A Real-Time Vision Transformers-Based System for Enhanced Driver Drowsiness Detection and Vehicle Safety Article Swipe
Anwar Jarndal
,
Hissam Tawfik
,
Ali I. Siam
,
Imad Alsyouf
,
Ali Cheaitou
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1109/access.2024.3522111
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1109/access.2024.3522111
Drowsy driving is a leading cause of fatal traffic accidents worldwide. Drowsy driving has emerged from modern societal trends such as long working hours, heavy reliance on vehicles, and insufficient sleep. Despite considerable efforts by researchers to develop efficient driver drowsiness detection systems, none so far has been widely adopted due to their high cost, intrusive nature, and ineffectiveness in challenging real-life situations. This paper presents a novel, real-time, non-intrusive, and cost-effective driver drowsiness detection system leveraging vision transformers (ViT). Our approach detects the driver’s face from each video frame and classifies the driver’s state as either ‘drowsy’ or ‘alert’ based on the entire facial image, as opposed to previous systems that rely on analyzing specific facial features. We demonstrate that the proposed Vision Transformers-based Driver Drowsiness Detection (ViT-DDD) system surpasses existing state-of-the-art methods, particularly in challenging scenarios such as drivers wearing glasses or sunglasses, or in different lighting conditions. The model was trained and evaluated on two widely used public drowsiness detection datasets, achieving classification accuracies of 98.89% on the NTHU-DDD dataset and 99.4% on the UTA-RLDD dataset. Furthermore, the system was successfully deployed on a Raspberry Pi microcomputer, integrated with an infrared camera, a GSM/GPS module, and a buzzer to alert the driver and report the drowsiness condition to the vehicle owner. Testing the prototype yielded highly promising results, with the system’s strong performance attributed to the ViT-DDD system and advanced hardware. The promising test results suggest the potential of this system in significantly reducing accidents caused by drowsy driving, with future work aiming to expand its capabilities and integration into broader vehicular systems.
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- Type
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- Language
- en
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- OA Status
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- OpenAlex ID
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All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W4405778644Canonical identifier for this work in OpenAlex
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https://doi.org/10.1109/access.2024.3522111Digital Object Identifier
- Title
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A Real-Time Vision Transformers-Based System for Enhanced Driver Drowsiness Detection and Vehicle SafetyWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-12-25Full publication date if available
- Authors
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Anwar Jarndal, Hissam Tawfik, Ali I. Siam, Imad Alsyouf, Ali CheaitouList of authors in order
- Landing page
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https://doi.org/10.1109/access.2024.3522111Publisher landing page
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://doi.org/10.1109/access.2024.3522111Direct OA link when available
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Computer science, Computer vision, Vehicle safety, Transformer, Artificial intelligence, Real-time computing, Embedded system, Automotive engineering, Electrical engineering, Engineering, VoltageTop concepts (fields/topics) attached by OpenAlex
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9Total citation count in OpenAlex
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2025: 9Per-year citation counts (last 5 years)
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36Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| referenced_works_count | 36 |
| abstract_inverted_index.a | 3, 66, 186, 195, 199 |
