Eye Gaze Estimation Invisible and IR Spectrum for Driver Monitoring System

Driver monitoring system has gained lot of popularity in automotive sector to ensure safety while driving. Collisions due to driver inattentiveness or driver fatigue or over reliance on autonomous driving features arethe major reasons for road accidents and fatalities. Driver monitoring systems aims to monitor various aspect of driving and provides appropriate warnings whenever required. Eye gaze estimation is a key element in almost all of the driver monitoring systems. Gaze estimation aims to find the point of gaze which is basically,” -where is driver looking”. This helps in understanding if the driver is attentively looking at the road or if he is distracted. Estimating gaze point also plays important role in many other applications like retail shopping, online marketing, psychological tests, healthcare etc. This paper covers the various aspects of eye gaze estimation for a driver monitoring system including sensor choice and sensor placement. There are multiple ways by which eye gaze estimation can be done. A detailed comparative study on two of the popular methods for gaze estimation using eye features is covered in this paper. An infra-red camera is used to capture data for this study. Method 1 tracks corneal reflection centre w.r.t the pupil centre and method 2 tracks the pupil centre w.r.t the eye centre to estimate gaze. There are advantages and disadvantages with both the methods which has been looked into. This paper can act as a reference for researchers working in the same field to understand possibilities and limitations of eye gaze estimation for driver monitoring system.

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In Cars (Are We Really Safest of All?): Interior Sensing and Emotional Opacity

10.31235/osf.io/cwmgd ◽

2021 ◽

Author(s):

Andrew McStay ◽

Lachlan Urquhart

Keyword(s):

Autonomous Driving ◽

Monitoring Systems ◽

Affective States ◽

The Road ◽

Social Trends ◽

Driver Monitoring ◽

European Regulations ◽

On The Road ◽

Vision Zero

This paper considers car driver monitoring systems that measure bodies to infer and react to emotions and other affective states. Situated within social trends in personalisation and automation, developers of driver monitoring systems promise increased safety on the road and comfort for cabin occupants. The impetus is threefold, namely: (1) European road safety policy seeks to vastly reduce road deaths using computational surveillance; (2) interest in the role of safety solutions based on in-cabin sensing of emotion and affective states of drivers and passengers; and 3) autonomous driving trends changing the nature of interactions between vehicle and driver. These systems are of special interest because they are backed by policy and standards initiatives, not least the European Union’s Vision Zero policy that seeks to reduce road death to zero, and industry-oriented safety programmes like the New Car Assessment Programme (NCAP). Informed by 13 expert interviews with interviewees working in and around in-cabin sensing developments, this paper identifies and explores features of emergent in-cabin profiling through emotional AI and biometric measures. It then carries ambivalent insights found into analysis of applicable European regulations, also finding a deep ambivalence in the politics of Emotional AI for interior sensing of cabins and occupants.

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Eye-Gaze Estimation Systems for Multi-Applications: An Implementation of Approach Based on Laptop Webcam

Diyala Journal for Pure Science ◽

10.24237/djps.1402.398c ◽

2018 ◽

Vol 14 (2) ◽

pp. 153-173 ◽

Cited By ~ 1

Author(s):

Jumana Waleed ◽

◽

Taha Mohammed Hasan ◽

Qutaiba Kadhim Abed

Keyword(s):

Eye Gaze ◽

Gaze Estimation ◽

Eye Gaze Estimation

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Detection of Road-Surface Anomalies Using a Smartphone Camera and Accelerometer

Sensors ◽

10.3390/s21020561 ◽

2021 ◽

Vol 21 (2) ◽

pp. 561

Author(s):

Taehee Lee ◽

Chanjun Chun ◽

Seung-Ki Ryu

Keyword(s):

Monitoring System ◽

Poor Performance ◽

Road Surface ◽

Monitoring Systems ◽

Advantages And Disadvantages ◽

Acceleration Data ◽

Road Surfaces ◽

Road Surface Monitoring ◽

Surface Monitoring ◽

Acquisition Method

Road surfaces should be maintained in excellent condition to ensure the safety of motorists. To this end, there exist various road-surface monitoring systems, each of which is known to have specific advantages and disadvantages. In this study, a smartphone-based dual-acquisition method system capable of acquiring images of road-surface anomalies and measuring the acceleration of the vehicle upon their detection was developed to explore the complementarity benefits of the two different methods. A road test was conducted in which 1896 road-surface images and corresponding three-axis acceleration data were acquired. All images were classified based on the presence and type of anomalies, and histograms of the maximum variations in the acceleration in the gravitational direction were comparatively analyzed. When the types of anomalies were not considered, it was difficult to identify their effects using the histograms. The differences among histograms became evident upon consideration of whether the vehicle wheels passed over the anomalies, and when excluding longitudinal anomalies that caused minor changes in acceleration. Although the image-based monitoring system used in this research provided poor performance on its own, the severity of road-surface anomalies was accurately inferred using the specific range of the maximum variation of acceleration in the gravitational direction.

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