Fusion of Optimized Indicators from Advanced Driver Assistance Systems (ADAS) for Driver Drowsiness Detection

Daza, Iván García; Bergasa, Luis M.; Bronte, Sebastián; Yebes, J. Javier; Almazán, Javier; Arroyo, Roberto

doi:10.3390/s140101106

Cited by 71 publications

(44 citation statements)

References 41 publications

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“…Deep learning techniques are becoming indispensable in autonomous driving, for tasks such as the recognition of people, cars, and lanes surrounding autonomous vehicles [24,[31][32][33]. In fact, deep learning can handle the numerous variables in a driving environment that otherwise hinder the development of conventional algorithms.…”

Section: Deep Learning-based Sensorsmentioning

confidence: 99%

An Automobile Environment Detection System Based on Deep Neural Network and its Implementation Using IoT-Enabled In-Vehicle Air Quality Sensors

Chung

Kim

2020

Sustainability

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This paper elucidates the development of a deep learning–based driver assistant that can prevent driving accidents arising from drowsiness. As a precursor to this assistant, the relationship between the sensation of sleep depravity among drivers during long journeys and CO2 concentrations in vehicles is established. Multimodal signals are collected by the assistant using five sensors that measure the levels of CO, CO2, and particulate matter (PM), as well as the temperature and humidity. These signals are then transmitted to a server via the Internet of Things, and a deep neural network utilizes this information to analyze the air quality in the vehicle. The deep network employs long short-term memory (LSTM), skip-generative adversarial network (GAN), and variational auto-encoder (VAE) models to build an air quality anomaly detection model. The deep learning models gather data via LSTM, while the semi-supervised deep learning models collect data via GANs and VAEs. The purpose of this assistant is to provide vehicle air quality information, such as PM alerts and sleep-deprived driving alerts, to drivers in real time and thereby prevent accidents.

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Section: Deep Learning-based Sensorsmentioning

confidence: 99%

An Automobile Environment Detection System Based on Deep Neural Network and its Implementation Using IoT-Enabled In-Vehicle Air Quality Sensors

Chung

Kim

2020

Sustainability

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“…Different functionalities are already available in commercial vehicles, such as, early mature technology, the Antilock Braking (ABS) [1], Traction Control System (TCS) [2], Electronic Stability Program(ESP) [3], Electric Power Steering (EPS) [4,5], and Electronic Braking System (EBS) [6,7], and Automatic Braking System (ABS) [8]. There are also newer technologies such as adaptive cruise control system (ACC) [9], Automatic Parking System (APS) [10], Anti-Collision System (ACS) [11,12,13],and others Advanced Driver Assistance Systems (ADAS) [14,15,16,17]. The ultimate goal of the technological development is to realize automatic driving.…”

Section: Introductionmentioning

confidence: 99%

The Lateral Tracking Control for the Intelligent Vehicle Based on Adaptive PID Neural Network

Gai-ning

Wen

et al. 2017

Sensors

110

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The intelligent vehicle is a complicated nonlinear system, and the design of a path tracking controller is one of the key technologies in intelligent vehicle research. This paper mainly designs a lateral control dynamic model of the intelligent vehicle, which is used for lateral tracking control. Firstly, the vehicle dynamics model (i.e., transfer function) is established according to the vehicle parameters. Secondly, according to the vehicle steering control system and the CARMA (Controlled Auto-Regression and Moving-Average) model, a second-order control system model is built. Using forgetting factor recursive least square estimation (FFRLS), the system parameters are identified. Finally, a neural network PID (Proportion Integral Derivative) controller is established for lateral path tracking control based on the vehicle model and the steering system model. Experimental simulation results show that the proposed model and algorithm have the high real-time and robustness in path tracing control. This provides a certain theoretical basis for intelligent vehicle autonomous navigation tracking control, and lays the foundation for the vertical and lateral coupling control.

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“…Moreover, the use of on-board sensors already available in the vehicle to analyze driver behaviour is a low-cost and powerful alternative to the vision-based monitoring systems [20,21]. However, these systems should not be treated like different alternatives, because they can be used together (fusioned) in order to obtain indicators for monitoring [22]. Hence, for the sake of completeness, in this paper review only “purely” vision-based monitoring systems have been reviewed.…”

Section: Introductionmentioning

confidence: 99%

Driver Distraction Using Visual-Based Sensors and Algorithms

Fernández¹,

Usamentiaga

Carús³

et al. 2016

Sensors

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Driver distraction, defined as the diversion of attention away from activities critical for safe driving toward a competing activity, is increasingly recognized as a significant source of injuries and fatalities on the roadway. Additionally, the trend towards increasing the use of in-vehicle information systems is critical because they induce visual, biomechanical and cognitive distraction and may affect driving performance in qualitatively different ways. Non-intrusive methods are strongly preferred for monitoring distraction, and vision-based systems have appeared to be attractive for both drivers and researchers. Biomechanical, visual and cognitive distractions are the most commonly detected types in video-based algorithms. Many distraction detection systems only use a single visual cue and therefore, they may be easily disturbed when occlusion or illumination changes appear. Moreover, the combination of these visual cues is a key and challenging aspect in the development of robust distraction detection systems. These visual cues can be extracted mainly by using face monitoring systems but they should be completed with more visual cues (e.g., hands or body information) or even, distraction detection from specific actions (e.g., phone usage). Additionally, these algorithms should be included in an embedded device or system inside a car. This is not a trivial task and several requirements must be taken into account: reliability, real-time performance, low cost, small size, low power consumption, flexibility and short time-to-market. The key points for the development and implementation of sensors to carry out the detection of distraction will also be reviewed. This paper shows a review of the role of computer vision technology applied to the development of monitoring systems to detect distraction. Some key points considered as both future work and challenges ahead yet to be solved will also be addressed.

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Fusion of Optimized Indicators from Advanced Driver Assistance Systems (ADAS) for Driver Drowsiness Detection

Cited by 71 publications

References 41 publications

An Automobile Environment Detection System Based on Deep Neural Network and its Implementation Using IoT-Enabled In-Vehicle Air Quality Sensors

An Automobile Environment Detection System Based on Deep Neural Network and its Implementation Using IoT-Enabled In-Vehicle Air Quality Sensors

The Lateral Tracking Control for the Intelligent Vehicle Based on Adaptive PID Neural Network

Driver Distraction Using Visual-Based Sensors and Algorithms

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