TY - INPR A1 - Han, Longfei A1 - Kefferpütz, Klaus A1 - Beyerer, Jürgen T1 - Decentralized Fusion of 3D Extended Object Tracking based on a B-Spline Shape Model N2 - Extended Object Tracking (EOT) exploits the high resolution of modern sensors for detailed environmental perception. Combined with decentralized fusion, it contributes to a more scalable and robust perception system. This paper investigates the decentralized fusion of 3D EOT using a B-spline curve based model. The spline curve is used to represent the side-view profile, which is then extruded with a width to form a 3D shape. We use covariance intersection (CI) for the decentralized fusion and discuss the challenge of applying it to EOT. We further evaluate the tracking result of the decentralized fusion with simulated and real datasets of traffic scenarios. We show that the CI-based fusion can significantly improve the tracking performance for sensors with unfavorable perspective. UR - https://doi.org/10.48550/arXiv.2504.18708 Y1 - 2025 UR - https://doi.org/10.48550/arXiv.2504.18708 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-59648 PB - arXiv CY - Ithaca ER - TY - INPR A1 - Han, Longfei A1 - Xu, Qiuyu A1 - Kefferpütz, Klaus A1 - Elger, Gordon A1 - Beyerer, Jürgen T1 - Applying Extended Object Tracking for Self-Localization of Roadside Radar Sensors N2 - Intelligent Transportation Systems (ITS) can benefit from roadside 4D mmWave radar sensors for large-scale traffic monitoring due to their weatherproof functionality, long sensing range and low manufacturing cost. However, the localization method using external measurement devices has limitations in urban environments. Furthermore, if the sensor mount exhibits changes due to environmental influences, they cannot be corrected when the measurement is performed only during the installation. In this paper, we propose self-localization of roadside radar data using Extended Object Tracking (EOT). The method analyses both the tracked trajectories of the vehicles observed by the sensor and the aerial laser scan of city streets, assigns labels of driving behaviors such as "straight ahead", "left turn", "right turn" to trajectory sections and road segments, and performs Semantic Iterative Closest Points (SICP) algorithm to register the point cloud. The method exploits the result from a down stream task -- object tracking -- for localization. We demonstrate high accuracy in the sub-meter range along with very low orientation error. The method also shows good data efficiency. The evaluation is done in both simulation and real-world tests. UR - https://doi.org/10.48550/arXiv.2407.03084 Y1 - 2024 UR - https://doi.org/10.48550/arXiv.2407.03084 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-58361 PB - arXiv CY - Ithaca ER - TY - INPR A1 - Han, Longfei A1 - Kefferpütz, Klaus A1 - Beyerer, Jürgen T1 - 3D Extended Object Tracking based on Extruded B-Spline Side View Profiles N2 - Object tracking is an essential task for autonomous systems. With the advancement of 3D sensors, these systems can better perceive their surroundings using effective 3D Extended Object Tracking (EOT) methods. Based on the observation that common road users are symmetrical on the right and left sides in the traveling direction, we focus on the side view profile of the object. In order to leverage of the development in 2D EOT and balance the number of parameters of a shape model in the tracking algorithms, we propose a method for 3D extended object tracking (EOT) by describing the side view profile of the object with B-spline curves and forming an extrusion to obtain a 3D extent. The use of B-spline curves exploits their flexible representation power by allowing the control points to move freely. The algorithm is developed into an Extended Kalman Filter (EKF). For a through evaluation of this method, we use simulated traffic scenario of different vehicle models and realworld open dataset containing both radar and lidar data. UR - https://doi.org/10.48550/arXiv.2503.10730 Y1 - 2025 UR - https://doi.org/10.48550/arXiv.2503.10730 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-58331 PB - arXiv CY - Ithaca ER - TY - CHAP A1 - Kefferpütz, Klaus A1 - McGuire, Kimberly T1 - Error-State Unscented Kalman-Filter for UAV Indoor Navigation T2 - 2022 25th International Conference on Information Fusion (FUSION) UR - https://doi.org/10.23919/FUSION49751.2022.9841385 KW - Indoor-navigation KW - Unscented Kalman Filter KW - Ultra-Wideband positioning KW - quadcopter navigation KW - Sensor data fusion Y1 - 2022 UR - https://doi.org/10.23919/FUSION49751.2022.9841385 SN - 978-1-7377497-2-1 PB - IEEE CY - Piscataway ER - TY - JOUR A1 - Senel, Numan A1 - Kefferpütz, Klaus A1 - Doycheva, Kristina A1 - Elger, Gordon T1 - Multi-Sensor Data Fusion for Real-Time Multi-Object Tracking JF - Processes N2 - Sensor data fusion is essential for environmental perception within smart traffic applications. By using multiple sensors cooperatively, the accuracy and probability of the perception are increased, which is crucial for critical traffic scenarios or under bad weather conditions. In this paper, a modular real-time capable multi-sensor fusion framework is presented and tested to fuse data on the object list level from distributed automotive sensors (cameras, radar, and LiDAR). The