Abstract
The task of driving is very complex and highly demanding for the individual. The optimal driver assistance strongly depends on the situation and the driver’s needs. In particular, this applies to driving manoeuvres as lane changes. Consequently, future advanced driver assistance systems will have to detect and assess driving situations as well as the driver’s intentions automatically before a driving manoeuvre is initiated.
The method proposed predicts situations of upcoming lane changes based on assessments of the environmental situation and the driver’s behaviour. For this purpose, information gained from a 360° sensory perception of the vehicle surroundings and from the analysis of the driver’s gaze behaviour is fused by means of a Bayesian network. The implemented algorithms work in real-time and provide a probabilistic estimation of the intention of the driver to perform a specific manoeuvre. The application of prediction was integrated into a test vehicle and evaluated by using real traffic data and driving studies.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Statistisches Bundesamt: Fachserie 8 Reihe 7, Verkehr – Verkehrsunfälle 2014. Statistisches Bundesamt, Wiesbaden (2016)
Bayly, M., Fildes, B., Regan, M., Young, K.: Review of crash effectiveness of intelligent transport systems. Deliverable D4.1.1–D6.2, TRACE project (2007). http://www.trace-project.org/publication/archives/trace-wp4-wp6-d4-1-1-d6-2.pdf
Kuge, N., Yamamura, T., Shimoyama, O., Liu, A.: A Driver Behavior Recognition Method Based on a Driver Model Framework. SAE Technical Paper, 2000-01-0349 (2000)
Berndt, H., Dietmayer, K.: Driver intention inference with vehicle onboard sensors. Proceedings of the IEEE International Conference on Vehicular Electronics and Safety (ICVES), pp 102–107 (2009)
Oliver, N., Pentland, A.P.: Graphical models for driver behavior recognition in a SmartCar. Proceedings of the IEEE Intelligent Vehicles Symposium, pp 7–12 (2000). IV 2000
Schubert, R., Wanielik, G.: Empirical evaluation of a unified bayesian object and situation assessment approach for lane change assistance. Proceedings of the IEEE International Conference Intelligent Transportation Systems (ITSC), pp 1471–1476 (2011)
Doshi, A., Trivedi, M.M.: On the Roles of Eye Gaze and Head Dynamics in Predicting Driver’s Intent to Change Lanes. IEEE Trans Intelligent Transportation Syst 10(3), 453–462 (2009)
Henning, M.J.: Preparation for lane change manoeuvres: Behavioural indicators and underlying cognitive processes, Ph.D. dissertation, Technische Universität Chemnitz (2010)
Lethaus, F., Rataj, J.: Do eye movements reflect driving manoeuvres? Intelligent Transport Syst Iet 1(3), 199–204 (2007)
Doshi, A., Moris, B.T., Trivedi, M.M.: On-road prediction of driver’s intent with multimodal sensory cues. IEEE Pervasive Comput 10(3), 22–34 (2011)
McCall, J.C., Trivedi, M.M., Wipf, D., Rao, B.: Lane Change Intent Analysis Using Robust Operators and Sparse Bayesian Learning. IEEE Computer Society Conference on Computer Vision and Pattern Recognition – Workshops, CVPR Workshops, pp 59–59 (2005)
Bauer, C.: A Driver-Specific Maneuver Prediction Model Based on Fuzzy Logic. Ph.D. dissertation, Freie Universität Berlin (2011)
Schroven, F., Giebel, T.: Fahrerintentionserkennung für Fahrerassistenzsysteme. Proceedings of 24. VDI/VW-Gemeinschaftstagung – Integrierte Sicherheit und Fahrerassistenzsysteme. Wolfsburg Bd. VDI-Berichte. VDI, Düsseldorf (2008)
Hupfer, C.: Deceleration to safety time (DST) – a useful figure to evaluate traffic safety. Proceedings of ICTCT Conference Seminar 3. Department of Traffic Planning and Engineering. (1997)
Leonhardt, V., Pech, T., Wanielik, G.: Data Fusion and Assessment for Maneuver Prediction including Driving Situation and Driver Behavior. Proceedings of the IEEE International Conference on Information Fusion, pp 1702–1708 (2016)
Pech, T., Lindner, P., Wanielik, G.: Head tracking based glance area estimation for driver behaviour modelling during lane change execution. Proceedings of the IEEE International Conference on Intelligent Transportation Systems (ITSC), pp 655–660 (2014)
Russel, S.J., Norvig, P.: Artificial intelligence: a modern approach. Prentice Hall/Pearson Education, Upper Saddle River (2003)
Rabiner, L.R., Juang, B.H.: An introduction to hidden markov models. IEEE Assp Mag 3, 4–16 (1986)
Lethaus, F., Baumann, M.R.K., Köster, F., Lemmer, K. (eds.): Using Pattern Recognition to Predict Driver Intention. Springer, Berlin, Heidelberg (2011)
Schubert, R., Schulze, K., Wanielik, G.: Situation assessment for automatic lane-change maneuvers. IEEE Trans Intelligent Transportation Syst 11(3), 607–616 (2010)
Schuster, M., Reuter, J., Wanielik, G.: Tracking of vehicles on near side lanes using multiple radar sensors. Proceedings of International Radar Conference, 2014. (2014)
Schubert, R., Adam, C., Obst, M., Mattern, N., Leonhardt, V., Wanielik, G.: Empirical evaluation of vehicular models for ego motion estimation. Proceedings of the IEEE International Conference Intelligent Vehicles Symposium (IV), pp 7–12 (2011)
Schubert, R., Adam, C., Richter, E., Bauer, S., Lietz, H., Wanielik, G.: Generalized probabilistic data association for vehicle tracking under clutter. Proceedings of the IEEE Intelligent Vehicles Symposium (IV), pp 962–968 (2012)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Fachmedien Wiesbaden GmbH
About this chapter
Cite this chapter
Leonhardt, V., Pech, T., Wanielik, G. (2018). Fusion of Driver Behaviour Analysis and Situation Assessment for Probabilistic Driving Manoeuvre Prediction. In: Bengler, K., Drüke, J., Hoffmann, S., Manstetten, D., Neukum, A. (eds) UR:BAN Human Factors in Traffic. ATZ/MTZ-Fachbuch. Springer Vieweg, Wiesbaden. https://doi.org/10.1007/978-3-658-15418-9_12
Download citation
DOI: https://doi.org/10.1007/978-3-658-15418-9_12
Published:
Publisher Name: Springer Vieweg, Wiesbaden
Print ISBN: 978-3-658-15417-2
Online ISBN: 978-3-658-15418-9
eBook Packages: EngineeringEngineering (R0)