为自动驾驶系统设计了更真实的多模式数据融合调度框架
Modeling and Scheduling of Fusion Patterns in Autonomous Driving Systems (Extended Version)
- 区分三类融合任务：定时触发、等待全部、立即融合
- 优化反应时间、信息年龄等多指标，实测性能显著提升
- 适合研究自动驾驶实时调度或系统设计的工程师
在自动驾驶系统（ADS）中，有向无环图（DAG）被广泛用于建模复杂的数据依赖和任务间通信。然而，现有DAG调度方法过度简化数据融合任务，假设固定的触发机制，无法捕捉真实ADS软件栈中的多样化融合模式。本文提出一种系统化框架，用于分析各类融合模式及其性能影响。该框架建模三种典型的融合任务类型：定时触发、等待全部、立即融合，全面涵盖真实场景中的融合行为。基于整数线性规划（ILP）的方法，可优化多个实时性能指标，包括反应时间、时间差异、信息年龄和响应时间，并生成可直接部署于实际平台的确定性离线调度方案。通过真实ADS案例研究、Raspberry Pi实现及随机生成的DAG进行评估，结果表明本框架能处理超出现有工作范围的多样融合模式，在相似场景下实现显著性能提升。
原文摘要 · Abstract (English)
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.
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