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Perceiving the surrounding multiple vehicles robustly and effectively is a very important step in building Advanced Driving Assistant System (ADAS) or autonomous vehicles. This thesis presents the design of the Light Detection and Ranging (LiDAR) perception system which consists of several sub-tasks: ground detection, object detection, object classification, and object tracking. It is believed that accomplishing these sub-tasks will provide a guideline to a vast range of potential autonomous vehicles applications. More specifically, a probability occupancy grid map based approach was developed for ground detection to address the issues of over-segmentation, under-segmentation and slow-segmentation by non-flat surface. Given the non-ground points, point cloud clustering algorithm is developed for object detection by using a Radially Bounded Nearest Neighbor (RBNN) method on the static Kd-tree. To identify the object, a supervised learning approach based on our LiDAR sensor for vehicle type classification is proposed. The proposed classification algorithm is used to classify the object into four different types: ``Sedan'', ``SUV'', ``Van'', and ``Truck''. To handle disturbances and motion uncertainties, a generalized form of Smooth Variable Structure Filter (SVSF) integrated with a combination of Hungarian algorithm (HA) and Probability Data Association Filter (PDAF), referred to as GSVSF-HA/PDAF, is developed. The developed approach is to overcome the multiple targets data association in the content of dynamics environment where the distribution of data is unpredictable. Last but not the least, a comprehensive experimental evaluation for each sub-task is presented to validate the robustness and effectiveness of our developed perception system. / Thesis / Doctor of Philosophy (PhD)

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Doctor of Philosophy / Electric scooters, or e-scooters, have become an increasingly popular form of transportation over the recent years. However, there have been numerous reports of safety concerns, crashes, and injuries for e-scooter riders and other road users as a result of e-scooter misuse. Until recently, very little formal research has been conducted on the safety of this micromobility solution. This dissertation describes a series of studies that have investigated the contributing factors to safety concerns and identified countermeasures, such as policy recommendations, design optimization, and training, that can be implemented with an end goal of improving e-scooter safety.

Full-motion driving simulators require efficient real-time high fidelity vehicle models in order to provide a more realistic vehicle response. Typically, multi-body models are used to represent the vehicle dynamics, but these have the unfortunate drawback of requiring the solution of a set of coupled differential algebraic equations (DAE). DAE's are not conducive to real-time implementation such as in a driving simulator, without a very expensive processing capability. The primary objective of this thesis is to show that multi-body models constructed from DAE's can be reasonably approximated with linear models using suspension elements that have nonlinear constitutive relationships. Three models were compared in this research, an experimental quarter-car test rig, a multi-body dynamics differential algebraic equation model, and a linear model with nonlinear suspension elements. Models constructed from differential algebraic equations are computationally expensive to compute and are difficult to realize for real-time simulations. Instead, a linear model with nonlinear elements was proposed for a more computationally efficient solution that would retain the nonlinearities of the suspension. Simplifications were made to the linear model with nonlinear elements to further reduce computation time for real-time simulation. The development process of each model is fully described in this thesis. Each model was excited with the same input and their outputs were compared. It was found that the linear model with nonlinear elements provides a reasonably good approximation of actual model with the differential algebraic equations. / Master of Science

Bus transit driving is an occupation that requires high concentration in driving and is demanding due to work overload, time pressure, and responsibility for lives. In 2006, there were 103 fatal crashes involving transit buses. As the number of distraction-related crashes increases, it is important to conduct a transit distraction study to reduce future crashes. This thesis focused on the analysis of the likelihood of the operator distraction behaviors and the analysis to find a predictive model to classify different distraction categories. An ordinal logistic regression was carried out to evaluate how age, gender, driving experience of the operators, and their driving frequencies accounts for the likelihood of 17 potential distracted driving behaviors. The results of this analysis showed that there were only 5 best models (p-value of model fit less than 0.005 and p-value of parallel line test more than 0.005) that could be constructed, including: listening to the radio/ CD/DVD/MP3 player (D1); picking Up and Holding 2-way Radio (D5); listening to the Dispatch Office broadcast (D6); adjusting switches/controls on dashboard (D15); and utilizing mentor ranger (D16). On the other hand, a discriminant analysis was performed to predict how different transit operator driving behaviors when exposed by 10 different distraction activities and 16 predictors were considered in this analysis. The final results showed that there are 4 predictors that seem to be able to classify distraction groups across all 4 models; those include segment length, average duration of idling time/stop delay at speed interval 0—4 km/hr, frequency of speed transitions that deviate by ± 0 to 4 km/hr from its speed, and frequency of speed transitions that deviate by ± 8 to 12 km/hr from its speed. / Master of Science

The present study attempted to validate modified versions of laboratory measures of behavioral impairment from alcohol for use in the field. A total of 195 students at various BAC levels, participated during two outdoor university-sponsored events. In addition to taking nonbehavioral measures of impairment, each subject was asked to perform and evaluate a reaction time task, balance test, and give a verbal index of impairment. The actual BAC of each subject was determined using a breath-alcohol testing machine. The results indicated that self-reported measures of impairment were the best predictors of BAC. Of the behavioral measures, reaction time and body balance performance accounted for the highest portion of the variance in predicting BAC. Subjects reported that actual BAC feedback would most likely result in behavior change, followed by performance on the progressive body balance and reaction time tasks. It was noted, however, that as BAC increased, poor test performance was less likely to result in behavior change. The need for continued research into behavioral measures of alcohol impairment are discussed as a means of promoting third-party intervention and"socially responsible drinking". / Master of Science

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