@inproceedings{MayerVolkersdorferHofbaueretal.2024, author = {Mayer, Kevin and Volkersdorfer, Tina and Hofbauer, Jenny and Heinl, Patrizia and Hof, Hans-Joachim}, title = {Vehicle Security Operations Center for Cooperative, Connected and Automated Mobility}, booktitle = {SECURWARE 2024: The Eighteenth International Conference on Emerging Security Information, Systems and Technologies}, editor = {Hussain, Fatima and Fries, Steffen}, publisher = {IARIA}, address = {[s. l.]}, isbn = {978-1-68558-206-7}, url = {https://www.thinkmind.org/library/SECURWARE/SECURWARE_2024/securware_2024_2_190_30080.html}, pages = {156 -- 164}, year = {2024}, language = {en} } @inproceedings{MichlHof2024, author = {Michl, Marco and Hof, Hans-Joachim}, title = {Towards a Stakeholder-Centric Trust Management Approach for the Automotive Ecosystem}, booktitle = {SECURWARE 2024: The Eighteenth International Conference on Emerging Security Information, Systems and Technologies}, editor = {Hussain, Fatima and Fries, Steffen}, publisher = {IARIA}, address = {[s. l.]}, isbn = {978-1-68558-206-7}, url = {https://www.thinkmind.org/library/SECURWARE/SECURWARE_2024/securware_2024_2_70_30036.html}, pages = {64 -- 70}, year = {2024}, language = {en} } @inproceedings{LoewMayerHof2024, author = {L{\"o}w, Jakob and Mayer, Kevin and Hof, Hans-Joachim}, title = {Fast Charging Communication and Cybersecurity: A Technology Review}, booktitle = {SECURWARE 2024: The Eighteenth International Conference on Emerging Security Information, Systems and Technologies}, editor = {Hussain, Fatima and Fries, Steffen}, publisher = {IARIA}, address = {[s. l.]}, isbn = {978-1-68558-206-7}, url = {https://www.thinkmind.org/library/SECURWARE/SECURWARE_2024/securware_2024_2_50_30030.html}, pages = {52 -- 57}, year = {2024}, language = {en} } @inproceedings{HofbauerMayer2024, author = {Hofbauer, Jenny and Mayer, Kevin}, title = {Blue Team Fundamentals: Roles and Tools in a Security Operations Center}, booktitle = {SECURWARE 2024: The Eighteenth International Conference on Emerging Security Information, Systems and Technologies}, editor = {Hussain, Fatima and Fries, Steffen}, publisher = {IARIA}, address = {[s. l.]}, isbn = {978-1-68558-206-7}, url = {https://www.thinkmind.org/library/SECURWARE/SECURWARE_2024/securware_2024_2_220_30089.html}, pages = {176 -- 184}, year = {2024}, language = {en} } @unpublished{MorrisTatschnerHeinletal.2024, author = {Morris, John and Tatschner, Stefan and Heinl, Michael P. and Heinl, Patrizia and Newe, Thomas and Plaga, Sven}, title = {Cybersecurity as a Service}, publisher = {arXiv}, address = {Ithaca}, doi = {https://doi.org/10.48550/arXiv.2402.13965}, year = {2024}, 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{DeloozWilleckeGarlichsetal.2022, author = {Delooz, Quentin and Willecke, Alexander and Garlichs, Keno and Hagau, Andreas-Christian and Wolf, Lars and Vinel, Alexey and Festag, Andreas}, title = {Analysis and Evaluation of Information Redundancy Mitigation for V2X Collective Perception}, volume = {10}, journal = {IEEE Access}, publisher = {IEEE}, address = {New York}, issn = {2169-3536}, doi = {https://doi.org/10.1109/ACCESS.2022.3170029}, pages = {47076 -- 47093}, year = {2022}, abstract = {Sensor data sharing enables vehicles to exchange locally perceived sensor data among each other and with the roadside infrastructure to increase their environmental awareness. It is commonly regarded as a next-generation vehicular communication service beyond the exchange of highly aggregated messages in the first generation. The approach is being considered in the European standardization process, where it relies on the exchange of locally detected objects representing anything safety-relevant, such as other vehicles or pedestrians, in periodically broadcasted messages to vehicles in direct communication range. Objects filtering methods for inclusion in a message are necessary to avoid overloading a channel and provoking unnecessary data processing. Initial studies provided in a pre-standardization report about sensor data sharing elaborated a first set of rules to filter objects based on their characteristics, such as their dynamics or type. However, these rules still lack the consideration of information received by other stations to operate. Specifically, to address the problem of information redundancy, several rules have been proposed, but their performance has not been evaluated yet comprehensively. In the present work, the rules are further analyzed, assessed, and compared. Functional and operational requirements are investigated. A performance evaluation is realized by discrete-event simulations in a scenario for a representative city with realistic vehicle densities and mobility patterns. A score and other redundancy-level metrics are elaborated to ease the evaluation and comparison of the filtering rules. Finally, improvements and future works to the filtering methods are proposed.