The main research objective of this dissertation is to understand the sensing and communication challenges to achieving cooperative perception among autonomous vehicles, and then, using the insights gained, guide the design of the suitable format of data to be exchanged, reliable and efficient data fusion algorithms on vehicles. By understanding what and how data are exchanged among autonomous vehicles, from a machine learning perspective, it is possible to realize precise cooperative perception on autonomous vehicles, enabling massive amounts of sensor information to be shared amongst vehicles. I first discuss the trustworthy perception information sharing on connected and autonomous vehicles. Then how to achieve effective cooperative perception on autonomous vehicles via exchanging feature maps among vehicles is discussed in the following. In the last methodology part, I propose a set of mechanisms to improve the solution proposed before, i.e., reducing the amount of data transmitted in the network to achieve an efficient cooperative perception. The effectiveness and efficiency of our mechanism is analyzed and discussed.

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The use of connected and autonomous ground and aerial vehicles is a promising solution to reduce accidents, improve the traffic efficiency, and provide various services ranging from delivery of goods to monitoring. Different from the current connected vehicles and autonomous vehicles, connected and autonomous vehicles (CAVs) combine autonomy and wireless connectivity and use both sensors and communication systems to increase their situational awareness and for their decision-making. However, in order to reap all the benefits of deploying CAVs, one must consider the interconnection between communication, control, and learning mechanisms for the CAV system design. The key goal of this dissertation is, thus, to develop foundational science that can be used for the design, analysis, and optimization of CAV systems while jointly taking into account the synergies among communication, control, and learning systems. First, a joint communication and control system design is developed for non-coordinated CAVs when performing autonomous path tracking. In particular, the maximum time delay requirements are derived to guarantee the stability of the controller when tracking two typical road scenarios (i.e., straight line and circular curve). Tools from optimization theory and risk theory are then used to jointly optimize the control system and power allocation for the communication network so as to maximize the number of vehicular links that meet the controller's delay requirements. Second, the joint control and communication design framework is extended to two coordinated CAVs applications, i.e., CAV platoons and unmanned aerial vehicle (UAV) swarms. Third, a distributed machine learning algorithm, i.e., federated learning (FL), is proposed for a swarm of connected and autonomous UAVs to execute tasks, such as coordinated trajectory planning and cooperative target recognition. In particular, a rigorous convergence analysis for FL is performed to show how wireless factors impact the FL convergence performance, and the design of UAV swarm networks is optimized to reduce the convergence time. Fourth, a new FL framework, called dynamic federated proximal (DFP) algorithm, is proposed for designing the autonomous controller of CAVs while considering the mobility of CAVs, the wireless fading channels, as well as the unbalanced and non independent and identically distributed data across CAVs. To improve the convergence of the proposed DFP algorithm, a contract-theoretic incentive mechanism is also proposed. Fifth, a wireless-enabled asynchronous federated learning (AFL) framework is proposed for urban air mobility (UAM) aircraft to collaboratively learn the turbulence prediction model. In particular, to characterize how UAM aircraft leverage wireless connectivity for AFL, a stochastic geometry based spatial model is developed and the wireless connectivity performance is analyzed. Then, a rigorous convergence analysis is performed for the proposed AFL framework to identify how fast the UAM aircraft converge to using the optimal turbulence prediction model. Sixth, based on the concordance order from stochastic ordering theory, a dependence control mechanism is proposed to improve the overall reliability of wireless networks for CAVs. Finally, to determine the optimal cache placement for CAVs, a novel spatio-temporal caching framework is proposed where the notion of graph motifs, i.e., the spatio-temporal communication patterns in wireless networks, is used. In conclusion, the frameworks presented in this dissertation will provide key fundamental guidelines to design, analyze, and optimize CAV systems. / Doctor of Philosophy / The evolution of transportation systems has always been the key to the progress of human societies. Recently, technology advances in sensing, autonomy, computing, and wireless connectivity ushered in the era of connected and autonomous vehicles (CAVs). In essence, CAVs rely on the data collected from sensors and wireless communication systems to automatically make the operation decision. If designed properly, the deployment of CAVs can improve the safety and the driving experience, increase the fuel efficiency and road capacity, as well as provide various services ranging from delivery of goods to monitoring. To reap all these benefits of deploying CAVs, one must address a number of technique challenges related to the wireless connectivity, autonomy, and autonomous learning for CAV systems. In particular, for CAV connectivity, the challenges include building a low latency and highly reliable network, using proper models for mobile radio channels, and determining the effective content dissemination strategy. At the control level, key considerations include guaranteeing stability and robustness for the controller when faced with measurement errors and wireless imperfections and rapidly adapting the CAV to dynamic environments. Meanwhile, when CAVs use machine learning to complete their tasks (e.g., object detection and environment monitoring), insufficient training data, privacy concerns, communication overhead, and limited energy are among the main challenges. Therefore, this dissertation develops the foundational science needed to design, analyze, and optimize CAVs while jointly taking into account the challenges within the wireless network, controller, and leaning mechanism design. To this end, various frameworks for the joint communication, control, and learning design and wireless network optimizations are proposed for different CAV applications. The results show that, using the proposed frameworks, the performance of CAVs can be optimized with more reliable communication systems, more stable controller, and improved learning mechanism, enabling intelligent transportation systems for the future smart cities.

