Zusammenfassung
Due to a large number of integrated advanced driver assistance systems (ADAS) the driver nowadays can hand over the driving task to the vehicle in specific, monotone driving scenarios. Short reaction times and the constant awareness of the computer reduces the number of accidents and thus increases safety. Currently available ADAS still need to be constantly monitored by the driver in case a situation appears that cannot be handled properly by the system.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
Literatur
[1] Glorot, X., Bengio, Y.: Understanding the difficulty of training deep feedforward neural networks. Aistats (2010) 9, S. 249-256.
[2] Krizhevsky, A., Sutskever, I., Hinton, G.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems (2012), pp. 1097-1105.
[3] Kasper D., Weidl G., Dang T., Breuel G., Tamke A., Wedel A., Rosenstiel W.: Object-oriented bayesian networks for detection of lane change maneuvers. IEEE Intelligent Transportation Systems Magazine (2012), vol. 4, no. 3, pp. 19–31.
[4] Meyer-Delius D., Plagemann C., and Burgard W.: Probabilistic situation recognition for vehicular traffic scenarios. IEEE Int. Conf. on Robotics and Automation (2009), pp. 459–464.
[5] Wissing C., Nattermann T., Glander K.-H., Hass C., Bertram T.: Lane Change Prediction by Combining Movement and Simulation based Probabilities. IFAC World Congress (2017) (accepted).
[6] Krueger M., Meuresch S., Stockem Novo A., Nattermann T., Glander K.-H., Bertram T.: Structural analysis of a neural network for lane change prediction for automated driving. 26. Workshop Computational Intelligence (2016).
[7] Behrisch, M.; et al.: SUMO–Simulation of Urban MObility. The Third International Conference on Advances in System Simulation (SIMUL 2011), Barcelona, Spain, pp. 1-6.
[8] Smith, L., Beckman, R., Baggerly, K.: TRANSIMS: Transportation analysis and simulation system. Los Alamos National Lab., NM (United States), 1995, pp. 1-10.
[9] Wissing C., Nattermann T., Glander K. H., Seewald A., Bertram T.: Environment Simulation for the Development, Evaluation and Verification of Underlying Algorithms for Automated Driving. AmE 2016 – Automotive meets Electronics; 7th GMM-Symposium (2016), pp. 1-6.
[10] Treiber, M., Helbing, D.: Realistische Mikrosimulation von Strassenverkehr mit einem einfachen Modell. 16th Symposium Simulationstechnik ASIM (2002), pp. 80.
[11] Yosinski, J., Clune, J., Bengio, Y., Lipson, H.: How transferable are features in deep neural networks? Advances in neural information processing systems (2014), pp. 3320-3328.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer Fachmedien Wiesbaden GmbH
About this paper
Cite this paper
Wissing, C., Glander, KH., Haß, C., Nattermann, T., Bertram, T. (2017). Development and test of a lane change prediction algorithm for automated driving. In: Isermann, R. (eds) Fahrerassistenzsysteme 2017. Proceedings. Springer Vieweg, Wiesbaden. https://doi.org/10.1007/978-3-658-19059-0_23
Download citation
DOI: https://doi.org/10.1007/978-3-658-19059-0_23
Published:
Publisher Name: Springer Vieweg, Wiesbaden
Print ISBN: 978-3-658-19058-3
Online ISBN: 978-3-658-19059-0
eBook Packages: Computer Science and Engineering (German Language)