Implementation of block device drivers in userspace of modern general-purpose operating systems, although possible, is fairly uncommon, poorly supported and usually achieves only low performance. Being able to implement high- performance drivers in userspace with ease would allow for faster iterations in storage research and would make it possible to design block devices which operate in radically different ways. In this thesis, we present Block Device in Userspace (BUSE), a Linux ker- nel module and communication protocol which makes it easy to develop userspace block-device drivers. Compared to the existing approaches, BUSE can scale on modern multicore architectures and provides at least 7x higher throughput with significantly simpler setup. Furthermore, the kernel module communicates with the userspace driver through shared memory, eliminating an extraneous memory copy. BUSE also solves the write-after-write and read- after-write consistency issues which stem from the use of multiple hardware queues in the Linux storage stack, allowing the implementation to focus on the domain of the problem. As a proof-of-concept, we implemented Block Device in S3 (BS3), a userspace block device implementation backed by Amazon S3 (or any other S3-compatible storage) on top of BUSE. BS3 can be used as a generic disk...

Detection of malicious logic on a hardware device is difficult to detect. This thesis proposes a device driver that emulates a hardware device and that device’s software driver. This device driver attacks the target system by accessing the hard disk in order to perform read and write transactions without the knowledge of the operating system or intrusion detection/prevention software. The attacks performed by the driver compromise the confidentiality, integrity, and availability of data on the target system’s disk drive. The attacks performed by the device driver have a less than one percent impact on system performance. This thesis, while tested in a Windows environment, applies to other operating systems (such as Linux/Unix, etc.) and thus has major implications for a wide range of users.

Use of cellular phones while driving, and safety implications thereof, has captured public and scientific interest. Previous research has shown that driver reactions and attention are impacted by cellular phone use. Generally, previous research studies have not focused on how visual attention and driver performance may interact. Strayer and colleagues found lower recognition for items present in the driving environment when drivers were using a cellular phone than when not using the phone; however, the tested items were not directly relevant to driving. Relevance to driving may have an impact on attention allocation. The current project used a mediumidelity driving simulator to extend previous research in two ways: 1) how attention is allocated across driving-relevant and -irrelevant items in the environment was investigated, and 2) driving performance measures and eye movement measures were considered together rather than in isolation to better illustrate the impact of cellular phone distraction on driver behavior. Results from driving performance measures replicated previous findings that vehicle control is negatively impacted by driver distraction. Interestingly, there were no interactions of relevance and distraction found, suggesting that participants responded to potential hazards similarly in driving-only and distraction conditions. In contrast to previous research, eye movement patterns (primarily measured by number of gazes) were impacted by distraction. Gaze patterns differed across relevance levels, with hazards receiving the most gazes, and signs receiving the fewest. The relative size of the critical items may have impacted gaze probability in this relatively undemanding driving environment. In contrast to the driving performance measures, the eye movement measures did show an interaction between distraction and relevance; thus, eye movements may be a more direct and more sensitive measure of driver attention. Recognition memory results were consistently near chance performance levels and did not reflect the patterns found in the eye movement or driving performance measures.

This thesis presents an econometric test of the offsetting behavior hypothesis concerning drivers of light trucks relative to cars. The main objective is to determine whether drivers of light trucks offset perceived safety benefits associated with larger vehicles by driving more aggressively than drivers of cars, subsequently causing more fatal crashes, holding all else constant. An empirical model using data on pedestrian fatalities across the United States over a five-year period is developed and analyzed in order to capture the desired results. Estimates provide substantial evidence in support of the offsetting behavior hypothesis. To strengthen the case for driver offsetting behavior beyond previous studies, the model is estimated again using pedalcyclist fatalities. The results also point to interesting conclusions regarding the effects of increased speed limits on the behavior of drivers.

