Forward Collision Avoidance Systems (FCAS) are a type of active safety system which have great potential for rear-end collision avoidance. These systems use either radar, lidar, or cameras to track objects in front of the vehicle. In the event of an imminent collision, the system will warn the driver, and, in some cases, can autonomously brake to avoid a crash. However, driver acceptance of the systems is paramount to the effectiveness of a FCAS system. Ideally, FCAS should only deliver an alert or intervene at the last possible moment to avoid nuisance alarms, and potentially have drivers disable the system. A better understanding of normal driving behavior can help designers predict when drivers would normally take avoidance action in different situations, and customize the timing of FCAS interventions accordingly. The overall research object of this dissertation was to characterize normal driver behavior in car following events based on naturalistic driving data. The dissertation analyzed normal driver behavior in car-following during both braking and lane change maneuvers. This study was based on the analysis of data collected in the Virginia Tech Transportation Institute 100-Car Naturalistic Driving Study which involved over 100 drivers operating instrumented vehicles in over 43,000 trips and 1.1 million miles of driving. Time to Collision in both braking and lane change were quantified as a function of vehicle speed and driver characteristics. In general, drivers were found to brake and change lanes more cautiously with increasing vehicle speed. Driver age and gender were found to have significant influence on both time to collision and maximum deceleration during braking. Drivers age 31-50 had a mean braking deceleration approximately 0.03 g greater than that of novice drivers (age 18-20), and female drivers had a marginal increase in mean braking deceleration as compared to male drivers. Lane change maneuvers were less frequent than braking maneuvers. Driver-specific models of TTC at braking and lane change were found to be well characterized by the Generalized Extreme Value distribution. Lastly, driver's intent to change lanes can be predicted using a bivariate normal distribution, characterizing the vehicle's distance to lane boundary and the lateral velocity of the vehicle. This dissertation presents the first large scale study of its kind, based on naturalistic driving data to report driver behavior during various car-following events. The overall goal of this dissertation is to provide a better understanding of driver behavior in normal driving conditions, which can benefit automakers who seek to improve FCAS effectiveness, as well as regulatory agencies seeking to improve FCAS vehicle tests. / Ph. D.

The driver-specific data from a naturalistic driving study provides car-following events in real-world driving situations, while additionally providing a wealth of information about the participating drivers. Reducing a naturalistic database into finite car-following events requires significant data reduction, validation, and calibration, often using manual procedures. The data collection performed herein included: the identification of commuting routes used by multiple drivers, the extraction of data along those routes, the identification of potential car-following events from the dataset, the visual validation of each car-following event, and the extraction of pertinent information from the database during each event identified. This thesis applies the developed process to generate car-following events from the 100-Car Study database, and applies the dataset to analyze four car-following models. The Gipps model was found to perform best for drivers with greater amounts of data in congested driving conditions, while the Rakha-Pasumarthy-Adjerid (RPA) model was best for drivers in uncongested conditions. The Gipps model was found to generate the lowest error value in aggregate, with the RPA model error 21 percent greater, and the Gaxis-Herman-Rothery model (GHR) and the Intelligent Driver Model (IDM) errors 143 percent and 86 percent greater, respectively. Additionally, the RPA model provides the flexibility for a driver to change vehicles without the need to recalibrate parameter values for that driver, and can also capture changes in roadway surface type and condition. With the error values close between the RPA and Gipps models, the additional advantages of the RPA model make it the recommended choice for simulation. / Master of Science

