Modeling and Scheduling of Fusion Patterns in Autonomous Driving Systems (Extended Version)

TL;DR

This paper introduces a systematic framework for modeling and scheduling diverse data fusion patterns in autonomous driving systems, improving real-time performance and handling complex fusion behaviors more accurately than prior approaches.

Contribution

It presents a novel ILP-based framework that models three fusion pattern types and optimizes multiple performance metrics for real-time autonomous driving applications.

Findings

Handles diverse fusion patterns beyond existing methods

Achieves significant performance improvements in real-world case studies

Demonstrates effectiveness on Raspberry Pi and synthetic DAGs

Abstract

In Autonomous Driving Systems (ADS), Directed Acyclic Graphs (DAGs) are widely used to model complex data dependencies and inter-task communication. However, existing DAG scheduling approaches oversimplify data fusion tasks by assuming fixed triggering mechanisms, failing to capture the diverse fusion patterns found in real-world ADS software stacks. In this paper, we propose a systematic framework for analyzing various fusion patterns and their performance implications in ADS. Our framework models three distinct fusion task types: timer-triggered, wait-for-all, and immediate fusion, which comprehensively represent real-world fusion behaviors. Our Integer Linear Programming (ILP)-based approach enables an optimization of multiple real-time performance metrics, including reaction time, time disparity, age of information, and response time, while generating deterministic offline schedules directly applicable to real platforms. Evaluation using real-world ADS case studies, Raspberry Pi implementation, and randomly generated DAGs demonstrates that our framework handles diverse fusion patterns beyond the scope of existing work, and achieves substantial performance improvements in comparable scenarios.