Advertisement

Cluster Computing

, Volume 20, Issue 4, pp 2967–2979 | Cite as

The research of prediction model on intelligent vehicle based on driver’s perception

  • Quanzhen GuanEmail author
  • Hong Bao
  • Zuxing Xuan
Article

Abstract

In the field of self-driving technology, the stability and comfort of the intelligent vehicle are the focus of attention. The paper applies cognitive psychology theory to the research of driving behavior and analyzes the behavior mechanism about the driver’s operation. Through applying the theory of hierarchical analysis, we take the safety and comfort of intelligent vehicle as the breakthrough point. And then we took the data of human drivers’ perception behavior as the training set and did regression analysis using the method of regression analysis of machine learning according to the charts of the vehicle speed and the visual field, the vehicle speed and the gaze point as well as the vehicle speed and the dynamic vision. At last we established linear and nonlinear regression models (including the logarithmic model) for the training set. The change in thinking is the first novelty of this paper. Last but not least important, we verified the accuracy of the model through the comparison of different regression analysis. Eventually, it turned out that using logarithmic relationship to express the relationship between the vehicle speed and the visual field, the vehicle speed and the gaze point as well as the vehicle speed and the dynamic vision is better than other models. In the aspect of application, we adopted the technology of multi-sensor fusion and transformed the acquired data from radar, navigation and image to log-polar coordinates, which makes us greatly simplify information when dealing with massive data problems from different sensors. This approach can not only reduce the complexity of the server’s processing but also drives the development of intelligent vehicle in information computing. We also make this model applied in the intelligent driver’s cognitive interactive debugging program, which can better explain and understand the intelligent driving behavior and improved the safety of intelligent vehicle to some extent.

Keywords

Cognitive psychology Driving behavior Logarithmic polar Intelligent vehicle Regression analysis Self-driving 

Notes

Acknowledgements

The work is supported by National Nature Science Foundation of China (Grant No. 91420202). We thank Professor Li Deyi who put forward the selective attention mechanism. We also thank Wang Xinfeng, Manan and Xu Xinkai for their help.

References

  1. 1.
    Li, D.: One Hundred Questions on the Intelligent Driving, p. 54. National Defense Industry Press, Beijing (2014)Google Scholar
  2. 2.
    Dehban, A., Sajedin, A., Bagher Menhaj, M.: A cognitive based driver’s steering behavior. In: 2016 International Conference on Control, Instrumentation, and Automation (ICCIA), January 2016, pp. 390–391Google Scholar
  3. 3.
    Schnelle, S., Wang, J., Su, H., Jagacinski, R.: A driver steering model with personalized desired path generation. IEEE Trans. Syst. Man Cybern. 47(1), 111–120 (2017)CrossRefGoogle Scholar
  4. 4.
    Driggs-Campbell, K., Bajcsy, R.: Comparing datasets for generalizing models of driver intent in dynamic environments. In: IEEE Intelligent Vehicles SymposiumGoogle Scholar
  5. 5.
    Tang, K., Zhu, S., Xu, Y., Wang, F.: Modeling drivers’ dynamic decision-making behavior during the phase transition period: an analytical approach based on hidden Markov model theory. IEEE Trans. Intell. Transp. Syst. 17(1), 206–207 (2016)CrossRefGoogle Scholar
  6. 6.
    Maran, F., Bruschetta, M., Beghi, A.: Study of a real-time, MPC based motion cueing procedure with time-varying prediction for different classes of drivers. In: 2016 American Control Conference (ACC), July 2016, pp. 1711–1712Google Scholar
  7. 7.
    Xiang, X., Zhou, K., Zhang, W., Qin, W., Mao, Q.: A closed-loop speed advisory model with driver’s behavior adaptability for eco-driving. IEEE Trans. Intell. Transp. Syst. 16(6), 3313–3314 (2015)CrossRefGoogle Scholar
  8. 8.
    Saifuzzaman, M., Zheng, Z.: Incorporating human-factors in car-following mode is: a review of recent developments and research needs. Transp. Res. C 48, 379–403 (2014)CrossRefGoogle Scholar
  9. 9.
    Tao, P.: Modelling of Driving Behavior Based on the Psychological Field Theory. Jilin University, Jilin (2012)Google Scholar
  10. 10.
    Zheng, L.: Modeling of Driving Behaviors Under Multi-directional Stimuli and Its Simulation Study. Tianjin University, Tianjin (2013)Google Scholar
  11. 11.
    Zhang, L., Zhu, H., Ma, T.: Model for vehicle driver’s behavior analysis based on Bayesian network. J. Transp. Inf. Saf. 32(186), 1–2 (2014)Google Scholar
  12. 12.
    Ji, B.: Research on Driving Behavior Prediction Method Based on Driver’s Visual Characteristics, pp. 1–6. Jilin University, Jilin (2014)Google Scholar
  13. 13.
    Tan, L.: Vision-Based Driving Behavior Modeling. Zhongnan University, Hunan (2014)Google Scholar
  14. 14.
    Ren, C.: The Uncertainty Modeling and Simulation of Motor Vehicle Driver Behavior, pp. 1–8. HeFei University of Technology, Anhui (2015)Google Scholar
  15. 15.
    Qu, T.: Driver Behavior Modeling Based on Stochastic Model Predictive Control, pp. 1–6. Jilin University, Jilin (2015)Google Scholar
  16. 16.
    Xu, Y., Li, S., Jiao, W., Pan, G.: Driving behavior identification system and application. Comput Sci. 42(9), 1–2 (2015)Google Scholar
  17. 17.
    Zhang, C., Yang, S., Pan, B., Zhao, Y.: Safety evaluation of expressway alignment based on spatial valid vision. J. Highw. Transp. Res. Dev. 27(9), 133 (2010)Google Scholar
  18. 18.
    Pei, H.: The relationship between the safety driving speed, the dynamic vision and visibility. J. Automob. Transp. Res. 14(4), 29 (1995)Google Scholar

Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  1. 1.Beijing Key Laboratory of Information Service EngineeringBeijing Union UniversityBeijingChina

Personalised recommendations