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The perception system is the key for autonomous vehicles to sense and understand the surrounding environment. As the cheapest and most mature sensor, monocular cameras create a rich and accurate visual representation of the world. The objective of this thesis is to investigate if camera-based deep learning models with transfer learning technique can achieve 2D object detection, License Plate Detection and Recognition (LPDR), and highway lane detection in real time. The You Only Look Once version 3 (YOLOv3) algorithm with and without transfer learning is applied on the Karlsruhe Institute of Technology and Toyota Technological Institute (KITTI) dataset for cars, cyclists, and pedestrians detection. This application shows that objects could be detected in real time and the transfer learning boosts the detection performance. The Convolutional Recurrent Neural Network (CRNN) algorithm with a pre-trained model is applied on multiple License Plate (LP) datasets for real-time LP recognition. The optimized model is then used to recognize Ontario LPs and achieves high accuracy. The Efficient Residual Factorized ConvNet (ERFNet) algorithm with transfer learning and a cubic spline model are modified and implemented on the TuSimple dataset for lane segmentation and interpolation. The detection performance and speed are comparable with other state-of-the-art algorithms. / Thesis / Master of Applied Science (MASc)

The LOOK, an iOS app, is a viewing time measure used to assess sexual interest. The measure is based on the assumption that sexual interest can be assessed by the amount of time a participant spends looking at an image. The purpose of this study was to examine the ability of the LOOK, a newly developed viewing time instrument, to accurately screen and diagnose individuals with deviant sexual interest. The profiles of known sexual offenders were compared to norm-referenced profiles of an exclusively heterosexual, non-pedophilic, male, college student population. Researchers were not able to find a fair constant multiplier that would allow for a positive screen of our offender sample while not over identifying our non-offender sample. Instead a graph was generated which showed the trends of offenders were closely related to those of non-offenders using Fischer’s Chi-square model. Additionally, when looking at the predictive validity of being able to identify victim demographics of known perpetrators based on Fischer’s Chi Square residuals, only 15.9% were found to have offense histories that were consistent with their profiles on the LOOK. The LOOK, using Fischer’s Chi-square model does not seem to be able to differentiate offenders from non-offenders. Future studies may include looking at the predictive nature of ipsative data.

<p>  </p> <p>For decades, agencies have collected infrastructure condition assessment data using dedicated equipment that require substantial capital investments and staff time to operate/drive. However, these techniques are challenging to scale network wide. The United States has over 8 million lane miles of roadways which generate almost 3 trillion vehicle miles annually.  Connected vehicles can now provide real-time data on a wide range of parameters such as vehicle speed, location, lane markings, and 3 axis acceleration. This dissertation develops techniques to validate, utilize and leverage connected vehicle data for infrastructure assessment and monitoring.  </p> <p><br></p> <p>Opportunities to employ connected vehicle data were examined in the following areas: quality of lane marking edge lines, width of lanes (particularly temporary lanes in construction zones), and pavement roughness. Quality of lane markings was evaluated using embedded lane keep assist data and equipment. In 2020 and 2021 over 5000 miles of pavement markings were evaluated on Indiana interstates. Comparisons between 2020 and 2021 data showed detection increase from 80.2% to 92.3%.  Although there are no industry standards for lane keep assist data, this study demonstrated both the importance and utility of partnering with the automotive industry to develop shared vision on acceptable lane quality. </p> <p><br></p> <p>A follow-up quantitative study was performed using a LiDAR vehicle to compare LiDAR values with those that are obtained from traditional retroreflectivity measurements used for contract acceptance and maintenance decisions. A comparison of LiDAR intensity to retroreflectivity (the industry standard) on 70 miles of US-52 and US-41 in Indiana was assessed and a linear regression found that the intensity values are comparable to retroreflectivity readings with an R2 of 0.87 and 0.63 for right edge and center skip lines respectively. These results suggest that LiDAR is a viable tool for monitoring of retroreflectivity of pavement markings that are strongly correlated with existing standards, but scale much better than traditional retroreflectivity measurement techniques.</p> <p><br></p> <p>The LiDAR data also provided the opportunity to evaluate how well modern vehicles measure lane width. This dissertation reports on over 200 miles of roadway and when compared to LiDAR and field measurements had a root mean square error of 0.24 feet. This data is valuable for agencies to quickly identify system wide where lane widths fall below acceptable design standards, typically 11-feet. </p> <p> </p> <p>The final connected vehicle data set evaluated was pavement roughness and compared with traditional dedicated vehicles collecting international roughness index (IRI) data. The study evaluated a 20-mile segment in 2022, and showed a linear regression between these data sets had an R2 of over 0.7, suggesting that connected vehicle roughness data can be utilized for network level monitoring of pavement quality. Scalability of these techniques is also illustrated with graphics characterizing IRI values obtained from almost 6 million records to evaluate improvements in Indiana construction zones and over 5,800 miles of I-80 in April of 2022 and October 2022.</p> <p><br></p> <p>Although connected vehicle data for infrastructure assessment is still in its infancy, these case studies demonstrate significant opportunities for public agencies to collect selected system wide infrastructure condition in near real-time, and in many cases at a lower cost than traditional techniques. </p>

