31. July 2021 - Providentia Editors

FloMo: Tractable Motion Prediction with Normalizing Flows

The future motion of traffic participants is inherently uncertain. To plan safely, therefore, an autonomous agent must take into account multiple possible trajectory outcomes and prioritize them. Recently, this problem has been addressed with generative neural networks. However, most generative models either do not learn the true underlying trajectory distribution reliably, or do not allow predictions to be associated with likelihoods. In our work, we model motion prediction directly as a density estimation problem with a normalizing flow between a noise distribution and the future motion distribution. Our model, named FloMo, allows likelihoods to be computed in a single network pass and can be trained directly with maximum likelihood estimation. Furthermore, we propose a method to stabilize training flows on trajectory datasets and a new data augmentation transformation that improves the performance and generalization of our model. Our method achieves state-of-the-art performance on three popular prediction datasets, with a significant gap to most competing models.




23. November 2021

Traffic: Added value by big data

A huge amount of data is needed as to distinguish cars from motorcycles and bicyclists, to recognize maneuvers, to analyze accidents and to simulate traffic.


15. November 2021

Automatisiertes und vernetztes Fahren: Testfeldmonitor macht Projekte und Testfelder transparent

24 Testfelder und 140 Projekte rund um das automatisierte und vernetzte Fahren gibt es in Deutschland. Kaum jemand kennt sie. Testfeldmonitor.de schafft Abhilfe.


9. October 2021

ITS World Congress: An industry on the way to the City Brain

Providentia++ will present the current state of research at the ITS World Congress. What consortium leader Prof. Alois Knoll expects from the leading congress for intelligent transport systems and what the external infrastructure has to do with a City Brain.