D. Phil. (Energy Studies) / Quantifying energy consumed and emissions produced by transport is essential for effective policy formulation and urban environmental management. Current first-world methods for determining vehicle emissions factors are technology and resource intensive, and results cannot be applied directly to cities in other parts of the world. There is a need for alternative cost-effective and accurate methods for determining real-world fuel consumption and emissions from vehicles in cities of the developing world. In this thesis, a new emissions simulation and inventory model is developed and implemented as a software tool. A novel application of low cost on-board diagnostics equipment and Global Positioning System sensors is devised to survey engine-operating parameters, driving conditions and vehicle usage profiles needed by the model. An emissions inventory is produced for the City of Johannesburg using the software tool and surveying method to demonstrate the overall process. The core contribution of this thesis is the logical development of data structures and software tools which link base engine-operating patterns (of engine speed and engine load), derived from the literature, to measured engine-operating patterns and vehicle activity from real-world driving. A range of real-world driving cycles and emission factors published by the Swiss Institute of Materials Science and Technology are transformed to produce the base engine-operating patterns and their corresponding emissions factors. The calculation of emission factors for real-world driving involves matching measured engineoperating patterns to combinations of the base engine-operating patterns using numerical methods. The method is validated using a cross validation technique. The emissions inventory application integrates measured engine-operating patterns, vehicle activity, fleet structure, fuel sales and the emissions simulation procedure to calculate total emissions. Fuel consumption and emissions of interest are CO2, CO, HC, NOx. Measurements of engine operating parameters and vehicle usage patterns were recorded for 30 privately owned passenger vehicles from the Johannesburg fleet. The selection included Euro-0 (a mixture of pre Euro-1 vehicles), Euro-2 and Euro-3 petrol vehicles, and Euro-2 diesel private passenger vehicles. Fifteen billion vehicle kilometres were driven in Johannesburg by private passenger vehicles per year consuming 325 million litres of diesel and 1 524 billion litres of petrol. iv Total emissions were estimated to be 4.13 Mt CO2, 82.77 kt CO, 9.15 kt HC, and 24.49 kt NOx. Between 88 and 93% of the total emissions were from vehicles which fall into the Euro-0 petrol category. Diesel vehicles did not make a significant contribution to CO and HC emissions but contributed 14% of the NOx and 19% of the CO2 emissions. During weekdays, 28 to 31% and 25 to 27% of the total fuel consumption and emissions were due to the morning commute and the evening commute periods respectively. Although minibus taxis, buses, freight and vehicle age significantly impact on total fuel consumption and emissions in cities they were not considered within the scope of this study. Vehicle usage patterns are analysed to produce spatial maps and diurnal charts of congestion on suburban roads, streets and highways within the Johannesburg municipal area. Times and locations of congestion are presented in terms of a standard congestion index, and suggestion given on how and where congestion problems could be addressed. This study shows that vehicle emissions inventories can be cost effectively produced by surveying engine-operating parameters and vehicle usage profiles using on-board diagnostics and Global Positioning System sensors and simulating emissions factors using a new emissions simulation and emissions inventory model.

This research investigated the association of street network connectivity differences across travel modes with travel behaviour – mode choice, distance traveled and number of trips. To date research on travel behaviour relationships with urban form has not developed empirical evidence on street designs as distinct networks for walking and driving. A street network having greater connectivity and continuity for the pedestrian mode of travel vis-à-vis the vehicular network, like the Fused Grid, will likely encourage more walking. This hypothesis was investigated using a quasi-experimental approach within a rational utility behavioural framework. Local travel behaviour is theorized to be affected by desire to access goods and services (broadly termed, ‘activities’) in the community where people live. Using inferential statistics, the research tested for relationships between measured street patterns and self-reported local travel by King County, WA households. The main variables were ratios (walking : driving) of network connectivity and density, in the vicinity of travel survey households. Demographics and household characteristics, as well as other behaviourally influential urban form factors (residential density, proximity of destinations, etc.), were included in regression models, allowing control for confounding factors. Findings suggest that street networks with connectivity that provides better routing for one mode of transportation over others encourage more travel by the favored mode. The regression model demonstrated that a change from a pure small-block grid to a modified grid (i.e. Fused Grid) can result in an 11.3% increase in odds of a home-based trip being walked. The modified street pattern like a Fused Grid is also associated with a 25.9% increase, over street patterns with equivalent route directness for walking and driving, in the odds a person will meet recommended levels of physical activity. Finally, the Fused Grid’s 10% increase in relative connectivity for pedestrians is associated with a 23% decrease in local vehicle travel distance (VMT), and its improved continuity is associated with increased walking trips and distance. Conclusions: Other factors being equal, residential street networks with either more direct routing for pedestrians or more pedestrian facilities relative to vehicular network are associated with improved odds of walking and reduced odds of driving. / Applied Science, Faculty of / Community and Regional Planning (SCARP), School of / Graduate

