TY - JOUR A1 - Delooz, Quentin A1 - Willecke, Alexander A1 - Garlichs, Keno A1 - Hagau, Andreas-Christian A1 - Wolf, Lars A1 - Vinel, Alexey A1 - Festag, Andreas T1 - Analysis and Evaluation of Information Redundancy Mitigation for V2X Collective Perception JF - IEEE Access N2 - 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. UR - https://doi.org/10.1109/ACCESS.2022.3170029 KW - Collective perception KW - information redundancy mitigation KW - road safety KW - sensor data sharing KW - V2X communications Y1 - 2022 UR - https://doi.org/10.1109/ACCESS.2022.3170029 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-28662 SN - 2169-3536 VL - 10 SP - 47076 EP - 47093 PB - IEEE CY - New York ER - TY - INPR A1 - Wan, Lei A1 - Zhao, Jianxin A1 - Wiedholz, Andreas A1 - Bied, Manuel A1 - Martinez de Lucena, Mateus A1 - Jagtap, Abhishek Dinkar A1 - Festag, Andreas A1 - Fröhlich, Antônio A1 - Keen, Hannan Ejaz A1 - Vinel, Alexey T1 - A Systematic Literature Review on Vehicular Collaborative Perception – A Computer Vision Perspective UR - https://doi.org/10.48550/arXiv.2504.04631 Y1 - 2025 UR - https://doi.org/10.48550/arXiv.2504.04631 PB - arXiv CY - Ithaca ER - TY - JOUR A1 - Wan, Lei A1 - Zhao, Jianxin A1 - Wiedholz, Andreas A1 - Bied, Manuel A1 - Martinez de Lucena, Mateus A1 - Jagtap, Abhishek Dinkar A1 - Festag, Andreas A1 - Fröhlich, Antônio A1 - Keen, Hannan Ejaz A1 - Vinel, Alexey T1 - A Systematic Literature Review on Vehicular Collaborative Perception—A Computer Vision Perspective JF - IEEE Transactions on Intelligent Transportation Systems N2 - The effectiveness of autonomous vehicles relies on reliable perception capabilities. Despite significant advancements in artificial intelligence and sensor fusion technologies, current single-vehicle perception systems continue to encounter limitations, notably visual occlusions and limited long-range detection capabilities. Collaborative Perception (CP), enabled by Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) communication, has emerged as a promising solution to mitigate these issues and enhance the reliability of autonomous systems. Beyond advancements in communication, the computer vision community is increasingly focusing on improving vehicular perception through collaborative approaches. However, a systematic literature review that thoroughly examines existing work and reduces subjective bias is still lacking. Such a systematic approach helps identify research gaps, recognize common trends across studies, and inform future research directions. In response, this study follows the PRISMA 2020 guidelines and includes 106 peer-reviewed articles. These publications are analyzed based on modalities, collaboration schemes, and key perception tasks. Through a comparative analysis, this review illustrates how different methods address practical issues such as pose errors, temporal latency, communication constraints, domain shifts, heterogeneity, and adversarial attacks. Furthermore, it critically examines evaluation methodologies, highlighting a misalignment between current metrics and CP’s fundamental objectives. By delving into all relevant topics in-depth, this review offers valuable insights into challenges, opportunities, and risks, serving as a reference for advancing research in vehicular collaborative perception. UR - https://doi.org/10.1109/TITS.2025.3631141 Y1 - 2025 UR - https://doi.org/10.1109/TITS.2025.3631141 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-64708 SN - 1558-0016 VL - 27 IS - 1 SP - 81 EP - 118 PB - IEEE CY - New York ER - TY - INPR A1 - Song, Rui A1 - Liu, Dai A1 - Chen, Dave Zhenyu A1 - Festag, Andreas A1 - Trinitis, Carsten A1 - Schulz, Martin A1 - Knoll, Alois T1 - Federated