Autonomous Vehicle Perception Validation With Learned Divergence Scoring
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Solution Overview
Problem
Evaluating the performance of autonomous vehicle perception systems is challenging due to the complexity of determining material versus immaterial errors in object recognition and tracking, making it difficult to quantify accuracy and requiring extensive manual tuning.
Innovation Solution
A machine-learned evaluation system uses multiple divergence metrics with context-aware weights to assess perception outputs, self-calibrating through unit tests and constraining weights to maintain interpretability and efficiency, allowing for automated and scalable evaluation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If naive accuracy measurement requiring identity between perception output and label is used, then measurement precision is improved, but device complexity and computational intractability worsen
Solution Approach 1:
The evaluation metric is segmented into multiple component divergences (e.g., positional divergence, dimensional divergence, orientation divergence) that can be computed independently and then aggregated. This breaks down the intractable identity comparison into manageable, computable components that maintain measurement precision while reducing computational complexity.
Solution Approach 2:
The approach transforms the evaluation from a discrete identity check to a continuous parameter-based comparison using divergence metrics. By changing the measurement parameters from binary match/mismatch to continuous divergence values, the system achieves tractable computation while preserving accuracy assessment capability.
2Measurement precision
If exhaustive list of comparison features is hand-tuned to determine material errors, then measurement precision is improved, but loss of time and ease of operation worsen
Solution Approach 1:
The evaluation system performs self-calibration by automatically learning the relative importance weights of different divergence components through unit tests. This eliminates the need for manual hand-tuning of comparison features, saving time while maintaining the ability to distinguish material from immaterial errors through data-driven weight optimization.
Solution Approach 2:
The system uses feedback from unit tests with known ground truth to automatically adjust and optimize the evaluation metric weights. This feedback loop enables the system to learn which divergence components are most important for detecting material errors, replacing manual tuning with automated adaptive calibration.
3Adaptability or versatility
If machine-learned model with learned parameters is used to map complex decision boundary, then adaptability is improved, but device complexity worsens
Solution Approach 1:
The machine-learned model applies different learned weights to different divergence components based on their local importance in specific contexts. This allows the system to adapt the evaluation criteria locally for different types of errors and scenarios, improving adaptability while keeping the overall model structure relatively simple and interpretable.
Data Source
AI summary
An example method includes (a) obtaining an object detection from a perception system that describes an object in an environment of the autonomous vehicle; (b) obtaining, from a reference dataset, a label that describes a reference position of the object in the environment; (c) determining a plurality of component divergence values respectively for a plurality of divergence metrics, wherein a respective divergence value characterizes a respective difference between the object detection and the label; (d) providing the plurality of component divergence values to a machine-learned model to generate a score that indicates an aggregate divergence between the object detection and the label, wherein the machine-learned model includes a plurality of learned parameters defining an influence of the plurality of component divergence values on the score; (e) evaluating a quality of a match between the object detection and the label based on the score.