TY - CHAP A1 - Schneegans, Jan A1 - Eilbrecht, Jan A1 - Zernetsch, Stefan A1 - Bieshaar, Maarten A1 - Doll, Konrad A1 - Stursberg, Olaf A1 - Sick, Bernhard T1 - Probabilistic VRU Trajectory Forecasting for Model-Predictive Planning A Case Study: Overtaking Cyclists T2 - 2021 IEEE Intelligent Vehicles Symposium Workshops (IV Workshops) KW - Fahrerassistenzsystem KW - Radfahrer KW - Autonomes Fahrzeug Y1 - 2021 U6 - https://doi.org/10.1109/IVWorkshops54471.2021.9669208 SP - 272 EP - 279 PB - IEEE ER - TY - JOUR A1 - Kress, Viktor A1 - Jeske, Fabian A1 - Zernetsch, Stefan A1 - Doll, Konrad A1 - Sick, Bernhard T1 - Pose and Semantic Map Based Probabilistic Forecast of Vulnerable Road Users’ Trajectories JF - IEEE Transactions on Intelligent Vehicles KW - Fahrerassistenzsystem KW - Autonomes Fahrzeug Y1 - 2023 U6 - https://doi.org/10.1109/TIV.2022.3149624 SN - 2379-8904 VL - 8 IS - 3 SP - 2592 EP - 2603 PB - Institute of Electrical and Electronics Engineers (IEEE) ER - TY - CHAP A1 - Hetzel, Manuel T1 - Intention Detection Using Environmental Context and Enhanced Training Data for Vulnerable Road Users T2 - Organic Computing - Doctoral Dissertation Colloquium 2022 KW - Fahrerassistenzsystem KW - Verkehrsteilnehmer KW - Verkehrsverhalten Y1 - 2023 VL - 2023 PB - Kassel University Press CY - Kassel ER - TY - CHAP A1 - Raiyn, Jamal A1 - Weidl, Galia T1 - Assessing Inattentiveness and Human Elements in Critical Driving Safety Events T2 - 9th European Congress on Computational Methods in Applied Sciences and Engineering 3-7 June 2024, Lisboa, Portugal N2 - Road accidents, with their potential for severe consequences, pose an ongoing global challenge. Within the multitude of factors contributing to these incidents, inattentiveness and the intricate human elements inherent in driving behaviors stand out as pivotal. As indicated by reports and studies on traffic safety, a significant share of accidents can be attributed to driver inattentiveness, encompassing activities such as texting, talking on the phone, or simply being distracted by the surrounding environment. Beyond these observable behaviors lie complex human elements, influenced by factors ranging from cognitive processes to emotional states, which significantly contribute to the occurrence and severity of critical safety events. Inattentiveness is defined as a state in which a driver's eye gaze behavior deviates from attentive driving patterns. It can be influenced by human factors and adverse weather conditions, serving as an indicator of an increased risk of inattentiveness and the potential to contribute to safety-critical events on the road. Recognition of inattentiveness occurs when the average gaze duration on the road or critical areas falls below a specified threshold. The driver's response time is crucial to the braking process of the vehicle and, therefore, has a significant impact on safety in critical situations. KW - Fahrerassistenzsystem KW - Aufmerksamkeit Y1 - 2024 UR - https://www.researchgate.net/publication/381283484_Assessing_Inattentiveness_and_Human_Elements_in_Critical_Driving_Safety_Events ER - TY - CHAP A1 - Raiyn, Jamal A1 - Chaar, Mohamad Mofeed A1 - Weidl, Galia T1 - Analysis of Driving Behavior in Adverse Weather Conditions T2 - IEEE 7th International Conference AND workshop in Óbuda on Electrical and Power Engineering (CANDO EPE 2024) N2 - This paper discusses the impact of Connected Cooperative and Automated Mobility (CCAM) on safety-critical events. The replacement of human drivers by autonomous vehicles (AVs) is promising improved traffic efficiency and reduction of car- crashes to zero using a baseline network traffic. Predicting driving behavior during car-following has been crucial for enhancing road safety while developing advanced driver assistance systems with adaptive cruise control. Human factors significantly influence the driving behavior of a vehicle. Thus, understanding the causal relations between human factors and driving behavior is essential for accurate prediction of vehicle behavior. This is important when autonomous vehicles are expected to behave (cooperatively, according to traffic rules and good praxis) in a human predictable manner, while driving in mixed traffic, involving autonomous, automated, and human driven vehicles. In this paper, we propose a methodology that combines convolutional neural networks (CNNs) with human factors analysis to predict driving behavior during car-following under adverse weather conditions (AWCs). KW - Autonomes Fahrzeug KW - Fahrerassistenzsystem KW - Wetter Y1 - 2024 UR - https://www.researchgate.net/publication/385591980_ANALYSIS_OF_DRIVING_BEHAVIOR_IN_ADVERSE_WEATHER_CONDITIONS