<p>  </p> <p>The last century has witnessed increased urban sprawl, motorization, and the attendant problems of congestion, safety, and emissions associated with current-day transportation systems. Contemporary literature suggests that emerging transportation technologies, including vehicle autonomy and connectivity, offer great promise in addressing these adversities. As such, highway agencies seek guidance on infrastructure preparations for connected and automated vehicle (CAV) operations. A key area of such preparations is the management of lanes to serve CAVs and human-driven vehicles (HDVs), including the deployment of dedicated lanes for CAVs. There is a need to address the demand and supply perspectives of CAV preparations. On the demand side, agencies need to model the trends and uncertainties of CAV market penetration and level of autonomy during the CAV transition period. On the supply side, agencies need to schedule the CAV-related roadway infrastructure in a way that progressively addresses the growing demand. </p> <p>In addressing these research questions, this dissertation first carries out an economics-based lane allocation for CAVs and HDVs in a highway corridor by determining the optimum number of CAVLs by minimizing road user cost. Next, the dissertation carries out such allocation considering the environment (community emissions cost). Third, the dissertation addresses elements of social and economic sustainability using a CAV-enabled tradable credit scheme that minimizes user travel time subject to social equity constraints. Further, this dissertation provides guidance on how CAV-dedicated lanes, in conjunction with market-based tradable travel credits, could enable the road agency to achieve maximum efficiency of the existing road infrastructure in the CAV transition period. The study framework can serve as a valuable decision-support tool for road agencies in their long-term planning and budgeting in anticipation of the CAV transition period. The key outcome of the framework is an optimal schedule for deploying CAV-dedicated lanes over a given analysis period of several decades in a manner commensurate with CAV demand projections and sustainability-related objectives and constraints.</p>

No-lane closure workzone is typical during the construction of open road tolling lanes of a toll plaza. The influence of no-lane closure on toll plazas' performance is unknown because very few studies have been conducted to address this topic. The open road tolling (ORT) has become the new trend of operating an efficient toll plaza. So, the upgrading of a toll plaza from gated E-pass to open road E-pass has become common. The better the toll plaza authority knows about the influence of this construction and congestion effects, the better it can serve the costumers. This project mainly deals with the effects of no-lane closure workzone on the toll plaza performance, and with the collected data, a model was developed predicting 15 minutes throughput and queue length. To better study the workzone impact on toll plaza performance, three sites with different characteristics were selected. They are Lake Jesup Mainline Plaza along the Seminole Expressway (SR-417), the Beachline West Expressway Toll Plaza along the SR-528 and Conway toll plaza along the Holland East-West Expressway (SR-408) in Orlando area of Central Florida. Data preparation includes demand, throughput, processing rates, and queue lengths of different toll categories. Data was collected during peak period for before and during the no-lane closure construction (phase 1) at SR-528 and Lake Jesup toll plaza at SR-417, and middle lane construction (phase 2) and after opening ORT lanes (phase 3) at Conway toll plaza at SR-408.Comparisons were conducted between non-construction stage and construction stage for non-lane closure workzone effects study using data from 417 and 528, and comparisons between middle-lane-construction and complete of construction stage for ORT impact study using data from 408. Analysis results showed that when the toll plaza is operating at or close to its capacity, the no-lane closure workzone can have a negative impact on its performance. But when the toll plaza's demand is lower than the capacity, the no-lane closure workzone has no impact at the toll plaza's performance. And the ORT lanes have a positive influence on the capacity and throughput of the toll plaza. After the impact of no-lane closure workzone on toll plaza has been analyzed, all the data from three toll plazas are put together and a model is built using the variables of Demand/Capacity ratio, percentage of each category of vehicles, E-pass, Automatic or Manual, number of Manual lanes, workzone or no-workzone. Throughput and Queue length can be predicted by this model.

