TY - CHAP A1 - Maksimovski, Daniel A1 - Facchi, Christian A1 - Festag, Andreas T1 - Priority Maneuver (PriMa) Coordination for Connected and Automated Vehicles T2 - 2021 IEEE International Intelligent Transportation Systems Conference (ITSC) UR - https://doi.org/10.1109/ITSC48978.2021.9564923 Y1 - 2021 UR - https://doi.org/10.1109/ITSC48978.2021.9564923 SN - 978-1-7281-9142-3 SP - 1083 EP - 1089 PB - IEEE CY - Piscataway ER - TY - JOUR A1 - Delooz, Quentin A1 - Festag, Andreas A1 - Vinel, Alexey T1 - Congestion Aware Objects Filtering for Collective Perception JF - Electronic Communications of the EASST N2 - This paper addresses collective perception for connected and automated driving. It proposes the adaptation of filtering rules based on the currently available channel resources, referred to as Enhanced DCC-Aware Filtering (EDAF). UR - http://dx.doi.org/10.14279/tuj.eceasst.80.1160 KW - V2X KW - Decentralized Congestion Control KW - Collective Perception Y1 - 2021 UR - http://dx.doi.org/10.14279/tuj.eceasst.80.1160 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-14761 SN - 1863-2122 VL - 80 PB - TU Berlin CY - Berlin ER - TY - CHAP A1 - Volk, Georg A1 - Delooz, Quentin A1 - Schiegg, Florian A1 - Bernuth, Alexander von A1 - Festag, Andreas A1 - Bringmann, Oliver T1 - Towards Realistic Evaluation of Collective Perception for Connected and Automated Driving T2 - 2021 IEEE International Intelligent Transportation Systems Conference (ITSC) UR - https://doi.org/10.1109/ITSC48978.2021.9564783 KW - Conferences KW - Pipelines KW - Information age KW - Delays KW - Vehicle-to-everything KW - Autonomous vehicles KW - Intelligent transportation systems Y1 - 2021 UR - https://doi.org/10.1109/ITSC48978.2021.9564783 SN - 978-1-7281-9142-3 SP - 1049 EP - 1056 PB - IEEE CY - Piscataway ER - TY - CHAP A1 - Hegde, Anupama A1 - Festag, Andreas T1 - Mode Switching Performance in Cellular-V2X T2 - 2020 IEEE Vehicular Networking Conference (VNC) UR - https://doi.org/10.1109/VNC51378.2020.9318394 KW - Switches KW - Synchronization KW - Base stations KW - Vehicle-to-everything KW - Resource management KW - Global navigation satellite system KW - Sensors Y1 - 2021 UR - https://doi.org/10.1109/VNC51378.2020.9318394 SN - 978-1-7281-9221-5 PB - IEEE CY - Piscataway ER - TY - CHAP A1 - Delooz, Quentin A1 - Riebl, Raphael A1 - Festag, Andreas A1 - Vinel, Alexey T1 - Design and Performance of Congestion-Aware Collective Perception T2 - 2020 IEEE Vehicular Networking Conference (VNC) UR - https://doi.org/10.1109/VNC51378.2020.9318335 KW - Logic gates KW - Wireless sensor networks KW - Wireless communication KW - Europe KW - Vehicle dynamics KW - Standards KW - Safety Y1 - 2021 UR - https://doi.org/10.1109/VNC51378.2020.9318335 SN - 978-1-7281-9221-5 PB - IEEE CY - Piscataway ER - TY - CHAP A1 - Senel, Numan A1 - Elger, Gordon A1 - Festag, Andreas T1 - Sensor Time Synchronization in Smart Road Infrastructure T2 - FISITA Web Congress 2020 Y1 - 2020 UR - https://www.fisita.com/library/f2020-acm-083 PB - FISITA CY - Bishops Stortford ER - TY - CHAP A1 - Delooz, Quentin A1 - Festag, Andreas T1 - Network Load Adaptation for Collective Perception in V2X Communication T2 - 2019 IEEE ICCVE, The 8th IEEE International Conference on Connected Vehicles and Expo (ICCVE), November 4-8, 2019, 2019 Conference Proceedings UR - https://doi.org/10.1109/ICCVE45908.2019.8964988 KW - V2X communications KW - collective perception KW - object filtering Y1 - 2020 UR - https://doi.org/10.1109/ICCVE45908.2019.8964988 SN - 978-1-7281-0142-2 PB - IEEE CY - Piscataway ER - TY - JOUR A1 - Hung, Shao-Chou A1 - Zhang, Xin A1 - Festag, Andreas A1 - Chen, Kwang-Cheng A1 - Fettweis, Gerhard T1 - Vehicle-Centric Network Association in Heterogeneous Vehicle-to-Vehicle Networks JF - IEEE Transactions on Vehicular Technology UR - https://doi.org/10.1109/TVT.2019.2910324 KW - Autonomous vehicular network KW - V2V KW - Lyapunov optimization KW - fairness KW - underlay KW - virtual cell KW - no-cell architecture Y1 - 2019 UR - https://doi.org/10.1109/TVT.2019.2910324 