Enhancing protection of vehicle drivers and road safety by deploying ADAS and Facial Features Pattern Analysis (FFPA) technologies

The latest technology associated with Intelligent Transportation Systems (ITS) have been designed with the aim to minimize the numbers of person injury in road accidents and improve the overall road safety. The driver behavior is one major concern in many accidents in HK urban road links. In particular, the driver\u27s attitudes, such as fatigue, drowsiness and concentration are the major causes to road accidents. It will affect the driver\u27s ability and decisions in properly controlling their vehicles. Very often, this kind of driver distraction is particularly obvious when driving after 2 to 3 hours from most research sources. In the traffic data sourced from Transport Department of HKSAR, around 82% of the personal injury in road accidents belongs to the driver\u27s fault. This paper used the latest technology and applied it to a group of transport vehicles, i.e. taxi. The objective is set up to monitor, record and analyze the fatigue and drowsiness situation of drivers by means of advanced AI system, facial recognition detection system (the sensors) and early warning devices (LDWS) via ADAS technology. The result will be used to give real time early warning and subsequent analysis for the transport operators or researchers for better and safer management of their transport fleets. The system aimed to have a good precaution and protection on all road users, including drivers, passengers and pedestrians. In turn, it largely saves our community resources, such as the medical and social services consumed on treating the injured persons

The effect of electronic word of mouth communication on purchase intention moderate by trust: a case online consumer of Bahawalpur Pakistan

The aim of this study is concerned with improving the previous research finding complete filling the research gaps and introducing the e-WOM on purchase intention and brand trust as a moderator between the e-WOM, and purchase intention an online user in Bahawalpur city Pakistan, therefore this study was a focus at linking the research gap of previous literature of past study based on individual awareness from the real-life experience. we collected data from the online user of the Bahawalpur Pakistan. In this study convenience sampling has been used to collect data and instruments of this study adopted from the previous study. The quantitative research methodology used to collect data, survey method was used to assemble data for this study, 300 questionnaire were distributed in Bahawalpur City due to the ease, reliability, and simplicity, effective recovery rate of 67% as a result 202 valid response was obtained for the effect of e-WOM on purchase intention and moderator analysis has been performed. Hypotheses of this research are analyzed by using Structural Equation Modeling (SEM) based on Partial Least Square (PLS). The result of this research is e-WOM significantly positive effect on purchase intention and moderator role of trust significantly affects the relationship between e-WOM, and purchase intention. The addition of brand trust in the model has contributed to the explanatory power, some studied was conduct on brand trust as a moderator and this study has contributed to the literature in this favor. significantly this study focused on current marketing research. Unlike past studies focused on western context, this study has extended the regional literature on e-WOM, and purchase intention to be intergrading in Bahawalpur Pakistan context. Lastly, future studies are recommended to examine the effect of trust in other countries allow for the comparison of the findings

Un nuevo sistema para detectar la distracción y la somnolencia utilizando el tiempo de tecnología de vuelo para vehículos inteligentes

Nowadays, most countries in the world suffer several traffic issues which generate public health problems such as deaths and injuries of drivers and pedestrians. In order to reduce these fatalities, a system for automatic detection of both distraction and drowsiness is presented in this research. Artificial intelligence, computer vision and time of flight (TOF) technologies are used to compute both distraction and drowsiness indexes, in real time. Several experiments have been developed in real conditions during the day, inside a real vehicle and in laboratory conditions, to prove the efficiency of the system.La mayoría de los países en el mundo sufren de varios problemas de tráfico que generan problemas de salud pública, tales como, excesivas muertes y lesiones de los conductores y los peatones. Con el fin de reducir estas cifras de siniestralidad, en esta investigación se presenta un sistema para la detección automática de la distracción y la somnolencia. Las tecnologías de inteligencia artificial, visión por computador y una cámara de tiempo de vuelo (TOF) son utilizadas para calcular los índices de distracción y somnolencia, en tiempo real. Varios experimentos se han desarrollado en condiciones reales durante el día, dentro de un vehículo real y en el laboratorio, para probar la eficiencia del sistema

Obstructive sleep apnoea and driver performance: prevalence, correlates and implications for driver fatigue