Within the set of all vehicle crashes that occur annually, of intersection-related crashes are over-represented. The research conducted here uses an empirical approach to study driver behavior at intersections, in a naturalistic paradigm. A data-mining algorithm was used to aggregate the data from two different naturalistic databases to obtain instances of unprotected turns at the same intersection. Several dependent variables were analyzed which included visual entropy, mean-duration of glances to locations in the driver's view, gap-acceptance/rejection time. Kinematic dependent variables include peak/average speed, and peak longitudinal and lateral acceleration. Results indicated that visual entropy and peak speed differs amongst drivers of the three age-groups (older, middle-age, teens) in the presence of traffic in the intersecting streams while negotiating a left turn. Although not significant, but approaching significance, were differences in gap acceptance times, with the older driver accepting larger gaps compared to the younger teen drivers. Significant differences were observed for peak speed and average speed during a left turn, with younger drivers exhibiting higher values for both. Overall, this research has resulted in contribution towards two types of engineering application. Firstly, the analyses of traffic levels, gap acceptance, and gap non-acceptance represented exploratory efforts, ones that ventured into new areas of technical content, using newly available naturalistic driving data. Secondly, the findings from this thesis are among the few that can be used to inform the further development, refinement, and testing of technology (and training) solutions intended to assist drivers in making successful turns and avoiding crashes at intersections. / Master of Science

This study is the first examination on the association between seven types of sleep disorder and driving risk using large-scale naturalistic driving study data involving more than 3,400 participants. Regression analyses revealed that females with restless leg syndrome or sleep apnea and drivers with insomnia, shift work sleep disorder, or periodic limb movement disorder are associated with significantly higher driving risk than other drivers without those conditons. Furthermore, despite a small number of observations, there is a strong indication of increased risk for narcoleptic drivers. The findings confirmed results from simulator and epidemiological studies that the driving risk increases amongst people with certain types of sleep disorders. However, this study did not yield evidence in naturalistic driving settings to confirm significantly increased driving risk associated with migraine in prior research. The inconsistency may be an indication that the significant decline in cognitive performance among drivers with sleep disorders observed in laboratory settings may not nessarily translate to an increase in actual driving risk. Further research is necessary to define how to incentivize drivers with specific sleep disorders to balance road safety and personal mobility. / Master of Science

Motor vehicle crashes are one of the leading causes of death in the United States. Traffic safety research targets at understanding the cause of crash, preventing the crash, and mitigating crash severity. This dissertation focuses on the driver-related traffic safety issues, in particular, on developing and implementing contemporary statistical modeling techniques on driving risk research on Naturalistic Driving Study data. The dissertation includes 5 chapters. In Chapter 1, I introduced the backgrounds of traffic safety research and naturalistic driving study. In Chapter 2, the state-of-practice statistical methods were implemented on individual driver risk assessment using NDS data. The study showed that critical-incident events and driver demographic characteristics can serve as good predictors for identifying risky drivers. In Chapter 3, I developed and evaluated a novel Bayesian random exposure method for Poisson regression models to account for situations where the exposure information needs to be estimated. Simulation studies and real data analysis on Cellphone Pilot Analysis study data showed that, random exposure models have significantly better model fitting performances and higher parameter coverage probabilities as compared to traditional fixed exposure models. The advantage is more apparent when the values of Poisson regression coefficients are large. In Chapter 4, I performed comprehensive simulation-based performance analyses to investigate the type-I error, power and coverage probabilities on summary effect size in classical meta-analysis models. The results shed some light for reference on the prospective and retrospective performance analysis in meta-analysis research. In Chapter 5, I implemented classical- and Bayesian-approach multi-group hierarchical models on 100-Car data. Simulation-based retrospective performance analyses were used to investigate the powers and parameter coverage probabilities among different hierarchical models. The results showed that under fixed-effects model context, complex secondary tasks are associated with higher driving risk. / Ph. D.

