Accurate Driver Detection Exploiting Invariant Characteristics of Smartphone Sensors
Distracted driving jeopardizes the safety of the driver and others. Numerous solutions have been proposed to prevent distracted driving, but the number of related accidents has not decreased. Such a deficiency comes from fragile system designs where drivers are detected exploiting sensory features f...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2019-06-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/19/11/2643 |
| _version_ | 1817988093809000448 |
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| author | DaeHan Ahn Homin Park Kyoosik Shin Taejoon Park |
| author_facet | DaeHan Ahn Homin Park Kyoosik Shin Taejoon Park |
| author_sort | DaeHan Ahn |
| collection | DOAJ |
| description | Distracted driving jeopardizes the safety of the driver and others. Numerous solutions have been proposed to prevent distracted driving, but the number of related accidents has not decreased. Such a deficiency comes from fragile system designs where drivers are detected exploiting sensory features from strictly controlled vehicle-riding actions and unreliable driving events. We propose a system called ADDICT (Accurate Driver Detection exploiting Invariant Characteristics of smarTphone sensors), which identifies the driver utilizing the inconsistency between gyroscope and magnetometer dynamics and the interplay between electromagnetic field emissions and engine startup vibrations. These features are invariantly observable regardless of smartphone positions and vehicle-riding actions. To evaluate the feasibility of ADDICT, we conducted extensive experiments with four participants and three different vehicles by varying vehicle-riding scenarios. Our evaluation results demonstrated that ADDICT identifies the driver’s smartphone with 89.1% average accuracy for all scenarios and >85% under the extreme scenario, at a marginal cost of battery consumption. |
| first_indexed | 2024-04-14T00:29:59Z |
| format | Article |
| id | doaj.art-3173fcbc8b1a44198412c94a40ee5757 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-14T00:29:59Z |
| publishDate | 2019-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-3173fcbc8b1a44198412c94a40ee57572022-12-22T02:22:34ZengMDPI AGSensors1424-82202019-06-011911264310.3390/s19112643s19112643Accurate Driver Detection Exploiting Invariant Characteristics of Smartphone SensorsDaeHan Ahn0Homin Park1Kyoosik Shin2Taejoon Park3Department of Robotics Engineering, Hanyang University, 55 Hanyang-Daehakro, Ansan-si 15588, Gyeonggi-do, KoreaBIGHEART, National University of Singapore, MD6, 13 Medical Drive #14-01, Singapore 117599, SingaporeDepartment of Robotics Engineering, Hanyang University, 55 Hanyang-Daehakro, Ansan-si 15588, Gyeonggi-do, KoreaDepartment of Robotics Engineering, Hanyang University, 55 Hanyang-Daehakro, Ansan-si 15588, Gyeonggi-do, KoreaDistracted driving jeopardizes the safety of the driver and others. Numerous solutions have been proposed to prevent distracted driving, but the number of related accidents has not decreased. Such a deficiency comes from fragile system designs where drivers are detected exploiting sensory features from strictly controlled vehicle-riding actions and unreliable driving events. We propose a system called ADDICT (Accurate Driver Detection exploiting Invariant Characteristics of smarTphone sensors), which identifies the driver utilizing the inconsistency between gyroscope and magnetometer dynamics and the interplay between electromagnetic field emissions and engine startup vibrations. These features are invariantly observable regardless of smartphone positions and vehicle-riding actions. To evaluate the feasibility of ADDICT, we conducted extensive experiments with four participants and three different vehicles by varying vehicle-riding scenarios. Our evaluation results demonstrated that ADDICT identifies the driver’s smartphone with 89.1% average accuracy for all scenarios and >85% under the extreme scenario, at a marginal cost of battery consumption.https://www.mdpi.com/1424-8220/19/11/2643driver detectioninvariant sensory characteristicsbuilt-in smartphone sensorsdistracted drivingdriving while distracted |
| spellingShingle | DaeHan Ahn Homin Park Kyoosik Shin Taejoon Park Accurate Driver Detection Exploiting Invariant Characteristics of Smartphone Sensors Sensors driver detection invariant sensory characteristics built-in smartphone sensors distracted driving driving while distracted |
| title | Accurate Driver Detection Exploiting Invariant Characteristics of Smartphone Sensors |
| title_full | Accurate Driver Detection Exploiting Invariant Characteristics of Smartphone Sensors |
| title_fullStr | Accurate Driver Detection Exploiting Invariant Characteristics of Smartphone Sensors |
| title_full_unstemmed | Accurate Driver Detection Exploiting Invariant Characteristics of Smartphone Sensors |
| title_short | Accurate Driver Detection Exploiting Invariant Characteristics of Smartphone Sensors |
| title_sort | accurate driver detection exploiting invariant characteristics of smartphone sensors |
| topic | driver detection invariant sensory characteristics built-in smartphone sensors distracted driving driving while distracted |
| url | https://www.mdpi.com/1424-8220/19/11/2643 |
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