Learning via Decentralized Dataset Distillation in Resource Constrained Edge Environments N2 - In federated learning, all networked clients contribute to the model training cooperatively. However, with model sizes increasing, even sharing the trained partial models often leads to severe communication bottlenecks in underlying networks, especially when communicated iteratively. In this paper, we introduce a federated learning framework FedD3 requiring only one-shot communication by integrating dataset distillation instances. Instead of sharing model updates in other federated learning approaches, FedD3 allows the connected clients to distill the local datasets independently, and then aggregates those decentralized distilled datasets (e.g. a few unrecognizable images) from networks for model training. Our experimental results show that FedD3 significantly outperforms other federated learning frameworks in terms of needed communication volumes, while it provides the additional benefit to be able to balance the trade-off between accuracy and communication cost, depending on usage scenario or target dataset. For instance, for training an AlexNet model on CIFAR-10 with 10 clients under non-independent and identically distributed (Non-IID) setting, FedD3 can either increase the accuracy by over 71% with a similar communication volume, or save 98% of communication volume, while reaching the same accuracy, compared to other one-shot federated learning approaches. UR - https://doi.org/10.48550/arXiv.2208.11311 Y1 - 2022 UR - https://doi.org/10.48550/arXiv.2208.11311 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-60054 PB - arXiv CY - Ithaca ER - TY - INPR A1 - Song, Rui A1 - Liang, Chenwei A1 - Xia, Yan A1 - Zimmer, Walter A1 - Cao, Hu A1 - Caesar, Holger A1 - Festag, Andreas A1 - Knoll, Alois T1 - CoDa-4DGS: Dynamic Gaussian Splatting with Context and Deformation Awareness for Autonomous Driving N2 - Dynamic scene rendering opens new avenues in autonomous driving by enabling closed-loop simulations with photorealistic data, which is crucial for validating end-to-end algorithms. However, the complex and highly dynamic nature of traffic environments presents significant challenges in accurately rendering these scenes. In this paper, we introduce a novel 4D Gaussian Splatting (4DGS) approach, which incorporates context and temporal deformation awareness to improve dynamic scene rendering. Specifically, we employ a 2D semantic segmentation foundation model to self-supervise the 4D semantic features of Gaussians, ensuring meaningful contextual embedding. Simultaneously, we track the temporal deformation of each Gaussian across adjacent frames. By aggregating and encoding both semantic and temporal deformation features, each Gaussian is equipped with cues for potential deformation compensation within 3D space, facilitating a more precise representation of dynamic scenes. Experimental results show that our method improves 4DGS's ability to capture fine details in dynamic scene rendering for autonomous driving and outperforms other self-supervised methods in 4D reconstruction and novel view synthesis. Furthermore, CoDa-4DGS deforms semantic features with each Gaussian, enabling broader applications. UR - https://doi.org/10.48550/arXiv.2503.06744 Y1 - 2025 UR - https://doi.org/10.48550/arXiv.2503.06744 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-58313 PB - arXiv CY - Ithaca ER - TY - CHAP A1 - Song, Rui A1 - Festag, Andreas A1 - Jagtap, Abhishek Dinkar A1 - Bialdyga, Maximilian A1 - Yan, Zhiran A1 - Otte, Maximilian A1 - Sadashivaiah, Sanath Tiptur A1 - Knoll, Alois T1 - First Mile: An Open Innovation Lab for Infrastructure-Assisted Cooperative Intelligent Transportation