Protolane Flowchart Validation for Autonomous Driving Scenarios
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Solution Overview
Problem
Autonomous vehicle systems face challenges in reliably navigating complex and unpredictable environments, requiring effective testing and validation of their decision-making processes to ensure trustworthy operation without human intervention.
Innovation Solution
The introduction of protolanes, which decompose roadways into manageable segments, allows for the simplification of testing and verification by assigning lane segments to corresponding protolanes, enabling the validation of autonomous vehicle operations across various scenarios and facilitating the reuse of testing results across similar geonets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If autonomous vehicle systems operate in complex and unpredictable environments, then the ability to handle diverse scenarios improves, but the complexity of testing and validation increases
Solution Approach 1:
The patent segments the continuous roadway into discrete lane segments and groups them into protolanes based on shared characteristics. This segmentation allows the testing system to break down complex validation into manageable units, where each protolane represents a specific driving scenario template that can be independently validated.
Solution Approach 2:
The patent creates abstract protolane models that serve as templates or copies of recurring roadway patterns. Instead of testing each unique physical lane configuration, the system validates against these copied protolane representations, significantly reducing testing complexity while maintaining comprehensive scenario coverage.
2Reliability
If comprehensive testing coverage is ensured across all possible driving scenarios, then reliability of autonomous operation improves, but the time and resources required for testing increase
Solution Approach 1:
The patent creates protolanes with universal characteristics that can represent multiple similar driving scenarios simultaneously. A single protolane validation can cover numerous real-world lane configurations that share common features, allowing one test to serve multiple validation purposes and significantly reducing total testing time.
Solution Approach 2:
The system performs preliminary clustering of lane segments into protolanes before actual validation begins. This pre-processing step organizes the testing space in advance, allowing the validation system to efficiently navigate and test against predetermined protolane templates rather than dealing with raw, unorganized lane segment data during the validation process.
3Adaptability or versatility
If testing is expanded to new geographic areas, then the versatility of autonomous vehicle navigation improves, but additional testing requirements increase complexity
Solution Approach 1:
When expanding to new geographic areas, the system creates new protolanes by copying and adapting existing protolane templates from previously validated regions. This allows the system to leverage validation results from one geographic area to inform testing in another, reducing the complexity burden of complete re-validation while ensuring appropriate local adaptations are tested.
Data Source
AI summary
Disclosed herein are system, method, and computer program product aspects for validating protolanes. For example, the method includes determining a protolane level flowchart from one or more flowcharts corresponding to one or more gates of a protolane selected from a set of protolanes of a geonet, wherein the one or more flowcharts correspond to conditions that can be encountered by an actor approaching the corresponding one or more gates, and wherein the geonet comprises a plurality of lane segments associated with the set of protolanes. A graph through the protolane level flowchart is generated that corresponds to actions of an actor. Reasoning of the actor is validated by testing the actions of the actor against the graph through the protolane level flowchart.