Construction of DAG Models for Autonomous Systems

Directed Acyclic Graphs (DAGs) are widely deployed as task models in autonomous systems, including vehicles and drones, to capture functional dependency. DAG scheduling has been extensively investigated by various communities to shorten makespan, under the common assumption that the model itself is...

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Bibliographic Details
Published in:2025 62nd ACM/IEEE Design Automation Conference (DAC) pp. 1 - 6
Main Authors: Huang, Jing, Jiang, Kuan, Wang, Weijie, Liang, Wei, Chang, Wanli
Format: Conference Proceeding
Language:English
Published: IEEE 22.06.2025
Subjects:
Autonomous systems
Heuristic algorithms
Real-time systems
Robustness
Scalability
Semantics
Time complexity
Time factors
Topology
Vehicle dynamics
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Summary:Directed Acyclic Graphs (DAGs) are widely deployed as task models in autonomous systems, including vehicles and drones, to capture functional dependency. DAG scheduling has been extensively investigated by various communities to shorten makespan, under the common assumption that the model itself is given a priori. This work studies a rarely touched problem - construction of DAG models - and considers time-triggered blended task chains predominant in autonomous systems. We report representation semantics and a topology optimization method. Experiments show that the average end-to-end response time reduction is 4.8 times of the conventional Floyd algorithm. Our time complexity is \mathcal{O}\left(n^{2}\right), making it suitable for handling dynamic tasks as well.
DOI:10.1109/DAC63849.2025.11133396