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GUO Lie, MA Yue, YUE Ming, QIN Zeng-ke. Overview of recognition and evaluation of driving characteristics and their applications in intelligent vehicles[J]. Journal of Traffic and Transportation Engineering, 2021, 21(2): 7-20. doi: 10.19818/j.cnki.1671-1637.2021.02.002
Citation: GUO Lie, MA Yue, YUE Ming, QIN Zeng-ke. Overview of recognition and evaluation of driving characteristics and their applications in intelligent vehicles[J]. Journal of Traffic and Transportation Engineering, 2021, 21(2): 7-20. doi: 10.19818/j.cnki.1671-1637.2021.02.002
shu

Overview of recognition and evaluation of driving characteristics and their applications in intelligent vehicles

doi: 10.19818/j.cnki.1671-1637.2021.02.002

  • School of Automotive Engineering, Dalian University of Technology, Dalian 116024, Liaoning, China

Funds:

National Natural Science Foundation of China 51975089

National Natural Science Foundation of China 61873047

More Information
  • Author Bio:

    GUO Lie(1978-), male, associate professor, PhD, guo_lie@dlut.edu.cn

  • Received Date: 2020-10-04
  • Publish Date: 2021-04-25
    Abstract
  • The methods for the recognition of driving characteristics, the research progress on driver takeover ability, and the application of driving characteristics to the field of intelligent vehicles were studied. The driver condition monitoring was divided into driver fatigue, distraction, and bad driving behavior monitoring. The research targets, methods, accuracy, judgment standards, and advantages and disadvantages of driver condition monitoring were summarized. The differences in various detection signals in the driver fatigue monitoring method were compared and analyzed. The methods for driver intention identification and prediction based on the fuzzy recognition and hidden Markov models were discussed and evaluated. The main steps and features of typical identification methods for driving style classification and identification were summarized. The influencing factors and evaluation criteria for driver takeover ability were analyzed. The major ways that driving characteristics were used to develop assistant driving systems with high user acceptance and excellent human-machine interaction performance were expounded. The approach considering the driving characteristics in human-machine co-driving cooperative control was summarized. Analysis result shows that driver condition monitoring methods based on the multi-sensor signal fusion can effectively avoid the disadvantages of single sensor-based methods, and increase the detection accuracy, and decrease the false alarms. Combining traditional prediction models with hybrid intelligent learning is the main solution for the online recognition and prediction of driving intentions. The identification of driving characteristics under complex conditions is the primary research focus. The research on driver takeover ability needs to be theoretical and systematic. Developing an integrated assistant driving technology based on driving characteristics and realizing the interaction of intention and control strategy between the driver and the assistant driving system under typical road conditions is a future research trend. Considering the driving characteristics of personalized drivers in the design of co-driving coefficients helps to improve the personalization, intelligence level, and environmental adaptability of human-machine co-driving systems. 4 tabs, 5 figs, 82 refs.

     

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