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The Traffic alert and Collision Avoidance System (TCAS) is an aircraft collision avoidance system designed to prevent mid-air collisions. During an advisory, danger is imminent, and TCAS is assumed to have better, more up-to-date information than the ground operated air traffic control (ATC) facility. Following a TCAS RA is generally the safe course of action during an advisory. However, pilot compliance with RAs is surprisingly low. Results from a TCAS monitoring study show pilots are not complying with many TCAS advisories. As revealed by pilot-submitted Aviation Safety Reporting System (ASRS) reports, this noncompliance could be attributed, in part, to pilot confusion to TCAS operation as well as misunderstandings of the appropriate response to a TCAS issued advisory. This thesis details the development and evaluation of a TCAS training program intended to improve pilots' understanding of TCAS use for collision avoidance in a range of traffic situations. The training program integrated Demonstration Based and Event Based Training techniques. Its efficacy was analyzed in an integrated ATC-cockpit simulator study in which eighteen commercial airline pilots were asked to complete the TCAS training program and afterwards experienced twelve experimental traffic events. The trained pilots' performance was compared to the performance of 16 baseline pilots who did not receive the modified training. Overall, the training program did have a significant impact on the pilots' behavior and response to TCAS advisories. The measure Time Pilots First Achieved Compliance decreased with the trained pilots, as did the measure Autopilot Disconnect Time After RA Initiation. Trained pilots exhibited less aggressive performance in response to a TCAS RA (including a decrease in the measures Altitude Deviation Over Duration Of RA, Average Vertical Rate Difference, Maximum Vertical Rate Difference, and Maximum Vertical Rate). The measure Percent Compliance did not significantly vary between trained and baseline pilots, although trained pilots had a more consistent response in the traffic event with conflicting ATC guidance. Finally, on the post-experiment questionnaires, pilots commented on their increase in understanding of TCAS as well as an increase in their trust in the advisory system. Results of this research inform TCAS training objectives provided by the FAA as well as the design of TCAS training. Additionally, conclusions extend more broadly to improved training techniques for other similar complex, time-critical situations.

Abstract: This thesis addresses the problem of graceful degradation for air traffic management systems (ATMS). The graceful degradation is the process by which the safety of the airspace is ensured in the event of failures or operational degradation in the system. After listing the main areas where failures and degradation can affect the ATMS, an ontology of the ATMS is proposed. The ontology allows to introduce failures at different levels, track their propagation throughout the system, and measure their operational impact. Then, two operational degradations are studied: The first degradation studied is a reduction in the landing capacity at San Francisco International Airport. The aircraft queueing process for terminal area is modeled and optimized to ensure a graceful degradation. The second degradation encompasses Communication, Navigation and Surveillance systems failures. The graceful degradation is ensured by increasing the spacing distance between aircraft, using novel algorithms of avoidance under uncertainties. Those algorithm also serve as probes to compare the degradation capabilities of different traffic configurations such as Miles-In-Trail and Free-Flight arrivals. Finally, this thesis focuses on monitoring the airspace for potential degradation. The ability and the difficulty of en-route traffic configuration are evaluated using degradation maps. Those maps can be used controller to rapidly and efficiently steer traffic from nominal mode of operations to mode of operations under abnormal conditions. Finally, a monitoring tool for terminal area is presented: the conformance of current flight to pre-identified typical operations is determined in real time. As the number of non-conforming aircraft increases, the complexity seen by air traffic controllers increases, and can become a threat for the airspace safety.

This thesis addresses the problems of collision avoidance and coalition formation of multiple UAVs in high density traffic environments, proposes simple and efficient algorithms as solutions, and discusses their applications in multiple UAV missions. First, the problem of collision avoidance among UAVs is considered and deconfliction algorithms are proposed. The efficacy of the proposed algorithms is tested using simulations involving random flights in high density traffic. Further, the proposed collision avoidance algorithms are implemented using realistic six degree of freedom UAV models. The studies in this thesis show that implementation of the proposed collision avoidance algorithms leads to a safer and efficient operational airspace occupied by multiple UAVs. Next, coalition formation in a search and prosecute mission involving a large number of UAVs and targets is considered. This problem is shown to be NP-hard and a sub-optimal but polynomial time coalition formation strategy is proposed. Simulations are carried out to show that this coalition formation algorithm works well. The coalition formation algorithm is then extended to handle situations where the UAVs have limited communication ranges. Finally, this thesis considers some multiple UAV missions that require the application of collision avoidance and coalition formation techniques. The problem of multiple UAV rendezvous is tackled by using (i) a consensus among the UAVs to attain rendezvous and (ii) the collision avoidance algorithm previously developed for safety. The thesis also considers a search and prosecute mission where the UAVs also have to avoid collisions among one another. In summary, the main contributions of this thesis include (a) novel collision avoidance algorithms, which are conceptually simple and easy to implement, for resolving path conflicts – both planar and three dimensional – in a high density traffic airspace with UAVs in free flight and (b) efficient coalition formation algorithms for search and prosecute task with large number of UAVs and targets where UAVs have limited communication ranges and targets are maneuvering. Simulations to evaluate the performance of algorithms based on these concepts to carry out realistic tasks by UAV swarms are also given.