ARTICLE
Automatic driving comfort analysis and intelligent identification of uncomfortable manoeuvres based on vehicle-following scenario
 
 
More details
Hide details
1
China Intelligent and Connected Vehicles, Research Institute Co., Ltd., Beijing, China
 
 
Submission date: 2021-11-03
 
 
Final revision date: 2022-03-01
 
 
Acceptance date: 2022-04-04
 
 
Online publication date: 2022-04-28
 
 
Publication date: 2022-04-30
 
 
Corresponding author
Jiacheng Feng   

Test and Validation Division, China Intelligent and Connected Vehicles (Beijing) Research Institute Co., Ltd, 100000, Beijing, China
 
 
Journal of Theoretical and Applied Mechanics 2022;60(2):303-316
 
KEYWORDS
TOPICS
ABSTRACT
Driving comfort and performance is of vital importance to evaluate the control quality of an automatic driving system. The control quality and calibration of the automatic driving system not only affects comfort but also psychological load and tension. Therefore, this paper proposed an analysis method of driving comfort combined with subjective and objec- tive factors, including multidimensional analysis based on the velocity domain, acceleration energy and power analysis, perceived risk and deviation analysis. Moreover, the feature of typical uncomfortable manoeuvres is analysed and generates an intelligent identification al- gorithm. It has been found that the uncomfortable identification performance is excellent (the accuracy reached 99%).
REFERENCES (18)
1.
Archer N.P.,Wang S., 2010, Application of the back propagation neural network algorithm with monotonicity constraints for two-group classification problems, Decision Sciences, 24, 1, 60-75.
 
2.
Burkhard G., Vos S, Munzinger N., Enders E., Schramm D., 2018, Requirements on Driving Dynamics in Autonomous Driving with Regard to Motion and Comfort, Springer Verlag.
 
3.
Cai S., Li K., Selesnick I., 2003, Matlab Implementation of Wavelet Transforms, Electrical Engineering at Polytechnic University, NY.
 
4.
Didier M, Landau K., 2005, Influence of driver characteristics and driving environment on the comfort evaluation of ACC-System, [In:] Adaptive Cruise Control, Lea & Febiger.
 
5.
Guan W., Yao Q., Gao Y., Lu B., 2015, Transient power quality detection and location of distribution network based on db4 wavelet transform, Dianli Xitong Baohu yu Kongzhi/Power System Protection and Control, 43, 8, 102-106.
 
6.
Hazarika N., Chen J.Z., Tsoi A.C., Sergejew A., 1997, Classification of EEG signals using the wavelet transform, Signal Processing, 59, 1, 61-72.
 
7.
Kusmirek S., Hana K., Socha V., Prucha J., Kutilek P., Svoboda Z., 2016, Postural instability assessment using trunk acceleration frequency analysis, European Journal of Physiotherapy, 18, 4, 237-244.
 
8.
Liu L., Jie C., Xu L., 2008, Realization and application research of BP neural network based on MATLAB, International Seminar on Future Biomedical Information Engineering, IEEE, 130-133.
 
9.
Moon T.K., Stirling W.C., 2000, Mathematical Methods and Algorithms for Signal Processing, Prentice Hall.
 
10.
Rihaczek A.W., 1968, Signal energy distribution in time and frequency, IEEE Transactions on Information Theory, 14, 3, 369-374.
 
11.
Rioul O., Vetterli M., 1991, Wavelets and signal processing, IEEE Signal Processing Magazine, 8, 4, 14-38.
 
12.
Tang T.-Q., Zhang J., Chen L., Shang H.-Y., 2017, Analysis of vehicle’s safety envelope under car-following model, Physica A: Statistical Mechanics and its Applications, 474, 127-133.
 
13.
Wang Y., Zhang Q., Zhang L., Hu Y., 2019, A method to automatic measuring riding comfort of autonomous vehicles: based on passenger subjective rating and vehicle parameters, [In:] Design, User Experience, and Usability, Springer International Publishing, 130-145.
 
14.
Xing J.L., 2007, BP neural network pattern and application, Journal of Shayang Teachers College.
 
15.
Yusof N.M., Karjanto J., Terken J., Delbressine F., Hassan M.Z., Rauterberg M., 2016, The exploration of autonomous vehicle driving styles: preferred longitudinal, lateral, and vertical accelerations, Proceedings of the 8th International Conference on Automotive User Interfaces and Interactive Vehicular Applications, 245252
 
16.
Zhang W.J., Yu S.X., Peng Y.F., Cheng Z.J., Wang C., 2015, Driving habits analysis on vehicle data using error back-propagation neural network algorithm, [In:] Computing Control Information and Education Engineering, H.-C. Liu, W.-P. Sung, W. Yao (Edit.), CRC Press.
 
17.
Zhao S.M., 2006, Comparison of BP algorithms in Matlab NN toolbox, Computer Simulation.
 
18.
Zhao Y., Zhang Y.-H., Lin J.-H., 2013, Summary on the pseudo-excitation method for vehicle random vibration PSD analysis, Applied Mathematics and Mechanics, 34, 2, 107-117.
 
eISSN:2543-6309
ISSN:1429-2955
Journals System - logo
Scroll to top