Automotive Functional Safety (FuSa) Challenges

Modern vehicles have become quite sophisticated, like a supercomputer on wheels. They integrate a vast number of electronic components, including thousands of chips, to deliver advanced functionalities ranging from infotainment to critical safety systems. This increasing complexity necessitates a robust approach to automotive Functional Safety (FuSa), a discipline focused on ensuring the absence of unreasonable risk due to hazards caused by malfunctioning electrical and electronic (E/E) systems in road vehicles. For engineers in the automotive market, understanding and implementing FuSa principles is mandatory to designing safe, reliable, and trustworthy vehicles.

Industry Standards and Cooperation

Ensuring uniform safety across the automotive industry requires collaboration and adherence to common frameworks. The International Organization for Standardization (ISO), in partnership with auto manufacturers (OEMs) and their suppliers, has developed key standards, most notably ISO 26262. This standard governs the functional safety of E/E systems within road vehicles. A critical element of ISO 26262 is the Automotive Safety Integrity Level (ASIL) classification, which ranges from ASIL-A (least stringent) to ASIL-D (most stringent). ASIL-D serves as a crucial benchmark for assessing and guaranteeing the reliability and safety of systems-on-chip (SoCs) and 3D integrated circuits (ICs) in applications where a failure could lead to severe consequences. OEMs collaborate with engineers to incorporate automotive-grade IP alongside ISO 26262-certified design and testing methodologies to create SoCs that meet stringent safety requirements.

Beyond ISO 26262, other standards contribute to vehicle safety. The Institute of Electrical and Electronics Engineers (IEEE), through its committee on IEEE P2851, sets guidelines for the design, implementation, and evaluation of safety-critical systems. These standards outline essential methods, description languages, data models, and databases that can be utilized across the industry in a technology-agnostic manner, contributing to safer vehicles and potentially reducing costs associated with redesigns and recalls due to safety issues. These standards also facilitate data exchange and interoperability throughout the vehicle’s lifecycle and evolve alongside emerging technologies like AI. The integration of more technology inherently introduces more risk, underscoring the importance of these safety frameworks.

Balancing Risk and Reward in Feature Integration

The integration of advanced technologies, such as Artificial Intelligence (AI) components into modern vehicles offers numerous benefits, including park assist and real-time analysis of driving conditions. This integration presents a balance between the potential rewards and inherent risks. High-performance SoCs that handle AI workloads consume more power, impacting energy efficiency, particularly in electric vehicles. 3D ICs introduce thermal management challenges that require effective cooling methods to ensure reliability and longevity, which is especially critical for the battery life and thermal stability of electric vehicles.

The addition of more chips and safety features inherently increases system complexity, leading to a higher risk of failure. Data security is another significant concern that must be addressed proactively. Material costs associated with these new technologies can impact profit margins and vehicle affordability. Functional safety necessitates a careful balancing act where OEMs must weigh safety mechanisms against budget constraints, performance requirements, and security considerations.

Security as an Indispensable Element of Safety

A fundamental principle in modern automotive engineering is that if the technology is not secure, it is not safe. To ensure reliable and predictable vehicle operation, tamper-proof data transfer among the numerous sensors and components is essential. Achieving this requires a comprehensive security approach that integrates security considerations from the earliest stages of design. This includes incorporating the extensive security expertise gained in the networking domain over the past three decades into in-vehicle network architectures. Recommended security measures encompass encryption for data in transit and at rest, multi-factor authentication, secure communication protocols, and regular security audits.

Hardware-based security features are vital in defending against potential threats. These features include secure enclaves, Trusted Execution Environments (TEEs), and Intrusion Detection and Prevention Systems (IDPS) that protect sensitive data and system integrity. The use of Hardware Security Modules (HSMs) and secure boot processes ensures that only authenticated and untampered firmware and software can operate within the vehicle’s Electronic Control Units (ECUs). Adherence to the ISO 21434 standard is also crucial for comprehensive vehicle security, as it covers the entire vehicle lifecycle, emphasizing risk management, organizational and technical requirements, and continuous monitoring. Since the components governing security also rely on chips, their safe operation must be ensured through proactive measures like predictive maintenance.

Proactive Reliability through Predictive Maintenance

Predictive maintenance leverages advanced analytics and machine learning algorithms to anticipate potential failures before they occur. This proactive approach can be applied to any part of the vehicle and is increasingly being used at the silicon level to predict chip degradation. Predictive maintenance techniques can monitor the health of critical components such as an engine’s electronic control unit (ECU) or the battery management system (BMS) in electric vehicles, allowing for timely maintenance before actual failures.

Achieving optimal results with predictive maintenance requires the vehicle operating system to analyze vast amounts of data using advanced technologies capable of identifying patterns and predicting potential failures with high precision. This often involves leveraging edge computing to process data locally within the vehicle and cloud computing to aggregate and analyze data at a larger scale. Advanced machine learning models are trained on both historical and real-time data to recognize early indicators of component degradation, such as subtle rises in operating temperature preceding a chip failure. A comprehensive framework for managing and effectively utilizing this extensive data is essential to fully realize the benefits of predictive maintenance.

Silicon Lifecycle Management

Silicon Lifecycle Management (SLM) provides a comprehensive strategy for managing the data and processes associated with the maintenance and service of vehicle components throughout their entire lifecycle. By integrating SLM with predictive maintenance, cybersecurity measures, and adherence to industry standards, manufacturers can ensure that maintenance activities are not only timely but also aligned with the overall vehicle service strategy.

Synopsys offers a broad portfolio of standards-based, automotive-grade IP, including interface, processor, security, and foundation IP, which are compliant with industry standards to accelerate SoC-level design and qualification. They also provide a comprehensive suite of integrated, standards-based SLM tools, IP, and methodologies that offer observability, analytics, and automation at the silicon level. Their Process, Voltage, and Temperature (PVT) Monitor IP, for example, is certified as ASIL-B ready and meets the AEC-Q100 Grade 2 standard. By gathering data at every stage of the product lifecycle, Synopsys’ SLM IP facilitate continuous analysis and provide actionable insights, improving design efficiency and quality while also predicting in-field chip degradation or failure. These automotive-grade IPs and the continuous insights derived from SLM are crucial for ensuring the long-term functional safety of modern vehicles. Synopsys’ SLM product family continuously monitors critical silicon metrics like voltage, temperature, margins, and health, enhancing the reliability and performance of ECUs, CPUs, GPUs, and other architectures through real-time edge and cloud analytics. This enables the assessment of silicon aging and reliability, providing valuable insights into both systemic and random defects and facilitating predictive maintenance to extend silicon lifecycle and reduce costs. Synopsys’ electronic digital twins (eDT) further support automotive development by enabling the validation of each step from SoC to ECU to E/E architecture before production, optimizing for performance, safety, reliability, quality, and security.

Summary

Ensuring automotive functional safety in today’s complex vehicles demands a holistic, silicon-to-systems approach. While modern safety features and sensors can reduce human errors, they also introduce new levels of complexity and risk. To maintain and enhance functional safety, the industry must continue to promote and refine essential standards, rigorously ensure the security of data within and around the vehicle, and leverage comprehensive approaches that provide end-to-end monitoring, verification, and predictability from the silicon level up to the complete vehicle system. Companies like Synopsys play a lead role by providing the necessary IP, tools, and methodologies to navigate these challenges and build safer, more reliable software-defined vehicles.

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