<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>

Downstream vehicles detect an emergency response vehicle (ERV) through sirens and/or strobe lights. These traditional warning systems do not give any recommendation about how to react, leaving the drivers confused and often adopting unsafe behavior while trying to open a passage for the ERV. In this research, an advanced intra-link emergency assistance system, that leverages the emerging technologies of the connected vehicle environment, is proposed. The proposed system assumes the presence of a centralized system that gathers/disseminates information from/to connected vehicles via vehicle-to-infrastructure (V2I) communications. The major contribution of this dissertation is the intra-link level support provided to ERV as well as non-ERVs. The proposed system provides network-wide assistance as it also considers the routing of ERVs. The core of the system is a mathematical program - a set of equations and inequalities - that generates, based on location and speed data from connected vehicles that are downstream of the ERV, the fastest intra-link ERV movement. It specifies for each connected non-ERV a final assigned position that the vehicle can reach comfortably along the link. The system accommodates partial market penetration levels and is applicable on large transportation link segments with signalized intersections. The system consists of three modules (1) an ERV route generation module, (2) a criticality analysis module and (2) the sequential optimization module. The first module determines the ERV's route (set of links) from the ERV's origin to the desired destination in the network. Based on this selected route, the criticality analysis module scans/filters the connected vehicles of interest and determines whether any of them should be provided with a warning/instruction message. As the ERV is moving towards its destination, new non-ERVs should be notified. When a group of non-ERVs is identified by the criticality analysis module, a sequential optimization module is activated. The proposed system is evaluated using simulation under different combinations of market penetration and congestion levels. Benefits in terms of ERV travel time with an average reduction of 9.09% and in terms of vehicular interactions with an average reduction of 35.46% and 81.38% for ERV/non-ERV and non-ERV/non-ERV interactions respectively are observed at 100% market penetration, when compared to the current practice where vehicles moving to the nearest edge. / Doctor of Philosophy / Downstream vehicles detect an emergency response vehicle (ERV) through sirens and/or strobe lights. These traditional warning systems do not give any recommendations about how to react, leaving the drivers confused and often adopting unsafe behavior while trying to open a passage for the ERV. In this research, an advanced intra-link emergency assistance system, that leverages the emerging technologies of the connected vehicle environment, is proposed. The proposed system assumes the presence of a centralized system that gathers/disseminates information from/to connected vehicles via vehicle-to-infrastructure (V2I) communications. The major contribution of this dissertation is the intra-link level support provided to ERV as well as non-ERVs. The proposed system provides network-wide assistance as it also considers the routing of ERVs. The core of the system is a mathematical program - a set of equations and inequalities - that generates, based on location and speed data from connected vehicles that are downstream of the ERV, the fastest intra-link ERV movement. It specifies for each connected non-ERV a final assigned position that the vehicle can reach comfortably along the link. The system accommodates partial market penetration levels and is applicable on large transportation link segments with signalized intersections. The system consists of three modules (1) an ERV route generation module, (2) a criticality analysis module and (2) the sequential optimization module. The first module determines the ERV’s route (set of links) from the ERV’s origin to the desired destination in the network. Based on this selected route, the criticality analysis module scans/filters the connected vehicles of interest and determines whether any of them should be provided with a warning/instruction message. As the ERV is moving towards its destination, new non-ERVs should be notified. When a group of non-ERVs is identified by the criticality analysis module, a sequential optimization module is activated. The proposed system is evaluated using simulation under different combinations of market penetration and congestion levels. Benefits in terms of ERV travel time with an average reduction of 9.09% and in terms of vehicular interactions with an average reduction of 35.46% and 81.38% for ERV/non-ERV and non-ERV/non-ERV interactions respectively are observed at 100% market penetration, when compared to the current practice where vehicles moving to the nearest edge.

