COURSE FORMAT & DELIVERY DETAILS Self-Paced, On-Demand Access with Lifetime Value
This course is designed for busy professionals who need flexibility without sacrificing depth or quality. From the moment you enroll, you gain self-paced, on-demand access to a fully comprehensive program in AI-driven functional safety for ISO 26262 compliance. There are no fixed start dates, no scheduled sessions, and no time commitments. You control your learning journey entirely, fitting it seamlessly into your life and work schedule. Fast Results with Real-World Relevance
Most learners complete the course within 6 to 8 weeks while dedicating just 4 to 6 hours per week. However, many report applying core techniques to their current projects within the first 10 modules, experiencing measurable clarity and confidence gains immediately. You can progress as quickly or as slowly as you need - every concept is structured in bite-sized, high-impact segments that build toward mastery without overwhelm. Lifetime Access, Future-Proofed Content
Your enrollment includes permanent, lifetime access to all course materials. This is not a time-limited subscription. As the field of AI in functional safety evolves, we update the content to reflect changes in best practices, regulatory expectations, and emerging tools. These updates are included at no extra cost, ensuring your knowledge stays current and your certification remains relevant for years to come. Available Anytime, Anywhere - Desktop or Mobile
Access your course 24/7 from any device, anywhere in the world. Whether you're reviewing materials on your laptop during a lunch break or studying from your phone during a commute, the platform is fully mobile-optimized and responsive. No downloads, no compatibility issues - just instant access with secure login from any internet-connected device. Direct Instructor Guidance & Professional Support
You are not learning in isolation. Throughout the course, you receive structured guidance from industry-experienced functional safety engineers with deep expertise in AI integration and ISO 26262 audits. Our support system is designed to answer your technical questions, clarify complex requirements, and help you apply concepts to real project scenarios. This isn’t a faceless program - it’s mentorship through structured, professional interaction. Certificate of Completion Issued by The Art of Service
Upon finishing the course, you will earn a formal Certificate of Completion issued by The Art of Service. This certification is globally recognized by engineering teams, automotive OEMs, Tier 1 suppliers, and compliance auditors. The Art of Service has trained over 130,000 professionals in functional safety, quality management, and systems engineering, making this credential a trusted signal of expertise. Your certificate includes a unique verification code, allowing employers and clients to validate your achievement instantly. Transparent Pricing - No Hidden Fees
What you see is exactly what you pay. There are no hidden fees, surprise charges, or recurring add-ons. The price includes full access to all materials, ongoing updates, instructor support, progress tracking, and your final certificate. We believe in fairness and clarity - especially when investing in your career. Accepted Payment Methods
We accept all major payment options including Visa, Mastercard, and PayPal. Transactions are processed securely through encrypted gateways, ensuring your financial information remains protected at all times. 100% Money-Back Guarantee - Satisfied or Refunded
We offer a complete satisfaction guarantee. If at any point you feel the course does not meet your expectations, you can request a full refund within 30 days of enrollment. There are no questions asked, no hoops to jump through. Your risk is entirely eliminated. This promise reflects our absolute confidence in the value and transformative power of this program. Secure Enrollment Confirmation & Access
After enrollment, you will receive an automated confirmation email. Your access details and login instructions will be sent separately once your course materials are prepared for delivery. This ensures a secure and accurate setup process for every learner. Will This Work for Me? Absolutely - Even If…
We understand that you may have doubts. Maybe you’re new to functional safety, or perhaps you’ve tried other programs that were too theoretical, outdated, or disconnected from real-world implementation. This course is different. It was built specifically for professionals like you - whether you’re a systems engineer, AI specialist, safety assessor, project manager, or compliance officer. Our learners include automotive software developers at Tier 1 suppliers who used this training to lead AI safety validation for autonomous driving features. Functional safety managers at OEMs have applied the hazard analysis frameworks to pass internal audits with zero findings. Independent consultants have leveraged the certification to triple their client fees. This works even if: you’re not an AI expert, you’ve never worked with ISO 26262 before, your company uses different toolchains, or you’re returning to engineering after years in another domain. The structure starts with foundational clarity and builds through practical application, ensuring you gain fluency regardless of your starting point. Social proof from past participants confirms the transformation: One systems engineer in Munich reported closing a critical safety gap in their ADAS stack within two weeks of starting the course. A Toronto-based validation lead used the AI fault injection techniques to reduce test cycle times by 40%. A Zurich-based startup CTO integrated the course’s safety case templates into their ISO certification package, saving over 200 hours of documentation effort. This course eliminates confusion, reduces your project risk, and positions you as a go-to expert in one of the most critical and high-demand engineering domains of the decade. Your success isn’t just possible - it’s engineered into every module. Your Investment is Protected, Your Growth is Guaranteed
Every element of this course - from the logical progression of content to the lifetime access and certification - is designed to reverse the risk for you. You are not gambling on vague promises. You are enrolling in a proven, structured, outcome-driven path to mastery. The combination of clarity, credibility, and risk reversal makes this the safest and highest-ROI investment you can make in your functional safety career.
EXTENSIVE & DETAILED COURSE CURRICULUM
Module 1: Foundations of AI and Functional Safety in the Automotive Domain - Understanding the convergence of AI and safety-critical systems
- Core principles of functional safety in automotive engineering
- Why traditional safety methods fall short with AI components
- Defining autonomy levels and their safety implications
- Key challenges in validating non-deterministic AI behavior
- Overview of AI use cases in ADAS and autonomous driving
- Introduction to neural networks and machine learning in vehicle systems
- Differences between classical software and AI-based safety architectures
- Understanding epistemic vs aleatoric uncertainty in AI models
- Role of data quality in AI safety assurance
- Introduction to safety culture in AI-integrated development
- Common misconceptions about AI safety
- Functional safety responsibilities in multidisciplinary teams
- Mapping AI lifecycle phases to safety activities
- Introduction to hazard types unique to learning systems
- Foundations of safety assurance arguments for probabilistic systems
Module 2: Deep Dive into ISO 26262 and Its Relevance to AI Systems - Structure and scope of ISO 26262 standards
- Understanding Part 1 to Part 12 and their relevance to AI
- How ISO 26262 addresses software, hardware, and systems
- Interpreting normative vs informative content
- Integrating AI modules within ISO 26262 compliant workflows
- Mapping AI safety activities to ISO 26262 work products
- Handling ambiguity in ISO 26262 for novel AI applications
- Differences between ASIL A, B, C, and D in AI contexts
- How ASIL decomposition interacts with AI subsystems
- Safety goals and safety requirements for AI-driven functions
- Top-down vs bottom-up derivation of safety requirements
- Understanding functional safety assessment boundaries
- Role of the safety manager in AI projects
- Verification and validation expectations under ISO 26262
- Documentation requirements for AI safety cases
- Interaction with other standards like SOTIF and ISO 21448
Module 3: Integrating AI into the Safety Lifecycle - Mapping the AI development lifecycle to ISO 26262 phases
- Safety initiation and concept phase for AI projects
- Defining operational design domain for AI functions
- Establishing safety goals for perception, planning, and control systems
- Incorporating AI modules in system architecture design
- Allocation of safety requirements to AI components
- Handling dynamic reconfiguration in AI systems
- Safety monitoring mechanisms for AI runtime behavior
- Integration of fallback strategies and degrading modes
- Interface safety between AI and non-AI modules
- Safety case evolution throughout development
- Version control and traceability for AI models
- Managing model drift and concept drift safety impacts
- Updating AI models post-deployment under safety constraints
- Change impact analysis for AI model updates
- Defining safety freeze points in iterative AI development
Module 4: Hazard Analysis and Risk Assessment for AI Functions - Conducting HARA for AI-based driving functions
- Identifying hazardous events specific to AI failures
- Assessing severity, exposure, and controllability with AI uncertainty
- Assigning ASIL levels when controllability is affected by AI limitations
- Handling unknown hazardous scenarios in perception systems
- Hazard propagation in deep learning pipelines
- Scenario-based hazard identification using edge cases
- Integrating natural language processing outputs into hazard analysis
- Temporal hazards in AI decision making
- Safety implications of delayed AI inference
- Hazard analysis for sensor fusion systems using AI
- Failure modes of training data under-representation
- Scenario mining techniques for rare events
- HARA for redundant AI vs non-AI system configurations
- Using hazard logs to drive AI testing priorities
- Managing evolving hazard profiles during AI retraining
Module 5: Safety Arguments and Assurance Cases for AI - Building structured safety arguments using GSN methodology
- Claim-subclaim-evidence structure for AI safety
- Addressing probabilistic claims in assurance cases
- Using Bayesian networks to support confidence arguments
- Modular safety case design for AI components
- Handling gaps in evidence due to AI black-box nature
- Justifying sufficient testing coverage with statistical methods
- Integrating tool qualification into safety arguments
- Automated generation of safety case fragments
- Managing assumptions and context limitations in claims
- Dealing with transferability of safety evidence
- Using fault tolerance arguments for AI ensembles
- Qualitative vs quantitative confidence in AI safety
- Linking safety case elements to ISO 26262 work products
- Peer review strategies for AI safety cases
- Preparing safety cases for third-party assessment
Module 6: AI-Specific Safety Techniques and Methods - Applying SOTIF and failure mode analysis to AI
- Formal specification of AI behavior boundaries
- Runtime monitoring of AI output consistency
- Defining operational envelopes for AI subsystems
- Input sanitization and anomaly detection for AI
- Output plausibility checking techniques
- Designing invariant checks for AI decisions
- Monotonicity and continuity constraints in neural network outputs
- Using shadow models for AI behavior validation
- Redundant AI systems with diverse architectures
- Variational autoencoders for out-of-distribution detection
- Confidence scoring and uncertainty quantification methods
- Calibration of neural network confidence outputs
- Detecting adversarial inputs in real-time
- Fail-safe default strategies for AI subsystems
- Safe reinforcement learning design patterns
Module 7: AI Model Development with Safety in Mind - Safety-aware neural network architecture selection
- Designing interpretable AI models without sacrificing performance
- Feature engineering with safety constraints
- Normalization and preprocessing for robust inference
- Bias mitigation techniques in training data
- Representativeness analysis of training datasets
- Curriculum learning strategies for safety-critical applications
- Transfer learning with safety validation
- Domain adaptation and its safety implications
- Handling class imbalance in safety contexts
- Active learning strategies guided by safety goals
- Prioritizing data collection based on hazard analysis
- Training with synthetic but safety-relevant scenarios
- Awareness of overfitting to non-hazardous data
- Regularization techniques to improve generalization
- Model compression and its safety impact assessment
Module 8: Data-Centric Safety for AI Systems - Data lifecycle management under functional safety
- Data quality metrics relevant to AI safety
- Traceability from data collection to model behavior
- Versioning strategies for training, validation, and test datasets
- Metadata standards for safety-critical datasets
- Data provenance and chain of custody tracking
- Labeling accuracy and its impact on safety performance
- Handling ambiguous or borderline cases in labeling
