TY - CHAP A1 - Hetzel, Manuel A1 - Reichert, Hannes A1 - Doll, Konrad A1 - Sick, Bernhard T1 - Reliable Probabilistic Human Trajectory Prediction for Autonomous Applications T2 - Computer Vision – ECCV 2024 Workshops, Milan, Italy, September 29–October 4, 2024, Proceedings, Part XVII KW - Autonomes System KW - Mensch-Maschine-Kommunikation Y1 - 2025 SN - 9783031915840 U6 - https://doi.org/https://doi.org/10.1007/978-3-031-91585-7_9 SN - 0302-9743 SP - 135 EP - 152 PB - Springer Nature CY - Cham ER - TY - CHAP A1 - Talluri, Kranthi Kumar A1 - Stang, Christopher A1 - Weidl, Galia T1 - Green Wave as an Integral Part for the Optimization of Traffic Efficiency and Safety: A Survey T2 - 2025 IEEE Intelligent Vehicles Symposium (IV) N2 - 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. KW - Verkehrsregelung KW - Smart City Y1 - 2025 U6 - https://doi.org/10.1109/IV64158.2025.11097706 SP - 2422 EP - 2429 PB - IEEE ER - TY - CHAP A1 - Talluri, Kranthi Kumar A1 - Weidl, Galia A1 - Kasuluru, Vaishnavi T1 - Accident-Driven Congestion Prediction and Simulation: An Explainable Framework Using Advanced Clustering and Bayesian Networks T2 - IEEE 102nd Vehicular Technology Conference N2 - 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. KW - Verkehrsstau KW - Verkehrsunfall KW - Maschinelles Lernen Y1 - 2025 UR - https://www.researchgate.net/publication/394121957_Accident-Driven_Congestion_Prediction_and_Simulation_An_Explainable_Framework_Using_Advanced_Clustering_and_Bayesian_Networks U6 - https://doi.org/10.48550/arXiv.2507.22529 ER - TY - CHAP A1 - Talluri, Kranthi Kumar A1 - Madsen, Anders L. A1 - Weidl, Galia T1 - Enhancing Safety Standards in Automated Systems Using Dynamic Bayesian Networks T2 - 2025 IEEE Intelligent Vehicles Symposium (IV) N2 - 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. KW - Unfallverhütung KW - Schnellfahrstrecke KW - Autonomes Fahrzeug Y1 - 2025 UR - https://www.researchgate.net/publication/391461422_Enhancing_Safety_Standards_in_Automated_Systems_Using_Dynamic_Bayesian_Networks U6 - https://doi.org/10.48550/arXiv.2505.02050 ER - TY - CHAP A1 - Serfling, Benjamin A1 - Reichert, Hannes A1 - Bayerlein, Lorenzo A1 - Doll, Konrad A1 - Radkhah-Lens, Kati T1 - LiDAR Based Semantic Perception for Forklifts in Outdoor Environments T2 - 2025 IEEE International Conference on Service Operations and Logistics, and Informatics (SOLI) KW - Gabelstapler KW - Autonomes Fahrzeug Y1 - 2025 U6 - https://doi.org/https://doi.org/10.1109/SOLI66289.2025.11396313 SP - 40 EP - 45 PB - IEEE ER - TY - CHAP A1 - Chaar, Mohamad Mofeed A1 - Raiyn, Jamal A1 - Weidl, Galia T1 - Predicting Depth Maps from Single RGB Images and Addressing Missing Information in Depth Estimation T2 - Proceedings of the 11th International Conference on Vehicle Technology and Intelligent Transport Systems N2 - 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. KW - Autonomes Fahrzeug KW - Tiefenbild Y1 - 2025 U6 - https://doi.org/10.5220/0013365900003941 SP - 549 EP - 556 PB - SCITEPRESS - Science and Technology Publications ER -