Obstructive sleep apnoea (OSA) is characterised by repetitive reductions or pauses in breathing during sleep due to upper airway narrowing or closure. Due to disruption to normal sleep patterns, many patients with OSA suffer from increased daytime sleepiness. Epidemiological studies have established a link between OSA and driver fatigue and accidents, generally showing a two to seven times increased risk of road traffic accidents in non-commercial drivers with OSA. There is emerging evidence that commercial drivers have a higher prevalence of OSA than the general population, being predominately male, middle-aged and overweight, three important risk factors for OSA. However, little is known about the relationship between OSA and driver sleepiness in commercial drivers, whether road accidents are increased in commercial drivers with OSA, and whether OSA interacts with other fatigue promoting factors, such as sleep deprivation, to further escalate road accident risk. One thousand randomly selected commercial drivers were surveyed in the field. In addition, 61 randomly selected NSW commercial drivers had in hospital sleep studies and daytime performance testing, including a PC based driving simulator task. The prevalence of OSA, defined as Respiratory Disturbance Index (RDI) < 10, was approximately 50% in NSW commercial drivers. Approximately one quarter of the drivers reported pathological daytime sleepiness, and 12-14% had both OSA and pathological daytime sleepiness. A diagnosis of OSA was the most important factor predicting excessive daytime sleepiness in these drivers: OSA was more important than 15 other work-related, lifestyle and medical factors that could be expected to promote, or be associated with, daytime sleepiness. Drivers with sleep apnoea syndrome (both OSA and pathological daytime sleepiness) had an increased driving accident risk, using driving simulator and daytime performance testing as proxy measures for accident risk. These results demonstrate the importance of OSA as a cause of driver fatigue in commercial drivers and suggest that all commercial drivers should be screened for the presence of sleep apnoea syndrome in order to potentially reduce road accident risk through treatment. A separate, but related body of work examined the combined effects of mild OSA and other fatigue promoting factors (sleep deprivation and circadian influences) on driving performance. Twenty nine subjects, consisting of a group with mild OSA and a group of non-OSA controls, were tested on several occasions throughout the night and day using an intensive performance battery, under both baseline conditions and after a period of 36 hours of total sleep deprivation. The results suggest that drivers with mild OSA are not different to the control group in their response to sleep deprivation or time of day influences. However, the subjects with mild OSA were less aware of their impairment due to sleep deprivation, which is of concern if drivers with OSA are relying on their subjective awareness of fatigue to make decisions about when to stop driving. A final perspective on OSA and driver fatigue is provided through a clinical case series of seven fall-asleep fatality associated MVA�s associated with unrecognised or under-treated sleep disorders. As well as demonstrating the day to day potential for devastating road accidents due, at least in part, to un-recognised or untreated sleep disorders, these cases also serve to highlight some of the current medico-legal controversies and difficulties in this area of driver fatigue. In conclusion, this body of work has provided novel information about the epidemiology and implications of OSA in commercial drivers, and about how OSA interacts with other fatigue promoting factors. Finally, it has explored some of the medico-legal issues that relate to sleep disorders and driver fatigue. As well as providing much needed information in the area of driver fatigue, at the same time this work raises many more questions and suggests areas of future research. For instance, such research should examine the relationship between objective accident rates and OSA/sleep apnoea syndrome in commercial drivers, the interaction between mild sleep apnoea syndrome and other fatigue risk factors, and driver perception of sleepiness prior to sleep onset in drivers with sleep disorders

Computer vision and laser scanner road environment perception

Data fusion procedure is presented to enhance classical Advanced Driver Assistance Systems (ADAS). The novel vehicle safety approach, combines two classical sensors: computer vision and laser scanner. Laser scanner algorithm performs detection of vehicles and pedestrians based on pattern matching algorithms. Computer vision approach is based on Haar-Like features for vehicles and Histogram of Oriented Gradients (HOG) features for pedestrians. The high level fusion procedure uses Kalman Filter and Joint Probabilistic Data Association (JPDA) algorithm to provide high level detection. Results proved that by means of data fusion, the performance of the system is enhanced.This work was supported by the Spanish Government through the Cicyt projects (GRANT TRA2010-20225-C03-01) and (GRANT TRA 2011-29454-C03-02). CAM through SEGAUTO-II (S2009IDPI-1509)

