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 - 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 - 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 -