3rd workshop on Unsupervised Learning for Automated Driving

Workshop description

Machine learning is nowadays omnipresent in automated driving, and applied on sensor data for perception, context cues for intent recognition, trajectories for path prediction and planning, scenario clustering for test specification, density estimation for anomalous event detection, and realistic data generation for simulation. Large annotated datasets and benchmarks are key to advancing the state-of-the-art, and have so far mainly involved supervised learning.

However, the supervised learning paradigm has its limitations, since (a) new sensors or environment conditions require new labelling to counter domain shift, (b) creating manual annotations is labour intensive and time consuming, (c) some properties may be infeasible to collect (e.g. rare events) or annotate (e.g. mental states), (d) sometimes the relevant structure in the data is unknown and must be discovered (e.g. clustering, dimensionality reduction).

On the other hand, unlabelled data is easily collected, increasing traction in IV to explore unsupervised learning, its semi-, weakly-, and self-supervised variants, transfer learning, and inferring probabilistic latent representations.

This workshop therefore explores the varied use of machine learning techniques on unlabelled, partially or automatically labelled data throughout all IV research domains, encouraging IV researchers to share novel developments, challenges and solutions.

 Workshop organizers

Contact information

for questions, please send us a mail at info[at]ulad-workshop.com