TY - GEN
T1 - Execution Flow Aware Profiling for ROS-based Autonomous Vehicle Software
AU - Wang, Shao Hua
AU - Tu, ChiaHeng
AU - Huang, Ching Chun
AU - Juang, Jyh Ching
N1 - Funding Information:
This work is financially supported in part by the "Intelligent Manufacturing Research Center" (iMRC) from The Featured Areas Research Center Program within the frame-work of the Higher Education Sprout Project by the Ministry of Education (MOE) in Taiwan. This work is supported in part by the Ministry of Science and Technology, Taiwan, under the grants MOST-110-2221-E-006-052, 110-2218-E-006-002, and 111-2221-E-006-116-MY3.
Publisher Copyright:
© 2022 ACM.
PY - 2022/8/29
Y1 - 2022/8/29
N2 - 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.
AB - 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.
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U2 - 10.1145/3547276.3548516
DO - 10.1145/3547276.3548516
M3 - Conference contribution
AN - SCOPUS:85147414162
T3 - ACM International Conference Proceeding Series
BT - 51st International Conference on Parallel Processing, ICPP 2022 - Workshop Proceedings
PB - Association for Computing Machinery
T2 - 51st International Conference on Parallel Processing, ICPP 2022
Y2 - 29 August 2022 through 1 September 2022
ER -