- Inter-rater reliability for annotation teams
- Bias detection and mitigation across dataset dimensions
- Geographic, demographic, and environmental diversity
- Corner case mining and injection into training sets
- Stress testing datasets for extreme conditions
- Simulator fidelity validation against real-world data
- Temperature, lighting, and weather coverage in datasets
- Data augmentation techniques with safety constraints
Module 9: Verification and Validation of AI Components - Defining coverage criteria for AI V&V
- Multi-level validation: unit, integration, system, and scenario
- Test case generation from hazard analysis
- Scenario-based testing with logical and physical parameters
- Monte Carlo simulation for probabilistic behavior testing
- Fault injection techniques for AI modules
- Adversarial testing and robustness evaluation
- Perception error simulation in virtual environments
- Assessing model degradation under sensor noise
- Temporal consistency testing for AI decision sequences
- Long-duration testing to uncover drift effects
- Replay testing using real-world edge cases
- Statistical confidence in test pass rates
- Defining stopping criteria for testing
- Equivalence partitioning for AI input spaces
- Boundary value analysis for neural network inputs
Module 10: Tool Qualification for AI Development and Testing - Understanding ISO 26262-8 tool classification
- Determining tool impact and error detection capability
- Tool qualification for AI training frameworks
- Justifying use of open-source AI libraries
- Documentation required for tool qualification
- Tool confidence levels and their justification
- Using qualified tools in safety workflows
- Qualification of simulation environments for AI testing
- Managing updates and version changes in qualified tools
- Tool chaining and integrated development environment safety
- Automated code generation from AI models
- Safety checks in automated tool pipelines
- Audit trail requirements for AI development tools
- Traceability between tool outputs and safety artifacts
- Independent tool verification strategies
- Using AI-assisted tools for safety analysis
Module 11: Functional Safety Monitoring and Runtime Assurance - Designing runtime monitors for AI inference
- Using watchdog timers with variable execution times
- Input range and distribution monitoring
- Output consistency and stability checks
- Latency monitoring for time-critical AI functions
- Health checks for AI model loading and initialization
- Detecting silent failures in AI subsystems
- Model signature validation at runtime
- Resource usage monitoring for safety implications
- Memory leak detection in long-running AI processes
- Thermal and processing load impact on AI reliability
- Integration with vehicle health monitoring systems
- Logging AI decisions with safety context
- Event-triggered recording for post-failure analysis
- Fault reaction strategies based on monitor outputs
- Graceful degradation pathways for AI subsystems
Module 12: AI in Perception Systems - Safety-Critical Design Patterns - Safety architecture for camera-based object detection
- Semantic segmentation with uncertainty maps
- Object detection using convolutional neural networks
- Handling occlusion and partial visibility safely
- Fusion of camera, radar, and LiDAR with AI
- Temporal consistency in object tracking
- Detecting and handling sensor spoofing attacks
- Confidence thresholds for object classification
- Safe ego-motion estimation using AI
- Lane detection with fallback models
- Free space detection using neural networks
- Handling adverse weather conditions in perception
- Adapting perception models to local regulations
- Localization using AI with integrity monitoring
- Digital map integration with AI perception
- Managing hallucinations in deep learning systems
Module 13: AI in Decision Making and Path Planning - Safety constraints in AI-based motion planning
- Behavior prediction for other road users
- Intention recognition using neural networks
- Uncertainty propagation in trajectory prediction
- Safe decision making under incomplete information
- Ethical considerations in AI driving policies
- Rule-based vs learning-based planner hybridization
- Defining safe fallback behaviors for AI planners
- Handling cut-in and sudden obstacle scenarios
- Lateral and longitudinal motion safety limits
- Interaction with vehicle dynamics constraints
- Velocity profile generation with safety margins
- Handling intersection negotiation with AI
- Risk field modeling using AI
- Safe merging and lane change strategies
- Handling construction zones and temporary obstacles
Module 14: Software and System Integration for AI Safety - Integrating AI modules into AUTOSAR architectures
- Handling AI in Classic vs Adaptive AUTOSAR
- Safety communication patterns between AI and control systems
- Data serialization and deserialization safety
- Handling timing jitter in AI inference
- Prioritization of AI tasks in real-time operating systems
- Memory allocation safety for AI workloads
- Preventing interference between safety and non-safety processes
- Secure boot with AI model integrity checks
- Over-the-air update safety for AI components
- Digital signature verification for model updates
- Rollback strategies for failed AI updates
- Version compatibility management
- Diagnostic trouble code generation for AI faults
- Service-oriented architecture integration
- Safety implications of microservices hosting AI models
Module 15: Managing AI Safety in Project Execution - Defining AI safety requirements traceability matrix
- Planning AI tasks within functional safety project timelines
- Estimating effort for AI safety activities
- Resource allocation for data, compute, and expertise
- Risk management strategies for AI development
- Milestone definition for AI safety validation
- Integrating AI teams with safety and systems engineering
- Communication protocols between disciplines
- Managing external AI vendors and suppliers
- Ensuring subcontractor compliance with safety standards
- Defining safety interfaces in system integration
- Change management processes for AI components
- Configuration management for AI models and datasets
- Security and safety co-engineering for AI
- Audit preparation for AI safety processes
- Lessons learned documentation for AI projects
Module 16: Certification and Audit Readiness - Preparing for independent functional safety assessment
- Documenting AI-specific work products for auditors
- Responding to auditor questions on AI uncertainty
- Demonstrating compliance with functional safety goals
- Presenting safety evidence for non-deterministic systems
- Handling ASIL D requirements for AI modules
- Compiling the safety case dossier
- Traceability from hazards to test results
- Tool qualification evidence package
- Model and data management records
- Process compliance with ISO 26262-6 and -8
- Handling auditor concerns about black-box AI
- Demonstrating sufficient V&V coverage
- Preparing for on-site assessment interviews
- Corrective action response procedures
- Post-certification surveillance planning
Module 17: Hands-On Projects and Real-World Applications - Project 1: Conducting HARA for an AI-based AEB system
- Project 2: Building a safety case for a neural network perception module
- Project 3: Designing runtime monitors for AI inference
- Project 4: Creating a test plan for an AI path planner
- Project 5: Developing a tool qualification package for an AI framework
- Project 6: Structuring data management for an autonomous vehicle project
- Project 7: Implementing fail-safe strategies for AI subsystems
- Project 8: Mapping AI development activities to ISO 26262 work products
- Project 9: Writing safety requirements for an object detection system
- Project 10: Constructing a GSN diagram for AI confidence
- Project 11: Designing a redundant perception system with AI diversity
- Project 12: Validating model performance on edge cases
- Project 13: Performing fault injection testing on an AI module
- Project 14: Creating a safety monitoring dashboard
- Project 15: Preparing a certification readiness checklist
- Project 16: Conducting a safety review meeting with stakeholders
Module 18: Continuing Your Career in AI-Driven Functional Safety - Building a personal brand in AI safety engineering
- Leveraging your certificate in job applications
- Networking with safety professionals and auditors
- Contributing to industry working groups
- Presenting at conferences on AI safety topics
- Transitioning into safety leadership roles
- Becoming an internal AI safety champion
- Consulting opportunities in functional safety
- Staying updated on regulatory developments
- Accessing advanced learning paths and specializations
- Joining professional safety engineering associations
- Using the certificate for client credibility
- Documenting career growth with project impact
- Creating an AI safety portfolio
- Preparing for internal and external audits as an expert
- Teaching AI safety principles to development teams
Module 1: Foundations of AI and Functional Safety in the Automotive Domain - Understanding the convergence of AI and safety-critical systems
- Core principles of functional safety in automotive engineering
- Why traditional safety methods fall short with AI components
- Defining autonomy levels and their safety implications
- Key challenges in validating non-deterministic AI behavior
- Overview of AI use cases in ADAS and autonomous driving
- Introduction to neural networks and machine learning in vehicle systems
- Differences between classical software and AI-based safety architectures
- Understanding epistemic vs aleatoric uncertainty in AI models
- Role of data quality in AI safety assurance
- Introduction to safety culture in AI-integrated development
- Common misconceptions about AI safety
- Functional safety responsibilities in multidisciplinary teams
- Mapping AI lifecycle phases to safety activities
- Introduction to hazard types unique to learning systems
- Foundations of safety assurance arguments for probabilistic systems
Module 2: Deep Dive into ISO 26262 and Its Relevance to AI Systems - Structure and scope of ISO 26262 standards
- Understanding Part 1 to Part 12 and their relevance to AI
- How ISO 26262 addresses software, hardware, and systems
- Interpreting normative vs informative content
- Integrating AI modules within ISO 26262 compliant workflows
- Mapping AI safety activities to ISO 26262 work products
- Handling ambiguity in ISO 26262 for novel AI applications
- Differences between ASIL A, B, C, and D in AI contexts
- How ASIL decomposition interacts with AI subsystems
- Safety goals and safety requirements for AI-driven functions
- Top-down vs bottom-up derivation of safety requirements
- Understanding functional safety assessment boundaries
- Role of the safety manager in AI projects
- Verification and validation expectations under ISO 26262
- Documentation requirements for AI safety cases
- Interaction with other standards like SOTIF and ISO 21448
Module 3: Integrating AI into the Safety Lifecycle - Mapping the AI development lifecycle to ISO 26262 phases
- Safety initiation and concept phase for AI projects
- Defining operational design domain for AI functions
- Establishing safety goals for perception, planning, and control systems
- Incorporating AI modules in system architecture design
- Allocation of safety requirements to AI components
- Handling dynamic reconfiguration in AI systems
- Safety monitoring mechanisms for AI runtime behavior
- Integration of fallback strategies and degrading modes
- Interface safety between AI and non-AI modules
- Safety case evolution throughout development
- Version control and traceability for AI models
- Managing model drift and concept drift safety impacts
- Updating AI models post-deployment under safety constraints
- Change impact analysis for AI model updates
- Defining safety freeze points in iterative AI development
Module 4: Hazard Analysis and Risk Assessment for AI Functions - Conducting HARA for AI-based driving functions
- Identifying hazardous events specific to AI failures
- Assessing severity, exposure, and controllability with AI uncertainty
- Assigning ASIL levels when controllability is affected by AI limitations
- Handling unknown hazardous scenarios in perception systems
- Hazard propagation in deep learning pipelines
- Scenario-based hazard identification using edge cases
- Integrating natural language processing outputs into hazard analysis
- Temporal hazards in AI decision making
- Safety implications of delayed AI inference
- Hazard analysis for sensor fusion systems using AI
- Failure modes of training data under-representation
- Scenario mining techniques for rare events
- HARA for redundant AI vs non-AI system configurations
- Using hazard logs to drive AI testing priorities
- Managing evolving hazard profiles during AI retraining
Module 5: Safety Arguments and Assurance Cases for AI - Building structured safety arguments using GSN methodology
- Claim-subclaim-evidence structure for AI safety
- Addressing probabilistic claims in assurance cases
- Using Bayesian networks to support confidence arguments
- Modular safety case design for AI components
- Handling gaps in evidence due to AI black-box nature
- Justifying sufficient testing coverage with statistical methods
- Integrating tool qualification into safety arguments
- Automated generation of safety case fragments
- Managing assumptions and context limitations in claims
- Dealing with transferability of safety evidence
- Using fault tolerance arguments for AI ensembles
