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ISO/PAS 8800 Road Vehicles - Safety and AI
ISO/PAS 8800 Road Vehicles - Safety and AI
Course Duration: 3 Days
This 3 day training course, Vehicle Safety – Impact of AI (artificial intelligence) Systems, is designed to discuss important aspects of vehicle safety when the ADAS or ADS (autonomous driving system) has AI sub-systems. With the increased use of AI systems in vehicle designs, new safety hazards might be introduced which necessitate that the corresponding safety of the vehicle be analyzed and mitigation measures be developed and realized. The course covers requirements and other content from the ISO/PAS 8800 specification.
Learning Objectives
- Understanding the application of ISO 8800 standards to safety-related systems that include one or more electrical and/or electronic (E/E) systems that use AI technology
- Understanding and identifying the risk of undesired safety-related behavior at the vehicle level.
- Select AI architectures, technologies, and measures for a typical project
- List and discuss safety analysis elements of AI systems for a typical project
- Detailed understanding of safety requirements
- Understanding principles that support the creation of a project-specific assurance argument for safety
Course Outline
Day One
· Chapter 1: Introduction and Interpret ISO/PAS 8800 conformity requirements
o Definition
o Key Concepts
o Conformity Conditions
o Conformity Tailoring and Rationale Documentation
· Chapter 2: AI System Safety Engineering
o Apply ISO 26262 tailoring to ML-based AI systems
o Interaction between AI systems and encompassing systems
o AI and system safety processes
o AI errors: Root Causes and Safety Impacts
o Breakout Exercise 1: Map AI model components to system architecture
· Chapter 3: AI Safety Lifecycle
o AI lifecycle phases
o Planning AI safety activities including risk control
o Align lifecycle with ISO 26262 and SOTIF processes
o Breakout Exercise 2: Tailoring an AI safety lifecycle for a given use case
· Chapter 4: Assurance Arguments
o Structured assurance arguments using GSN or CAE
o Residual risk and completeness of safety claims
o Argument components for work products and AI lifecycle outputs
o Breakout Exercise 3: Structure assurance arguments using GSN with evidence
Day Two
· Chapter 5: Safety Requirements
o AI-specific insufficiencies and performance limitations
o Input space, target metrics, and KPIs for AI safety
o AI safety requirements and acceptance criteria
o Breakout Exercise 4: Draft AI safety requirements based on input space and ML type
· Chapter 6: Architecture and Measures
o Architectural patterns for safety in AI systems
o Design measures and AI technologies that mitigate failure or uncertainty
o Traceability between architecture and AI safety requirements
o Breakout Exercise 5: Compare and critique architectural patterns for AI system safety
· Chapter 7: Dataset Lifecycle
o Dataset safety analysis, design, and verification
o AI safety requirements and input space definitions for datasets
o Dataset integrity across the AI lifecycle
o Breakout Exercise 6: Analyze sample dataset lifecycle and validate against safety goals
· Chapter 8: Verification & Validation
o V&V activities across system and component levels
o Test oracles and evaluation against safety KPIs
o Sufficiency of AI testing
o Breakout Exercise 7: Design test cases for component and system-level AI testing
Day Three
· Chapter 9: Safety Analysis
o Root cause and fault analysis
o Linking analysis to risk mitigation, safety requirements, and datasets
o Refining data and test specs based on identified faults
o Breakout Exercise 8: Perform root cause analysis on AI failures using FMEA/STPA
· Chapter 10: Operational Measures
o Define monitoring and reapproval processes
o Respond to operational risks and insufficiencies
o Use field data to support retraining and assurance updates
o Breakout Exercise 9: Develop operational monitoring and revalidation protocols
· Chapter 11: Confidence in Tools
o Evaluate AI toolchains for reliability and correctness
o Apply principles for trustworthy AI development environments
o Document confidence arguments for software tools and platforms
o Breakout Exercise 10 : Assess a development toolchain and justify confidence level
· Final Exam
Who Should Attend
This seminar is ideal for professionals involved in the development, deployment, and assurance of AI systems in safety-critical automotive applications. It is designed for Functional Safety Engineer, AI/ML Engineers Working in Automotive Systems, Systems Engineers and Safety Architects, Quality and Compliance Engineers, Verification and Validation (V&V) Specialists, Safety Managers and Project Managers.
