TY - JOUR A1 - Obermaier, Christina A1 - Riebl, Raphael A1 - Al-Bayatti, Ali H. A1 - Khan, Sarmadullah A1 - Facchi, Christian T1 - Measuring the Realtime Capability of Parallel-Discrete-Event-Simulations JF - Electronics N2 - Speeding up Discrete Event Simulations (DESs) is a broad research field. Promising Parallel Discrete Event Simulation (PDES) approaches with optimistic and conservative synchronisation schemes have emerged throughout the years. However, in the area of real-time simulation, PDESs are rarely considered. This is caused by the complex problem of fitting parallel executed DES models to a real-time clock. Hence, this paper gives an extensive review of existing conservative and optimistic synchronisation schemes for PDESs. It introduces a metric to compare their real-time capabilities to determine whether they can be used for soft or firm real-time simulation. Examples are given on how to apply this metric to evaluate PDESs using synthetic and real-world examples. The results of the investigation reveal that no final answer can be given if PDESs can be used for soft or firm real-time simulation as they are. However, boundary conditions were defined, which allow a use-case specific evaluation of the real-time capabilities of a certain parallel executed DES. Using this in-depth knowledge and can lead to predictability of the real-time behaviour of a simulation run. UR - https://doi.org/10.3390/electronics10060636 KW - real-time simulation KW - Vehicular Ad Hoc Networks KW - Discrete Event Simulation KW - network simulation Y1 - 2021 UR - https://doi.org/10.3390/electronics10060636 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-10883 SN - 2079-9292 VL - 10 IS - 6 PB - MDPI CY - Basel ER - TY - JOUR A1 - Flores Fernández, Alberto A1 - Sánchez Morales, Eduardo A1 - Botsch, Michael A1 - Facchi, Christian A1 - García Higuera, Andrés T1 - Generation of Correction Data for Autonomous Driving by Means of Machine Learning and On-Board Diagnostics JF - Sensors N2 - A highly accurate reference vehicle state is a requisite for the evaluation and validation of Autonomous Driving (AD) and Advanced Driver Assistance Systems (ADASs). This highly accurate vehicle state is usually obtained by means of Inertial Navigation Systems (INSs) that obtain position, velocity, and Course Over Ground (COG) correction data from Satellite Navigation (SatNav). However, SatNav is not always available, as is the case of roofed places, such as parking structures, tunnels, or urban canyons. This leads to a degradation over time of the estimated vehicle state. In the present paper, a methodology is proposed that consists on the use of a Machine Learning (ML)-method (Transformer Neural Network—TNN) with the objective of generating highly accurate velocity correction data from On-Board Diagnostics (OBD) data. The TNN obtains OBD data as input and measurements from state-of-the-art reference sensors as a learning target. The results show that the TNN is able to infer the velocity over ground with a Mean Absolute Error (MAE) of 0.167 kmh (0.046 ms) when a database of 3,428,099 OBD measurements is considered. The accuracy decreases to 0.863 kmh (0.24 ms) when only 5000 OBD measurements are used. Given that the obtained accuracy closely resembles that of state-of-the-art reference sensors, it allows INSs to be provided with accurate velocity correction data. An inference time of less than 40 ms for the generation of new correction data is achieved, which suggests the possibility of online implementation. This supports a highly accurate estimation of the vehicle state for the evaluation and validation of AD and ADAS, even in SatNav-deprived environments. UR - https://doi.org/10.3390/s23010159 KW - On-Board Diagnostics KW - Machine Learning KW - Transformer Neural Network KW - Autonomous Driving KW - ADAS KW - Inertial Navigation Systems Y1 - 2022 UR - https://doi.org/10.3390/s23010159 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-30464 SN - 1424-8220 VL - 23 IS - 1 PB - MDPI CY - Basel ER - TY - JOUR A1 - Brandmeier, Thomas A1 - Facchi, Christian A1 - Kucsera, Anja A1 - Lauerer, Christian A1 - Overbeck, Georg T1 - Richtungsweisende Forschungskonzepte an der Hochschule Ingolstadt JF - Die Neue Hochschule Y1 - 2012 UR - https://www.hlb.de/fileadmin/hlb-global/downloads/dnh/full/2012/DNH_2012_1.pdf VL - 2012 IS - 1 SP - 14 EP - 17 PB - Hochschullehrerbund hlb CY - Bonn ER - TY - JOUR A1 - Facchi, Christian A1 - Overbeck, Georg A1 - Lohmeier, Anne-Sophie T1 - AWARE – strategische Partnerschaft mit Brasilien an der Technischen Hochschule Ingolstadt JF - Die Neue Hochschule Y1 - 2016 UR - https://www.hlb.de/fileadmin/hlb-global/downloads/dnh/full/2016/DNH_2016-6.pdf VL - 2016 IS - 6 SP - 166 EP - 169 PB - Hochschullehrerbund hlb CY - Bonn ER - TY - JOUR A1 - Overbeck, Georg A1 - Facchi, Christian