2022 International Conference on Embedded Software (EMSOFT)最新文献_第2页

Industry-track: System-Level Logical Execution Time for Automotive Software Development 行业跟踪:汽车软件开发的系统级逻辑执行时间

2022 International Conference on Embedded Software (EMSOFT) Pub Date : 2022-10-01 DOI: 10.1109/EMSOFT55006.2022.00017

Kai-Björn Gemlau, H. V. Hasseln, R. Ernst

{"title":"Industry-track: System-Level Logical Execution Time for Automotive Software Development","authors":"Kai-Björn Gemlau, H. V. Hasseln, R. Ernst","doi":"10.1109/EMSOFT55006.2022.00017","DOIUrl":"https://doi.org/10.1109/EMSOFT55006.2022.00017","url":null,"abstract":"The way how automotive software is developed has rapidly evolved with the introduction of heterogeneous hardware/software architectures. Nevertheless, the requirement for deterministic behavior of safety-critical cause-effect chains persists unchanged. As a side effect of the shared platform, complex dependencies between critical and non-critical functions arise, demanding a model-based approach to handle time determinism throughout the design process. Limited to the scope of a single component, the Logical Execution Time (LET) paradigm provides such an abstraction of the runtime behavior. It has been successfully introduced in AUTOSAR to mitigate the design complexity, ensure a deterministic timing behavior and facilitate a lock-free communication. This paper discusses how the scope of LET can be extended to the system level, enabling an efficient design of distributed AUTOSAR software, where robustness towards platform changes plays a key role. System-Level Logical Execution Time (SL-LET) is currently in the process of AUTOSAR standardization, supported by a joint group of industry and academic partners.","PeriodicalId":371537,"journal":{"name":"2022 International Conference on Embedded Software (EMSOFT)","volume":"161 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116417361","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Industry-track: Challenges in Rebooting Autonomy with Deep Learned Perception 行业专题:用深度学习感知重新启动自主的挑战

2022 International Conference on Embedded Software (EMSOFT) Pub Date : 2022-10-01 DOI: 10.1109/EMSOFT55006.2022.00016

Michael Abraham, Aaron Mayne, Tristan Perez, Ítalo Romani de Oliveira, Huafeng Yu, Chiao Hsieh, Yangge Li, Dawei Sun, S. Mitra

{"title":"Industry-track: Challenges in Rebooting Autonomy with Deep Learned Perception","authors":"Michael Abraham, Aaron Mayne, Tristan Perez, Ítalo Romani de Oliveira, Huafeng Yu, Chiao Hsieh, Yangge Li, Dawei Sun, S. Mitra","doi":"10.1109/EMSOFT55006.2022.00016","DOIUrl":"https://doi.org/10.1109/EMSOFT55006.2022.00016","url":null,"abstract":"Deep learning (DL) models are becoming effective in solving computer-vision tasks such as semantic segmentation, object tracking, and pose estimation on real-world captured images. Reliability analysis of autonomous systems that use these DL models as part of their perception systems have to account for the performance of these models. Autonomous systems with traditional sensors have tried-and-tested reliability assessment processes with modular design, unit tests, system integration, compositional verification, certification, etc. In contrast, DL perception modules relies on data-driven or learned models. These models do not capture uncertainty and often lack robustness. Also, these models are often updated throughout the lifecycle of the product when new data sets become available. However, the integration of an updated DL-based perception requires a reboot and start afresh of the reliability assessment and operation processes for autonomous systems. In this paper, we discuss three challenges related to specifying, verifying, and operating systems that incorporate DL-based perception. We illustrate these challenges through two concrete and open source examples.","PeriodicalId":371537,"journal":{"name":"2022 International Conference on Embedded Software (EMSOFT)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128930039","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Work-in-Progress: Accuracy-Area Efficient Online Fault Detection for Robust Neural Network Software-Embedded Microcontrollers 鲁棒神经网络软件嵌入式微控制器的精度区域高效在线故障检测

2022 International Conference on Embedded Software (EMSOFT) Pub Date : 2022-10-01 DOI: 10.1109/EMSOFT55006.2022.00008

Juneseo Chang, Sejong Oh, Daejin Park

引用次数: 0

Programming Autonomous Machines : Special Session Paper 自主机器编程:特别会议论文

2022 International Conference on Embedded Software (EMSOFT) Pub Date : 2022-09-06 DOI: 10.1109/EMSOFT55006.2022.00018

Shaoshan Liu, Xiaoming Li, Tongsheng Geng, Stéphane Zuckerman, J. Gaudiot

引用次数: 0