Course Materials
Each participant will receive a course manual and a workbook including all team breakout exercises.
Note: Omnex does not provide copies of standard(s) and/or compliant specifications during training courses, but clients are encouraged to have their own copy.
Note: Omnex does not provide copies of standard(s) during training courses, but clients are encouraged to have their own copy.
Pre-Requisite
Familiarity with ISO 26262 and SOTIF standard, understanding of AI/ML concepts, especially in safety-critical systems and background in vehicle development processes and risk analysis will be helpful. Additional knowledge of vehicle hardware, software, and ADS perception systems will be beneficial.
ISO/PAS 8800 Road Vehicles - Safety and AI Program is available in multiple locations globally, including the USA, Canada, Mexico, India, Europe, Thailand, Singapore, Middle East and China.
MICHAEL DOWN
315 E. Eisenhower Parkway, Suite 300, Ann Arbor, Michigan - 48108, USATel: (734) 761-4940
Fax: (734) 761-4966
Michael Down is a Senior Consultant with extensive Engineering, Quality and Reliability experience. Whether it may be in Product Development, Manufacturing, or Quality Management Systems, his greatest desires are to improve clients understanding and improving system to provide optimum performance, quality and durability of the product or process design. He also well understands the need for reducing costs while continually improving quality & compliance/conformance.
Mr. Down has extensive experience working in the automotive industry from manufacturing and assembly to vehicle design development and software/hardware Reliability, DMFEA and PFMEAs. He spent over 32 years working for GM in the quality engineering, statistical problem solving and continuous improvement and teaching. Have taught thousands of employees over the years in relation to FMEA, Probability and Statistics, SPC, System Thinking, Deming, Reliability, and statistical problem solving. Used SPC principles to manufacturing processes at GM, increasing line efficiency and reducing cost, saving GM millions of dollars. Applied DOE to advanced design and process development, identifying critical variables and optimizing process performance. Statistically solved process and product issues in relation to casting, metal fabrication, electronics, injection molding and SMC plastics. Also, statistically solved issues in relation to stamping, heat treating, paint, and in relation to issues with oxygen sensors.
In addition, he was instrumental in the development of the GM Powertrain PFMEA guidelines. Managed quality engineers in manufacturing and assembling. Was a part of the leadership group that directed the Statistical Network within GM (assisted in facilitating Deming seminars and assisted in the training of his courses and seminar) Mike also represented GM at both SAE and AIAG, providing extensive guidance and input to the development of Global Automotive Standards reference documents on Quality and Core tools, including PFMEA, APQP/CP, PPAP, SPC, MSA, and DRBFM reference documents. Mike has been involved with FMEA standards and including developing and teaching FMEAs since the 1990s. Today, Mike is actively working on the SAE J1739 committee updating the FMEA standard to reflect AIAG-VDA FMEA.
Specialties: Training and support the development of DFMEAs and PFMEAs for FMEA 4th edition and AIAG-VDA FMEA. Lead for PQMS training development, IQFMEA tech expert, taught and developed DFA and Robust engineering courses. Deming expert, facilitation and application, DOE trainer and implementer, Represent GM at SAE and at AIAG. Expert in the area of AIAG-VDA FMEA, SPC, MSA, FMEA 4th edition, and DRBFM
EDUCATION
Bachelor of Science, Electrical Engineering, MTU, Bachelor Industrial Management in Electronic Engineering Technology from Baker College, and a Master Degree in Applied Statistics from Oakland University
GREG GRUSKA
315 E. Eisenhower Parkway, Suite 300, Ann Arbor, Michigan - 48108, USATel: (734) 761-4940
Fax: (734) 761-4966
Greg Gruska is the Omnex Champion for APQP, PPAP, FMEA, ISO 26262, Lean Six Sigma and a Fellow of the American Society for Quality (ASQ). His strength in ISO 26262 is a strong understanding and experience in systems engineering and reliability/safety analysis in both hardware and software development. Greg managed the Quality Engineering Activity at Chevrolet. This group provided benchmarking, quality engineering and statistical support to all divisional and corporate activities and their suppliers. Besides the application of statistics within the design, manufacturing, and support environments, this group was active in the development of new technologies and training in these areas. Greg additional served as a Divisional and Corporate consultant in Statistical Engineering and Management. He has traveled extensively in assisting engineering, financial, and support staffs and manufacturing plants in the investigation and solution of problems affecting quality, new product development, product failures and customer satisfaction.
