UR:BAN
Funding
BMWi (Federal Ministry of Economics and Technology)
Duration
04/2012-03/2016
Partners
18 partners from industry and research, additionally:
Description
The aim of UR:BAN is to develop advanced driver assistance and traffic management systems for cities. Novel assistance functions provide the driver with information in complex traffic situations, but benefits arise only if the information flow is intelligently filtered to avoid over-loading. Safe, comfortable and stress-free driving in cities should be achieved by optimizing the interaction between driver and assistance. TUC is involved in the subproject "Intention detection and behaviour prediction". Early indicators (e.g. glances, driving behaviour) are investigated regarding their potential to predict future actions of the driver. This information shall manage various systems and provide the driver with customized assistance already before entering a situation.
Contact
People
Dipl. Ing. Philipp Lindner, Dipl. Wirtsch.-Ing. Martin Jentsch
Publications, talks, poster
Beggiato, M., Pech, T., Leonhardt, V., Lindner, P., Wanielik, G., Bullinger-Hoffmann, A., & Krems, J. F. (2017). Lane Change Prediction: From Driver Characteristics, Maneuver Types and Glance Behavior to a Real-Time Prediction Algorithm. In K. Bengler, S. Hoffmann, D. Manstetten, A. Neukum, & J. Drüke, (Eds.) UR:BAN Human Factors in Traffic. Approaches for Safe, Efficient and Stressfree Urban Traffic. (pp. 205-221). Wiesbaden: Springer Vieweg. doi:10.1007/978-3-658-15418-9_11
Beggiato, M., & Krems, J. F. (2015). Real-time assessment of demanding driving scenarios. In C. Bermeitinger, A. Mojzisch, & W. Greve (Eds.), TeaP 2015 - Abstracts of the 57th Conference of Experimental Psychology (p. 35). Lengerich: Pabst Science Publishers.
Beggiato, M., & Krems, J. F. (2014). Dynamische Aufmerksamkeitsverteilung im Straßenverkehr: Analyse von Blickmustern vor Fahrstreifenwechseln. In O. Güntürkün (Ed.). 49. Kongress der Deutschen Gesellschaft für Psychologie, 21.-25. September 2014 (p. 526). Lengerich: Pabst Science Publishers.
Beggiato, M., Pech, T., & Krems, J. F. (2014). The predictive potential of driver characteristics for lane changes on urban arterial roads. Poster presented at the 5th conference on Applied Human Factors and Ergonomics (AHFE), 19.-23 July 2014, Krakow, Poland.
Beggiato, M., & Krems, J. F. (2013). Sequence analysis of glance patterns to predict lane changes on urban arterial roads. Paper presented at 6. Tagung Fahrerassistenz - Der Weg zum automatischen Fahren, Munich, 28.-29.11.2013. mediatum.ub.tum.de/node?id=1187197
Bocklisch, F., Bocklisch, S.F., Beggiato, M., & Krems, J. F. (2017). Adaptive fuzzy pattern classification for the online detection of driver lane change intention. Neurocomputing. doi:10.1016/j.neucom.2017.02.089
Bocklisch, F., Bocklisch, S., Beggiato, M., & Krems, J. F. (2017). Fuzzy Pattern Classification for the Online Detection of Driver Lane Change Intention. In T. Goschke, A. Bolte, & C. Kirschbaum (Eds.), TeaP 2017 - Abstracts of the 59th Conference of Experimental Psychology (p. 232). Lengerich: Pabst Science Publishers.
Griesbach, K., Beggiato, M., & Hoffmann, K. H. (2021). Lane Change Prediction With an Echo State Network and Recurrent Neural Network in the Urban Area. IEEE Transactions on Intelligent Transportation Systems, doi:10.1109/TITS.2021.3058035
Griesbach, K., Hoffmann, K., & Beggiato, M. (2020). Prediction of lane change by echo state networks. Transportation Research Part C: Emerging Technologies, 121, 102841. doi:10.1016/j.trc.2020.102841
Griesbach K., Hoffmann K.H., & Beggiato M. (2019). Lane Change Prediction Using an Echo State Network. In: W. Karwowski & T. Ahram (Eds.), Intelligent Human Systems Integration 2019. Advances in Intelligent Systems and Computing, Vol. 903 (pp. 69-75). Cham, Switzerland: Springer. doi:10.1007/978-3-030-11051-2_11