Connected Vehicle (CV) is a motorized vehicle that can communicate with its interior and exterior surroundings. Connected Vehicle focuses on localized vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) to support safety, mobility and environmental applications. In this work, a simulation framework is presented. The framework quantifies Connected Vehicle performance in a forward collision warning situation. The simulation framework evaluates the performance using a vehicular traffic simulator with data from an intersection in Toronto, ON Canada. Various communication methodologies are evaluated at different Connected Vehicle market penetration rates. While DSRC is an interference limited communication methodology and visible light communications is a line-of-sight communication, the combination of both is evaluated to quantify the vehicular network safety performance in terms of time to collision. The performance of DSRC in a vehicular network is quantified in an interference dominant environment and the VLC performance in the vehicular network is evaluated at different weather conditions. In a specific vehicular traffic situation namely for- ward collision warning, this research quantified the VLC performance improvement in vehicular network safety to be 11% in addition to DSRC.This work concludes with the simulation and prototyping of camera communications for vehicular applications. Specifically this thesis presents multiple input / multiple output camera communications link utilizing a luminary array as a transmitter and two orthogonal low cost rolling shutter cameras as a receiver with the purpose of increasing the achievable data rate with one camera. This work has demonstrated that there is at most a doubling in the data rate using two cameras over a single one. This data rate increase is achievable using a specific camera setup namely orthogonal cameras. / Thesis / Master of Applied Science (MASc)

Vehicular Ad-hoc Networks (VANETs) provide networks for smart vehicles and will enable future systems to provide services that enhance the overall transportation experience. However, these applications require consideration to possible damage to both property and human life. Communication between vehicles requires data immutability and user privacies to provide safe operation of the system. Blockchains can provide these properties and more to create a more secure and decentralized system. However, a chain’s security comes from the chain length. VANETs’ ephemeral connections provide harm limits how much data can be exchanged during vehicle rendezvous. This thesis investigates lightweight blockchains that operate with lower overheads. A survey of current techniques to accomplish this are discussed in Chapter 1. Two techniques are demonstrated within two separate environments to demonstrate the network overhead reductions when using a lightweight blockchain with respect to network and storage loads within these VANET environments.

No description available.

An analysis of crash data shows that the number of fatal school bus related crashes has remained nearly constant over the past ten years, despite an increase in available safety-improving technology. One of the main concerns related to school bus safety is the issue of illegally passing a stopped school bus. To improve safety around stopped school buses, this dissertation presents a Concept of Operations for a connected vehicle application to improve safety around stopped school buses using Dedicated Short Range Communication. The focus of this application is to increase awareness of stopped school buses or bus stops that are obscured from the driver's view. An on-road naturalistic driving experiment evaluated driver response to an in-vehicle message to warn drivers that they were approaching a school bus that was stopped around a curve. The dissertation concludes by using microsimulation to evaluate the impact of implementing specialized speed control algorithms to reduce vehicle speeds near bus stops along high speed roads. The simulation evaluated the effect of the reduce speed zone on travel time and emissions when the system was considered as a pre-timed speed limit and also when the system was modeled as a connected vehicle system. / Ph. D.