ER - TY - CHAP A1 - Kaspar, Dietmar A1 - Weidl, Galia A1 - Dang, Thao A1 - Breuel, Gabi A1 - Tamke, Andreas A1 - Rosenstiel, Wolfgang T1 - Erkennung von Fahrmanövern mit objektorientierten Bayes-Netzen T2 - 7. Workshop. Fahrerassistenzsysteme. FAS2011 N2 - In diesem Artikel wird ein Ansatz zur Erkennung von Spurwechselmanövern mit Hilfe von objekt-orientierten Bayes Netzen beschrieben. Dieser Ansatz ist eine Erweiterung von Grundlagenarbeiten zur Einscherererkennung. Zunächst werden die zur Erkennung von Spurwechselvorgängen erforderlichen Fahrsituationsmerkmale vorgestellt. Darauf aufbauend wird das entwickelte objekt-orientierte Bayes Netz zur Modellierung der Spurwechsel erläutert. Dabei wird ein Spurwechsel als eine Beziehung zwischen zwei Fahrzeugen betrachtet. Dabei kann ein Fahrzeug in der Spur bleiben, die Spur nach links oder nach rechts wechseln. Durch die Kombination der Möglichkeiten der beiden Fahrzeuge entstehen 9 Klassen. Aus den relativen Positionen der Fahrzeuge ergeben sich aus den 9 Situationsklassen 27 mögliche Spurwechselmanöver. Dabei ist das Einschervorgang ein Sonderfall der modellierten Fahrmanöver. KW - ITS (Intelligent Transportation Systems) KW - Artificial Intelligence KW - Fahrerassistenzsystem Y1 - 2011 UR - https://www.researchgate.net/publication/249226427_Erkennung_von_Fahrmanovern_mit_objektorientierten_Bayes-Netzen#fullTextFileContent ER - TY - JOUR A1 - Kaspar, Dietmar A1 - Weidl, Galia A1 - Dang, Thao A1 - Breuel, Gabi A1 - Tamke, Andreas A1 - Rosenstiel, Wolfgang T1 - Object-Oriented Bayesian Networks for Detection of Lane Change Maneuvers JF - IEEE Intelligent Transportation Systems Magazine N2 - In this paper we introduce a novel approach towards the recognition of typical driving maneuvers in structured highway scenarios and identify some of the key benefits of traffic scene modeling with object-oriented Bayesian networks (OOBNs). The approach exploits the advantages of an introduced lane-related coordinate system together with individual occupancy grids for all vehicles. This combination allows for an efficient classification of the existing vehiclelane and vehicle-vehicle relations in a traffic scene and thus substantially improves the understanding of complex traffic scenes. We systematically propagate probabilities and variances within our network which results in probabilistic sets of the modeled driving maneuvers. Using this generic approach, we are able to classify a total of 27 driving maneuvers including merging and object following. KW - ITS (Intelligent Transportation Systems) KW - Artificial Intelligence KW - Fahrerassistenzsystem Y1 - 2011 UR - https://www.researchgate.net/publication/249023568_Object-Oriented_Bayesian_Networks_for_Detection_of_Lane_Change_Maneuvers#fullTextFileContent U6 - https://doi.org/10.1109/IVS.2011.5940468 IS - 4(3) SP - 19 EP - 31 ER - TY - CHAP A1 - Weidl, Galia A1 - Breuel, Gabi T1 - Overall Probabilistic Framework for Modeling and Analysis of Intersection Situations T2 - Advanced Microsystems for Automotive Applications 2012 N2 - We propose a system design for preventive traffic safety in general intersection situations involving all present traffic participants (vehicles and vulnerable road users) in the context of their environment and traffic rules. It exploits the developed overall probabilistic framework for modeling and analysis of intersection situations under uncertainties in the scene, in measured data or in communicated information. It proposes OOBN modeling for the cognitive assessment of potential and real danger in intersection situations and presents schematically an algorithm for multistage cognitive situation assessment. A concept for the interaction between situation assessment and the proposed Proactive coaching Safety Assistance System (PaSAS) is outlined. The assessment of danger in a situation development serves as a filter for the output and intensity of HMI-signals for directing driver's attention to essentials. KW - ITS (Intelligent Transportation Systems) KW - Artificial Intelligence KW - Fahrerassistenzsystem Y1 - 2012 UR - https://link.springer.com/chapter/10.1007/978-3-642-29673-4_24 SN - 978-3-642-29673-4 U6 - https://doi.org/10.1007/978-3-642-29673-4_24 SP - 257 EP - 268 PB - Springer ER - TY - JOUR A1 - Kaspar, Dietmar A1 - Weidl, Galia A1 - Dang, Thao A1 - Breuel, Gabi A1 - Tamke, Andreas A1 - Wedel, Andreas A1 - Rosenstiel, Wolfgang T1 - Object-Oriented Bayesian Networks for Detection of Lane Change Maneuvers JF - IEEE Intelligent Transportation Systems Magazine N2 - This article introduces a novel approach towards the recognition of typical driving maneuvers in