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Driver Fatigue Detection Systems Using Multi-Sensors, Smartphone, and Cloud-Based Computing Platforms: A Comparative

Qaisar Abbas1, Abdullah Alsheddy1

  • 1College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi Arabia.

Sensors (Basel, Switzerland)
|December 30, 2020
PubMed
Summary
This summary is machine-generated.

This study reviews Internet of Things (IoT) architectures for driver fatigue detection systems (DFDs) to enhance road safety. It highlights challenges and compares multi-sensor, smartphone, and cloud-based approaches for predicting unsafe driving styles.

Keywords:
cloud computingconvolutional neural networkdeep learningdriver fatigue detectionmobile sensor networkmulti-sensormultimodal features learningrecurrent neural networksmartwatch

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Transportation Engineering

Background:

  • Driver fatigue is a major cause of road accidents, necessitating advanced detection systems.
  • Internet of Things (IoT) cloud-based applications offer emerging solutions for smart cities to mitigate traffic accidents.
  • Existing low-cost, computerized driver fatigue detection systems (DFDs) utilize multi-sensors and mobile/cloud computing.

Purpose of the Study:

  • To review and compare state-of-the-art approaches for predicting unsafe driving styles using three common IoT-based architectures.
  • To highlight the differences in multimodal feature processing among multi-sensor, smartphone-based, and cloud-based architectures.
  • To discuss challenges faced by machine learning, particularly deep learning (DL), in predicting driver hypovigilance across these architectures.

Main Methods:

  • A review of current literature on IoT-based driver fatigue detection systems.
  • State-of-the-art comparisons using driving simulators to integrate multimodal driver features.
  • Analysis of online data sources and public multimodal datasets for training and testing network architectures.
  • Evaluation of Multi-Access Edge Computing (MEC) and 5G networks' impact on DL-based DFD systems.

Main Results:

  • Major differences in multimodal feature processing exist among multi-sensor, smartphone, and cloud-based IoT architectures for DFDs.
  • Deep learning models face specific challenges in predicting driver hypovigilance within these distinct IoT architectures.
  • Driving simulator experiments provide a basis for comparing DFD system performance using multimodal features.
  • Analysis indicates potential for MEC and 5G to improve DFD system response times.

Conclusions:

  • There is a significant research gap in implementing DFD systems on MEC and 5G technologies using multimodal features and DL architecture.
  • Understanding the performance limitations of different IoT architectures is crucial for real-time DFD implementation.
  • Further research is needed to optimize DFD systems by leveraging advanced networking and AI techniques for enhanced road safety.