Work-in-Progress: Multi-Deadline DAG Scheduling Model for Autonomous Driving Systems

Abstract

Autoware is an autonomous driving system implemented on Robot Operation System (ROS) 2, where an end-to-end timing guarantee is crucial to ensure safety. However, existing ROS 2 cause-effect chain models for analyzing end-toend latency struggle to accurately represent the complexities of Autoware, particularly regarding sync callbacks, queue consumption patterns, and feedback loops. To address these problems, we propose a new scheduling model that decomposes the end-to-end timing constraints of Autoware into local relative deadlines for each sub-DAG. This multi-deadline DAG scheduling model avoids the need for complex analysis of data flows through queues and loops, while ensuring that all callbacks receive data within correct intervals. Furthermore, we extend the Global Earliest Deadline First (GEDF) algorithm for the proposed model and evaluate its effectiveness using a synthetic workload derived from Autoware.