Effects of circadian rhythm phase alteration on physiological and psychological variables: Implications to pilot performance (including a partially annotated bibliography)

The effects of environmental synchronizers upon circadian rhythmic stability in man and the deleterious alterations in performance and which result from changes in this stability are points of interest in a review of selected literature published between 1972 and 1980. A total of 2,084 references relevant to pilot performance and circadian phase alteration are cited and arranged in the following categories: (1) human performance, with focus on the effects of sleep loss or disturbance and fatigue; (2) phase shift in which ground based light/dark alteration and transmeridian flight studies are discussed; (3) shiftwork; (4)internal desynchronization which includes the effect of evironmental factors on rhythmic stability, and of rhythm disturbances on sleep and psychopathology; (5) chronotherapy, the application of methods to ameliorate desynchronization symptomatology; and (6) biorythm theory, in which the birthdate based biorythm method for predicting aircraft accident susceptability is critically analyzed. Annotations are provided for most citations

Detection of Driver Drowsiness and Distraction Using Computer Vision and Machine Learning Approaches

Drowsiness and distracted driving are leading factor in most car crashes and near-crashes. This research study explores and investigates the applications of both conventional computer vision and deep learning approaches for the detection of drowsiness and distraction in drivers. In the first part of this MPhil research study conventional computer vision approaches was studied to develop a robust drowsiness and distraction system based on yawning detection, head pose detection and eye blinking detection. These algorithms were implemented by using existing human crafted features. Experiments were performed for the detection and classification with small image datasets to evaluate and measure the performance of system. It was observed that the use of human crafted features together with a robust classifier such as SVM gives better performance in comparison to previous approaches. Though, the results were satisfactorily, there are many drawbacks and challenges associated with conventional computer vision approaches, such as definition and extraction of human crafted features, thus making these conventional algorithms to be subjective in nature and less adaptive in practice. In contrast, deep learning approaches automates the feature selection process and can be trained to learn the most discriminative features without any input from human. In the second half of this research study, the use of deep learning approaches for the detection of distracted driving was investigated. It was observed that one of the advantages of the applied methodology and technique for distraction detection includes and illustrates the contribution of CNN enhancement to a better pattern recognition accuracy and its ability to learn features from various regions of a human body simultaneously. The comparison of the performance of four convolutional deep net architectures (AlexNet, ResNet, MobileNet and NASNet) was carried out, investigated triplet training and explored the impact of combining a support vector classifier (SVC) with a trained deep net. The images used in our experiments with the deep nets are from the State Farm Distracted Driver Detection dataset hosted on Kaggle, each of which captures the entire body of a driver. The best results were obtained with the NASNet trained using triplet loss and combined with an SVC. It was observed that one of the advantages of deep learning approaches are their ability to learn discriminative features from various regions of a human body simultaneously. The ability has enabled deep learning approaches to reach accuracy at human level.

Integration of body sensor networks and vehicular ad-hoc networks for traffic safety

The emergence of Body Sensor Networks (BSNs) constitutes a new and fast growing trend for the development of daily routine applications. However, in the case of heterogeneous BSNs integration with Vehicular ad hoc Networks (VANETs) a large number of difficulties remain, that must be solved, especially when talking about the detection of human state factors that impair the driving of motor vehicles. The main contributions of this investigation are principally three: (1) an exhaustive review of the current mechanisms to detect four basic physiological behavior states (drowsy, drunk, driving under emotional state disorders and distracted driving) that may cause traffic accidents is presented; (2) A middleware architecture is proposed. This architecture can communicate with the car dashboard, emergency services, vehicles belonging to the VANET and road or street facilities. This architecture seeks on the one hand to improve the car driving experience of the driver and on the other hand to extend security mechanisms for the surrounding individuals; and (3) as a proof of concept, an Android real-time attention low level detection application that runs in a next-generation smartphone is developed. The application features mechanisms that allow one to measure the degree of attention of a driver on the base of her/his EEG signals, establish wireless communication links via various standard wireless means, GPRS, Bluetooth and WiFi and issue alarms of critical low driver attention levels.Peer ReviewedPostprint (author's final draft