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

Driver-centered pervasive application for heart rate measurement

People spend a significant amount of time daily in the driving seat and some health complexity is possible to happen like heart-related problems, and stroke. Driver’s health conditions may also be attributed to fatigue, drowsiness, or stress levels when driving on the road. Drivers’ health is important to make sure that they are vigilant when they are driving on the road. A driver-centered pervasive application is proposed to monitor a driver’s heart rate while driving. The input will be acquired from the interaction between the driver and embedded sensors at the steering wheel, which is tied to a Bluetooth link with an Android smartphone. The driver can view his historical data easily in tabular or graph form with selected filters using the application since the sensor data are transferred to a real-time database for storage and analysis. The application is coupled with the tool to demonstrate an opportunity as an aftermarket service for vehicles that are not equipped with this technology

A review of technologies for heart attack monitoring systems

Every year, approximately 1.35 million people die in car accidents. One of the causes of traffic accidents is a heart attack while driving. Common heart attack warning signs are pain or discomfort in the chest or one or both arms or shoulders, light-headedness, faintness, cold sweat, and shortness of breath. When having a heart attack, a car driver has strong pain in the centre or left side of the chest. Current technology for heart attack detection is based on sensory signal properties such as the electrocardiogram (ECG), heart rate and oxygen saturation (SpO2). This paper is intended to give the readers an overview of technologies for heart attack monitoring system that has been used at the hospital, at the home and in the vehicle. The result shows that ECG, heart rate and SpO2 properties are mostly used by numerous researchers for heart attack monitoring systems at hospitals. Meanwhile, many researchers developed a system by using heart rate, ECG, SpO2 and images as properties for heart attack monitoring systems at home. Existing technologies for heart attack monitoring systems in the vehicle used heart rate and ECG as properties in a system. However, there are no review papers yet on heart attack monitoring systems using image processing in vehicles. We believe that researchers and practitioners will embrace this technology by addressing image processing in the heart attack monitoring system in vehicles

A novel Big Data analytics and intelligent technique to predict driver's intent

Modern age offers a great potential for automatically predicting the driver's intent through the increasing miniaturization of computing technologies, rapid advancements in communication technologies and continuous connectivity of heterogeneous smart objects. Inside the cabin and engine of modern cars, dedicated computer systems need to possess the ability to exploit the wealth of information generated by heterogeneous data sources with different contextual and conceptual representations. Processing and utilizing this diverse and voluminous data, involves many challenges concerning the design of the computational technique used to perform this task. In this paper, we investigate the various data sources available in the car and the surrounding environment, which can be utilized as inputs in order to predict driver's intent and behavior. As part of investigating these potential data sources, we conducted experiments on e-calendars for a large number of employees, and have reviewed a number of available geo referencing systems. Through the results of a statistical analysis and by computing location recognition accuracy results, we explored in detail the potential utilization of calendar location data to detect the driver's intentions. In order to exploit the numerous diverse data inputs available in modern vehicles, we investigate the suitability of different Computational Intelligence (CI) techniques, and propose a novel fuzzy computational modelling methodology. Finally, we outline the impact of applying advanced CI and Big Data analytics techniques in modern vehicles on the driver and society in general, and discuss ethical and legal issues arising from the deployment of intelligent self-learning cars

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

Development of Human-Computer Interactive Interface for Intelligent Automotive

The wide application of information technology and network technology in automobiles has made great changes in the Human-computer interaction. This paper studies the influence of Human-computer interaction modes on driving safety, comfort and efficiency based on physical interaction, touch screen control interaction, augmented reality, speech interaction and somatosensory interaction. The future Human-com-puter interaction modes such as multi-channel Human-computer interaction mode and Human-computer interaction mode based on biometrics and perception techno-logy are also discussed. At last, the method of automobile Human-computer interaction design based on the existing technology is proposed, which has certain guiding significance for the current automobile Human-computer interaction interface design

