@article{ObermaierRieblAlBayattietal.2021, author = {Obermaier, Christina and Riebl, Raphael and Al-Bayatti, Ali H. and Khan, Sarmadullah and Facchi, Christian}, title = {Measuring the Realtime Capability of Parallel-Discrete-Event-Simulations}, volume = {10}, pages = {636}, journal = {Electronics}, number = {6}, publisher = {MDPI}, address = {Basel}, issn = {2079-9292}, doi = {https://doi.org/10.3390/electronics10060636}, year = {2021}, abstract = {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.}, language = {en} } @article{FloresFernandezSanchezMoralesBotschetal.2022, author = {Flores Fern{\´a}ndez, Alberto and S{\´a}nchez Morales, Eduardo and Botsch, Michael and Facchi, Christian and Garc{\´i}a Higuera, Andr{\´e}s}, title = {Generation of Correction Data for Autonomous Driving by Means of Machine Learning and On-Board Diagnostics}, volume = {23}, pages = {159}, journal = {Sensors}, number = {1}, publisher = {MDPI}, address = {Basel}, issn = {1424-8220}, doi = {https://doi.org/10.3390/s23010159}, year = {2022}, abstract = {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.}, language = {en} } @article{BrandmeierFacchiKucseraetal.2012, author = {Brandmeier, Thomas and Facchi, Christian and Kucsera, Anja and Lauerer, Christian and Overbeck, Georg}, title = {Richtungsweisende Forschungskonzepte an der Hochschule Ingolstadt}, volume = {2012}, journal = {Die Neue Hochschule}, number = {1}, publisher = {Hochschullehrerbund hlb}, address = {Bonn}, url = {https://www.hlb.de/fileadmin/hlb-global/downloads/dnh/full/2012/DNH_2012_1.pdf}, pages = {14 -- 17}, year = {2012}, language = {de} } @article{FacchiOverbeckLohmeier2016, author = {Facchi, Christian and Overbeck, Georg and Lohmeier, Anne-Sophie}, title = {AWARE - strategische Partnerschaft mit Brasilien an der Technischen Hochschule Ingolstadt}, volume = {2016}, journal = {Die Neue Hochschule}, number = {6}, publisher = {Hochschullehrerbund hlb}, address = {Bonn}, url = {https://www.hlb.de/fileadmin/hlb-global/downloads/dnh/full/2016/DNH_2016-6.pdf}, pages = {166 -- 169}, year = {2016}, language = {de} } @article{OverbeckFacchi2020, author = {Overbeck, Georg and Facchi, Christian}, title = {Herausforderung strategische Netzwerke: Von Wunschdenken und Verstetigung}, volume = {2020}, journal = {Die Neue Hochschule}, number = {1}, publisher = {Hochschullehrerbund hlb}, address = {Bonn}, url = {https://www.hlb.de/fileadmin/hlb-global/downloads/dnh/full/2020/DNH_2020-1.pdf}, pages = {12 -- 15}, year = {2020}, language = {de} } @article{JonesJanickeFacchietal.2017, author = {Jones, Kevin and Janicke, Helge and Facchi, Christian and Maglaras, Leandros}, title = {Editorial: Introduction to the special issue of the journal of information security and applications on "ICS \& SCADA cyber security"}, volume = {2017}, journal = {Journal of Information Security and Applications}, number = {34, Part 2}, publisher = {Elsevier}, address = {Amsterdam}, issn = {2214-2126}, doi = {https://doi.org/10.1016/j.jisa.2017.05.009}, pages = {152}, year = {2017}, language = {en} } @article{GuentherRieblWolfetal.2018, author = {G{\"u}nther, Hendrik-J{\"o}rn and Riebl, Raphael and Wolf, Lars and Facchi, Christian}, title = {The effect of decentralized congestion control on collective perception in dense traffic scenarios}, volume = {2018}, journal = {Computer Communications}, number = {122}, publisher = {Elsevier}, address = {Amsterdam}, issn = {0140-3664}, doi = {https://doi.org/10.1016/j.comcom.2018.03.009}, pages = {76 -- 83}, year = {2018}, language = {en} } @article{FloresFernandezWurstSanchezMoralesetal.2022, author = {Flores Fern{\´a}ndez, Alberto and Wurst, Jonas and S{\´a}nchez Morales, Eduardo and Botsch, Michael and Facchi, Christian and Garc{\´i}a Higuera, Andr{\´e}s}, title = {Probabilistic Traffic Motion Labeling for Multi-Modal Vehicle Route Prediction}, volume = {22}, pages = {4498}, journal = {Sensors}, number = {12}, publisher = {MDPI}, address = {Basel}, issn = {1424-8220}, doi = {https://doi.org/10.3390/s22124498}, year = {2022}, abstract = {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.