DRAPP: An end-to-end Latency Evaluation Tool for Containerized ROS Applications

The advent of Software-Defined Vehicles (SDVs) has revolutionized the automotive industry by enabling rapid innovation through the integration of software-driven functionalities. The Scalable Open Architecture for Embedded Edge (SOAFEE) framework, an innovative open software architecture, integrates cloud-native methodologies into in-vehicle environments. By leveraging this framework, the containerization of Robot Operating System (ROS) applications has gained widespread adoption for deploying and maintaining autonomous driving applications. However, real-time performance remains a critical challenge, particularly in addressing end-to-end (E2E) latency within ROS systems to ensure responsiveness and timely task execution. Existing tools, such as the Chain-Aware ROS Evaluation Tool (CARET), have contributed significantly to realtime performance evaluation. Nevertheless, these tools exhibit limitations when assessing E2E latency in containerized environments involving multiple containers. To address this gap, we introduce Distributed ROS Application Performance Profiler (DRAPP), a solution referenced CARET with enhancements, designed specifically to evaluate E2E latency in ROS-based applications. Our approach includes experiments using Autoware, deployed across multiple containers within the SOAFEE framework, with a primary focus on analyzing E2E processing latency. Preliminary empirical results demonstrate that DRAPP outperforms existing tools in both usability and effectiveness for latency evaluation, representing a significant step forward in the performance assessment of in-vehicle software for autonomous systems.