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 - TY - CHAP A1 - Hegde, Anupama A1 - Delooz, Quentin A1 - Mariyaklla, Chethan L. A1 - Festag, Andreas A1 - Klingler, Florian T1 - Radio Resource Allocation for Collective Perception in 5G-NR Vehicle-to-X Communication Systems T2 - 2023 IEEE Wireless Communications and Networking Conference (WCNC): Proceedings UR - https://doi.org/10.1109/WCNC55385.2023.10118606 KW - Cellular V2X KW - radio resource allocation KW - scheduling KW - collective perception Y1 - 2023 UR - https://doi.org/10.1109/WCNC55385.2023.10118606 SN - 978-1-6654-9122-8 SN - 1558-2612 PB - IEEE CY - Piscataway ER -