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 - TY - CHAP A1 - Song, Rui A1 - Hegde, Anupama A1 - Senel, Numan A1 - Knoll, Alois A1 - Festag, Andreas T1 - Edge-Aided Sensor Data Sharing in Vehicular Communication Networks T2 - 2022 IEEE 95th Vehicular Technology Conference: (VTC2022-Spring) Proceedings UR - https://doi.org/10.1109/VTC2022-Spring54318.2022.9860849 KW - sensor data sharing and fusion KW - cooperative perception KW - vehicular communication KW - distributed estimation KW - measurement noise Y1 - 2022 UR - https://doi.org/10.1109/VTC2022-Spring54318.2022.9860849 SN - 978-1-6654-8243-1 SN - 2577-2465 PB - IEEE CY - Piscataway (NJ) ER - 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 - TY - INPR 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 N2 - Machine learning (ML) has revolutionized transportation systems, enabling autonomous driving and smart traffic services. Federated learning (FL) overcomes privacy constraints by training ML models in distributed systems, exchanging model parameters instead of raw data. However, the dynamic states of connected vehicles affect the network connection quality and influence the FL performance. To tackle this challenge, we propose a contextual client selection pipeline that uses Vehicle-to-Everything (V2X) messages to select clients based on the predicted communication latency. The pipeline includes: (i) fusing V2X messages, (ii) predicting future traffic topology, (iii) pre-clustering clients based on local data distribution similarity, and (iv) selecting clients with minimal latency for future model aggregation. Experiments show that our pipeline outperforms baselines on various datasets, particularly in non-iid settings. UR - https://doi.org/10.48550/arXiv.2305.11654 Y1 - 2023 UR - https://doi.org/10.48550/arXiv.2305.11654 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-41441 PB - arXiv CY - Ithaca ER - TY - CHAP A1 - Jagtap, Abhishek Dinkar A1 - Song, Rui A1 - Sadashivaiah, Sanath Tiptur A1 - Festag, Andreas T1 - V2X-Gaussians: Gaussian Splatting for Multi-Agent Cooperative Dynamic Scene Reconstruction T2 - 2025 IEEE Intelligent Vehicles Symposium (IV) UR - https://doi.org/10.1109/IV64158.2025.11097436 Y1 - 2025 UR - https://doi.org/10.1109/IV64158.2025.11097436 SN - 979-8-3315-3803-3 SP - 1033 EP - 1039 PB - IEEE CY - Piscataway ER - TY - CHAP A1 - Maksimovski, Daniel A1 - Festag, Andreas A1 - Facchi, Christian T1 - A Survey on Decentralized Cooperative Maneuver Coordination for Connected and Automated Vehicles T2 - Proceedings of the 7th International Conference on Vehicle Technology and Intelligent Transport Systems N2 - V2X communications can be applied for maneuver coordination of automated vehicles, where the vehicles exchange messages to inform each other of their driving intentions and to negotiate for joint maneuvers. For motion and maneuver planning of automated vehicles, the cooperative maneuver coordination extends the perception range of the sensors, enhances the planning horizon and allows complex interactions among the vehicles. For specific scenarios, various schemes for maneuver coordination of connected automated vehicles exist. Recently, several proposals for maneuver coordination have been made that address generic instead of specific scenarios and apply different schemes for the message exchange of driving intentions and maneuver negotiation. This paper presents use cases for maneuver coordination and classifies existing generic approaches for decentralized maneuver coordination considering implicit and explicit trajectory broadcast, cost values and space-time reservation. We systematically describe the approaches, compare them and derive future research topics. UR - https://doi.org/10.5220/0010442501000111 KW - V2X Communications KW - Cooperative Driving KW - Maneuver Coordination KW - Automated Vehicle Y1 - 2021 UR - https://doi.org/10.5220/0010442501000111 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-14806 SN - 978-989-758-513-5 SN - 2184-495X SP - 100 EP - 111 PB - SciTePress CY - Setúbal ER - TY - CHAP A1 - Hegde, Anupama A1 - Stahl, Ringo A1 - Lobo, Silas A1 - Festag, Andreas T1 - Modeling Cellular Network Infrastructure in SUMO T2 - SUMO Conference Proceedings N2 - Communication networks are becoming an increasingly important part of the mobility system. They allow traffic participants to be connected and to exchange information related to traffic and roads. The information exchange impacts the behavior of traffic participants, such as the selection of travel routes or their mobility dynamics. Considering