| abstract_inverted_index.Pi | 188 |
| abstract_inverted_index.We | 118 |
| abstract_inverted_index.an | 192 |
| abstract_inverted_index.as | 20, 95, 106, 139 |
| abstract_inverted_index.by | 34, 249 |
| abstract_inverted_index.in | 59, 135, 146, 244 |
| abstract_inverted_index.is | 2 |
| abstract_inverted_index.of | 6, 167, 241 |
| abstract_inverted_index.on | 26, 101, 113, 156, 169, 175, 185 |
| abstract_inverted_index.or | 98, 143, 145 |
| abstract_inverted_index.so | 44 |
| abstract_inverted_index.to | 36, 51, 108, 201, 210, 227, 256 |
| abstract_inverted_index.Our | 80 |
| abstract_inverted_index.The | 150, 234 |
| abstract_inverted_index.and | 28, 57, 70, 90, 154, 173, 198, 205, 231, 260 |
| abstract_inverted_index.due | 50 |
| abstract_inverted_index.far | 45 |
| abstract_inverted_index.has | 13, 46 |
| abstract_inverted_index.its | 258 |
| abstract_inverted_index.the | 83, 92, 102, 121, 170, 176, 180, 203, 207, 211, 215, 222, 228, 239 |
| abstract_inverted_index.two | 157 |
| abstract_inverted_index.was | 152, 182 |
| abstract_inverted_index.This | 63 |
| abstract_inverted_index.been | 47 |
| abstract_inverted_index.each | 87 |
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| abstract_inverted_index.rely | 112 |
| abstract_inverted_index.such | 19, 138 |
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| abstract_inverted_index.that | 111, 120 |
| abstract_inverted_index.this | 242 |
| abstract_inverted_index.used | 159 |
| abstract_inverted_index.with | 191, 221, 252 |
| abstract_inverted_index.work | 254 |
| abstract_inverted_index.99.4% | 174 |
| abstract_inverted_index.alert | 202 |
| abstract_inverted_index.based | 100 |
| abstract_inverted_index.cause | 5 |
| abstract_inverted_index.cost, | 54 |
| abstract_inverted_index.fatal | 7 |
| abstract_inverted_index.frame | 89 |
| abstract_inverted_index.heavy | 24 |
| abstract_inverted_index.model | 151 |
| abstract_inverted_index.paper | 64 |
| abstract_inverted_index.state | 94 |
| abstract_inverted_index.their | 52 |
| abstract_inverted_index.video | 88 |
| abstract_inverted_index.(ViT). | 79 |
| abstract_inverted_index.98.89% | 168 |
| abstract_inverted_index.Driver | 125 |
| abstract_inverted_index.Drowsy | 0, 11 |
| abstract_inverted_index.Vision | 123 |
| abstract_inverted_index.aiming | 255 |
| abstract_inverted_index.buzzer | 200 |
| abstract_inverted_index.caused | 248 |
| abstract_inverted_index.driver | 39, 72, 204 |
| abstract_inverted_index.drowsy | 250 |
| abstract_inverted_index.either | 96 |
| abstract_inverted_index.entire | 103 |
| abstract_inverted_index.expand | 257 |
| abstract_inverted_index.facial | 104, 116 |
| abstract_inverted_index.future | 253 |
| abstract_inverted_index.highly | 218 |
| abstract_inverted_index.hours, | 23 |
| abstract_inverted_index.image, | 105 |
| abstract_inverted_index.modern | 16 |
| abstract_inverted_index.novel, | 67 |
| abstract_inverted_index.owner. | 213 |
| abstract_inverted_index.public | 160 |
| abstract_inverted_index.report | 206 |
| abstract_inverted_index.sleep. | 30 |
| abstract_inverted_index.strong | 224 |
| abstract_inverted_index.system | 75, 129, 181, 230, 243 |
| abstract_inverted_index.trends | 18 |
| abstract_inverted_index.vision | 77 |
| abstract_inverted_index.widely | 48, 158 |
| abstract_inverted_index.Despite | 31 |
| abstract_inverted_index.GSM/GPS | 196 |
| abstract_inverted_index.Testing | 214 |
| abstract_inverted_index.ViT-DDD | 229 |
| abstract_inverted_index.adopted | 49 |
| abstract_inverted_index.broader | 263 |
| abstract_inverted_index.camera, | 194 |
| abstract_inverted_index.dataset | 172 |
| abstract_inverted_index.detects | 82 |
| abstract_inverted_index.develop | 37 |
| abstract_inverted_index.drivers | 140 |
| abstract_inverted_index.driving | 1, 12 |
| abstract_inverted_index.efforts | 33 |
| abstract_inverted_index.emerged | 14 |
| abstract_inverted_index.glasses | 142 |
| abstract_inverted_index.leading | 4 |
| abstract_inverted_index.module, | 197 |
| abstract_inverted_index.nature, | 56 |
| abstract_inverted_index.opposed | 107 |
| abstract_inverted_index.results | 237 |
| abstract_inverted_index.suggest | 238 |