- Qualitative vs quantitative confidence in AI safety
- Linking safety case elements to ISO 26262 work products
- Peer review strategies for AI safety cases
- Preparing safety cases for third-party assessment
Module 6: AI-Specific Safety Techniques and Methods - Applying SOTIF and failure mode analysis to AI
- Formal specification of AI behavior boundaries
- Runtime monitoring of AI output consistency
- Defining operational envelopes for AI subsystems
- Input sanitization and anomaly detection for AI
- Output plausibility checking techniques
- Designing invariant checks for AI decisions
- Monotonicity and continuity constraints in neural network outputs
- Using shadow models for AI behavior validation
- Redundant AI systems with diverse architectures
- Variational autoencoders for out-of-distribution detection
- Confidence scoring and uncertainty quantification methods
- Calibration of neural network confidence outputs
- Detecting adversarial inputs in real-time
- Fail-safe default strategies for AI subsystems
- Safe reinforcement learning design patterns
Module 7: AI Model Development with Safety in Mind - Safety-aware neural network architecture selection
- Designing interpretable AI models without sacrificing performance
- Feature engineering with safety constraints
- Normalization and preprocessing for robust inference
- Bias mitigation techniques in training data
- Representativeness analysis of training datasets
- Curriculum learning strategies for safety-critical applications
- Transfer learning with safety validation
- Domain adaptation and its safety implications
- Handling class imbalance in safety contexts
- Active learning strategies guided by safety goals
- Prioritizing data collection based on hazard analysis
- Training with synthetic but safety-relevant scenarios
- Awareness of overfitting to non-hazardous data
- Regularization techniques to improve generalization
- Model compression and its safety impact assessment
Module 8: Data-Centric Safety for AI Systems - Data lifecycle management under functional safety
- Data quality metrics relevant to AI safety
- Traceability from data collection to model behavior
- Versioning strategies for training, validation, and test datasets
- Metadata standards for safety-critical datasets
- Data provenance and chain of custody tracking
- Labeling accuracy and its impact on safety performance
- Handling ambiguous or borderline cases in labeling
- Inter-rater reliability for annotation teams
- Bias detection and mitigation across dataset dimensions
- Geographic, demographic, and environmental diversity
- Corner case mining and injection into training sets
- Stress testing datasets for extreme conditions
- Simulator fidelity validation against real-world data
- Temperature, lighting, and weather coverage in datasets
- Data augmentation techniques with safety constraints
Module 9: Verification and Validation of AI Components - Defining coverage criteria for AI V&V
- Multi-level validation: unit, integration, system, and scenario
- Test case generation from hazard analysis
- Scenario-based testing with logical and physical parameters
- Monte Carlo simulation for probabilistic behavior testing
- Fault injection techniques for AI modules
- Adversarial testing and robustness evaluation
- Perception error simulation in virtual environments
- Assessing model degradation under sensor noise
- Temporal consistency testing for AI decision sequences
- Long-duration testing to uncover drift effects
- Replay testing using real-world edge cases
- Statistical confidence in test pass rates
- Defining stopping criteria for testing
- Equivalence partitioning for AI input spaces
- Boundary value analysis for neural network inputs
Module 10: Tool Qualification for AI Development and Testing - Understanding ISO 26262-8 tool classification
- Determining tool impact and error detection capability
- Tool qualification for AI training frameworks
- Justifying use of open-source AI libraries
- Documentation required for tool qualification
- Tool confidence levels and their justification
- Using qualified tools in safety workflows
- Qualification of simulation environments for AI testing
- Managing updates and version changes in qualified tools
- Tool chaining and integrated development environment safety
- Automated code generation from AI models
- Safety checks in automated tool pipelines
- Audit trail requirements for AI development tools
- Traceability between tool outputs and safety artifacts
- Independent tool verification strategies
- Using AI-assisted tools for safety analysis
Module 11: Functional Safety Monitoring and Runtime Assurance - Designing runtime monitors for AI inference
- Using watchdog timers with variable execution times
- Input range and distribution monitoring
- Output consistency and stability checks
- Latency monitoring for time-critical AI functions
- Health checks for AI model loading and initialization
- Detecting silent failures in AI subsystems
- Model signature validation at runtime
- Resource usage monitoring for safety implications
- Memory leak detection in long-running AI processes
- Thermal and processing load impact on AI reliability
- Integration with vehicle health monitoring systems
- Logging AI decisions with safety context
- Event-triggered recording for post-failure analysis
- Fault reaction strategies based on monitor outputs
- Graceful degradation pathways for AI subsystems
Module 12: AI in Perception Systems - Safety-Critical Design Patterns - Safety architecture for camera-based object detection
- Semantic segmentation with uncertainty maps
- Object detection using convolutional neural networks
- Handling occlusion and partial visibility safely
- Fusion of camera, radar, and LiDAR with AI
- Temporal consistency in object tracking
- Detecting and handling sensor spoofing attacks
- Confidence thresholds for object classification
- Safe ego-motion estimation using AI
- Lane detection with fallback models
- Free space detection using neural networks
- Handling adverse weather conditions in perception
- Adapting perception models to local regulations
- Localization using AI with integrity monitoring
- Digital map integration with AI perception
- Managing hallucinations in deep learning systems
Module 13: AI in Decision Making and Path Planning - Safety constraints in AI-based motion planning
- Behavior prediction for other road users
- Intention recognition using neural networks
- Uncertainty propagation in trajectory prediction
- Safe decision making under incomplete information
- Ethical considerations in AI driving policies
- Rule-based vs learning-based planner hybridization
- Defining safe fallback behaviors for AI planners
- Handling cut-in and sudden obstacle scenarios
- Lateral and longitudinal motion safety limits
- Interaction with vehicle dynamics constraints
- Velocity profile generation with safety margins
- Handling intersection negotiation with AI
- Risk field modeling using AI
- Safe merging and lane change strategies
- Handling construction zones and temporary obstacles
Module 14: Software and System Integration for AI Safety - Integrating AI modules into AUTOSAR architectures
- Handling AI in Classic vs Adaptive AUTOSAR
- Safety communication patterns between AI and control systems
- Data serialization and deserialization safety
- Handling timing jitter in AI inference
- Prioritization of AI tasks in real-time operating systems
- Memory allocation safety for AI workloads
- Preventing interference between safety and non-safety processes
- Secure boot with AI model integrity checks
- Over-the-air update safety for AI components
- Digital signature verification for model updates
- Rollback strategies for failed AI updates
- Version compatibility management
- Diagnostic trouble code generation for AI faults
- Service-oriented architecture integration
- Safety implications of microservices hosting AI models
Module 15: Managing AI Safety in Project Execution - Defining AI safety requirements traceability matrix
- Planning AI tasks within functional safety project timelines
- Estimating effort for AI safety activities
- Resource allocation for data, compute, and expertise
- Risk management strategies for AI development
- Milestone definition for AI safety validation
- Integrating AI teams with safety and systems engineering
- Communication protocols between disciplines
- Managing external AI vendors and suppliers
- Ensuring subcontractor compliance with safety standards
- Defining safety interfaces in system integration
- Change management processes for AI components
- Configuration management for AI models and datasets
- Security and safety co-engineering for AI
- Audit preparation for AI safety processes
- Lessons learned documentation for AI projects
Module 16: Certification and Audit Readiness - Preparing for independent functional safety assessment
- Documenting AI-specific work products for auditors
- Responding to auditor questions on AI uncertainty
- Demonstrating compliance with functional safety goals
- Presenting safety evidence for non-deterministic systems
- Handling ASIL D requirements for AI modules
- Compiling the safety case dossier
- Traceability from hazards to test results
- Tool qualification evidence package
- Model and data management records
- Process compliance with ISO 26262-6 and -8
- Handling auditor concerns about black-box AI
- Demonstrating sufficient V&V coverage
- Preparing for on-site assessment interviews
- Corrective action response procedures
- Post-certification surveillance planning
Module 17: Hands-On Projects and Real-World Applications - Project 1: Conducting HARA for an AI-based AEB system
- Project 2: Building a safety case for a neural network perception module
- Project 3: Designing runtime monitors for AI inference
- Project 4: Creating a test plan for an AI path planner
- Project 5: Developing a tool qualification package for an AI framework
- Project 6: Structuring data management for an autonomous vehicle project
- Project 7: Implementing fail-safe strategies for AI subsystems
- Project 8: Mapping AI development activities to ISO 26262 work products
- Project 9: Writing safety requirements for an object detection system
- Project 10: Constructing a GSN diagram for AI confidence
- Project 11: Designing a redundant perception system with AI diversity
- Project 12: Validating model performance on edge cases
- Project 13: Performing fault injection testing on an AI module
- Project 14: Creating a safety monitoring dashboard
- Project 15: Preparing a certification readiness checklist
- Project 16: Conducting a safety review meeting with stakeholders
Module 18: Continuing Your Career in AI-Driven Functional Safety - Building a personal brand in AI safety engineering
- Leveraging your certificate in job applications
- Networking with safety professionals and auditors
- Contributing to industry working groups
- Presenting at conferences on AI safety topics
- Transitioning into safety leadership roles
- Becoming an internal AI safety champion
- Consulting opportunities in functional safety
- Staying updated on regulatory developments
- Accessing advanced learning paths and specializations
- Joining professional safety engineering associations
- Using the certificate for client credibility
- Documenting career growth with project impact
- Creating an AI safety portfolio
- Preparing for internal and external audits as an expert
- Teaching AI safety principles to development teams
- Structure and scope of ISO 26262 standards
- Understanding Part 1 to Part 12 and their relevance to AI
- How ISO 26262 addresses software, hardware, and systems
- Interpreting normative vs informative content
- Integrating AI modules within ISO 26262 compliant workflows
- Mapping AI safety activities to ISO 26262 work products
- Handling ambiguity in ISO 26262 for novel AI applications
- Differences between ASIL A, B, C, and D in AI contexts
- How ASIL decomposition interacts with AI subsystems
- Safety goals and safety requirements for AI-driven functions
- Top-down vs bottom-up derivation of safety requirements
- Understanding functional safety assessment boundaries
- Role of the safety manager in AI projects
- Verification and validation expectations under ISO 26262
- Documentation requirements for AI safety cases
- Interaction with other standards like SOTIF and ISO 21448
Module 3: Integrating AI into the Safety Lifecycle - Mapping the AI development lifecycle to ISO 26262 phases
- Safety initiation and concept phase for AI projects
- Defining operational design domain for AI functions
- Establishing safety goals for perception, planning, and control systems
- Incorporating AI modules in system architecture design
- Allocation of safety requirements to AI components
- Handling dynamic reconfiguration in AI systems
- Safety monitoring mechanisms for AI runtime behavior
- Integration of fallback strategies and degrading modes
- Interface safety between AI and non-AI modules
- Safety case evolution throughout development
- Version control and traceability for AI models
- Managing model drift and concept drift safety impacts
- Updating AI models post-deployment under safety constraints
- Change impact analysis for AI model updates
- Defining safety freeze points in iterative AI development
Module 4: Hazard Analysis and Risk Assessment for AI Functions - Conducting HARA for AI-based driving functions
- Identifying hazardous events specific to AI failures
- Assessing severity, exposure, and controllability with AI uncertainty
- Assigning ASIL levels when controllability is affected by AI limitations
