Estimation of Driver’s Danger Level when Accessing the Center Console for Safe Driving

Lee, Hyun-Soon; Oh, Sun-Young; Jo, Daeseong; Kang, Bo‐Yeong

doi:10.3390/s18103392

Cited by 3 publications

(2 citation statements)

References 19 publications

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“…In order to implement a safe ADAS, Lee et al [38] and Yin et al [39] proposed a method for analyzing a driver's movement while driving and focusing on a driving task. In their proposed method, the risk level was estimated using the hand movement information of the driver.…”

Section: Related Workmentioning

confidence: 99%

Efficient Vehicle Detection and Distance Estimation Based on Aggregated Channel Features and Inverse Perspective Mapping from a Single Camera

Kim

2019

Symmetry

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In this paper a method for detecting and estimating the distance of a vehicle driving in front using a single black-box camera installed in a vehicle was proposed. In order to apply the proposed method to autonomous vehicles, it was required to reduce the throughput and speed-up the processing. To do this, the proposed method decomposed the input image into multiple-resolution images for real-time processing and then extracted the aggregated channel features (ACFs). The idea was to extract only the most important features from images at different resolutions symmetrically. A method of detecting an object and a method of estimating a vehicle’s distance from a bird’s eye view through inverse perspective mapping (IPM) were applied. In the proposed method, ACFs were used to generate the AdaBoost-based vehicle detector. The ACFs were extracted from the LUV color, edge gradient, and orientation (histograms of oriented gradients) of the input image. Subsequently, by applying IPM and transforming a 2D input image into 3D by generating an image projected in three dimensions, the distance between the detected vehicle and the autonomous vehicle was detected. The proposed method was applied in a real-world road environment and showed accurate results for vehicle detection and distance estimation in real-time processing. Thus, it was showed that our method is applicable to autonomous vehicles.

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Section: Related Workmentioning

confidence: 99%

Efficient Vehicle Detection and Distance Estimation Based on Aggregated Channel Features and Inverse Perspective Mapping from a Single Camera

Kim

2019

Symmetry

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“…Specifically, inattention, fatigue, and drowsiness detection became significant subjects since those mental status are deeply associated with accidents in diverse areas [ 3 , 4 ]. Among them, inattention, which is a mental status of decreased attention, arouses wide concern in both academia and industry due to many catastrophic accidents reported to be caused by the inattention [ 5 ].…”

Section: Introductionmentioning

confidence: 99%

A BCI Based Alerting System for Attention Recovery of UAV Operators

Park

Shin

et al. 2021

Sensors

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As unmanned aerial vehicles have become popular, the number of accidents caused by an operator’s inattention have increased. To prevent such accidents, the operator should maintain an attention status. However, limited research has been conducted on the brain-computer interface (BCI)-based system with an alerting module for the operator’s attention recovery of unmanned aerial vehicles. Therefore, we introduce a detection and alerting system that prevents an unmanned aerial vehicle operator from falling into inattention status by using the operator’s electroencephalogram signal. The proposed system consists of the following three components: a signal processing module, which collects and preprocesses an electroencephalogram signal of an operator, an inattention detection module, which determines whether an inattention status occurred based on the preprocessed signal, and, lastly, an alert providing module that presents stimulus to an operator when inattention is detected. As a result of evaluating the performance with a real-world dataset, it was shown that the proposed system successfully contributed to the recovery of operator attention in the evaluating dataset, although statistical significance could not be established due to the small number of subjects.

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Design and Implementation Accurate Three Distance Sensors Device Using Neural Network

Zuhair Khaleel,

Raheem

2024

Tikrit j. eng. sci.

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This paper suggested and created a proposed device for sensing an accurate distance from 5 to 400 cm with three sensors using the proposed on-line Feedforward Backpropagation Neural Network (FFBPN). Firstly, preparing a dataset (distances) with two distances utilizing Arduino and measured distance calculated from three sensors (2 ultrasonic and 1 IR). Secondly, target distances are calculated from the ground manually using a distance tape tool. Thirdly, interfacing Arduino and MATLAB using USB easily saved datasets from Arduino directly to MATLAB, then training, and testing data. The proposed device divides reading distances into three distances, getting from three sensors to get accurate distances. The first reading was 5-10 cm from down ultrasonic sensor 3. The second reading was 10-80 cm from middle IR sensor 2. The third reading was 80-400 cm from up ultrasonic sensor 1. The dataset was created and improved using the suggested FFBPN to create a very accurate distance gadget with three sensors. A suggested FFBPN consisted of 3 layers: The first input measured distance layer (from 3 sensors), the second hidden layer with ten neurons, and the third output layer distance. The numerical results showed that the proposed distance device was significantly accurate due to regression result R=1 using the proposed Neural Network (NN), which meanings that the device had 100% fitting. It had the best validation performance in 464 epochs, i.e., is 0.0036122. Furthermore, the proposed on-line FFBPN was fast because the training time equals three seconds. This distance device was implemented for use in robotic and radar applications to detect objects accurately because this device successfully detects near and far objects.

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Estimation of Driver’s Danger Level when Accessing the Center Console for Safe Driving

Cited by 3 publications

References 19 publications

Efficient Vehicle Detection and Distance Estimation Based on Aggregated Channel Features and Inverse Perspective Mapping from a Single Camera

Efficient Vehicle Detection and Distance Estimation Based on Aggregated Channel Features and Inverse Perspective Mapping from a Single Camera

A BCI Based Alerting System for Attention Recovery of UAV Operators

Design and Implementation Accurate Three Distance Sensors Device Using Neural Network

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