Latency Measurement for Autonomous Driving Software Using Data Flow Extraction

Tobias Betz, Maximilian Schmeller, Andreas Korb, Johannes Betz

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Scopus citations

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.

Original languageEnglish
Title of host publicationIV 2023 - IEEE Intelligent Vehicles Symposium, Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9798350346916
DOIs
StatePublished - 2023
Event34th IEEE Intelligent Vehicles Symposium, IV 2023 - Anchorage, United States
Duration: 4 Jun 20237 Jun 2023

Publication series

NameIEEE Intelligent Vehicles Symposium, Proceedings
Volume2023-June

Conference

Conference34th IEEE Intelligent Vehicles Symposium, IV 2023
Country/TerritoryUnited States
CityAnchorage
Period4/06/237/06/23

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