}, language = {en} } @inproceedings{MeilingerHassSchieberetal.2023, author = {Meilinger, Florian and Haß, Jan and Schieber, Christina and Schweiger, Hans-Georg}, title = {Investigation of the hazard potential of lithium-ion cells in case of overcharging with automotive oriented high-voltage levels}, booktitle = {Applied Research Conference 2023 Conference Proceedings: 3rd July 2023 Technische Hochschule Ingolstadt}, editor = {Vaculin, Ondrej}, publisher = {Technische Hochschule Ingolstadt}, address = {Ingolstadt}, doi = {https://doi.org/10.57825/repo_in-5244}, pages = {254 -- 259}, year = {2023}, language = {en} } @inproceedings{WalterSchweiger2023, author = {Walter, Gero and Schweiger, Hans-Georg}, title = {Structural Health Monitoring of Hydrogen Pressure Vessels for Fuel Cell Electric Vehicles}, booktitle = {Applied Research Conference 2023 Conference Proceedings: 3rd July 2023 Technische Hochschule Ingolstadt}, editor = {Vaculin, Ondrej}, publisher = {Technische Hochschule Ingolstadt}, address = {Ingolstadt}, doi = {https://doi.org/10.57825/repo_in-5244}, pages = {282 -- 287}, year = {2023}, language = {en} } @inproceedings{WierlingSchweiger2023, author = {Wierling, Charlotte and Schweiger, Hans-Georg}, title = {Comparison of Facilities for Abuse Tests on Hydrogen Storage Vessels with Regard to Safety-Related Aspects}, booktitle = {Applied Research Conference 2023 Conference Proceedings: 3rd July 2023 Technische Hochschule Ingolstadt}, editor = {Vaculin, Ondrej}, publisher = {Technische Hochschule Ingolstadt}, address = {Ingolstadt}, doi = {https://doi.org/10.57825/repo_in-5244}, pages = {233 -- 238}, year = {2023}, language = {en} } @article{LoewBayerlHof2022, author = {L{\"o}w, Jakob and Bayerl, Dominik and Hof, Hans-Joachim}, title = {Implementation of a Software Based Glitching Detection Mechanism}, volume = {15}, journal = {International Journal on Advances in Security}, number = {1\&2}, publisher = {IARIA}, address = {[s. l.]}, issn = {1942-2636}, url = {https://www.iariajournals.org/security/tocv15n12.html}, pages = {31 -- 40}, year = {2022}, language = {en} } @article{MayerBayerlHof2021, author = {Mayer, Kevin and Bayerl, Dominik and Hof, Hans-Joachim}, title = {{\"U}berwachung in modernen Fahrzeugen}, volume = {45}, journal = {Datenschutz und Datensicherheit - DuD}, subtitle = {Ergebnisse einer fahrzeugforensischen Untersuchung}, number = {6}, publisher = {Springer}, address = {Berlin}, issn = {1862-2607}, doi = {https://doi.org/10.1007/s11623-021-1459-5}, pages = {399 -- 403}, year = {2021}, language = {de} } @article{BazziSepulcreDeloozetal.2024, author = {Bazzi, Alessandro and Sepulcre, Miguel and Delooz, Quentin and Festag, Andreas and Vogt, Jonas and Wieker, Horst and Berens, Friedbert and Spaanderman, Paul}, title = {Multi-Channel Operation for the Release 2 of ETSI Cooperative Intelligent Transport Systems}, volume = {8}, journal = {IEEE Communications Standards Magazine}, number = {1}, publisher = {IEEE}, address = {New York}, issn = {2471-2833}, doi = {https://doi.org/10.1109/MCOMSTD.0001.2200080}, pages = {28 -- 35}, year = {2024}, abstract = {Vehicles and road infrastructure are starting to be equipped with vehicle-to-everything (V2X) communication solutions to increase road safety and provide new services to drivers and passengers. In Europe, the deployment is based on a set of Release 1 standards developed by ETSI to support basic use cases for cooperative intelligent transport systems (C-ITS). For them, the capacity of a single 10 MHz channel in the ITS band at 5.9 GHz is considered sufficient. At the same time, the ITS stakeholders are working toward several advanced use cases, which imply a significant increment of data traffic and the need for multiple channels. To address this issue, ETSI has recently standardized a new multi-channel operation (MCO) concept for flexible, efficient, and future-proof use of multiple channels. This new concept is defined in a set of new specifications that represent the foundation for the future releases of C-ITS standards. The present article provides a comprehensive review of the new set of specifications, describing the main entities that extend the C-ITS architecture at the different layers of the protocol stack. In addition, the article provides representative examples that describe how these MCO standards will be used in the future and discusses some of the main open issues arising. The review and analysis of this article facilitate the understanding and motivation of the new set of Release 2 ETSI specifications for MCO and the identification of new research opportunities.