A Multi-Feature Fusion Algorithm for Fatigue Driving Detection Considering Individual Driver Differences
Fatigue driving is one of the crucial factors causing traffic accidents. Most existing fatigue driving detection algorithms overlook individual driver characteristics, potentially leading to misjudgments. This article presents a novel detection algorithm that utilizes facial multi-feature fusion, th...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-04-01
|
| Series: | Algorithms |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1999-4893/18/5/247 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849711413163982848 |
|---|---|
| author | Meng Zhou Xiaoyi Zhou Zhijian Li Xinyue Liu Chengming Chen |
| author_facet | Meng Zhou Xiaoyi Zhou Zhijian Li Xinyue Liu Chengming Chen |
| author_sort | Meng Zhou |
| collection | DOAJ |
| description | Fatigue driving is one of the crucial factors causing traffic accidents. Most existing fatigue driving detection algorithms overlook individual driver characteristics, potentially leading to misjudgments. This article presents a novel detection algorithm that utilizes facial multi-feature fusion, thoroughly considering the driver’s individual characteristics. To improve the judging accuracy of the driver’s facial expressions, a personalized threshold is proposed based on the normalization of the driver’s eyes and mouth opening and closing instead of the traditional average threshold, as individual drivers have different eye and mouth sizes. Given the dynamic changes in fatigue level, a sliding window model is designed for further calculating blinking duration ratio (BF), yawning frequency (YF), and nodding frequency (NF), and these evaluation indexes are used in the feature fusion model. The reliability of the algorithm is verified by the actual test results, which show that the detection accuracy reaches 95.6% and shows good application potential in fatigue detection applications. In this way, facial multi-feature fusion and fully considering the driver’s individual characteristics makes fatigue driving detection more accurate. |
| format | Article |
| id | doaj-art-4f0da4e490c84c0ebacc7f27f579c141 |
| institution | DOAJ |
| issn | 1999-4893 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Algorithms |
| spelling | doaj-art-4f0da4e490c84c0ebacc7f27f579c1412025-08-20T03:14:38ZengMDPI AGAlgorithms1999-48932025-04-0118524710.3390/a18050247A Multi-Feature Fusion Algorithm for Fatigue Driving Detection Considering Individual Driver DifferencesMeng Zhou0Xiaoyi Zhou1Zhijian Li2Xinyue Liu3Chengming Chen4College of Engineering Science and Technology, Shanghai Ocean University, Shanghai 201306, ChinaChina Institute of Marine Technology and Economy, Beijing 100081, ChinaCollege of Engineering Science and Technology, Shanghai Ocean University, Shanghai 201306, ChinaCollege of Engineering Science and Technology, Shanghai Ocean University, Shanghai 201306, ChinaCollege of Engineering Science and Technology, Shanghai Ocean University, Shanghai 201306, ChinaFatigue driving is one of the crucial factors causing traffic accidents. Most existing fatigue driving detection algorithms overlook individual driver characteristics, potentially leading to misjudgments. This article presents a novel detection algorithm that utilizes facial multi-feature fusion, thoroughly considering the driver’s individual characteristics. To improve the judging accuracy of the driver’s facial expressions, a personalized threshold is proposed based on the normalization of the driver’s eyes and mouth opening and closing instead of the traditional average threshold, as individual drivers have different eye and mouth sizes. Given the dynamic changes in fatigue level, a sliding window model is designed for further calculating blinking duration ratio (BF), yawning frequency (YF), and nodding frequency (NF), and these evaluation indexes are used in the feature fusion model. The reliability of the algorithm is verified by the actual test results, which show that the detection accuracy reaches 95.6% and shows good application potential in fatigue detection applications. In this way, facial multi-feature fusion and fully considering the driver’s individual characteristics makes fatigue driving detection more accurate.https://www.mdpi.com/1999-4893/18/5/247fatigue drivingfatigue detectionpersonalized thresholdsmulti-feature fusion |
| spellingShingle | Meng Zhou Xiaoyi Zhou Zhijian Li Xinyue Liu Chengming Chen A Multi-Feature Fusion Algorithm for Fatigue Driving Detection Considering Individual Driver Differences Algorithms fatigue driving fatigue detection personalized thresholds multi-feature fusion |
| title | A Multi-Feature Fusion Algorithm for Fatigue Driving Detection Considering Individual Driver Differences |
| title_full | A Multi-Feature Fusion Algorithm for Fatigue Driving Detection Considering Individual Driver Differences |
| title_fullStr | A Multi-Feature Fusion Algorithm for Fatigue Driving Detection Considering Individual Driver Differences |
| title_full_unstemmed | A Multi-Feature Fusion Algorithm for Fatigue Driving Detection Considering Individual Driver Differences |
| title_short | A Multi-Feature Fusion Algorithm for Fatigue Driving Detection Considering Individual Driver Differences |
| title_sort | multi feature fusion algorithm for fatigue driving detection considering individual driver differences |
| topic | fatigue driving fatigue detection personalized thresholds multi-feature fusion |
| url | https://www.mdpi.com/1999-4893/18/5/247 |
| work_keys_str_mv | AT mengzhou amultifeaturefusionalgorithmforfatiguedrivingdetectionconsideringindividualdriverdifferences AT xiaoyizhou amultifeaturefusionalgorithmforfatiguedrivingdetectionconsideringindividualdriverdifferences AT zhijianli amultifeaturefusionalgorithmforfatiguedrivingdetectionconsideringindividualdriverdifferences AT xinyueliu amultifeaturefusionalgorithmforfatiguedrivingdetectionconsideringindividualdriverdifferences AT chengmingchen amultifeaturefusionalgorithmforfatiguedrivingdetectionconsideringindividualdriverdifferences AT mengzhou multifeaturefusionalgorithmforfatiguedrivingdetectionconsideringindividualdriverdifferences AT xiaoyizhou multifeaturefusionalgorithmforfatiguedrivingdetectionconsideringindividualdriverdifferences AT zhijianli multifeaturefusionalgorithmforfatiguedrivingdetectionconsideringindividualdriverdifferences AT xinyueliu multifeaturefusionalgorithmforfatiguedrivingdetectionconsideringindividualdriverdifferences AT chengmingchen multifeaturefusionalgorithmforfatiguedrivingdetectionconsideringindividualdriverdifferences |