AI-Driven Driver Behavior Assessment Through Vehicle and Health Monitoring for Safe Driving—A Survey
Safe driving is a serious and challenging concern that is dependent on driver behaviors including aggressive, distracted, fatigued, and drowsy driving. In a smart society, in-vehicle monitoring of drivers and detecting abnormal driving behavior as anomalies can reduce the rate of road crashes. Exist...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10486888/ |
| _version_ | 1797217375706152960 |
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| author | Shumayla Yaqoob Giacomo Morabito Salvatore Cafiso Giuseppina Pappalardo Ata Ullah |
| author_facet | Shumayla Yaqoob Giacomo Morabito Salvatore Cafiso Giuseppina Pappalardo Ata Ullah |
| author_sort | Shumayla Yaqoob |
| collection | DOAJ |
| description | Safe driving is a serious and challenging concern that is dependent on driver behaviors including aggressive, distracted, fatigued, and drowsy driving. In a smart society, in-vehicle monitoring of drivers and detecting abnormal driving behavior as anomalies can reduce the rate of road crashes. Existing surveys cover the various schemes for detecting on-road driver behaviors through sensing data. We have identified a gap in the linkage of driver’s health or vehicle conditions to abnormal behaviors that are not yet covered. This work presents a taxonomy of schemes, and the analytical evaluation and identifies the open research challenges. The work specifically investigates the modeling of driver behavior and the detection of abnormal behavior, utilizing techniques such as AI-based image processing, signal processing, and traditional algorithmic approaches. This analysis encompasses methods and features based on both driver health and vehicle monitoring with the ultimate goal of ensuring safe driving. More specifically, existing approaches are classified in a coherent taxonomy by reviewing traditional mathematical, machine learning, and deep learning-based schemes. Moreover, a summary of schemes is presented to highlight the key points followed by a comprehensive analytical discussion. It aids in pinpointing research issues by leveraging the insights gained from the comparison. Finally, the work highlights the open research challenges for the researchers to provide innovative solutions. |
| first_indexed | 2024-04-24T12:00:52Z |
| format | Article |
| id | doaj.art-6f83f5db071d48278b7a2748e001a8e9 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-24T12:00:52Z |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-6f83f5db071d48278b7a2748e001a8e92024-04-08T23:00:59ZengIEEEIEEE Access2169-35362024-01-0112480444805610.1109/ACCESS.2024.338377510486888AI-Driven Driver Behavior Assessment Through Vehicle and Health Monitoring for Safe Driving—A SurveyShumayla Yaqoob0https://orcid.org/0000-0002-0696-5546Giacomo Morabito1https://orcid.org/0000-0002-8714-4001Salvatore Cafiso2https://orcid.org/0000-0002-7247-0365Giuseppina Pappalardo3https://orcid.org/0000-0002-9793-1885Ata Ullah4https://orcid.org/0000-0003-3603-1709Department of Electrical Electronic Computer and Telecommunication Engineering, University of Catania, Catania, ItalyDepartment of Electrical Electronic Computer and Telecommunication Engineering, University of Catania, Catania, ItalyDepartment of Civil Engineering and Architecture, University of Catania, Catania, ItalyDepartment of Civil Engineering and Architecture, University of Catania, Catania, ItalyDepartment of Computer Science, National University of Modern Languages, Islamabad, PakistanSafe driving is a serious and challenging concern that is dependent on driver behaviors including aggressive, distracted, fatigued, and drowsy driving. In a smart society, in-vehicle monitoring of drivers and detecting abnormal driving behavior as anomalies can reduce the rate of road crashes. Existing surveys cover the various schemes for detecting on-road driver behaviors through sensing data. We have identified a gap in the linkage of driver’s health or vehicle conditions to abnormal behaviors that are not yet covered. This work presents a taxonomy of schemes, and the analytical evaluation and identifies the open research challenges. The work specifically investigates the modeling of driver behavior and the detection of abnormal behavior, utilizing techniques such as AI-based image processing, signal processing, and traditional algorithmic approaches. This analysis encompasses methods and features based on both driver health and vehicle monitoring with the ultimate goal of ensuring safe driving. More specifically, existing approaches are classified in a coherent taxonomy by reviewing traditional mathematical, machine learning, and deep learning-based schemes. Moreover, a summary of schemes is presented to highlight the key points followed by a comprehensive analytical discussion. It aids in pinpointing research issues by leveraging the insights gained from the comparison. Finally, the work highlights the open research challenges for the researchers to provide innovative solutions.https://ieeexplore.ieee.org/document/10486888/Driver behavioranomalieshealthcaresafe drivingabnormal behaviorsmachine learning |
| spellingShingle | Shumayla Yaqoob Giacomo Morabito Salvatore Cafiso Giuseppina Pappalardo Ata Ullah AI-Driven Driver Behavior Assessment Through Vehicle and Health Monitoring for Safe Driving—A Survey IEEE Access Driver behavior anomalies healthcare safe driving abnormal behaviors machine learning |
| title | AI-Driven Driver Behavior Assessment Through Vehicle and Health Monitoring for Safe Driving—A Survey |
| title_full | AI-Driven Driver Behavior Assessment Through Vehicle and Health Monitoring for Safe Driving—A Survey |
| title_fullStr | AI-Driven Driver Behavior Assessment Through Vehicle and Health Monitoring for Safe Driving—A Survey |
| title_full_unstemmed | AI-Driven Driver Behavior Assessment Through Vehicle and Health Monitoring for Safe Driving—A Survey |
| title_short | AI-Driven Driver Behavior Assessment Through Vehicle and Health Monitoring for Safe Driving—A Survey |
| title_sort | ai driven driver behavior assessment through vehicle and health monitoring for safe driving x2014 a survey |
| topic | Driver behavior anomalies healthcare safe driving abnormal behaviors machine learning |
| url | https://ieeexplore.ieee.org/document/10486888/ |
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