TY - CHAP A1 - Raiyn, Jamal A1 - Weidl, Galia T1 - Improving Autonomous Vehicle Reasoning with Non-Monotonic Logic: Advancing Safety and Performance in Complex Environments T2 - IEEE International Smart Cities Conference (ISC2) KW - ITS (Intelligent Transportation Systems) KW - Artificial Intelligence KW - Künstliche Intelligenz KW - Autonomes Fahrzeug Y1 - 2023 UR - https://www.researchgate.net/publication/375128760_Improving_Autonomous_Vehicle_Reasoning_with_Non-Monotonic_Logic_Advancing_Safety_and_Performance_in_Complex_Environments U6 - https://doi.org/10.1109/ISC257844.2023.10293463 ER - TY - CHAP A1 - Valencia, Yeimy A1 - Normann, Marc A1 - Sapsai, Iryna A1 - Abke, Jörg A1 - Madsen, Anders L. A1 - Weidl, Galia T1 - Learning Style Classification by Using Bayesian Networks Based on the Index of Learning Style T2 - ECSEE '23: Proceedings of the 5th European Conference on Software Engineering Education, June 2023 KW - Lernstil KW - Fragebogen Y1 - 2023 U6 - https://doi.org/10.1145/3593663.3593685 SP - 73 EP - 82 ER - TY - CHAP A1 - Weidl, Galia A1 - Raiyn, Jamal A1 - Berres, Stefan T1 - Does a livable city profit from a shared CCAM Shuttle Bus on demand? T2 - International Symposium on Transportation Data & Modelling (ISTDM2023), June 2023 N2 - Livable cities measure quality-of-life factors such as transportation, convenience of daily life, education, and a safe and stable built and natural environment. Livability of a city includes also some social and psychological factors, like emotion and perception. How do we realize the advantages of new technology under mixed traffic conditions, while observing all daily requirements on safety, convenience and high education needs? KW - ITS (Intelligent Transportation Systems) KW - Artificial Intelligence KW - Stadtentwicklung KW - Stadtplanung KW - Digitalisierung Y1 - 2023 UR - https://www.researchgate.net/publication/370492569_Does_a_livable_city_profit_from_a_shared_CCAM_Shuttle_Bus_on_demand#fullTextFileContent ER - TY - CHAP A1 - Chaar, Mohamad Mofeed A1 - Weidl, Galia A1 - Raiyn, Jamal T1 - Analyse the effect of fog on the perception T2 - Conference: International Symposium on Transportation Data & Modelling (ISTDM 2023) KW - ITS (Intelligent Transportation Systems) KW - Artificial Intelligence KW - Autonomes Fahrzeug KW - Fahrerassistenzsystem KW - Nebel Y1 - 2023 UR - https://www.researchgate.net/publication/369484982_Analyse_the_effect_of_fog_on_the_perception#fullTextFileContent ER - TY - CHAP A1 - Raiyn, Jamal A1 - Weidl, Galia T1 - Naturalistic Driving Studies Data Analysis Based on a Convolutional Neural Network T2 - VEHITS 2023: 9th International Conference on Vehicle Technology and Intelligent Transport Systems N2 - The new generation of autonomous vehicles (AVs) are being designed to act autonomously and collect travel data based on various smart devices and sensors. The goal is to enable AVs to operate under their own power. Naturalistic driving studies (NDSs) collect data continuously from real traffic activities, in order not to miss any safety-critical event. In NDSs of AVs, however, the data they collect is influenced by various sources that degrade their forecasting accuracy. A convolutional neural network (CNN) is proposed to process a large amount of traffic data in different formats. A CNN can detect anomalies in traffic data that negatively affect traffic efficiency and identify the source of data anomalies, which can help reduce traffic congestion and vehicular queuing. KW - Autonomes Fahrzeug KW - Künstliche Intelligenz Y1 - 2023 UR - https://www.researchgate.net/publication/368332673_Naturalistic_Driving_Studies_Data_Analysis_Based_on_a_Convolutional_Neural_Network#fullTextFileContent U6 - https://doi.org/10.5220/0011839600003479 ER - TY - CHAP A1 - Jarosch, Oliver A1 - Naujoks, Frederik A1 - Wandtner, Bernhard A1 - Gold, Christian A1 - Marberger, Claus A1 - Weidl, Galia A1 - Schrauf, Michael T1 - The Impact of Non-Driving Related Tasks on Take-over Performance in Conditionally Automated Driving – A Review of the Empirical Evidence T2 - 9. Tagung Automatisiertes Fahren, München, Partner TÜV Süd, November 2019 N2 - Conditional automated driving (CAD) systems (SAE level 3) will soon be introduced to the public market. This automation level is designed to take care of all aspects of the dynamic driving task in specific application areas and does not require the driver to continuously monitor the system performance. However, in contrast to higher levels of automation the "fallback-ready" user always has to be able to regain control if requested by the system. As CAD allows the driver to engage in non-driving-related tasks (NDRTs) past human factors research has looked at their effects on takeover time and quality especially in short-term takeover situations. In order to understand how takeover performance is impacted by different NDRTs, this paper summarizes and