{"title":"基于ros的自动驾驶软件执行流程感知分析","authors":"Shao-Hua Wang, Chia-Heng Tu, C. Huang, J. Juang","doi":"10.1145/3547276.3548516","DOIUrl":null,"url":null,"abstract":"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.","PeriodicalId":255540,"journal":{"name":"Workshop Proceedings of the 51st International Conference on Parallel Processing","volume":"664 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Execution Flow Aware Profiling for ROS-based Autonomous Vehicle Software\",\"authors\":\"Shao-Hua Wang, Chia-Heng Tu, C. Huang, J. Juang\",\"doi\":\"10.1145/3547276.3548516\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"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.\",\"PeriodicalId\":255540,\"journal\":{\"name\":\"Workshop Proceedings of the 51st International Conference on Parallel Processing\",\"volume\":\"664 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-08-29\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Workshop Proceedings of the 51st International Conference on Parallel Processing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3547276.3548516\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Workshop Proceedings of the 51st International Conference on Parallel Processing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3547276.3548516","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Execution Flow Aware Profiling for ROS-based Autonomous Vehicle Software
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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