<div>The National Aeronautics and Space Administration (NASA) has a vision for Advanced Air Mobility (AAM) based on safely introducing aviation services to missions that were previously not served or under-served. Many potential AAM missions lie in metropolitan areas that are beset by various types of uncertainty and potential constraints. Radio interference from other electronic devices can render unreliable communication between flying vehicles to ground operators. Buildings have irregular surfaces that degrade GPS localization performance. Skyscrapers can induce spontaneous turbulence that degrades vehicles' navigational accuracy. However, the potential market demands for aerial passenger-carrying and package delivery services have attracted investments. For example, Google WingX, Amazon Prime Air, and Joby Aviation are well-known companies developing AAM systems and services. If the market visions are realized, how will safety be assessed and maintained with high-density AAM operations?</div><div><br></div><div>While there are multiple technology candidates for realizing high-density AAM operations in urban environments, the means to accomplish the requisite first step of assessing the airspace safety of an integrated AAM eco-system from the candidate technologies is crucial but as yet unclear. This dissertation proposes an entropy-based framework for assessing the airspace safety level for low-altitude airspace in an AAM setting. The framework includes a conceptual model for depicting the information flows between air vehicles and an air traffic authority (ATA) and the use of a probability distribution to represent the traffic state. Subsequently, the framework embeds three airspace-level metrics for assessing airspace safety and uncertainty levels. The traffic safety severity metric quantifies the traffic safety level. The traffic entropy quantifies the uncertainty level of the traffic state distribution. Finally, the temperature is the ratio of the traffic safety severity to the traffic entropy. The temperature is similar to the traffic safety severity but gives a higher weight to the instance with a safe traffic state. </div><div><br></div><div>Simulation studies show that the combined use of the three metrics can evaluate relative airspace safety levels even if the unsafe conditions do not occur. The use cases include using the metrics for real-time airspace safety level monitoring and comparing the design of airspace systems and operational strategies. Additionally, this study demonstrates using a heat map to visualize vehicle-level metrics and assess designs of UAM airspace structures. The contribution of this study includes two parts. First, the temperature metric can heuristically assess a probability function. Based on the definition of the cost function, the temperature metric gives a higher weighting to the instance of the probability function with a lower cost value. This study constructs several triggers for predicting if a near-miss event would happen in the airspace. The temperature-based trigger has a better prediction accuracy than the cost-function-based trigger. Secondly, the temperature can visualize the safety level of an airspace structure with the considerations of the environmental and vehicle state measurement uncertainty. The locations with high-temperature values indicate that the regions are more likely to have endangered vehicles. Although this framework does not provide any means of resolving the unsafe conditions, it can be powerful in the comparison of different airspace design concepts and identify the weaknesses of either airspace design or operational strategies. </div>

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Emergency landing is a critical safety mechanism for aerial vehicles. Commercial aircraft have triply-redundant systems that greatly increase the probability that the pilot will be able to land the aircraft at a designated airfield in the event of an emergency. In general aviation, the chances of always reaching a designated airfield are lower, but the successful pilot might use landmarks and other visual information to safely land in unprepared locations. For small unmanned aircraft systems (sUAS), triply- or even doubly-redundant systems are unlikely due to size, weight, and power constraints. Additionally, there is a growing demand for beyond visual line of sight (BVLOS) operations, where an sUAS operator would be unable to guide the vehicle safely to the ground. This thesis presents a machine vision-based approach to emergency landing for small unmanned aircraft systems. In the event of an emergency, the vehicle uses a pre-compiled database of potential landing sites to select the most accessible location to land based on vehicle health. Because it is impossible to know the current state of any ground environment, a camera is used for real-time visual feedback. Using the recently developed Recursive-RANSAC algorithm, an arbitrary number of moving ground obstacles can be visually detected and tracked. If obstacles are present in the selected ditch site, the emergency landing system chooses a new ditch site to mitigate risk. This system is called Safe2Ditch.