Distracted driving detection based on the fusion of deep learning and causal reasoning

Ping, Peng; Huang, Chuanfeng; Ding, Weiping; Liu, Yongkang; Miyajima, Chiyomi; Takeda, Kazuya

doi:10.1016/j.inffus.2022.08.009

Cited by 32 publications

(12 citation statements)

References 47 publications

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“…Four popular CNN architectures dedicated to this task provide extensive results comparisons. Furthermore, various adjustments were made to the input image, as well as the impact of the image size on performance [56,57]. To train and test the network, a large collection of driving data was collected, consisting of 11 long recordings of driving activities for 10 people in 2 different cars.…”

Section: Of 19mentioning

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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Section: Of 19mentioning

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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show abstract

“…In the same year, Zhang et al proposed ShuffleNet, which introduced grouped convolution in deep separable convolution, greatly improving network performance. [10][11][12][13] MobileNetV2 is chosen as the backbone network model in this paper because it can assure excellent recognition accuracy while lowering the number of parameters.…”

Section: Convolutional Neural Network and Deep Learningmentioning

confidence: 99%

“…proposed ShuffleNet, which introduced grouped convolution in deep separable convolution, greatly improving network performance 10 – 13 …”

Section: Related Workmentioning

confidence: 99%

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Distracted driving behavior recognition based on improved MobileNetV2

Bai,

Li,

Zhang

et al. 2023

J. Electron. Imag.

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.In recent years, research on distracted driving behavior recognition has made significant progress, with an increasing number of researchers focusing on deep-learning-based algorithms. Aiming at the problems of the existing distracted driving recognition algorithm, such as its oversized model and difficulty in adapting to low computing environments, a lightweight network MobileNetV2, is chosen as the backbone network and improved to design a distracted driving behavior detection method that is both accurate and practical. The Ghost module is employed to replace point-by-point convolution to reduce the computation, the Leaky ReLU function helps mitigate the problem of dead neurons, as it prevents gradients from becoming zero for negative inputs. Finally, the channel pruning algorithm is used to further reduce the model parameters. The experiment results on the State Farm dataset show that the model’s test accuracy can reach 94.66%, and the number of parameters is only 0.23 M. The improved model has significantly fewer parameters than the baseline model, which demonstrates the effectiveness and applicability of the method.

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“…Peng Ping et al [ 26 ] show that distracted driving is one of the leading causes of road accidents caused by driver carelessness. Distracting activities include safe driving, texting right, drinking, talking to the passenger, left phone usage, hair or makeup, texting left, Reaching behind, and adjusting the radio.…”

Section: Literature Reviewmentioning

confidence: 99%

Unusual Driver Behavior Detection in Videos Using Deep Learning Models

Abosaq

Ramzan

Althobiani

et al. 2022

Sensors

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Anomalous driving behavior detection is becoming more popular since it is vital in ensuring the safety of drivers and passengers in vehicles. Road accidents happen for various reasons, including health, mental stress, and fatigue. It is critical to monitor abnormal driving behaviors in real time to improve driving safety, raise driver awareness of their driving patterns, and minimize future road accidents. Many symptoms appear to show this condition in the driver, such as facial expressions or abnormal actions. The abnormal activity was among the most common causes of road accidents, accounting for nearly 20% of all accidents, according to international data on accident causes. To avoid serious consequences, abnormal driving behaviors must be identified and avoided. As it is difficult to monitor anyone continuously, automated detection of this condition is more effective and quicker. To increase drivers’ recognition of their driving behaviors and prevent potential accidents, a precise monitoring approach that detects abnormal driving behaviors and identifies abnormal driving behaviors is required. The most common activities performed by the driver while driving is drinking, eating, smoking, and calling. These types of driver activities are considered in this work, along with normal driving. This study proposed deep learning-based detection models for recognizing abnormal driver actions. This system is trained and tested using a newly created dataset, including five classes. The main classes include Driver-smoking, Driver-eating, Driver-drinking, Driver-calling, and Driver-normal. For the analysis of results, pre-trained and fine-tuned CNN models are considered. The proposed CNN-based model and pre-trained models ResNet101, VGG-16, VGG-19, and Inception-v3 are used. The results are compared by using the performance measures. The results are obtained 89%, 93%, 93%, 94% for pre-trained models and 95% by using the proposed CNN-based model. Our analysis and results revealed that our proposed CNN base model performed well and could effectively classify the driver’s abnormal behavior.

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Distracted driving detection based on the fusion of deep learning and causal reasoning

Cited by 32 publications

References 47 publications

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

Distracted driving behavior recognition based on improved MobileNetV2

Unusual Driver Behavior Detection in Videos Using Deep Learning Models

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