infrastructure-based networks, the information exchange depends on the availability of the network infrastructure and the quality of the communication links. Specifically in urban areas, today’s 4G and 5G networks deploy small cells of high capacity, which do not provide ubiquitous cellular coverage due to their small range, signal blocking, etc. Therefore, the accurate modeling of the network infrastructure and its integration in simulation scenarios in microscopic traffic simulation software is gaining relevance. Unlike traffic infrastructure, such as traffic lights, the simulation of a cellular network infrastructure is not natively supported in SUMO. Instead, the protocols, functions and entities of the communication system with the physical wireless transmission are modeled in a dedicated and specialized network simulator that is coupled with SUMO. The disadvantage of this approach is that the simulated SUMO entities, typically vehicles, are not aware which portions of the roads are covered by wireless cells and what quality the wireless communication links have. In this paper, we propose a method for modeling the cellular infrastructure in SUMO that introduces a cellular coverage layer to SUMO. This layer models cell sites in a regular hexagonal grid, where each site is served by a base station. Following commonly accepted guidelines for the evaluation of cellular communication system, the method facilitates standardized and realistic modeling of the cellular coverage, including cell sites, antenna characteristics, cell association and handover. In order to ease the applicability of the method, we describe the work flow to create cell sites. As a representative case, we have applied the method to InTAS, the SUMO Ingolstadt traffic scenario and applied real data for the cellular infrastructure. We validate the approach by simulating a Cellular V2X system with sidelink connectivity in an urban macro cell environment by coupling SUMO enhanced by the proposed connectivity sublayer with ARTERY-C, a network simulator for Cellular V2X. As a proof-of-concept, we present a signal-to-interference noise ratio (SINR) coverage map and further evaluate the impact of different types of interference. We also demonstrate the effect of advanced features of cellular networks such as inter-cell interference coordination (ICIC) and sidelink communication modes of Cellular V2X with dynamic switching between the in-coverage and out-of-coverage mode. UR - https://doi.org/10.52825/scp.v2i.97 Y1 - 2022 UR - https://doi.org/10.52825/scp.v2i.97 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-14785 SN - 2750-4425 VL - 2 SP - 99 EP - 113 PB - TIB Open Publishing CY - Hannover ER - TY - CHAP A1 - Delooz, Quentin A1 - Festag, Andreas A1 - Vinel, Alexey T1 - Revisiting Message Generation Strategies for Collective Perception in Connected and Automated Driving T2 - VEHICULAR 2020: The Ninth International Conference on Advances in Vehicular Systems, Technologies and Applications Y1 - 2020 UR - https://www.thinkmind.org/index.php?view=article&articleid=vehicular_2020_1_80_30039 SN - 978-1-61208-795-5 SP - 46 EP - 52 PB - IARIA CY - [s. l.] ER - TY - JOUR A1 - Maksimovski, Daniel A1 - Festag, Andreas A1 - Facchi, Christian T1 - Adaptive Message Generation Rules for V2X Maneuver Coordination Service JF - IEEE Access N2 - 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. UR - https://doi.org/10.1109/ACCESS.2026.3652364 Y1 - 2026 UR - https://doi.org/10.1109/ACCESS.2026.3652364 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-66051 SN - 2169-3536 VL - 14 SP - 6417 EP - 6437 PB - IEEE CY - New York ER - TY - JOUR A1 - Delooz, Quentin A1 - Vinel, Alexey A1 - Festag, Andreas T1 - Optimizing the channel resource usage for sensor data sharing with V2X communications JF - at - Automatisierungstechnik UR - https://doi.org/10.1515/auto-2022-0162 KW - V2X communications KW - sensor data sharing KW - Cooperative ITS KW - Intelligent Transport Systems Y1 - 2023 UR - https://doi.org/10.1515/auto-2022-0162 SN - 2196-677X VL - 71 IS - 4 SP - 311 EP - 317 PB - De Gruyter CY - Berlin ER - TY - CHAP A1 - Hegde, Anupama A1 - Festag, Andreas T1 - Artery-C: An OMNeT++ Based Discrete Event Simulation Framework for Cellular V2X T2 - MSWiM '20: Proceedings of the 23rd International ACM Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems UR - https://doi.org/10.1145/3416010.3423240 KW - LTE-V2X KW - Cellular V2X KW - resource allocation KW - simulation Y1 - 2020 UR - https://doi.org/10.1145/3416010.3423240 SN - 978-1-4503-8117-8 SP - 47 EP - 51 PB - ACM CY - New York ER -