@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 Finally, improvements and future works to the filtering methods are proposed.}, language = {en} } @unpublished{WanZhaoWiedholzetal.2025, author = {Wan, Lei and Zhao, Jianxin and Wiedholz, Andreas and Bied, Manuel and Martinez de Lucena, Mateus and Jagtap, Abhishek Dinkar and Festag, Andreas and Fr{\"o}hlich, Ant{\^o}nio and Keen, Hannan Ejaz and Vinel, Alexey}, title = {A Systematic Literature Review on Vehicular Collaborative Perception - A Computer Vision Perspective}, publisher = {arXiv}, address = {Ithaca}, doi = {https://doi.org/10.48550/arXiv.2504.04631}, year = {2025}, language = {en} } @article{WanZhaoWiedholzetal.2025, author = {Wan, Lei and Zhao, Jianxin and Wiedholz, Andreas and Bied, Manuel and Martinez de Lucena, Mateus and Jagtap, Abhishek Dinkar and Festag, Andreas and Fr{\"o}hlich, Ant{\^o}nio and Keen, Hannan Ejaz and Vinel, Alexey}, title = {A Systematic Literature Review on Vehicular Collaborative Perception—A Computer Vision Perspective}, volume = {27}, journal = {IEEE Transactions on Intelligent Transportation Systems}, number = {1}, publisher = {IEEE}, address = {New York}, issn = {1558-0016}, doi = {https://doi.org/10.1109/TITS.2025.3631141}, pages = {81 -- 118}, year = {2025}, abstract = {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.}, language = {en} } @unpublished{SongLiuChenetal.2022, author = {Song, Rui and Liu, Dai and Chen, Dave Zhenyu and Festag, Andreas and Trinitis, Carsten and Schulz, Martin and Knoll, Alois}, title = {Federated Learning via Decentralized Dataset Distillation in Resource Constrained Edge Environments}, publisher = {arXiv}, address = {Ithaca}, doi = {https://doi.org/10.48550/arXiv.2208.11311}, year = {2022}, abstract = {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.}, language = {en} } @unpublished{SongLiangXiaetal.2025, author = {Song, Rui and Liang, Chenwei and Xia, Yan and Zimmer, Walter and Cao, Hu and Caesar, Holger and Festag, Andreas and Knoll, Alois}, title = {CoDa-4DGS: Dynamic Gaussian Splatting with Context and Deformation Awareness for Autonomous Driving}, publisher = {arXiv}, address = {Ithaca}, doi = {https://doi.org/10.48550/arXiv.2503.06744}, year = {2025}, abstract = {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.}, language = {en} } @inproceedings{SongFestagJagtapetal.2024, author = {Song, Rui and Festag, Andreas and Jagtap, Abhishek Dinkar and Bialdyga, Maximilian and Yan, Zhiran and Otte, Maximilian and Sadashivaiah, Sanath Tiptur and Knoll, Alois}, title = {First Mile: An Open Innovation Lab for Infrastructure-Assisted Cooperative Intelligent Transportation Systems}, booktitle = {2024 IEEE Intelligent Vehicles Symposium (IV)}, publisher = {IEEE}, address = {Piscataway}, isbn = {979-8-3503-4881-1}, doi = {https://doi.org/10.1109/IV55156.2024.10588500}, pages = {1635 -- 1642}, year = {2024}, language = {en} } @inproceedings{HegdeLoboFestag2022, author = {Hegde, Anupama and Lobo, Silas and Festag, Andreas}, title = {Cellular-V2X for Vulnerable Road User Protection in Cooperative ITS}, booktitle = {2022 18th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob)}, publisher = {IEEE}, address = {Piscataway}, isbn = {978-1-6654-6975-3}, doi = {https://doi.org/10.1109/WiMob55322.2022.9941707}, pages = {118 -- 123}, year = {2022}, language = {en} } @inproceedings{SongLiuChenetal.2023, author = {Song, Rui and Liu, Dai and Chen, Dave Zhenyu and Festag, Andreas and Trinitis, Carsten and Schulz, Martin and Knoll, Alois}, title = {Federated Learning via Decentralized Dataset Distillation in Resource-Constrained Edge Environments}, booktitle = {IJCNN 2023 Conference Proceedings}, publisher = {IEEE}, address = {Piscataway}, isbn = {978-1-6654-8867-9}, doi = {https://doi.org/10.1109/IJCNN54540.2023.10191879}, year = {2023}, language = {en} } @inproceedings{FritzscheFestag2018, author = {Fritzsche, Richard and Festag, Andreas}, title = {Reliability Maximization with Location-Based Scheduling for Cellular-V2X Communications in Highway Scenarios}, booktitle = {2018 16th International Conference on Intelligent Transportation Systems Telecommunications (ITST) Proceedings}, publisher = {IEEE}, address = {Piscataway}, isbn = {978-1-5386-5544-3}, doi = {https://doi.org/10.1109/ITST.2018.8566935}, year = {2018}, language = {en} } @inproceedings{LoboFestagFacchi2023, author = {Lobo, Silas and Festag, Andreas and Facchi, Christian}, title = {Enhancing the Safety of Vulnerable Road Users: Messaging Protocols for V2X Communication}, booktitle = {2022 IEEE 96th Vehicular Technology Conference (VTC2022-Fall) Proceedings}, publisher = {IEEE}, address = {Piscataway}, isbn = {978-1-6654-5468-1}, doi = {https://doi.org/10.1109/VTC2022-Fall57202.2022.10012775}, year = {2023}, language = {en} } @inproceedings{FritzscheFestag2018, author = {Fritzsche, Richard and Festag, Andreas}, title = {Location-Based Scheduling for Cellular V2V Systems in Highway Scenarios}, booktitle = {2018 IEEE 87th Vehicular Technology Conference (VTC Spring) Proceedings}, publisher = {IEEE}, address = {Piscataway}, isbn = {978-1-5386-6355-4}, doi = {https://doi.org/10.1109/VTCSpring.2018.8417744}, year = {2018}, language = {en} } @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} } @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} } @inproceedings{FestagUdupaGarciaetal.2021, author = {Festag, Andreas and Udupa, Shrivatsa and Garcia, Lourdes and Wellens, Ralf and Hecht, Matthias and Ulfig, Pierre}, title = {End-to-End Performance Measurements of Drone Communications in 5G Cellular Networks}, booktitle = {2021 IEEE 94th Vehicular Technology Conference (VTC2021-Fall): Proceedings}, publisher = {IEEE}, address = {Piscataway}, isbn = {978-1-6654-1368-8}, doi = {https://doi.org/10.1109/VTC2021-Fall52928.2021.9625429}, year = {2021}, language = {en} } @inproceedings{MaksimovskiFacchiFestag2024, author = {Maksimovski, Daniel and Facchi, Christian and Festag, Andreas}, title = {Packet Rate Control for Maneuver Coordination in Congested V2X Communication Environments}, booktitle = {2024 IEEE 100th Vehicular Technology Conference (VTC2024-Fall), Proceedings}, publisher = {IEEE}, address = {Piscataway}, isbn = {979-8-3315-1778-6}, doi = {https://doi.org/10.1109/VTC2024-Fall63153.2024.10758036}, year = {2024}, language = {en} } @inproceedings{FestagSong2021, author = {Festag, Andreas and Song, Rui}, title = {Analysis of Existing Approaches for Information Sharing in Cooperative Intelligent Transport Systems}, booktitle = {FISITA World Congress 2021}, subtitle = {SENSORIS and V2X Messaging}, publisher = {FISITA}, address = {Bishops Stortford}, url = {https://www.fisita.com/library/f2020-acm-012}, year = {2021}, language = {en} }