compares available results according to the NDRT's impact on the sensoric, motoric and cognitive transition. In addition, aspects of arousal and motivation are considered. Due to the heterogeneity of the empirical work and the available data practically relevant effects can only be attested for NDRTs that cause severe discrepancies between the current driver state and the requirements of the takeover task, such as sensoric and motoric unavailability. The paper concludes by discussing methodological issues and recommending the development of standardized methods for the future. KW - ITS (Intelligent Transportation Systems) KW - Artificial Intelligence KW - Fahrerassistenzsystem KW - Autonomes Fahrzeug Y1 - 2019 UR - https://www.researchgate.net/publication/338644809_The_Impact_of_Non-Driving_Related_Tasks_on_Take-over_Performance_in_Conditionally_Automated_Driving_-_A_Review_of_the_Empirical_Evidence#fullTextFileContent ER - TY - CHAP A1 - Weidl, Galia A1 - Madsen, Anders L. A1 - Tereshchenko, Viacheslav A1 - Zhang, Wei A1 - Stevens Ruixi, Wang A1 - Kasper, Dietmar T1 - Situation Awareness and Early Recognition of Traffic Maneuvers T2 - Proceedings of The 9th EUROSIM Congress on Modelling and Simulation, EUROSIM 2016, The 57th SIMS Conference on Simulation and Modelling SIMS 2016 N2 - We outline the challenges of situation awareness with early and accurate recognition of traffic maneuvers and how to assess them. This includes also an overview of the available data and derived situation features,handling of data uncertainties, modelling and the approach for maneuver recognition. An efficient and effective solution, meeting the automotive requirements, is successfully deployed and tested on a prototype car. Test driving results show that earlier recognition of intended maneuver is feasible on average 1 second (and up to 6.72 s) before the actual lane-marking crossing. The even earlier maneuver recognition is dependent on the earlier recognition of surrounding vehicles. KW - ITS (Intelligent Transportation Systems) KW - Artificial Intelligence KW - Fahrerassistenzsystem Y1 - 2018 UR - https://www.researchgate.net/publication/329785176_Situation_Awareness_and_Early_Recognition_of_Traffic_Maneuvers#fullTextFileContent U6 - https://doi.org/10.3384/ecp171428 ER - TY - JOUR A1 - Weidl, Galia A1 - Madsen, Anders L. A1 - Wang, Stevens Ruixi A1 - Kaspar, Dietmar A1 - Karlsen, Martin T1 - Early and Accurate Recognition of Highway Traffic Maneuvers Considering Real-World Application : A Novel Framework Using Bayesian Networks JF - IEEE Intelligent Transportation Systems Magazine N2 - This paper presents a novel application of artificial cognitive systems to traffic scene understanding and early recognition of highway maneuvers. This is achieved by use of Bayesian networks for knowledge representation, to mimic the human reasoning on situation analysis and to manage inherited uncertainties in the automotive domain, that requires efficient and effective analysis of high volume and frequency data streams. The maneuver recognition uses features, analyzing the observed vehicles behavior and available free space on the target lane. Dynamic Bayesian networks (DBN) capture the motion of the own and surrounding vehicles as a dynamic process, following the trend development of lateral motion features. The static and dynamic models for maneuver recognition are statistically evaluated with real highway driving data sequences. The DBN demonstrates earlier recognition (∼ 1.1 seconds) and higher accuracy (1.12% error rate) with more stable performance than the static models. The system is deployed on an experimental vehicle, where a Divide-and-conquer approach to inference in object-oriented Bayesian networks is introduced and its implementation is shown to require computation time of 0.15 milliseconds and ROM memory ∼ 10 2 kilobytes. The promising performance evaluation results are confirmed by test drives in real highway traffic. KW - ITS (Intelligent Transportation Systems) KW - Artificial Intelligence KW - Fahrerassistenzsystem KW - Künstliche Intelligenz Y1 - 2018 UR - https://www.researchgate.net/publication/323453898_Early_and_Accurate_Recognition_of_Highway_Traffic_Maneuvers_Considering_Real-World_Application_A_Novel_Framework_Using_Bayesian_Networks#fullTextFileContent U6 - https://doi.org/10.1109/MITS.2018.2842049 VL - 2018 IS - 10/3 SP - 146 EP - 158 ER - TY - CHAP A1 - Weidl, Galia A1 - Madsen, Anders L. A1 - Tereshchenko, Viacheslav A1 - Kaspar, Dietmar A1 - Breuel, Gabi T1 - Early Recognition of Maneuvers in Highway Traffic T2 - European Conference on Symbolic and Quantitative Approaches to Reasoning and Uncertainty, July 2015 N2 - This paper presents an application of Bayesian networks where early recognition of traffic maneuver intention is achieved using features of lane change, representing the relative dynamics between vehicles on the same