A Temporal–Spatial Deep Learning Approach for Driver Distraction Detection Based on EEG Signals

Li, Guofa; Yan, Weiquan; Shen, Li; Qu, Xingda; Chu, Wenbo; Cao, Dongpu

doi:10.1109/tase.2021.3088897

Cited by 62 publications

(23 citation statements)

References 40 publications

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“…However, Support Vector Machine achieved the highest accuracy of 95.19% after a period of training and an F1-score of 95% as shown in Table 6. Finally, Name entity recognition (NER) [44] was utilized as a sub-task for information extraction. It located and classified named entities mentioned in the unstructured command into pre-defined categories.…”

Section: Resultsmentioning

confidence: 99%

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Multimodel System for Driver Distraction Detection and Elimination

et al. 2022

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On average 3,700 people lose their lives on roads every day due to car accidents as a result of drivers' distraction. In this research, a proposed hybrid approach is presented. The approach is based on deep learning to detect the driver's actions and eliminate the driver's distraction as a packed solution. The detection is performed by analyzing the driver's actions and his head pose. The elimination is made by using voice commands that are based on trigger words, speech to text, and text classification models to access the car's functions such as the air-condition, radio, etc. The results of the driver's actions classification showed 94.1% accuracy on the AUC benchmark database for driver distraction achieving the state-of-the-art accuracy on this benchmark. The results of the command to text classification is 95.19% while the results of the head pose estimation show a 6.21-degree MAE in face angles detection. With using our car commands dataset, the domain of the speech recognition output is more focused on car commands. The previously mentioned algorithms are beneficial to the safety of the driver. He can use his voice to operate the car accessories. His alert state is monitored and he is warned through an alarm if a distraction is detected. However, this research is not concerned with the detection of retinal abnormalities such as sleeping with eyes open. The results of real-time testing show 0.080 second response time for the driver's behavior classification and command following with the use of graphical processing units.

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Section: Resultsmentioning

confidence: 99%

“…After choosing the optimizer and performing data augmentation, classifying the sentences started. Name entity recognition (NER) [44] is utilized as a sub-task for information extraction. It seeks to locate and classify named entities mentioned in unstructured text into pre-defined categories.…”

Section: ) Speech Recognition and Command Classificationmentioning

confidence: 99%

Multimodel System for Driver Distraction Detection and Elimination

et al. 2022

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“…Moreover, the effectiveness of our proposed method will be limited at night or in other environmental conditions with unclear vision [11,59]. Given that both traditional and end-to-end deep learning technologies have been well developed to fuse information from multiple sources for better environmental perception in recent years [59,60], comprehensively using information fusion technologies for the development of autonomous driving technologies will be another perspective of our future work.…”

Section: Discussionmentioning

confidence: 99%

Continuous decision‐making for autonomous driving at intersections using deep deterministic policy gradient

Shen

et al. 2021

IET Intelligent Trans Sys

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Intersections have been identified as the most complex and accident-prone traffic scenarios on road. Making appropriate decisions at intersections for driving safety, efficiency, and comfort become a challenging task for autonomous vehicles (AVs). The existing research on AV decision-making at intersections either considers a single scenario only with discrete behaviour outputs or ignores the requirements for driving efficiency and comfort. To address these problems, this study proposed a deep reinforcement learning based continuous decision-making method to make AVs drive through intersections. The proposed method establishes an end-to-end decision-making framework by using a convolutional neural network to map the relationship between traffic images and vehicle operations. The interaction between the AV and other vehicles was modelled as a Markov decision process (MDP), and a deep deterministic policy gradient algorithm was employed to solve the MDP problem and obtain the optimal driving policy. The top three accident-prone crossing path crash scenarios at intersections were realized in CARLA to verify the effectiveness of the proposed method. The experimental results demonstrated that the developed method could provide effective policies to ensure driving safety and efficiency while considering driving comfort for autonomous driving at intersections in all the examined scenarios.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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“…The model performed well on the Lilong Distracted Driving Behavior dataset while being implemented on a limited computation budget. A unique approach is proposed in [15] that uses both spatial and temporal information of electroencephalography (EEG) signals as an input to a deep learning model. The relationship between the driver distraction and the EEG signal in the time domain is mapped through gated recurrent units (GRUs) and CNNs.…”

Section: Single-based Deep Learning Modelsmentioning

confidence: 99%

E2DR: A Deep Learning Ensemble-Based Driver Distraction Detection with Recommendations Model

Aljasim

Kashef

2022

Sensors

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The increasing number of car accidents is a significant issue in current transportation systems. According to the World Health Organization (WHO), road accidents are the eighth highest top cause of death around the world. More than 80% of road accidents are caused by distracted driving, such as using a mobile phone, talking to passengers, and smoking. A lot of efforts have been made to tackle the problem of driver distraction; however, no optimal solution is provided. A practical approach to solving this problem is implementing quantitative measures for driver activities and designing a classification system that detects distracting actions. In this paper, we have implemented a portfolio of various ensemble deep learning models that have been proven to efficiently classify driver distracted actions and provide an in-car recommendation to minimize the level of distractions and increase in-car awareness for improved safety. This paper proposes E2DR, a new scalable model that uses stacking ensemble methods to combine two or more deep learning models to improve accuracy, enhance generalization, and reduce overfitting, with real-time recommendations. The highest performing E2DR variant, which included the ResNet50 and VGG16 models, achieved a test accuracy of 92% as applied to state-of-the-art datasets, including the State Farm Distracted Drivers dataset, using novel data splitting strategies.

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A Temporal–Spatial Deep Learning Approach for Driver Distraction Detection Based on EEG Signals

Cited by 62 publications

References 40 publications

Multimodel System for Driver Distraction Detection and Elimination

Multimodel System for Driver Distraction Detection and Elimination

Continuous decision‐making for autonomous driving at intersections using deep deterministic policy gradient

E2DR: A Deep Learning Ensemble-Based Driver Distraction Detection with Recommendations Model

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