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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. 

This paper presents Latency Management Executor (LaME), a theory-guided adaptive scheduling framework that enhances real-time performance in ROS 2 through dynamic resource allocation and hybrid priority-driven scheduling. LaME introduces the concept of threadclasses to dynamically adjust system configurations, ensuring response-time guarantees for real-time chains while maintaining starvation freedom for best-effort chains. By implementing adaptive resource allocation and continuous runtime monitoring, LaME provides robust response times even under fluctuating workloads and resource constraints. We implement our framework for the Autoware reference system and perform our evaluation on an Nvidia Jetson platform. Our results demonstrate that LaME successfully adapts to changing resource availability and workload surges, and effectively balances real-time guarantees with overall system throughput. 

In recent studies aimed at enhancing the analyzability and real-time performance of ROS 2, there has been insufficient emphasis on the importance of different scheduling options, including global, partitioned, and semi-partitioned approaches, particularly when multiple CPU cores are involved. In this work, we enabled the partitioned and semi-partitioned scheduling for ROS 2 multi-threaded executors and discussed the opportunities and the potential issues associated with it.