@incollection{HetzelReichertDolletal.2025, author = {Hetzel, Manuel and Reichert, Hannes and Doll, Konrad and Sick, Bernhard}, title = {Reliable Probabilistic Human Trajectory Prediction for Autonomous Applications}, series = {Computer Vision - ECCV 2024 Workshops, Milan, Italy, September 29-October 4, 2024, Proceedings, Part XVII}, booktitle = {Computer Vision - ECCV 2024 Workshops, Milan, Italy, September 29-October 4, 2024, Proceedings, Part XVII}, publisher = {Springer Nature}, address = {Cham}, isbn = {9783031915840}, issn = {0302-9743}, doi = {https://doi.org/10.1007/978-3-031-91585-7_9}, pages = {135 -- 152}, year = {2025}, subject = {Autonomes System}, language = {en} } @inproceedings{TalluriStangWeidl2025, author = {Talluri, Kranthi Kumar and Stang, Christopher and Weidl, Galia}, title = {Green Wave as an Integral Part for the Optimization of Traffic Efficiency and Safety: A Survey}, series = {2025 IEEE Intelligent Vehicles Symposium (IV)}, booktitle = {2025 IEEE Intelligent Vehicles Symposium (IV)}, publisher = {IEEE}, doi = {10.1109/IV64158.2025.11097706}, pages = {2422 -- 2429}, year = {2025}, abstract = {Green Wave provides practical and advanced solutions to improve traffic efficiency and safety through network coordination. Nevertheless, the complete potential of Green Wave systems has yet to be explored. Utilizing emerging technologies and advanced algorithms, such as AI or V2X, would aid in achieving more robust traffic management strategies, especially when integrated with Green Wave. This work comprehensively surveys existing traffic control strategies that enable Green Waves and analyzes their impact on future traffic management systems and urban infrastructure. Understanding previous research on traffic management and its effect on traffic efficiency and safety helps explore the integration of Green Wave solutions with smart city initiatives for effective traffic signal coordination. This paper also discusses the advantages of using Green Wave strategies for emission reduction and considers road safety issues for vulnerable road users, such as pedestrians and cyclists. Finally, the existing challenges and research gaps in building robust and successful Green Wave systems are discussed to articulate explicitly the future requirement of sustainable urban transport.}, subject = {Verkehrsregelung}, language = {en} } @inproceedings{TalluriWeidlKasuluru2025, author = {Talluri, Kranthi Kumar and Weidl, Galia and Kasuluru, Vaishnavi}, title = {Accident-Driven Congestion Prediction and Simulation: An Explainable Framework Using Advanced Clustering and Bayesian Networks}, series = {IEEE 102nd Vehicular Technology Conference}, booktitle = {IEEE 102nd Vehicular Technology Conference}, doi = {10.48550/arXiv.2507.22529}, year = {2025}, abstract = {Traffic congestion due to uncertainties, such as accidents, is a significant issue in urban areas, as the ripple effect of accidents causes longer delays, increased emissions, and safety concerns. To address this issue, we propose a robust framework for predicting the impact of accidents on congestion. We implement Automated Machine Learning (AutoML)-enhanced Deep Embedding Clustering (DEC) to assign congestion labels to accident data and predict congestion probability using a Bayesian Network (BN). The Simulation of Urban Mobility (SUMO) simulation is utilized to evaluate the correctness of BN predictions using evidence-based scenarios. Results demonstrate that the AutoML-enhanced DEC has outperformed traditional clustering approaches. The performance of the proposed BN model achieved an overall accuracy of 95.6\%, indicating its ability to understand the complex relationship of accidents causing congestion. Validation in SUMO with evidence-based scenarios demonstrated that the BN model's prediction of congestion states closely matches those of SUMO, indicating the high reliability of the proposed BN model in ensuring smooth urban mobility.}, subject = {Verkehrsstau}, language = {en} } @inproceedings{TalluriMadsenWeidl2025, author = {Talluri, Kranthi Kumar and Madsen, Anders L. and Weidl, Galia}, title = {Enhancing Safety Standards in Automated Systems Using Dynamic Bayesian Networks}, series = {2025 IEEE Intelligent Vehicles Symposium (IV)}, booktitle = {2025 IEEE Intelligent Vehicles Symposium (IV)}, doi = {10.48550/arXiv.2505.02050}, year = {2025}, abstract = {Cut-in maneuvers in high-speed traffic pose critical challenges that can lead to abrupt braking and collisions, necessitating safe and efficient lane change strategies. We propose a Dynamic Bayesian Network (DBN) framework to integrate lateral evidence with safety assessment models, thereby predicting lane changes and ensuring safe cut-in maneuvers effectively. Our proposed framework comprises three key probabilistic hypotheses (lateral evidence, lateral safety, and longitudinal safety) that facilitate the decision-making process through dynamic data processing and assessments of vehicle positions, lateral velocities, relative distance, and Time-to-Collision (TTC) computations. The DBN model's performance compared with other conventional approaches demonstrates superior performance in crash reduction, especially in critical high-speed scenarios, while maintaining a competitive performance in low-speed scenarios. This paves the way for robust, scalable, and efficient safety validation in automated driving systems.}, subject = {Unfallverh{\"u}tung}, language = {en} } @inproceedings{SerflingReichertBayerleinetal.2025, author = {Serfling, Benjamin and Reichert, Hannes and Bayerlein, Lorenzo and Doll, Konrad and Radkhah-Lens, Kati}, title = {LiDAR Based Semantic Perception for Forklifts in Outdoor Environments}, series = {2025 IEEE International Conference on Service Operations and Logistics, and Informatics (SOLI)}, booktitle = {2025 IEEE International Conference on Service Operations and Logistics, and Informatics (SOLI)}, publisher = {IEEE}, doi = {https://doi.org/10.1109/SOLI66289.2025.11396313}, pages = {40 -- 45}, year = {2025}, subject = {Gabelstapler}, language = {en} } @inproceedings{ChaarRaiynWeidl2025, author = {Chaar, Mohamad Mofeed and Raiyn, Jamal and Weidl, Galia}, title = {Predicting Depth Maps from Single RGB Images and Addressing Missing Information in Depth Estimation}, series = {Proceedings of the 11th International Conference on Vehicle Technology and Intelligent Transport Systems}, booktitle = {Proceedings of the 11th International Conference on Vehicle Technology and Intelligent Transport Systems}, publisher = {SCITEPRESS - Science and Technology Publications}, doi = {10.5220/0013365900003941}, pages = {549 -- 556}, year = {2025}, abstract = {Depth imaging is a crucial area in Autonomous Driving Systems (ADS), as it plays a key role in detecting and measuring objects in the vehicle's surroundings. However, a significant challenge in this domain arises from missing information in Depth images, where certain points are not measurable due to gaps or inconsistencies in pixel data. Our research addresses two key tasks to overcome this challenge. First, we developed an algorithm using a multi-layered training approach to generate Depth images from a single RGB image. Second, we addressed the issue of missing information in Depth images by applying our algorithm to rectify these gaps, resulting in Depth images with complete and accurate data. We further tested our algorithm on the Cityscapes dataset and successfully resolved the missing information in its Depth images, demonstrating the effectiveness of our approach in real-world urban environments.}, subject = {Autonomes Fahrzeug}, language = {en} }