Active Learning of Driving Scenario Trajectories

no code implementations • 6 Aug 2021 • Sanna Jarl, Linus Aronsson, Sadegh Rahrovani, Morteza Haghir Chehreghani

In this study, we develop a generic active learning framework to annotate driving trajectory time series data.

Active Learning Autonomous Vehicles +2

A Generic Framework for Clustering Vehicle Motion Trajectories

no code implementations • 25 Sep 2020 • Fazeleh S. Hoseini, Sadegh Rahrovani, Morteza Haghir Chehreghani

The development of autonomous vehicles requires having access to a large amount of data in the concerning driving scenarios.

Autonomous Vehicles Clustering +2

A Deep Learning Framework for Generation and Analysis of Driving Scenario Trajectories

no code implementations • 28 Jul 2020 • Andreas Demetriou, Henrik Alfsvåg, Sadegh Rahrovani, Morteza Haghir Chehreghani

Second, we develop an architecture based on Recurrent Autoencoder with GANs to obviate the variable length issue, wherein we train a GAN to learn/generate the latent representations of original trajectories.

Anomaly Detection Autonomous Driving +2