Building Reliability into AI through Deterministic Engineering

As software-defined vehicle (SDV) programs evolve, automotive development is at a major turning point. While the cycle of feature updates is shortening and the interaction between systems is becoming increasingly sophisticated, it is more important than ever to meet strict requirements for safety, reliability, and long-term maintainability. In this environment, generative AI is already beginning to be integrated into engineering workflows. While it contributes to accelerating development speed, direct application to safety-critical systems is considered difficult due to characteristics such as non-deterministic behavior, lack of understanding of physical properties, and traceability constraints. If the output of generative AI is introduced without restrictions, ensuring verification, certification, and traceability becomes a major challenge.

Model-Based Design addresses these challenges through deterministic execution, executable specifications, and simulation based on the laws of physics. MathWorks integrates these strengths, directly embedding generative AI-assisted capabilities into Model-Based Design tools, thereby simultaneously accelerating workflows and meeting the long-term reliability and certification requirements demanded by automotive software.

Simulation as the Foundation of Trust

In generative AI-driven engineering, simulation is the foundation of reliability. Simulation provides an environment where system behavior can be verified early and repeatedly. In Model-Based Design, closed-loop simulations can be executed within a continuous development pipeline, allowing even artifacts involving generative AI to be continuously verified in a virtual environment long before the software reaches the physical hardware.

Closed-loop simulation reveals defects—such as instability, timing issues, saturation, and integration errors—that only become apparent through the real-time interaction of software, hardware, and physical dynamics. Unlike traditional software testing, which verifies code logic in isolation, simulation verifies the overall behavior of the system against required specifications under realistic operating conditions, enabling earlier detection of issues directly related to safety and performance.

In advanced organizations, 'shift-left' is not a one-time effort. Virtual verification is integrated directly into Continuous Integration and Continuous Delivery (CI/CD) pipelines, with automated builds and simulations running for every change. By constantly evaluating models against representative scenarios and criteria, verification becomes an ongoing activity rather than an intermittent task.

Scalable Development for Evolving E/E Architectures

The electrical/electronic (E/E) architecture of vehicles is transitioning from ECU-centric configurations to zonal and centralized computing platforms. Software must not be tied to specific hardware; it must operate reliably on heterogeneous computing resources ranging from small controllers to high-performance automotive computers, while possessing portability and scalability.

Model-Based Design meets this demand by separating system behavior and software intent from hardware implementation. Engineers build executable models that serve as a reliable 'single source of truth'. From these models, production-ready code can be generated for a variety of processors and OSs, including hardware accelerators such as GPUs, DSPs, and NPUs, and AI inference engines. This approach allows AI-based features, such as virtual sensors, to be developed and verified at the system level, enhancing cross-platform efficiency and consistency while minimizing the need for algorithm redesign for each target.

Enhancing Collaboration through Model-Based Design

Amidst increasing complexity, engineering organizations are required to transform the very nature of collaboration. Embedding simulation, virtualization, and automated verification directly into CI/CD workflows enables rapid iteration across software, AI models, and hardware acceleration strategies. The model-centric approach enhances organizational agility in software-defined and AI-driven vehicle development while maintaining robustness, safety, and long-term maintainability.

Integrating AI into Deterministic Workflows

In automotive development, AI is most effective when embedded within a deterministic modeling framework. In Model-Based Design tools, generative AI-generated content is automatically tied to existing interfaces, data definitions, and architectural constraints. Model Context Pro