Systems T2 - 2024 IEEE Intelligent Vehicles Symposium (IV) UR - https://doi.org/10.1109/IV55156.2024.10588500 Y1 - 2024 UR - https://doi.org/10.1109/IV55156.2024.10588500 SN - 979-8-3503-4881-1 SP - 1635 EP - 1642 PB - IEEE CY - Piscataway ER - TY - CHAP A1 - Hegde, Anupama A1 - Lobo, Silas A1 - Festag, Andreas T1 - Cellular-V2X for Vulnerable Road User Protection in Cooperative ITS T2 - 2022 18th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob) UR - https://doi.org/10.1109/WiMob55322.2022.9941707 KW - Radio resource allocation in Cellular-V2X KW - VRU safety and protection KW - messaging services Y1 - 2022 UR - https://doi.org/10.1109/WiMob55322.2022.9941707 SN - 978-1-6654-6975-3 SP - 118 EP - 123 PB - IEEE CY - Piscataway ER - TY - CHAP A1 - Song, Rui A1 - Liu, Dai A1 - Chen, Dave Zhenyu A1 - Festag, Andreas A1 - Trinitis, Carsten A1 - Schulz, Martin A1 - Knoll, Alois T1 - Federated Learning via Decentralized Dataset Distillation in Resource-Constrained Edge Environments T2 - IJCNN 2023 Conference Proceedings UR - https://doi.org/10.1109/IJCNN54540.2023.10191879 Y1 - 2023 UR - https://doi.org/10.1109/IJCNN54540.2023.10191879 SN - 978-1-6654-8867-9 PB - IEEE CY - Piscataway ER - TY - CHAP A1 - Fritzsche, Richard A1 - Festag, Andreas T1 - Reliability Maximization with Location-Based Scheduling for Cellular-V2X Communications in Highway Scenarios T2 - 2018 16th International Conference on Intelligent Transportation Systems Telecommunications (ITST) Proceedings UR - https://doi.org/10.1109/ITST.2018.8566935 KW - Optimal scheduling KW - Power system reliability KW - Probability KW - Throughput KW - Reliability KW - Signal to noise ratio KW - Road transportation Y1 - 2018 UR - https://doi.org/10.1109/ITST.2018.8566935 SN - 978-1-5386-5544-3 PB - IEEE CY - Piscataway ER - TY - CHAP A1 - Lobo, Silas A1 - Festag, Andreas A1 - Facchi, Christian T1 - Enhancing the Safety of Vulnerable Road Users: Messaging Protocols for V2X Communication T2 - 2022 IEEE 96th Vehicular Technology Conference (VTC2022-Fall) Proceedings UR - https://doi.org/10.1109/VTC2022-Fall57202.2022.10012775 KW - road safety KW - vulnerable road users KW - V2X communication KW - messaging KW - collective perception KW - CPM KW - VAM KW - VRU Y1 - 2023 UR - https://doi.org/10.1109/VTC2022-Fall57202.2022.10012775 SN - 978-1-6654-5468-1 PB - IEEE CY - Piscataway ER - TY - CHAP A1 - Fritzsche, Richard A1 - Festag, Andreas T1 - Location-Based Scheduling for Cellular V2V Systems in Highway Scenarios T2 - 2018 IEEE 87th Vehicular Technology Conference (VTC Spring) Proceedings UR - https://doi.org/10.1109/VTCSpring.2018.8417744 KW - Power system reliability KW - Probability KW - Optimal scheduling KW - Receivers KW - Radio transmitters KW - Road transportation KW - Fading channels Y1 - 2018 UR - https://doi.org/10.1109/VTCSpring.2018.8417744 SN - 978-1-5386-6355-4 PB - IEEE CY - Piscataway ER - TY - JOUR 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 JF - IEEE Communications Standards Magazine 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 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. UR - https://doi.org/10.1109/MCOMSTD.0001.2200080 Y1 - 2024 UR - https://doi.org/10.1109/MCOMSTD.0001.2200080 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-58154 SN - 2471-2833 VL - 8 IS - 1 SP - 28 EP - 35 PB - IEEE CY - New York ER - TY - JOUR A1 - Song, Rui A1 - Zhou, Liguo A1 - Lyu, Lingjuan A1 - Festag, Andreas A1 - Knoll, Alois T1 - ResFed: Communication-Efficient Federated Learning With Deep Compressed Residuals JF - IEEE Internet of Things Journal N2 - 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. UR - https://doi.org/10.1109/JIOT.2023.3324079 Y1 - 2023 UR - https://doi.org/10.1109/JIOT.2023.3324079 