Latency Measurement for Autonomous Driving Software Using Data Flow Extraction

Abstract

Real-time capability and robust software behavior have emerged as crucial issues since autonomous vehicles must react reliably to various traffic conditions when operating on our streets. The objective of our work is to understand and examine the processing latency of a software stack for autonomous vehicles. In this paper, we propose a framework based on ros2_tracing that automatically extracts implicit and explicit data flow from large-scale ROS 2-based autonomous driving software. It can measure the end-to-end latency and the individual components it is composed of. Using a static analysis, the implicit dependencies can be extracted. The method was used to analyze a software stack for autonomous vehicles. Compared to previous work that requires a manual definition of node-internal data dependencies and often does not follow the data flows completely, this paper provides a more feasible and comprehensive toolkit for analyzing real-world ROS 2 systems.