Objective Evaluation of Motion Cueing Algorithms for Vehicle Driving Simulator Based on Criteria Importance through Intercriteria Correlation (CRITIC) Weight Method Combined with Gray Correlation Analysis
Abstract
:1. Introduction
2. Materials and Methods
2.1. Visual–Vestibular Interaction Model and Evaluation Scheme
2.1.1. Visual–Vestibular Interaction Model
2.1.2. Model of Tilting Angle Perception of Head
2.1.3. Improved Scheme of MCA Perceptual Fidelity Evaluation
2.2. Establishment of the Evaluation Indicator System
2.2.1. Key Performance Indicators
2.2.2. Weights of Indicators at the First Level
2.2.3. Criteria Importance through Intercriteria Correlation (CRITIC)
2.2.4. The Method of Gray Relational Analysis
2.2.5. Determine Combined Weights
2.3. Example Analysis
3. Results
3.1. Apparatus and the MCAs
3.2. Experimental Procedure
4. Discussion
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Type | Advantages | Disadvantages |
---|---|---|
CMCA | Fewer parameters, fast calculation. | Fixed parameters, poor applicability. |
AMCA | Filter parameters can be adjusted in real time. | The stability is poor, and the optimization efficiency is low. |
OMCA | Human perception error is considered for the first time. | The parameters are fixed, and the calculation is complicated. |
FMCA | Regulators are plentiful and flexible. | The algorithm structure is complex. |
MPC-MCA | Parameters and constraints have a more intuitive relationship. | It is highly dependent on human perception model, which is not perfect. |
Indicator | Correlation | Coefficient | Average | Weight |
---|---|---|---|---|
NAAD | 0.123 | 0.218 | 0.1705 | 0.17 |
NPC | 0.425 | 0.495 | 0.460 | 0.47 |
ED | 0.347 | 0.357 | 0.352 | 0.36 |
Second Level | CRITIC Weight | GRA Weight | Combined Weight | Third Level | CRITIC Weight | GRA Weight | Combined Weight |
---|---|---|---|---|---|---|---|
Angular Velocity | 0.3784 | 0.2277 | 0.3031 | R | 0.2858 | 0.4074 | 0.3466 |
P | 0.2826 | 0.2831 | 0.2829 | ||||
Y | 0.4315 | 0.3095 | 0.3705 | ||||
Specific Force | 0.0591 | 0.3415 | 0.2003 | H | 0.5138 | 0.2867 | 0.4002 |
T | 0.2768 | 0.2873 | 0.2821 | ||||
V | 0.2095 | 0.4260 | 0.3177 | ||||
Linear Velocity | 0.2775 | 0.2414 | 0.2594 | H | 0.2627 | 0.3333 | 0.2980 |
T | 0.3208 | 0.2760 | 0.2984 | ||||
V | 0.4166 | 0.3907 | 0.4036 | ||||
Head Angle | 0.2850 | 0.1894 | 0.2372 |
Second Level | CRITIC Weight | GRA Weight | Combined Weight | Third Level | CRITIC Weight | GRA Weight | Combined Weight |
---|---|---|---|---|---|---|---|
Angular Velocity | 0.3127 | 0.2330 | 0.2729 | R | 0.7947 | 0.3048 | 0.5498 |
P | 0.0931 | 0.3603 | 0.2267 | ||||
Y | 0.1122 | 0.3349 | 0.2235 | ||||
Specific Force | 0.4044 | 0.2600 | 0.3322 | H | 0.9067 | 0.2877 | 0.5972 |
T | 0.0501 | 0.3439 | 0.1969 | ||||
V | 0.0433 | 0.3685 | 0.2059 | ||||
Linear Velocity | 0.1626 | 0.2791 | 0.2209 | H | 0.6965 | 0.3262 | 0.5114 |
