Ambient Technology in Vehicles: The Benefits and Risks

Bennakhi, Ahmad; Safar, Maytham

doi:10.1016/j.procs.2016.04.223

Cited by 12 publications

(6 citation statements)

References 8 publications

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“…A constant effort is being made by automotive companies to improve the human machine interface, which is a key component of car development. They have tried to develop various complex original systems, integrating smart AR devices such as Google glass [39], RGB-D sensors for 3D motion recognition such us Microsoft Kinect [40] or other methods involving sound-for instance, rendering engine sound through the car audio system [12]. The potential of AR systems has a twofold purpose: to enrich the driving experience and to prevent accidents by increasing the driver's perception and attention [41].…”

Section: Ar In Drivingmentioning

confidence: 99%

The Application of Augmented Reality in the Automotive Industry: A Systematic Literature Review

2020

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Augmented reality (AR) is a fairly new technology enabling human machine interaction by superimposing virtual information on a real environment. Potential applications can be found in many areas of research from recent years. This study presents a systematic review of existing AR systems in the automotive field, synthesizing 55 studies from 2002 to 2019. The main research questions are: where AR technology has been applied within the automotive industry, what is the purpose of its application, what are the general characteristics of these systems, and what are the emphasized benefits and challenges of using AR in this field? The aim of this paper is to provide an insight into the AR applications and technologies in the automotive field.

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Section: Ar In Drivingmentioning

confidence: 99%

The Application of Augmented Reality in the Automotive Industry: A Systematic Literature Review

2020

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“…While driving a vehicle, drivers are often occupied with many other activities such as using a mobile phone, listening to the radio, or having a conversation with a passenger, etc. Moreover, new advanced in-vehicle information systems embedded in the modern vehicles could create distracted driving scenarios and may affect the driving performance [2][3][4]. Thus, these secondary activities, i.e., activities not related to driving require extra cognitive processes in ways that the driver can still keep their eyes on the road and hands on the steering wheel while being involved in other activities at the same time, and this refers to the 'cognitive load activities'.…”

Section: Introductionmentioning

confidence: 99%

Towards Intelligent Data Analytics: A Case Study in Driver Cognitive Load Classification

Ahmed

Begum

2020

Brain Sciences

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One debatable issue in traffic safety research is that the cognitive load by secondary tasks reduces primary task performance, i.e., driving. In this paper, the study adopted a version of the n-back task as a cognitively loading secondary task on the primary task, i.e., driving; where drivers drove in three different simulated driving scenarios. This paper has taken a multimodal approach to perform ‘intelligent multivariate data analytics’ based on machine learning (ML). Here, the k-nearest neighbour (k-NN), support vector machine (SVM), and random forest (RF) are used for driver cognitive load classification. Moreover, physiological measures have proven to be sophisticated in cognitive load identification, yet it suffers from confounding factors and noise. Therefore, this work uses multi-component signals, i.e., physiological measures and vehicular features to overcome that problem. Both multiclass and binary classifications have been performed to distinguish normal driving from cognitive load tasks. To identify the optimal feature set, two feature selection algorithms, i.e., sequential forward floating selection (SFFS) and random forest have been applied where out of 323 features, a subset of 42 features has been selected as the best feature subset. For the classification, RF has shown better performance with F1-score of 0.75 and 0.80 than two other algorithms. Moreover, the result shows that using multicomponent features classifiers could classify better than using features from a single source.

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“…The use of in-vehicle information systems (IVISs) trend is critical [7] due to the fact they manual, induce visual, and cognitive distraction may have an effect on performance of driving in qualitatively distinct ways. Moreover, the advancement and incidence of personal communique gadgets has exacerbated the problem during these remaining year's [8].…”

Section: Literature Reviewmentioning

confidence: 99%

Real Time Eye Tracking and Detection- A Driving Assistance System

Said¹,

Kork²,

Beyrouthy³

et al. 2018

Adv. sci. technol. eng. syst. j.

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Distraction, drowsiness, and fatigue are the main factors of car accidents recently. To solve such problems, an Eye-tracking system based on camera is proposed in this paper. The system detects the driver's Distraction or sleepiness and gives an alert to the driver as an assistance system. The camera best position is chosen to be on the dashboard without distracting the driver. The system will detect the driver's face and eyes by using Viola-Jones Algorithm that includes Haar Classifiers that showed significant advantages regarding processing time and correct detection algorithms. A prepared scenario is tested in a designed simulator that is used to simulate real driving conditions in an indoor environment. The system is added in real-vehicle and tested in an outdoor environment. Whenever the system detects the distraction or sleepiness of the driver, the driver will be alerted through a displayed message on a screen and an audible sound for more attention. The results show the accuracy of the system with a correct detection rate of 82% for indoor tests and 72.8 % for the outdoor environment.

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Ambient Technology in Vehicles: The Benefits and Risks

Cited by 12 publications

References 8 publications

The Application of Augmented Reality in the Automotive Industry: A Systematic Literature Review

The Application of Augmented Reality in the Automotive Industry: A Systematic Literature Review

Towards Intelligent Data Analytics: A Case Study in Driver Cognitive Load Classification

Real Time Eye Tracking and Detection- A Driving Assistance System

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