Driver distraction detection using single convolutional neural network

Kim, Whui; Choi, Heon‐Jin; Jang, Byungtae; Lim, Jinsu

doi:10.1109/ictc.2017.8190898

Cited by 30 publications

(14 citation statements)

References 14 publications

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“…These models outperform the traditional machine learning methods. Kim et al [ 31 ] proposed to use Inception-ResNet [ 32 ] and MobileNet [ 26 ] to classify posture distraction. It was shown that fine-tuned models outperformed training from scratch, and MobileNet outperformed Inception-ResNet.…”

Section: Literature Reviewmentioning

confidence: 99%

Optimally-Weighted Image-Pose Approach (OWIPA) for Distracted Driver Detection and Classification

Koay

Chuah

Chow

et al. 2021

Sensors

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Distracted driving is the prime factor of motor vehicle accidents. Current studies on distraction detection focus on improving distraction detection performance through various techniques, including convolutional neural networks (CNNs) and recurrent neural networks (RNNs). However, the research on detection of distracted drivers through pose estimation is scarce. This work introduces an ensemble of ResNets, which is named Optimally-weighted Image-Pose Approach (OWIPA), to classify the distraction through original and pose estimation images. The pose estimation images are generated from HRNet and ResNet. We use ResNet101 and ResNet50 to classify the original images and the pose estimation images, respectively. An optimum weight is determined through grid search method, and the predictions from both models are weighted through this parameter. The experimental results show that our proposed approach achieves 94.28% accuracy on AUC Distracted Driver Dataset.

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Section: Literature Reviewmentioning

confidence: 99%

Optimally-Weighted Image-Pose Approach (OWIPA) for Distracted Driver Detection and Classification

Koay

Chuah

Chow

et al. 2021

Sensors

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show abstract

“…Different cognitive and behavioral parameters of drivers have been analyzed in the literature, such as sleepiness, 48 stress level, 49 distraction level, 50 and among others. The combination of several cognitive parameters can provide additional information to increase the performance of accident prevention systems.…”

Section: Driver Cognitive Parameters Informationmentioning

confidence: 99%

On the design of a human–robot interaction strategy for commercial vehicle driving based on human cognitive parameters

Vásconez

Carvajal

Cheein

2019

Advances in Mechanical Engineering

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A proper design of human–robot interaction strategies based on human cognitive factors can help to compensate human limitations for safety purposes. This work is focused on the development of a human–robot interaction system for commercial vehicle (Renault Twizy) driving, that uses driver cognitive parameters to improve driver’s safety during day and night tasks. To achieve this, eye blink behavior measurements are detected using a convolutional neural network, which is capable of operating under variable illumination conditions using an infrared camera. Percentage of eye closure measure values along with blink frequency are used to infer diver’s sleepiness level. The use of such algorithm is validated with experimental tests for subjects under different sleep-quality conditions. Additional cognitive parameters are also analyzed for the human–robot interaction system such as driver sleep quality, distraction level, stress level, and the effects related to not wearing glasses. Based on such driver cognitive state parameters, a human–robot interaction strategy is proposed to limit the speed of a Renault Twizy vehicle by intervening its acceleration and braking system. The proposed human–robot interaction strategy can increase safety during driving tasks for both users and pedestrians.

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“…To solve this problem, we propose a Multi-Task Mobilenets (MT-Mobilenets)-based lightweight driver monitoring system that uses a resource sharing device such as the driver’s mobile phone. Our previous work [18] showed it is possible to recognize driver’s facial behaviors related to driver status recognition without relying on face detection and tracking by using Mobilenets, which is smaller and faster than AlexNet, VGG, and FlowNet. MT-Mobilenets is an improved method from the Mobilenets of the previous research.…”

Section: Introductionmentioning

confidence: 99%

Lightweight Driver Monitoring System Based on Multi-Task Mobilenets

Kim

Jung

Choi

2019

Sensors

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Research on driver status recognition has been actively conducted to reduce fatal crashes caused by the driver’s distraction and drowsiness. As in many other research areas, deep-learning-based algorithms are showing excellent performance for driver status recognition. However, despite decades of research in the driver status recognition area, the visual image-based driver monitoring system has not been widely used in the automobile industry. This is because the system requires high-performance processors, as well as has a hierarchical structure in which each procedure is affected by an inaccuracy from the previous procedure. To avoid using a hierarchical structure, we propose a method using Mobilenets without the functions of face detection and tracking and show this method is enabled to recognize facial behaviors that indicate the driver’s distraction. However, frames per second processed by Mobilenets with a Raspberry pi, one of the single-board computers, is not enough to recognize the driver status. To alleviate this problem, we propose a lightweight driver monitoring system using a resource sharing device in a vehicle (e.g., a driver’s mobile phone). The proposed system is based on Multi-Task Mobilenets (MT-Mobilenets), which consists of the Mobilenets’ base and multi-task classifier. The three Softmax regressions of the multi-task classifier help one Mobilenets base recognize facial behaviors related to the driver status, such as distraction, fatigue, and drowsiness. The proposed system based on MT-Mobilenets improved the accuracy of the driver status recognition with Raspberry Pi by using one additional device.

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Driver distraction detection using single convolutional neural network

Cited by 30 publications

References 14 publications

Optimally-Weighted Image-Pose Approach (OWIPA) for Distracted Driver Detection and Classification

Optimally-Weighted Image-Pose Approach (OWIPA) for Distracted Driver Detection and Classification

On the design of a human–robot interaction strategy for commercial vehicle driving based on human cognitive parameters

Lightweight Driver Monitoring System Based on Multi-Task Mobilenets

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