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Found 5 papers, 1 papers with code
PITA: Physics-Informed Trajectory Autoencoder
To resolve this issue, we propose the novel Physics-Informed Trajectory Autoencoder (PITA) architecture, whichincorporates a physical dynamics model into the loss functionof the autoencoder.
LDFA: Latent Diffusion Face Anonymization for Self-driving Applications
Therefore, datasets used to train perception models of ITS must contain a significant number of vulnerable road users.
Space, Time, and Interaction: A Taxonomy of Corner Cases in Trajectory Datasets for Automated Driving
Based on these predictions - and additional contextual information such as the course of the road, (traffic) rules, and interaction with other road users - the highly automated vehicle (HAV) must be able to reliably and safely perform the task assigned to it, e. g., moving from point A to B.
Fast and Robust Ground Surface Estimation from LIDAR Measurements using Uniform B-Splines
We propose a fast and robust method to estimate the ground surface from LIDAR measurements on an automated vehicle.
An Application-Driven Conceptualization of Corner Cases for Perception in Highly Automated Driving
Systems and functions that rely on machine learning (ML) are the basis of highly automated driving.
