Workshop summary
International Workshop on Reliability of Autonomous Intelligent Systems (RAIS 2022) seeks to bring together researchers and practitioners to exchange and discuss the reliability issues of the emerging autonomous intelligent systems (AIS).
The AIS, such as the autopilots and autonomous vehicles (AVs), the AV-based logistics systems and transportation systems, the unmanned intelligent manufacturing systems, etc., are exponentially fast-growing and leads to a new era of industrial revolution. With the recent popularity and attention received by the AIS-related industry, the reliability and safety issues of AIS have caused wide public concerns and a nerve of public anxiety, especially when fatal accidents occurred due to the critical failures of AIS. This year’s RAIS centers around two key scopes to bring researchers with the diverse backgrounds, including reliability, AI, autonomous vehicles and other industrial autonomous systems, to come up together with in-depth discussion and solutions for both reliability issues and autonomous intelligence: (1) How to better take recent advances in reliability techniques and tools to further improve quality of AIS, and (2) How to develop reliability theory and methods for AI-Intensive AIS. As we have seen, reliability engineering has already significantly contributed to traditional automotive industry, while for AVs and other AI-Intensive AIS are still at a very early stage.
RAIS 2022 will, therefore, be a workshop, which seeks to develop a cross-domain community that systematically looks into both areas from the new perspective. The workshop will explore the emerging techniques and tools for assessing, predicting, and improving the safety, security, quality, and reliability of advanced AIS that in turn help the development of reliability modeling methods and techniques. We hope RAIS could facilitate to improve autopilot systems with high quality, as well as accelerate the process of system engineering and quality assurance.
Our Theme, Goals and Relevance
Theme
The theme of the workshop is to leverage traditional reliability techniques to better understand autonomous intelligent systems and draw strong connections between the two. We aim to apply reliability methods to accomplish the tasks such as failure prediction for autonomous intelligent systems, as well as advance and complement the existing reliability theory and practice for AI-Intensive System.
Goals
Our main goal is to shed light on the direction of applying the principles of reliability theory and methods to autonomous intelligent systems and therefore evaluate the robustness of AI-intensive autonomous techniques. The workshop also aims to leverage reliability techniques to advance the efficiency, accuracy, effectiveness, and usefulness of current AI-intensive autonomous techniques.
Relevance
The audience of ISSRE focuses on general reliability issues of hardware/software systems, which is highly related and would be benefited by leveraging the reliability techniques for autonomous intelligent systems. In addition, it would be helpful for the ISSRE community to know at what aspect the reliability theory and methodology should be improved for the AI-Intensive System (e.g., autonomous vehicles) and make a broader impact to the community.
Venue & Format
Workshop format - October 30, 2022
8:40am - 9:00am UTC+8
Opening
9:00am - 11:00am UTC+8
Session 1: Keynote
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(9:00am - 9:50am) Keynote Talk 1: Software and Hardware Reliability of Autonomous Systems
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(10:00am - 10:50am) Keynote Talk 2: Commercialization and Operation Promotion of Large-Scale Simulation Test for Autonomous Driving Cars
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Session 2: Presentations (Research Paper: 20min; Abstract: 15min)
11:00am - 12:15pm UTC+8; Safety Assessment
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(11:00 - 11:20) 0187 Safety Assessment: From Black-Box to White-Box
Iwo Kurzidem, Adam Misik, Philipp Schleiss and Simon Burton
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(11:20 - 11:40) 1356 Arguing Safety of an Improved Autonomous Vehicle From Safe Operation Before the Change: New Results
Robab Aghazadeh Chakherlou, Kizito Salako and Lorenzo Strigini
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(11:40 - 12:00) 5236 A Systematic Approach on Developing Autopilot Sensor Monitoring System for Autonomous Delivery Vehicle Based on STPA Method
Guangshuang Ge, Yan-Fu Li and Liangliang Sun
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(12:00 - 12:15) 7962 Disclosing the Pringles Syndrome in Tesla FSD Vehicles
Shengjian Guo and Zhisheng Hu
(11:00 - 11:20) 0187 Safety Assessment: From Black-Box to White-Box
Iwo Kurzidem, Adam Misik, Philipp Schleiss and Simon Burton
(11:20 - 11:40) 1356 Arguing Safety of an Improved Autonomous Vehicle From Safe Operation Before the Change: New Results
Robab Aghazadeh Chakherlou, Kizito Salako and Lorenzo Strigini
(11:40 - 12:00) 5236 A Systematic Approach on Developing Autopilot Sensor Monitoring System for Autonomous Delivery Vehicle Based on STPA Method
Guangshuang Ge, Yan-Fu Li and Liangliang Sun
(12:00 - 12:15) 7962 Disclosing the Pringles Syndrome in Tesla FSD Vehicles
Shengjian Guo and Zhisheng Hu
14:00pm - 15:00pm UTC+8; Reliability Modeling and Enhancement
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(14:00 - 14:15) 5179 Biologically Plausible Spiking Neural Network for Fault Diagnosis of Intelligent Autonomous Systems
Huan Wang and Yan-Fu Li
