Welcome to your essential guide to functional safety, tailored specifically for product developers. In a world where technology is increasingly integrated into every aspect of our lives—from industrial robots to autonomous vehicles—the potential for harm from product malfunctions makes functional safety not just important, but critical. This webinar cuts through the complexity to provide a clear understanding of what functional safety truly entails and why it's critical for product success. We'll start by defining functional safety not by its often-confusing official terms, but as a structured methodology for managing risk through defined engineering processes, essential product design requirements, and probabilistic analysis. The “north star” goals? To ensure your product not only works reliably but, if it does fail, it does so in a safe and predictable manner. We'll dive into two fundamental concepts: the Safety Lifecycle, a detailed engineering process focused on design quality to minimize systematic failures, and Probabilistic, Performance-Based Design using reliability metrics to minimize random hardware failures. You'll learn about IEC 61508, the foundational standard for functional safety, and how numerous industry-specific standards derive from it. The webinar will walk you through the Engineering Design phases: analyzing hazards and required risk reduction, realizing optimal designs, and ensuring safe operation. We'll demystify the Performance Concept and the critical Safety Integrity Level (SIL), explaining its definition, criteria (systematic capability, architectural constraints, PFD), and how it relates to industry-specific priorities. Discover key Design Verification techniques like DFMEA/DDMA and FMEDA, emphasizing how these tools help identify and address problems early in development. We'll detail the FMEDA technique showing how design decisions directly impact predictions like safe and dangerous failure rates, diagnostic coverage, and useful life. Finally, we'll cover Functional Safety Certification, explaining its purpose, process, and what adjustments to your development process can set you up for success.

The development and deployment of safe and dependable AI models is crucial in applications where functional safety is a key concern. Given the rapid advancement in AI research and the relative novelty of the safe-AI domain, there is an increasing need for a workflow that balances stability with adaptability. This work proposes a transparent, complete, yet flexible and lightweight workflow that highlights both reliability and qualifiability. The core idea is that the workflow must be qualifiable, which demands the use of qualified tools. Tool qualification is a resource-intensive process, both in terms of time and cost. We therefore place value on a lightweight workflow featuring a minimal number of tools with limited features. The workflow is built upon an extended ONNX model description allowing for validation of AI algorithms from their generation to runtime deployment. This validation is essential to ensure that models are validated before being reliably deployed across different runtimes, particularly in mixed-criticality systems. Keywords-AI workflows, safe-AI, dependable-AI, functional safety, v-model development

Making AI safe and dependable requires the generation of dependable models and dependable execution of those models. We propose redundant execution as a well-known technique that can be used to ensure reliable execution of the AI model. This generic technique will extend the application scope of AI-accelerators that do not feature well-documented safety or dependability properties. Typical redundancy techniques incur at least double or triple the computational expense of the original. We adopt a co-design approach, integrating reliable model execution with non-reliable execution, focusing that additional computational expense only where it is strictly necessary. We describe the design, implementation and some preliminary results of a hybrid CNN.

Ensuring the safety of road vehicles at an acceptable level requires the absence of any unreasonable risk arising from all potential hazards linked to the intended au-tomated driving function and its implementation. The assurance that there are no unreasonable risks stemming from hazardous behaviours associated to functional insufficiencies is denoted as safety of intended functionality (SOTIF), a concept outlined in the ISO 21448 standard. In this context, the acquisition of real driving data is considered essential for the verification and validation. For this purpose, we are currently developing a method with which data collect-ed representatively from production vehicles can be modelled into a knowledge-based system in the future. A system that represents the probabilities of occur-rence of concrete driving scenarios over the statistical population of road traffic and makes them usable. The method includes the qualitative and quantitative ab-straction of the drives recorded by the sensors in the vehicles, the possibility of subsequent wireless transmission of the abstracted data from the vehicles and the derivation of the distributions and correlations of scenario parameters. This paper provides a summary of the research project and outlines its central idea. To this end, among other things, the needs for statistical information and da-ta from road traffic are elaborated from ISO 21448, the current state of research is addressed, and methodical aspects are discussed.