}, language = {en} } @article{SongZhouLyuetal.2023, author = {Song, Rui and Zhou, Liguo and Lyu, Lingjuan and Festag, Andreas and Knoll, Alois}, title = {ResFed: Communication-Efficient Federated Learning With Deep Compressed Residuals}, volume = {11}, journal = {IEEE Internet of Things Journal}, number = {6}, publisher = {IEEE}, address = {New York}, issn = {2327-4662}, doi = {https://doi.org/10.1109/JIOT.2023.3324079}, pages = {9458 -- 9472}, year = {2023}, abstract = {Federated learning allows for cooperative training among distributed clients by sharing their locally learned model parameters, such as weights or gradients. However, as model size increases, the communication bandwidth required for deployment in wireless networks becomes a bottleneck. To address this, we propose a residual-based federated learning framework (ResFed) that transmits residuals instead of gradients or weights in networks. By predicting model updates at both clients and the server, residuals are calculated as the difference between updated and predicted models and contain more dense information than weights or gradients. We find that the residuals are less sensitive to an increasing compression ratio than other parameters, and hence use lossy compression techniques on residuals to improve communication efficiency for training in federated settings. With the same compression ratio, ResFed outperforms current methods (weight- or gradient-based federated learning) by over 1.4× on federated data sets, including MNIST, FashionMNIST, SVHN, CIFAR-10, CIFAR-100, and FEMNIST, in client-to-server communication, and can also be applied to reduce communication costs for server-to-client communication.}, language = {en} } @article{HegdeSongFestag2023, author = {Hegde, Anupama and Song, Rui and Festag, Andreas}, title = {Radio Resource Allocation in 5G-NR V2X: A Multi-Agent Actor-Critic Based Approach}, volume = {11}, journal = {IEEE Access}, publisher = {IEEE}, address = {New York}, issn = {2169-3536}, doi = {https://doi.org/10.1109/ACCESS.2023.3305267}, pages = {87225 -- 87244}, year = {2023}, abstract = {The efficiency of radio resource allocation and scheduling procedures in Cellular Vehicle-to-X (Cellular V2X) communication networks directly affects link quality in terms of latency and reliability. However, owing to the continuous movement of vehicles, it is impossible to have a centralized coordinating unit at all times to manage the allocation of radio resources. In the unmanaged mode of the fifth generation new radio (5G-NR) V2X, the sensing-based semi-persistent scheduling (SB-SPS) loses its effectiveness when V2X data messages become aperiodic with varying data sizes. This leads to misinformed resource allocation decisions among vehicles and frequent resource collisions. To improve resource selection, this study formulates the Cellular V2X communication network as a decentralized multi-agent networked markov decision process (MDP) where each vehicle agent executes an actor-critic-based radio resource scheduler. Developing further the actor-critic methodology for the radio resource allocation problem in Cellular V2X, two variants are derived: independent actor-critic (IAC) and shared experience actor-critic (SEAC). Results from simulation studies indicate that the actor-critic schedulers improve reliability, achieving a 15-20\% higher probability of reception under high vehicular density scenarios with aperiodic traffic patterns.}, language = {en} } @article{BauderKubjatkoSchweiger2023, author = {Bauder, Maximilian and Kubjatko, Tibor and Schweiger, Hans-Georg}, title = {Cooperative Awareness Messages' Generation Frequencies, Trigger Distributions, and Pseudonym Changes of First Commercially Deployed Vehicles in Real Operating Scenarios}, volume = {11}, journal = {IEEE Access}, publisher = {IEEE}, address = {New York}, issn = {2169-3536}, doi = {https://doi.org/10.1109/ACCESS.2023.3292576}, pages = {69708 -- 69719}, year = {2023}, abstract = {Cooperative Intelligent Transport Systems have been deployed in mass-produced vehicles in Europe (Golf 8, ID.3, etc.) since 2019, promising major improvements in vehicle safety, efficiency, and comfort. Equipped with Vehicle-to-X communication, the vehicles directly send information about their driving status to the environment. The data are already used for many Vehicle-to-X communication use-cases specified by the Car-2-Car Communication Consortium to achieve the above-mentioned improvements. This study focuses on using Vehicle-2-X communication and its messages for accident analysis. For this purpose, we investigated the advantage in temporal resolution of the continuously sent Cooperative Awareness Message compared to the Event-Data-Recorder for accident reconstruction in the pre-crash. A high resolution of