Autonomous Driving Rule Grading Using a Domain-Specific Language
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Solution Overview
Problem
Autonomous vehicles face challenges in efficiently grading and improving their driving operations due to the complexity and volume of scenarios, making it difficult for human users to manually review and audit their performance at accelerated computational rates.
Innovation Solution
A system and method using a domain-specific language to parse rules and generate conditions for evaluating driving operations, comparing observations to these conditions, and generating a grading summary to assess compliance, which can autonomously adjust future operations and store reports for user analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual review and audit of driving operations is performed, then accuracy of performance assessment is improved, but productivity and computational rate deteriorate
Solution Approach 1:
The patent introduces an automated grading system with domain-specific language parsers and rule engines as intermediaries between raw driving operation data and performance assessments. These intermediaries automatically parse driving scenarios, apply safety rules and grading criteria, and generate performance grades, replacing manual human review while maintaining assessment accuracy through systematic rule-based evaluation
Solution Approach 2:
The patent replaces the mechanical human review process with an automated computational system that uses domain-specific language parsing, rule condition evaluation, and automated grading algorithms. This substitution enables high-speed processing of driving operations at computational rates far exceeding human capabilities while maintaining consistent and objective performance assessment
2Measurement precision
If comprehensive rules and observations are analyzed for each driving operation, then measurement precision is improved, but device complexity and processing time worsen
Solution Approach 1:
The patent transforms complex driving operation data into standardized numerical observations and metrics that can be systematically compared against rule conditions. By converting qualitative driving behaviors into quantifiable parameters (e.g., distances, speeds, positions), the system enables precise grading through mathematical comparisons while managing complexity through parameter standardization
Solution Approach 2:
The patent segments the grading system into distinct modular components: domain-specific language parsers that interpret rules, rule condition generators that break down grading criteria, observation extractors that identify relevant driving parameters, and grading engines that compute results. This segmentation allows each component to handle specific tasks independently, reducing overall system complexity while maintaining comprehensive grading accuracy
Data Source
AI summary
A method may include obtaining input information that describes a driving operation of a vehicle and obtaining a rule that indicates an approved driving operation of the vehicle. The method may include parsing the rule using a domain-specific language to generate rule conditions in which the domain-specific language is a programming language that is specifically designed for analyzing driving operations of vehicles. The method may include representing the input information as observations relating to the vehicle in which each of the observations is comparable to one or more of the rules. The method may include comparing the observations to one or more respective comparable rule conditions and generating a grading summary that evaluates how well the observations satisfy the respective comparable rule conditions based on the comparison. A future driving operation of the vehicle may be adjusted based on the grading summary.