T | 0.2202 | 0.2980 | 0.2591 | ||||
V | 0.0833 | 0.3758 | 0.2295 | ||||
Head Angle | 0.1203 | 0.2278 | 0.1740 |
Second Level | CRITIC Weight | GRA Weight | Combined Weight | Third Level | CRITIC Weight | GRA Weight | Combined Weight |
---|---|---|---|---|---|---|---|
Angular Velocity | 0.0974 | 0.2572 | 0.1774 | R | 0.5154 | 0.3067 | 0.4110 |
P | 0.3879 | 0.2904 | 0.3392 | ||||
Y | 0.0967 | 0.4030 | 0.2498 | ||||
Specific Force | 0.2400 | 0.2756 | 0.2578 | H | 0.7089 | 0.2977 | 0.5033 |
T | 0.2549 | 0.3135 | 0.2842 | ||||
V | 0.0363 | 0.3888 | 0.2125 | ||||
Linear Velocity | 0.2092 | 0.2737 | 0.2414 | H | 0.2094 | 0.3833 | 0.2963 |
T | 0.0667 | 0.3744 | 0.2206 | ||||
V | 0.7239 | 0.2423 | 0.4831 | ||||
Head Angle | 0.4535 | 0.1934 | 0.3234 |
CMCA (a) | CMCA (b) | CMCA (c) | CMCA (d) | CMCA (e) | |
---|---|---|---|---|---|
Participant 1 | 7 | 6.5 | 7.5 | 8 | 8 |
Participant 2 | 6 | 5 | 6 | 7 | 8 |
Participant 3 | 6 | 5 | 7 | 8 | 9 |
Participant 4 | 5 | 6.5 | 7 | 8 | 8.5 |
Participant 5 | 4 | 7 | 5 | 6 | 7 |
Participant 6 | 4 | 6 | 5 | 5.5 | 6 |
Participant 7 | 5.5 | 6 | 6 | 7 | 8 |
Participant 8 | 6 | 6.5 | 7 | 8 | 8.5 |
Participant 9 | 5 | 5.5 | 7 | 6 | 7.5 |
Participant 10 | 6 | 8.5 | 6.5 | 7 | 7.5 |
The average | 5.45 | 6.25 | 6.40 | 7.05 | 7.80 |
The MCAs | CMCA (a) | CMCA (b) | CMCA (c) | CMCA (d) | CMCA (e) |
---|---|---|---|---|---|
Subjective evaluation results | 0.165 | 0.190 | 0.194 | 0.214 | 0.237 |
Objective evaluation results | 0.207 | 0.202 | 0.200 | 0.199 | 0.192 |
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Jiang, X.; Chen, X.; Jiao, Y.; Zhang, L. Objective Evaluation of Motion Cueing Algorithms for Vehicle Driving Simulator Based on Criteria Importance through Intercriteria Correlation (CRITIC) Weight Method Combined with Gray Correlation Analysis. Machines 2024, 12, 344. https://doi.org/10.3390/machines12050344
Jiang X, Chen X, Jiao Y, Zhang L. Objective Evaluation of Motion Cueing Algorithms for Vehicle Driving Simulator Based on Criteria Importance through Intercriteria Correlation (CRITIC) Weight Method Combined with Gray Correlation Analysis. Machines. 2024; 12(5):344. https://doi.org/10.3390/machines12050344
Chicago/Turabian StyleJiang, Xue, Xiafei Chen, Yiyang Jiao, and Lijie Zhang. 2024. "Objective Evaluation of Motion Cueing Algorithms for Vehicle Driving Simulator Based on Criteria Importance through Intercriteria Correlation (CRITIC) Weight Method Combined with Gray Correlation Analysis" Machines 12, no. 5: 344. https://doi.org/10.3390/machines12050344
APA StyleJiang, X., Chen, X., Jiao, Y., & Zhang, L. (2024). Objective Evaluation of Motion Cueing Algorithms for Vehicle Driving Simulator Based on Criteria Importance through Intercriteria Correlation (CRITIC) Weight Method Combined with Gray Correlation Analysis. Machines, 12(5), 344. https://doi.org/10.3390/machines12050344