Using AI in driver development

With the rapid development of technologies and growing requirements to software quality, development automation is becoming a key factor of competitiveness. The use of artificial intelligence (AI) in driver development can significantly reduce development time, minimize errors, and improve the quality of the final product.

OEM documentation contains:

• Specifications and technical requirements;
• Algorithms of operation of metering devices;
• Description of interaction protocols.

This is an extensive (but not always exhaustive) manual that includes:

• Hardware interfaces and communication protocols - a detailed description of the data exchange sequence;
• Signal processing algorithms - specifications for collecting, filtering and interpreting measured parameters.

However, the large volume and complexity of such documentation as well as its incompleteness or low quality often create obstacles for quick creation of new drivers. This is where artificial intelligence comes to the rescue, capable of analyzing and structuring large arrays of technical data.

Benefits of using AI in driver development

Utilizing AI to assist with manufacturer documentation provides the following benefits:

• Reduction in development time. Using AI can cut the time it takes to create a new driver in half. The technology helps compress information from documentation, extract relevant data on demand, and generate code snippets (e.g., driver methods).
• Minimizing errors. AI helps identify and correct common errors in documentation (e.g., inaccuracies in protocol descriptions). This reduces the risk of bugs in the finished driver.

Algorithm for using AI

During the development process using AI, the following algorithm was formed:

1. Data Preparation. Documentation is converted to vector format (using embedding models such as text-embedding-ada-002 from OpenAI) and stored in a vector database.
2. Semantic Search. The user formulates a query (e.g.: “Provide a description of the clock archive protocol and the algorithm for reading it ”). The query is converted into a vector, after which the database is searched. The found relevant fragments are passed to the analysis model (e.g., Deepseek R1) along with the text prompt.
3. Code generation. Based on the received context, the AI generates basic driver methods in the selected programming language (in this case - Golang).

It is important to note that AI does not replace the understanding of device protocols, but it speeds up access to information, simplifies its interpretation and thus optimizes the development process.