Comprehensive study of driver behavior monitoring systems using computer vision and machine learning techniques
Abstract The flourishing realm of advanced driver-assistance systems (ADAS) as well as autonomous vehicles (AVs) presents exceptional opportunities to enhance safe driving. An essential aspect of this transformation involves monitoring driver behavior through observable physiological indicators, inc...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
SpringerOpen
2024-02-01
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| Series: | Journal of Big Data |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s40537-024-00890-0 |
| _version_ | 1827327203299295232 |
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| author | Fangming Qu Nolan Dang Borko Furht Mehrdad Nojoumian |
| author_facet | Fangming Qu Nolan Dang Borko Furht Mehrdad Nojoumian |
| author_sort | Fangming Qu |
| collection | DOAJ |
| description | Abstract The flourishing realm of advanced driver-assistance systems (ADAS) as well as autonomous vehicles (AVs) presents exceptional opportunities to enhance safe driving. An essential aspect of this transformation involves monitoring driver behavior through observable physiological indicators, including the driver’s facial expressions, hand placement on the wheels, and the driver’s body postures. An artificial intelligence (AI) system under consideration alerts drivers about potentially unsafe behaviors using real-time voice notifications. This paper offers an all-embracing survey of neural network-based methodologies for studying these driver bio-metrics, presenting an exhaustive examination of their advantages and drawbacks. The evaluation includes two relevant datasets, separately categorizing ten different in-cabinet behaviors, providing a systematic classification for driver behaviors detection. The ultimate aim is to inform the development of driver behavior monitoring systems. This survey is a valuable guide for those dedicated to enhancing vehicle safety and preventing accidents caused by careless driving. The paper’s structure encompasses sections on autonomous vehicles, neural networks, driver behavior analysis methods, dataset utilization, and final findings and future suggestions, ensuring accessibility for audiences with diverse levels of understanding regarding the subject matter. |
| first_indexed | 2024-03-07T14:56:40Z |
| format | Article |
| id | doaj.art-9de97d423e7d4be280850b0883ec8d1e |
| institution | Directory Open Access Journal |
| issn | 2196-1115 |
| language | English |
| last_indexed | 2024-03-07T14:56:40Z |
| publishDate | 2024-02-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Journal of Big Data |
| spelling | doaj.art-9de97d423e7d4be280850b0883ec8d1e2024-03-05T19:22:39ZengSpringerOpenJournal of Big Data2196-11152024-02-0111114410.1186/s40537-024-00890-0Comprehensive study of driver behavior monitoring systems using computer vision and machine learning techniquesFangming Qu0Nolan Dang1Borko Furht2Mehrdad Nojoumian3Department of Electrical Engineering and Computer Science, Florida Atlantic UniversityDepartment of Electrical Engineering and Computer Science, Florida Atlantic UniversityDepartment of Electrical Engineering and Computer Science, Florida Atlantic UniversityDepartment of Electrical Engineering and Computer Science, Florida Atlantic UniversityAbstract The flourishing realm of advanced driver-assistance systems (ADAS) as well as autonomous vehicles (AVs) presents exceptional opportunities to enhance safe driving. An essential aspect of this transformation involves monitoring driver behavior through observable physiological indicators, including the driver’s facial expressions, hand placement on the wheels, and the driver’s body postures. An artificial intelligence (AI) system under consideration alerts drivers about potentially unsafe behaviors using real-time voice notifications. This paper offers an all-embracing survey of neural network-based methodologies for studying these driver bio-metrics, presenting an exhaustive examination of their advantages and drawbacks. The evaluation includes two relevant datasets, separately categorizing ten different in-cabinet behaviors, providing a systematic classification for driver behaviors detection. The ultimate aim is to inform the development of driver behavior monitoring systems. This survey is a valuable guide for those dedicated to enhancing vehicle safety and preventing accidents caused by careless driving. The paper’s structure encompasses sections on autonomous vehicles, neural networks, driver behavior analysis methods, dataset utilization, and final findings and future suggestions, ensuring accessibility for audiences with diverse levels of understanding regarding the subject matter.https://doi.org/10.1186/s40537-024-00890-0Driver behavior monitoring systemDriver behavior classificationComputer visionMachine learningAutonomous vehicles |
| spellingShingle | Fangming Qu Nolan Dang Borko Furht Mehrdad Nojoumian Comprehensive study of driver behavior monitoring systems using computer vision and machine learning techniques Journal of Big Data Driver behavior monitoring system Driver behavior classification Computer vision Machine learning Autonomous vehicles |
| title | Comprehensive study of driver behavior monitoring systems using computer vision and machine learning techniques |
| title_full | Comprehensive study of driver behavior monitoring systems using computer vision and machine learning techniques |
| title_fullStr | Comprehensive study of driver behavior monitoring systems using computer vision and machine learning techniques |
| title_full_unstemmed | Comprehensive study of driver behavior monitoring systems using computer vision and machine learning techniques |
| title_short | Comprehensive study of driver behavior monitoring systems using computer vision and machine learning techniques |
| title_sort | comprehensive study of driver behavior monitoring systems using computer vision and machine learning techniques |
| topic | Driver behavior monitoring system Driver behavior classification Computer vision Machine learning Autonomous vehicles |
| url | https://doi.org/10.1186/s40537-024-00890-0 |
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