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Monte Carlo Dropout for Steering Uncertainty by huuhdfc is a document available to read on EtoBox.
This paper discusses the calibration of uncertainty models for steering angle estimation in autonomous vehicles using deep neural networks. It compares various uncertainty models, including Monte Carlo dropout, bootstrap ensembling, and Gaussian mixtures, to enhance the reliability of steering predictions by quantifying model confidence. The authors emphasize the importance of well-calibrated uncertainty estimates to improve decision-making in complex driving scenarios and ensure safety in autonomous drivin
- Author
- huuhdfc
- Language
- EN