}, language = {en} } @article{NeumeierBajpaiNeumeieretal.2022, author = {Neumeier, Stefan and Bajpai, Vaibhav and Neumeier, Marion and Facchi, Christian and Ott, J{\"o}rg}, title = {Data Rate Reduction for Video Streams in Teleoperated Driving}, volume = {23}, journal = {IEEE Transactions on Intelligent Transportation Systems}, number = {10}, publisher = {IEEE}, address = {Piscataway}, issn = {1558-0016}, doi = {https://doi.org/10.1109/TITS.2022.3171718}, pages = {19145 -- 19160}, year = {2022}, language = {en} } @article{RieblMonzVargaetal.2016, author = {Riebl, Raphael and Monz, Markus and Varga, Simon and Maglaras, Leandros and Janicke, Helge and Al-Bayatti, Ali H. and Facchi, Christian}, title = {Improved Security Performance for VANET Simulations}, volume = {49}, journal = {IFAC-PapersOnLine}, number = {30}, publisher = {Elsevier}, address = {Amsterdam}, issn = {2405-8963}, doi = {https://doi.org/10.1016/j.ifacol.2016.11.173}, pages = {233 -- 238}, year = {2016}, language = {en} } @article{BacherlerMoszkowskiFacchi2013, author = {Bacherler, Christian and Moszkowski, Ben and Facchi, Christian}, title = {Supporting Test Code Generation with an Easy to Understand Business Rule Language}, volume = {6}, journal = {International Journal on Advances in Software}, number = {1 \& 2}, publisher = {IARIA}, address = {[s. l.]}, issn = {1942-2628}, url = {http://www.iariajournals.org/software/tocv6n12.html}, pages = {69 -- 79}, year = {2013}, language = {en} } @article{TrappMeyerFacchietal.2011, author = {Trapp, Peter and Meyer, Markus and Facchi, Christian and Janicke, Helge and Siewe, Fran{\c{c}}ois}, title = {Building CPU Stubs to Optimize CPU Bound Systems: An Application of Dynamic Performance Stubs}, volume = {4}, journal = {International Journal on Advances in Software}, number = {1\&2}, publisher = {IARIA}, address = {[s. l.]}, issn = {1942-2628}, url = {https://www.iariajournals.org/software/tocv4n12.html}, pages = {189 -- 206}, year = {2011}, language = {en} } @article{MaksimovskiFestagFacchi2026, author = {Maksimovski, Daniel and Festag, Andreas and Facchi, Christian}, title = {Adaptive Message Generation Rules for V2X Maneuver Coordination Service}, volume = {14}, journal = {IEEE Access}, publisher = {IEEE}, address = {New York}, issn = {2169-3536}, doi = {https://doi.org/10.1109/ACCESS.2026.3652364}, pages = {6417 -- 6437}, year = {2026}, abstract = {Maneuver coordination enables connected and automated vehicles (CAVs) to collaboratively plan, negotiate, and execute driving maneuvers, aiming to enhance safety, traffic flow, and energy efficiency. As part of the Vehicle-to-Everything (V2X) communication system, it operates as a dedicated service that relies on detailed, bidirectional interactions between vehicles, unlike traditional broadcast-based V2X services. While maneuver coordination is the focus of ongoing research and standardization, the rules that govern when and how vehicles generate Maneuver Coordination Messages (MCMs) within the Maneuver Coordination Service (MCS) are still not fully defined. These rules are essential for ensuring timely and reliable coordination. This paper proposes three adaptive MCM generation strategies based on the operation mode of the MCS, maneuver priority, and current channel load. The first approach defines MCM rules for intent sharing, maneuver negotiation, and execution phases, factoring in maneuver importance. The second adjusts the frequency of intent MCMs when a negotiation message is received from another vehicle. The third adapts frequency based on real-time channel load during intent sharing. For comparison, two baseline strategies are also evaluated. The proposed rules are tested for highway merging and lane change use cases in traffic scenarios with increasing vehicle density and high channel loads. A comprehensive evaluation is conducted using metrics related to the network, message generation frequency, congestion control, and maneuver coordination. The results demonstrate significantly enhanced channel efficiency and communication reliability across all metrics. For example, under the final approach, maneuver negotiation time is reduced by a factor of five in congested V2X environments.}, language = {en} }