@unpublished{HanKefferpuetzBeyerer2025, author = {Han, Longfei and Kefferp{\"u}tz, Klaus and Beyerer, J{\"u}rgen}, title = {Decentralized Fusion of 3D Extended Object Tracking based on a B-Spline Shape Model}, publisher = {arXiv}, address = {Ithaca}, doi = {https://doi.org/10.48550/arXiv.2504.18708}, year = {2025}, abstract = {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can significantly improve the tracking performance for sensors with unfavorable perspective.}, language = {en} } @unpublished{HanXuKefferpuetzetal.2024, author = {Han, Longfei and Xu, Qiuyu and Kefferp{\"u}tz, Klaus and Elger, Gordon and Beyerer, J{\"u}rgen}, title = {Applying Extended Object Tracking for Self-Localization of Roadside Radar Sensors}, publisher = {arXiv}, address = {Ithaca}, doi = {https://doi.org/10.48550/arXiv.2407.03084}, year = {2024}, abstract = {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.}, language = {en} } @unpublished{HanKefferpuetzBeyerer2025, author = {Han, Longfei and Kefferp{\"u}tz, Klaus and Beyerer, J{\"u}rgen}, title = {3D Extended Object Tracking based on Extruded B-Spline Side View Profiles}, publisher = {arXiv}, address = {Ithaca}, doi = {https://doi.org/10.48550/arXiv.2503.10730}, year = {2025}, abstract = {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.}, language = {en} } @inproceedings{KefferpuetzMcGuire2022, author = {Kefferp{\"u}tz, Klaus and McGuire, Kimberly}, title = {Error-State Unscented Kalman-Filter for UAV Indoor Navigation}, booktitle = {2022 25th International Conference on Information Fusion (FUSION)}, publisher = {IEEE}, address = {Piscataway}, isbn = {978-1-7377497-2-1}, doi = {https://doi.org/10.23919/FUSION49751.2022.9841385}, year = {2022}, language = {en} } @article{SenelKefferpuetzDoychevaetal.2023, author = {Senel, Numan and Kefferp{\"u}tz, Klaus and Doycheva, Kristina and Elger, Gordon}, title = {Multi-Sensor Data Fusion for Real-Time Multi-Object Tracking}, volume = {11}, pages = {501}, journal = {Processes}, number = {2}, publisher = {MDPI}, address = {Basel}, issn = {2227-9717}, doi = {https://doi.org/10.3390/pr11020501}, year = {2023}, abstract = {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.}, language = {en} } @inproceedings{HanKefferpuetzElgeretal.2024, author = {Han, Longfei and Kefferp{\"u}tz, Klaus and Elger, Gordon and Beyerer, J{\"u}rgen}, title = {FlexSense: Flexible Infrastructure Sensors for Traffic Perception}, booktitle = {2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC)}, publisher = {IEEE}, address = {Piscataway}, isbn = {979-8-3503-9946-2}, doi = {https://doi.org/10.1109/ITSC57777.2023.10422616}, pages = {3810 -- 3816}, year = {2024}, language = {en} } @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} } @article{BeutenmuellerBrostekDobersteinetal.2025, author = {Beutenm{\"u}ller, Frank and Brostek, Lukas and Doberstein, Christian and Han, Longfei and Kefferp{\"u}tz, Klaus and Obstbaum, Martin and Pawlowski, Antonia and R{\"o}ssert, Christian and Sas-Brunschier, Lucas and Sch{\"o}n, Thilo and Sichermann, J{\"o}rg}, title = {DISRUPT - Decentralized Intelligent System for Road User Prediction and Tracking}, pages = {2025-01-0294}, journal = {SAE Technical Paper}, publisher = {SAE}, address = {Warrendale}, issn = {0148-7191}, doi = {https://doi.org/10.4271/2025-01-0294}, year = {2025}, language = {en} } @inproceedings{HanKefferpuetzBeyerer2025, author = {Han, Longfei and Kefferp{\"u}tz, Klaus and Beyerer, J{\"u}rgen}, title = {Improving B-Spline-Based 3D Extended Object Tracking Using Doppler Measurements}, booktitle = {Proceedings of the 2025 IEEE Radar Conference (RadarConf25), October 4-9, 2025, Krak{\´o}w, Poland}, editor = {Rupniewski, Marek and Blunt, Shannon and Misiurewicz, Jacek and Greco, Maria Sabrina and Himed, Braham}, publisher = {IEEE}, address = {Piscataway}, isbn = {979-8-3315-4433-1}, doi = {https://doi.org/10.1109/RadarConf2559087.2025.11205008}, pages = {599 -- 604}, year = {2025}, language = {en} } @article{ZieherOlcayKefferpuetzetal.2024, author = {Zieher, Simon and Olcay, Ertug and Kefferp{\"u}tz, Klaus and Salamat, Babak and Olzem, Sebastian-Sven and Elsbacher, Gerhard and Meeß, Henri}, title = {Drones for automated parcel delivery: Use case identification and derivation of technical requirements}, volume = {2024}, pages = {101253}, journal = {Transportation Research Interdisciplinary Perspectives}, number = {28}, publisher = {Elsevier}, address = {Amsterdam}, issn = {2590-1982}, doi = {https://doi.org/10.1016/j.trip.2024.101253}, year = {2024}, abstract = {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.}, language = {en} }