This content is not included in
your SAE MOBILUS subscription, or you are not logged in.
The Evaluation of the Driving Capability for Drivers Based on Vehicle States and Fuzzy-ANP Model
Technical Paper
2022-01-7000
ISSN: 0148-7191, e-ISSN: 2688-3627
Annotation ability available
Sector:
Language:
English
Abstract
In partly autonomous driving such as level 2 or level 3 automatic driving from SAE international classification, the switching of the driving right between the human driver and the machine plays an important role in the driving process of vehicle [1]. In this paper, the data collected from vehicle states and the driving behavior of drivers is completed via a simulator and self-report questionnaires. A fuzzy analytic network process (Fuzzy-ANP) model is developed to evaluate the driving capability of the drivers in real time from vehicle states due to its direct inherent link to the change of the driving state of drivers Moreover, in this model, the idea of group decision and multi-index fusion is adopted. The questionnaire is required to identify the experimental results from the simulator. The results show that the proposed Fuzzy-ANP model can evaluate the driving capability of the participants in real time accurately. As a real and objective driving state from vehicle, it is a good mirror to reflect the driving state or the driving capability from drivers. The driving capability is an important criterion or standard to decide the switching of the driving right between human driver and machine in autonomous vehicles.
Authors
Citation
Wang, K., Liu, L., Zheng, L., and Zeng, D., "The Evaluation of the Driving Capability for Drivers Based on Vehicle States and Fuzzy-ANP Model," SAE Technical Paper 2022-01-7000, 2022, https://doi.org/10.4271/2022-01-7000.Also In
References
- Martens , M.H. and Beukel , Van den The Road to Automated Driving: Dual Mode and Human Factors Considerations 16th IEEE Intelligent Transportation Systems Conference Proceedings 2262 2267
- Stanton , N.A. , Dunoyer , A. , and Leatherland , A. Detection of New in-Path Targets by Drivers Using Stop&go Adaptive Cruise Control Appl. Ergon. 42 4 2011 592 601
- Sagberg , F. A Review of Research on Driving Styles and Road Safety Human Factors 57 7 2015 1248 1275
- Xiaoming , R. and Qing , X. Research of Driver’s State Model Journal of System Simulation 24 9 2012 1993 1998
- Yuyan , Y. Exploration on the Formation Law of Automobile Driving Skills Vocational and Technical Education in China 24 7 2006 26 28
- Linfen , W. and Shubai , X. An Introduction to Analytic Hierarchy Process People’s University Publication House 1990 263 303
- Zuylen , V. , Henk , J. , Muller , T.H.J. , Miska , M.P. Driving Behavior Model for Microscopic Online Simulation Based on Remote-Sensing and Equipped-Vehicle Data TRB 85th Annual Meeting Compendium of Papers 2006 1609 1614
- Caird , J.K. , Willness , C.R. , Steel , P. , and Scialfa , C. A meta-Analysis of the Effects of Cell Phones on Driver Performance Accident Analysis and Prevention 40 2008 1282 1293
- Pavelka , M. Indication of Driver Fatigue with Help of Steering Wheel Movement 7th International Conference on Automatic Control, Modeling and Simulation 2005 13 15
- Takei , Y. Furukawa , Y.
- You , J. , et al. 2005 34 38
- Wang , F. , Wu , S. , Zhang , W. , Xu , Z. et al. Multiple Nonlinear Features Fusion Based Driving Fatigue Detection Biomedical Signal Processing and Control 62 2020 102075
- Wang , H. , Dragomir , A. , Abbasi , N.I. , Li , J. et al. A Novel Real-Time Driving Fatigue Detection System Based on Wireless Dry EEG Cognitive neurodynamics 12 4 2018 365 376
- Ma , Y. , Chen , B. , Li , R. , Wang , C. et al. Driving Fatigue Detection from EEG using a Modified PCANet Method Computational Intelligence and Neuroscience 2019 2019
- Dang , W. , Gao , Z. , Lv , D. , Sun , X. et al. Rhythm-Dependent Multilayer Brain Network for the Detection of Driving Fatigue IEEE Journal of Biomedical and Health Informatics 25 3 2020 693 700
- Zhang , W. , Wang , F. , Wu , S. , Xu , Z. et al. Partial Directed Coherence Based Graph Convolutional Neural Networks for Driving Fatigue Detection Review of Scientific Instruments 91 7 2020 074713
- Yingshi , G. , Guo Yanjun , F. , and Rui. Operation and Gazing Behavior of Driver during Lane Change Journal of Changan University (Natural Science Edition) 34 4 2014 115 119
- Sanders A. An Introduction to Unreal Engine 4 Taylor and Francis
- Osafune , T. , Takahashi , T. , Kiyama , N. , Sobue , T. et al. Analysis of Accident Risks from Driving Behaviors International journal of intelligent transportation systems research 15 3 2017 192 202
- Toledo , T. , Musicant , O. , and Lotan , T. In-Vehicle Data Recorders for Monitoring and Feedback on Drivers’ Behavior Transportation Research Part C: Emerging Technologies 6 3 2008 320 331
- Zhenhai , G. , DinhDat , L. , Hongyu , H. , Ziwen , Y. et al. Driver Drowsiness Detection Based on Time Series Analysis of Steering Wheel Angular Velocity In 2017 9th International Conference on Measuring Technology and Mechatronics Automation (ICMTMA) IEEE 2017 99 101
- Verster , J.C. and Roth , T. Effects of Central Nervous System Drugs on Driving: Speed Variability Versus Standard Deviation of Lateral Position as Outcome Measure of the on-the-Road Driving Test Human Psychopharmacology: Clinical and Experimental 29 1 2014
- Simons-morton , B.G. , Klauer , S.G. , Ouimet , M.C. et al. Naturalistic Teenage Driving Study: Findings and Lessons Learned Journal of Safety Research 54 41 2015 e29 e44
- Hirokawa , M. , Uesugi , N. , Furugori , S. et al. Effect of Haptic Assistance on Learning Vehicle Reverse Parking Skills IEEE Transactions on Haptics 7 3 2014 334 344
- Weihua , Z. and Xinxu , Z. Validity and Reliability of Safety Driving Scale under Low Visibility Journal of Guangxi University(Nat Sci Ed) 41 5 2016 1545 1551
- Wei , L. and Hongming , H. Reliability Fuzzy Valuation of Existing Concrete Structure Based on Analytic Network Process Journal of Agricultural University of Hubei 2 40 2017 119 128
- Saaty , T.L. and Vargas , L.G. Decision Making with Dependence and Feedback: The Analytic Network Process Pittsburgh RWS publications 1996
- Pingfan , L. and Dianhai , W. The Impact of Dialing on Mobile Phones to Drivers’ Mental Workload and Driving Behavior Journal of Transport Information and Safety 2010 103 107