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 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.