abstract: There are significantly higher rates of pilot error events during surface operations at night than during the day. Events include incidents, accidents, wrong surface takeoffs and landings, hitting objects, turning on the wrong taxiway, departing the runway surface, among others. There is evidence to suggest that these events are linked to situational awareness. Improvements to situational awareness can be accomplished through training to instruct pilots to increase attention outside of the cockpit while taxiing at night. However, the Federal Aviation Administration (FAA) night time requirements are relatively low to obtain a private pilot certification. The purpose of this study was to determine the effect of flight training experience on conducting safe and incident-free surface operations at night, collect pilot opinions on night training requirements and resources, and analyze the need for night time on flight reviews. A survey was distributed to general aviation pilots and 239 responses were collected to be analyzed. The responses indicated a higher observed incident rate at night than during the day, however there were no significant effects of night training hours or type of training received (Part 61, Part 141/142, or both) on incident rate. Additionally, higher total night hours improved pilot confidence at night and decreased incident rate. The overall opinions indicated that FAA resources on night flying were effective in providing support, but overall pilots were not in support of or against adding night time requirements to flight reviews and found night training requirements to be somewhat effective. / Dissertation/Thesis / Masters Thesis Aerospace Engineering 2020

Over the last few years, the concept of civil Unmanned Aircraft System(s) (UAS) has been realised, with small UASs commonly used in industries such as law enforcement, agriculture and mapping. With increased development in other areas, such as logistics and advertisement, the size and range of civil UAS is likely to grow. Taken to the logical conclusion, it is likely that large scale UAS will be operating in civil airspace within the next decade. Although the airborne operations of civil UAS have already gathered much research attention, work is also required to determine how UAS will function when on the ground. Motivated by the assumption that large UAS will share ground facilities with manned aircraft, this thesis describes the preliminary development of an Automated Taxiing System(ATS) for UAS operating at civil aerodromes. To allow the ATS to function on the majority of UAS without the need for additional hardware, a visual sensing approach has been chosen, with the majority of work focusing on monocular image processing techniques. The purpose of the computer vision system is to provide direct sensor data which can be used to validate the vehicle s position, in addition to detecting potential collision risks. As aerospace regulations require the most robust and reliable algorithms for control, any methods which are not fully definable or explainable will not be suitable for real-world use. Therefore, non-deterministic methods and algorithms with hidden components (such as Artificial Neural Network (ANN)) have not been used. Instead, the visual sensing is achieved through a semantic segmentation, with separate segmentation and classification stages. Segmentation is performed using superpixels and reachability clustering to divide the image into single content clusters. Each cluster is then classified using multiple types of image data, probabilistically fused within a Bayesian network. The data set for testing has been provided by BAE Systems, allowing the system to be trained and tested on real-world aerodrome data. The system has demonstrated good performance on this limited dataset, accurately detecting both collision risks and terrain features for use in navigation.