The use of artificial intelligence (AI) and AI methods in the workplace holds both great opportunities as well as risks to occupational safety and discrimination. In addition to legal regulation, technical standards will play a key role in mitigating such risk by defining technical requirements for development and testing of AI systems. This paper provides an overview and assessment of existing international, European and German standards as well as those currently under development. The paper is part of the research project "ExamAI - Testing and Auditing of AI systems" and focusses on the use of AI in an industrial production environment as well as in the realm of human resource management (HR).

Autonomous vehicle technology is almost ready for widespread deployment--but people aren't ready for autonomous technology. This is because they don't yet trust the technology to make decisions fully on its own--thus inhibiting driver-assisted vehicles from transforming to truly autonomous vehicles. We accept a certain level of failures in technology like our laptops, smartphones and Wi-Fi because those limitations are merely inconveniences and we can live with that. Building a vehicle requires safety, security and automotive-quality considerations. But when it comes to technology where our lives are dependent on its performance, we have to hold it to a higher standard.

Recent advances in Reinforcement Learning (RL) combined with Deep Learning (DL) have demonstrated impressive performance in complex tasks, including autonomous driving. The use of RL agents in autonomous driving leads to a smooth human-like driving experience, but the limited interpretability of Deep Reinforcement Learning (DRL) creates a verification and certification bottleneck. Instead of relying on RL agents to learn complex tasks, we propose HPRL - Hierarchical Program-triggered Reinforcement Learning, which uses a hierarchy consisting of a structured program along with multiple RL agents, each trained to perform a relatively simple task. The focus of verification shifts to the master program under simple guarantees from the RL agents, leading to a significantly more interpretable and verifiable implementation as compared to a complex RL agent. The evaluation of the framework is demonstrated on different driving tasks, and NHTSA precrash scenarios using CARLA, an open-source dynamic urban simulation environment.

Most people surveyed about autonomous are comfortable with the technology and yet billions continue ... [ ] to be invested in the technology. In a recent survey by Myplanet of various technologies, "autonomous driving" came in as the most uncomfortable of the thirty-five technologies at 66.8% of the Americans surveyed. To put that in perspective, one of the technologies near the middle of the pack was "surgical robot" at 42% negative, which translates into "I'd rather your'bot cuts me open than have it drive me to the corner store." As summarized well by Jason Cottrell, Myplanet CEO, "Customers have made up their minds about autonomous driving and it's skewed heavily to the negative." Other studies, in fact, corroborate that level of fear (e.g.

I already posted on one automotive panel at this year's Arm TechCon. A second I attended was a more open-ended discussion on where we're really at in autonomous driving. Most of you probably agree we've passed the peak of the hype curve and are now into the long slog of trying to connect hope to reality. There are a lot of challenges, not all technical; this panel did a good job (IMHO) of exposing some of the tough questions and acknowledging that answers are still in short supply. I left even more convinced that autonomous driving is still a hard problem needing a lot more investment and a lot more time to work through.

Job Description The successful candidate will own all aspects of regulatory compliance processes/practices programs (including driving continuous improvement) within Intel's Internet of Things (IoT) Group. Product Regulatory Engr will support growth through expanded footprint in key verticals such as transportation, industrial/energy, retail, home/buildings etc through development and implementation of product regulatory test plans to ensure that the system development platforms (or products) meet regulatory requirements for countries where the products/platforms will be shipped. Responsibilities include: supporting design teams on product safety, functional safety, connectivity (WiFi-BT, Zigbee, Cellular, RFID, NFC), and EMC related issues.; Minimum QualificationsBS in Electrical Engineering, Physics or related field.• 3 years with regulatory certifications in product safety, EMC and/or RF/wireless• 3 years Rf/ Wireless regulatory certifications – FCC, PTCRB, experience with any of the carriers – ATT, Verizon, Nokia, Siemens, etc• 3 years EMC regulatory certification – FCC, CE, CISPRPreferred qualifications: – MS or PhD in electrical engineering or physics or related field preferred.- Unrestricted right to work in the US without sponsorship- Global product regulatory knowledge; inclusive of wireless safety EMC- Experience with wireless RF test methods and equipment- Experience in the design of wireless/RF systems including antennas is highly desired.-