An Effort to Detect Vehicle Drivers Drowsy State Based on the Speed Analysis
Detection of the drivers drowsy state is still an actual task since it is a reason for a significant number of traffic accidents. The carried out literature review showed that a significant number of approaches rely on special equipment for driver state identification. At the same time, efficient op...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
FRUCT
2021-05-01
|
| Series: | Proceedings of the XXth Conference of Open Innovations Association FRUCT |
| Subjects: | |
| Online Access: | https://www.fruct.org/publications/fruct29/files/Shi.pdf |
| _version_ | 1818736592457564160 |
|---|---|
| author | Nikolay Shilov Alexey Kashevnik |
| author_facet | Nikolay Shilov Alexey Kashevnik |
| author_sort | Nikolay Shilov |
| collection | DOAJ |
| description | Detection of the drivers drowsy state is still an actual task since it is a reason for a significant number of traffic accidents. The carried out literature review showed that a significant number of approaches rely on special equipment for driver state identification. At the same time, efficient operation of computer vision-based techniques heavily depends on the lighting conditions, which are usually not good in a moving car. The paper presents a research effort aiming at using speed recordings to identify the drivers state. For this purpose, the speed recordings are analyzed as a time series, and its characteristics are used as features for the classification task. The results show that the suggested approach is viable and promising. |
| first_indexed | 2024-12-18T00:39:36Z |
| format | Article |
| id | doaj.art-c2fb0a5588df4a49b7ae1be2a96d654f |
| institution | Directory Open Access Journal |
| issn | 2305-7254 2343-0737 |
| language | English |
| last_indexed | 2024-12-18T00:39:36Z |
| publishDate | 2021-05-01 |
| publisher | FRUCT |
| record_format | Article |
| series | Proceedings of the XXth Conference of Open Innovations Association FRUCT |
| spelling | doaj.art-c2fb0a5588df4a49b7ae1be2a96d654f2022-12-21T21:26:56ZengFRUCTProceedings of the XXth Conference of Open Innovations Association FRUCT2305-72542343-07372021-05-0129132432910.23919/FRUCT52173.2021.9435466An Effort to Detect Vehicle Drivers Drowsy State Based on the Speed AnalysisNikolay Shilov0Alexey Kashevnik1SPC RAS, RussiaSPC RAS, RussiaDetection of the drivers drowsy state is still an actual task since it is a reason for a significant number of traffic accidents. The carried out literature review showed that a significant number of approaches rely on special equipment for driver state identification. At the same time, efficient operation of computer vision-based techniques heavily depends on the lighting conditions, which are usually not good in a moving car. The paper presents a research effort aiming at using speed recordings to identify the drivers state. For this purpose, the speed recordings are analyzed as a time series, and its characteristics are used as features for the classification task. The results show that the suggested approach is viable and promising.https://www.fruct.org/publications/fruct29/files/Shi.pdfvehicledriver monitoringdriver's state identificationmachine learning |
| spellingShingle | Nikolay Shilov Alexey Kashevnik An Effort to Detect Vehicle Drivers Drowsy State Based on the Speed Analysis Proceedings of the XXth Conference of Open Innovations Association FRUCT vehicle driver monitoring driver's state identification machine learning |
| title | An Effort to Detect Vehicle Drivers Drowsy State Based on the Speed Analysis |
| title_full | An Effort to Detect Vehicle Drivers Drowsy State Based on the Speed Analysis |
| title_fullStr | An Effort to Detect Vehicle Drivers Drowsy State Based on the Speed Analysis |
| title_full_unstemmed | An Effort to Detect Vehicle Drivers Drowsy State Based on the Speed Analysis |
| title_short | An Effort to Detect Vehicle Drivers Drowsy State Based on the Speed Analysis |
| title_sort | effort to detect vehicle drivers drowsy state based on the speed analysis |
| topic | vehicle driver monitoring driver's state identification machine learning |
| url | https://www.fruct.org/publications/fruct29/files/Shi.pdf |
| work_keys_str_mv | AT nikolayshilov anefforttodetectvehicledriversdrowsystatebasedonthespeedanalysis AT alexeykashevnik anefforttodetectvehicledriversdrowsystatebasedonthespeedanalysis AT nikolayshilov efforttodetectvehicledriversdrowsystatebasedonthespeedanalysis AT alexeykashevnik efforttodetectvehicledriversdrowsystatebasedonthespeedanalysis |