Lane Segment Clustering with Hybrid Metrics for AV Testing
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Solution Overview
Problem
Autonomous vehicles struggle to adapt to complex driving scenarios due to the lack of human-like adaptability, necessitating techniques to ensure safe operation under various conditions and gain public trust.
Innovation Solution
A system for clustering lane segments based on trained labels using a metric learning model to group similar segments, allowing for efficient testing and validation of autonomous vehicle performance across multiple scenarios.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If lane segments are tested individually to ensure comprehensive safety validation, then testing completeness is improved, but testing time and resource consumption increase significantly
Solution Approach 1:
The patent merges similar lane segments into clusters based on geometric and contextual features, allowing multiple segments to be validated together through representative samples. This clustering approach maintains safety validation completeness while reducing the number of individual tests required, directly addressing the contradiction between thorough testing and time consumption.
Solution Approach 2:
The patent creates prototype lane segments that represent entire clusters of similar segments. By validating these prototypes, the system effectively copies the validation results to all similar segments, ensuring comprehensive safety coverage without testing each segment individually, thus resolving the time versus completeness contradiction.
2Productivity
If the number of lane segment tests is reduced through grouping, then testing efficiency is improved, but testing coverage may be compromised
Solution Approach 1:
The patent applies different clustering strategies to different types of lane segments based on their specific characteristics. By maintaining local quality distinctions through feature-based clustering (geometry, context, topology), the system ensures that segments with similar safety-critical properties are grouped together, preserving testing coverage while improving efficiency through intelligent grouping.
Solution Approach 2:
The patent changes the parameter space by selecting specific geometric and contextual features for clustering. This parameter-based approach ensures that segments are grouped by meaningful characteristics that affect safety, allowing efficient testing while maintaining comprehensive coverage of critical variations through multi-dimensional feature analysis.
3Ease of manufacture
If traditional clustering methods are used without domain-specific metrics, then implementation simplicity is maintained, but clustering accuracy for autonomous vehicle testing is insufficient
Solution Approach 1:
The patent introduces domain-specific distance metrics as intermediaries between traditional clustering algorithms and lane segment data. These metrics serve as specialized mediators that incorporate autonomous vehicle testing requirements, bridging the gap between simple implementation and high accuracy by providing tailored comparison functions for geometric and contextual features.
Solution Approach 2:
The patent changes the measurement parameters by defining custom distance metrics that reflect autonomous vehicle safety requirements. Instead of using generic clustering parameters, the system employs domain-specific parameters (geometric similarity, contextual relevance, topological relationships) that maintain implementation simplicity while dramatically improving clustering accuracy for the specific application.
Data Source
AI summary
Disclosed herein are system, method, and computer program product embodiments for clustering lane segments of a roadway in order to improve and simplify autonomous vehicle behavior testing. The approaches disclosed herein provide a hybrid methodology of dividing lane segments into hard features and soft features, and using a metric learning model trained in a supervised process on the entirety of lane segment features to cluster the lane segments based on the soft features. These clustered lane segments can then be assigned to what is termed as protolanes, where a single set of tests applied to a given protolane is considered valid across all of the lane segments assigned to the protolane.