Doctor of Philosophy / Self-driving cars promise a future with safer roads and reduced traffic incidents and fatalities. This future hinges on the car's accurate understanding of its surrounding environment; however, the reliability of the algorithms that form this perception is not always guaranteed and adverse traffic and environmental conditions can significantly diminish the performance of these algorithms. To solve this problem, this research builds on the idea that enabling cars to share and exchange information via communication allows them to extend the range and quality of their perception beyond their capability. To that end, this research formulates a robust and flexible framework for cooperative perception, explores how connected vehicles can learn to collaborate to improve their perception, and introduces an affordable, experimental vehicle platform for connected autonomy research.

With the advent of electric vehicles (EVs) and the proliferation of vehicle technologies like drive-by-wire and autonomous driving, advanced communication protocols to connect vehicles and the infrastructure have been proposed. However, practical large-scale deployments have been hindered due to caveats such as hardware, and infrastructure demands — including the security of vehicles, given their ubiquitous nature and direct correlation to human safety. As part of this thesis, we look at deploying a practical solution to adopt a secure large-scale vehicle-to-everything (V2X) communication architecture. Then, we also try to analyze and detect vulnerabilities in vehicle-to-grid communication for electric vehicles. In the first work, we analyze, build a proof of concept and evaluate the use of commercial off-the-shelf (COTS) smartphones as secure cellular-vehicle-to-everything (CV2X) radios. Here, we study the various possible network topologies considering the long-term evolution (LTE) technology with necessary latency requirements considering security and the associated overhead. We further simulate the proposed method by considering real-world scalability for practical deployment. In the second work, we analyze the ISO15118 standard for EV-to-electric grid communication involving high levels of energy exchange. We develop a grammatical fuzzing architecture to assess and evaluate the implementation of the standard on a road-deployed vehicle to detect security vulnerabilities and shortcomings. / Master of Science / The technology around vehicles and the transportation infrastructure has immensely advanced in the last few decades. Today we have advanced technologies like driver assistance, automated driving, and access to multimedia within our vehicles. And deploying such technologies has only been possible due to advancements in the electronics embedded in the vehicles and surrounding infrastructure. Opportunely, we can further improve the technologies to include numerous safety features by connecting vehicles and infrastructure via communication networks. However, this poses immense challenges regarding the scaling of communication infrastructure for the timely exchange of data and its security. But, given the proliferation of cellular technology, the ubiquitous nature of smartphones, and their capabilities, we propose and evaluate the idea of using commercial off-the-shelf (COTS) smartphones to connect vehicles and the infrastructure to exchange data securely. The first work of this thesis details the analysis and evaluation of the system and the network for a secure COTS-based cellular-vehicle-to-everything architecture, including a proof of concept hardware implementation and additional simulations. Additionally, in light of climate policies and cleaner transportation alternatives, we are moving from gasoline-based internal combustion engines to electric vehicles, requiring the transfer of extended amounts of electric energy from the electric grid to the batteries in the vehicles. In light of the same, ISO 15118 standard was developed to reduce repetitive efforts and standardize the communication and exchange of this energy. But as with any new technology, especially involving communication, new attack vectors for malicious entities open up. Therefore, we study this new standard and develop a novel fuzzing architecture to test the implementation of the standard on deployed real-world vehicles for security vulnerabilities and robustness. Again, as this is a nascent technology and standard, a fuzzing approach would accelerate the detection of edge cases and threats before these are exploited to cause harm to human life and property.