Methods to detect and reduce driver stress: a review

Automobiles are the most common modes of transportation in urban areas. An alert mind is a prerequisite while driving to avoid tragic accidents; however, driver stress can lead to faulty decision-making and cause severe injuries. Therefore, numerous techniques and systems have been proposed and implemented to subdue negative emotions and improve the driving experience. Studies show that conditions such as the road, state of the vehicle, weather, as well as the driver’s personality, and presence of passengers can affect driver stress. All the above-mentioned factors significantly influence a driver’s attention. This paper presents a detailed review of techniques proposed to reduce and recover from driving stress. These technologies can be divided into three categories: notification alert, driver assistance systems, and environmental soothing. Notification alert systems enhance the driving experience by strengthening the driver’s awareness of his/her physiological condition, and thereby aid in avoiding accidents. Driver assistance systems assist and provide the driver with directions during difficult driving circumstances. The environmental soothing technique helps in relieving driver stress caused by changes in the environment. Furthermore, driving maneuvers, driver stress detection, driver stress, and its factors are discussed and reviewed to facilitate a better understanding of the topic

Towards hybrid driver state monitoring : review, future perspectives and the role of consumer electronics

The purpose of this paper is to bring together multiple literature sources which present innovative methodologies for the assessment of driver state, driving context and performance by means of technology within a vehicle and consumer electronic devices. It also provides an overview of ongoing research and trends in the area of driver state monitoring. As part of this review a model of a hybrid driver state monitoring system is proposed. The model incorporates technology within a vehicle and multiple broughtin devices for enhanced validity and reliability of recorded data. Additionally, the model draws upon requirement of data fusion in order to generate unified driver state indicator(-s) that could be used to modify in-vehicle information and safety systems hence, make them driver state adaptable. Such modification could help to reach optimal driving performance in a particular driving situation. To conclude, we discuss the advantages of integrating hybrid driver state monitoring system into a vehicle and suggest future areas of research

Development of rear-end collision avoidance in automobiles

The goal of this work is to develop a Rear-End Collision Avoidance System for automobiles. In order to develop the Rear-end Collision Avoidance System, it is stated that the most important difference from the old practice is the fact that new design approach attempts to completely avoid collision instead of minimizing the damage by over-designing cars. Rear-end collisions are the third highest cause of multiple vehicle fatalities in the U.S. Their cause seems to be a result of poor driver awareness and communication. For example, car brake lights illuminate exactly the same whether the car is slowing, stopping or the driver is simply resting his foot on the pedal. In the development of Rear-End Collision Avoidance System (RECAS), a thorough review of hardware, software, driver/human factors, and current rear-end collision avoidance systems are included. Key sensor technologies are identified and reviewed in an attempt to ease the design effort. The characteristics and capabilities of alternative and emerging sensor technologies are also described and their performance compared. In designing a RECAS the first component is to monitor the distance and speed of the car ahead. If an unsafe condition is detected a warning is issued and the vehicle is decelerated (if necessary). The second component in the design effort utilizes the illumination of independent segments of brake lights corresponding to the stopping condition of the car. This communicates the stopping intensity to the following driver. The RECAS is designed the using the LabVIEW software. The simulation is designed to meet several criteria: System warnings should result in a minimum load on driver attention, and the system should also perform well in a variety of driving conditions. In order to illustrate and test the proposed RECAS methods, a Java program has been developed. This simulation animates a multi-car, multi-lane highway environment where car speeds are assigned randomly, and the proposed RECAS approaches demonstrate rear-end collision avoidance successfully. The Java simulation is an applet, which is easily accessible through the World Wide Web and also can be tested for different angles of the sensor

An Ambient Agent Model for Monitoring and Analysing Dynamics of Complex Human Behaviour

In ambient intelligent systems, monitoring of a human could consist of more complex tasks than merely identifying whether a certain value of a sensor is above a certain threshold. Instead, such tasks may involve monitoring of complex dynamic interactions between human and environment. In order to enable such more complex types of monitoring, this paper presents a generic agent-based framework. The framework consists of support on various levels of system design, namely: (1) the top level, including the interaction between agents, (2) the agent level, providing support on the design of individual agents, and (3) the level of monitoring complex dynamic behaviour, allowing the specification of the aforementioned complex monitoring properties within the agents. The approach is exemplified by a large case study concerning the assessment of driving behaviour, and is applied to two smaller cases as well (concerning fall detection of elderly, and assistance of naval operations, respectively), which are briefly described. These case studies have illustrated that the presented framework enables developers within ambient intelligence to build systems with more expressiveness regarding their monitoring focus. Moreover, they have shown that the framework is easy to use and applicable in a wide variety of domains. © 2011 - IOS Press and the authors. All rights reserved