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


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.


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



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.


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Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

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

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