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Predicting overtaking manoeuvres via CAN-Bus data

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Abstract

In order to resolve the conflict between the support and autocracy of advanced driver assistance systems (ADAS), system control must incorporate the driver him/herself. At the Human Factors Institute of the Bundeswehr University Munich, research is therefore being conducted on the prediction of driver intention, which can greatly increase the specificity of driver assistance. This article describes the development of an algorithm which is based on currently available vehicle sensors and which is able to predict overtaking manoeuvres with a high level of reliability.

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References

  1. Moetsch, M.: Blink mal wieder. AutoBild, 2005, 19, pp. 18–20

    Google Scholar 

  2. LeBlanc, D., Sayer, J., Winkler, C., Ervin, R., Bogard, S., Devonshire, J., Mefford, M., Hagan, M., Bareket, Z., Goodsell, R. & Gordon, T.: Road departure crash warning system field operational test: Methodology and results. Technical report, The University of Michigan Transportation Research Institute, 2006. http://www-nrd.nhtsa.dot.gov/pdf/nrd-12/RDCW-Final-Report-Vol.1_JUNE.pdf

    Google Scholar 

  3. Alkim, T.P., Bootsma, G. & Hoogendoorn, S.P.: Field Operational Test „The Assisted Driver“. Intelligent Vehicles Symposium, pp.1198–1203, 13–15 June 2007, Istanbul, Turkey

    Chapter  Google Scholar 

  4. Zabyshny, A.A. & Ragland, D.R.: False Alarms and Human-Machine Warning Systems. UC Berkeley Traffic Safety Center, 2003. Paper UCB-TSCSC-RR-2003-07. http://repositories.cdlib.org/its/tsc/UCB-TSCSC-RR-2003-07

    Google Scholar 

  5. Kompass, K. & Huber, W.: Advanced Driver Assistance: Chances and Limitations on the Way to Improved Active Safety. SAE Technical Paper, No. 2007-01-1738, 2007

    Google Scholar 

  6. Blaschke, C., Färber, B. & Limbacher, R.: Online-detection of driver distraction. Proceedings of the 4th International Conference on Traffic and Transport Psychology, Washington D.C., USA, 1.–4. September 2008

    Google Scholar 

  7. Schmitt, J. & Färber, B.: Verbesserung von FASAS durch Fahrerabsichtserkennung mit Fuzzy Logic. In: Fahrer im 21. Jahrhundert, VDI-Bericht 1919, Düsseldorf: VDI-Verlag 2005

    Google Scholar 

  8. Oliver, N. & Pentland, A.P.: Graphical models for driver behavior recognition in a SmartCar. Proceedings of IEEE Conference on Intelligent Vehicles, Dearborn (MI), USA, 2000, pp. 7–12

    Google Scholar 

  9. Kuge, N., Yamamura, T., Shimoyama, O. & Liu, A.: A driver behaviour recognition method based on a driver model framework. Proceedings of the 2000 SAE World Congress, Detroit (MI), USA, 2000, pp. 469–476

    Google Scholar 

  10. Ohasi, K., Yamaguchi, T. & Tamai, I.: Humane automotive system using driver intention recognition. Proceedings of the SICE Annual Conference in Sapporo, Japan, 2004, pp. 1164–1167

    Google Scholar 

  11. Salvucci, D.D.: Inferring driver intent: A case study in lane-change detection. Proceedings of the Human Factors Ergonomics Society 48th Annual Meeting. Santa Monica, USA, 2004, pp. 2228–2231

    Google Scholar 

  12. Dagli, I., Brost, M. und Breuel, G.: Action recognition and prediction for driver assistance systems using dynamic belief networks. Proceedings of the Conference on Agent Technologies, Infrastructures, Tools and Applications for E-Services, Erfurt, Deutschland, 2002, pp. 179–194

    Google Scholar 

  13. Heckhausen, H., Gollwitzer, P.M., Weinert, F.E.: Jenseits des Rubikon: Der Wille in den Humanwissenschaften. Berlin: Springer Verlag 1987

    Book  Google Scholar 

  14. Zadeh, L.A.: Fuzzy Sets. Information and Control, 8 (3), 1965, pp. 338–353

    MathSciNet  MATH  Google Scholar 

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Blaschke, C., Schmitt, J. & Färber, B. Predicting overtaking manoeuvres via CAN-Bus data. ATZ Worldw 110, 47–51 (2008). https://doi.org/10.1007/BF03225045

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