A Fuzzy-Logic Approach to Dynamic Bayesian Severity Level Classification of Driver Distraction Using Image Recognition

open access articleDetecting and classifying driver distractions is crucial in the prevention of road accidents. These distractions impact both driver behavior and vehicle dynamics. Knowing the degree of driver distraction can aid in accident prevention techniques, including transitioning of control to a level 4 semi- autonomous vehicle, when a high distraction severity level is reached. Thus, enhancement of Advanced Driving Assistance Systems (ADAS) is a critical component in the safety of vehicle drivers and other road users. In this paper, a new methodology is introduced, using an expert knowledge rule system to predict the severity of distraction in a contiguous set of video frames using the Naturalistic Driving American University of Cairo (AUC) Distraction Dataset. A multi-class distraction system comprises the face orientation, drivers’ activities, hands and previous driver distraction, a severity classification model is developed as a discrete dynamic Bayesian (DDB). Furthermore, a Mamdani-based fuzzy system was implemented to detect multi- class of distractions into a severity level of safe, careless or dangerous driving. Thus, if a high level of severity is reached the semi-autonomous vehicle will take control. The result further shows that some instances of driver’s distraction may quickly transition from a careless to dangerous driving in a multi-class distraction context

Robotic Mobile Holder (For CAR Dashboards)

In the current smart tech world, there is an immense need of automating tasks and processes to avoid human intervention, save time and energy. Nowadays, mobile phones have become one of the essential things for human beings either to call someone, connect to the internet, while driving people need mobile phones to receive or make a call, use google maps to know the routes and many more. Normally in cars, mobile holders are placed on the dashboard to hold the mobile and the orientation of the phone needs to be changed according to the driver's convenience manually, but the driver may distract from driving while trying to access mobile phone which may lead to accidents. To solve this problem, an auto adjustable mobile holder is designed in such a way that it rotates according to the movement of the driver and also it can even alert the driver when he feels drowsiness. Image Processing is used to detect the movement of the driver which is then processed using LabVIEW software and NI myRIO hardware. NI Vision development module is used to perform face recognition and servo motors are used to rotate the holder in the required position. Simulation results show that the proposed system has achieved maximum accuracy in detecting faces, drowsiness and finding the position coordinates

Deep Learning Systems for Advanced Driving Assistance

Next generation cars embed intelligent assessment of car driving safety through innovative solutions often based on usage of artificial intelligence. The safety driving monitoring can be carried out using several methodologies widely treated in scientific literature. In this context, the author proposes an innovative approach that uses ad-hoc bio-sensing system suitable to reconstruct the physio-based attentional status of the car driver. To reconstruct the car driver physiological status, the author proposed the use of a bio-sensing probe consisting of a coupled LEDs at Near infrared (NiR) spectrum with a photodetector. This probe placed over the monitored subject allows to detect a physiological signal called PhotoPlethysmoGraphy (PPG). The PPG signal formation is regulated by the change in oxygenated and non-oxygenated hemoglobin concentration in the monitored subject bloodstream which will be directly connected to cardiac activity in turn regulated by the Autonomic Nervous System (ANS) that characterizes the subject's attention level. This so designed car driver drowsiness monitoring will be combined with further driving safety assessment based on correlated intelligent driving scenario understanding

Algorithm for Monitoring Head/Eye Motion for Driver Alertness with one Camera

Visual methods and systems are described for detecting alterness and vigilance of persons under the conditions of fatigue, lack of sleep, and exposure to mind altering substances such as alcohol or drugs. In particular, the invention can have particular application for truck drivers, bus drivers, train operators, pilots and watercraft controllers and stationary heavy equipment operators, and students and employees during either daytime or nighttime conditions. The invention robustly tracks a person\u27s head and facial features with a single on-board camera with a fully automatic system, that can intitalize automatically, and can reinitialize when it needs to and provide outputs in realtime. The system can classify rotation in all viewing directions, detects eye/mouth occlusion, detects eye blinking, and recovers the 3D (three dimensional) gaze of the eyes. In addition, the system is able to track both through occlusion like eye blinking and through occlusion like rotation. Outputs can be visual and sound alarms to the driver directly..

