3rd workshop on Unsupervised Learning for Automated Driving

Half-day workshop at the IEEE Intelligent Vehicles Symposium 2022, Sunday, June 5
supported by the IEEE ITS Technical Committee Self Driving Automobiles

 

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. Share novel methodological developments, challenges and solutions for exploiting unlabelled data and reducing the annotation bottleneck.

 

Keynote speakers

Vasileios Belagiannis

Vasileios Belagiannis

Otto-von-Guericke-Universität Magdeburg

Claudius Glaeser

Claudius Glaeser

Robert Bosch GmbH

Daniel Kondermann

Daniel Kondermann

Quality Match GmbH

Holger Caesar

Holger Caesar

Motional / TU Delft

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.

Schedule

Preliminary agenda

  • 13:30 – 13:40: [10 min] WS organizer – Welcome and introduction
  • 13:40 – 14:15: [35 min] 1st Keynote – Prof. Dr. Vasileios Belagiannis (Otto-von-Guericke-Universität Magdeburg)
  • 14:15 – 14:50: [35 min] 2nd Keynote – Dr. Claudius Glaeser (Robert Bosch GmbH)
  • 14:50 – 15:05: [15 min] Coffee Break – 15 min
  • 15:05 – 15:40: [35 min] 3rd Keynote – Dr. Daniel Kondermann (Quality Match GmbH)
  • 15:40 – 16:15: [35 min] 4th Keynote – Dr. Holger Caesar (Motional / TU Delft)
  • 16:15 – 16:20: [05 min] Coffee Break – 5 min
  • 16:20 – 17:00: [40 min] Roundtable discussion: All keynote speakers and audience

Main conference

Previous editions

  • ULAD 2019 at IEEE Intelligent Vehicles Symposium 2019
  • ULAD 2020 at IEEE Intelligent Vehicles Symposium 2020

Contact information

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

Topics of interest

  • unsupervised, semi-supervised, and weakly-supervised learning
  • self-supervised learning
  • domain adaptation
  • GANs & VAEs
  • missing data
  • representation learning
  • density estimation
  • anomaly detection
  • clustering and data analysis
  • generative graphical models
  • latent variable models
  • non-parametric models
  • artificial data generation and simulation