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Found 5 papers, 4 papers with code
Two is Better Than One: Digital Siblings to Improve Autonomous Driving Testing
Our empirical evaluation shows that the ensemble failure predictor by the digital siblings is superior to each individual simulator at predicting the failures of the digital twin.
Mind the Gap! A Study on the Transferability of Virtual vs Physical-world Testing of Autonomous Driving Systems
In this paper, we shed light on the problem of generalizing testing results obtained in a driving simulator to a physical platform and provide a characterization and quantification of the sim2real gap affecting SDC testing.
Taxonomy of Real Faults in Deep Learning Systems
The growing application of deep neural networks in safety-critical domains makes the analysis of faults that occur in such systems of enormous importance.
Misbehaviour Prediction for Autonomous Driving Systems
Deep Neural Networks (DNNs) are the core component of modern autonomous driving systems.
Signal Processing
BrainNet: A Multi-Person Brain-to-Brain Interface for Direct Collaboration Between Brains
Two of the three subjects are "Senders" whose brain signals are decoded using real-time EEG data analysis to extract decisions about whether to rotate a block in a Tetris-like game before it is dropped to fill a line.
Human-Computer Interaction Neurons and Cognition