Flooding can severely disrupt transportation systems. When safety measures are limited to road closures, vehicles affected by the flooding have an origin, destination, or path segment that is closed or soon-to-be flooded during the trip's duration. This thesis introduces a framework to provide routing assistance and trip cancellation recommendations to affected vehicles. The framework relies on the connected vehicle environment for real-time link performance measures and flood data and evaluates the trip of the vehicle to determine whether it is affected by the flood or not. If the vehicle is affected and can still leave its origin, the framework generates the corresponding routing assistance in the form of hyperpath(s) or set of alternative paths. On the other hand, a vehicle with a closed origin receives a warning to wait at origin, while a vehicle with an affected destination is assigned to a new safe one. This framework is tested on two transportation networks. The evaluation of the framework's scalability to different network sizes and the sensitivity of the results to various flood characteristics, policy-related variables and other dependencies are performed using simulated vehicle data and hypothetical flood scenarios. The computation times depends on the network size and flood depth but have generally an average of 1.47 seconds for the largest tested network and deepest tested flood. The framework has the potential to alleviate the impacts and inconveniences associated with flooding. / Master of Science / Flooding is a natural hazard that occurs with heavy rainfalls and high tides. In extreme situations, a flood in an area results in the evacuation of its occupant. Yet, in many cases, a flood is less severe and may only result in roads closures without necessitating evacuation. During these situations, and as transportation engineers, our ultimate goal is to maintain efficient and safe traffic operations. This thesis introduces a framework that focuses on providing routing assistance to affected vehicles and sending warnings to unaffected ones. It relies on the future connected vehicle environment which enables the communication between a traffic management center and equipped vehicles. The traffic management center collects and processes the information about the link performance measures and the weather and flood forecasts and sends them to the connected vehicles. Each vehicle has an in-vehicle navigation system in which the proposed framework is embedded. The framework, depending on the vehicle’s origin, destination, path and departure time and based on the flood’s characteristics, determines whether the vehicle is affected or not. If the vehicle is unaffected, it will receive a warning with the areas to avoid in case of any deviation and it can resume its trip as intended. If affected, the vehicle will either receive a warning to stay at its origin or routing guidance in the form of hyperpath or a set of alternative paths. The proposed framework has been evaluated on two transportation networks modeled in VISSIM based on the city of Virginia Beach, VA. Using simulated vehicle data and generated flood scenarios, several tests were executed to evaluate the scalability of the framework to different transportation networks along with the sensitivity of the results to variation in flood characteristics, policy-dependent variables and other dependencies. Concentrated, more intense and deeper floods resulted in a higher impact on the system. Yet, the analysis of the output is highly dependent on the location of the origin and destination of the vehicles with respect to the flooded roads. Thus, a lot of the output explanation are specific. Computation time increased with the increase in network size and in the flood depth. Nevertheless, it is still small and reasonable and further increase in both parameters (network size and flood depth) can be tested in future along with multiple techniques that minimize the computation time. This framework addresses the flooding hazard which road users are experiencing more and more nowadays. This hazard brings risks and inconveniences to our daily life. Thus, the development of this framework is of great interest to our society as it is a promising tool that has the potential to offer benefits, in terms of safety and mobility, to roads users exposed to a flood hazard. Its first implementation shows that it is a timely application with a potential to perform even better with future improvements.

With connected and autonomous vehicles, no optimal standard or framework currently exists, outlining the right level of information sharing for cooperative autonomous driving. Cooperative Perception is proposed among vehicles, where every vehicle is transformed into a moving sensor platform that is capable of sharing information collected using its on-board sensors. This helps extend the line of sight and field of view of autonomous vehicles, which otherwise suffer from blind spots and occlusions. This increase in situational awareness promotes safe driving over a short range and improves traffic flow efficiency over a long range. This thesis proposes a methodology for cooperative perception for autonomous vehicles over a short range. The problem of cooperative perception is broken down into sub-tasks of cooperative relative localization and map merging. Cooperative relative localization is achieved using visual and inertial sensors, where a computer-vision based camera relative pose estimation technique, augmented with position information, is used to provide a pose-fix that is subsequently updated by dead reckoning using an inertial sensor. Prior to map merging, a technique for object localization using a monocular camera is proposed that is based on the Inverse Perspective Mapping technique. A mobile multi-robot testbed was developed to emulate autonomous vehicles and the proposed method was implemented on the testbed to detect pedestrians and also to respond to the perceived hazard. Potential traffic scenarios where cooperative perception could prove crucial were tested and the results are presented in this thesis. / MS / Perception in Autonomous Vehicles is limited to the field of view of the vehicles’ onboard sensors and the environment may not be fully perceivable due to the presence of blind spots and occlusions. To overcome this limitation, Vehicle-to-Vehicle wireless communication could be leveraged to exchange locally sensed information among vehicles within the vicinity. Vehicles may share information about their own position, heading and velocity or go one step further and share information about their surroundings as well. This latter form of cooperative perception extends each vehicle’s field of view and line of sight, and helps increase situational awareness. The result is an increase in safety over a short range whereas communication over a long range could help improve traffic flow efficiency. This thesis proposes one such technique for cooperative perception over a short range. The system uses visual and inertial sensors to perform cooperative localization between two vehicles sharing a common field of view, which allows one vehicle to locate the other vehicle in its frame of reference. Subsequently, information about objects in the surroundings of one vehicle, localized using a visual sensor is relayed to the other vehicle through communication. A mobile multi-robot testbed was developed to emulate autonomous vehicles and to experimentally evaluate the proposed method through a series of driving scenario test cases in which cooperative perception could be effective and crucial to the safety and comfort of driving.

