@inproceedings{HanXuKefferpuetzetal.2024, author = {Han, Longfei and Xu, Qiuyu and Kefferp{\"u}tz, Klaus and Lu, Ying and Elger, Gordon and Beyerer, J{\"u}rgen}, title = {Scalable Radar-based Roadside Perception: Self-localization and Occupancy Heat Map for Traffic Analysis}, booktitle = {2024 IEEE Intelligent Vehicles Symposium (IV)}, publisher = {IEEE}, address = {Piscataway}, isbn = {979-8-3503-4881-1}, doi = {https://doi.org/10.1109/iv55156.2024.10588397}, pages = {1651 -- 1657}, year = {2024}, language = {en} } @inproceedings{OlcayMeessElger2024, author = {Olcay, Ertug and Meeß, Henri and Elger, Gordon}, title = {Dynamic Obstacle Avoidance for UAVs using MPC and GP-Based Motion Forecast}, booktitle = {2024 European Control Conference (ECC)}, publisher = {IEEE}, address = {Piscataway}, isbn = {978-3-9071-4410-7}, doi = {https://doi.org/10.23919/ECC64448.2024.10591083}, pages = {1024 -- 1031}, year = {2024}, language = {en} } @inproceedings{KettelgerdesPandeyUnruhetal.2024, author = {Kettelgerdes, Marcel and Pandey, Amit and Unruh, Denis and Erdogan, H{\"u}seyin and Wunderle, Bernhard and Elger, Gordon}, title = {Automotive LiDAR Based Precipitation State Estimation Using Physics Informed Spatio-Temporal 3D Convolutional Neural Networks (PIST-CNN)}, booktitle = {2023 29th International Conference on Mechatronics and Machine Vision in Practice (M2VIP)}, publisher = {IEEE}, address = {Piscataway}, isbn = {979-8-3503-2562-1}, doi = {https://doi.org/10.1109/M2VIP58386.2023.10413394}, year = {2024}, 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{StreckHerschelWallrathetal.2022, author = {Streck, Egor and Herschel, Reinhold and Wallrath, Patrick and Sunderam, M. and Elger, Gordon}, title = {Comparison of Two Different Radar Concepts for Pedestrian Protection on Bus Stops}, booktitle = {Proceedings of the 11th International Conference on Sensor Networks}, editor = {Prasad, Venkatesha and Pesch, Dirk and Ansari, Nirwan and Benavente-Peces, C{\´e}sar}, publisher = {SciTePress}, address = {Set{\´u}bal}, isbn = {978-989-758-551-7}, issn = {2184-4380}, doi = {https://doi.org/10.5220/0010777100003118}, pages = {89 -- 96}, year = {2022}, abstract = {This paper presents the joint work from the "HORIS" project, with a focus on pedestrian detection at bus-stops by radar sensors mounted in the infrastructure to support future autonomous driving and protecting pedestrians in critical situations. Two sensor systems are investigated and evaluated. The first based on single radar sensor phase-sensitive raw data analysis and the second based on sensor data fusion of cluster data with two radar sensors using neural networks to predict the position of pedestrians.}, language = {en} } @unpublished{SongLyuJiangetal.2023, author = {Song, Rui and Lyu, Lingjuan and Jiang, Wei and Festag, Andreas and Knoll, Alois}, title = {V2X-Boosted Federated Learning for Cooperative Intelligent Transportation Systems with Contextual Client Selection}, publisher = {arXiv}, address = {Ithaca}, doi = {https://doi.org/10.48550/arXiv.2305.11654}, year = {2023}, abstract = {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.}, 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} } @article{BhanderiAgrawalElger2025, author = {Bhanderi, Savankumar and Agrawal, Shiva and Elger, Gordon}, title = {Deep segmentation of 3+1D radar point cloud for real-time roadside traffic user detection}, volume = {15}, pages = {38489}, journal = {Scientific Reports}, publisher = {Springer Nature}, address = {London}, issn = {2045-2322}, doi = {https://doi.org/10.1038/s41598-025-23019-6}, year = {2025}, abstract = {Smart cities rely on intelligent infrastructure to enhance road safety, optimize traffic flow, and enable vehicle-to-infrastructure (V2I) communication. A key component of such infrastructure is an efficient and real-time perception system that accurately detects diverse traffic participants. Among various sensing modalities, automotive radar is one of the best choices due to its robust performance in adverse weather and low-light conditions. However, due to low spatial resolution, traditional clustering-based approaches for radar object detection often struggle with vulnerable road user detection and nearby object separation. Hence, this paper proposes a deep learning-based D radar point cloud clustering methodology tailored for smart infrastructure-based perception applications. This approach first performs semantic segmentation of the radar point cloud, followed by instance segmentation to generate well-formed clusters with class labels using a deep neural network. It also detects single-point objects that conventional methods often miss. The described approach is developed and experimented using a smart infrastructure-based sensor setup and it performs segmentation of the point cloud in real-time. Experimental results demonstrate 95.35\% F1-macro score for semantic segmentation and 91.03\% mean average precision (mAP) at an intersection over union (IoU) threshold of 0.5 for instance segmentation. Further, the complete pipeline operates at 43.61 frames per second with a memory requirement of less than 0.7 MB on the edge device (Nvidia Jetson AGX Orin).}, language = {en} } @inproceedings{MaksimovskiFestagFacchi2021, author = {Maksimovski, Daniel and Festag, Andreas and Facchi, Christian}, title = {A Survey on Decentralized Cooperative Maneuver Coordination for Connected and Automated Vehicles}, booktitle = {Proceedings of the 7th International Conference on Vehicle Technology and Intelligent Transport Systems}, publisher = {SciTePress}, address = {Set{\´u}bal}, isbn = {978-989-758-513-5}, issn = {2184-495X}, doi = {https://doi.org/10.5220/0010442501000111}, pages = {100 -- 111}, year = {2021}, abstract = {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.}, language = {en} } @inproceedings{HegdeStahlLoboetal.2022, author = {Hegde, Anupama and Stahl, Ringo and Lobo, Silas and Festag, Andreas}, title = {Modeling Cellular Network Infrastructure in SUMO}, volume = {2}, booktitle = {SUMO Conference Proceedings}, publisher = {TIB Open Publishing}, address = {Hannover}, issn = {2750-4425}, doi = {https://doi.org/10.52825/scp.v2i.97}, pages = {99 -- 113}, year = {2022}, abstract = {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.}, language = {en} }