Vision-based Detection of Mobile Device Use While Driving

The aim of this study was to explore the feasibility of an automatic vision-based solution to detect drivers using mobile devices while operating their vehicles. The proposed system comprises of modules for vehicle license plate localisation, driver’s face detection and mobile phone interaction. The system were then implemented and systematically evaluated using suitable image datasets. The strengths and weaknesses of individual modules were analysed and further recommendations made to improve the overall system’s performance

Improvements on the enforcement process based on intelligent transportation techniques: model and mechanisms for electronic reporting, offence notification and evidence generation

Enforcement activities in the road traffi c context have shown to be one of the key factors for reducing fatalities. However, despite their evolution (both in their underlying legislation and their technical means), there are several aspects that may be subject to improvement. Three of them are on the focus of this thesis. First, victims of offenders are usually not able to report them, as there are not enough data to support their claims. Second, there is a significant delay between the offence and its notification, which negatively affects to its educational purpose. Third, the offender does not have the practical chance to defend herself (i.e. claim her innocence or, at least, that it was a less serious offence) as there are no suitable attesting elements. In order to contribute on these issues, recent advances on data processing, communication and sensing capabilities of vehicles conform an interesting technological context. These new capabilities are the basis over which a new family of services, called Intelligent Transportation Systems (ITS) are being developed. Despite the new opportunities provided by ITSs, it does not exist an adequate framework to guide the introduction of these new techniques in the surveillance of the adherence to the road traffi c rules. Thus, there is a lack of a clear view on how these techniques may help on the aforementioned problems. The general goal of this thesis is to provide the technical basis for the realization of an ITS-enhanced electronic road traffi c administrative enforcement process. Particularly, four contributions are developed in this thesis. First, an enforcement process model is proposed, based on the results of the European VERA2 project. The model describes the entities, the stakeholders, the data at stake and the underlying security considerations. It conforms the aforementioned framework that enables identifying where to introduce the required ITS enhancements. Based on the previous model, the remaining contributions focus on the development of specific mechanisms where the enforcement actors (the offender, the offence witnesses, the victims and the Authority) participate actively through ITS-related technologies. Thus, the second contribution is a mechanism that enables victims to report their offenders. In order to prevent this action to be noticeable by the reported driver, the report information is embedded into innocuous-looking messages by means of steganography. As the educational purpose of the punishment grows with its immediacy, the third contribution is a protocol to send an offence notification to the offending vehicle. Thanks to the human-machine interface of the vehicle, the offender is able to realize about the fine even during the same trip in which the offence was committed. Finally, in order to ensure that the driver has adequate means to defend herself against unfair punishments, a protocol to create evidences on its recent driving behavior has been proposed. Such evidences are based on the sensorial perceptions by surrounding vehicles, which are contacted using ITS communication technologies. At the light of these contributions, this thesis opens the door to upcoming developments that may end into a fully automated enforcement process. ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------Uno de los factores m as críticos para la reducción de la siniestralidad en las carreteras es la vigilancia del cumplimiento de las normas de circulación. A pesar de la evolución de los procedimientos y técnicas para efectuar dicha vigilancia (tanto en el ámbito normativo como en el técnico), existen algunos factores que son susceptibles de mejora. Tres de ellos constituyen el foco principal de esta tesis. En primer lugar, las víctimas de los infractores no disponen de medios prácticos para denunciarles, pues habitualmente no existen datos que permitan acreditar la descripción de los hechos manifestada. En segundo lugar, existe un intervalo significativo de tiempo entre la comisión de la infracción y la recepción de la notificación de la correspondiente denuncia, lo que afecta negativamente a la capacidad educativa de las sanciones. En tercer lugar, el supuesto infractor no dispone de medios prácticos para defenderse, pues habitualmente no se cuenta con elementos que soporten su argumento. Con el fin de contribuir a estas cuestiones, los avances recientes en materia de procesamiento de información, transmisión de información y percepción sensorial en los vehículos constituyen un contexto tecnológico interesante. Estas nuevas capacidades son la base sobre la que se construyen los Sistemas Inteligentes de Transporte (habitualmente referidos mediante sus siglas en ingl es, ITS). A pesar del desarrollo constante de dichos sistemas, no existe un marco adecuado para la utilización de dichas capacidades en el ámbito de la vigilancia del cumplimiento de las normas de circulación. Así, se detecta una carencia de una visión clara de cómo estas nuevas técnicas pueden contribuir a resolver los aspectos problemáticos identificados anteriormente. El objetivo general de esta tesis es proporcionar la base técnica para el desarrollo de un procedimiento administrativo sancionador en el ámbito del tr áfico que aproveche las oportunidades que plantean los ITS. En particular, en esta tesis se desarrollan cuatro contribuciones. En primer lugar, se propone un modelo de procedimiento administrativo sancionador, extendiendo los resultados del proyecto de investigación europeo VERA2. Este modelo describe las entidades participantes, los interesados, la información en juego y las consideraciones de seguridad subyacentes. Este modelo constituye el antedicho marco y permite identificar la forma de introducir las tecnologías ITS en dicho proceso. Basándose en este modelo, las contribuciones restantes se centran en el desarrollo de mecanismos espec íficos en los que los actores del proceso (el infractor, los testigos, las víctimas y la Autoridad) participan activamente empleando tecnologías relacionadas con los ITS. Así, la segunda contribuci ó es un mecanismo que permite a las víctimas denunciar a los infractores. Con el objetivo de impedir que dicha denuncia sea conocida por el infractor, el mensaje es introducido mediante técnicas esteganográficas en otro mensaje aparentemente inofensivo. La tercera contribución es el envío de la notificaci on de forma directa al vehí culo infractor, lo cual pretende incrementar la inmediatez del proceso (ya que se le puede presentar al infractor durante la conducción) y, con ello, su eficacia educativa. Finalmente, para promover que el conductor disponga de los medios adecuados para defenderse de sanciones supuestamente injustas, se propone un protocolo para la creaci on de evidencias que describan su comportamiento reciente en lo que respecta a la conducción. Dichas evidencias se basan en las percepciones sensoriales de los vehículos cercanos, los cuales son contactados empleando tecnologías de comunicaci on relacionadas con los ITS. A la vista de estas contribuciones, esta tesis abre la puerta al futuro desarrollo de un proceso sancionador completamente automatizado

