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Found 7 papers, 1 papers with code
Histogram-based Deep Learning for Automotive Radar
Compared to existing methods, the design of our approach is extremely simple: it boils down to computing a point cloud histogram and passing it through a multi-layer perceptron.
DeepHybrid: Deep Learning on Automotive Radar Spectra and Reflections for Object Classification
Automated vehicles need to detect and classify objects and traffic participants accurately.
Improving Uncertainty of Deep Learning-based Object Classification on Radar Spectra using Label Smoothing
The focus of this article is to learn deep radar spectra classifiers which offer robust real-time uncertainty estimates using label smoothing during training.
DiagViB-6: A Diagnostic Benchmark Suite for Vision Models in the Presence of Shortcut and Generalization Opportunities
We also argue that it is necessary for DNNs to exploit GO to overcome shortcut learning.
Test-Time Adaptation to Distribution Shift by Confidence Maximization and Input Transformation
This paper focuses on the fully test-time adaptation setting, where only unlabeled data from the target distribution is required.
Investigation of Uncertainty of Deep Learning-based Object Classification on Radar Spectra
Deep learning (DL) has recently attracted increasing interest to improve object type classification for automotive radar. In addition to high accuracy, it is crucial for decision making in autonomous vehicles to evaluate the reliability of the predictions; however, decisions of DL networks are non-transparent.
Test-Time Adaptation to Distribution Shifts by Confidence Maximization and Input Transformation
This paper focuses on the fully test-time adaptation setting, where only unlabeled data from the target distribution is required.