The need for developing proper habits, attitudes, and skills on the part of motorists is being continually and convincingly impressed on the public mind by the tremendous daily accident toll. Great strides have been made in engineering, both with respect to the construction of motor vehicles and the construction of highways. Traffic law enforcement agencies have become increasingly effective in recent years. Both good engineering and proper law enforcement are necessary, but to secure a very appreciable reduction in traffic accidents authorities are agreed that there must be an effective driver-education program. There have been many analysis of the causes of traffic accidents and all of them clearly indicate that human factors--wrong attitudes, bad habits, lack of skill, and ignorance--account for the majority of accidents. Certainly education must play the major role in any program which aims to eradicate these causes. Good driving is far from an instinctive accomplishment. It must be learned through close application, constant practice, and perhaps most important, a sincere desire to be a good driver A systematic plan of driver education should not only prepare students to drive safely, but should imbue them with a sense of their responsibilities as pedestrians and, by giving them an understanding of the purpose behind traffic laws, develop into a willingness to observe traffic rules and regulations. Driving training schools and traffic courses are being given in many of our secondary schools today as a vital answer to the growing need "teaching by experience those who are about to take their place on the 'open highway'." Driver education and driver training do not rest entirely upon the need for better mechanical operation of motor cars. It is not enough to each a student merely how to manipulate a vehicle. Other habits, are just as important. Courtesy on the highway is of the same nature as courtesy anywhere, and, cultivated in one sphere of activity, tends to carry over into other spheres. The same can be said for respect for law and order, good sportsmanship, and all the other desirable attitudes which these courses foster. The main purpose of safety education is to teach people what knowledge is necessary to prevent accidents, to develop the skills and habits necessary to make this knowledge of safety function automatically in emergencies, and equally important to develop attitudes and appreciations of importance in safe driving.

There are many types of power sources that are used for many different applications. In this thesis, a programmable source is designed, built and tested. The programmable source is able to generate three-phase output signals from three different input voltage signals using the ADMC401 DSP board. The programmable source is unique in that it can reproduce any input signal and amplify the input signal. This is done by pulse-width modulation (PWM). The programmable source was designed with gate driver circuits, a motor controller, switches, filters, comparators and other electronic components. Thermal protection and applications for this programmable source are presented in this thesis. Also, test data taken when a squirrel cage induction motor was powered by the programmable source is presented.

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To improve the safety and comfort of intelligent vehicles, advanced driver models offer promising solutions. However, several shortcomings of these models prevent them from being widely applied in reality. To address these shortcomings, advanced artificial intelligence algorithms in conjunction with the sufficient driving environmental factors are proposed based on real-life driving data. More specifically, three typical problems will be addressed in this thesis: Mandatory Lane Changing (MLC) suggestion at the highway entrance; Discretionary Lane Changing (DLC) intention prediction; Car-Following gap model considering the effect of cuts-in from the adjacent lanes. For the MLC suggestion system, in which the main challenges are efficient decision making and high prediction accuracy of both non-merge and merge events, an additional gated branch neural network (GBNN) is proposed. The proposed GBNN algorithm not only achieves the highest accuracy among conventional binary classifiers in terms of great performance on the non-merge accuracy, the merge accuracy, and receiver operating characteristic score but also takes less time. For the DLC, we propose a recurrent neural network (RNN)-based time series classifier with a gated recurrent units (GRU) architecture to predict the surrounding vehicles’ intention. It can predict the surrounding vehicles’ lane changing maneuver 0.8 s in advance at a recall and precision of 99.5% and 98.7%, respectively, which outperforms conventional algorithms such as the Hidden Markov Model (HMM). Finally, drivers are typically faced with two competing challenges when following a preceding vehicle. A method is proposed to address the problem through an overall objective function of car-following gap and velocity. Based on this, seeking the strategic car-following gap translates to finding the optimal solution that minimizes the overall objective function. With the support of field data, the method along with concrete models are instantiated and the application of the method is elaborated. / Thesis / Doctor of Philosophy (PhD) / Lane changing and car following are the two most frequently encountered driving behaviours for intelligent vehicles. Substantial research has been carried out and several prototypes have been developed by universities as well as companies. However, the low accuracy and high computational cost prevent the existing lane changing models from providing safer and more reliable decisions for intelligent vehicles. In the existing car-following models, there are also few models that consider the effects of cut-ins from adjacent lanes which may result in their poor accuracy and efficiency. To address these obstacles, advanced artificial intelligence algorithms combined with sufficient driving environmental factors are proposed due to their promise of providing accurate, efficient, and robust lane changing and car-following models. The main part of this thesis is composed of three journal papers. Paper 1 proposed a gated branch neural network for a mandatory lane changing suggestion system at the on-ramps of highways; paper 2 developed a recurrent neural network time-series algorithm to predict the surrounding vehicles’ discretionary lane changing intention in advance; paper 3 researched the strategic car-following gap model considering the effect of cut-ins from adjacent lanes.