Developing a Visitor Profile: The Hill of Tara for Hill of Tara Conservation Management Plan

A Conservation Management Plan for the State-owned lands at the Hill of Tara was commissioned by the Minister for Culture, Heritage and the Gaeltacht in January 2018. As an element of this at the request of the Heritage Council and the Discovery Programme, a visitor profile was undertaken by staff and students of the School of Hospitality Management and Tourism, Dublin Institute of Technology (DIT). According to the Office of Public Works (OPW), over 200,000 people visit Tara archaeological site each year, and the site is being actively promoted as part of the Ireland’s Ancient East brand by Fáilte Ireland. As part of the Conservation Management Plan consideration is required as to how the site should be managed in the future, and a visitor profile is the first step in identifying aspects of visitation to the site. The objective of the research work was to develop a visitor profile of the Tara archaeological site in County Meath. The survey was administered to visitors to the Hill of Tara over one day, 18 July 2018. The survey was administered by three volunteer students from the BA in Tourism Management (DT406), DIT, facilitated by Dr. Catherine Gorman. These students were informed of the survey and were offered an opportunity to volunteer to undertake the wor

Measuring Information Security Awareness Efforts in Social Networking Sites – A Proactive Approach

For Social Network Sites to determine the effectiveness of their Information Security Awareness (ISA) techniques, many measurement and evaluation techniques are now in place to ensure controls are working as intended. While these techniques are inexpensive, they are all incident- driven as they are based on the occurrence of incident(s). Additionally, they do not present a true reflection of ISA since cyber-incidents are hardly reported. They are therefore adjudged to be post-mortem and risk permissive, the limitations that are inacceptable in industries where incident tolerance level is low. This paper aims at employing a non-incident statistic approach to measure ISA efforts. Using an object- oriented programming approach, PhP is employed as the coding language with MySQL database engine at the back-end to develop sOcialistOnline – a Social Network Sites (SNS) fully secured with multiple ISA techniques. Rather than evaluating the effectiveness of ISA efforts by success of attacks or occurrence of an event, password scanning is implemented to proactively measure the effects of ISA techniques in sOcialistOnline. Thus, measurement of ISA efforts is shifted from detective and corrective to preventive and anticipatory paradigms which are the best forms of information security approach

Detecting Rogue Nodes In Vehicular Ad-hoc Networks (DETER)

Vehicular ad hoc Networks (VANETs) are self-organizing networks of vehicles equipped with radios and processors. VANETs are very promising as they can make driving safer by improving road awareness through sharing of information from sensors. Vehicles communicate with each other wirelessly to exchange information and this exchange of information is susceptible to attacks of different kinds. There are some very important issues that need to be resolved before VANETs can be deployed on large scale. Security and privacy issues are undoubtedly the most important factors that need to be resolved. Amongst various problems to be solved in VANETs is the issue of rogue nodes and their impact on the network. This thesis discusses the problems associated with the security and privacy of vehicular networks in the presence of rogue nodes. The rogue nodes can share / inject false data in the network which can cause serious harm. The techniques proposed make VANETs secure and prevent them from the harmful impact of rogue nodes. The proposed work makes the network safer by making it fault tolerant and resilient in the presence of rogue nodes that can be detected and reported. As VANETs are highly dynamic and fast moving so, a data centric scheme is proposed that can determine if a node is rogue or not just by analysing its data. The work then enhances the developed mechanism by applying hypothesis testing and other statistical techniques to detect intrusions in the network by rogue nodes. The technique is simulated using OMNET++, SUMO and VACAMobil and the results obtained have been presented, discussed and compared to previous works. In order to prevent rogue nodes from becoming part of the VANETs this thesis also presents a novel framework for managing the digital identity in the vehicular networks. This framework authenticates the user and the vehicle separately from two authorities and allows him to communicate securely with the infrastructure using IBE (Identity Based Encryption). The proposed technique also preserves the privacy of the user. The proposed scheme allows traceability and revocation so that users can be held accountable and penalised. The results have been compared to previous works of similar nature. The thesis also discusses the Sybil attack and how to detect them using game theory in a VANET environment