T1 - Herausforderung strategische Netzwerke: Von Wunschdenken und Verstetigung JF - Die Neue Hochschule Y1 - 2020 UR - https://www.hlb.de/fileadmin/hlb-global/downloads/dnh/full/2020/DNH_2020-1.pdf VL - 2020 IS - 1 SP - 12 EP - 15 PB - Hochschullehrerbund hlb CY - Bonn ER - TY - JOUR A1 - Jones, Kevin A1 - Janicke, Helge A1 - Facchi, Christian A1 - Maglaras, Leandros T1 - Editorial: Introduction to the special issue of the journal of information security and applications on “ICS & SCADA cyber security” JF - Journal of Information Security and Applications UR - https://doi.org/10.1016/j.jisa.2017.05.009 Y1 - 2017 UR - https://doi.org/10.1016/j.jisa.2017.05.009 SN - 2214-2126 VL - 2017 IS - 34, Part 2 SP - 152 PB - Elsevier CY - Amsterdam ER - TY - JOUR A1 - Günther, Hendrik-Jörn A1 - Riebl, Raphael A1 - Wolf, Lars A1 - Facchi, Christian T1 - The effect of decentralized congestion control on collective perception in dense traffic scenarios JF - Computer Communications UR - https://doi.org/10.1016/j.comcom.2018.03.009 KW - Collective Perception KW - Decentralized Congestion Control KW - ITS-G5 KW - VANET KW - V2X Y1 - 2018 UR - https://doi.org/10.1016/j.comcom.2018.03.009 SN - 0140-3664 VL - 2018 IS - 122 SP - 76 EP - 83 PB - Elsevier CY - Amsterdam ER - TY - JOUR A1 - Speth, Thomas A1 - Riebl, Raphael A1 - Brandmeier, Thomas A1 - Facchi, Christian A1 - Jumar, Ulrich A1 - Al-Bayatti, Ali H. T1 - VANET Coverage Analysis for GPS Augmentation Data in Rural Area JF - IFAC-PapersOnLine UR - https://doi.org/10.1016/j.ifacol.2016.11.112 KW - Communication networks KW - Global positioning system KW - Vehicular ad hoc networks KW - Intelligent transportation system KW - Real time kinematic KW - Portable Base Y1 - 2016 UR - https://doi.org/10.1016/j.ifacol.2016.11.112 SN - 2405-8963 VL - 49 IS - 30 SP - 245 EP - 250 PB - Elsevier CY - Amsterdam ER - TY - JOUR A1 - Flores Fernández, Alberto A1 - Wurst, Jonas A1 - Sánchez Morales, Eduardo A1 - Botsch, Michael A1 - Facchi, Christian A1 - García Higuera, Andrés T1 - Probabilistic Traffic Motion Labeling for Multi-Modal Vehicle Route Prediction JF - Sensors N2 - The prediction of the motion of traffic participants is a crucial aspect for the research and development of Automated Driving Systems (ADSs). Recent approaches are based on multi-modal motion prediction, which requires the assignment of a probability score to each of the multiple predicted motion hypotheses. However, there is a lack of ground truth for this probability score in the existing datasets. This implies that current Machine Learning (ML) models evaluate the multiple predictions by comparing them with the single real trajectory labeled in the dataset. In this work, a novel data-based method named Probabilistic Traffic Motion Labeling (PROMOTING) is introduced in order to (a) generate probable future routes and (b) estimate their probabilities. PROMOTING is presented with the focus on urban intersections. The generation of probable future routes is (a) based on a real traffic dataset and consists of two steps: first, a clustering of intersections with similar road topology, and second, a clustering of similar routes that are driven in each cluster from the first step. The estimation of the route probabilities is (b) based on a frequentist approach that considers how traffic participants will move in the future given their motion history. PROMOTING is evaluated with the publicly available Lyft database. The results show that PROMOTING is an appropriate approach to estimate the probabilities of the future motion of traffic participants in urban intersections. In this regard, PROMOTING can be used as a labeling approach for the generation of a labeled dataset that provides a probability score for probable future routes. Such a labeled dataset currently does not exist and would be highly valuable for ML approaches with the task of multi-modal motion prediction. The code is made open source. UR - https://doi.org/10.3390/s22124498 KW - PROMOTING KW - automated driving systems KW - autonomous vehicles KW - multi-modal KW - motion prediction KW - route prediction KW - machine learning KW - real traffic data Y1 - 2022 UR - https://doi.org/10.3390/s22124498 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-24341 SN - 1424-8220 VL - 22 IS - 12 PB - MDPI CY - Basel ER - TY - JOUR A1 - Neumeier, Stefan A1 - Bajpai, Vaibhav A1 - Neumeier, Marion A1 - Facchi, Christian A1 - Ott, Jörg T1 - Data Rate Reduction for Video Streams in Teleoperated Driving JF - IEEE Transactions on Intelligent Transportation Systems UR - https://doi.org/10.1109/TITS.2022.3171718 KW - bandwidth optimization KW - teleoperated driving KW - user study KW - video stream Y1 - 2022 UR - https://doi.org/10.1109/TITS.2022.3171718 SN - 1558-0016 VL - 23 IS - 10 SP - 19145 EP - 19160 PB - IEEE CY - Piscataway ER -