Here you can download the KI Wissen public deliverables:

Public deliverables


D1 Catalogue, characterisation and representation of relevant domain knowledge for L3 to L5 driving functions / D4 Methods for data-independent validation of the predictions and decisions of an AI function

Deliverables 1 and 4(pdf:14 MB)

D2 Learning techniques for integrating formalised knowledge and network knowledge for training reduction and performance improvement

Deliverable 2(pdf:7 MB)

Scientific contributions and publications from KI Wissen are listed below:

  1. Dominik Grundt, Sorin Liviu Jurj: Verification of Sigmoidal Artificial Neural Networks using iSAT. Präsentiert auf dem 7th International Workshop on Symbolic-Numeric Methods for Reasoning about CPS and IoT.

  2. Gesina Schwalbe, Bettina Finzel: XAI Method Properties: A (Meta-)study. In: Data Mining and Knowledge Discovery, Special Issue on Explainable and Interpretable Machine Learning and Data Mining.

  3. Sorin Liviu Jurj, Dominik Grundt, Tino Werner, Philipp Borchers, Eike Möhlmann: Increasing the Safety of Adaptive Cruise Control using Physics-guided Reinforcement Learning. In: Energies, Special Issue "Advances in Automated Driving Systems".
  4. Gesina Schwalbe: Concept Embedding Analysis: A Review. In: Artificial Intelligence Review, März 2022.
  5. Daniel Bogdoll, Moritz Nekolla, Tim Joseph, J. Marius Zöllner: Quantification of Actual Road User Behavior on the Basis of Given Traffic Rules. In: 33rd IEEE Intelligent Vehicles Symposium, 05.-09.06.2022.
  6. Jörg Reichardt: Trajectories as Markov States for Long Term Traffic Scene Prediction. UniDAS FAS Workshop, 09.05.2022.
  7. Tianming Qiu: SViT: Hybrid Vision Transformer Models with Scattering Transform. In: 32nd IEEE International Workshop on Machine Learning for Signal Processing (MLSP 2022), Xi'an, China, 22.08.2022.
  8. Sorin Liviu Jurj, Tino Werner, Dominik Grundt, Eike Möhlmann: Towards Safe and Sustainable Autonomous Vehicles using Environmentally-Friendly Criticality Metrics. Sustainability, Special Issue "Research on Sustainable Transportation and Urban Traffic”, 07.06.2022
  9. Dominik Grundt, Eike Möhlmann: Towards Runtime Monitoring of Complex System Requirements for Autonomous Driving Functions. In: Formal Methods for Autonomous Systems 2022.
  10. Abdul Hannan Khan, Mohsin Munir, Ludger van Elst: F2DNet: Fast Focal Detection Network for Pedestrian Detection. In: 26th International Conference on Pattern Recognition, Montréal, 22.-25.08.2022.
  11. Julian Wörmann, Daniel Bogdoll, Etienne Bührle, Han Chen, Evaristus Fuh Chuo, Kostadin Cvejoski, Tobias Gleißner, Philip Gottschall, Stefan Griesche, Christian Hellert, Christian Hesels, et al.: Knowledge Augmented Machine Learning with Applications in Autonomous Driving: A Survey.
  12. Alexander Steen, David Fuenmayor, Tobias Gleißner, Geoff Sutcliffe, Christoph Benzmüller: Automated Reasoning in Non-classical Logics in the TPTP World, Haifa, Israel, 11.08.2022
  13. Kumar Manas, Stefan Zwicklbauer, Adrian Paschke: Robust Traffic Rules and Knowledge Representation for Conflict Resolution in Autonomous Driving, Virtual, 26.09.22
  14. Abdul Hannan Khan: Localized Semantic Feature Mixers for Efficient Pedestrian Detection in Autonomous Driving, The IEEE/CVF Conference on Computer Vision and Pattern Recognition 2023, Vancouver Canada, 18.-22.06.2023
  15. Himanshu Agarwal, Christian Brunner, Tobias Latka, Stefan Pilar von Pilchau: A Causal Model for Physics-Conform Vehicle Trajectories, IEEE ITSC-2023, 24.-28.09.2023
  16. Adrien Wantiez, Tianming Qiu, Stefan Matthes, Hao Shen: Scene Understanding for Autonomous Driving Using Visual Question Answering, Gold coast, Queensland, Australia, 18 Jun 2023
  17. Yue Yao, Joerg Reichardt, Daniel Goehring: An Empirical Bayes Analysis of Object Trajectory Representation Models, ITSC 2023 Bilbao, Spain, 24.-28.09.2023



Here the public KI Wissen presentations are listed.



Stefan Rudolph, Tobias Latka: Towards Causality-Driven Reinforcement LEarning for Autonomous Driving

Presentation at Computational Science Lab Seminar, 20.01.2022(pdf:1 MB)

Here you can find more project material.

Project materials