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 - 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 - 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 -