the temporal accident scene is essential for the doubtless clarification of accidents. Therefore, we analyzed the generation frequencies and the trigger conditions of the Cooperative Awareness Message of vehicles available on the European market while driving in real traffic and during different parking maneuvers. Also, the pseudonym change was analyzed. We found that the average generation frequency of the Cooperative Awareness Message is higher than the 2.00 Hz recording frequency of the event data recorder for all scenarios investigated. Especially on rural roads and highways, the temporal resolution is advantageous. Since many serious accidents occur on rural roads, the use of Cooperative Awareness Messages could significantly benefit the reconstruction of the accident scene. Thus, we recommend increasing efforts to use the Cooperative Awareness Message as digital data for accident analysis and encourage more scientists to deal with this topic.}, language = {en} } @inproceedings{DietlFacchi2025, author = {Dietl, Laura and Facchi, Christian}, title = {Is Maximum Entropy Deep Inverse Reinforcement Learning Suitable for Pedestrian Path Prediction?}, booktitle = {Proceedings of the 3rd Cognitive Mobility Conference}, editor = {Z{\"o}ldy, M{\´a}t{\´e}}, publisher = {Springer}, address = {Cham}, isbn = {978-3-031-81799-1}, doi = {https://doi.org/10.1007/978-3-031-81799-1_20}, pages = {215 -- 229}, year = {2025}, language = {en} } @inproceedings{MaksimovskiFacchi2025, author = {Maksimovski, Daniel and Facchi, Christian}, title = {Priority-Based Cooperative Driving Planner for V2X Maneuver Coordination Application}, booktitle = {2025 IEEE 22nd Consumer Communications \& Networking Conference (CCNC)}, publisher = {IEEE}, address = {Piscataway}, isbn = {979-8-3315-0805-0}, doi = {https://doi.org/10.1109/CCNC54725.2025.10975982}, year = {2025}, language = {en} } @inproceedings{LoboFestagFacchi2025, author = {Lobo, Silas and Festag, Andreas and Facchi, Christian}, title = {Adaptive Message Prioritization: How to Prioritize VRU Awareness Messages in a Congested V2X Network}, booktitle = {2024 IEEE 27th International Conference on Intelligent Transportation Systems (ITSC)}, publisher = {IEEE}, address = {Piscataway}, isbn = {979-8-3315-0592-9}, doi = {https://doi.org/10.1109/ITSC58415.2024.10919704}, pages = {423 -- 430}, year = {2025}, language = {en} } @inbook{DeloozMaksimovskiFestagetal.2024, author = {Delooz, Quentin and Maksimovski, Daniel and Festag, Andreas and Facchi, Christian}, title = {Design and Evaluation of V2X Communication Protocols for Cooperatively Interacting Automobiles}, booktitle = {Cooperatively Interacting Vehicles: Methods and Effects of Automated Cooperation in Traffic}, editor = {Stiller, Christoph and Althoff, Matthias and Burger, Christoph and Deml, Barbara and Eckstein, Lutz and Flemisch, Frank}, publisher = {Springer}, address = {Cham}, isbn = {978-3-031-60494-2}, doi = {https://doi.org/10.1007/978-3-031-60494-2_6}, pages = {159 -- 199}, year = {2024}, abstract = {This chapter studies two key communication services for the support of cooperative driving capabilities using Vehicle-to-Everything (V2X) communications: sensor data sharing and maneuver coordination. Based on the current state of the art in research and pre-standardization of V2X communications, we enhance the protocol design for both services and assess their performance by discrete-event simulations in highway and city scenarios. The first part of this chapter addresses the performance improvement of sensor data sharing by two complementary strategies. The shared sensor data are adapted to the available resources on the used channel. Furthermore, the redundancy of the transmitted information is reduced to lower the load on the wireless channel, whereas several approaches are proposed and assessed. The second part of the chapter analyzes cooperative maneuver coordination protocols. We propose a distributed approach based on the explicit exchange of V2X messages, which introduces priorities in maneuver coordination and studies several communication patterns for the negotiation and coordination of maneuvers among two and more vehicles. The results demonstrate the potential of V2X communications for automated driving, showcase several approaches for enhancements of sensor data sharing and maneuver coordination, and indicate the performance of these enhancements.}, language = {en} } @unpublished{SongZhouLyuetal.2022, author = {Song, Rui and Zhou, Liguo and Lyu, Lingjuan and Festag, Andreas and Knoll, Alois}, title = {ResFed: Communication Efficient Federated Learning by Transmitting Deep Compressed Residuals}, publisher = {arXiv}, address = {Ithaca}, doi = {https://doi.org/10.48550/arXiv.2212.05602}, year = {2022}, language = {en} }