Evaluating the Explainable AI Method Grad-CAM for Breath Classification on Newborn Time Series Data

With the digitalization of health care systems, artificial intelligence becomes more present in medicine. Especially machine learning shows great potential for complex tasks such as time series classification, usually at the cost of transparency and comprehensibility. This leads to a lack of trust by humans and thus hinders its active usage. Explainable artificial intelligence tries to close this gap by providing insight into the decision-making process, the actual usefulness of its different methods is however unclear. This paper proposes a user study based evaluation of the explanation method Grad-CAM with application to a neural network for the classification of breaths in time series neonatal ventilation data. We present the perceived usefulness of the explainability method by different stakeholders, exposing the difficulty to achieve actual transparency and the wish for more in-depth explanations by many of the participants.

Infrared Beacons for Robust Localization

This paper presents a localization system that uses infrared beacons and a camera equipped with an optical band-pass filter. Our system can reliably detect and identify individual beacons at 100m distance regardless of lighting conditions. We describe the camera and beacon design as well as the image processing pipeline in detail. In our experiments, we investigate and demonstrate the ability of the system to recognize our beacons in both daytime and nighttime conditions. High precision localization is a key enabler for automated vehicles but remains unsolved, despite strong recent improvements. Our low-cost, infrastructure-based approach helps solve the localization problem. All datasets are made available.

Cyber-Physical Mobility Lab An Open-Source Platform for Networked and Autonomous Vehicles

We introduce our Cyber-Physical Mobility Lab (CPM Lab), a development environment for networked and autonomous vehicles. It consists of 20 model-scale vehicles for experiments and a simulation environment. We show our four-layered architecture that enables the seamless use of the same software in simulations and in experiments without any adaptions. A Data Distribution Service (DDS) based middleware allows to adapt the number of vehicles during experiments in a seamless manner. Experiments with the 20 vehicles can be extended by unlimited additional simulated vehicles. Another layer is responsible for synchronizing all entities following a logical execution time approach. We pursue an open policy in the CPM Lab and will publish the entire code as well as construction plans online. Additionally, we will offer a remote-access to the CPM Lab using a web interface. The remote-access will be publicly available. The CPM Lab allows researchers as well as students from different disciplines to see their ideas develop into reality.

Networked and Autonomous Model-scale Vehicles for Experiments in Research and Education

This paper presents the $\mathrm{\mu}$Car, a 1:18 model-scale vehicle with Ackermann steering geometry developed for experiments in networked and autonomous driving in research and education. The vehicle is open source, moderately costed and highly flexible, which allows for many applications. It is equipped with an inertial measurement unit and an odometer and obtains its pose via WLAN from an indoor positioning system. The two supported operating modes for controlling the vehicle are (1) computing control inputs on external hardware, transmitting them via WLAN and applying received inputs to the actuators and (2) transmitting a reference trajectory via WLAN, which is then followed by a controller running on the onboard Raspberry Pi Zero W. The design allows identical vehicles to be used at the same time in order to conduct experiments with a large amount of networked agents.