@inproceedings{RaiynWeidl2024, author = {Raiyn, Jamal and Weidl, Galia}, title = {Assessing Inattentiveness and Human Elements in Critical Driving Safety Events}, series = {9th European Congress on Computational Methods in Applied Sciences and Engineering 3-7 June 2024, Lisboa, Portugal}, booktitle = {9th European Congress on Computational Methods in Applied Sciences and Engineering 3-7 June 2024, Lisboa, Portugal}, year = {2024}, abstract = {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.}, subject = {Fahrerassistenzsystem}, language = {en} } @inproceedings{RaiynChaarWeidl2024, author = {Raiyn, Jamal and Chaar, Mohamad Mofeed and Weidl, Galia}, title = {Analysis of Driving Behavior in Adverse Weather Conditions}, series = {IEEE 7th International Conference AND workshop in {\´O}buda on Electrical and Power Engineering (CANDO EPE 2024)}, booktitle = {IEEE 7th International Conference AND workshop in {\´O}buda on Electrical and Power Engineering (CANDO EPE 2024)}, year = {2024}, abstract = {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).}, subject = {Autonomes Fahrzeug}, language = {en} } @inproceedings{KasparWeidlDangetal.2011, author = {Kaspar, Dietmar and Weidl, Galia and Dang, Thao and Breuel, Gabi and Tamke, Andreas and Rosenstiel, Wolfgang}, title = {Erkennung von Fahrman{\"o}vern mit objektorientierten Bayes-Netzen}, series = {7. Workshop. Fahrerassistenzsysteme. FAS2011}, booktitle = {7. Workshop. Fahrerassistenzsysteme. FAS2011}, year = {2011}, abstract = {In diesem Artikel wird ein Ansatz zur Erkennung von Spurwechselman{\"o}vern mit Hilfe von objekt-orientierten Bayes Netzen beschrieben. Dieser Ansatz ist eine Erweiterung von Grundlagenarbeiten zur Einscherererkennung. Zun{\"a}chst werden die zur Erkennung von Spurwechselvorg{\"a}ngen erforderlichen Fahrsituationsmerkmale vorgestellt. Darauf aufbauend wird das entwickelte objekt-orientierte Bayes Netz zur Modellierung der Spurwechsel erl{\"a}utert. 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 M{\"o}glichkeiten der beiden Fahrzeuge entstehen 9 Klassen. Aus den relativen Positionen der Fahrzeuge ergeben sich aus den 9 Situationsklassen 27 m{\"o}gliche Spurwechselman{\"o}ver. Dabei ist das Einschervorgang ein Sonderfall der modellierten Fahrman{\"o}ver.}, subject = {Fahrerassistenzsystem}, language = {de} } @article{KasparWeidlDangetal.2011, author = {Kaspar, Dietmar and Weidl, Galia and Dang, Thao and Breuel, Gabi and Tamke, Andreas and Rosenstiel, Wolfgang}, title = {Object-Oriented Bayesian Networks for Detection of Lane Change Maneuvers}, series = {IEEE Intelligent Transportation Systems Magazine}, journal = {IEEE Intelligent Transportation Systems Magazine}, number = {4(3)}, doi = {10.1109/IVS.2011.5940468}, pages = {19 -- 31}, year = {2011}, abstract = {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.}, subject = {Fahrerassistenzsystem}, language = {en} } @incollection{WeidlBreuel2012, author = {Weidl, Galia and Breuel, Gabi}, title = {Overall Probabilistic Framework for Modeling and Analysis of Intersection Situations}, series = {Advanced Microsystems for Automotive Applications 2012}, booktitle = {Advanced Microsystems for Automotive Applications 2012}, publisher = {Springer}, isbn = {978-3-642-29673-4}, doi = {10.1007/978-3-642-29673-4_24}, pages = {257 -- 268}, year = {2012}, abstract = {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.