Alert button
Picture for Yutaka Matsubara

Yutaka Matsubara

Alert button

Nagoya University

Safety design concepts for statistical machine learning components toward accordance with functional safety standards

Aug 04, 2020
Akihisa Morikawa, Yutaka Matsubara

Figure 1 for Safety design concepts for statistical machine learning components toward accordance with functional safety standards
Figure 2 for Safety design concepts for statistical machine learning components toward accordance with functional safety standards
Figure 3 for Safety design concepts for statistical machine learning components toward accordance with functional safety standards
Figure 4 for Safety design concepts for statistical machine learning components toward accordance with functional safety standards

In recent years, curial incidents and accidents have been reported due to un-intended control caused by misjudgment of statistical machine learning (SML), which include deep learning. The international functional safety standards for Electric/Electronic/Programmable (E/E/P) systems have been widely spread to improve safety. However, most of them do not recom-mended to use SML in safety critical systems so far. In practical the new concepts and methods are urgently required to enable SML to be safely used in safety critical systems. In this paper, we organize five kinds of technical safety concepts (TSCs) for SML components toward accordance with functional safety standards. We discuss not only quantitative evaluation criteria, but also development process based on XAI (eXplainable Artificial Intelligence) and Automotive SPICE to improve explainability and reliability in development phase. Fi-nally, we briefly compare the TSCs in cost and difficulty, and expect to en-courage further discussion in many communities and domain.

Viaarxiv icon

Towards Identifying and closing Gaps in Assurance of autonomous Road vehicleS -- a collection of Technical Notes Part 2

Feb 28, 2020
Robin Bloomfield, Gareth Fletcher, Heidy Khlaaf, Philippa Ryan, Shuji Kinoshita, Yoshiki Kinoshit, Makoto Takeyama, Yutaka Matsubara, Peter Popov, Kazuki Imai, Yoshinori Tsutake

Figure 1 for Towards Identifying and closing Gaps in Assurance of autonomous Road vehicleS -- a collection of Technical Notes Part 2
Figure 2 for Towards Identifying and closing Gaps in Assurance of autonomous Road vehicleS -- a collection of Technical Notes Part 2
Figure 3 for Towards Identifying and closing Gaps in Assurance of autonomous Road vehicleS -- a collection of Technical Notes Part 2
Figure 4 for Towards Identifying and closing Gaps in Assurance of autonomous Road vehicleS -- a collection of Technical Notes Part 2

This report provides an introduction and overview of the Technical Topic Notes (TTNs) produced in the Towards Identifying and closing Gaps in Assurance of autonomous Road vehicleS (Tigars) project. These notes aim to support the development and evaluation of autonomous vehicles. Part 1 addresses: Assurance-overview and issues, Resilience and Safety Requirements, Open Systems Perspective and Formal Verification and Static Analysis of ML Systems. This report is Part 2 and discusses: Simulation and Dynamic Testing, Defence in Depth and Diversity, Security-Informed Safety Analysis, Standards and Guidelines.

* Authors of the individual notes are indicated in the text 
Viaarxiv icon

Towards Identifying and closing Gaps in Assurance of autonomous Road vehicleS -- a collection of Technical Notes Part 1

Feb 28, 2020
Robin Bloomfield, Gareth Fletcher, Heidy Khlaaf, Philippa Ryan, Shuji Kinoshita, Yoshiki Kinoshit, Makoto Takeyama, Yutaka Matsubara, Peter Popov, Kazuki Imai, Yoshinori Tsutake

Figure 1 for Towards Identifying and closing Gaps in Assurance of autonomous Road vehicleS -- a collection of Technical Notes Part 1
Figure 2 for Towards Identifying and closing Gaps in Assurance of autonomous Road vehicleS -- a collection of Technical Notes Part 1
Figure 3 for Towards Identifying and closing Gaps in Assurance of autonomous Road vehicleS -- a collection of Technical Notes Part 1
Figure 4 for Towards Identifying and closing Gaps in Assurance of autonomous Road vehicleS -- a collection of Technical Notes Part 1

This report provides an introduction and overview of the Technical Topic Notes (TTNs) produced in the Towards Identifying and closing Gaps in Assurance of autonomous Road vehicleS (Tigars) project. These notes aim to support the development and evaluation of autonomous vehicles. Part 1 addresses: Assurance-overview and issues, Resilience and Safety Requirements, Open Systems Perspective and Formal Verification and Static Analysis of ML Systems. Part 2: Simulation and Dynamic Testing, Defence in Depth and Diversity, Security-Informed Safety Analysis, Standards and Guidelines.

* Authors of individual Topic Notes are indicated in the body of the report 
Viaarxiv icon