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Correcting Learning-based Perception for Safety
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safetymachine learningautonomous systemsperception correction
2609.22108
Builder Relevance
1h ago80%
Abstract
This paper proposes a two-step strategy for correcting ML-based state estimation to enhance safety in autonomous systems.
Reality Card
Core Claim
The proposed runtime perception correction strategy preserved safety in 73% of scenarios where traditional perception-based control systems led to safety violations.
Method / Result
Achieved a 73% success rate in preserving safety across 45 ACC scenarios.
Limitations
The method could not recover 27% of scenarios due to non-conformant construction of preimages of perception contracts.
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