With the rapid improvement of the Advanced Driver Assistant System (ADAS), autonomous driving has become one of the most common hot topics in recent years. While driving, many technologies related to autonomous driving choose to use the sensors installed on the vehicle to collect the information of road status and the environment outside. This aims to warn the driver to perceive the potential danger in the fastest time, which has become the focus of autonomous driving in recent years. Although autonomous driving brings plenty of conveniences to people, the safety of it is still facing difficulties. During driving, even the experienced driver can not guarantee focus on the status of the road all the time. Thus, lane departure warning system (LDWS) becomes developed. The purpose of LDWS is to determine whether the vehicle is in the safe driving area. If the vehicle is out of this area, LDWS will detect it and alert the driver by the sensors, such as sound and vibration, in order to make the driver back to the safe driving area. This thesis proposes a novel lightweight LDWS model LEHA, which divides the entire LDWS into three stages: image preprocessing, lane detection, and lane departure recognition. Different from the deep learning methods of LDWS, our LDWS model LEHA can achieve high accuracy and efficiency by relying only on simple hardware. The image preprocessing stage aims to process the original road image to remove the noise which is irrelevant to the detection result. In this stage, we apply a novel algorithm of grayscale preprocessing to convert the road image to a grayscale image, which removes the color of it. Then, we design a binarization method to greatly extract the lane lines from the background. A newly-designed image smoothing is added to this stage to reduce most of the noise, which improves the accuracy of the following lane detection stage. After obtaining the processed image, the lane detection stage is applied to detect and mark the lane lines. We use region of interest (ROI) to remove the irrelevant parts of the road image to reduce the detection time. After that, we introduce the Canny edge detection method, which aims to extract the edges of the lane lines. The last step of LDWS in the lane detection stage is a novel Hough transform method, the purpose of it is to detect the position of the lane and mark it. Finally, the lane departure recognition stage is used to calculate the deviation distance between the vehicle and the centerline of the lane to determine whether the warning needs to turn on. In the last part of this paper, we present the experiment results which show the comparison results of different lane conditions. We do the statistic of the proposed LDWS accuracy in terms of detection and departure. The detection rate of our proposed LDWS is 98.2% and the departure rate of it is 99.1%. The average processing time of our proposed LDWS is 20.01 x 10⁻³s per image.

Safety in transportation systems is of paramount concern to society; many improvements have been made in recent decades and yet thousands of fatalities still occur annually. Work zones in particular are areas with increased safety risks in transit networks. Advances in electronics have now allowed engineers to merge powerful computing and communication technologies with modern automotive and vehicular technology, known as connected vehicle. Connected vehicle will allow vehicles to exchange data wirelessly with each other and infrastructure to improve safety, mobility and sustainability. This thesis presents a paper that focuses on evaluating the impact of connected vehicle on work zone traffic safety. A dynamic route guidance system based on decaying average-travel-time and shortest path routing was developed and tested in a microscopic traffic simulation environment to avoid routes with work zones. To account for the unpredictable behaviour and psychology of driver’s response to information, three behaviour models, in the form of multinomial distributions, are proposed and studied in this research. The surrogate safety measure improved Time to Collision was used to gauge network safety at various market penetrations of connected vehicles. Results show that higher market penetrations of connected vehicles decrease network safety due to increased average travel distance, while the safest conditions, 5%-10% reduction in critical Time to Collision events, were observed at market penetrations of 20%-40% connected vehicle, with network safety strongly influenced by behaviour model. / Thesis / Master of Applied Science (MASc)

A transformative revolution in transportation is coming with the advent of Au-tonomous Vehicles (AVs), which are expected to increase mobility, reduce trafficcongestion, and save fuel. Although AVs present significant advantages, they alsopose substantial challenges, particularly when it comes to security and safety. Theaim of this study is to map out the existing knowledge in order to facilitate furtherresearch and development, which will hasten the rollout of secure and reliable au-tonomous vehicles. This, in turn, will enable a sustainable and efficient future fortransportation. Research on AV safety and security is reviewed in this thesis in acomprehensive systematic literature review. The search process identified a total of283 studies published between 2019 and 2022, out of which 24 studies were selectedthrough a multi-stage process according to our predefined protocol. Based on re-search topics in selected studies, our findings have a significant impact on the fieldof Artificial Intelligence and automated vehicles. Based on our findings, we canprovide a summary of current knowledge regarding the safety, security, and stabilityimplications of autonomous vehicles. Simulations, real-life experiments, and physi-cal tests were all used in the selected articles for evaluation. Aside from the excellentresults, we identified many limitations of the articles, including the limitations of thedata sets, the analysis of unusual events, and the verification practices.

