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Karsten Kreutz and Julian Eggert, "Analysis of a Generalized Intelligent Driver Model for merging situations", IEEE Intelligent Vehicle Symposium 2021, pp. 34-41, 2021.

Abstract

In this paper, we analyze an extension of the Intelligent Driver Model (IDM) for its application on single situation prediction in merging situations. For this purpose, we first extend the original, longitudinal single car following IDM with several terms. First, we include a consideration of more than a single leading car to be able to deal with pressure from back, as required for anticipatory acceleration. Second, we use a virtual projection of...



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Francesco Romagnoli, "Electric load time series forecasting and relative predictions on simulation model ", UNIVERSITA’ POLITECNICA DELLE MARCHE, ANCONA, IT, 2021.

Abstract

Time series forecasting is an important area of machine learning because there are so many prediction problems that involve a time component. A normal machine learning dataset is a collection of observations, while a time series dataset adds an explicit order dependence between observations, represented by time dimension. This additional dimension is both a constraint and a structure that provides a source of addit...



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Karsten Kreutz and Julian Eggert, "Analysis of the Generalized Intelligent Driver Model (GIDM) for uncontrolled intersections", IEEE Intelligent Transportation Systems Conference (ITSC) 2021, pp. 3223-3230, 2021.

Abstract

In this paper, we propose and analyze a Generalized Intelligent Driver Model (GIDM) as an extension of the Intelligent Driver Model (IDM) for its applicability to model uncontrolled intersection scenarios. For this purpose, we extend the original longitudinal car-following IDM with several terms:(1) for anticipatory acceleration capabilities, we include the most nearby backward agent, (2) we consider cars on paths that will cross the own path by...



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Christiane Wiebel, Matti Krüger, Patricia Wollstadt, "Measuring inter- and intra-individual differences in visual scan patterns in a driving simulator experiment using active information storage", PLOS ONE, vol. 16, no. 3, pp. e0248166, 2021.

Abstract

Scan pattern analysis has been discussed as a promising tool in the context of real-time gaze-based applications [1]. In particular, information-theoretic measures of scan path predictability, such as the gaze transition entropy (GTE), have been proposed for detecting relevant changes in user state or task demand [2]. These measures model scan patterns as first-order Markov chains, assuming that only the location of the previous fixation is predi...



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Duc Nguyen, Ewout Zwanenburg, Steffen Limmer, Wessel Luijben, Markus Olhofer, Thomas Bäck, "Fourier Transform and Machine Learning Techniques for Fault Detection and Diagnosis of Induction Motors", International Conference on Prognostics and Health Management, 2021.

Abstract

Induction motors are widely used in different industry areas and can experience various kinds of faults in stators and rotors. In general, fault detection and diagnosis techniques for induction motors can be supervised by measuring quantities such as noise, vibration, and temperature. The installation of mechanical sensors in order to assess the health conditions of a machine is typically only done for expensive or load-critical machines, where ...



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Martin Stadie, Tobias Rodemann, Andre Burger, Florian Jomrich, Steffen Limmer, Sven Rebhan, Hibiki Saeki, "V2B Vehicle to Building Charging Manager", EVTeC: 5th International Electric Vehicle Technology Conference 2021, 2021.

Abstract

Due to a fast rise in the share of renewable energy with a corresponding destabilizing impact on the energy grid and a rapidly growing share of electric vehicles (EVs), the smart integration of electric mobility and facility energy management promises substantial social, ecologic, and economical benefits for drivers, facility and grid operators, and society in general. Vehicle-to-grid (V2G) technologies, which allow a bi-directional energy flow t...



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Samuele Vinanzi, "Developmental Collaborative Intelligence for Embodied Agents ", University of Manchester, 2021.

Abstract

Robots stand at the heart of a techno-scientific revolution which promises to alter the way in which we conceive our society. Recent discoveries point towards a future in which artificial agents will become fully integrated in our social structures, thus becoming important actors in our life. In this scenario, it will be critical for them to be able to understand us in the most human-like fashion and to assist us in our routines. We state that...



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Stefan Fuchs and Anna Belardinelli, "Gaze-Based Intention Estimation for Shared Autonomy in Pick-and-Place Tasks", Frontiers in Neurorobotics, vol. 15, pp. 33, 2021.

Abstract

Shared autonomy aims at combining robotic and human control in the execution of remote, teleoperated tasks. This cooperative interaction cannot be brought about without the robot first recognizing the current human intention in a fast and reliable way so that a suitable assisting plan can be quickly instantiated and executed. Eye movements have long been known to be highly predictive of the cognitive agenda unfolding during manual tasks and ...



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Thomas Uriot, Dario Izzo, Luís Simões, Rasit Abay, Nils Einecke, Sven Rebhan, Jose Martinez-Heras, Francesca Letizia, Jan Siminski, Klaus Merz, "Spacecraft Collision Avoidance Challenge: design and results of a machine learning competition", Astrodynamics, 2021.

Abstract

Spacecraft collision avoidance procedures have become an essential part of satellite operations. Complex and constantly updated estimates of the collision risk between orbiting objects inform various operators who can then plan risk mitigation measures. Such measures can be aided by the development of suitable machine learning (ML) models that predict, for example, the evolution of the collision risk over time. In October 2019, in an attempt to s...



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Charlie Street, Bruno Lacerda, Manuel Mühlig, Nick Hawes, "Congestion-Aware Planning for Multi-Robot Systems", IEEE Transactions on Robotics, pp. 1-19, 2021.

Abstract

Abstract: If a set of multi-robot plans are ​resilient​, the performance of the robots at execution time match our expectations of behaviour made at planning time. To achieve resilience requires modelling the execution-time interactions between robots. One such mode of interaction is ​congestion​. Congestion occurs when multiple robots are present in the same part of the environment simultaneously. This can have an adverse effect on...



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