}, subject = {Fahrerassistenzsystem}, language = {en} } @article{KasparWeidlDangetal.2012, author = {Kaspar, Dietmar and Weidl, Galia and Dang, Thao and Breuel, Gabi and Tamke, Andreas and Wedel, Andreas and Rosenstiel, Wolfgang}, title = {Object-Oriented Bayesian Networks for Detection of Lane Change Maneuvers}, series = {IEEE Intelligent Transportation Systems Magazine}, volume = {2012}, journal = {IEEE Intelligent Transportation Systems Magazine}, number = {4/3}, doi = {10.1109/MITS.2012.2203229}, pages = {19 -- 31}, year = {2012}, abstract = {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.}, subject = {Fahrerassistenzsystem}, language = {en} } @inproceedings{WeidlSinghalPetrichetal.2013, author = {Weidl, Galia and Singhal, Virat and Petrich, Dominik and Kaspar, Dietmar and Wedel, Andreas and Breuel, Gabi}, title = {Collision Risk Prediction and Warning at Road Intersections Using an Object Oriented Bayesian Network}, series = {5th International Conference Automotive User Interfaces and Interactive Vehicular Applications (Automotive UI,13), Oct.28-30, 2013}, booktitle = {5th International Conference Automotive User Interfaces and Interactive Vehicular Applications (Automotive UI,13), Oct.28-30, 2013}, doi = {10.1145/2516540.2516577}, year = {2013}, abstract = {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.}, subject = {Fahrerassistenzsystem}, language = {en} } @inproceedings{SeeligerWeidlPetrichetal.2014, author = {Seeliger, Florian and Weidl, Galia and Petrich, Dominik and Naujoks, Frederik and Breuel, Gabi and Neukum, Alexandra and Dietmayer, Klaus}, title = {Advisory warnings based on cooperative perception}, series = {IEEE Intelligent Vehicles Symposium Proceedings, June 2014}, booktitle = {IEEE Intelligent Vehicles Symposium Proceedings, June 2014}, doi = {10.1109/IVS.2014.6856479}, year = {2014}, abstract = {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.}, subject = {Fahrerassistenzsystem}, language = {en} } @inproceedings{WeidlMadsenKasparetal.2014, author = {Weidl, Galia and Madsen, Anders L. and Kaspar, Dietmar and Breuel, Gabi}, title = {Optimizing Bayesian networks for recognition of driving maneuvers to meet the automotive requirements}, series = {The 2014 IEEE Multi-Conference on Systems and Control (MSC 2014)}, booktitle = {The 2014 IEEE Multi-Conference on Systems and Control (MSC 2014)}, doi = {10.1109/ISIC.2014.6967630}, year = {2014}, abstract = {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.}, subject = {Fahrerassistenzsystem}, language = {en} } @article{NaujoksGrattenthalerNeukumetal.2015, author = {Naujoks, Frederik and Grattenthaler, Heidi and Neukum, Alexandra and Weidl, Galia and Petrich, Dominik}, title = {Effectiveness of advisory warnings based on cooperative perception}, series = {IET Intelligent Transport Systems}, volume = {2015}, journal = {IET Intelligent Transport Systems}, number = {9}, doi = {10.1049/iet-its.2014.0190}, pages = {606 -- 617}, year = {2015}, abstract = {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.}, subject = {Fahrerassistenzsystem}, language = {en} } @inproceedings{Weidl2015, author = {Weidl, Galia}, title = {Freiraumbewertung f{\"u}r Spurwechselman{\"o}ver mit Bayes-Netzen}, series = {7. VDI/VDE Fachtagung AUTOREG Auf dem Weg zum automatisierten Fahren}, booktitle = {7. VDI/VDE Fachtagung AUTOREG Auf dem Weg zum automatisierten Fahren}, year = {2015}, abstract = {Kurzfassung Diese Arbeit stellt ein robustes wissensbasiertes Verfahren zur L{\"u}ckenbewertung f{\"u}r Spur-wechselman{\"o}ver vor. Zur Modellierung wurden dynamische Bayes-Netzwerke eingesetzt und mit Hilfe von Lernalgorithmen die Erkennungsleistung verbessert. Die Testergebnisse zeigen eine sehr hohe Trefferquote.