Certified Autonomy Safety Professional - Machine Learning
General Information
- Code
- UL05
- Duration
- 2 Days
Artificial intelligence (AI) plays a significant role in the automotive industry as it competes to develop and market vehicles that can perform tasks that previously required an experienced driver.
Nowadays, carmakers and suppliers must face the difficulties of developing all the operational and tactical functions required to operate a vehicle in on-road traffic that performs driving maneuvers better than an experienced driver.
This machine learning (ML) for automotive safety assurance training provides guidance for the safe development of ML components into the state-of-the-art safety frameworks used for automotive, including functional safety (FuSa) andSafety of Intended Functionality (SOTIF).
This training also includes the reference information of novel autonomy safety standards such as:
- UL 4600, the Standard for Evaluation of Autonomous Products
- ISO/TR 4804:2020, Road Vehicles - Safety and Cybersecurity for Automated Driving Systems - Design, Verification and Validation
- ISO/IEC DTR 5469, Artificial intelligence - Functional safety and AI systems
- ISO/AWI PAS 8800, Road Vehicles - Safety and Artificial Intelligence
- ISO/IEC TR 24029-1:2021, Artificial Intelligence (AI) - Assessment of the Robustness of Neural Networks - Part 1: Overview
- ISO/IEC DIS 24029-2, Artificial intelligence (AI) - Assessment of the Robustness of Neural Networks - Part 2: Methodology for the Use of Formal Methods
This training will help you:
- apply ML concepts within FuSa and SOTIF frameworks
- develop safer, more robust ML algorithms to solve autonomy safety problems
- obtain sufficient guidance to define, specify, develop, evaluate, deploy and monitor ML algorithms in the context of autonomy safety
- provide ML safety analysis that supports completeness and correctness for safer autonomous vehicles to an acceptable society risk level
Contents
Day 1
- Intro
- Automotive safety and AI standards
- Deployment of AI into systems
- Factors to consider for AI safety
- Running example
- Specification and design (SOTIF-specific)
- AI safety culture
- Concept phase
- System development phase
Day 2
- Hardware development phase
- Software development phase
- Verification and validation (V&V) strategy
- Software verification phase
- Hardware verification phase
- System verification phase
- Vehicle verification and validation phase
- Assessment
- Operations, decommissioning, service and maintenance
Target audience
Engineers working with advanced driver assistance systems(ADAS) and autonomous vehicles (AV), such as automotiveengineers, safety engineers, data scientists, project leaders,testing personnel
Prerequisites
- UL Certified Artificial Intelligence Professional * Foundations training oder praktische Erfahrung in der Entwicklung von ML-Modellen
- Vorkenntnisse in SOTIF (ISO 21448) und funktionaler Sicherheit (ISO 26262)
Examination
Participants who complete the full two days of training are eligible to take a two-hour certification exam on the morning of the third day.
Those who pass the exam are individually certified as a UL Certified Autonomy Safety Professional – Machine Learning or UL-CASP.
Any Questions?
Any questions about our engineering service offers? Feel free to call us!