Traffic signals typically produce vehicle stops and thus increase vehicle fuel consumption levels. Vehicle stops produced by traffic signals, decrease vehicle fuel economy on arterial roads making it significantly lower than that on freeways. Eco-Cooperative Adaptive Cruise Control (Eco-CACC) systems can improve vehicle fuel efficiency by receiving Signal Phasing and Timing (SPaT) data form downstream signalized intersections via vehicle-to-infrastructure communication. The algorithm that was developed in an earlier study provides advisory speed recommendations to drivers to reduce vehicle fuel consumption levels in the vicinity of traffic signalized intersections. The research presented in this thesis enhances the algorithm by adding a queue length estimation component and incorporates the algorithm in the INTEGRATION microscopic traffic simulation software to test the system under varying conditions. The enhanced Eco-CACC algorithm is then tested in a simulation environment considering different levels of connected vehicle (CV) market penetration levels. The simulation analysis demonstrates that the algorithm is able to reduce the vehicle fuel consumption level by as high as 40%. Moreover, the overall benefits of the proposed algorithm is evaluated for different intersection configurations and CV market penetration rates (MPRs). The results demonstrate that for single lane approaches, the algorithm can reduce the overall fuel consumption levels and that higher MPRs result in larger savings. While for multilane approaches, lower MPRs produce negative impacts on fuel efficiency; only when MPRs are greater than 30%, can the algorithm work effectively in reducing fuel consumption levels. Subsequently a sensitivity analysis is conducted. The sensitivity analysis demonstrates that higher market penetration rates of Eco-CACC enabled vehicles can improve the environmental benefits of the algorithm, and the overall savings in fuel consumption are as high as 19% when all vehicles are equipped with the system. While, on multi-lane approaches, the algorithm has negative impacts on fuel consumption levels when the market penetration rate is lower than 30 percent. The analysis also indicates that the length of control segments, the SPaT plan, and the traffic demand levels affect the algorithm performance significantly. The study further demonstrates that the algorithm has negative impacts on fuel consumption levels when the network is over-saturated. / Master of Science

Assuming that vehicle connectivity technology matures and connected vehicles hit the market, many of the running vehicles will be equipped with highly sophisticated sensors and communication hardware. Along with the goal of eliminating human distracted driving and increasing vehicle automation, it is necessary to develop novel intersection control strategies. Accordingly, the research presented in this dissertation develops an innovative system that controls the movement of vehicles using cooperative cruise control system (CACC) capabilities entitled: iCACC (intersection management using CACC). In the iCACC system, the main assumption is that the intersection controller receives vehicle requests from vehicles and advises each vehicle on the optimum course of action by ensuring no crashes occur while at the same time minimizing the intersection delay. In addition, an innovative framework has been developed (APP framework) using the iCACC platform to prioritize the movements of vehicles based on the number of passengers in the vehicle. Using CACC and vehicle-to-infrastructure connectivity, the system was also applied to a single-lane roundabout. In general terms, this application is considered quite similar to the concept of metering single-lane entrance ramps. The proposed iCACC system was tested and compared to three other intersection control strategies, namely: traffic signal control, an all-way stop control (AWSC), and a roundabout, considering different traffic demand levels ranging from low to high levels of congestion (volume-to-capacity ration from 0.2 to 0.9). The simulated results showed savings in delay and fuel consumption in the order of 90 to 45 %, respectively compared to AWSC and traffic signal control. Delays for the roundabout and the iCACC controller were comparable. The simulation results showed that fuel consumption for the iCACC controller was, on average, 33%, 45% and 11% lower than the fuel consumption for the traffic signal, AWSC and roundabout control strategies, respectively. In summary, the developed iCACC system is an innovative system because of its ability to optimize/model different levels of vehicle automation market penetrations, weather conditions, vehicle classes/models, shared movements, roundabouts, and passenger priority. In addition, the iCACC is capable of capturing the heterogeneity of roadway users (cyclists, pedestrians, etc.) using a video detection technique developed in this dissertation effort. It is anticipated that the research findings will contribute to the application of automated systems, connected vehicle technology, and the future of driverless vehicle management. Finally, the public acceptability of the new advanced in-vehicle technologies is a challenging task and this research will provide valuable feedback for researchers, automobile manufacturers, and decision makers in making the case to introduce such systems. / Ph. D.

Driving through an un-signalized intersection creates multiple opportunities for missed or misunderstood information. Stop signs, in particular, can be stolen, covered by vegetation, or rotated out of place, leading to an absence of information, contributing to inappropriate decision-making and crashes. Stop controlled intersections have also been shown to be a source of unnecessary delay and emissions due to their frequent, often inappropriate use. Using connected vehicle technology, it is possible to place an electronic stop sign within the vehicle that tells the driver to stop when a conflict in the intersection is imminent, thus reducing the probability of missed information by the driver, and decreasing the amount of unnecessary delay, fuel consumption, and emissions. Before implementing any new technology, it is important to assess it from both a transportation engineering and human factors standpoint to assess the value of the system. The objective of this study was to assess several key benefits of an adaptive in-vehicle stop display as well as to determine if there are any negative safety implications with the use of this system. This assessment was accomplished through a test track experiment where participants experienced conditions where a standard R1-1 stop sign was displayed on the in-vehicle display, as well as an experimental sign, which informed them to proceed through the intersection with caution. Data collected from in-vehicle sensors was analyzed, and results indicate that the implementation of this technology reduces delay, decreases fuel consumption, and does not instigate any safety decrements. / Master of Science