<p>Connected and autonomous vehicle (CAV) technologies provide disruptive and transformational opportunities for innovations toward intelligent transportation systems. Compared with human driven vehicles (HDVs), the CAVs can reduce reaction time and human errors, increase traffic mobility and will be more knowledgeable due to vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication. CAVs’ potential to reduce traffic accidents, improve vehicular mobility and promote eco-driving is immense. However, the new characteristics and capabilities of CAVs will significantly transform the future of transportation, including the dissemination of traffic information, traffic flow dynamics and network equilibrium flow. This dissertation seeks to realize and enhance the application of CAVs by specifically advancing the research in three connected topics: (1) modeling and controlling information flow propagation within a V2V communication environment, (2) designing a real-time deployable cooperative control mechanism for CAV platoons, and (3) modeling network equilibrium flow with a mix of CAVs and HDVs. </p> <p>Vehicular traffic congestion in a V2V communication environment can lead to congestion effects for information flow propagation due to full occupation of the communication channel. Such congestion effects can impact not only whether a specific information packet of interest is able to reach a desired location, but also the timeliness needed to influence traffic system performance. This dissertation begins with exploring spatiotemporal information flow propagation under information congestion effects, by introducing a two-layer macroscopic model and an information packet relay control strategy. The upper layer models the information dissemination in the information flow regime, and the lower layer model captures the impacts of traffic flow dynamics on information propagation. Analytical and numerical solutions of the information flow propagation wave (IFPW) speed are provided, and the density of informed vehicles is derived under different traffic conditions. Hence, the proposed model can be leveraged to develop a new generation of information dissemination strategies focused on enabling specific V2V information to reach specific locations at specific points in time.</p> <p>In a V2V-based system, multiclass information (e.g., safety information, routing information, work zone information) needs to be disseminated simultaneously. The application needs of different classes of information related to vehicular reception ratio, the time delay and spatial coverage (i.e., distance it can be propagated) are different. To meet the application needs of multiclass information under different traffic and communication environments, a queuing strategy is proposed for each equipped vehicle to disseminate the received information. It enables control of multiclass information flow propagation through two parameters: 1) the number of communication servers and 2) the communication service rate. A two-layer model is derived to characterize the IFPW under the designed queuing strategy. Analytical and numerical solutions are derived to investigate the effects of the two control parameters on information propagation performance in different information classes. </p> <p>Third, this dissertation also develops a real-time implementable cooperative control mechanism for CAV platoons. Recently, model predictive control (MPC)-based platooning strategies have been developed for CAVs to enhance traffic performance by enabling cooperation among vehicles in the platoon. However, they are not deployable in practice as they require anembedded optimal control problem to be solved instantaneously, with platoon size and prediction horizon duration compounding the intractability. Ignoring the computational requirements leads to control delays that can deteriorate platoon performance and cause collisions between vehicles. To address this critical gap, this dissertation first proposes an idealized MPC-based cooperative control strategy for CAV platooning based on the strong assumption that the problem can be solved instantaneously. It then develops a deployable model predictive control with first-order approximation (DMPC-FOA) that can accurately estimate the optimal control decisions of the idealized MPC strategy without entailing control delay. Application of the DMPC-FOA approach for a CAV platoon using real-world leading vehicle trajectory data shows that it can dampen the traffic oscillation effectively, and can lead to smooth deceleration and acceleration behavior of all following vehicles.</p> <p>Finally, this dissertation also develops a multiclass traffic assignment model for mixed traffic flow of CAVs and HDVs. Due to the advantages of CAVs over HDVs, such as reduced value of time, enhanced quality of travel experience, and seamless situational awareness and connectivity, CAV users can differ in their route choice behavior compared to HDV users, leading to mixed traffic flows that can significantly deviate from the single-class HDV traffic pattern. However, due to a lack of quantitative models, there is limited knowledge on the evolution of mixed traffic flows in a traffic network. To partly bridge this gap, this dissertation proposes a multiclass traffic assignment model. The multiclass model captures the effect of knowledge level of traffic conditions on route choice of both CAVs and HDVs. In addition, it captures the characteristics of mixed traffic flow such as the difference in value of time between HDVs and CAVs and the asymmetry in their driving interactions, thereby enhancing behavioral realism in the modeling. New solution algorithms will be developed to solve the multiclass traffic assignment model. The study results can assist transportation decision-makers to design effective planning and operational strategies to leverage the advantages of CAVs and manage traffic congestion under mixed traffic flows.</p> <p>This dissertation deepens our understanding of the characteristics and phenomena in domains of traffic information dissemination, traffic flow dynamics and network equilibrium flow in the age of connected and autonomous transportation. The findings of this dissertation can assist transportation managers in designing effective traffic operation and planning strategies to fully exploit the potential of CAVs to improve system performance related to traffic safety, mobility and energy consumption. </p>