SN - 1939-9359 VL - 68 IS - 6 SP - 5981 EP - 5996 PB - IEEE CY - New York ER - TY - JOUR A1 - Hegde, Anupama A1 - Festag, Andreas T1 - Mode Switching Strategies in Cellular-V2X JF - IFAC-PapersOnLine UR - https://doi.org/10.1016/j.ifacol.2019.08.052 KW - Cellular-V2X KW - in-coverage mode KW - out-of-coverage mode KW - mode switching KW - latency Y1 - 2019 UR - https://doi.org/10.1016/j.ifacol.2019.08.052 SN - 2405-8963 VL - 52 IS - 8 SP - 81 EP - 86 PB - Elsevier CY - Amsterdam ER - TY - JOUR A1 - Kühlmorgen, Sebastian A1 - Lu, Hongsheng A1 - Festag, Andreas A1 - Kenney, John A1 - Gemsheim, Sebastian A1 - Fettweis, Gerhard T1 - Evaluation of Congestion-Enabled Forwarding With Mixed Data Traffic in Vehicular Communications JF - IEEE Transactions on Intelligent Transportation Systems UR - https://doi.org/10.1109/TITS.2018.2890619 KW - Vehicular communication (V2X) KW - VANETs KW - decentralized congestion control KW - Gatekeeper KW - rate adaptation KW - LIMERIC KW - multi-hop KW - contention-based forwarding KW - RORA Y1 - 2019 UR - https://doi.org/10.1109/TITS.2018.2890619 SN - 1558-0016 VL - 21 IS - 1 SP - 233 EP - 247 PB - IEEE CY - New York ER - TY - CHAP A1 - Maksimovski, Daniel A1 - Facchi, Christian A1 - Festag, Andreas ED - Klein, Cornel ED - Jarke, Matthias ED - Helfert, Markus ED - Berns, Karsten ED - Gusikhin, Oleg T1 - Cooperative Driving: Research on Generic Decentralized Maneuver Coordination for Connected and Automated Vehicles T2 - Smart Cities, Green Technologies, and Intelligent Transport Systems: 10th International Conference, SMARTGREENS 2021, and 7th International Conference, VEHITS 2021, Virtual Event, April 28-30, 2021, Revised Selected Papers UR - https://doi.org/10.1007/978-3-031-17098-0_18 KW - V2X communication KW - cooperative maneuver coordination KW - connected vehicles Y1 - 2022 UR - https://doi.org/10.1007/978-3-031-17098-0_18 SN - 978-3-031-17097-3 SN - 978-3-031-17098-0 SP - 348 EP - 370 PB - Springer CY - Cham ER - TY - CHAP A1 - Kühlmorgen, Sebastian A1 - Schmager, Patrick A1 - Festag, Andreas A1 - Fettweis, Gerhard T1 - Simulation-Based Evaluation of ETSI ITS-G5 and Cellular-VCS in a Real-World Road Traffic Scenario T2 - 2018 IEEE 88th Vehicular Technology Conference (VTC-Fall) Proceedings UR - https://doi.org/10.1109/VTCFall.2018.8691011 KW - Protocols KW - Cams KW - Device-to-device communication KW - Roads KW - Long Term Evolution KW - Vehicle-to-everything KW - Data models Y1 - 2018 UR - https://doi.org/10.1109/VTCFall.2018.8691011 SN - 978-1-5386-6358-5 SN - 2577-2465 PB - IEEE CY - Piscataway ER - TY - CHAP A1 - Song, Rui A1 - Zhou, Liguo A1 - Lakshminarasimhan, Venkatnarayanan A1 - Festag, Andreas A1 - Knoll, Alois T1 - Federated Learning Framework Coping with Hierarchical Heterogeneity in Cooperative ITS T2 - 2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC) KW - Training KW - Deep learning KW - Data privacy KW - Federated learning KW - Roads KW - Computational modeling KW - Data models Y1 - 2022 SN - 978-1-6654-6880-0 SP - 3502 EP - 3508 PB - IEEE CY - Piscataway ER - TY - CHAP A1 - Delooz, Quentin A1 - Festag, Andreas A1 - Vinel, Alexey A1 - Lobo, Silas T1 - Simulation-Based Performance Optimization of V2X Collective Perception by Adaptive Object Filtering T2 - 2023 IEEE Intelligent Vehicles Symposium (IV): Proceedings UR - https://doi.org/10.1109/IV55152.2023.10186788 KW - V2X KW - sensor data sharing KW - vehicular communications KW - Collective Perception KW - message generation Y1 - 2023 UR - https://doi.org/10.1109/IV55152.2023.10186788 SN - 979-8-3503-4691-6 PB - IEEE CY - Piscataway ER - TY - JOUR A1 - Goeckenjan, Maren A1 - Festag, Andreas A1 - Kather, Jakob Nikolas T1 - Entscheidungsfindungen in der Reproduktionsmedizin BT - von der menschlichen zur künstlichen Intelligenz JF - Gynäkologische Endokrinologie T2 - Decision-making in reproductive medicine: from human to artificial intelligence UR - https://doi.org/10.1007/s10304-022-00489-9 Y1 - 2023 UR - https://doi.org/10.1007/s10304-022-00489-9 SN - 1610-2908 VL - 21 IS - 1 SP - 72 