Execution Flow Aware Profiling for ROS-based Autonomous Vehicle Software

Abstract

The complexity of the Robot Operating System (ROS) based autonomous software grows as autonomous vehicles get more intelligent. It is a big challenge for system designers to rapidly understand runtime behaviors and performance of such sophisticated software because the conventional tools are insufficient for characterizing the high-level interactions of the modules within the software. In this paper, a new graphical representation, execution flow graph, is devised to represent the execution sequences and related performance statistics of the ROS modules. The execution flow aware profiling is applied on the autonomous software, Autoware and Navigation Stack, with encouraging results.