- Handling unknown hazardous scenarios in perception systems
- Hazard propagation in deep learning pipelines
- Scenario-based hazard identification using edge cases
- Integrating natural language processing outputs into hazard analysis
- Temporal hazards in AI decision making
- Safety implications of delayed AI inference
- Hazard analysis for sensor fusion systems using AI
- Failure modes of training data under-representation
- Scenario mining techniques for rare events
- HARA for redundant AI vs non-AI system configurations
- Using hazard logs to drive AI testing priorities
- Managing evolving hazard profiles during AI retraining
Module 5: Safety Arguments and Assurance Cases for AI - Building structured safety arguments using GSN methodology
- Claim-subclaim-evidence structure for AI safety
- Addressing probabilistic claims in assurance cases
- Using Bayesian networks to support confidence arguments
- Modular safety case design for AI components
- Handling gaps in evidence due to AI black-box nature
- Justifying sufficient testing coverage with statistical methods
- Integrating tool qualification into safety arguments
- Automated generation of safety case fragments
- Managing assumptions and context limitations in claims
- Dealing with transferability of safety evidence
- Using fault tolerance arguments for AI ensembles
- Qualitative vs quantitative confidence in AI safety
- Linking safety case elements to ISO 26262 work products
- Peer review strategies for AI safety cases
- Preparing safety cases for third-party assessment
Module 6: AI-Specific Safety Techniques and Methods - Applying SOTIF and failure mode analysis to AI
- Formal specification of AI behavior boundaries
- Runtime monitoring of AI output consistency
- Defining operational envelopes for AI subsystems
- Input sanitization and anomaly detection for AI
- Output plausibility checking techniques
- Designing invariant checks for AI decisions
- Monotonicity and continuity constraints in neural network outputs
- Using shadow models for AI behavior validation
- Redundant AI systems with diverse architectures
- Variational autoencoders for out-of-distribution detection
- Confidence scoring and uncertainty quantification methods
- Calibration of neural network confidence outputs
- Detecting adversarial inputs in real-time
- Fail-safe default strategies for AI subsystems
- Safe reinforcement learning design patterns
Module 7: AI Model Development with Safety in Mind - Safety-aware neural network architecture selection
- Designing interpretable AI models without sacrificing performance
- Feature engineering with safety constraints
- Normalization and preprocessing for robust inference
- Bias mitigation techniques in training data
- Representativeness analysis of training datasets
- Curriculum learning strategies for safety-critical applications
- Transfer learning with safety validation
- Domain adaptation and its safety implications
- Handling class imbalance in safety contexts
- Active learning strategies guided by safety goals
- Prioritizing data collection based on hazard analysis
- Training with synthetic but safety-relevant scenarios
- Awareness of overfitting to non-hazardous data
- Regularization techniques to improve generalization
- Model compression and its safety impact assessment
Module 8: Data-Centric Safety for AI Systems - Data lifecycle management under functional safety
- Data quality metrics relevant to AI safety
- Traceability from data collection to model behavior
- Versioning strategies for training, validation, and test datasets
- Metadata standards for safety-critical datasets
- Data provenance and chain of custody tracking
- Labeling accuracy and its impact on safety performance
- Handling ambiguous or borderline cases in labeling
- Inter-rater reliability for annotation teams
- Bias detection and mitigation across dataset dimensions
- Geographic, demographic, and environmental diversity
- Corner case mining and injection into training sets
- Stress testing datasets for extreme conditions
- Simulator fidelity validation against real-world data
- Temperature, lighting, and weather coverage in datasets
- Data augmentation techniques with safety constraints
Module 9: Verification and Validation of AI Components - Defining coverage criteria for AI V&V
- Multi-level validation: unit, integration, system, and scenario
- Test case generation from hazard analysis
- Scenario-based testing with logical and physical parameters
- Monte Carlo simulation for probabilistic behavior testing
- Fault injection techniques for AI modules
- Adversarial testing and robustness evaluation
- Perception error simulation in virtual environments
- Assessing model degradation under sensor noise
- Temporal consistency testing for AI decision sequences
- Long-duration testing to uncover drift effects
- Replay testing using real-world edge cases
- Statistical confidence in test pass rates
- Defining stopping criteria for testing
- Equivalence partitioning for AI input spaces
- Boundary value analysis for neural network inputs
Module 10: Tool Qualification for AI Development and Testing - Understanding ISO 26262-8 tool classification
- Determining tool impact and error detection capability
- Tool qualification for AI training frameworks
- Justifying use of open-source AI libraries
- Documentation required for tool qualification
- Tool confidence levels and their justification
- Using qualified tools in safety workflows
- Qualification of simulation environments for AI testing
- Managing updates and version changes in qualified tools
- Tool chaining and integrated development environment safety
- Automated code generation from AI models
- Safety checks in automated tool pipelines
- Audit trail requirements for AI development tools
- Traceability between tool outputs and safety artifacts
- Independent tool verification strategies
- Using AI-assisted tools for safety analysis
Module 11: Functional Safety Monitoring and Runtime Assurance - Designing runtime monitors for AI inference
- Using watchdog timers with variable execution times
- Input range and distribution monitoring
- Output consistency and stability checks
- Latency monitoring for time-critical AI functions
- Health checks for AI model loading and initialization
- Detecting silent failures in AI subsystems
- Model signature validation at runtime
- Resource usage monitoring for safety implications
- Memory leak detection in long-running AI processes
- Thermal and processing load impact on AI reliability
- Integration with vehicle health monitoring systems
- Logging AI decisions with safety context
- Event-triggered recording for post-failure analysis
- Fault reaction strategies based on monitor outputs
- Graceful degradation pathways for AI subsystems
Module 12: AI in Perception Systems - Safety-Critical Design Patterns - Safety architecture for camera-based object detection
- Semantic segmentation with uncertainty maps
- Object detection using convolutional neural networks
- Handling occlusion and partial visibility safely
- Fusion of camera, radar, and LiDAR with AI
- Temporal consistency in object tracking
- Detecting and handling sensor spoofing attacks
- Confidence thresholds for object classification
- Safe ego-motion estimation using AI
- Lane detection with fallback models
- Free space detection using neural networks
- Handling adverse weather conditions in perception
- Adapting perception models to local regulations
- Localization using AI with integrity monitoring
- Digital map integration with AI perception
- Managing hallucinations in deep learning systems
Module 13: AI in Decision Making and Path Planning - Safety constraints in AI-based motion planning
- Behavior prediction for other road users
- Intention recognition using neural networks
- Uncertainty propagation in trajectory prediction
- Safe decision making under incomplete information
- Ethical considerations in AI driving policies
- Rule-based vs learning-based planner hybridization
- Defining safe fallback behaviors for AI planners
- Handling cut-in and sudden obstacle scenarios
- Lateral and longitudinal motion safety limits
- Interaction with vehicle dynamics constraints
- Velocity profile generation with safety margins
- Handling intersection negotiation with AI
- Risk field modeling using AI
- Safe merging and lane change strategies
- Handling construction zones and temporary obstacles
Module 14: Software and System Integration for AI Safety - Integrating AI modules into AUTOSAR architectures
- Handling AI in Classic vs Adaptive AUTOSAR
- Safety communication patterns between AI and control systems
- Data serialization and deserialization safety
- Handling timing jitter in AI inference
- Prioritization of AI tasks in real-time operating systems
- Memory allocation safety for AI workloads
- Preventing interference between safety and non-safety processes
- Secure boot with AI model integrity checks
- Over-the-air update safety for AI components
- Digital signature verification for model updates
- Rollback strategies for failed AI updates
- Version compatibility management
- Diagnostic trouble code generation for AI faults
- Service-oriented architecture integration
- Safety implications of microservices hosting AI models
Module 15: Managing AI Safety in Project Execution - Defining AI safety requirements traceability matrix
- Planning AI tasks within functional safety project timelines
- Estimating effort for AI safety activities
- Resource allocation for data, compute, and expertise
- Risk management strategies for AI development
- Milestone definition for AI safety validation
- Integrating AI teams with safety and systems engineering
- Communication protocols between disciplines
- Managing external AI vendors and suppliers
- Ensuring subcontractor compliance with safety standards
- Defining safety interfaces in system integration
- Change management processes for AI components
- Configuration management for AI models and datasets
- Security and safety co-engineering for AI
- Audit preparation for AI safety processes
- Lessons learned documentation for AI projects
Module 16: Certification and Audit Readiness - Preparing for independent functional safety assessment
- Documenting AI-specific work products for auditors
- Responding to auditor questions on AI uncertainty
- Demonstrating compliance with functional safety goals
- Presenting safety evidence for non-deterministic systems
- Handling ASIL D requirements for AI modules
- Compiling the safety case dossier
- Traceability from hazards to test results
- Tool qualification evidence package
- Model and data management records
- Process compliance with ISO 26262-6 and -8
- Handling auditor concerns about black-box AI
- Demonstrating sufficient V&V coverage
- Preparing for on-site assessment interviews
- Corrective action response procedures
- Post-certification surveillance planning
Module 17: Hands-On Projects and Real-World Applications - Project 1: Conducting HARA for an AI-based AEB system
- Project 2: Building a safety case for a neural network perception module
- Project 3: Designing runtime monitors for AI inference
- Project 4: Creating a test plan for an AI path planner
- Project 5: Developing a tool qualification package for an AI framework
- Project 6: Structuring data management for an autonomous vehicle project
- Project 7: Implementing fail-safe strategies for AI subsystems
- Project 8: Mapping AI development activities to ISO 26262 work products
- Project 9: Writing safety requirements for an object detection system
- Project 10: Constructing a GSN diagram for AI confidence
- Project 11: Designing a redundant perception system with AI diversity
- Project 12: Validating model performance on edge cases
- Project 13: Performing fault injection testing on an AI module
- Project 14: Creating a safety monitoring dashboard
- Project 15: Preparing a certification readiness checklist
- Project 16: Conducting a safety review meeting with stakeholders
Module 18: Continuing Your Career in AI-Driven Functional Safety - Building a personal brand in AI safety engineering
- Leveraging your certificate in job applications
- Networking with safety professionals and auditors
- Contributing to industry working groups
- Presenting at conferences on AI safety topics
- Transitioning into safety leadership roles
- Becoming an internal AI safety champion
- Consulting opportunities in functional safety
- Staying updated on regulatory developments
- Accessing advanced learning paths and specializations
- Joining professional safety engineering associations
- Using the certificate for client credibility
- Documenting career growth with project impact
- Creating an AI safety portfolio
- Preparing for internal and external audits as an expert
- Teaching AI safety principles to development teams
- Conducting HARA for AI-based driving functions
- Identifying hazardous events specific to AI failures
- Assessing severity, exposure, and controllability with AI uncertainty
- Assigning ASIL levels when controllability is affected by AI limitations
- Handling unknown hazardous scenarios in perception systems
- Hazard propagation in deep learning pipelines
- Scenario-based hazard identification using edge cases
- Integrating natural language processing outputs into hazard analysis
- Temporal hazards in AI decision making
- Safety implications of delayed AI inference
- Hazard analysis for sensor fusion systems using AI
- Failure modes of training data under-representation
- Scenario mining techniques for rare events
- HARA for redundant AI vs non-AI system configurations