Adaptive ramp metering (ARM) is a widely popular intelligent transportation system (ITS) tool that boasts the ability to reduce congestion and streamline traffic flow during peak hour periods while maintaining a lower implementation cost than traditional methods such as freeway widening. This thesis explores the effectiveness of ARM implementation on an 18 mile segment of the Interstate 80 (I-80) corridor in the Bay Area residing in northern California. Smaller segments of this particular segment were analyzed to determine the effective length of ARM on efficiency at various lengths originating from a known bottleneck location. Efficiency values were also compared against a control segment of the Interstate 280 (I-280) in San Jose to provide a test site experiencing similar traffic congestion but without any ARM implementation. An Empirical Bayes analysis was conducted to provide the foundation of a safety evaluation of the ramp metering implementation and determine a counterfactual estimate of expected collisions had ARM implementation not occurred. It was found that the installation of the ramp meters did allow for some marginal increases in efficiency but may not be entirely associated with ARM implementation due to a variety of external factors as well as showing inconsistent behavior between analyzed segments. Regarding safety, the predictive model estimates 32.8 collisions to occur along a 0.5 mile segment within a three-year timeframe if ARM were not installed, which implies substantial improvements in safety conditions. However additional efficiency and safety data within the “after” period may be necessary to provide a more robust and conclusive evaluation as the ARM system is still relatively new.

Over the past few years, the Utah Department of Transportation (UDOT) has developed a system called the Signal Performance Metrics System (SPMS) to evaluate the performance of signalized intersections. This system currently provides data summaries for several performance measures including: 1) Purdue Coordination Diagram, 2) Speed, 3) Approach Volume, 4) Purdue Phase Termination Charts, 5) Split Monitor, 6) Turning Movement Volume Counts, 7) Arrivals on Red, and 8) Approach Delay. There is a need to know the accuracy of the data that are being collected by the Wavetronix SmartSensor Matrix and displayed in the SPMS. The TAC members determined that the following factors would affect the accuracy of radar-based traffic sensors the most: sensor position, number of approach lanes, and volume level. The speed limit factor was added to the study after most of the data collection was completed. The purpose of this research was to collect data at various intersections to determine the accuracy of the data collected by the Wavetronix SmartSensor Matrix.A Mixed Model Analysis of Variance (ANOVA) was employed to analyze the effects that each factor had on the accuracy of the traffic volume count. A total of 14 tests were performed to examine the effects of the factors on traffic volume count accuracy. The sensor position factor was not found to be a statistically significant factor affecting the accuracy of traffic volume counts. The effect of speed limit on traffic volume count accuracy was determined to be inconclusive due to the lack of samples to be tested. The remaining two factors, volume level and number of approach lanes, were found to have a statistically significant effect on the accuracy of traffic volume counts. Based on these two factors, a matrix was created to meet the needs of UDOT to present accuracy values on the SPMS website. This matrix includes the mean, 95 percent confidence interval of the mean, standard deviation, number of samples, and the minimum number of samples needed.

This research evaluates Intelligent Transportation System (ITS) implementation strategies to improve the safety of a freeway once a potential of a crash is detected. Among these strategies are Variable Speed Limit (VSL) and ramp metering. VSL are ITS devices that are commonly used to calm traffic in an attempt to relieve congestion and enhance throughput. With proper use, VSL can be more cost effective than adding more lanes. In addition to maximizing the capacity of a roadway, a different aspect of VSL can be realized by the potential of improving traffic safety. Through the use of multiple microscopic traffic simulations, best practices can be determined, and a final recommendation can be made. Ramp metering is a method to control the amount of traffic flow entering from on-ramps to achieve a better efficiency of the freeway. It can also have a potential benefit in improving the safety of the freeway. This thesis pursues the goal of a best-case implementation of VSL. Two loading scenarios, a fully loaded case (90% of ramp maximums) and an off-peak loading case (60% of ramp maximums), at multiple stations with multiple implementation methods are strategically attempted until a best-case implementation is found. The final recommendation for the off-peak loading is a 15 mph speed reduction for 2 miles upstream and a 15 mph increase in speed for the 2 miles downstream of the detector that shows a high crash potential. The speed change is to be implemented in 5 mph increments every 10 minutes. The recommended case is found to reduce relative crash potential from .065 to -.292, as measured by a high-speed crash prediction algorithm (Abdel-Aty et al. 2005). A possibility of crash migration to downstream and upstream locations was observed, however, the safety and efficiency benefits far outweigh the crash migration potential. No final recommendation is made for the use of VSL in the fully loaded case (low-speed case); however, ramp metering indicated a promising potential for safety improvement.