Highways play a vivacious role in a country's economic growth, by facilitating movement of both goods and people from one place to another. Over a short period of time, innovation in automobile and information technology has seen an unprecedented growth and this exploratory research highlights the impact of advent of innovative technologies like Autonomous and Connected Vehicles, Internet of Things applications and Big Data analytics on highway operators, as reflected in the opinions of organizations around the world (highway operators, toll agencies, suppliers, consultants and associations). The opinions were collected on a Likert scale type online survey, which was later tested for its empirical significance with non-parametric Binomial and Wilcoxon signed rank tests, supported by descriptive analysis. The research results clearly indicate that these technologies and products are not far from realization and while on one hand they would facilitate highway operations on the other hand they may pose some serious challenges for operators.

Over the past few years automotive and technology companies have made significant advances in what has been traditionally a completely human function: driving. Crash avoidance features such as lane departure warning and forward collision warning are becoming increasingly more common and cheaper to obtain, even on non-luxury vehicles. Technology companies and auto manufacturers have announced plans to have self-driving vehicles ready for public use as early as 2020. The mass adoption of automated vehicles (AVs) could significantly change surface transportation as we know it today. This thesis is intended to provide a technical analysis of the potential impacts of AVs on current light-duty vehicle miles traveled (VMT) and parking decisions, the economic desirability of widespread deployment of partially automated technologies, and methods for existing roadways to transition to connected and automated vehicle (CAV) transportation, so that policymakers can make more informed decisions during the transition to CAVs. This work takes a look at AVs from a point in time where vehicles are equipped with driver assistance systems (Level 1) to a point in time where AVs are driverless (Level 5) and can self-park. The results of this work indicate that the fleet-wide adoption of partially automated crash avoidance technologies could provide net-benefit of about $4 billion at current system effectiveness and could provide an annual net-benefit up to $202 billion if all relevant crashes could be prevented. About 25% of all crashes could be addressed by the crash avoidance technologies examined in this dissertation. Over time, as technologies become more effective and cheaper due to economies of scale, greater benefits than the $4 billion could be realized. As automated technologies become more advanced and widespread, existing roadways will need to be able to accommodate these vehicles. This work investigates the effects of a dedicated truck platoon lane on congestion on the Pennsylvania Turnpike and provides a method for existing roadways and highways to determine viable platoon demonstration sites. The initial results suggest that there are several sections of turnpike that could serve as commercial truck platoon demonstration site while still providing a high LOS to all other vehicles. Once AVs can safely and legally drive unoccupied, vehicles will no longer be limited to their driver’s destination and can search for cheaper parking in more distant parking locations. This work simulates a fleet of privately owned vehicles (POVs) in search of cheaper parking in Seattle, using a rectangular grid throughout the study area. Model results indicate that we are not likely to see significant increase in vehicle miles traveled (VMT) and energy use from cars moving from downtown parking lots to cheaper parking in distance locations but at higher penetration rates, parking lot revenues could likely decline to the point where operating a lot is unsustainable economically, if no parking demand management policies are implemented. Driverless vehicles also promise to increase mobility for those in underserved populations. This work estimates bounds on the potential increases in travel in a fully automated vehicle environment due to an increase in mobility from the non-driving and senior populations and people with travel-restrictive medical conditions. Three demand wedges were established in order to conduct a first-order bounding analysis. The combination of the results from all three demand wedges represents an upper bound of 295 billion miles or a 14% increase in annual light-duty VMT for the US population 19 and older. AV technology holds much promise in providing a more accessible and safe transportation system. This thesis can help policymakers and stakeholders maximize the benefits and minimize the challenges.