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(14:15 - 14:30) 9337 A Spiral-FMEA Approach for Continuous Reliability Enhancement of Autonomous Delivery Vehicle (ADV)
Liangliang Sun and Yan-Fu Li
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(14:30 - 14:45) 5701 Joint Optimization of Production Lot Sizing and Preventive Maintenance Threshold Based on Nonlinear Degradation
Li Qu, Junli Liao, Kaiye Gao and Li Yang
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(14:45 - 15:00) 7319 Remaining useful lifetime analysis based on functional variance process
Linjie Qin and Yan Shen
(14:00 - 14:15) 5179 Biologically Plausible Spiking Neural Network for Fault Diagnosis of Intelligent Autonomous Systems
Huan Wang and Yan-Fu Li
(14:15 - 14:30) 9337 A Spiral-FMEA Approach for Continuous Reliability Enhancement of Autonomous Delivery Vehicle (ADV)
Liangliang Sun and Yan-Fu Li
(14:30 - 14:45) 5701 Joint Optimization of Production Lot Sizing and Preventive Maintenance Threshold Based on Nonlinear Degradation
Li Qu, Junli Liao, Kaiye Gao and Li Yang
(14:45 - 15:00) 7319 Remaining useful lifetime analysis based on functional variance process
Linjie Qin and Yan Shen
15:10pm - 16:20pm UTC+8; Simulation & Testing
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(15:10 - 15:30) 3412 Colour Space Defence: Simple, Intuitive, but Effective
Pei Yang, Jing Wang and Huan Wang
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(15:30 - 15:50) 7036 A Survey on Autonomous Driving System Simulators
Jixiang Zhou, Yi Zhang, Shengjian Guo and Yan Guo
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(15:50 - 16:05) 1147 Disclosing the Fragility Problem of Virtual Safety Testing for Autonomous Driving Systems
Zhisheng Hu and Shengjian Guo
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(16:05 - 16:20) 1622 A Simulation Studly of UAS Risk-aware Path Planning in Mitigating Third-party Risks Considering Fight Volume
Xinyu He, Chengper Jiang Lishuai li and Henk A. P. Blom.
(15:10 - 15:30) 3412 Colour Space Defence: Simple, Intuitive, but Effective
Pei Yang, Jing Wang and Huan Wang
(15:30 - 15:50) 7036 A Survey on Autonomous Driving System Simulators
Jixiang Zhou, Yi Zhang, Shengjian Guo and Yan Guo
(15:50 - 16:05) 1147 Disclosing the Fragility Problem of Virtual Safety Testing for Autonomous Driving Systems
Zhisheng Hu and Shengjian Guo
(16:05 - 16:20) 1622 A Simulation Studly of UAS Risk-aware Path Planning in Mitigating Third-party Risks Considering Fight Volume
Xinyu He, Chengper Jiang Lishuai li and Henk A. P. Blom.
16:20pm - 16:30pm UTC+8
Session 3: Closing Remarks
Important Dates
Paper/Abstract Submission
August 1st, 2022Notification
August 19th, 2022Registration
Sept. 30th, 2022Camera-ready Paper
Sept. 20th, 2022Workshop Date
Oct. 30th, 2022Submission instructions
Q: What types of contributions are accepted?
This workshop accepts regular research papers within 6 pages and short papers within 4 pages. The submissions will be under the peer review process and EI-indexed after acceptance.
Presentation-Only abstracts are also welcomed, with only a 1-2 pages abstract required. The accepted abstracts will be listed in the final program but will not appear in the proceedings of the ISSRE conference.
Submitted papers must conform to the two-column IEEE conference publication format. Templates for LaTeX and Microsoft Word are available from:
http://www.ieee.org/conferences_events/conferences/publishing/templates.html
please use the letter format template and conference option.
Papers should be submitted in the PDF format: they must not exceed page limit. Submissions will be handled via EasyChair. Papers must neither have been previously accepted for publication nor be under submission in another conference or journal. For your paper to be published in the proceedings, at least one of the authors of the paper must register for the conference and confirm that she/he will present the paper in person.
Q: How is the review and evaluation process to decide which submissions to accept?
All papers will be evaluated in terms of the following criteria:
Originality or potential for impact: The submission presents a particularly novel collation of historical work, insight or approach towards new/future work, and/or is potentially disruptive of current practice or common knowledge.
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Soundness: The submission makes a coherent argument, substantiated by historical analysis, cogent analytical argument, or appropriately-scoped initial empirical results.
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Relevance: The submission appropriately considers and puts itself in context with respect to the relevant literature.
Specific topics of interest include, but are not limited to the following subject categories:
- Reliability modelling, assessment and optimization of Autonomous Intelligent Systems
- PHM of Autonomous Intelligent Systems
- Availability and safety of Autonomous Intelligent Systems
- Quality Assurance of Autonomous Intelligent Systems
- Testing and verification of Autonomous Intelligent Systems
- Hardware, software & AI reliability of Autonomous Intelligent Systems
- Defects, errors, failures, defects and bugs of Autonomous Intelligent Systems
- Robustness, adversarial attack and defense in Autonomous Intelligent Systems
- System and software security of Autonomous Intelligent Systems
- Systems (software and hardware) engineering of Autonomous Intelligent Systems
- Human-Machine interaction issues in Autonomous Intelligent Systems
- Empirical studies on Autonomous Intelligent Systems
- Industry practices