TY - CHAP A1 - Schmid, Maximilian A1 - Hermann, Joseph A1 - Bhogaraju, Sri Krishna A1 - Elger, Gordon T1 - Reliability of SAC Solders under Low and High Stress Conditions T2 - 2022 IEEE 9th Electronics System-Integration Technology Conference (ESTC) UR - https://doi.org/10.1109/ESTC55720.2022.9939394 KW - reliability KW - solder joint cracking KW - SAC solder KW - transient thermal analysis KW - SAM Y1 - 2022 UR - https://doi.org/10.1109/ESTC55720.2022.9939394 SN - 978-1-6654-8947-8 SP - 553 EP - 559 PB - IEEE CY - Piscataway ER - TY - CHAP A1 - Maksimovski, Daniel A1 - Facchi, Christian A1 - Festag, Andreas T1 - Priority Maneuver (PriMa) Coordination for Connected and Automated Vehicles T2 - 2021 IEEE International Intelligent Transportation Systems Conference (ITSC) UR - https://doi.org/10.1109/ITSC48978.2021.9564923 Y1 - 2021 UR - https://doi.org/10.1109/ITSC48978.2021.9564923 SN - 978-1-7281-9142-3 SP - 1083 EP - 1089 PB - IEEE CY - Piscataway 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 N1 - Die veröffentlichte Version dieses Preprints ist ebenfalls in diesem Repositorium verzeichnet, s. https://opus4.kobv.de/opus4-haw/frontdoor/index/index/docId/4784 PB - arXiv CY - Ithaca ER - TY - CHAP 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 T2 - CoPerception: Collaborative Perception and Learning, ICRA 2023 workshop Y1 - 2023 UR - https://drive.google.com/file/d/1-OQAeNryd7-lksCTuWnhhyydpFSnw5On/view?usp=share_link N1 - Der Nachweis einer Preprint-Version dieser Veröffentlichung ist ebenfalls in diesem Repositorium verzeichnet, s. https://opus4.kobv.de/opus4-haw/frontdoor/index/index/docId/4144 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 SN - 2750-4425 VL - 2 SP - 99 EP - 113 PB - TIB Open Publishing CY - Hannover ER - TY - JOUR A1 - Schmid, Maximilian A1 - Zippelius, Andreas A1 - Hanß, Alexander A1 - Böckhorst, Stephan A1 - Elger, Gordon T1 - Investigations on High-Power LEDs and Solder Interconnects in Automotive Application: Part II - Reliability JF - IEEE Transactions on Device and Materials Reliability UR - https://doi.org/10.1109/TDMR.2023.3300355 KW - LED KW - non-destructive testing KW - reliability KW - solder KW - scanning acoustic microscopy (SAM) KW - thermal impedance (Zth) KW - thermal resistant (Rth) KW - X-ray KW - transient thermal analysis (TTA) Y1 - 2023 UR - https://doi.org/10.1109/TDMR.2023.3300355 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-39651 SN - 1558-2574 SN - 1530-4388 VL - 23 IS - 3 SP - 419 EP - 429 PB - IEEE CY - Piscataway ER - TY - JOUR A1 - Agrawal, Shiva A1 - Bhanderi, Savankumar A1 - Elger, Gordon T1 - Semi-Automatic Annotation of 3D Radar and Camera for Smart Infrastructure-Based Perception JF - IEEE Access N2 - Environment perception using camera, radar, and/or lidar sensors has significantly improved in the last few years because of deep learning-based methods. However, a large group of these methods fall into the category of supervised learning, which requires a considerable amount of annotated data. Due to uncertainties in multi-sensor data, automating the data labeling process is extremely challenging; hence, it is performed manually to a large extent. Even though full automation of such a process is difficult, semiautomation can be a significant step to ease this process. However, the available work in this regard is still very limited; hence, in this paper, a novel semi-automatic annotation methodology is developed for labeling RGB camera images and 3D automotive radar point cloud data using a smart infrastructure-based sensor setup. This paper also describes a new method for 3D radar background subtraction to remove clutter and a new object category, GROUP, for radar-based object detection for closely located vulnerable road users. To validate the work, a dataset named INFRA-3DRC is created using this methodology, where 75% of the labels are automatically generated. In addition, a radar cluster classifier and an image classifier are developed, trained, and tested on this dataset, achieving accuracy of 98.26% and 94.86%, respectively. The dataset and Python scripts are available at https://fraunhoferivi.github.io/INFRA-3DRC-Dataset/. UR - https://doi.org/10.1109/ACCESS.2024.3373310 Y1 - 2024 UR - https://doi.org/10.1109/ACCESS.2024.3373310 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-46180 SN - 2169-3536 VL - 12 SP - 34325 EP - 34341 PB - IEEE CY - New York ER - TY - CHAP A1 - Meess, Henri A1 - Gerner, Jeremias A1 - Hein, Daniel A1 - Schmidtner, Stefanie A1 - Elger, Gordon T1 - Reinforcement Learning for Traffic Signal Control Optimization BT - A Concept for Real-World Implementation T2 - AAMAS '22: Proceedings of the 21st International Conference on Autonomous Agents and Multiagent Systems KW - Multi-Agent Reinforcement Learning in real-world KW - MARL KW - traffic optimization KW - multimodal traffic KW - DRL Y1 - 2022 UR - https://dl.acm.org/doi/10.5555/3535850.3536081 SN - 978-1-4503-9213-6 SP - 1699 EP - 1701 PB - International Foundation for Autonomous Agents and Multiagent Systems CY - Richland ER - TY - CHAP A1 - Meess, Henri A1 - Gerner, Jeremias A1 - Hein, Daniel A1 - Schmidtner, Stefanie A1 - Elger, Gordon ED - Kieu, Minh Le ED - Dam, Koen H. van ED - Thompson, Jason ED - Malleson, Nick ED - Heppenstall, Alison ED - Ge, Jiaqi T1 - Real World Traffic Optimization by Reinforcement Learning: A Concept T2 - International Workshop on Agent-Based Modelling of Urban Systems (ABMUS) Proceedings: 2022 UR - https://doi.org/10.6084/m9.figshare.19733800.v1 Y1 - 2022 UR - https://doi.org/10.6084/m9.figshare.19733800.v1 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-29290 SP - 49 EP - 54 PB - figshare CY - [s. l.] ER - TY - CHAP A1 - Hegde, Anupama A1 - Lobo, Silas A1 - Festag, Andreas T1 - Cellular-V2X for Vulnerable Road User Protection in Cooperative ITS T2 - 2022 18th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob) UR - https://doi.org/10.1109/WiMob55322.2022.9941707 KW - Radio resource allocation in Cellular-V2X KW - VRU safety and protection KW - messaging services Y1 - 2022 UR - https://doi.org/10.1109/WiMob55322.2022.9941707 SN - 978-1-6654-6975-3 SN - 2160-4894 SP - 118 EP - 123 PB - IEEE CY - Piscataway ER -