Fixed-object crashes have long time been considered as major roadway safety concerns. While previous relevant studies tended to address such crashes in the context of roadway departures, and heavily relied on police-reported accidents data, this study integrated the SHRP2 NDS and RID data for analyses, which fully depicted the prior to, during, and after crash scenarios. A total of 1,639 crash, near-crash events, and 1,050 baseline events were acquired. Three analysis methods: logistic regression, support vector machine (SVM) and artificial neural network (ANN) were employed for two responses: crash occurrence and severity level. Logistic regression analyses identified 16 and 10 significant variables with significance levels of 0.1, relevant to driver, roadway, environment, etc. for two responses respectively. The logistic regression analyses led to a series of findings regarding the effects of explanatory variables on fixed-object event occurrence and associated severity level. SVM classifiers and ANN models were also constructed to predict these two responses. Sensitivity analyses were performed for SVM classifiers to infer the contributing effects of input variables. All three methods obtained satisfactory prediction performance, that was around 88% for fixed-object event occurrence and 75% for event severity level, which indicated the effectiveness of NDS event data on depicting crash scenarios and roadway safety analyses. / Master of Science / Fixed-object crashes happen when a single vehicle strikes a roadway feature such as a curb or a median, or runs off the road and hits a roadside feature such as a tree or utility pole. They have long time been considered as major highway safety concerns due to their high frequency, fatality rate, and associated property cost. Previous studies relevant to fixed-object crashes tended to address such crashes in the contexture of roadway departures, and heavily relied on police-reported accident data. However, many fixed-object crashes involved objects in roadway such as traffic control devices, roadway debris, etc. The police-reported accident data were found to be weak in depicting scenarios prior to, during crashes. Also, many minor crashes were often kept unreported. The Second Strategic Highway Research Program (SHRP2) Naturalistic Driving Study (NDS) is the largest NDS project launched across the country till now, aimed to study driver behavior or, performance-related safety problems under real-world scenarios. The data acquisition systems (DASs) equipped on participated vehicles collect vehicle kinematics, roadway, traffic, environment, and driver behavior data continuously, which enable researchers to address such crash scenarios closely. This study integrated SHRP2 NDS and roadway information database (RID) data to conduct a comprehensive analysis of fixed-object crashes. A total of 1,639 crash, near-crash events relevant to fixed objects and animals, and 1,050 baseline events were used. Three analysis methods: logistic regression, support vector machine (SVM) and artificial neural network (ANN) were employed for two responses: crash occurrence and severity level. The logistic regression analyses identified 16 and 10 variables with significance levels of 0.1 for fixed-object event occurrence and severity level models respectively. The influence of explanatory variables was discussed in detail. SVM classifiers and ANN models were also constructed to predict the fixed-object crash occurrence and severity level. Sensitivity analyses were performed for SVM classifiers to infer the contributing effects of input variables. All three methods achieved satisfactory prediction accuracies of around 88% for crash occurrence prediction and 75% for crash severity level prediction, which suggested the effectiveness of NDS event data on depicting crash scenarios and roadway safety analyses.

Thesis (MEng)--Stellenbosch University, 2014. / ENGLISH ABSTRACT: Driving is a complex task that requires both the physical ability to drive a vehicle and the cognitive ability to do so safely. The ability to correctly integrate and apply information from the driving environment is essential for safe driving. In South Africa approximately 33 people per 100 000 population are killed annually in road accidents. Recent mortality data from South Africa has indicated that the age group 15 to 19 years old are the age group most likely to be involved in fatal vehicle crashes. Novice driver behaviour has been confirmed as problematic across the globe and extensive research into novice driver behaviour has been conducted to understand and ultimately to curb novice driver deaths. Very little is known about South African novice drivers. This lack of knowledge makes it difficult to plan for training, education or preparing young South African novice drivers for the challenges they are likely to face on the road. This study is a first stepping stone to understand this problem. This study utilises naturalistic driving studies as a method to explore differences between novice and experienced driver behaviour at a few preselected location types. Since 2005 naturalistic driving studies (NDS) have been employed extensively in the rest of the world and this study is South Africa’s first small attempt to employ this methodology and apply it to specifically novice driver behaviour. This thesis therefore not only explores novice driver behaviour in the context of South Africa, but also provides an overview of how the ND methodology can be developed for use in South Africa. The document provides an overview of both novice driver behaviour and naturalistic driving study methodologies from abroad. Where available, reference to South African research and reports are made. The literature review considers demographic, developmental and personality factors that could potentially (and have internationally been proven to) influence novice driver behaviour in the context of society, family and physical environments. Popular theories that have been applied to novice driver behaviour are reviewed. These theories include the Theory of Planned Behaviour, Social Learning Theory and the Theory of Intent. On the methodology side, the technology, its application as well as challenges and successes of the ND methodology are reviewed. The research process is described in terms of the participants and their risk attitudes to road traffic safety prior and after the study. The research process also details the specifications of the technology used, the data collected and the associated processes to make the data manageable. The research process took a number of unexpected turns which included the development of a coding scheme for the image material. Initially it was thought that this coding scheme should be predefined. However once the coding process commenced it was clear that in-vivo coding was necessary for inclusion of all elements of the environment and the behaviour. These elements differed from video to video and participant to participant. Grounded theory was introduced in an attempt to explain the novice behaviour. Although the data analysed was not extensive enough to substantiate the use of grounded theory it is considered useful in operationalizing this coding scheme in future. In addition to learning how to work with the data collection systems and how to integrate different types of quantitative and qualitative data in different formats, it also became clear that a strategy for managing large databases should be considered. This was an unexpected spin-off and is currently being investigated. The findings of the study showed that certain behaviours (such as the left scanning of a driving environment) were neglected not only by novice drivers but also by experienced drivers. Further investigations could include research into understanding this phenomenon. The preselected site types included stop streets, traffic lights, traffic circles and intersections. Traffic lights and intersections in particular have in recent years been highlighted as hazardous locations in Pretoria, where the study took place. Differences in behaviours were highlighted for intersections but not for traffic lights, stop streets or traffic circles. However the difference in the proportion of time that novice and experienced drivers took to scan their environments around these preselected hazardous locations differed significantly. Experienced drivers were much more thorough than their novice counterparts. This study was aimed at investigating the differences between novice and experienced drivers and aimed to develop recommendations that could potentially have implications for changing the driver training and education milieu in SA. However, the sample size (both participants and material selected for analysis) was too small to make meaningful recommendations towards change in this industry. It did however show clear differences between novice and experienced drivers, even in South Africa, and that this research needs to be expanded. The potential of this research for South Africa is enormous and could quite possibly, in future, change the way in which South Africans drive.

