in autonomous driving systems highlight the significance of response time analysis. In autonomous driving systems, the complexities of response time analysis stem from different periods of tasks, tasks with high computational demands (high-load tasks), and Directed Acyclic Graph...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10857337/ |
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| Summary: | Recent developments in autonomous driving systems highlight the significance of response time analysis. In autonomous driving systems, the complexities of response time analysis stem from different periods of tasks, tasks with high computational demands (high-load tasks), and Directed Acyclic Graph (DAG) structures. Although Cause-Effect Chain can consider tasks that are activated at different periods, there is no method that can handle high-load tasks. This paper proposes an allocation algorithm that assigns multiple processor cores to a single task and response time analysis using the Cause-Effect Chain in systems that include high-load tasks is realized. In addition, this paper extends a response time bound of Cause-Effect Chain, and proposes a response time bound of systems with DAG structures. The proposed algorithm and the proposed response time bound are evaluated by task sets generated from measured autonomous driving data. The evaluation results show that the proposed algorithm consistently indicates higher acceptance rates than existing algorithms. Furthermore, the proposed response time bound for the DAG can estimate response times that are shorter than existing bounds. |
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| ISSN: | 2169-3536 |