System and method for translating natural language traffic rules into formal logic for autonomous moving vehicles

A method for preparing a machine learning system for translating natural language traffic rules into formal logic representations for autonomous moving vehicles,

• wherein the machine learning system comprises a pre-trained language model which has been trained on large volumes of text data to assign probabilities to sequences of words, wherein preparation method of the machine learning systems comprises either
• using the pre-trained language model is used for prompting in the following way:
• input for the pre-trained language model are natural language instructions and language traffic rules, and
• presenting a number of inputs and corresponding formal logic target outputs as examples to the machine learning system so that the machine learning system will learn to generate a formal logic traffic rule when prompted with natural language instructions and a natural language traffic rule;
• or
• fine-tuning the machine learning system using training data comprising language traffic rules as training input and corresponding formal logic traffic rules as target output for the pre-trained language model;
• wherein the language traffic rules comprise at least one of natural language rules and/or synthetic language traffic rules.