Unbiased confidence estimates of neural networks are crucial especially for safety-critical applications. Many methods have been developed to calibrate biased confidence estimates. Though there is a variety of methods for classification, the field of object detection has not been addressed yet. Therefore, we present a novel framework to measure and calibrate biased (or miscalibrated) confidence estimates of object detection methods. The main difference to related work in the field of classifier calibration is that we also use additional information of the regression output of an object detector for calibration. Our approach allows, for the first time, to obtain calibrated confidence estimates with respect to image location and box scale. In addition, we propose a new measure to evaluate miscalibration of object detectors. Finally, we show that our developed methods outperform state-of-the-art calibration models for the task of object detection and provides reliable confidence estimates across different locations and scales.
[Paper] A Wizard of Oz Field Study to Understand Non-Driving-Related Activities, Trust, and Acceptance of Automated Vehicles
Understanding user needs and behavior in automated vehicles (AVs) while traveling is essential for future in-vehicle interface and service design. Since AVs are not yet market-ready, current knowledge about AV use and perception is based […]
[Paper] User-driven development (UDD): Ansätze und Methoden zur erfolgreichen Umsetzung neuer Mobilitätskonzepte
Ansätze und Methoden zur erfolgreichen Umsetzung neuer Mobilitätskonzepte Autonome Fahrzeuge und Flugtaxis, die per Smartphone bedarfsgerecht angefordert und über eine App abgerechnet werden, Transportdrohnen, die Güter zustellen, etc. – mit solchen Bildern vermitteln die beteiligten […]
[Paper] “Help, Accident Ahead!”: Using Mixed Reality Environments in Automated Vehicles to Support Occupants After Passive Accident Experiences
Currently, car assistant systems mainly try to prevent accidents. Increasing built-in car technology also extends the potential applications in vehicles. Future cars might have virtual windshields that augment the traffic or individual virtual assistants interacting […]