Scientific publications

Highlight papers by the staff of Fraunhofer IKS


Out-Of-Distribution Detection Transformer

A serious problem in image classification is that a trained model might perform well for input data that originates from the same distribution as the data available for model training but performs much worse for out-of-distribution (OOD) samples. This paper proposes a first-of-its-kind OOD detection architecture named OODformer that leverages the contextualization capabilities of the transformer.


Situation-Aware Model Refinement for Semantic Image Segmentation

The quality of semantic image segmentation models can be affected by external factors such as weather or daytime. Those factors can lead to safety-critical mistakes. In this work, we propose a systematic approach to detect and alleviate such weaknesses of semantic segmentation models.


Enhanced System Awareness as Basis for Resilience of Autonomous Vehicles

Automated cars have to take correct decisions in complex situations. For this, the understanding of a vehicle system’s own capabilities and the environmental context is crucial. We introduce our approach of enhancing the system awareness of vehicles to handle changes gracefully, while optimizing the overall performance.


Safety Assurance of Machine Learning for Chassis Control Functions

This paper describes the application of machine learning techniques and an assurance case for a safety-relevant chassis control system. The paper highlights how the choice of machine learning approach supports the assurance case, especially regarding the explainability of the algorithm and its robustness.


Collaborative Perception in Automated Driving

In this position paper “Towards Collaborative Perception in Automated Driving: Combining Vehicle and Infrastructure Perspectives” the authors propose a framework to merge the vehicle and the infrastructure perspective enabling collaborative perception and thus to enhance the dependability of the environment perception of automated vehicles in complex scenarios.


Behavior Prediction of Cyber-Physical Systems for Dynamic Risk Assessment

Cyber-physical systems such as autonomous vehicles must function safely and be self-adaptive and predictive to do so. In her paper, Marta Grobelna sketches how reachability analysis in combination with game theory can be used to predict risk of hazardous situations.


Safe Interaction of Automated Forklifts and Humans

Co-working of automated systems and humans must be safe. Fraunhofer IKS therefore proposes an architecture that uses infrastructure sensors to prevent human-robot collisions at blind corners with respect to automated forklifts. We use a warehouse simulation to verify our approach and to estimate the impact on an automated forklift’s performance.


Domain Shifts in Reinforcement Learning: Identifying Disturbances in Environments

In their poster and their poster paper Tom Haider and his colleagues present a Markov Decision Process (MDP) to formalize changes in the environment. This allows End-to-End Deep Reinforcement Learning systems to better deal with situations they have not been trained to deal with.


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You can access all scientific publications of Frauenhofer IKS and former Fraunhofer ESK in the database Fraunhofer-Publica. You may use the "inst=(iks or esk)" tag in the keyword form to specifically search for publications written by scientists working with our institute.


Gereon Weiß

Contact Press / Media

Dr. Gereon Weiß

Department Head Self-Adaptive Software Systems

Hansastr. 32
80686 Munich

Phone +49 89 547088-348

Karsten Roscher

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Karsten Roscher

Departement Head Dependable Perception & Imaging

Hansastr. 32
80686 Munich

Phone +49 89 547088-349

Philipp Schleiß

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Philipp Schleiß

Department Head Systems Safety Engineering

Hansastr. 32
80686 Munich

Phone +49 89 547088-398