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

TL;DR

This paper introduces a multi-deadline DAG scheduling model for Autoware autonomous driving systems, improving end-to-end latency guarantees by decomposing timing constraints and extending scheduling algorithms.

Contribution

It proposes a novel scheduling model that simplifies latency analysis in complex ROS 2 systems and extends the GEDF algorithm for better real-time performance.

Findings

Effective end-to-end latency guarantees achieved

Simplified analysis of complex data flows in Autoware

Extended GEDF algorithm improves scheduling performance

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-to-end 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.