Human-Centric Detection and Mitigation Approach for Various Levels of Cell Phone-Based Driver Distractions

abstract: Driving a vehicle is a complex task that typically requires several physical interactions and mental tasks. Inattentive driving takes a driver’s attention away from the primary task of driving, which can endanger the safety of driver, passenger(s), as well as pedestrians. According to several traffic safety administration organizations, distracted and inattentive driving are the primary causes of vehicle crashes or near crashes. In this research, a novel approach to detect and mitigate various levels of driving distractions is proposed. This novel approach consists of two main phases: i.) Proposing a system to detect various levels of driver distractions (low, medium, and high) using a machine learning techniques. ii.) Mitigating the effects of driver distractions through the integration of the distracted driving detection algorithm and the existing vehicle safety systems. In phase- 1, vehicle data were collected from an advanced driving simulator and a visual based sensor (webcam) for face monitoring. In addition, data were processed using a machine learning algorithm and a head pose analysis package in MATLAB. Then the model was trained and validated to detect different human operator distraction levels. In phase 2, the detected level of distraction, time to collision (TTC), lane position (LP), and steering entropy (SE) were used as an input to feed the vehicle safety controller that provides an appropriate action to maintain and/or mitigate vehicle safety status. The integrated detection algorithm and vehicle safety controller were then prototyped using MATLAB/SIMULINK for validation. A complete vehicle power train model including the driver’s interaction was replicated, and the outcome from the detection algorithm was fed into the vehicle safety controller. The results show that the vehicle safety system controller reacted and mitigated the vehicle safety status-in closed loop real-time fashion. The simulation results show that the proposed approach is efficient, accurate, and adaptable to dynamic changes resulting from the driver, as well as the vehicle system. This novel approach was applied in order to mitigate the impact of visual and cognitive distractions on the driver performance.Dissertation/ThesisDoctoral Dissertation Applied Psychology 201

Fundamental study for constructing a system to assist the left visual field of older drivers - Effectiveness of the alternative of the left front side-view mirror by the central visual field -

The purpose of this paper is to establish the basics of the systems that assist visibility of the left visual field for older drivers. The display was located either the left which corresponded to a left side mirror, or within the central effective visual field. Participants performed multiple tasks where tracking task using a steering wheel was a primary task, and judgment of situations using a left or front display was a secondary task. How the display location affected the judgment performance was explored for both young and older adults. We counted the number of the warning during the tracking task and measured the percentage correct reaction to displayed stimulus and reaction sensitivity. We investigated how these measures ware affected by age and display location. Mean warning number during the tracking tasks, the percentage correct recognition of situations and d' was affected age and display location. The central display was found to increase the percentage correct recognitions of situations

Eye tracking use in researching driver distraction: A scientometric and qualitative literature review approach

Many factors affect road safety, but research constantly shows that drivers are the major cause of critical situations that could potentially lead to a traffic accident in road traffic. Visual information is a crucial part of input information into the driving process; therefore, distractions of overt visual attention can potentially have a large impact on driving safety. Modern eye tracking technology enables researchers to gain precise insight into the direction and movement of a driver’s gaze during various distractions. As this is an evolving and currently very relevant field of road safety research, the present paper sets out to analyse the current state of the research field and the most relevant publications that use eye tracking for research of distractions to a driver’s visual attention. With the use of scientometrics and a qualitative review of the 139 identified publications that fit the inclusion criteria, the results revealed a currently expanding research field. The narrow research field is interdisciplinary in its core, as evidenced by the dispersion of publication sources and research variables. The main research gaps identified were performing research in real conditions, including a wider array of distractions, a larger number of participants, and increasing interdisciplinarity of the field with more author cooperation outside of their primary co-authorship networks

Investigation of low-cost infrared sensing for intelligent deployment of occupant restraints

In automotive transport, airbags and seatbelts are effective at restraining the driver and passenger in the event of a crash, with statistics showing a dramatic reduction in the number of casualties from road crashes. However, statistics also show that a small number of these people have been injured or even killed from striking the airbag, and that the elderly and small children are especially at risk of airbag-related injury. This is the result of the fact that in-car restraint systems were designed for the average male at an average speed of 50 km/hr, and people outside these norms are at risk. Therefore one of the future safety goals of the car manufacturers is to deploy sensors that would gain more information about the driver or passenger of their cars in order to tailor the safety systems specifically for that person, and this is the goal of this project. This thesis describes a novel approach to occupant detection, position measurement and monitoring using a low-cost thermal imaging based system, which is a departure from traditional video camera-based systems, and at an affordable price. Experiments were carried out using a specially designed test rig and a car driving simulator with members of the public. Results have shown that the thermal imager can detect a human in a car cabin mock up and provide crucial real-time position data, which could be used to support intelligent restraint deployment. Other valuable information has been detected such as whether the driver is smoking, drinking a hot or cold drink, using a mobile phone, which can help to infer the level of driver attentiveness or engagement