Innovative Framework for Distracted-Driving Alert System Based on Deep Learning

Lin, Peng-Wei; Hsu, Chih-Ming

doi:10.1109/access.2022.3186674

Cited by 4 publications

(3 citation statements)

References 75 publications

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“…After experimentation, it was proven that the proposed model achieved higher accuracy (98%) and runs 3.44 times faster than the existing ResNet-101 (91%) model at lower running costs. A basic architecture was designed by Peng-Wei Lin, et al [22] for building a driver alerting that takes into account vehicle condition and driver behaviour. In the projected dataset, the motive force behaviour from the SFD3 dataset and the results of 3D object detection using KITTI's modified KFPN are integrated.…”

Section: Fig 3 [9]mentioning

confidence: 99%

Deep Learning Approaches to Detect Distracted Driving Activities: A Survey

Reddy¹,

Deshmukh²,

Lokesh³

et al. 2023

IJRASET

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With increase in the use of advanced systems on vehicle dashboards such as modern infotainment systems and mobile phones, we have come to observe a proportional increase in the distraction of drivers. Distracted driving has proven to be a vital cause for most modern-day road crashes due to lane variation, and inattention on the road. Hence, efficient mechanisms to tackle this problem have to be studied and developed. So far, many attempts have been made on the identification of distracted driving in the form of computer vision techniques and ML/DL approaches. This research effort suggests numerous approaches to assess driver behaviour and identify alleged distractions, thereby offering robust solutions to reduce distracted driving-related traffic accidents.

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Section: Fig 3 [9]mentioning

confidence: 99%

Deep Learning Approaches to Detect Distracted Driving Activities: A Survey

Reddy¹,

Deshmukh²,

Lokesh³

et al. 2023

IJRASET

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show abstract

“…The second issue is the frequency with which the drivers perform the task. Even a small task, but performed frequently, can pose a safety concern [19,20].…”

Section: Introductionmentioning

confidence: 99%

Sensor-Based Classification of Primary and Secondary Car Driver Activities Using Convolutional Neural Networks

Doniec¹,

Konior²,

Sieciński³

et al. 2023

Preprint

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To drive safely, the driver must be aware of the surroundings, pay attention to the road traffic, and be ready to adapt to new circumstances. Most studies on driving safety focus on detecting anomalies in driver behavior and monitoring the cognitive capabilities of drivers. In our study, we proposed a classifier for basic activities in driving a car, based on a similar approach that could be applied to the recognition of basic activities in daily life, that is, using electrooculographic (EOG) signals and a one-dimensional convolutional neural network (1D CNN). Our classifier achieved an accuracy of 80% for the 16 primary and secondary activities. The accuracy related to primary activities in driving, including crossroad, parking, roundabout was 97.9%, 96.8%, 97.4%, and 99.5%, respectively. The F1 score for secondary driving actions (0.99) was higher than for primary driving activities (0.93–0.94). Furthermore, using the same algorithm, it was possible to distinguish four secondary activities related to activities of daily life and secondary when driving a car.

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“…The second issue is the frequency with which the drivers perform the task. Even a task that is small, but performed frequently, can pose a safety concern [ 2 , 3 , 19 , 21 , 22 ].…”

Section: Introductionmentioning

confidence: 99%

Sensor-Based Classification of Primary and Secondary Car Driver Activities Using Convolutional Neural Networks

Doniec

Konior

Sieciński

et al. 2023

Sensors

View full text Add to dashboard Cite

To drive safely, the driver must be aware of the surroundings, pay attention to the road traffic, and be ready to adapt to new circumstances. Most studies on driving safety focus on detecting anomalies in driver behavior and monitoring cognitive capabilities in drivers. In our study, we proposed a classifier for basic activities in driving a car, based on a similar approach that could be applied to the recognition of basic activities in daily life, that is, using electrooculographic (EOG) signals and a one-dimensional convolutional neural network (1D CNN). Our classifier achieved an accuracy of 80% for the 16 primary and secondary activities. The accuracy related to activities in driving, including crossroad, parking, roundabout, and secondary activities, was 97.9%, 96.8%, 97.4%, and 99.5%, respectively. The F1 score for secondary driving actions (0.99) was higher than for primary driving activities (0.93–0.94). Furthermore, using the same algorithm, it was possible to distinguish four activities related to activities of daily life that were secondary activities when driving a car.

show abstract

Innovative Framework for Distracted-Driving Alert System Based on Deep Learning

Cited by 4 publications

References 75 publications

Deep Learning Approaches to Detect Distracted Driving Activities: A Survey

Deep Learning Approaches to Detect Distracted Driving Activities: A Survey

Sensor-Based Classification of Primary and Secondary Car Driver Activities Using Convolutional Neural Networks

Sensor-Based Classification of Primary and Secondary Car Driver Activities Using Convolutional Neural Networks

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