Autonomous Vehicle Planning Profiling With Complexity-Weighted Scoring
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Solution Overview
Problem
Existing solutions for evaluating the motion planning performance of autonomous vehicles fail to accurately assess performance across varying traffic conditions, as they typically rely on a simple average score across all frames without considering the complexity of different stages in a driving scenario.
Innovation Solution
A method that calculates a weighted score for each planning cycle based on individual performance scores and weights derived from factors like path curvature, obstacles, and junction size, using high-definition maps and perception messages to account for the complexity of each stage in the driving scenario.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a simple average score across all frames is used to evaluate motion planning performance, then the evaluation process is simple and fast, but the evaluation accuracy is insufficient because it does not account for varying traffic conditions at different stages
Solution Approach 1:
The patent divides the driving scenario into multiple stages based on traffic conditions (e.g., approaching intersection, through intersection, leaving intersection). Each stage is evaluated separately with stage-specific weights, allowing the system to capture varying planning performance across different traffic contexts rather than using a single average score.
Solution Approach 2:
Different weights are assigned to different stages of the driving scenario based on their importance and complexity. Critical stages like approaching and through intersection receive higher weights, while less critical stages receive lower weights. This local differentiation improves evaluation accuracy by reflecting the varying significance of different driving phases.
2Measurement precision
If stage-specific weights are assigned based on traffic condition complexity, then the evaluation accuracy improves, but the computational complexity and data processing requirements increase
Solution Approach 1:
The system pre-defines stage boundaries and weight assignments based on typical driving scenarios and traffic condition patterns. By establishing these evaluation criteria beforehand, the system reduces real-time computational burden while maintaining accurate stage-based evaluation across different driving contexts.
3Measurement precision
If multiple factors like path curvature, obstacles, and junction size are considered for weighting, then the evaluation becomes more comprehensive and accurate, but the calculation complexity and time required increase
Solution Approach 1:
The patent extracts and focuses on the most critical factors for motion planning evaluation (path curvature, obstacles, junction size) while excluding less relevant variables. This selective approach maintains comprehensive evaluation of key planning aspects while reducing computational complexity compared to considering all possible driving parameters.
Data Source
AI summary
In one embodiment, an exemplary method includes the operations of receiving, at a profiling application, a record file recorded by the ADV for a driving scenario in an area, and a high definition map matching the area; extracting planning messages and perception messages from the record file; and aligning the planning message and the perception messages based on their timestamps. The method further includes calculating an individual performance score for each planning cycle of the ADV for the driving scenario based on the planning messages; calculating a weight for each planning cycle based on the perception messages and the high definition map; and then calculating a weighted score for the driving scenario based on individual performance scores and their corresponding weights.