Over the past decade, unmanned aerial vehicles (UAVs) have demonstrated increasing
attention and promise. They demonstrate great potential for application in both civilian and …

Deep neural networks have emerged as a widely used and effective means for tackling
complex, real-world problems. However, a major obstacle in applying them to safety-critical …

One approach to designing the decision making logic for an aircraft collision avoidance
system is to frame the problem as Markov decision process and optimize the system using …

One of the most difficult challenges in robot motion planning is to account for the behavior of
other moving agents, such as humans. Commonly, practitioners employ predictive models to …

In response to a series of midair collisions involving commercial airliners, Lincoln Laboratory
was directed by the Federal Aviation Administration in the 1970s to participate in the …

Deep neural networks have emerged as a widely used and effective means for tackling
complex, real-world problems. However, a major obstacle in applying them to safety-critical …

Unmanned aircraft systems must demonstrate a capability to sense and avoid air traffic as
part of a layered conflict management system to enable safe operations in the National …

Models for predicting aircraft motion are an important component of modern aeronautical
systems. These models help aircraft plan collision avoidance maneuvers and help conduct …

This paper proposes an effective approach for modelling and assessing the risks associated
with unmanned aerial vehicles (UAVs) integrated into national airspace system (NAS). Two …

EXECUTIVE SUMMARY The Traffic Alert and Collision Avoidance System (TCAS), currently
mandated on all large transport aircraft, has been shown to significantly reduce the risk of …