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Found 7 papers, 2 papers with code
A Joint Approach Towards Data-Driven Virtual Testing for Automated Driving: The AVEAS Project
With growing complexity and responsibility of automated driving functions in road traffic and growing scope of their operational design domains, there is increasing demand for covering significant parts of development, validation, and verification via virtual environments and simulation models.
An Approach to Systematic Data Acquisition and Data-Driven Simulation for the Safety Testing of Automated Driving Functions
With growing complexity and criticality of automated driving functions in road traffic and their operational design domains (ODD), there is increasing demand for covering significant proportions of development, validation, and verification in virtual environments and through simulation models.
MAP-Former: Multi-Agent-Pair Gaussian Joint Prediction
This paper addresses that gap by introducing a novel approach to motion prediction, focusing on predicting agent-pair covariance matrices in a ``scene-centric'' manner, which can then be used to model Gaussian joint PDFs for all agent-pairs in a scene.
JointMotion: Joint Self-supervision for Joint Motion Prediction
We present JointMotion, a self-supervised learning method for joint motion prediction in autonomous driving.
RedMotion: Motion Prediction via Redundancy Reduction
Predicting the future motion of traffic agents is vital for self-driving vehicles to ensure their safe operation.
MaskedFusion360: Reconstruct LiDAR Data by Querying Camera Features
In self-driving applications, LiDAR data provides accurate information about distances in 3D but lacks the semantic richness of camera data.
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.
