@article{DeloozWilleckeGarlichsetal.2022, author = {Delooz, Quentin and Willecke, Alexander and Garlichs, Keno and Hagau, Andreas-Christian and Wolf, Lars and Vinel, Alexey and Festag, Andreas}, title = {Analysis and Evaluation of Information Redundancy Mitigation for V2X Collective Perception}, volume = {10}, journal = {IEEE Access}, publisher = {IEEE}, address = {New York}, issn = {2169-3536}, doi = {https://doi.org/10.1109/ACCESS.2022.3170029}, pages = {47076 -- 47093}, year = {2022}, abstract = {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.}, language = {en} } @article{DeloozFestagVinel2021, author = {Delooz, Quentin and Festag, Andreas and Vinel, Alexey}, title = {Congestion Aware Objects Filtering for Collective Perception}, volume = {80}, journal = {Electronic Communications of the EASST}, publisher = {TU Berlin}, address = {Berlin}, issn = {1863-2122}, doi = {http://dx.doi.org/10.14279/tuj.eceasst.80.1160}, year = {2021}, abstract = {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).}, language = {en} } @inproceedings{DeloozRieblFestagetal.2021, author = {Delooz, Quentin and Riebl, Raphael and Festag, Andreas and Vinel, Alexey}, title = {Design and Performance of Congestion-Aware Collective Perception}, booktitle = {2020 IEEE Vehicular Networking Conference (VNC)}, publisher = {IEEE}, address = {Piscataway}, isbn = {978-1-7281-9221-5}, doi = {https://doi.org/10.1109/VNC51378.2020.9318335}, year = {2021}, language = {en} } @inproceedings{DeloozFestagVineletal.2023, author = {Delooz, Quentin and Festag, Andreas and Vinel, Alexey and Lobo, Silas}, title = {Simulation-Based Performance Optimization of V2X Collective Perception by Adaptive Object Filtering}, booktitle = {2023 IEEE Intelligent Vehicles Symposium (IV): Proceedings}, publisher = {IEEE}, address = {Piscataway}, isbn = {979-8-3503-4691-6}, doi = {https://doi.org/10.1109/IV55152.2023.10186788}, year = {2023}, language = {en} } @inproceedings{DeloozFestagVinel2020, author = {Delooz, Quentin and Festag, Andreas and Vinel, Alexey}, title = {Revisiting Message Generation Strategies for Collective Perception in Connected and Automated Driving}, booktitle = {VEHICULAR 2020: The Ninth International Conference on Advances in Vehicular Systems, Technologies and Applications}, publisher = {IARIA}, address = {[s. l.]}, isbn = {978-1-61208-795-5}, url = {https://www.thinkmind.org/index.php?view=article\&articleid=vehicular_2020_1_80_30039}, pages = {46 -- 52}, year = {2020}, language = {en} } @unpublished{WanZhaoWiedholzetal.2025, author = {Wan, Lei and Zhao, Jianxin and Wiedholz, Andreas and Bied, Manuel and Martinez de Lucena, Mateus and Jagtap, Abhishek Dinkar and Festag, Andreas and Fr{\"o}hlich, Ant{\^o}nio and Keen, Hannan Ejaz and Vinel, Alexey}, title = {A Systematic Literature Review on Vehicular Collaborative Perception - A Computer Vision Perspective}, publisher = {arXiv}, address = {Ithaca}, doi = {https://doi.org/10.48550/arXiv.2504.04631}, year = {2025}, language = {en} } @article{WanZhaoWiedholzetal.2025, author = {Wan, Lei and Zhao, Jianxin and Wiedholz, Andreas and Bied, Manuel and Martinez de Lucena, Mateus and Jagtap, Abhishek Dinkar and Festag, Andreas and Fr{\"o}hlich, Ant{\^o}nio and Keen, Hannan Ejaz and Vinel, Alexey}, title = {A Systematic Literature Review on Vehicular Collaborative Perception—A Computer Vision Perspective}, volume = {27}, journal = {IEEE Transactions on Intelligent Transportation Systems}, number = {1}, publisher = {IEEE}, address = {New York}, issn = {1558-0016}, doi = {https://doi.org/10.1109/TITS.2025.3631141}, pages = {81 -- 118}, year = {2025}, abstract = {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.}, language = {en} } @article{DeloozVinelFestag2023, author = {Delooz, Quentin and Vinel, Alexey and Festag, Andreas}, title = {Optimizing the channel resource usage for sensor data sharing with V2X communications}, volume = {71}, journal = {at - Automatisierungstechnik}, number = {4}, publisher = {De Gruyter}, address = {Berlin}, issn = {2196-677X}, doi = {https://doi.org/10.1515/auto-2022-0162}, pages = {311 -- 317}, year = {2023}, language = {en} }