Autonomous Driving Grading Algorithm for Performance Evaluation
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Solution Overview
Problem
Current autonomous vehicle systems lack an effective method to quantify and evaluate performance in simulation environments, particularly in novel driving situations, and fail to provide comprehensive training and comparison between different driving systems.
Innovation Solution
A grading algorithm that uses feature weighting and complexity metrics to assess autonomous driving systems, incorporating human grading scores and breaking down driving tasks into subtasks to generate performance metrics, enabling continuous grading and unbiased comparison.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If autonomous vehicle software is tested through manual evaluation methods, then evaluation accuracy may be maintained, but testing efficiency and productivity are significantly reduced
Solution Approach 1:
The system enables autonomous self-evaluation by having the autonomous vehicle perform its own grading through the cognitive model and performance metric generation, eliminating the need for external manual evaluation while maintaining objective and consistent measurement across all test scenarios
Solution Approach 2:
The system transforms subjective evaluation parameters into objective quantifiable metrics by breaking down driving scenarios into discrete features and subtasks, each with measurable performance indicators that can be automatically calculated and compared
2Measurement precision
If comprehensive performance metrics are generated through detailed feature analysis, then evaluation precision is improved, but system complexity increases
Solution Approach 1:
The driving scenario is segmented into discrete features and subtasks that can be independently evaluated. Each feature represents a specific aspect of driving performance that can be measured separately, allowing comprehensive evaluation without overwhelming system complexity
Solution Approach 2:
The cognitive model serves as an intermediary layer between raw sensor data and performance metrics, automatically interpreting sensor inputs and control outputs according to predefined driving rules and scenarios, thereby simplifying the overall evaluation architecture
3Measurement precision
If the system evaluates all driving features in detail, then measurement completeness is improved, but processing time and loss of time increase
Solution Approach 1:
Driving scenarios, features, and evaluation criteria are pre-configured and stored in the system before actual testing begins. This preliminary setup allows the system to quickly retrieve and apply appropriate evaluation metrics during real-time or near-real-time testing without performing complex analysis from scratch
Solution Approach 2:
By dividing the evaluation into independent feature assessments and subtask completions, the system can process multiple features in parallel or selectively focus on critical features, reducing overall processing time while maintaining comprehensive evaluation
Data Source
AI summary
The present application generally relates to methods and apparatus for evaluating and assigning a performance metric to a driver response to a driving scenario. More specifically, the application teaches a method and apparatus for breaking a scenario into features, assigning each feature a grade and generating an overall grade in response to a weighted combination of the grades.