Background: The rise in the total number of seniors will lead to a considerable increase in the prevalence of persons with dementia (PWD), the number of senior drivers and the amount of drivers with dementia. Understanding how this life event is experienced by PWD and their caregivers is paramount to policy development and planning. Methods: Descriptive qualitative study using secondary data. There were 25 participants over the age of 65, whose monthly phone call conversations were analyzed using analytic induction, to find links and create a theoretically based hypothesis regarding the experience of driving cessation. A standardized questionnaire was used to guide the telephone data collection. Results: PWD may experience emotions of anger when they don’t understand why they are no longer allowed to drive. When PWD understand why they are no longer allowed to drive, they seem to accept and self-regulate their driving cessation. Cognitive tests and physicians instructing PWD they have to cease driving, are not perceived to be helpful in this understanding. Public transportation and alternate means of personal transportation are potentially associated with the acceptance of driving cessation. Conclusions: PWD might not understand the link between cognitive assessments, their memory, and their driving performance. A systems level approach to improved access to transportation and on-road driving tests might make it easier for PWD to understand and accept the lifestyle changes that come with driving cessation.

The Pedestrian Detection System (PDS) has become a significant area of research designed to protect pedestrians. Despite the huge number of research work, the most current PDSs are designed to detect pedestrians without knowing their distances from cars. In fact, a priori knowledge of the distance between a car and pedestrian allows this system to make the appropriate decision in order to avoid collisions. Typical methods of distance measurement require additional equipment (e.g., Radars) which, unfortunately, cannot identify objects. Moreover, traditional stereo-vision methods have poor precision in long-range conditions. In this thesis, we use the keypoint-based feature extraction method to generate the parallax in a binocular vision system in order to measure a detectable object; this is used instead of a disparity map. Our method enhances the tolerance to instability of a moving vehicle; and, it also enables binocular measurement systems to be equipped with a zoom lens and to have greater distance between cameras. In addition, we designed a crossover re-detection and tracking method in order to reinforce the robustness of the system (one camera helps the other reduce detection errors). Our system is able to measure the distance between cars and pedestrians; and, it can also be used efficiently to measure the distance between cars and other objects such as Traffic signs or animals. Through a real word experiment, the system shows a 7.5% margin of error in outdoor and long-range conditions.

Reductions in speed and, more critically, speed variability between vehicles are thought to reduce crash risk in work zones. Numerous factors, such as lane width and lateral clearance and activity level, have been shown to influence speed but very little research has considered how multiple factors might interact to affect driver performance in work zones. This study evaluated the effect of work zone barrier type, presence of a lateral buffer, and work zone activity level on measures of speed and lane position. Twelve middle aged and twelve senior subjects drove in a National Advanced Driving Simulator (NADS) MiniSim. The subjects drove faster and with less variability in work zones with concrete barriers. Measures of speed and lane position were more heterogeneous across groups with 42-inch channelizers compared to drums. Speed was reduced and more variable in work areas with a high level of activity than in areas with a low level of activity. On the whole, the presence of a lateral buffer reduced speed variability in the high activity areas but this response was not uniform across all drivers. This research demonstrates that driving simulators can be used to evaluate how work zone factors may interact with one another to affect driver performance for different driver groups. While the results from this study corresponded to observations from actual work zones, the driving simulator must be validated with on-road data before generalizations can be made.