Vehicle platooning is an advanced vehicle maneuver that allows for the simultaneous control of several vehicles traveling on the roadway cite{al2010experimental}. Automated platoons, when activated in tractor trailer convoys, have a high potential of increasing the fuel efficiency and improving the utilization of roadways by allowing more vehicles to share the road at the same time. The increased fuel efficiency translates to lower cost on goods and motivates a more environmentally friendly and sustainable economy. In order to achieve the promised fuel savings from vehicle platooning, the vehicles need to follow each other at shorter headways than in typical driving scenarios. The reduced separation distance between the lead and follow vehicle reduces visibility and the reaction time available for the follow vehicle; this renders most modern Active Driver Assist Systems (ADAS) ineffective since they are not designed for operation in such short headway conditions. The focus of this work is related to understanding and improving the failures of Lane Keep Assist (LKA) systems in the follow vehicles of a platoon. In this work, the source of lane detection degradation when using a monocular forward facing camera in short headway platooning is identified. Furthermore, a novel lane augmentation algorithm is proposed to improve the lane detection capability of follow vehicles in a platoon. The lane augmentation process utilizes a longitudinal transformation of lane parameters from the lead to the follow vehicles. The transformation utilizes an accurate understanding of the relative spatial position and orientation of the two vehicles. The transformation also requires a reliable communication system between the two vehicles such as a Vehicle-to-Vehicle (V2V) module. The work presented in this thesis develops theory, simulation and verification using real world data of the proposed cooperative lane augmentation. The results of this work indicate that it is possible to improve vehicle platooning performance by distributing the required sensing across multiple agents of the platoon. / Master of Science / Vehicle platooning is an advanced vehicle maneuver that allows for the simultaneous control of several vehicles traveling on the roadway cite{al2010experimental}. Automated platoons, when activated in tractor trailer convoys, have a high potential of increasing the fuel efficiency and improving the utilization of our roadways by allowing more vehicles to share the road at the same time. The increased fuel efficiency translates to lower cost on goods and motivates a more environmentally friendly and sustainable economy. In order to achieve the promised fuel savings from vehicle platooning, the vehicles need to follow each other at closer distances (headway) than in typical driving scenarios. The reduced separation distance between the lead and follow vehicle reduces visibility and the reaction time available for the follow vehicle; this renders most modern Active Driver Assist Systems (ADAS) ineffective since they are not designed for operation in such short headway conditions. The focus of this work is related to understanding and improving the failures of Lane Keep Assist (LKA) systems - the automated system used to keep the vehicle in the center of the lane - in the follow vehicles of a platoon. In the proposed scenario, the LKA uses a single forward facing camera to detect the lane lines ahead of the vehicle. The detected lanes serve as inputs to the lateral position (steering) controller in order to keep the vehicle in the center of the lane. In this work, the source of lane detection degradation in a follow vehicle of a short headway platoon is identified. Furthermore, a novel cooperative lane detection algorithm is proposed to improve the lane detection capability of the follow vehicles. The proposed algorithm utilizes lane information transformed from the lead to follow vehicle frame. The transformation utilizes the relative spatial position and orientation of the two vehicles. Additionally, a reliable communication protocol between the vehicles is required to transport the lane information. The work presented in this thesis develops theory, simulation and verification using real world data of the proposed algorithm. The results of this work indicate that lane keeping performance in a platoon can be improved using cooperative lane detection.