EP - 77 PB - Springer CY - Heidelberg ER - TY - JOUR A1 - Zhou, Liguo A1 - Song, Rui A1 - Chen, Guang A1 - Festag, Andreas A1 - Knoll, Alois T1 - Residual encoding framework to compress DNN parameters for fast transfer JF - Knowledge-Based Systems UR - https://doi.org/10.1016/j.knosys.2023.110815 Y1 - 2023 UR - https://doi.org/10.1016/j.knosys.2023.110815 SN - 1872-7409 SN - 0950-7051 VL - 2023 IS - 277 PB - Elsevier CY - Amsterdam ER - TY - INPR A1 - Bazzi, Alessandro A1 - Sepulcre, Miguel A1 - Delooz, Quentin A1 - Festag, Andreas A1 - Vogt, Jonas A1 - Wieker, Horst A1 - Berens, Friedbert A1 - Spaanderman, Paul T1 - Multi-Channel Operation for the Release 2 of ETSI Cooperative Intelligent Transport Systems N2 - 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 towards 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 paper provides a comprehensive review of the new set of specifications, describing the main entities extending the C-ITS architecture at the different layers of the protocol stack, In addition, the paper 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 paper facilitate the understanding and motivation of the new set of Release 2 ETSI specifications for MCO and the identification of new research opportunities. UR - https://doi.org/10.48550/arXiv.2305.19863 Y1 - 2023 UR - https://doi.org/10.48550/arXiv.2305.19863 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-41498 PB - arXiv CY - Ithaca ER - TY - CHAP A1 - Maksimovski, Daniel A1 - Facchi, Christian A1 - Festag, Andreas T1 - A Framework of Use Cases, Scenarios, and Metrics for Evaluation of V2X Maneuver Coordination T2 - 2024 IEEE Vehicular Networking Conference (VNC) UR - https://doi.org/10.1109/VNC61989.2024.10575992 Y1 - 2024 UR - https://doi.org/10.1109/VNC61989.2024.10575992 SN - 979-8-3503-6270-1 SP - 1 EP - 8 PB - IEEE CY - Piscataway ER - TY - CHAP A1 - Song, Rui A1 - Lyu, Lingjuan A1 - Jiang, Wei A1 - Festag, Andreas A1 - Knoll, Alois T1 - V2X-Boosted Federated Learning for Cooperative Intelligent Transportation Systems with Contextual Client Selection T2 - CoPerception: Collaborative Perception and Learning, ICRA 2023 workshop Y1 - 2023 UR - https://drive.google.com/file/d/1-OQAeNryd7-lksCTuWnhhyydpFSnw5On/view?usp=share_link ER - TY - INPR A1 - Song, Rui A1 - Liang, Chenwei A1 - Cao, Hu A1 - Yan, Zhiran A1 - Zimmer, Walter A1 - Gross, Markus A1 - Festag, Andreas A1 - Knoll, Alois T1 - Collaborative Semantic Occupancy Prediction with Hybrid Feature Fusion in Connected Automated Vehicles N2 - Collaborative perception in automated vehicles leverages the exchange of information between agents, aiming to elevate perception results. Previous camera-based collaborative 3D perception methods typically employ 3D bounding boxes or bird's eye views as representations of the environment. However, these approaches fall short in offering a comprehensive 3D environmental prediction. To bridge this gap, we introduce the first method for collaborative 3D semantic occupancy prediction. Particularly, it improves local 3D semantic occupancy predictions by hybrid fusion of (i) semantic and occupancy task features, and (ii) compressed orthogonal attention features shared between vehicles. Additionally, due to the lack of a collaborative perception dataset designed for semantic occupancy prediction, we augment a current collaborative perception dataset to include 3D collaborative semantic occupancy labels for a more robust evaluation. The experimental findings highlight that: (i) our collaborative semantic occupancy predictions excel above the results from single vehicles by over 30%, and (ii) models anchored on semantic occupancy outpace state-of-the-art collaborative 3D detection techniques in subsequent perception applications, showcasing enhanced accuracy and enriched semantic-awareness in road environments. UR - https://doi.org/10.48550/arXiv.2402.07635 Y1 - 2024 UR - https://doi.org/10.48550/arXiv.2402.07635 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-46035 PB - arXiv CY - Ithaca ER - TY - JOUR A1 - Bauder, Maximilian