Greg is also an active/writing member of the MSA, SPC, FMEA, and EFMEA Manual subcommittees of the American Automotive industry�s Supplier Quality Requirements Task Force which is part of the international task force governing TS-16949. Greg is an adjunct professor at Madonna University. He has advanced degrees in mathematics and engineering from the University of Detroit, Michigan State University and Wayne State University. He was the Deming Memorial Lecturer at the Sheffield Hallam University for the year 2000.
Greg is a charter member of the Greater Detroit Deming Study Group and the W. E. Deming Institute. He is an ASQ certified Quality Engineer, a licensed Professional Engineer (CA - Quality) and a member of the Board of Examiners of and Judge for the Michigan Quality Leadership Award (1994-2011). Greg is on the writing committee of AIAG on FMEA, a member of the SAE Functional Safety Committee (J2980) and is considered one of the foremost authorities on risk management in the world. He has considerable hardware and software experience in Automotive applications.
MARY E. ROWZEE, ASQ FELLOW
315 E. Eisenhower Parkway, Suite 300, Ann Arbor, Michigan - 48108, USATel: (734) 761-4940
Fax: (734) 761-4966
Mary Rowzee is an Omnex consultant with extensive experience and achievements in Quality Systems development, implementation and auditing to ISO 9000 series and IATF 16949 standards; Six Sigma Black Belt Problem Solving and Advance Quality Tools including: Design and Process FMEA, Design and Process Verification and Test Planning, Complex Statistical Analyses and Reliability Prediction, Modelling and Risk Reduction. Mary is a writing member of AIAG-VDA FMEA 1st edition and the Core Tools Guidelines: SPC 2nd edition, MSA 4th edition, EFMEA 1st edition, PPAP 4th edition and APQP 2nd edition.
Mary has been actively leading industry practices and application of ISO 26262 Functional Safety Standard for Electrical/ Electronic Products; Software FMEAs, ASPICE, CMMI and Quality; Supplemental Monitoring and Systems Response (MSR) FMEAs; Safety of the Intended Functionality (SOTIF) ISO 21448 and use of Safety Engineering tools (Reliability Block Diagrams, Hazard and Risk Analyses, Addressing ASIL rated risks) in Advanced Driver Assistance Systems (ADAS). She also served as GM Global representative on AIAG-VDA and SAE Quality Standards development teams.
Mary has worked for Daimler Chrysler Fiat, TRW and recently GM working as a Senior Engineer ADAS Electrical sub-systems quality for Autonomous Vehicles. She was the Quality and Reliability Resource on ADAS Electrical Sub-systems teams, used in Autonomous Vehicles. For GM she worked with internal and first tier supplier teams to develop Safety Analyses and Design FMEAs on Electrical, Mechanical and Software products in support of ISO 26262 requirements. Mary also assisted in the establishment and implementation of an aggressive Advanced Product Quality Process within GM and Supply Base. Additionally, at GM, she served as in-house consultant and coach to more than 5,000 product engineers in Six Sigma project development and implementation. Mary was an Operational Excellence Master for the GM Quality organization, leading and facilitating the highest impact, most financially significant corporate projects, in addition to teaching many courses on Six Sigma tools and techniques. Also at GM, Mary served as the Senior Leader for Global Design and Process FMEA. In this she revitalized the use of FMEAS within General Motors by developing and teaching all live and web based FMEA classes in North America and developing criteria and assessment processes for Global FMEA software selection.
While at Daimler Chrysler Fiat she served as the manager of Product and Process Integrity. In this position she supported interior and electrical product development (SMTs) areas in writing technical specifications, developing reliability requirements, constructing and executing designed experiments, developing FMEAs and Validation plans.
EDUCATION
Mary has Bachelor of Arts (BA), Psychology and Human Factors from University of Delaware, Newark, DE. She also has a Master of Science (MS), Industrial Psychology and Applied Statistics from University of Akron, Akron, OH. Mary hold numerous certifications including: Certified Reliability Engineer CRE, Certified Manager of Quality and Organizational Excellence CQM/OE, Certified Quality Engineer CQE, Certified Quality Auditor CQA, Registrar Accreditation Board Quality Auditor.