A Context Aware Classification System for Monitoring Driver’s Distraction Levels

Understanding the safety measures regarding developing self-driving futuristic cars is a concern for decision-makers, civil society, consumer groups, and manufacturers. The researchers are trying to thoroughly test and simulate various driving contexts to make these cars fully secure for road users. Including the vehicle’ surroundings offer an ideal way to monitor context-aware situations and incorporate the various hazards. In this regard, different studies have analysed drivers’ behaviour under different case scenarios and scrutinised the external environment to obtain a holistic view of vehicles and the environment. Studies showed that the primary cause of road accidents is driver distraction, and there is a thin line that separates the transition from careless to dangerous. While there has been a significant improvement in advanced driver assistance systems, the current measures neither detect the severity of the distraction levels nor the context-aware, which can aid in preventing accidents. Also, no compact study provides a complete model for transitioning control from the driver to the vehicle when a high degree of distraction is detected. The current study proposes a context-aware severity model to detect safety issues related to driver’s distractions, considering the physiological attributes, the activities, and context-aware situations such as environment and vehicle. Thereby, a novel three-phase Fast Recurrent Convolutional Neural Network (Fast-RCNN) architecture addresses the physiological attributes. Secondly, a novel two-tier FRCNN-LSTM framework is devised to classify the severity of driver distraction. Thirdly, a Dynamic Bayesian Network (DBN) for the prediction of driver distraction. The study further proposes the Multiclass Driver Distraction Risk Assessment (MDDRA) model, which can be adopted in a context-aware driving distraction scenario. Finally, a 3-way hybrid CNN-DBN-LSTM multiclass degree of driver distraction according to severity level is developed. In addition, a Hidden Markov Driver Distraction Severity Model (HMDDSM) for the transitioning of control from the driver to the vehicle when a high degree of distraction is detected. This work tests and evaluates the proposed models using the multi-view TeleFOT naturalistic driving study data and the American University of Cairo dataset (AUCD). The evaluation of the developed models was performed using cross-correlation, hybrid cross-correlations, K-Folds validation. The results show that the technique effectively learns and adopts safety measures related to the severity of driver distraction. In addition, the results also show that while a driver is in a dangerous distraction state, the control can be shifted from driver to vehicle in a systematic manner

Shaping the future through Artificial Intelligent technologies to reduce vehicle accidents in Abu Dhabi

Traffic accidents (TAs) constitute one of the top killers in the United Arab Emirates (UAE). Without practical approaches to address the TAs in Abu Dhabi, the region is likely to experience continued economic losses and health burden to the affected families and country. This study identified interventions and solutions for mitigating TAs in Abu Dhabi. The study was guided by research questions that focused on the causes of accidents and how to mitigate TA. The study was based on descriptive observational methodology where quantitative data was collected using a detailed survey questionnaire (n= 300) that assessed various aspects relating to the driver’s behaviour. The 2007 to 2017 MVC injuries baseline data were also analyzed. Data on TAs control strategies from existing studies were used to assess the artificial intelligent approaches in road safety management. The quantitative data analysis was carried out using SPSS software and Microsoft Excel software. The study findings showed that the most common traffic problems on Abu Dhabi's roads include driver-related factors, vehicular factors, and road condition-related factors. Risky overtaking, violation of the need to keep a safe distance and violation of speed limits were noted as the significant violations associated with the traffic problems on Abu Dhabi’s roads. The baseline data analysis findings indicated that the three regions in Abu Dhabi registered a general reduction in TAs over the 10 years (2007 to 2017). However, the reduction in Al Ain was minimal over the study period. The study’s findings relating to the forecasting of the accident trends showed that the Western region and Abu Dhabi would continue to experience a reduction in TAs in the future while the frequency of accidents in Al Ain will increase between 2017 and 2024. Most of the accidents in Abu Dhabi are associated with driver behaviour. The identified risky driver behaviours include the failure to keep adequate distance, maintain recommended speeds, and reckless driving. The study also noted the need to adopt artificial intelligent based interventions to limit the occurrence of accidents and enhance road safety. Based on the reported findings, management of the traffic problems need to focus on controlling risky driver behaviours. Road safety authorities in Abu Dhabi should adopt artificial intelligent approaches in the management of road safety