The increase and changes in the demand for emergency care require pro-active responses from the designers and implementers of the emergency care system. The role of Emergency Care Practitioner (ECP) was introduced in England to improve the delivery of emergency care in the community. The role was evaluated using cost-benefit approach and compared with other existing emergency care roles. An analysis of the cognitive elements (situation awareness (SA) and naturalistic decision making (NDM)) of the ECP job was proposed considering the mental efforts involved. While the cost-benefit approach can justify further spending on developing the role, a cognitive approach can provide the evidence in ensuring the role is developed to fulfil its purpose. A series of studies were carried out to describe SA and NDM amongst ECPs in an ambulance service in England. A study examined decision-making process using Critical Decision Method interviews which revealed the main processes in making decision and how information was used to develop SA. Based on the findings, the subsequent studies focus on the non-clinical factors that influence SA and decision making. Data from a scoping study were used to develop a socio-technical systems framework based on existing models and frameworks. The framework was then used to guide further exploration of SA and NDM. Emergency calls that were assigned to ECPs over a period of 8 months were analysed. The analysis revealed system-related influences on the deployment of ECPs. Interviews with the ECPs enabled the identification of influences on their decision-making with respect to patient care. Goal-directed task analysis was used to identify the decision points and information requirements of the ECPs. The findings and the framework were then evaluated via a set of studies based on an ethnographic approach. Participant observations with 13 ECPs were carried out. Field notes provided further insight into the characteristics of jobs assigned to the ECPs. It was possible to map the actual information used by the ECP to their information needs. The sources of the information were classified according to system levels. A questionnaire based on factors influencing decision-making was tested with actual cases. It was found that the items in the questionnaire could reliably measure factors that influence decision-making. Overall, the studies identify factors that have direct and indirect influences on the ECP job. A coherent model for the whole emergency care systems can be developed to build safety into the care delivery process. Further development of the ECP role need to consider the support for cognitive tasks in light of the findings reported in this thesis.