lane and the free space to neighbor vehicles back and front on the target lane. The classifiers have been deployed on the automotive target platform, which has severe constraints on time and space performance of the system. The test driving has been performed with encouraging results. Even earlier recognition is possible by considering the trend development of features, characterizing the dynamic driving process. The preliminary test results confirm feasibility. KW - Fahrerassistenzsystem Y1 - 2015 UR - https://www.researchgate.net/publication/300646375_Early_Recognition_of_Maneuvers_in_Highway_Traffic#fullTextFileContent U6 - https://doi.org/10.1007/978-3-319-20807-7_48 ER - TY - CHAP A1 - Weidl, Galia T1 - Freiraumbewertung für Spurwechselmanöver mit Bayes-Netzen T2 - 7. VDI/VDE Fachtagung AUTOREG Auf dem Weg zum automatisierten Fahren N2 - Kurzfassung Diese Arbeit stellt ein robustes wissensbasiertes Verfahren zur Lückenbewertung für Spur-wechselmanöver vor. Zur Modellierung wurden dynamische Bayes-Netzwerke eingesetzt und mit Hilfe von Lernalgorithmen die Erkennungsleistung verbessert. Die Testergebnisse zeigen eine sehr hohe Trefferquote. KW - ITS (Intelligent Transportation Systems) KW - Artificial Intelligence KW - Fahrerassistenzsystem Y1 - 2015 UR - https://www.researchgate.net/publication/279886700_Freiraumbewertung_fur_Spurwechselmanover_mit_Bayes-Netzen#fullTextFileContent ER - TY - JOUR A1 - Naujoks, Frederik A1 - Grattenthaler, Heidi A1 - Neukum, Alexandra A1 - Weidl, Galia A1 - Petrich, Dominik T1 - Effectiveness of advisory warnings based on cooperative perception JF - IET Intelligent Transport Systems N2 - Cooperative perception makes it possible – in addition to emergency warnings – to provide drivers with early advisory warnings about potentially dangerous driving situations. Based on research results pertaining to imminent crash warnings, it was expected that the effectiveness of such advisory warnings depends on situation-specific anticipations by the driver. During a simulator study, N = 20 drivers went through a wide range of longitudinal traffic and intersection scenarios. The scenarios varied in the possibility to anticipate traffic conflicts (Anticipation: high vs. low) and were completed under different visibility conditions (Visibility: obstructed vs. visible), with and without driver assistance based on cooperative perception (i.e., visual-auditory advisory warnings two seconds prior to the last-possible warning moment; assistance: no assistance vs. with assistance). The warning concept was based on empirical pre-studies and previously validated on a public test intersection. During non-assisted driving, critical situations were mainly experienced when the possibility to anticipate traffic conflicts was low. Visual obstructions lead to a further increase in the frequency of critical situations. Furthermore, the results indicate a clear mitigation of critical encounters when providing early advisory warnings which is independent from sight obstructions. This applies particularly to surprising and unexpected scenarios and thus illustrates the potential of cooperative perception to enhance active traffic safety. KW - ITS (Intelligent Transportation Systems) KW - Artificial Intelligence KW - Fahrerassistenzsystem Y1 - 2015 UR - https://www.researchgate.net/publication/276248479_Effectiveness_of_advisory_warnings_based_on_cooperative_perception#fullTextFileContent U6 - https://doi.org/10.1049/iet-its.2014.0190 VL - 2015 IS - 9 SP - 606 EP - 617 ER - TY - CHAP A1 - Weidl, Galia A1 - Madsen, Anders L. A1 - Kaspar, Dietmar A1 - Breuel, Gabi T1 - Optimizing Bayesian networks for recognition of driving maneuvers to meet the automotive requirements T2 - The 2014 IEEE Multi-Conference on Systems and Control (MSC 2014) N2 - An Object Oriented Bayesian Network for recognition of maneuver in highway traffic has demonstrated an acceptably high recognition performance on a prototype car with a Linux PC having an i7 processor. This paper is focusing on keeping the high recognition performance of the original OOBN, while evaluating alternative modelling techniques and their impact on the memory and time requirements of an ECU-processor for automotive applications. New challenges are faced, when the prediction horizon is to be further extended. KW - ITS (Intelligent Transportation Systems) KW - Artificial Intelligence KW - Fahrerassistenzsystem KW - Künstliche Intelligenz Y1 - 2014 UR - https://www.researchgate.net/publication/286570923_Optimizing_Bayesian_networks_for_recognition_of_driving_maneuvers_to_meet_the_automotive_requirements#fullTextFileContent U6 - https://doi.org/10.1109/ISIC.2014.6967630 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 - 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 - 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 - 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 -