@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} } @inproceedings{RaiynWeidl2023, author = {Raiyn, Jamal and Weidl, Galia}, title = {Improving Autonomous Vehicle Reasoning with Non-Monotonic Logic: Advancing Safety and Performance in Complex Environments}, series = {IEEE International Smart Cities Conference (ISC2)}, booktitle = {IEEE International Smart Cities Conference (ISC2)}, doi = {10.1109/ISC257844.2023.10293463}, year = {2023}, subject = {K{\"u}nstliche Intelligenz}, language = {en} } @inproceedings{WeidlRaiynBerres2023, author = {Weidl, Galia and Raiyn, Jamal and Berres, Stefan}, title = {Does a livable city profit from a shared CCAM Shuttle Bus on demand?}, series = {International Symposium on Transportation Data \& Modelling (ISTDM2023), June 2023}, booktitle = {International Symposium on Transportation Data \& Modelling (ISTDM2023), June 2023}, year = {2023}, abstract = {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?}, subject = {Stadtentwicklung}, 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} } @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{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} } @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 = {Early Recognition of Maneuvers in Highway Traffic}, series = {The 13th European Conference Symbolic and Qualitative Approaches to Reasoning With Uncertainties, ECSQARU, 15-17 July 2015}, booktitle = {The 13th European Conference Symbolic and Qualitative Approaches to Reasoning With Uncertainties, ECSQARU, 15-17 July 2015}, 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} } @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} }