Failure Modes for Deep Learning-Based Online Mapping: How to Measure and Address Them

1. 1Identify memorization of input features as a failure mode
2. 2Disentangle overfitting to known map geometries
3. 3Use evaluation subsets to control for geographical proximity and geometric similarity
4. 4Develop measures to quantify failure modes and track model performance

AI engineers and researchers working on autonomous driving projects benefit from understanding failure modes to improve model generalization, while product managers can use this knowledge to inform product development and testing strategies

💡 Disentangling memorization and overfitting is crucial to improving model generalization in deep learning-based online mapping

🚗💡 Deep learning-based online mapping for autonomous driving can fail due to memorization and overfitting. New framework helps measure and address these failure modes