You are driving down the street. You have been down this same road almost every day for the past decade. Home is just past the next intersection. As you regain speed after halting at the \u2018Stop\u2019 sign, a driver screeches to a stop just inches from your door. He had not planned for a full stop at the sign. Adrenaline rushes through you, but everyone is okay. So, you head home. You resolve to ask for a traffic light at the intersection during the next community meeting.<\/p>\n
Pilots sometimes find themselves in similar situations. They missed a command from Air Traffic Control (ATC). Another aircraft lost its radio ability. The instruments provided a bad signal. Even if a collision is imminent, there is no way to come to a full stop mid-flight. The pilot must be aware of potential risks and plan for them.<\/p>\n
The Grand Canyon mid-air collision in 1956 spurred the Federal Aviation Administration (FAA) into action. The current solution, Traffic Collision Avoidance System (TCAS), was first implemented 25 years later. Required on all aircraft weighing over 12,600lbs or carrying more than 19 passengers, this system works by constantly broadcasting each aircraft\u2019s altitude and bearing. [1] When a nearby aircraft is detected, it announces a verbal instruction to the pilot to ascend or descend. TCAS instruction take priority over everything else, including ATC; however, it depends on the pilot responding in time and correctly. Still, the pilot may not be able to comply: the aircraft is too close to the ground, its aerodynamic capabilities prevent action, or the fuel levels are too low. Even so, TCAS has proven its usefulness many times over.<\/p>\n
The 2001 Japan Airlines incident [2] and the 2002 Uberlingen collision [3] proved that TCAS is far from perfect. Automatic Dependent Surveillance \u2013 Broadcast (ADS-B) is a revolutionary technology set for implementation in the US airspace starting in 2020. ADS-B accounts for a much larger set of inputs \u2013 type of aircraft, runway occupancy, weather, etc. \u2013 and provides much more comprehensive guidance to pilots in at-risk situations. [4]<\/p>\n
The complexity of correctly matching an output given the expanded number of inputs has necessitated the use of machine learning. Using clustering, ADS-B transforms scattered flight data points into individual continuous flight trajectories. Learning by observing actual pilot action under different input conditions, the system then teaches how to best respond to a wide range of situations. [5] It would not have been possible for the system to reach the same conclusion using traditional solver algorithms in a reasonable amount of time (NP-Hard). Millions of flight hours have already been logged to train ADS-B systems.<\/p>\n
ADS-B is set to be included on a much larger set of aircraft, including those that fly Visual Flight Rules (VFR). Longer term, there are proposals to expand the system to fuel trucks, baggage handlers and other moving pieces involved in aircraft operations. [4]<\/p>\n
Air travel has become ubiquitous globally. Despite better pilot training and cutting-edge technologies, the increase in complexity of airspace management has outpaced this progress. Collaboration will be key to maximizing the effectiveness and benefits of ADS-B.<\/p>\n
To engage research scientists and stakeholders (pilots, ATC, etc.), the FAA needs to find ways to balance ADS-B data security concerns with the added learning that can be gained. Like how Uber uses data to optimize their products, ADS-B data can be used to proactively predict and prescribe improvements to flight patterns and ground movement at airports. Such optimizations can ensure that the occurrence rates of high-risk situations under normal operating conditions is close to zero. Under stressed conditions, this learning can inform where the key players should focus their attention.<\/p>\n
The density of and variability in air traffic is going to increase. ADS-B can help optimize the global airspace in addition to its primary safety benefits. The data can be used to understand where margin can be repurposed and where more is needed; machine learning can ensure that FAA\u2019s ability to meet customer needs is maximized while holding extremely high safety standards. Additionally, such systems can help prepare for the introduction of drones to commercial airspace.<\/p>\n
As with autonomous vehicles, an adequately large data set provided to machine learning algorithms will push the expected safety levels from such systems to beyond those of human pilots. Would you trust your aircraft if it was fully autonomous? Longer term, would you be comfortable with decoupling the pilot from the aircraft i.e. ground-based pilots are available to take over cockpit-less aircraft when needed?<\/p>\n
A similar system has been proposed for ground-based autonomous vehicles. Each car would broadcast its details, reducing the load on other cars to determine their surroundings. Do you see value in integrating such systems with ADS-B? How would you balance the benefits here with the added complexity and implementation delay it would result in?<\/p>\n
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Bibliography<\/u><\/p>\n How can you reduce the likelihood of mid-air aircraft collisions? Can the solution be a pre-cursor to fully autonomous flight? This article explores the use of machine learning to replace traditional Traffic Collision Avoidance Systems (TCAS).<\/p>\n","protected":false},"author":11467,"featured_media":32666,"comment_status":"open","ping_status":"closed","template":"","categories":[889,4771,2115,346],"class_list":["post-32665","hck-submission","type-hck-submission","status-publish","has-post-thumbnail","hentry","category-aviation","category-faa","category-flight","category-machine-learning","hck-taxonomy-organization-federal-aviation-administration","hck-taxonomy-industry-aerospace","hck-taxonomy-country-united-states"],"connected_submission_link":"https:\/\/d3.harvard.edu\/platform-rctom\/assignment\/rc-tom-challenge-2018\/","yoast_head":"\n\n