- Using hazard logs to drive AI testing priorities
- Managing evolving hazard profiles during AI retraining
Module 5: Safety Arguments and Assurance Cases for AI - Building structured safety arguments using GSN methodology
- Claim-subclaim-evidence structure for AI safety
- Addressing probabilistic claims in assurance cases
- Using Bayesian networks to support confidence arguments
- Modular safety case design for AI components
- Handling gaps in evidence due to AI black-box nature
- Justifying sufficient testing coverage with statistical methods
- Integrating tool qualification into safety arguments
- Automated generation of safety case fragments
- Managing assumptions and context limitations in claims
- Dealing with transferability of safety evidence
- Using fault tolerance arguments for AI ensembles
- Qualitative vs quantitative confidence in AI safety
- Linking safety case elements to ISO 26262 work products
- Peer review strategies for AI safety cases
- Preparing safety cases for third-party assessment
Module 6: AI-Specific Safety Techniques and Methods - Applying SOTIF and failure mode analysis to AI
- Formal specification of AI behavior boundaries
- Runtime monitoring of AI output consistency
- Defining operational envelopes for AI subsystems
- Input sanitization and anomaly detection for AI
- Output plausibility checking techniques
- Designing invariant checks for AI decisions
- Monotonicity and continuity constraints in neural network outputs
- Using shadow models for AI behavior validation
- Redundant AI systems with diverse architectures
- Variational autoencoders for out-of-distribution detection
- Confidence scoring and uncertainty quantification methods
- Calibration of neural network confidence outputs
- Detecting adversarial inputs in real-time
- Fail-safe default strategies for AI subsystems
- Safe reinforcement learning design patterns
Module 7: AI Model Development with Safety in Mind - Safety-aware neural network architecture selection
- Designing interpretable AI models without sacrificing performance
- Feature engineering with safety constraints
- Normalization and preprocessing for robust inference
- Bias mitigation techniques in training data
- Representativeness analysis of training datasets
- Curriculum learning strategies for safety-critical applications
- Transfer learning with safety validation
- Domain adaptation and its safety implications
- Handling class imbalance in safety contexts
- Active learning strategies guided by safety goals
- Prioritizing data collection based on hazard analysis
- Training with synthetic but safety-relevant scenarios
- Awareness of overfitting to non-hazardous data
- Regularization techniques to improve generalization
- Model compression and its safety impact assessment
Module 8: Data-Centric Safety for AI Systems - Data lifecycle management under functional safety
- Data quality metrics relevant to AI safety
- Traceability from data collection to model behavior
- Versioning strategies for training, validation, and test datasets
- Metadata standards for safety-critical datasets
- Data provenance and chain of custody tracking
- Labeling accuracy and its impact on safety performance
- Handling ambiguous or borderline cases in labeling
- Inter-rater reliability for annotation teams
- Bias detection and mitigation across dataset dimensions
- Geographic, demographic, and environmental diversity
- Corner case mining and injection into training sets
- Stress testing datasets for extreme conditions
- Simulator fidelity validation against real-world data
- Temperature, lighting, and weather coverage in datasets
- Data augmentation techniques with safety constraints
Module 9: Verification and Validation of AI Components - Defining coverage criteria for AI V&V
- Multi-level validation: unit, integration, system, and scenario
- Test case generation from hazard analysis
- Scenario-based testing with logical and physical parameters
- Monte Carlo simulation for probabilistic behavior testing
- Fault injection techniques for AI modules
- Adversarial testing and robustness evaluation
- Perception error simulation in virtual environments
- Assessing model degradation under sensor noise
- Temporal consistency testing for AI decision sequences
- Long-duration testing to uncover drift effects
- Replay testing using real-world edge cases
- Statistical confidence in test pass rates
- Defining stopping criteria for testing
- Equivalence partitioning for AI input spaces
- Boundary value analysis for neural network inputs
Module 10: Tool Qualification for AI Development and Testing - Understanding ISO 26262-8 tool classification
- Determining tool impact and error detection capability
- Tool qualification for AI training frameworks
- Justifying use of open-source AI libraries
- Documentation required for tool qualification
- Tool confidence levels and their justification
- Using qualified tools in safety workflows
- Qualification of simulation environments for AI testing
- Managing updates and version changes in qualified tools
- Tool chaining and integrated development environment safety
- Automated code generation from AI models
- Safety checks in automated tool pipelines
- Audit trail requirements for AI development tools
- Traceability between tool outputs and safety artifacts
- Independent tool verification strategies
- Using AI-assisted tools for safety analysis
Module 11: Functional Safety Monitoring and Runtime Assurance - Designing runtime monitors for AI inference
- Using watchdog timers with variable execution times
- Input range and distribution monitoring
- Output consistency and stability checks
- Latency monitoring for time-critical AI functions
- Health checks for AI model loading and initialization
- Detecting silent failures in AI subsystems
- Model signature validation at runtime
- Resource usage monitoring for safety implications
- Memory leak detection in long-running AI processes
- Thermal and processing load impact on AI reliability
- Integration with vehicle health monitoring systems
- Logging AI decisions with safety context
- Event-triggered recording for post-failure analysis
- Fault reaction strategies based on monitor outputs
- Graceful degradation pathways for AI subsystems
Module 12: AI in Perception Systems - Safety-Critical Design Patterns - Safety architecture for camera-based object detection
- Semantic segmentation with uncertainty maps
- Object detection using convolutional neural networks
- Handling occlusion and partial visibility safely
- Fusion of camera, radar, and LiDAR with AI
- Temporal consistency in object tracking
- Detecting and handling sensor spoofing attacks
- Confidence thresholds for object classification
- Safe ego-motion estimation using AI
- Lane detection with fallback models
- Free space detection using neural networks
- Handling adverse weather conditions in perception
- Adapting perception models to local regulations
- Localization using AI with integrity monitoring
- Digital map integration with AI perception
- Managing hallucinations in deep learning systems
Module 13: AI in Decision Making and Path Planning - Safety constraints in AI-based motion planning
- Behavior prediction for other road users
- Intention recognition using neural networks
- Uncertainty propagation in trajectory prediction
- Safe decision making under incomplete information
- Ethical considerations in AI driving policies
- Rule-based vs learning-based planner hybridization
- Defining safe fallback behaviors for AI planners
- Handling cut-in and sudden obstacle scenarios
- Lateral and longitudinal motion safety limits
- Interaction with vehicle dynamics constraints
- Velocity profile generation with safety margins
- Handling intersection negotiation with AI
- Risk field modeling using AI
- Safe merging and lane change strategies
- Handling construction zones and temporary obstacles
Module 14: Software and System Integration for AI Safety - Integrating AI modules into AUTOSAR architectures
- Handling AI in Classic vs Adaptive AUTOSAR
- Safety communication patterns between AI and control systems
- Data serialization and deserialization safety
- Handling timing jitter in AI inference
- Prioritization of AI tasks in real-time operating systems
- Memory allocation safety for AI workloads
- Preventing interference between safety and non-safety processes
- Secure boot with AI model integrity checks
- Over-the-air update safety for AI components
- Digital signature verification for model updates
- Rollback strategies for failed AI updates
- Version compatibility management
- Diagnostic trouble code generation for AI faults
- Service-oriented architecture integration
- Safety implications of microservices hosting AI models
Module 15: Managing AI Safety in Project Execution - Defining AI safety requirements traceability matrix
- Planning AI tasks within functional safety project timelines
- Estimating effort for AI safety activities
- Resource allocation for data, compute, and expertise
- Risk management strategies for AI development
- Milestone definition for AI safety validation
- Integrating AI teams with safety and systems engineering
- Communication protocols between disciplines
- Managing external AI vendors and suppliers
- Ensuring subcontractor compliance with safety standards
- Defining safety interfaces in system integration
- Change management processes for AI components
- Configuration management for AI models and datasets
- Security and safety co-engineering for AI
- Audit preparation for AI safety processes
- Lessons learned documentation for AI projects
Module 16: Certification and Audit Readiness - Preparing for independent functional safety assessment
- Documenting AI-specific work products for auditors
- Responding to auditor questions on AI uncertainty
- Demonstrating compliance with functional safety goals
- Presenting safety evidence for non-deterministic systems
- Handling ASIL D requirements for AI modules
- Compiling the safety case dossier
- Traceability from hazards to test results
- Tool qualification evidence package
- Model and data management records
- Process compliance with ISO 26262-6 and -8
- Handling auditor concerns about black-box AI
- Demonstrating sufficient V&V coverage
- Preparing for on-site assessment interviews
- Corrective action response procedures
- Post-certification surveillance planning
Module 17: Hands-On Projects and Real-World Applications - Project 1: Conducting HARA for an AI-based AEB system
- Project 2: Building a safety case for a neural network perception module
- Project 3: Designing runtime monitors for AI inference
- Project 4: Creating a test plan for an AI path planner
- Project 5: Developing a tool qualification package for an AI framework
- Project 6: Structuring data management for an autonomous vehicle project
- Project 7: Implementing fail-safe strategies for AI subsystems
- Project 8: Mapping AI development activities to ISO 26262 work products
- Project 9: Writing safety requirements for an object detection system
- Project 10: Constructing a GSN diagram for AI confidence
- Project 11: Designing a redundant perception system with AI diversity
- Project 12: Validating model performance on edge cases
- Project 13: Performing fault injection testing on an AI module
- Project 14: Creating a safety monitoring dashboard
- Project 15: Preparing a certification readiness checklist
- Project 16: Conducting a safety review meeting with stakeholders
Module 18: Continuing Your Career in AI-Driven Functional Safety - Building a personal brand in AI safety engineering
- Leveraging your certificate in job applications
- Networking with safety professionals and auditors
- Contributing to industry working groups
- Presenting at conferences on AI safety topics
- Transitioning into safety leadership roles
- Becoming an internal AI safety champion
- Consulting opportunities in functional safety
- Staying updated on regulatory developments
- Accessing advanced learning paths and specializations
- Joining professional safety engineering associations
- Using the certificate for client credibility
- Documenting career growth with project impact
- Creating an AI safety portfolio
- Preparing for internal and external audits as an expert
- Teaching AI safety principles to development teams
- Applying SOTIF and failure mode analysis to AI
- Formal specification of AI behavior boundaries
- Runtime monitoring of AI output consistency
- Defining operational envelopes for AI subsystems
- Input sanitization and anomaly detection for AI
- Output plausibility checking techniques
- Designing invariant checks for AI decisions
- Monotonicity and continuity constraints in neural network outputs
- Using shadow models for AI behavior validation
- Redundant AI systems with diverse architectures
- Variational autoencoders for out-of-distribution detection
- Confidence scoring and uncertainty quantification methods
- Calibration of neural network confidence outputs
- Detecting adversarial inputs in real-time
- Fail-safe default strategies for AI subsystems
- Safe reinforcement learning design patterns
Module 7: AI Model Development with Safety in Mind - Safety-aware neural network architecture selection
- Designing interpretable AI models without sacrificing performance
- Feature engineering with safety constraints
- Normalization and preprocessing for robust inference
- Bias mitigation techniques in training data
- Representativeness analysis of training datasets
- Curriculum learning strategies for safety-critical applications
- Transfer learning with safety validation
- Domain adaptation and its safety implications