Emergency response times have been shown to be directly correlated with mortality rates of out-of-hospital patients. Studies have been conducted to show the relationship between time and mortality rates until patients receive the proper treatment. With more cardiac arrests and other life threatening illnesses occurring in the United States, more emergency calls will be required as well. As of today, technological advancements have been made to reduce response times, but human factors still require certain procedures, causing delays in the run time and increasing the rate of mortality. Here we show the results of emergency response times with the market penetration of connected and autonomous vehicles. With connected and autonomous vehicles, the average time emergency vehicles spend on the roadways can be significantly decreased. Safety procedures with human drivers can be eliminated, giving the emergency vehicle a proper right-of-way through virtual emergency lanes and removing the need to slow down and avoid vehicles at intersections or during periods of heavy congestion. Our results show a three minute decrease in response time under full market penetration of the technology, reducing the mortality rate and increasing the potential to save lives.

The Indiana Department of Transportation (INDOT) manages and maintains over 3,000 miles of interstates across the state. Assessing lane marking quality is an important part of agency asset tracking and typically occurs annually. The current process requires agency staff to travel the road and collect representative measurements. This is quite challenging for high volume multi-lane facilities. Furthermore, it does not scale well to the additional 5,200 centerline miles of non-interstate routes. <div><br></div><div>Modern vehicles now have technology on them called “Lane Keep Assist” or LKA, that monitor lane markings and notify the driver if they are deviating from the lane. This thesis evaluates the feasibility of monitoring when the LKA systems can and cannot detect lane markings as an alternative to traditional pavement marking asset management techniques. This information could also provide guidance on what corridors are prepared for level 3 autonomous vehicle travel and which locations need additional attention. </div><div><br></div><div>In this study, a 2019 Subaru Legacy with LKA technology was utilized to detect pavement markings in both directions along Interstates I-64, I-65, I-69, I-70, I-74, I90, I-94 and I-465 in Indiana during the summer of 2020. The data was collected in the right most lane for all interstates except for work zones that required temporary lane changes. The data was collected utilizing two go-pro cameras, one facing the dashboard collecting LKA information and one facing the roadway collecting photos of the user’s experience. Images were taken at 0.5 second frequency and were GPS tagged. Data collection occurred on over 2,500 miles and approximately 280,000 images were analyzed. The data provided outputs of: No Data, Excluded, Both Lanes Not Detected, Right Lane Not Detected, Left Lane Not Detected, and Both Lanes Detected. </div><div><br></div><div>The data was processed and analyzed to create spatial plots signifying locations where markings were detectable and locations where markings were undetected. Overall, across 2,500 miles of travel (right lane only), 77.6% of the pavement markings were classified as both detected. The study found</div><div><br></div><div>• 2.6% the lane miles were not detected on both the left and right side </div><div>• 5.2% the lane miles were not detected on the left side </div><div>• 2.0% the lane miles were not detected on the right side 8 </div><div><br></div><div>Lane changes, inclement weather, and congestion caused 12.5% of the right travel lane miles to be excluded. The methodology utilized in this study provides an opportunity to complement the current methods of evaluating pavement marking quality by transportation agencies. </div><div><br></div><div>The thesis concludes by recommending large scale harvesting of LKA from a variety of vendors so that complete lane coverage during all weather and light conditions can be collected so agencies have an accurate assessment of how their pavement markings perform with modern LKA technology. Not only will this assist in identifying areas in need of pavement marking maintenance, but it will also provide a framework for agencies and vehicle OEM’s to initiate dialog on best practices for marking lines and exchanging information.</div>

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<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>

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