}, subject = {Fahrerassistenzsystem}, language = {de} } @inproceedings{WeidlMadsenTereshchenkoetal.2015, author = {Weidl, Galia and Madsen, Anders L. and Tereshchenko, Viacheslav and Kaspar, Dietmar and Breuel, Gabi}, title = {Early Recognition of Maneuvers in Highway Traffic}, series = {European Conference on Symbolic and Quantitative Approaches to Reasoning and Uncertainty, July 2015}, booktitle = {European Conference on Symbolic and Quantitative Approaches to Reasoning and Uncertainty, July 2015}, doi = {10.1007/978-3-319-20807-7_48}, year = {2015}, abstract = {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.}, subject = {Fahrerassistenzsystem}, language = {en} } @article{WeidlMadsenWangetal.2018, author = {Weidl, Galia and Madsen, Anders L. and Wang, Stevens Ruixi and Kaspar, Dietmar and Karlsen, Martin}, title = {Early and Accurate Recognition of Highway Traffic Maneuvers Considering Real-World Application : A Novel Framework Using Bayesian Networks}, series = {IEEE Intelligent Transportation Systems Magazine}, volume = {2018}, journal = {IEEE Intelligent Transportation Systems Magazine}, number = {10/3}, doi = {10.1109/MITS.2018.2842049}, pages = {146 -- 158}, year = {2018}, abstract = {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.}, subject = {Fahrerassistenzsystem}, language = {en} } @inproceedings{WeidlMadsenTereshchenkoetal.2018, author = {Weidl, Galia and Madsen, Anders L. and Tereshchenko, Viacheslav and Zhang, Wei and Stevens Ruixi, Wang and Kasper, Dietmar}, title = {Situation Awareness and Early Recognition of Traffic Maneuvers}, series = {Proceedings of The 9th EUROSIM Congress on Modelling and Simulation, EUROSIM 2016, The 57th SIMS Conference on Simulation and Modelling SIMS 2016}, booktitle = {Proceedings of The 9th EUROSIM Congress on Modelling and Simulation, EUROSIM 2016, The 57th SIMS Conference on Simulation and Modelling SIMS 2016}, doi = {10.3384/ecp171428}, year = {2018}, abstract = {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.}, subject = {Fahrerassistenzsystem}, language = {en} } @inproceedings{JaroschNaujoksWandtneretal.2019, author = {Jarosch, Oliver and Naujoks, Frederik and Wandtner, Bernhard and Gold, Christian and Marberger, Claus and Weidl, Galia and Schrauf, Michael}, title = {The Impact of Non-Driving Related Tasks on Take-over Performance in Conditionally Automated Driving - A Review of the Empirical Evidence}, series = {9. Tagung Automatisiertes Fahren, M{\"u}nchen, Partner T{\"U}V S{\"u}d, November 2019}, booktitle = {9. Tagung Automatisiertes Fahren, M{\"u}nchen, Partner T{\"U}V S{\"u}d, November 2019}, year = {2019}, abstract = {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.}, subject = {Fahrerassistenzsystem}, language = {en} } @inproceedings{ChaarWeidlRaiyn2023, author = {Chaar, Mohamad Mofeed and Weidl, Galia and Raiyn, Jamal}, title = {Analyse the effect of fog on the perception}, series = {Conference: International Symposium on Transportation Data \& Modelling (ISTDM 2023)}, booktitle = {Conference: International Symposium on Transportation Data \& Modelling (ISTDM 2023)}, year = {2023}, subject = {Autonomes Fahrzeug}, language = {en} }