This study examines the safety effects of the improvements made on multi-lane arterials. The improvements were divided into two categories 1) corridor level improvements, and 2) intersection improvements. Empirical Bayes method, which is one of the most accepted approaches for conducting before-after evaluations, has been used to assess the safety effects of the improvement projects. Safety effects are estimated not only in terms of all crashes but also rear-end (most common type) as well as severe crashes (crashes involving incapacitating and/or fatal injuries) and also angle crashes for intersection improvements. The Safety Performance Functions (SPFs) used in this study are negative binomial crash frequency estimation models that use the information on ADT, length of the segments, speed limit, and number of lanes for corridors. And for intersections the explanatory variables used are ADT, number of lanes, speed limit on major road, and number of lanes on the minor road. GENMOD procedure in SAS was used to develop the SPFs. Corridor SPFs are segregated by crash groups (all, rear-end, and severe), length of the segments being evaluated, and land use (urban, suburban and rural). The results of the analysis show that the resulting changes in safety following corridor level improvements vary widely. Although the safety effect of projects involving the same type of improvement varied, the overall effectiveness of each of the corridor level improvements were found to be positive in terms of reduction in crashes of each crash type considered (total, severe, and rear-end) except for resurfacing projects where the total number of crashes slightly increased after the roadway section is resurfaced. Evaluating additional improvements carried out with resurfacing activities showed that all (other than sidewalk improvements for total crashes) of them consistently led to improvements in safety of multilane arterial sections. It leads to the inference that it may be a good idea to take up additional improvements if it is cost effective to do them along with resurfacing. It was also found that the addition of turning lanes (left and/or right) and paving shoulders were two improvements associated with a project�s relative performance in terms of reduction in rear-end crashes. No improvements were found to be associated with a resurfacing project�s relative performance in terms of changes in (i.e., reducing) severe crashes. For intersection improvements also the individual results of each project varied widely. Except for adding turn lane(s) all other improvements showed a positive impact on safety in terms of reducing the number of crashes for all the crash types (total, severe, angle, and rear-end) considered. Indicating that the design guidelines for this work type have to be revisited and safety aspect has to be considered while implementing them. In all it can be concluded that FDOT is doing a good job in selecting the sites for treatment and it is very successful in improving the safety of the sections being treated although the main objective(s) of the treatments are not necessarily safety related.

