Extraction of optimal fatigue‐driving steering indicators considering individual differences
Abstract Extracting effective steering indicators is critical for fatigue‐identification based on steering behaviour. However, considering individual differences, the best calculation parameters of the individual driver's steering indicators are different. The indicator's performance decre...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2021-05-01
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| Series: | IET Intelligent Transport Systems |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/itr2.12048 |
| Summary: | Abstract Extracting effective steering indicators is critical for fatigue‐identification based on steering behaviour. However, considering individual differences, the best calculation parameters of the individual driver's steering indicators are different. The indicator's performance decreases by using unified parameters rather than the individual driver's best parameters. The authors propose a model extracting individual driver's optimal fatigue steering indicators calculated by the individual driver's best parameters. Individual driver's naturalistic driving data are analysed by the model. First, indicators in sober and fatigue state are examined by the Wilcoxon test, and |Z| represents the indicator's fatigue‐identification performance. And the function reflecting correspondences between calculation parameters and indicator's fatigue‐identification performance is constructed. Then the function is optimized using particle swarm optimization to obtain individual driver's best calculation parameters maximizing |Z|. Finally, effective indicators calculated by the individual driver's best parameters constitute the individual driver's optimal fatigue indicator set. Indicators calculated by the individual driver's best parameters and unified parameters are used to establish the individual fatigue‐identification model. The average identification accuracy of models using individual driver's best parameters and unified parameters is 87.4% and 79.1%, which indicates using individual driver's optimal fatigue indicators can improve identification accuracy. This study can provide references for individualized fatigue‐driving identification |
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| ISSN: | 1751-956X 1751-9578 |