modular multi-sensor fusion architecture receives an object list (untracked objects) from each sensor. The fusion framework combines classical data fusion algorithms, as it contains a coordinate transformation module, an object association module (Hungarian algorithm), an object tracking module (unscented Kalman filter), and a movement compensation module. Due to the modular design, the fusion framework is adaptable and does not rely on the number of sensors or their types. Moreover, the method continues to operate because of this adaptable design in case of an individual sensor failure. This is an essential feature for safety-critical applications. The architecture targets environmental perception in challenging time-critical applications. The developed fusion framework is tested using simulation and public domain experimental data. Using the developed framework, sensor fusion is obtained well below 10 milliseconds of computing time using an AMD Ryzen 7 5800H mobile processor and the Python programming language. Furthermore, the object-level multi-sensor approach enables the detection of changes in the extrinsic calibration of the sensors and potential sensor failures. A concept was developed to use the multi-sensor framework to identify sensor malfunctions. This feature will become extremely important in ensuring the functional safety of the sensors for autonomous driving. UR - https://doi.org/10.3390/pr11020501 KW - environmental perception KW - sensor fusion KW - autonomous vehicle KW - unscented Kalman filter KW - object tracking KW - roadside units Y1 - 2023 UR - https://doi.org/10.3390/pr11020501 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-31989 SN - 2227-9717 VL - 11 IS - 2 PB - MDPI CY - Basel ER - TY - CHAP A1 - Han, Longfei A1 - Kefferpütz, Klaus A1 - Elger, Gordon A1 - Beyerer, Jürgen T1 - FlexSense: Flexible Infrastructure Sensors for Traffic Perception T2 - 2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC) UR - https://doi.org/10.1109/ITSC57777.2023.10422616 Y1 - 2024 UR - https://doi.org/10.1109/ITSC57777.2023.10422616 SN - 979-8-3503-9946-2 SP - 3810 EP - 3816 PB - IEEE CY - Piscataway ER - TY - CHAP A1 - Han, Longfei A1 - Xu, Qiuyu A1 - Kefferpütz, Klaus A1 - Lu, Ying A1 - Elger, Gordon A1 - Beyerer, Jürgen T1 - Scalable Radar-based Roadside Perception: Self-localization and Occupancy Heat Map for Traffic Analysis T2 - 2024 IEEE Intelligent Vehicles Symposium (IV) UR - https://doi.org/10.1109/iv55156.2024.10588397 Y1 - 2024 UR - https://doi.org/10.1109/iv55156.2024.10588397 SN - 979-8-3503-4881-1 SP - 1651 EP - 1657 PB - IEEE CY - Piscataway ER - TY - JOUR A1 - Beutenmüller, Frank A1 - Brostek, Lukas A1 - Doberstein, Christian A1 - Han, Longfei A1 - Kefferpütz, Klaus A1 - Obstbaum, Martin A1 - Pawlowski, Antonia A1 - Rössert, Christian A1 - Sas-Brunschier, Lucas A1 - Schön, Thilo A1 - Sichermann, Jörg T1 - DISRUPT - Decentralized Intelligent System for Road User Prediction and Tracking JF - SAE Technical Paper UR - https://doi.org/10.4271/2025-01-0294 Y1 - 2025 UR - https://doi.org/10.4271/2025-01-0294 SN - 0148-7191 SN - 2688-3627 PB - SAE CY - Warrendale ER - TY - CHAP A1 - Han, Longfei A1 - Kefferpütz, Klaus A1 - Beyerer, Jürgen ED - Rupniewski, Marek ED - Blunt, Shannon ED - Misiurewicz, Jacek ED - Greco, Maria Sabrina ED - Himed, Braham T1 - Improving B-Spline-Based 3D Extended Object Tracking Using Doppler Measurements T2 - Proceedings of the 2025 IEEE Radar Conference (RadarConf25), October 4–9, 2025, Kraków, Poland UR - https://doi.org/10.1109/RadarConf2559087.2025.11205008 Y1 - 2025 UR - https://doi.org/10.1109/RadarConf2559087.2025.11205008 SN - 979-8-3315-4433-1 SP - 599 EP - 604 PB - IEEE CY - Piscataway ER - TY - JOUR A1 - Zieher, Simon A1 - Olcay, Ertug A1 - Kefferpütz, Klaus A1 - Salamat, Babak A1 - Olzem, Sebastian-Sven A1 - Elsbacher, Gerhard A1 - Meeß, Henri T1 - Drones for automated parcel delivery: Use case identification and derivation of technical requirements JF - Transportation Research Interdisciplinary Perspectives N2 - Drone-based parcel delivery is a rapidly growing domain posing many technological and operational challenges. Drone technologies have been under increasing levels of investigation and development over the past years. Since the realization of drone-based delivery can only be economically feasible at large scales, it requires processes that address the outstanding challenges holistically. However, the majority of research focuses on individual aspects such as environmental perception, route planning, operational design, and business models without addressing the challenges in the overall system. This paper conducts a quantitative analysis of the requirements for identified use cases that are considered relevant for the upcoming future. We provide a comprehensive technology overview and derive requirements and specifications for the targeted use cases that are not fulfilled by the current state-of-the-art. The use cases are not only related to last-mile delivery but also long-distance (hub-to-hub) logistics as well as courier and express delivery. UR - https://doi.org/10.1016/j.trip.2024.101253 Y1 - 2024 UR - https://doi.org/10.1016/j.trip.2024.101253 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-53303 SN - 2590-1982 VL - 2024 IS - 28 PB - Elsevier CY - Amsterdam ER -