- Handling class imbalance in safety contexts
- Active learning strategies guided by safety goals
- Prioritizing data collection based on hazard analysis
- Training with synthetic but safety-relevant scenarios
- Awareness of overfitting to non-hazardous data
- Regularization techniques to improve generalization
- Model compression and its safety impact assessment
Module 8: Data-Centric Safety for AI Systems - Data lifecycle management under functional safety
- Data quality metrics relevant to AI safety
- Traceability from data collection to model behavior
- Versioning strategies for training, validation, and test datasets
- Metadata standards for safety-critical datasets
- Data provenance and chain of custody tracking
- Labeling accuracy and its impact on safety performance
- Handling ambiguous or borderline cases in labeling
- Inter-rater reliability for annotation teams
- Bias detection and mitigation across dataset dimensions
- Geographic, demographic, and environmental diversity
- Corner case mining and injection into training sets
- Stress testing datasets for extreme conditions
- Simulator fidelity validation against real-world data
- Temperature, lighting, and weather coverage in datasets
- Data augmentation techniques with safety constraints
Module 9: Verification and Validation of AI Components - Defining coverage criteria for AI V&V
- Multi-level validation: unit, integration, system, and scenario
- Test case generation from hazard analysis
- Scenario-based testing with logical and physical parameters
- Monte Carlo simulation for probabilistic behavior testing
- Fault injection techniques for AI modules
- Adversarial testing and robustness evaluation
- Perception error simulation in virtual environments
- Assessing model degradation under sensor noise
- Temporal consistency testing for AI decision sequences
- Long-duration testing to uncover drift effects
- Replay testing using real-world edge cases
- Statistical confidence in test pass rates
- Defining stopping criteria for testing
- Equivalence partitioning for AI input spaces
- Boundary value analysis for neural network inputs
Module 10: Tool Qualification for AI Development and Testing - Understanding ISO 26262-8 tool classification
- Determining tool impact and error detection capability
- Tool qualification for AI training frameworks
- Justifying use of open-source AI libraries
- Documentation required for tool qualification
- Tool confidence levels and their justification
- Using qualified tools in safety workflows
- Qualification of simulation environments for AI testing
- Managing updates and version changes in qualified tools
- Tool chaining and integrated development environment safety
- Automated code generation from AI models
- Safety checks in automated tool pipelines
- Audit trail requirements for AI development tools
- Traceability between tool outputs and safety artifacts
- Independent tool verification strategies
- Using AI-assisted tools for safety analysis
Module 11: Functional Safety Monitoring and Runtime Assurance - Designing runtime monitors for AI inference
- Using watchdog timers with variable execution times
- Input range and distribution monitoring
- Output consistency and stability checks
- Latency monitoring for time-critical AI functions
- Health checks for AI model loading and initialization
- Detecting silent failures in AI subsystems
- Model signature validation at runtime
- Resource usage monitoring for safety implications
- Memory leak detection in long-running AI processes
- Thermal and processing load impact on AI reliability
- Integration with vehicle health monitoring systems
- Logging AI decisions with safety context
- Event-triggered recording for post-failure analysis
- Fault reaction strategies based on monitor outputs
- Graceful degradation pathways for AI subsystems
Module 12: AI in Perception Systems - Safety-Critical Design Patterns - Safety architecture for camera-based object detection
- Semantic segmentation with uncertainty maps
- Object detection using convolutional neural networks
- Handling occlusion and partial visibility safely
- Fusion of camera, radar, and LiDAR with AI
- Temporal consistency in object tracking
- Detecting and handling sensor spoofing attacks
- Confidence thresholds for object classification
- Safe ego-motion estimation using AI
- Lane detection with fallback models
- Free space detection using neural networks
- Handling adverse weather conditions in perception
- Adapting perception models to local regulations
- Localization using AI with integrity monitoring
- Digital map integration with AI perception
- Managing hallucinations in deep learning systems
Module 13: AI in Decision Making and Path Planning - Safety constraints in AI-based motion planning
- Behavior prediction for other road users
- Intention recognition using neural networks
- Uncertainty propagation in trajectory prediction
- Safe decision making under incomplete information
- Ethical considerations in AI driving policies
- Rule-based vs learning-based planner hybridization
- Defining safe fallback behaviors for AI planners
- Handling cut-in and sudden obstacle scenarios
- Lateral and longitudinal motion safety limits
- Interaction with vehicle dynamics constraints
- Velocity profile generation with safety margins
- Handling intersection negotiation with AI
- Risk field modeling using AI
- Safe merging and lane change strategies
- Handling construction zones and temporary obstacles
Module 14: Software and System Integration for AI Safety - Integrating AI modules into AUTOSAR architectures
- Handling AI in Classic vs Adaptive AUTOSAR
- Safety communication patterns between AI and control systems
- Data serialization and deserialization safety
- Handling timing jitter in AI inference
- Prioritization of AI tasks in real-time operating systems
- Memory allocation safety for AI workloads
- Preventing interference between safety and non-safety processes
- Secure boot with AI model integrity checks
- Over-the-air update safety for AI components
- Digital signature verification for model updates
- Rollback strategies for failed AI updates
- Version compatibility management
- Diagnostic trouble code generation for AI faults
- Service-oriented architecture integration
- Safety implications of microservices hosting AI models
Module 15: Managing AI Safety in Project Execution - Defining AI safety requirements traceability matrix
- Planning AI tasks within functional safety project timelines
- Estimating effort for AI safety activities
- Resource allocation for data, compute, and expertise
- Risk management strategies for AI development
- Milestone definition for AI safety validation
- Integrating AI teams with safety and systems engineering
- Communication protocols between disciplines
- Managing external AI vendors and suppliers
- Ensuring subcontractor compliance with safety standards
- Defining safety interfaces in system integration
- Change management processes for AI components
- Configuration management for AI models and datasets
- Security and safety co-engineering for AI
- Audit preparation for AI safety processes
- Lessons learned documentation for AI projects
Module 16: Certification and Audit Readiness - Preparing for independent functional safety assessment
- Documenting AI-specific work products for auditors
- Responding to auditor questions on AI uncertainty
- Demonstrating compliance with functional safety goals
- Presenting safety evidence for non-deterministic systems
- Handling ASIL D requirements for AI modules
- Compiling the safety case dossier
- Traceability from hazards to test results
- Tool qualification evidence package
- Model and data management records
- Process compliance with ISO 26262-6 and -8
- Handling auditor concerns about black-box AI
- Demonstrating sufficient V&V coverage
- Preparing for on-site assessment interviews
- Corrective action response procedures
- Post-certification surveillance planning
Module 17: Hands-On Projects and Real-World Applications - Project 1: Conducting HARA for an AI-based AEB system
- Project 2: Building a safety case for a neural network perception module
- Project 3: Designing runtime monitors for AI inference
- Project 4: Creating a test plan for an AI path planner
- Project 5: Developing a tool qualification package for an AI framework
- Project 6: Structuring data management for an autonomous vehicle project
- Project 7: Implementing fail-safe strategies for AI subsystems
- Project 8: Mapping AI development activities to ISO 26262 work products
- Project 9: Writing safety requirements for an object detection system
- Project 10: Constructing a GSN diagram for AI confidence
- Project 11: Designing a redundant perception system with AI diversity
- Project 12: Validating model performance on edge cases
- Project 13: Performing fault injection testing on an AI module
- Project 14: Creating a safety monitoring dashboard
- Project 15: Preparing a certification readiness checklist
- Project 16: Conducting a safety review meeting with stakeholders
Module 18: Continuing Your Career in AI-Driven Functional Safety - Building a personal brand in AI safety engineering
- Leveraging your certificate in job applications
- Networking with safety professionals and auditors
- Contributing to industry working groups
- Presenting at conferences on AI safety topics
- Transitioning into safety leadership roles
- Becoming an internal AI safety champion
- Consulting opportunities in functional safety
- Staying updated on regulatory developments
- Accessing advanced learning paths and specializations
- Joining professional safety engineering associations
- Using the certificate for client credibility
- Documenting career growth with project impact
- Creating an AI safety portfolio
- Preparing for internal and external audits as an expert
- Teaching AI safety principles to development teams
- Data lifecycle management under functional safety
- Data quality metrics relevant to AI safety
- Traceability from data collection to model behavior
- Versioning strategies for training, validation, and test datasets
- Metadata standards for safety-critical datasets
- Data provenance and chain of custody tracking
- Labeling accuracy and its impact on safety performance
- Handling ambiguous or borderline cases in labeling
- Inter-rater reliability for annotation teams
- Bias detection and mitigation across dataset dimensions
- Geographic, demographic, and environmental diversity
- Corner case mining and injection into training sets
- Stress testing datasets for extreme conditions
- Simulator fidelity validation against real-world data
- Temperature, lighting, and weather coverage in datasets
- Data augmentation techniques with safety constraints
Module 9: Verification and Validation of AI Components - Defining coverage criteria for AI V&V
- Multi-level validation: unit, integration, system, and scenario
- Test case generation from hazard analysis
- Scenario-based testing with logical and physical parameters
- Monte Carlo simulation for probabilistic behavior testing
- Fault injection techniques for AI modules
- Adversarial testing and robustness evaluation
- Perception error simulation in virtual environments
- Assessing model degradation under sensor noise
- Temporal consistency testing for AI decision sequences
- Long-duration testing to uncover drift effects
- Replay testing using real-world edge cases
- Statistical confidence in test pass rates
- Defining stopping criteria for testing
- Equivalence partitioning for AI input spaces
- Boundary value analysis for neural network inputs
Module 10: Tool Qualification for AI Development and Testing - Understanding ISO 26262-8 tool classification
- Determining tool impact and error detection capability
- Tool qualification for AI training frameworks
- Justifying use of open-source AI libraries
- Documentation required for tool qualification
- Tool confidence levels and their justification
- Using qualified tools in safety workflows
- Qualification of simulation environments for AI testing
- Managing updates and version changes in qualified tools
- Tool chaining and integrated development environment safety
- Automated code generation from AI models
- Safety checks in automated tool pipelines
- Audit trail requirements for AI development tools
- Traceability between tool outputs and safety artifacts
- Independent tool verification strategies
- Using AI-assisted tools for safety analysis
Module 11: Functional Safety Monitoring and Runtime Assurance - Designing runtime monitors for AI inference
- Using watchdog timers with variable execution times
- Input range and distribution monitoring
- Output consistency and stability checks
- Latency monitoring for time-critical AI functions
- Health checks for AI model loading and initialization
- Detecting silent failures in AI subsystems
- Model signature validation at runtime
- Resource usage monitoring for safety implications
- Memory leak detection in long-running AI processes
- Thermal and processing load impact on AI reliability
- Integration with vehicle health monitoring systems
- Logging AI decisions with safety context
- Event-triggered recording for post-failure analysis
- Fault reaction strategies based on monitor outputs
- Graceful degradation pathways for AI subsystems
Module 12: AI in Perception Systems - Safety-Critical Design Patterns - Safety architecture for camera-based object detection
- Semantic segmentation with uncertainty maps
- Object detection using convolutional neural networks
- Handling occlusion and partial visibility safely
- Fusion of camera, radar, and LiDAR with AI
