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Home > Archives > Volume 16, No 2 (2018) > Article

DOI: 10.14704/nq.2018.16.2.1182

The Driver’s Steering Feel Assessment Using EEG and EMG signals

Gao Zhenhai


Whereas the existing steering feel evaluation methods fail to objectively describe subjective feelings, this paper successfully implements physiological features analysis of both mental and physical workload. Several drivers were invited to attend double-lane change tests, during which the electroencephalogram and surface electromyogram signals of their shoulder muscles were obtained. The steering feel was rated subjectively after each test run. Through the comparison of subjective ratings, it was found that physiological features of both mental and physical workload were correlated with maneuverability and lane-change ability. This research sheds new light on measuring driver’s response in performance evaluation and provides valuable references for steering feel quantification.


Driver, Steering Feel, Subjective Evaluation, EEG, EMG

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Brookhuis KA, Dick DW, and Wiel HJ. Behavioural impacts of advanced driver assistance systems–an overview. European Journal of Transport and Infrastructure Research 2001; 1(3): 245-253.

Data S, Frigerio F. Objective evaluation of handling quality. Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering 2002;216(4): 297-305.

De Luca C. The use of surface electromyography in biomechanics. Journal of applied biomechanics 1997; 13:135-163.

Farah G, Hewson D, Duchene J. Surface electromyography as a tool to assess the responses of vehicle passengers to lateral accelerations: Part I. Extraction of relevant muscular activities from noisy recordings. Journal of Electromyography and Kinesiology 2006; 16(6): 669-676.

Farah G, Petit-Boulanger C, Hewson D, Duchêne J. Surface electromyography as a tool to assess the responses of vehicle passengers to lateral accelerations. Part II: Objective comparison of vehicles, Journal of Electromyography and Kinesiology 2006; 16(6): 677-684.

Gao ZH, Li CZ, Hu HY, Zhao H, Chen CY, Yu HL. Experimental study of young male drivers’ responses to vehicle collision using EMG of lower extremity. Bio-medical materials and engineering 2015; 26(s1): S563-S573.

Gao ZH, Li CZ, Hu HY, Zhao H, Chen CY, Yu HL. Study of the influence of muscle activation on a driver's lower extremity injury. International journal of crashworthiness 2016; 21(3): 191-197.

Gheorghe LA, Takashi S. Brain waves measurement based evaluation of mental workload related to visual information while driving. SAE International Journal of Passenger Cars-Mechanical Systems 2011; 4(2011-01-0593): 578-585.

Gómez G, Nybacka M, Drugge L, Bakker E. Machine learning to classify and predict objective and subjective assessments of vehicle dynamics: the case of steering feel. Vehicle System Dynamics 2016; 56(1): 150-171.

Haufe S, Treder M, Gugler M, Sagebaum M., Curio G, Blankertz B. EEG potentials predict upcoming emergency brakings during simulated driving. Journal of neural engineering 2011; 8(5): 056001.

Kolich M. Using Failure Mode and Effects Analysis to design a comfortable automotive driver seat. Applied ergonomics 2014; 45(4): 1087-1096.

Liu YH. Method for measuring a driver’s steering efficiency using electromyography. Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering 2014; 228(10): 1170-1184.

Mastrigt S, Kamp I, Veen S, Vink P, Bosch T. The influence of active seating on car passengers' perceived comfort and activity levels. Applied ergonomics 2015; 47: 211-219.

Matsushita A, Takanami K, Takeda N, Takahashi M. Subjective evaluation and vehicle behavior in lane-change maneuver. SAE Technical Paper 1980; 800845.

Nash C, Cole, DJ, Bigler R. A review of human sensory dynamics for application to models of driver steering and speed control. Biological cybernetics 2016; 110(2-3): 91-116.

Nash C, Cole DJ. Modelling the effect of sensory dynamics on a driver’s control of a nonlinear vehicle. Advanced Vehicle Control: Proceedings of the 13th International Symposium on Advanced Vehicle Control (AVEC'16), Munich, Germany 2016; 317.

Nash C, Cole DJ. Modelling the influence of sensory dynamics on linear and nonlinear driver steering control. Vehicle System Dynamics 2017; 1-30.

Moa S, Hyunb Y, Kimb C, Kimc D, Kang H. Correlation between muscle contraction and vehicle dynamics in a real driving. Advances in Affective and Pleasurable Design 2012; 196-201.

Pick AJ, Cole DJ. Driver steering and muscle activity during a lane-change manoeuver, Vehicle system dynamics, 2007; 45(9): 781-805.

Rothhämel M, Ijkema J, Drugge L. A method to find correlations between steering feel and vehicle handling properties using a moving base driving simulator. Vehicle System Dynamics 2011; 49(12): 1837-1854.

Shi GB, Zhang X, Lin Y. (2007). Subjective fuzzy evaluation of steering feel for electric power steering system. Journal of Jilin University (Engineering and Technology Edition) 2007; 37(4): 751-755.

Sinclair J, Taylor P, Hebron J, Brooks D, Hurst H, Atkins S. The reliability of electromyographic normalization methods for cycling analyses. Journal of human kinetics 2015; 46(1): 19-27.

Smith ME, Gevins A, Brown H, Karnik A, Du R. Monitoring task loading with multivariate EEG measures during complex forms of human-computer interaction. Human Factors 2001; 43(3): 366-380.

Yang, GS, Yingzi L, and Prabir B. A driver fatigue recognition model based on information fusion and dynamic Bayesian network. Information Sciences 2010; 180(10): 1942-1954.

Zhang X., Shi GB. Objective evaluation of electric power steering steering feel, Journal of Mechanical Engineering 2009; 45(9): 171-175.

Zheng RC, Nakano K, Okamoto Y, Ohori M, Hori S, Suda Y. (2013). Evaluation of Sternocleidomastoid Muscle Activity of a Passenger in Response to a Vehicle's Lateral Acceleration While Slalom Driving. IEEE Transactions on Human-Machine Systems 2013; 43(4): 405-415.