A1 - Festag, Andreas A1 - Kubjatko, Tibor A1 - Schweiger, Hans-Georg T1 - Data accuracy in Vehicle-to-X cooperative awareness messages: An experimental study for the first commercial deployment of C-ITS in Europe JF - Vehicular Communications N2 - Cooperative Intelligent Transportation Systems have achieved a mature technology stage and are in an early phase of mass deployment in Europe. Relying on Vehicle-to-X communication, these systems were primarily developed to improve traffic safety, efficiency, and driving comfort. However, they also offer great opportunities for other use cases. One of them is forensic accident analysis, where the received data provide details about the status of other traffic participants, give insights into the accident scenario, and therefore help in understanding accident causes. A high accuracy of the sent information is essential: For safety use cases, such as traffic jam warning, a poor accuracy of the data may result in wrong driver information, undermine the usability of the system and even create new safety risks. For accident analysis, a low accuracy may prevent the correct reconstruction of an accident. This paper presents an experimental study of the first generation of Cooperative Intelligent Transportation Systems in Europe. The results indicate a high accuracy for most of the data fields in the Vehicle-to-X messages, namely speed, acceleration, heading and yaw rate information, which meet the accuracy requirements for safety use cases and accident analysis. In contrast, the position data, which are also carried in the messages, have larger errors. Specifically, we observed that the lateral position still has an acceptable accuracy. The error of the longitudinal position is larger and may compromise safety use cases with high accuracy requirements. Even with limited accuracy, the data provide a high value for the accident analysis. Since we also found that the accuracy of the data increases for newer vehicle models, we presume that Vehicle-to-X data have the potential for exact accident reconstruction. UR - https://doi.org/10.1016/j.vehcom.2024.100744 Y1 - 2024 UR - https://doi.org/10.1016/j.vehcom.2024.100744 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-48060 SN - 2214-2096 VL - 2024 IS - 47 PB - Elsevier CY - Amsterdam ER - TY - JOUR A1 - Song, Rui A1 - Xu, Runsheng A1 - Festag, Andreas A1 - Ma, Jiaqi A1 - Knoll, Alois T1 - FedBEVT: Federated Learning Bird's Eye View Perception Transformer in Road Traffic Systems JF - IEEE Transactions on Intelligent Vehicles N2 - Bird's eye view (BEV) perception is becoming increasingly important in the field of autonomous driving. It uses multi-view camera data to learn a transformer model that directly projects the perception of the road environment onto the BEV perspective. However, training a transformer model often requires a large amount of data, and as camera data for road traffic are often private, they are typically not shared. Federated learning offers a solution that enables clients to collaborate and train models without exchanging data but model parameters. In this paper, we introduce FedBEVT, a federated transformer learning approach for BEV perception. In order to address two common data heterogeneity issues in FedBEVT: (i) diverse sensor poses, and (ii) varying sensor numbers in perception systems, we propose two approaches - Federated Learning with Camera-Attentive Personalization (FedCaP) and Adaptive Multi-Camera Masking (AMCM), respectively. To evaluate our method in real-world settings, we create a dataset consisting of four typical federated use cases. Our findings suggest that FedBEVT outperforms the baseline approaches in all four use cases, demonstrating the potential of our approach for improving BEV perception in autonomous driving. UR - https://doi.org/10.1109/TIV.2023.3310674 Y1 - 2023 UR - https://doi.org/10.1109/TIV.2023.3310674 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-42789 SN - 2379-8904 SN - 2379-8858 VL - 9 IS - 1 SP - 958 EP - 969 PB - IEEE CY - New York ER -