The automotive industry is in the midst of a transformation. As vehicles become smarter and more reliant on software, developers are increasingly at the center of innovation. The challenge? Developing automotive software requires compliance with stringent safety standards like ISO 26262, ASPICE, and AUTOSAR. This article details the concept of an automotive embedded coding assistant and how it can support developers in creating compliant embedded C/C++ code, following requirements from standards like AUTOSAR, which reference MISRA C guidelines.

The need for an automotive coding assistant is driven by several converging trends. The automotive sector is transitioning from traditional mechanical engineering to a software-defined vehicle (SDV) model, where a car’s value is increasingly defined by its software features. This shift demands scalable development practices to manage the complexity of autonomous driving, electric powertrains, and advanced connectivity. Recent advances in AI and machine learning now enable assistants to deeply understand automotive software, helping developers focus on innovation rather than repetitive tasks or extensive documentation searches.

What Is an Automotive Coding Assistant?

An automotive coding assistant is an intelligent companion for developers in the automotive industry, designed to streamline coding processes, boost productivity, and ensure high-quality code. It leverages advances in generative AI, natural language processing, and machine learning to help developers solve the many challenges of automotive software development. Imagine a virtual collaborator that can provide suggestions and write code for AUTOSAR components or walk you through regulatory compliance standards and their implications for the respective code.

Potential Challenges and Considerations

While the promise of an automotive coding assistant is enormous, there are challenges to address. The assistant must be designed with a deep understanding of safety and security requirements, which are non-negotiable in the automotive space. Developers may have concerns about the reliability of AI recommendations. Trust can be built by providing clear references to the sources of each suggestion, such as code repositories or official documentation.

Sketch of an Automotive Software Development Assistant

The assistant can generate code snippets based on developer goals, whether it’s a simple vehicle sensor readout or a complex algorithm for battery management. It helps refactor legacy code to improve compliance, readability, and maintainability. Moreover, the assistant identifies existing code implementations that fulfill certain functions while meeting regulatory requirements, enabling developers to reuse proven solutions.

Current automotive coding assistants and Large Language Models (LLMs) can generate C/C++ code but aren’t specifically trained for embedded automotive systems. Enhancements can be made by fine-tuning existing models to align with automotive standards or by using prompt engineering to guide the model’s responses. An agent can act as an intermediary, enhancing developer prompts with relevant requirements to ensure the generated code meets industry standards. This mechanism can also be used to make existing code compliant with regulatory standards.

Another important feature is finding existing compliant code implementations in private code repositories. This is done using a process called retrieval-augmented generation (RAG). Essentially, the codebase is analyzed and broken down into searchable components, which are stored in a specialized database. The assistant can then search this database to quickly find and recommend the most relevant code snippets, making it easier for developers to reuse proven, compliant solutions in their projects.

Conclusion: Driving Into the Future

The automotive coding assistant is more than just a tool—it’s a partner for every developer navigating the complexities of the automotive software landscape. As the industry continues its shift toward software-defined vehicles, the need for such intelligent assistants will only grow. They promise to reduce development time, enhance safety, and ultimately accelerate the journey toward a future of connected, autonomous, and intelligent vehicles.