It is the period of the World's history, where the technological progress reached a level that enables the first steps towards the development of vehicles with automated driving capabilities. The swift response from the significant portion of the industry resulted in a race, the final line set at the introduction of vehicles with full automated driving capabilities. Vehicles with automated driving capabilities target making driving safer, more comfortable, and economically more efficient by assisting the driver or by taking responsibilities for different driving tasks. While vehicles with assistance and partial automation capabilities are already in series production, the ultimate goal is in the introduction of vehicles with full automated driving capabilities. Reaching this level of automation will require shifting all responsibilities, including the responsibility for the overall vehicle safety, from the human to the computer-based system responsible for the automated driving functionality (i.e., the Automated Driving System (ADS)). Such a shift makes the ADS highly safe-critical, requiring a safety level comparable to an aircraft system. It is paramount to understand that ensuring such a level of safety is a complex interdisciplinary challenge. Traditional approaches for ensuring safety require the use of fault-tolerance techniques that are unproven when it comes to the automated driving domain. Moreover, existing safety assurance methods (e.g., ISO 26262) suffer from requirements incompleteness in the automated driving context. The use of artificial intelligence-based components in the ADS further complicate the matter due to their non-deterministic behavior. At present, there is no single straightforward solution for these challenges. Instead, the consensus of cross-domain experts is to use a set of complementary safety methods that together are sufficient to ensure the required level of safety. In the context of that, runtime monitors that verify the safe operation of the ADS during execution, are a promising complementary approach for ensuring safety. However, to develop a runtime monitoring solution for ADS, one has to handle a wide range of challenges. On a conceptual level, the complex and opaque technology used in ADS often make researchers ask the question ``how should ADS be verified in order to judge it is operating safely?". Once the initial Runtime Verification (RV) concept is developed, researchers and practitioners have to deal with research and engineering challenges encountered during the realization of the RV approaches into an actual runtime monitoring solution for ADS. These challenges range from, estimating different safety parameters of the runtime monitors, finding solutions for different technical problems, to meeting scalability and efficiency requirements. The focus of this thesis is to propose novel runtime monitoring solutions for verifying the safe operation of ADS. This encompasses (i) defining novel RV approaches explicitly tailored for automated driving, and (ii) developing concepts, methods, and architectures for realizing the RV approaches into an actual runtime monitoring solution for ADS. Contributions to the former include defining two runtime RV approaches, namely the Computer Vision Monitor (CVM) and the Safe Driving Envelope Verification. Contributions to the latter include (i) estimating the sufficient diagnostic test interval of the runtime verification approaches (in particular the CVM), (ii) addressing the out-of-sequence measurement problem in sensor fusion-based ADS, and (iii) developing an architectural solution for improving the scalability and efficiency of the runtime monitoring solution. / RetNet

Alloy is a lightweight formal modeling tool that generates instances of a software specification to check properties of the design. This work demonstrates the use of Alloy for the rapid development of autonomous vehicle driving protocols. We contribute two driving protocols: a Normal protocol that represents the unpredictable yet safe driving behavior of typical human drivers, and a Connected protocol that employs connected technology for cooperative autonomous driving. Using five properties that define safe and productive driving actions, we analyze the performance of our protocols in mixed traffic. Lightweight formal modeling is a valuable way to reason about driving protocols early in the development process because it can automate the checking of safety and productivity properties and prevent costly design flaws.

The human brain acts as an intelligent sensor by helping in effective signal communication and execution of logical functions and instructions, thus, coordinating all functions of the human body. More importantly, it shows the potential to combine prior knowledge with adaptive learning, thus ensuring constant improvement. These qualities help the brain to interact efficiently with both, the body (brain-body) as well as the environment (brain-environment). This dissertation attempts to apply the brain-body-environment interactions (BBEI) to elevate human existence and enhance our day-to-day experiences. For instance, when one stepped out of the house in the past, one had to carry keys (for unlocking), money (for purchasing), and a phone (for communication). With the advent of smartphones, this scenario changed completely and today, it is often enough to carry just one's smartphone because all the above activities can be performed with a single device. In the future, with advanced research and progress in BBEI interactions, one will be able to perform many activities by dictating it in one's mind without any physical involvement. This dissertation aims to shift the paradigm of existing brain-computer-interfaces from just ‘control' to ‘monitor, control, enhance, and restore' in three main areas - healthcare, transportation safety, and cryptography. In healthcare, measures were developed for understanding brain-body interactions by correlating cerebral autoregulation with brain signals. The variation in estimated blood flow of brain (obtained through EEG) was detected with evoked change in blood pressure, thus, enabling EEG metrics to be used as a first hand screening tool to check impaired cerebral autoregulation. To enhance road safety, distracted drivers' behavior in various multitasking scenarios while driving was identified by significant changes in the time-frequency spectrum of the EEG signals. A distraction metric was calculated to rank the severity of a distraction task that can be used as an intuitive measure for distraction in people - analogous to the Richter scale for earthquakes. In cryptography, brain-environment interactions (BBEI) were qualitatively and quantitatively modeled to obtain cancelable biometrics and cryptographic keys using brain signals. Two different datasets were used to analyze the key generation process and it was observed that neurokeys established for every subject-task combination were unique, consistent, and can be revoked and re-issued in case of a breach. This dissertation envisions a future where humans and technology are intuitively connected by a seamless flow of information through ‘the most intelligent sensor', the brain.

京都大学 / 新制・課程博士 / 博士(人間健康科学) / 甲第23129号 / 人健博第91号 / 新制||人健||6(附属図書館) / 京都大学大学院医学研究科人間健康科学系専攻 / (主査)教授 澤本 伸克, 教授 十一 元三, 教授 髙橋 良輔 / 学位規則第4条第1項該当 / Doctor of Human Health Sciences / Kyoto University / DFAM

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