Ancrée dans le courant de la Transformative Consumer Research (Mick et al., 2012), cette recherche explore la manifestation du bien-être alimentaire à partir de l’analyse de souvenirs d’expériences vécues par le consommateur. Dans une approche naturaliste (Lincoln et Guba, 1985) et interprétativiste, notre recherche qualitative combine trois types de recueil de données pour accéder aux expériences vécues des consommateurs : l’entretien phénoménologique, la photo-élicitation et le journal personnel. En suivant un processus d’analyse fondé sur la Grounded Theory (Glaser et Strauss, 1967), nos résultats présentent deux éléments majeurs de l’expérience de bien-être alimentaire : le rôle de l’expression des valeurs personnelles (Schwartz, 1992) dans l’expérience de bien-être alimentaire et l’existence de trois composantes-clés dans le processus global de l’expérience. Les résultats nous permettent alors de définir l’expérience de bien-être alimentaire comme une expérience eudémonique, congruente avec les valeurs ou types motivationnels de la personne. Elle est soutenue par différentes conditions facilitantes (attention portée au temps qualitatif, aux produits et recettes ainsi qu’au contexte physique de l’expérience) ; elle implique différents processus expérientiels (pleine maîtrise de l’expérience, immersion, initiation, partage, réminiscence et reproduction du souvenir et processus sensoriel) ; enfin, elle fait émerger des résultantes émotionnelles positives pouvant se combiner (fierté, joie, enthousiasme, gratitude, détente, réconfort et nostalgie). Nos résultats nous conduisent à proposer une taxonomie du bien-être alimentaire selon quatre types : le bien-être alimentaire de conservatisme, de transcendance de soi, d’ouverture au changement et d’affirmation de soi. Ils mettent au jour la définition d’eudémonie alimentaire qui caractérise l’alignement des expériences alimentaires et des significations attribuées au bien-être sur les propres valeurs de l’individu. Enfin, cette recherche propose des recommandations managériales en direction des pouvoirs publics, des associations de défense des consommateurs et organisations non gouvernementales ainsi qu’à tous les acteurs des filières alimentaires et distributeurs investis et/ou concernés par la question du bien-être du consommateur. / Rooted in the Transformative Consumer Research (Mick et al., 2012), this research explores the manifestation of food well-being using consumers' memories of lived experiences.  In a naturalistic (Lincoln and Guba, 1985) and interpretativist approach, this qualitative research combines three types of data which grant us access to lived experiences: the phenomenological interview, photo-elicitation and the personal diary. By following a grounded analysis process theory (Glaser and Strauss, 1967), our results present two major elements defining the experience of food well-being: the role of the expression of personal values (Schwartz, 1992) in the experience of food well-being and the presence of three key components in the overall process of the experiment. The results then allow us to define the experience of food well-being as a eudemonic experience linked to the motivational values of the person. This is supported by different enabling conditions (attention to quality of time, products and recipes as well as the physical context of the experience); experiential processes (full mastery of the experience, immersion, initiation, sharing, reminiscence and reproduction of memory, and sensory process); and positive emotions (pride, joy, enthusiasm, gratitude, relaxation, comfort and nostalgia). Our results propose a taxonomy of food well-being according to 4 principles: the food well-being of conservation, the food well-being of self-transcendence, the food well-being of openness to change and the food well-being of self-enhancement. These concepts define food eudemonia characterized by the confluence of food related experiences and meanings attributed to well-being with the individual's own values. Finally, this research proposes managerial recommendations for public authorities, consumer protection associations and non-governmental organizations, as well as all the actors in food chains and distributors invested and / or concerned by consumer well-being issues.

This thesis aims to examine which views of the human being that are manifested in the Swedish and Dutch legislation on prostitution. The theoretical framework consists of the humanist, the christian and the naturalistic views of the human being in combination with the concepts of autonomy and human dignity. The question is which views of the human being are manifested in the Swedish and Dutch legislation on prostitution? According to this thesis all three views of the human being can be traced in the legislations. The humanist view of the human being is most clearly manifested, in both the Swedish and Dutch legislation on prostitution, however their views are from different angles. Whereas the view of the Swedish legislation is that the autonomy and human dignity of the seller is violated in prostitution, the Dutch legislation on the other hand sees autonomy as the seller’s right to choose prostitution. In Dutch legislation both seller and buyer are seen as autonomous beings with ability to reason, and prostitution is seen as a trade transaction. In Swedish legislation the seller is seen as vulnerable and exposed and the buyer is seen as a criminal, and both seller and buyer are in need of change and help, in different ways.