- Temporal consistency in object tracking
- Detecting and handling sensor spoofing attacks
- Confidence thresholds for object classification
- Safe ego-motion estimation using AI
- Lane detection with fallback models
- Free space detection using neural networks
- Handling adverse weather conditions in perception
- Adapting perception models to local regulations
- Localization using AI with integrity monitoring
- Digital map integration with AI perception
- Managing hallucinations in deep learning systems
Module 13: AI in Decision Making and Path Planning - Safety constraints in AI-based motion planning
- Behavior prediction for other road users
- Intention recognition using neural networks
- Uncertainty propagation in trajectory prediction
- Safe decision making under incomplete information
- Ethical considerations in AI driving policies
- Rule-based vs learning-based planner hybridization
- Defining safe fallback behaviors for AI planners
- Handling cut-in and sudden obstacle scenarios
- Lateral and longitudinal motion safety limits
- Interaction with vehicle dynamics constraints
- Velocity profile generation with safety margins
- Handling intersection negotiation with AI
- Risk field modeling using AI
- Safe merging and lane change strategies
- Handling construction zones and temporary obstacles
Module 14: Software and System Integration for AI Safety - Integrating AI modules into AUTOSAR architectures
- Handling AI in Classic vs Adaptive AUTOSAR
- Safety communication patterns between AI and control systems
- Data serialization and deserialization safety
- Handling timing jitter in AI inference
- Prioritization of AI tasks in real-time operating systems
- Memory allocation safety for AI workloads
- Preventing interference between safety and non-safety processes
- Secure boot with AI model integrity checks
- Over-the-air update safety for AI components
- Digital signature verification for model updates
- Rollback strategies for failed AI updates
- Version compatibility management
- Diagnostic trouble code generation for AI faults
- Service-oriented architecture integration
- Safety implications of microservices hosting AI models
Module 15: Managing AI Safety in Project Execution - Defining AI safety requirements traceability matrix
- Planning AI tasks within functional safety project timelines
- Estimating effort for AI safety activities
- Resource allocation for data, compute, and expertise
- Risk management strategies for AI development
- Milestone definition for AI safety validation
- Integrating AI teams with safety and systems engineering
- Communication protocols between disciplines
- Managing external AI vendors and suppliers
- Ensuring subcontractor compliance with safety standards
- Defining safety interfaces in system integration
- Change management processes for AI components
- Configuration management for AI models and datasets
- Security and safety co-engineering for AI
- Audit preparation for AI safety processes
- Lessons learned documentation for AI projects
Module 16: Certification and Audit Readiness - Preparing for independent functional safety assessment
- Documenting AI-specific work products for auditors
- Responding to auditor questions on AI uncertainty
- Demonstrating compliance with functional safety goals
- Presenting safety evidence for non-deterministic systems
- Handling ASIL D requirements for AI modules
- Compiling the safety case dossier
- Traceability from hazards to test results
- Tool qualification evidence package
- Model and data management records
- Process compliance with ISO 26262-6 and -8
- Handling auditor concerns about black-box AI
- Demonstrating sufficient V&V coverage
- Preparing for on-site assessment interviews
- Corrective action response procedures
- Post-certification surveillance planning
Module 17: Hands-On Projects and Real-World Applications - Project 1: Conducting HARA for an AI-based AEB system
- Project 2: Building a safety case for a neural network perception module
- Project 3: Designing runtime monitors for AI inference
- Project 4: Creating a test plan for an AI path planner
- Project 5: Developing a tool qualification package for an AI framework
- Project 6: Structuring data management for an autonomous vehicle project
- Project 7: Implementing fail-safe strategies for AI subsystems
- Project 8: Mapping AI development activities to ISO 26262 work products
- Project 9: Writing safety requirements for an object detection system
- Project 10: Constructing a GSN diagram for AI confidence
- Project 11: Designing a redundant perception system with AI diversity
- Project 12: Validating model performance on edge cases
- Project 13: Performing fault injection testing on an AI module
- Project 14: Creating a safety monitoring dashboard
- Project 15: Preparing a certification readiness checklist
- Project 16: Conducting a safety review meeting with stakeholders
Module 18: Continuing Your Career in AI-Driven Functional Safety - Building a personal brand in AI safety engineering
- Leveraging your certificate in job applications
- Networking with safety professionals and auditors
- Contributing to industry working groups
- Presenting at conferences on AI safety topics
- Transitioning into safety leadership roles
- Becoming an internal AI safety champion
- Consulting opportunities in functional safety
- Staying updated on regulatory developments
- Accessing advanced learning paths and specializations
- Joining professional safety engineering associations
- Using the certificate for client credibility
- Documenting career growth with project impact
- Creating an AI safety portfolio
- Preparing for internal and external audits as an expert
- Teaching AI safety principles to development teams
- Understanding ISO 26262-8 tool classification
- Determining tool impact and error detection capability
- Tool qualification for AI training frameworks
- Justifying use of open-source AI libraries
- Documentation required for tool qualification
- Tool confidence levels and their justification
- Using qualified tools in safety workflows
- Qualification of simulation environments for AI testing
- Managing updates and version changes in qualified tools
- Tool chaining and integrated development environment safety
- Automated code generation from AI models
- Safety checks in automated tool pipelines
- Audit trail requirements for AI development tools
- Traceability between tool outputs and safety artifacts
- Independent tool verification strategies
- Using AI-assisted tools for safety analysis
Module 11: Functional Safety Monitoring and Runtime Assurance - Designing runtime monitors for AI inference
- Using watchdog timers with variable execution times
- Input range and distribution monitoring
- Output consistency and stability checks
- Latency monitoring for time-critical AI functions
- Health checks for AI model loading and initialization
- Detecting silent failures in AI subsystems
- Model signature validation at runtime
- Resource usage monitoring for safety implications
- Memory leak detection in long-running AI processes
- Thermal and processing load impact on AI reliability
- Integration with vehicle health monitoring systems
- Logging AI decisions with safety context
- Event-triggered recording for post-failure analysis
- Fault reaction strategies based on monitor outputs
- Graceful degradation pathways for AI subsystems
Module 12: AI in Perception Systems - Safety-Critical Design Patterns - Safety architecture for camera-based object detection
- Semantic segmentation with uncertainty maps
- Object detection using convolutional neural networks
- Handling occlusion and partial visibility safely
- Fusion of camera, radar, and LiDAR with AI
- Temporal consistency in object tracking
- Detecting and handling sensor spoofing attacks
- Confidence thresholds for object classification
- Safe ego-motion estimation using AI
- Lane detection with fallback models
- Free space detection using neural networks
- Handling adverse weather conditions in perception
- Adapting perception models to local regulations
- Localization using AI with integrity monitoring
- Digital map integration with AI perception
- Managing hallucinations in deep learning systems
Module 13: AI in Decision Making and Path Planning - Safety constraints in AI-based motion planning
- Behavior prediction for other road users
- Intention recognition using neural networks
- Uncertainty propagation in trajectory prediction
- Safe decision making under incomplete information
- Ethical considerations in AI driving policies
- Rule-based vs learning-based planner hybridization
- Defining safe fallback behaviors for AI planners
- Handling cut-in and sudden obstacle scenarios
- Lateral and longitudinal motion safety limits
- Interaction with vehicle dynamics constraints
- Velocity profile generation with safety margins
- Handling intersection negotiation with AI
- Risk field modeling using AI
- Safe merging and lane change strategies
- Handling construction zones and temporary obstacles
Module 14: Software and System Integration for AI Safety - Integrating AI modules into AUTOSAR architectures
- Handling AI in Classic vs Adaptive AUTOSAR
- Safety communication patterns between AI and control systems
- Data serialization and deserialization safety
- Handling timing jitter in AI inference
- Prioritization of AI tasks in real-time operating systems
- Memory allocation safety for AI workloads
- Preventing interference between safety and non-safety processes
- Secure boot with AI model integrity checks
- Over-the-air update safety for AI components
- Digital signature verification for model updates
- Rollback strategies for failed AI updates
- Version compatibility management
- Diagnostic trouble code generation for AI faults
- Service-oriented architecture integration
- Safety implications of microservices hosting AI models
Module 15: Managing AI Safety in Project Execution - Defining AI safety requirements traceability matrix
- Planning AI tasks within functional safety project timelines
- Estimating effort for AI safety activities
- Resource allocation for data, compute, and expertise
- Risk management strategies for AI development
- Milestone definition for AI safety validation
- Integrating AI teams with safety and systems engineering
- Communication protocols between disciplines
- Managing external AI vendors and suppliers
- Ensuring subcontractor compliance with safety standards
- Defining safety interfaces in system integration
- Change management processes for AI components
- Configuration management for AI models and datasets
- Security and safety co-engineering for AI
- Audit preparation for AI safety processes
- Lessons learned documentation for AI projects
Module 16: Certification and Audit Readiness - Preparing for independent functional safety assessment
- Documenting AI-specific work products for auditors
- Responding to auditor questions on AI uncertainty
- Demonstrating compliance with functional safety goals
- Presenting safety evidence for non-deterministic systems
- Handling ASIL D requirements for AI modules
- Compiling the safety case dossier
- Traceability from hazards to test results
- Tool qualification evidence package
- Model and data management records
- Process compliance with ISO 26262-6 and -8
- Handling auditor concerns about black-box AI
- Demonstrating sufficient V&V coverage
- Preparing for on-site assessment interviews
- Corrective action response procedures
- Post-certification surveillance planning
Module 17: Hands-On Projects and Real-World Applications - Project 1: Conducting HARA for an AI-based AEB system
- Project 2: Building a safety case for a neural network perception module
- Project 3: Designing runtime monitors for AI inference
- Project 4: Creating a test plan for an AI path planner
- Project 5: Developing a tool qualification package for an AI framework
- Project 6: Structuring data management for an autonomous vehicle project
- Project 7: Implementing fail-safe strategies for AI subsystems
- Project 8: Mapping AI development activities to ISO 26262 work products
- Project 9: Writing safety requirements for an object detection system
- Project 10: Constructing a GSN diagram for AI confidence
- Project 11: Designing a redundant perception system with AI diversity
- Project 12: Validating model performance on edge cases
- Project 13: Performing fault injection testing on an AI module
- Project 14: Creating a safety monitoring dashboard
- Project 15: Preparing a certification readiness checklist
- Project 16: Conducting a safety review meeting with stakeholders
Module 18: Continuing Your Career in AI-Driven Functional Safety - Building a personal brand in AI safety engineering
- Leveraging your certificate in job applications
- Networking with safety professionals and auditors
- Contributing to industry working groups
- Presenting at conferences on AI safety topics
- Transitioning into safety leadership roles
- Becoming an internal AI safety champion
- Consulting opportunities in functional safety
- Staying updated on regulatory developments
- Accessing advanced learning paths and specializations
- Joining professional safety engineering associations
- Using the certificate for client credibility
- Documenting career growth with project impact
- Creating an AI safety portfolio
- Preparing for internal and external audits as an expert
- Teaching AI safety principles to development teams
- Safety architecture for camera-based object detection
- Semantic segmentation with uncertainty maps
- Object detection using convolutional neural networks
