@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{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{AgrawalSongDoychevaetal.2023, author = {Agrawal, Shiva and Song, Rui and Doycheva, Kristina and Knoll, Alois and Elger, Gordon}, title = {Intelligent Roadside Infrastructure for Connected Mobility}, booktitle = {Smart Cities, Green Technologies, and Intelligent Transport Systems: 11th International Conference, SMARTGREENS 2022 and 8th International Conference, VEHITS 2022: Revised Selected Papers}, editor = {Klein, Cornel and Jarke, Matthias and Ploeg, Jeroen and Helfert, Markus and Berns, Karsten and Gusikhin, Oleg}, publisher = {Springer}, address = {Cham}, isbn = {978-3-031-37470-8}, issn = {1865-0937}, doi = {https://doi.org/10.1007/978-3-031-37470-8_6}, pages = {134 -- 157}, year = {2023}, 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{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} } @inproceedings{SongHegdeSeneletal.2022, author = {Song, Rui and Hegde, Anupama and Senel, Numan and Knoll, Alois and Festag, Andreas}, title = {Edge-Aided Sensor Data Sharing in Vehicular Communication Networks}, booktitle = {2022 IEEE 95th Vehicular Technology Conference: (VTC2022-Spring) Proceedings}, publisher = {IEEE}, address = {Piscataway (NJ)}, isbn = {978-1-6654-8243-1}, issn = {2577-2465}, doi = {https://doi.org/10.1109/VTC2022-Spring54318.2022.9860849}, year = {2022}, language = {en} } @inproceedings{SongZhouLakshminarasimhanetal.2022, author = {Song, Rui and Zhou, Liguo and Lakshminarasimhan, Venkatnarayanan and Festag, Andreas and Knoll, Alois}, title = {Federated Learning Framework Coping with Hierarchical Heterogeneity in Cooperative ITS}, booktitle = {2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC)}, publisher = {IEEE}, address = {Piscataway}, isbn = {978-1-6654-6880-0}, pages = {3502 -- 3508}, year = {2022}, language = {en} } @article{ZhouSongChenetal.2023, author = {Zhou, Liguo and Song, Rui and Chen, Guang and Festag, Andreas and Knoll, Alois}, title = {Residual encoding framework to compress DNN parameters for fast transfer}, volume = {2023}, pages = {110815}, journal = {Knowledge-Based Systems}, number = {277}, publisher = {Elsevier}, address = {Amsterdam}, issn = {1872-7409}, doi = {https://doi.org/10.1016/j.knosys.2023.110815}, year = {2023}, language = {en} } @article{SongXuFestagetal.2023, author = {Song, Rui and Xu, Runsheng and Festag, Andreas and Ma, Jiaqi and Knoll, Alois}, title = {FedBEVT: Federated Learning Bird's Eye View Perception Transformer in Road Traffic Systems}, volume = {9}, journal = {IEEE Transactions on Intelligent Vehicles}, number = {1}, publisher = {IEEE}, address = {New York}, issn = {2379-8904}, doi = {https://doi.org/10.1109/TIV.2023.3310674}, pages = {958 -- 969}, year = {2023}, abstract = {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.}, language = {en} } @inproceedings{AgrawalSongKohlietal.2022, author = {Agrawal, Shiva and Song, Rui and Kohli, Akhil and Korb, Andreas and Andre, Maximilian and Holzinger, Erik and Elger, Gordon}, title = {Concept of Smart Infrastructure for Connected Vehicle Assist and Traffic Flow Optimization}, booktitle = {Proceedings of the 8th International Conference on Vehicle Technology and Intelligent Transport Systems}, editor = {Ploeg, Jeroen and Helfert, Markus and Berns, Karsten and Gusikhin, Oleg}, publisher = {SciTePress}, address = {Set{\´u}bal}, isbn = {978-989-758-573-9}, issn = {2184-495X}, doi = {https://doi.org/10.5220/0011068800003191}, pages = {360 -- 367}, year = {2022}, abstract = {The smart infrastructure units can play a vital role to develop smart cities of the future and in assisting automated vehicles on the road by providing extended perception and timely warnings to avoid accidents. This paper focuses on the development of such an infrastructure unit, that is specifically designed for a pedestrian crossing junction. It can control traffic lights at the junction by real-time environment perception through its sensors and can optimize the flow of vehicles and passing vulnerable road users (VRUs). Moreover, it can assist on-road vehicles by providing real-time information and critical warnings via a v2x module. This paper further describes different use-cases of the work, all major hardware components involved in the development of smart infrastructure unit, referred to as an edge, different sensor fusion approaches using the camera, radar, and lidar mounted on the edge for environment perception, various modes of communication including v2x, system design}, language = {en} } @inproceedings{JagtapSongSadashivaiahetal.2025, author = {Jagtap, Abhishek Dinkar and Song, Rui and Sadashivaiah, Sanath Tiptur and Festag, Andreas}, title = {V2X-Gaussians: Gaussian Splatting for Multi-Agent Cooperative Dynamic Scene Reconstruction}, booktitle = {2025 IEEE Intelligent Vehicles Symposium (IV)}, publisher = {IEEE}, address = {Piscataway}, isbn = {979-8-3315-3803-3}, doi = {https://doi.org/10.1109/IV64158.2025.11097436}, pages = {1033 -- 1039}, year = {2025}, language = {en} }