Evaluation Platform for Tracing of Autonomous Driving System Combined AUTOSAR AP and ROS 2

Abstract

Autonomous driving systems demand strict real-time properties and safety for practical realization, and development is progressing in heterogeneous environments where the industry standard AUTomotive Open System ARchitecture (AUTOSAR) Adaptive Platform (AUTOSAR AP) and Robot Operating System 2 (ROS 2) coexist; however, end-to-end latency analysis in such mixed environments remains a challenge. A framework called “Combined AUTOSAR AP and ROS 2 Tracing Framework (CART)” was proposed in a previous study to integrate trace data from both platforms; however, its evaluation was limited to functional verification with small-scale applications, and its effectiveness in an environment with complexity and high load close to actual autonomous driving systems was unverified. Therefore, this article proposes the “Evaluation Platform for Tracing of Autonomous Driving System Combined AUTOSAR AP and ROS 2,” which extends CART and integrates CARLA, a high-fidelity simulator, and a practical autonomous driving software stack (Autoware). This platform targets the sequence from the reception of sensor data by the autonomous driving system, through processing via ROS 2 and AUTOSAR AP, to the emission of control commands immediately before they are forwarded to the simulator, enabling the tracking of overall system behavior and the identification of bottlenecks in complex mixed environments. The results indicate that integrated tracing and latency analysis using CART can be applied to a mixed AUTOSAR AP and ROS 2 autonomous driving stack executed in a cloud-based simulator-driven evaluation environment. Furthermore, quantitative evaluation confirmed that the CPU and memory overhead from the ara::log-based tracing method adopted by CART remain at a low level comparable to the ROS 2 standard tracing tool, affirming its viability for large-scale systems. The proposed platform supports detailed performance evaluation and reduces manual effort by enabling unified end-to-end latency analysis across the heterogeneous AUTOSAR AP and ROS 2 autonomous-driving stack.