Run-time safety monitoring framework for AI-based systems: automated driving cases
Intelligent systems based on artificial intelligence techniques are increasing and are recently being accepted in the automotive domain. In the competition of automobile makers to provide fully automated vehicles, it is perceived that artificial intelligence will profoundly influence the automotive...
| Main Authors: | , , |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
IEEE
2019
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| Online Access: | http://psasir.upm.edu.my/id/eprint/78104/ http://psasir.upm.edu.my/id/eprint/78104/1/Run-time%20safety%20monitoring%20framework%20for%20AI-based%20systems%20automated%20driving%20cases.pdf |
| _version_ | 1848858414721007616 |
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| author | Osman, Mohd Hafeez Kugele, Stefan Shafaei, Sina |
| author_facet | Osman, Mohd Hafeez Kugele, Stefan Shafaei, Sina |
| author_sort | Osman, Mohd Hafeez |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | Intelligent systems based on artificial intelligence techniques are increasing and are recently being accepted in the automotive domain. In the competition of automobile makers to provide fully automated vehicles, it is perceived that artificial intelligence will profoundly influence the automotive electric and electronic architecture in the future. However, while such systems provide highly advanced functions, safety risk increases as AI-based systems may produce uncertain output and behaviour. In this paper, we devise a run-time safety monitoring framework for AI-based intelligence systems focusing on autonomous driving functions. In detail, this paper describes (i) the characteristics of a safety monitoring framework; (ii) the safety monitoring framework itself, and (iii) we develop a prototype and implement the framework for two critical driving functions: Lane detection and object detection. Through an implementation of the framework to a prototypic control environment, we show the possibility of this framework in the real context. Finally, we discuss the techniques used in developing the safety monitoring framework and describes the encountered challenges. |
| first_indexed | 2025-11-15T12:13:04Z |
| format | Conference or Workshop Item |
| id | upm-78104 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T12:13:04Z |
| publishDate | 2019 |
| publisher | IEEE |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-781042020-06-03T06:32:50Z http://psasir.upm.edu.my/id/eprint/78104/ Run-time safety monitoring framework for AI-based systems: automated driving cases Osman, Mohd Hafeez Kugele, Stefan Shafaei, Sina Intelligent systems based on artificial intelligence techniques are increasing and are recently being accepted in the automotive domain. In the competition of automobile makers to provide fully automated vehicles, it is perceived that artificial intelligence will profoundly influence the automotive electric and electronic architecture in the future. However, while such systems provide highly advanced functions, safety risk increases as AI-based systems may produce uncertain output and behaviour. In this paper, we devise a run-time safety monitoring framework for AI-based intelligence systems focusing on autonomous driving functions. In detail, this paper describes (i) the characteristics of a safety monitoring framework; (ii) the safety monitoring framework itself, and (iii) we develop a prototype and implement the framework for two critical driving functions: Lane detection and object detection. Through an implementation of the framework to a prototypic control environment, we show the possibility of this framework in the real context. Finally, we discuss the techniques used in developing the safety monitoring framework and describes the encountered challenges. IEEE 2019 Conference or Workshop Item PeerReviewed text en http://psasir.upm.edu.my/id/eprint/78104/1/Run-time%20safety%20monitoring%20framework%20for%20AI-based%20systems%20automated%20driving%20cases.pdf Osman, Mohd Hafeez and Kugele, Stefan and Shafaei, Sina (2019) Run-time safety monitoring framework for AI-based systems: automated driving cases. In: 26th Asia-Pacific Software Engineering Conference (APSEC 2019), 2-5 Dec. 2019, Putrajaya, Malaysia. (pp. 442-449). 10.1109/APSEC48747.2019.00066 |
| spellingShingle | Osman, Mohd Hafeez Kugele, Stefan Shafaei, Sina Run-time safety monitoring framework for AI-based systems: automated driving cases |
| title | Run-time safety monitoring framework for AI-based systems: automated driving cases |
| title_full | Run-time safety monitoring framework for AI-based systems: automated driving cases |
| title_fullStr | Run-time safety monitoring framework for AI-based systems: automated driving cases |
| title_full_unstemmed | Run-time safety monitoring framework for AI-based systems: automated driving cases |
| title_short | Run-time safety monitoring framework for AI-based systems: automated driving cases |
| title_sort | run-time safety monitoring framework for ai-based systems: automated driving cases |
| url | http://psasir.upm.edu.my/id/eprint/78104/ http://psasir.upm.edu.my/id/eprint/78104/ http://psasir.upm.edu.my/id/eprint/78104/1/Run-time%20safety%20monitoring%20framework%20for%20AI-based%20systems%20automated%20driving%20cases.pdf |