PARD: A <u>D</u> ata Flow <u>A</u> ware <u>P</u> rofiler for <u>R</u> OS Based Autonomous Driving Software

Robot operating system (ROS) is an open-source software platform that is well-suited for building complex robotic systems. Autonomous driving systems are one of the emerging robotic systems that are built on top of ROS. However, complicated communications among ROS processes pose a great challenge for system designers to design efficient computing hardware in autonomous vehicles. This complexity stems from the implicit data dependencies among these processes. When a process is bound by computation or communication, it can lead to increased data generation time for subsequent processes in the dependency chain. Unfortunately, existing tools for ROS are either built for tracking the communication latencies or revealing the communication performance with abstracted performance graphs. For analyzing computational resource utilization, general-purpose profiling tools like perf should be further adopted. Substantial human effort is required to consolidate and analyze all the performance data generated by the above tools so as to pinpoint potential performance issues. To address this challenge, a ROS-based data flow aware profiler PARD is proposed. PARD employs data flow aware analysis methods to automatically analyze collected ROS performance data, and highlights potential computation and communication performance issues on our profile graph, helping users promptly identify system bottlenecks. Thanks to the proposed data analysis methods, the system designers are able to identify and solve the performance issues rapidly through the graphical data representation that accentuates the potential performance issues. The experimental results on the commercial-grade ROS-based autonomous driving software Autoware demonstrate PARD is useful for quickly identifying the potential performance issues for autonomous valet parking. Moreover, its overall impact on the system is around 2%, indicating that PARD is indeed useful for accelerating hardware design exploration in autonomous driving vehicles.