structured highway scenarios and shows some key benefits of traffic scene modeling with object-oriented Bayesian networks (OOBNs). The approach exploits the advantages of an introduced lane-related coordinate system together with individual occupancy schedule grids for all modeled vehicles. This combination allows an efficient classification of the existing vehicle-lane and vehicle-vehicle relations in traffic scenes and thus substantially improves the understanding of complex traffic scenes. Probabilities and variances within the network are propagated systematically which results in probabilistic sets of the modeled driving maneuvers. Using this generic approach, the network is able to classify a total of 27 driving maneuvers including merging and object following. KW - ITS (Intelligent Transportation Systems) KW - Artificial Intelligence KW - Fahrerassistenzsystem KW - Künstliche Intelligenz Y1 - 2012 UR - https://www.researchgate.net/publication/249012939_Object-Oriented_Bayesian_Networks_for_Detection_of_Lane_Change_Maneuvers#fullTextFileContent U6 - https://doi.org/10.1109/MITS.2012.2203229 VL - 2012 IS - 4/3 SP - 19 EP - 31 ER - TY - CHAP A1 - Weidl, Galia A1 - Singhal, Virat A1 - Petrich, Dominik A1 - Kaspar, Dietmar A1 - Wedel, Andreas A1 - Breuel, Gabi T1 - Collision Risk Prediction and Warning at Road Intersections Using an Object Oriented Bayesian Network T2 - 5th International Conference Automotive User Interfaces and Interactive Vehicular Applications (Automotive UI,13), Oct.28-30, 2013 N2 - This paper describes a novel approach to situation analysis at intersections using object-oriented Bayesian networks. The Bayesian network infers the collision probability for all vehicles approaching the intersection, while taking into account traffic rules, the digital street map, and the sensors' uncertainties. The environment perception is fused from communicated data, vehicles local perception and self-localization. Thus, a cooperatively validated set of data is obtained to characterize all objects involved in a situation (resolving occlusions). The system is tested with data, acquired by vehicles with heterogenic equipment (without/with perception). In a first step the probabilistic mapping of a vehicle onto a fixed set of traffic lanes and forward motion predictions is introduced. Second, criticality measures are evaluated for these motion predictions to infer the collision probability. In our test vehicle this probability is then used to warn the driver of a possible hazardous situation. It serves as a likelihood alarm parameter for deciding the intensity of HMI acoustic signals to direct the driver's attention. First results in various simulated and live real-time scenarios show, that a collision can be predicted up to two seconds before a possible impact by applying the developed Bayesian network. The extension of this network to further situation features is the content of ongoing research. KW - ITS (Intelligent Transportation Systems) KW - Artificial Intelligence KW - Fahrerassistenzsystem KW - Künstliche Intelligenz Y1 - 2013 UR - https://www.researchgate.net/publication/267764514_Collision_Risk_Prediction_and_Warning_at_Road_Intersections_Using_an_Object_Oriented_Bayesian_Network#fullTextFileContent U6 - https://doi.org/10.1145/2516540.2516577 ER - TY - CHAP A1 - Seeliger, Florian A1 - Weidl, Galia A1 - Petrich, Dominik A1 - Naujoks, Frederik A1 - Breuel, Gabi A1 - Neukum, Alexandra A1 - Dietmayer, Klaus T1 - Advisory warnings based on cooperative perception T2 - IEEE Intelligent Vehicles Symposium Proceedings, June 2014 N2 - The Ko-PER (cooperative perception) research project aims at improvements of active traffic safety through cooperative perception systems. Within the project a prototype of a cooperative warning system was realized. This system provides early advisory warnings which are especially useful in critical situations with occluded conflict partners. The development process was accompanied by a series of driving simulator studies to determine both the potential to reduce traffic conflicts and important design characteristics of early advisory warning signals. The most important details of the prototype system's components inter-vehicle information-fusion and situation analysis are described and the achieved warning timings are compared to the results of the driving simulator studies. KW - ITS (Intelligent Transportation Systems) KW - Artificial Intelligence KW - Fahrerassistenzsystem Y1 - 2014 UR - https://www.researchgate.net/publication/264732322_Advisory_warnings_based_on_cooperative_perception#fullTextFileContent U6 - https://doi.org/10.1109/IVS.2014.6856479 ER -