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-46371 SN - 2327-4662 VL - 11 IS - 6 SP - 9458 EP - 9472 PB - IEEE CY - New York ER - TY - JOUR A1 - Hegde, Anupama A1 - Song, Rui A1 - Festag, Andreas T1 - Radio Resource Allocation in 5G-NR V2X: A Multi-Agent Actor-Critic Based Approach JF - IEEE Access N2 - 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. UR - https://doi.org/10.1109/ACCESS.2023.3305267 KW - Cellular V2X KW - radio resource allocation KW - deep reinforcement learning Y1 - 2023 UR - https://doi.org/10.1109/ACCESS.2023.3305267 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-39352 SN - 2169-3536 VL - 11 SP - 87225 EP - 87244 PB - IEEE CY - New York ER - TY - CHAP A1 - Lobo, Silas A1 - Festag, Andreas A1 - Facchi, Christian T1 - Adaptive Message Prioritization: How to Prioritize VRU Awareness Messages in a Congested V2X Network T2 - 2024 IEEE 27th International Conference on Intelligent Transportation Systems (ITSC) UR - https://doi.org/10.1109/ITSC58415.2024.10919704 Y1 - 2025 UR - https://doi.org/10.1109/ITSC58415.2024.10919704 SN - 979-8-3315-0592-9 SP - 423 EP - 430 PB - IEEE CY - Piscataway ER - TY - CHAP A1 - Delooz, Quentin A1 - Maksimovski, Daniel A1 - Festag, Andreas A1 - Facchi, Christian ED - Stiller, Christoph ED - Althoff, Matthias ED - Burger, Christoph ED - Deml, Barbara ED - Eckstein, Lutz ED - Flemisch, Frank T1 - Design and Evaluation of V2X Communication Protocols for Cooperatively Interacting Automobiles T2 - Cooperatively Interacting Vehicles: Methods and Effects of Automated Cooperation in Traffic N2 - 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. UR - https://doi.org/10.1007/978-3-031-60494-2_6 Y1 - 2024 UR - https://doi.org/10.1007/978-3-031-60494-2_6 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-50008 SN - 978-3-031-60494-2 SN - 978-3-031-60493-5 SP - 159 EP - 199 PB - Springer CY - Cham ER - TY - INPR A1 - Song, Rui A1 - Zhou, Liguo A1 - Lyu, Lingjuan A1 - Festag, Andreas A1 - Knoll, Alois T1 - ResFed: Communication Efficient Federated Learning by Transmitting Deep Compressed Residuals UR - https://doi.org/10.48550/arXiv.2212.05602 Y1 - 2022 UR - https://doi.org/10.48550/arXiv.2212.05602 PB - arXiv CY - Ithaca ER - TY - CHAP A1 - Festag, Andreas A1 - Udupa, Shrivatsa A1 - Garcia, Lourdes A1 - Wellens, Ralf A1 - Hecht, Matthias A1 - Ulfig, Pierre T1 - End-to-End Performance Measurements of Drone Communications in 5G Cellular Networks T2 - 2021 IEEE 94th Vehicular Technology Conference (VTC2021-Fall): Proceedings UR - https://doi.org/10.1109/VTC2021-Fall52928.2021.9625429 KW - drone KW - measurements KW - end-to-end performance KW - 5G KW - network slicing KW - BVLOS Y1 - 2021 UR - https://doi.org/10.1109/VTC2021-Fall52928.2021.9625429 SN - 978-1-6654-1368-8 PB - IEEE CY - Piscataway ER - TY - CHAP A1 - Maksimovski, Daniel A1 - Facchi, Christian A1 - Festag, Andreas T1 - Packet Rate Control for Maneuver Coordination in Congested V2X Communication Environments T2 - 2024 IEEE 100th Vehicular Technology Conference (VTC2024-Fall), Proceedings UR - https://doi.org/10.1109/VTC2024-Fall63153.2024.10758036 Y1 - 2024 UR - https://doi.org/10.1109/VTC2024-Fall63153.2024.10758036 SN - 979-8-3315-1778-6 PB - IEEE CY - Piscataway ER - TY - CHAP A1 - Festag, Andreas A1 - Song, Rui T1 - Analysis of Existing Approaches for Information Sharing in Cooperative Intelligent Transport Systems BT - SENSORIS and V2X Messaging T2 - FISITA World Congress 2021 Y1 - 2021 UR - https://www.fisita.com/library/f2020-acm-012 N1 - Schreibfehler im Titel auf Website der FISITA Library: "Analysis of Existing Approaches for Information Sharingin Cooperative Intelligent Transport Systems" PB - FISITA CY - Bishops Stortford ER -