Traffic Rule Translation Into Formal Logic for Autonomous Vehicles
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Solution Overview
Problem
Current methods for formalizing traffic rules in autonomous vehicles rely on manual translation from natural language to formal logic, which is not scalable and requires human expertise, limiting their applicability and efficiency.
Innovation Solution
A machine learning system utilizing large pre-trained language models, such as T5 or GPT-3, is trained to automatically translate natural language traffic rules into formal logic representations, with a human-in-the-loop for fine-tuning, leveraging synthetic examples and neural networks to generate and fine-tune the models for accurate formal representations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual translation from natural language to formal logic is used, then accuracy of traffic rule formalization is improved, but scalability and efficiency deteriorate
Solution Approach 1:
The patent segments the traffic rule formalization process into multiple components: natural language processing module, formal logic generation module, and validation module. This segmentation allows automated processing while maintaining accuracy through specialized sub-components, resolving the contradiction between automation efficiency and formalization accuracy.
Solution Approach 2:
The patent introduces an intermediary machine learning model that translates natural language traffic rules into formal logic representations. This intermediary automates the translation process, improving scalability and efficiency while maintaining accuracy through trained model predictions, thus resolving the contradiction between manual accuracy and automated productivity.
2Productivity
If automated translation using machine learning is used, then scalability and efficiency are improved, but complexity of the system increases
Solution Approach 1:
The patent employs a universal machine learning model that can handle multiple traffic rule scenarios and formal logic types through a single system architecture. This multi-functionality reduces overall system complexity compared to having separate specialized systems for each traffic rule type, while maintaining high scalability and efficiency.
Solution Approach 2:
The patent uses template-based formal logic structures that can be copied and adapted for different traffic rule scenarios. This copying approach simplifies the system by providing reusable formal logic patterns, reducing the complexity of generating formal representations from scratch for each new traffic rule.
3Productivity
If automated translation using machine learning is used, then productivity is improved, but reliability of formal logic generation deteriorates
Solution Approach 1:
The patent implements a feedback mechanism where generated formal logic is validated against expected outputs and traffic rule semantics. This feedback loop identifies and corrects errors in automated translation, maintaining reliability while preserving the productivity benefits of machine learning-based automation.
Solution Approach 2:
The patent performs preliminary training of the machine learning model using curated datasets of traffic rules and their formal logic representations. This preliminary action ensures the model learns accurate translation patterns before deployment, improving the reliability of formal logic generation while maintaining high translation efficiency during operation.
Data Source
Figure 1~2
AI summary
The invention relates to a system and method for translating natural language traffic rules into formal logic including a training method for a machine learning system and can be used in the context of advanced driver assistance systems or autonomous driving systems (ADAS or AD) for vehicles or other autonomous moving vehicles like robots and drones. A method for method for preparing a machine learning system for translating natural language traffic rules into formal logic representations for autonomous moving (or driving) vehicles. The machine learning system comprises a (large) pre-trained language model which has been trained on large volumes of text data to assign probabilities to sequences of words. The machine learning system is prepared by one of the following alternatives: i. 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, - a number of inputs and corresponding formal logic target outputs are presented as (few shot) 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. ii. The machine learning system is fine-tuned 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 (or the machine learning system, respectively). The language traffic rules comprise natural language rules and/or synthetic language traffic rules.