Uncovering Underexplored Runtime Behaviors in ROS2-Based Autonomous Systems

Autonomous systems built on ROS2 are increasingly deployed in safety- and performance-critical domains such as autonomous driving and mobile robotics. While existing research has proposed various timing analyses and scheduling strategies for ROS2, many rely on simplified assumptions that do not hold in real-world applications. In this paper, we present a detailed empirical study of ROS2-based autonomous applications, uncovering underexplored runtime behaviors that significantly impact both real-time and functional performance. These include the importance of partial cause-effect chains, dynamic execution paths and timing variability, non-FIFO data access patterns, and computation threads uncontrolled by ROS2 executors. We extend an existing tracing tool to support Transform Library and ROS2’s Action entity, enabling reconstruction and analysis of realistic cause-effect chains. Our findings are validated through experiments in simulated autonomous robot scenarios and a case study using the Autoware autonomous driving framework. Together, our results highlight the need for rethinking ROS2 modeling, scheduling and analysis to better reflect the realities of autonomous systems.