| abstract_inverted_index.systems | 110 |
| abstract_inverted_index.traffic | 8 |
| abstract_inverted_index.trained | 153 |
| abstract_inverted_index.vehicle | 212 |
| abstract_inverted_index.wearing | 141 |
| abstract_inverted_index.working | 22 |
| abstract_inverted_index.yielded | 217 |
| abstract_inverted_index.NTHU-DDD | 171 |
| abstract_inverted_index.UTA-RLDD | 177 |
| abstract_inverted_index.advanced | 232 |
| abstract_inverted_index.approach | 81 |
| abstract_inverted_index.dataset. | 178 |
| abstract_inverted_index.deployed | 184 |
| abstract_inverted_index.driving, | 251 |
| abstract_inverted_index.existing | 131 |
| abstract_inverted_index.infrared | 193 |
| abstract_inverted_index.lighting | 148 |
| abstract_inverted_index.methods, | 133 |
| abstract_inverted_index.presents | 65 |
| abstract_inverted_index.previous | 109 |
| abstract_inverted_index.proposed | 122 |
| abstract_inverted_index.reducing | 246 |
| abstract_inverted_index.reliance | 25 |
| abstract_inverted_index.results, | 220 |
| abstract_inverted_index.societal | 17 |
| abstract_inverted_index.specific | 115 |
| abstract_inverted_index.systems, | 42 |
| abstract_inverted_index.systems. | 265 |
| abstract_inverted_index.(ViT-DDD) | 128 |
| abstract_inverted_index.Detection | 127 |
| abstract_inverted_index.Raspberry | 187 |
| abstract_inverted_index.accidents | 9, 247 |
| abstract_inverted_index.achieving | 164 |
| abstract_inverted_index.analyzing | 114 |
| abstract_inverted_index.condition | 209 |
| abstract_inverted_index.datasets, | 163 |
| abstract_inverted_index.detection | 41, 74, 162 |
| abstract_inverted_index.different | 147 |
| abstract_inverted_index.efficient | 38 |
| abstract_inverted_index.evaluated | 155 |
| abstract_inverted_index.features. | 117 |
| abstract_inverted_index.hardware. | 233 |
| abstract_inverted_index.intrusive | 55 |
| abstract_inverted_index.potential | 240 |
| abstract_inverted_index.promising | 219, 235 |
| abstract_inverted_index.prototype | 216 |
| abstract_inverted_index.real-life | 61 |
| abstract_inverted_index.scenarios | 137 |
| abstract_inverted_index.surpasses | 130 |
| abstract_inverted_index.vehicles, | 27 |
| abstract_inverted_index.vehicular | 264 |
| abstract_inverted_index.Drowsiness | 126 |
| abstract_inverted_index.accuracies | 166 |
| abstract_inverted_index.attributed | 226 |
| abstract_inverted_index.classifies | 91 |
| abstract_inverted_index.drowsiness | 40, 73, 161, 208 |
| abstract_inverted_index.integrated | 190 |
| abstract_inverted_index.leveraging | 76 |
| abstract_inverted_index.real-time, | 68 |
| abstract_inverted_index.worldwide. | 10 |
| abstract_inverted_index.challenging | 60, 136 |
| abstract_inverted_index.conditions. | 149 |
| abstract_inverted_index.demonstrate | 119 |
| abstract_inverted_index.integration | 261 |
| abstract_inverted_index.performance | 225 |
| abstract_inverted_index.researchers | 35 |
| abstract_inverted_index.situations. | 62 |
| abstract_inverted_index.sunglasses, | 144 |
| abstract_inverted_index.Furthermore, | 179 |
| abstract_inverted_index.capabilities | 259 |
| abstract_inverted_index.considerable | 32 |
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| abstract_inverted_index.successfully | 183 |
| abstract_inverted_index.transformers | 78 |
| abstract_inverted_index.significantly | 245 |
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| abstract_inverted_index.cost-effective | 71 |
| abstract_inverted_index.microcomputer, | 189 |
| abstract_inverted_index.non-intrusive, | 69 |
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| abstract_inverted_index.Transformers-based | 124 |
| abstract_inverted_index.‘alert’ | 99 |
| abstract_inverted_index.‘drowsy’ | 97 |
| cited_by_percentile_year.max | 99 |
| cited_by_percentile_year.min | 98 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 5 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/13 |
| sustainable_development_goals[0].score | 0.6499999761581421 |
| sustainable_development_goals[0].display_name | Climate action |
| citation_normalized_percentile.value | 0.96820412 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |