Soft Computing

, Volume 22, Issue 5, pp 1457–1466 | Cite as

Sensor-based risk perception ability network design for drivers in snow and ice environmental freeway: a deep learning and rough sets approach

  • Wei Zhao
  • Liangjie Xu
  • Jing Bai
  • Menglu Ji
  • Troy Runge


Due to factors such as snow and ice impeding drivers’ vision, the number of automobile crashes significantly rises during winter months. This study sets forth an automatic evaluation network of the risk perceived ability for motorists driving on the freeway in snow and ice environments, using a deep learning approach and the rough sets technique. First, a naturalistic driving experiment involving thirteen licensed drivers was conducted on a freeway in Jilin, China, with a crash hot spot set prior to the start of the experiment. Then multi-sensor (eye-trackers, mini-cameras, and speed detectors) apparatuses, collecting both images and numerical data, were utilized. Afterward, restricted Boltzmann machine was used to develop a deep belief network (DBN) along with training procedures. Rough sets technique was added as judgment in output layer of the DBN. Finally, fixation duration, pupil size, changes in speed, etc., were used as input impact factors and the perception conditions were used as output variables to train the network. Furthermore, after comparing the DBN-based risk perception ability network with Naïve Bayes and BP-ANN (artificial neural networks with back propagations), the results indicate that the DBN-FS not only outperforms both Naïve Bayes and BP-ANN, but also improves the accuracy of perceiving risky conditions. This approach can provide reference for the design of hazard detection systems of partially automated vehicles.


Freeway curves Fuzzy sets DBN Deep learning Risk perception ability 



I have disclosed those interests fully to Research Projects of Social Science and Humanity on Young Fund of the Ministry of Education of China (16YJCZH157), while this work also supported by Social science planning project of Inner Mongolia (2015JDA008) and National Natural Science Foundation of China (71764020).

Compliance with ethical standards

Conflict of interest

No potential conflict of interest was reported by the authors.


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Copyright information

© Springer-Verlag GmbH Germany 2017

Authors and Affiliations

  1. 1.School of TransportationWuhan University of TechnologyWuhanChina
  2. 2.School of Economics and ManagementInner Mongolia University of Science and TechnologyBaotouChina
  3. 3.Key Laboratory of Intelligent Perception and Image Understanding of Ministry of EducationXidian UniversityXi’anChina
  4. 4.College of Agricultural and Life SciencesUniversity of Wisconsin-MadisonMadisonUSA

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