TY - GEN
T1 - Latency Measurement for Autonomous Driving Software Using Data Flow Extraction
AU - Betz, Tobias
AU - Schmeller, Maximilian
AU - Korb, Andreas
AU - Betz, Johannes
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85167964444&partnerID=8YFLogxK
U2 - 10.1109/IV55152.2023.10186686
DO - 10.1109/IV55152.2023.10186686
M3 - Conference contribution
AN - SCOPUS:85167964444
T3 - IEEE Intelligent Vehicles Symposium, Proceedings
BT - IV 2023 - IEEE Intelligent Vehicles Symposium, Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 34th IEEE Intelligent Vehicles Symposium, IV 2023
Y2 - 4 June 2023 through 7 June 2023
ER -