- Handling occlusion and partial visibility safely
- Fusion of camera, radar, and LiDAR with AI
- Temporal consistency in object tracking
- Detecting and handling sensor spoofing attacks
- Confidence thresholds for object classification
- Safe ego-motion estimation using AI
- Lane detection with fallback models
- Free space detection using neural networks
- Handling adverse weather conditions in perception
- Adapting perception models to local regulations
- Localization using AI with integrity monitoring
- Digital map integration with AI perception
- Managing hallucinations in deep learning systems
Module 13: AI in Decision Making and Path Planning - Safety constraints in AI-based motion planning
- Behavior prediction for other road users
- Intention recognition using neural networks
- Uncertainty propagation in trajectory prediction
- Safe decision making under incomplete information
- Ethical considerations in AI driving policies
- Rule-based vs learning-based planner hybridization
- Defining safe fallback behaviors for AI planners
- Handling cut-in and sudden obstacle scenarios
- Lateral and longitudinal motion safety limits
- Interaction with vehicle dynamics constraints
- Velocity profile generation with safety margins
- Handling intersection negotiation with AI
- Risk field modeling using AI
- Safe merging and lane change strategies
- Handling construction zones and temporary obstacles
Module 14: Software and System Integration for AI Safety - Integrating AI modules into AUTOSAR architectures
- Handling AI in Classic vs Adaptive AUTOSAR
- Safety communication patterns between AI and control systems
- Data serialization and deserialization safety
- Handling timing jitter in AI inference
- Prioritization of AI tasks in real-time operating systems
- Memory allocation safety for AI workloads
- Preventing interference between safety and non-safety processes
- Secure boot with AI model integrity checks
- Over-the-air update safety for AI components
- Digital signature verification for model updates
- Rollback strategies for failed AI updates
- Version compatibility management
- Diagnostic trouble code generation for AI faults
- Service-oriented architecture integration
- Safety implications of microservices hosting AI models
Module 15: Managing AI Safety in Project Execution - Defining AI safety requirements traceability matrix
- Planning AI tasks within functional safety project timelines
- Estimating effort for AI safety activities
- Resource allocation for data, compute, and expertise
- Risk management strategies for AI development
- Milestone definition for AI safety validation
- Integrating AI teams with safety and systems engineering
- Communication protocols between disciplines
- Managing external AI vendors and suppliers
- Ensuring subcontractor compliance with safety standards
- Defining safety interfaces in system integration
- Change management processes for AI components
- Configuration management for AI models and datasets
- Security and safety co-engineering for AI
- Audit preparation for AI safety processes
- Lessons learned documentation for AI projects
Module 16: Certification and Audit Readiness - Preparing for independent functional safety assessment
- Documenting AI-specific work products for auditors
- Responding to auditor questions on AI uncertainty
- Demonstrating compliance with functional safety goals
- Presenting safety evidence for non-deterministic systems
- Handling ASIL D requirements for AI modules
- Compiling the safety case dossier
- Traceability from hazards to test results
- Tool qualification evidence package
- Model and data management records
- Process compliance with ISO 26262-6 and -8
- Handling auditor concerns about black-box AI
- Demonstrating sufficient V&V coverage
- Preparing for on-site assessment interviews
- Corrective action response procedures
- Post-certification surveillance planning
Module 17: Hands-On Projects and Real-World Applications - Project 1: Conducting HARA for an AI-based AEB system
- Project 2: Building a safety case for a neural network perception module
- Project 3: Designing runtime monitors for AI inference
- Project 4: Creating a test plan for an AI path planner
- Project 5: Developing a tool qualification package for an AI framework
- Project 6: Structuring data management for an autonomous vehicle project
- Project 7: Implementing fail-safe strategies for AI subsystems
- Project 8: Mapping AI development activities to ISO 26262 work products
- Project 9: Writing safety requirements for an object detection system
- Project 10: Constructing a GSN diagram for AI confidence
- Project 11: Designing a redundant perception system with AI diversity
- Project 12: Validating model performance on edge cases
- Project 13: Performing fault injection testing on an AI module
- Project 14: Creating a safety monitoring dashboard
- Project 15: Preparing a certification readiness checklist
- Project 16: Conducting a safety review meeting with stakeholders
Module 18: Continuing Your Career in AI-Driven Functional Safety - Building a personal brand in AI safety engineering
- Leveraging your certificate in job applications
- Networking with safety professionals and auditors
- Contributing to industry working groups
- Presenting at conferences on AI safety topics
- Transitioning into safety leadership roles
- Becoming an internal AI safety champion
- Consulting opportunities in functional safety
- Staying updated on regulatory developments
- Accessing advanced learning paths and specializations
- Joining professional safety engineering associations
- Using the certificate for client credibility
- Documenting career growth with project impact
- Creating an AI safety portfolio
- Preparing for internal and external audits as an expert
- Teaching AI safety principles to development teams
- Integrating AI modules into AUTOSAR architectures
- Handling AI in Classic vs Adaptive AUTOSAR
- Safety communication patterns between AI and control systems
- Data serialization and deserialization safety
- Handling timing jitter in AI inference
- Prioritization of AI tasks in real-time operating systems
- Memory allocation safety for AI workloads
- Preventing interference between safety and non-safety processes
- Secure boot with AI model integrity checks
- Over-the-air update safety for AI components
- Digital signature verification for model updates
- Rollback strategies for failed AI updates
- Version compatibility management
- Diagnostic trouble code generation for AI faults
- Service-oriented architecture integration
- Safety implications of microservices hosting AI models
Module 15: Managing AI Safety in Project Execution - Defining AI safety requirements traceability matrix
- Planning AI tasks within functional safety project timelines
- Estimating effort for AI safety activities
- Resource allocation for data, compute, and expertise
- Risk management strategies for AI development
- Milestone definition for AI safety validation
- Integrating AI teams with safety and systems engineering
- Communication protocols between disciplines
- Managing external AI vendors and suppliers
- Ensuring subcontractor compliance with safety standards
- Defining safety interfaces in system integration
- Change management processes for AI components
- Configuration management for AI models and datasets
- Security and safety co-engineering for AI
- Audit preparation for AI safety processes
- Lessons learned documentation for AI projects
Module 16: Certification and Audit Readiness - Preparing for independent functional safety assessment
- Documenting AI-specific work products for auditors
- Responding to auditor questions on AI uncertainty
- Demonstrating compliance with functional safety goals
- Presenting safety evidence for non-deterministic systems
- Handling ASIL D requirements for AI modules
- Compiling the safety case dossier
- Traceability from hazards to test results
- Tool qualification evidence package
- Model and data management records
- Process compliance with ISO 26262-6 and -8
- Handling auditor concerns about black-box AI
- Demonstrating sufficient V&V coverage
- Preparing for on-site assessment interviews
- Corrective action response procedures
- Post-certification surveillance planning
Module 17: Hands-On Projects and Real-World Applications - Project 1: Conducting HARA for an AI-based AEB system
- Project 2: Building a safety case for a neural network perception module
- Project 3: Designing runtime monitors for AI inference
- Project 4: Creating a test plan for an AI path planner
- Project 5: Developing a tool qualification package for an AI framework
- Project 6: Structuring data management for an autonomous vehicle project
- Project 7: Implementing fail-safe strategies for AI subsystems
- Project 8: Mapping AI development activities to ISO 26262 work products
- Project 9: Writing safety requirements for an object detection system
- Project 10: Constructing a GSN diagram for AI confidence
- Project 11: Designing a redundant perception system with AI diversity
- Project 12: Validating model performance on edge cases
- Project 13: Performing fault injection testing on an AI module
- Project 14: Creating a safety monitoring dashboard
- Project 15: Preparing a certification readiness checklist
- Project 16: Conducting a safety review meeting with stakeholders
Module 18: Continuing Your Career in AI-Driven Functional Safety - Building a personal brand in AI safety engineering
- Leveraging your certificate in job applications
- Networking with safety professionals and auditors
- Contributing to industry working groups
- Presenting at conferences on AI safety topics
- Transitioning into safety leadership roles
- Becoming an internal AI safety champion
- Consulting opportunities in functional safety
- Staying updated on regulatory developments
- Accessing advanced learning paths and specializations
- Joining professional safety engineering associations
- Using the certificate for client credibility
- Documenting career growth with project impact
- Creating an AI safety portfolio
- Preparing for internal and external audits as an expert
- Teaching AI safety principles to development teams
- Preparing for independent functional safety assessment
- Documenting AI-specific work products for auditors
- Responding to auditor questions on AI uncertainty
- Demonstrating compliance with functional safety goals
- Presenting safety evidence for non-deterministic systems
- Handling ASIL D requirements for AI modules
- Compiling the safety case dossier
- Traceability from hazards to test results
- Tool qualification evidence package
- Model and data management records
- Process compliance with ISO 26262-6 and -8
- Handling auditor concerns about black-box AI
- Demonstrating sufficient V&V coverage
- Preparing for on-site assessment interviews
- Corrective action response procedures
- Post-certification surveillance planning
Module 17: Hands-On Projects and Real-World Applications - Project 1: Conducting HARA for an AI-based AEB system
- Project 2: Building a safety case for a neural network perception module
- Project 3: Designing runtime monitors for AI inference
- Project 4: Creating a test plan for an AI path planner
- Project 5: Developing a tool qualification package for an AI framework
- Project 6: Structuring data management for an autonomous vehicle project
- Project 7: Implementing fail-safe strategies for AI subsystems
- Project 8: Mapping AI development activities to ISO 26262 work products
- Project 9: Writing safety requirements for an object detection system
- Project 10: Constructing a GSN diagram for AI confidence
- Project 11: Designing a redundant perception system with AI diversity
- Project 12: Validating model performance on edge cases
- Project 13: Performing fault injection testing on an AI module
- Project 14: Creating a safety monitoring dashboard
- Project 15: Preparing a certification readiness checklist
- Project 16: Conducting a safety review meeting with stakeholders
Module 18: Continuing Your Career in AI-Driven Functional Safety - Building a personal brand in AI safety engineering
- Leveraging your certificate in job applications
- Networking with safety professionals and auditors
- Contributing to industry working groups
- Presenting at conferences on AI safety topics
- Transitioning into safety leadership roles
- Becoming an internal AI safety champion
- Consulting opportunities in functional safety
- Staying updated on regulatory developments
- Accessing advanced learning paths and specializations
- Joining professional safety engineering associations
- Using the certificate for client credibility
- Documenting career growth with project impact
- Creating an AI safety portfolio
- Preparing for internal and external audits as an expert
- Teaching AI safety principles to development teams
- Building a personal brand in AI safety engineering
- Leveraging your certificate in job applications
- Networking with safety professionals and auditors
- Contributing to industry working groups
- Presenting at conferences on AI safety topics
- Transitioning into safety leadership roles
- Becoming an internal AI safety champion
- Consulting opportunities in functional safety
- Staying updated on regulatory developments
- Accessing advanced learning paths and specializations
- Joining professional safety engineering associations
- Using the certificate for client credibility
- Documenting career growth with project impact
- Creating an AI safety portfolio
- Preparing for internal and external audits as an expert
- Teaching AI safety principles to development teams