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<p>  </p> <p>Historically, agencies have been reliant on physical infrastructure, crash data, manual data collection, and modeling to evaluate their road networks. Over the past several years, enhanced probe data has become commercially available and has shown itself to be a relatively inexpensive and scalable way to evaluate the performance of road networks. In January 2022 alone, 11.3 billion passenger vehicle trajectory waypoints and 279 million passenger vehicle event records were logged in the state of Indiana. This data, typically segmented into vehicle trajectory waypoints and vehicle event records, contains a variety of information including, but not limited to, location, speed, heading, and timestamp. </p> <p>One use for this enhanced probe data is the evaluation of traffic signals for safety improvements. Typically, agencies require 3 – 5 years of crash data to be able to statistically identify intersections in need of safety improvements. This study compared crash data over a 4.5-year period at 8 signalized intersections to one month of weekday hard-braking and hard-acceleration data from July 2019. A Spearman’s rank-order correlation test was used, and a strong to very strong correlation between event data and crashes could be found indicating that just one month of event data could be an adequate substitute for 3 – 5 years of crash data. </p> <p>The representativeness of this data is often a major concern for many agencies as the usefulness of the data is only as good as the data itself. This paper describes and demonstrates a methodology for measuring connected vehicle penetration using data provided by state highway performance monitoring stations. This study looked at 1.7 billion count station vehicle counts and 70 million connected vehicle records across 381 count stations in 11 different states (California, Connecticut, Georgia, Indiana, Minnesota, North Carolina, Ohio, Pennsylvania, Texas, Utah, and Wisconsin). Across the 11 states and 381 stations, the average percent penetration was 3.8% in August 2020 and 3.9% in August 2021. Drilling down to August 2021, the percent penetration observed among the 187 interstate stations varied from 1.6% in Indiana to 10.0% in Wisconsin. A similar comparison of 162 non-interstate count stations showed a variation of 2.1% in MN and 18.0% in WI on non-interstates. </p>

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This thesis focuses on developing an intelligent energy management strategy for eco-driving in Connected and Autonomous Hybrid Electric Vehicles (CA-HEV's), which can be implemented in real-time. The strategy is divided into two layers, i.e. the upper level controller and the lower level controller. The upper level controller can be executed on the remote server. It is responsible for extracting the information from the driver about the trip and the vehicle information using the communication capabilities of the CA-HEV. The gathered information is then utilized by dynamic programming (DP), which is implemented in a bi-layer fashion to reduce the computation burden on the server. The outer layer of the DP algorithm and the optimal velocity trajectory and the inner layer optimizes the power distribution in the powertrain to minimize fuel consumption alongside maintaining charge balance conditions. These global optimal results are evaluated for an ideal environment without any traffic information. The lower level controller is responsible for real-time implementation on vehicles in the real world environment and is based on a well-accredited reinforcement learning (RL) strategy, i.e., Q-learning. The RL-based controller optimally distributes the power in a CA-HEV and maintains charge balance conditions. Furthermore, the RL-based controller is also trained on the remote server based on global optimal results obtained from the DP algorithm. The optimal parameter information is then resent to the vehicle's embedded controller for real-time implementation. Simulations are performed for Toyata Prius (2010) on MATLAB and Simulink, and road information is gathered from SUMO. Simulation results provide a comparative study between the global optimal and the RL-based controller. To validate the adaptiveness of the RL-based controller, it is also tested on two approximate real-world drivecycles and its performance is compared against global optimal results evaluated using DP. / Thesis / Master of Applied Science (MASc)