Commuters in urban areas suffer from traffic congestion on a daily basis. The increasing number of vehicles and vehicle miles traveled (VMT) are exacerbating this congested roadway problem for society. Although literature contains numerous studies that strive to propose solutions to this congestion problem, the problem is still prevalent today. Traffic congestion problem affects society’s quality of life socially, economically, and environmentally. In order to alleviate the unsustainable impacts of the congested roadway problem, Intelligent Transportation Systems (ITS) has been utilized to improve sustainable transportation systems in the world. The purpose of this thesis is to analyze the sustainable impacts and performance of the utilization of ITS in the United States. This thesis advances the body of knowledge of sustainability impacts of ITS related congestion relief through a triple bottom line (TBL) evaluation in the United States. TBL impacts analyze from a holistic perspective, rather than considering only the direct economic benefits. A critical approach to this research was to include both the direct and the indirect environmental and socio-economic impacts associated with the chain of supply paths of traffic congestion relief. To accomplish this aim, net benefits of ITS implementations are analyzed in 101 cities in the United States. In addition to the state level results, seven metropolitan cities in Florida are investigated in detail among these 101 cities. For instance, the results of this study indicated that Florida saved 1.38 E+05 tons of greenhouse gas emissions (tons of carbon dioxide equivalent), $420 million of annual delay reduction costs, and $17.2 million of net fuel-based costs. Furthermore, to quantify the relative impact and sustainability performance of different ITS technologies, several ITS solutions are analyzed in terms of total costs (initial and operation & maintenance costs) and benefits (value of time, emissions, and safety). To account for the uncertainty in benefit and cost ii analyses, a fuzzy-data envelopment analysis (DEA) methodology is utilized instead of the traditional DEA approach for sustainability performance analysis. The results using the fuzzy-DEA approach indicate that some of the ITS investments are not efficient compared to other investments where as all of them are highly effective investments in terms of the cost/benefit ratios approach. The TBL results of this study provide more comprehensive picture of socio-economic benefits which include the negative and indirect indicators and environmental benefits for ITS related congestion relief. In addition, sustainability performance comparisons and TBL analysis of ITS investments contained encouraging results to support decision makers to pursue ITS projects in the future.

The original FlexiChain and its descendants are a revolutionary distributed ledger technology (DLT) for cyber-physical systems (CPS) and their embedded systems (ES). FlexiChain, a DLT implementation, uses cryptography, distributed ledgers, peer-to-peer communications, scalable networks, and consensus. FlexiChain facilitates data structure agreements. This thesis offers a Block Directed Acyclic Graph (BDAG) architecture to link blocks to their forerunners to speed up validation. These data blocks are securely linked. This dissertation introduces Proof of Rapid Authentication, a novel consensus algorithm. This innovative method uses a distributed file to safely store a unique identifier (UID) based on node attributes to verify two blocks faster. This study also addresses CPS hardware security. A system of interconnected, user-unique identifiers allows each block's history to be monitored. This maintains each transaction and the validators who checked the block to ensure trustworthiness and honesty. We constructed a digital version that stays in sync with the distributed ledger as all nodes are linked by a NodeChain. The ledger is distributed without compromising node autonomy. Moreover, FlexiChain Layer 0 distributed ledger is also introduced and can connect and validate Layer 1 blockchains. This project produced a DAG-based blockchain integration platform with hardware security. The results illustrate a practical technique for creating a system depending on diverse applications' needs. This research's design and execution showed faster authentication, less cost, less complexity, greater scalability, higher interoperability, and reduced power consumption.

No description available.

In this thesis, multiple on-road vehicle tracking problem is explored, with greater consideration of road constraints and interactions between vehicles. A comprehensive method for tracking multiple on-road vehicles is proposed by making use of domain knowledge of on-road vehicle motion. Starting with raw measurements provided by sensors, bias correction methods for sensors commonly used in vehicle tracking are briefly introduced and a fast but effective bias correction method for airborne video sensor is proposed. In the proposed method, by assuming errors in sensor parameter measurements are close to zero, the bias is separately addressed in converted measurements of target position by a linear term of errors in sensor parameter measurements. Based on this model, the bias is efficiently estimated by addressing it while tracking or using measurements of targets that are observed by multiple airborne video sensors simultaneously. The proposed method is compared with other airborne video bias correction methods through simulations. The numerical results demonstrate the effectiveness of the proposed method for correcting bias as well as its high computational efficiency. Then, a novel tracking algorithm that utilizes domain knowledge of on-road vehicle motion, i.e., road-map information and interactions among vehicles, by integrating a car-following model into a road coordinate system, is proposed for tracking multiple vehicles on single-lane roads. This algorithm is extended for tracking multiple vehicles on multi-lane roads: The road coordinate system is extended to two-dimension to express lanes on roads and a lane-changing model is integrated for modeling lane-changing behavior of vehicles. Since the longitudinal and lateral motions are mutually dependent, the longitudinal and lateral states of vehicles are estimated sequentially in a recursive manner. Two estimation strategies are proposed: a) The unscented Kalman filter combined with the multiple hypothesis tracking framework to estimate longitudinal and lateral states of vehicles, respectively. b) A unified particle filter framework with a specifically designed computationally-efficient joint sampling method to estimate longitudinal and lateral states of vehicles jointly. Both of two estimation methods can handle unknown parameters in motion models. A posterior Cramer-Rao lower bound is derived for quantifying achievable estimation accuracy in both single-lane and multi-lane cases, respectively. Numerical results show that the proposed algorithms achieve better track accuracy and consistency than conventional multi-vehicle tracking algorithms, which assumes that vehicles move independently of one another. / Thesis / Doctor of Philosophy (PhD)