<p dir="ltr">Road safety analysis typically relies on the correlation between road surface conditions, lane marking status, or lane width and crash data. Traditionally, this data is surveyed in the field after road construction or car accidents, which is labor-intensive, time-consuming, and hazardous. With the development of mobile mapping systems (MMS) in recent years, the ability to collect lane marking retroreflectivity or lane width information has been greatly improved. By utilizing Light Detection and Ranging (LiDAR) point clouds and RGB images captured by MMS, it is possible to establish lane marking inventory that includes the conditions of pavement markers (such as lane marking retroreflectivity and lane width) for road safety analysis.</p><p dir="ltr">This dissertation aims to develop a comprehensive framework to extract lane markings and report their characteristics using MMS datasets for transportation safety. The proposed approaches include geometric/morphological and deep learning-based approaches based on the LiDAR point clouds acquired by MMS. A normalization strategy is developed to ensure consistent intensity values across laser beams/LiDAR units/MMS for the same objects, thereby enhancing the lane marking extraction. In addition, an image-aided LiDAR approach is proposed to improve the extraction process further. Following the extraction, lane marking classification and characterization, including intensity profile generation and lane width estimation, are conducted to establish comprehensive lane marking inventory.</p><p dir="ltr">To evaluate the proposed strategies, lane marking extraction with and without intensity normalization is also conducted. The results show that the proposed intensity normalization significantly improves the performance of lane marking extraction, regardless of the approach or data used. The geometric approach using normalized intensity achieves F1-scores higher than 90%, outperforming the learning-based models. Furthermore, the intensity derived from two different MMS is compared for performance evaluation, and the agreement of normalized intensity values is within a range of 3.1 to 3.8 (the used MMS provide intensity as an integer number within 0 to 255). Through the normalization, a positive linear relationship between LiDAR normalized intensity and retroreflectivity is found, with the strongest relationship providing an R² of 0.72 and a Pearson's correlation coefficient of 0.85. A comparison of the correlation between original/normalized intensity and retroreflectivity revealed a stronger correlation between original intensity and retroreflectivity. For image-aided LiDAR approach, the image information indeed enhanced the LiDAR-based lane marking extraction approach, as evidenced by the highest F1-score (92.5%) of the image-aided LiDAR approach, outperforming the LiDAR-based (90.3%) and image-based (77.8%) ones. Specifically, the recall increases by 4.0% – from 87.6% (LiDAR-based) to 91.6% (image-aided LiDAR) – surpasses the slight improvement in the precision of 0.2% – from 93.2% (LiDAR-based) to 93.4% (image-aided LiDAR).</p><p dir="ltr">Finally, a Potree-based web portal is developed to visualize the results derived through the proposed lane marking extraction/classification/characterization strategies. This portal includes a function that enables the projection between 2D images and 3D point clouds, as well as tools for displaying intensity profiles and lane width estimates. It is capable of rendering a large dataset of {approximately 4.2 billion LiDAR points} in around ten seconds and allows for the visualization of intensity profiles and lane width estimates. Users can select points of interest in an intensity profile/lane width plot. This selection will result in the corresponding point being showcased in the LiDAR data on the web portal. Furthermore, the LiDAR point can be projected onto the corresponding image.</p><p dir="ltr">The above proposed strategies facilitate the investigation of the relationship between LiDAR intensity and mobile retroreflectivity. To ensure quality control, lane markings derived from geometric and learning-based extraction approaches were compared. These strategies were evaluated using two MMS (equipped with multiple imaging and LiDAR sensors), covering extensive road segments exceeding 400 miles. Furthermore, a reporting mechanism based on multi-modal data from various MMS sensors was implemented to visualize the results derived from the proposed strategies and to serve as a quality control tool. Consequently, the proposed strategies are easily adaptable for different MMS or the regular updating of lane marking inventory.</p>

This thesis focuses on evaluating the appropriate speed at which vehicles should travel under different traffic conditions on freeways and its impact on crash frequency. The common belief is that the lower speed results in fewer crashes as reduced speed provides drivers with more time to react effectively and avoid collisions. However, this perspective overlooks the interplay among traffic speed, average spacing between consecutive vehicles, and the distance available for stopping a vehicle. Hence, we propose a safety parameter termed ‘Safety Correlate' (SCORE), which is defined as the proportion of average spacing relative to the stopping distance. To determine the relationship between SCORE and crash frequency, data from 366 4-lane urban freeway segments located in Virginia was analyzed and a Random-effects Poisson Lognormal model was developed. The obtained result indicated that the safety parameter SCORE is negatively associated with the annual hourly crash frequency, implying that the lesser the average spacing as a proportion of the stopping distance while traffic flow remains constant, the more frequent will be the crashes. Additionally, this research presents an application of SCORE in setting variable speed limits under various traffic flows. Overall, the study results provide valuable insights by investigating SCORE to improve traffic safety. Also, this research would help practitioners and policymakers to incorporate safety aspects while setting variable speed limits on freeways.

Ever since they were formulated in the Middle Ages, St. Thomas Aquinas' famous Five Ways to demonstrate the existence of God have been frequently debated. During this process there have been several misconceptions of what Aquinas actually meant, especially when discussing his cosmological arguments. While previous researchers have managed to tease out why Aquinas accepts some infinite regresses and rejects others, I attempt to add on to this by demonstrating the centrality of his metaphysics in his argument from motion. Aquinas cannot be properly understood or debated with a contemporary view of causality, but rather must wrestle with the concepts he actually employs in the arguments. To demonstrate this, I will compare the Thomistic argument from motion to the contemporary Kalām cosmological argument of William Lane Craig. Although some may consider it beneficial to base theistic arguments on more modern principles, this analysis shows that the metaphysical framework used by Aquinas is much less vulnerable to the rebuttals that otherwise challenge the Kalām argument, and that their differences in strength rest on their differences in metaphysics.