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Aviation safety is one of the key focus areas of the aerospace industry as it involves safety of passengers, crew, assets etc. Due to advancements in technology, aviation safety has reached to safest levels compared to last few decades. In spite of declining trends in in-air accident rate, ground accidents are increasing due to ever increasing air traffic and human factors in the airport. Majority of the accidents occur during initial and final phases of the flight. Rapid increase in air traffic would pose challenge in ensuring safety and best utilization of Airports, Airspace and assets. In current scenario multiple systems like Runway Debris Monitoring System, Runway Incursion Detection System, Obstacle avoidance system and Traffic Collision Avoidance System are used for collision prediction and alerting in airport environment. However these approaches are standalone in nature and have limitations in coverage, performance and are dependent on onboard equipment.

Cloud phase discrimination, coupled with measurements of liquid water content (LWC) and ice water content (IWC) as well as the detection and discrimination of supercooled large droplets (SLD), are of primary importance in aviation safety due to several high-profile incidents over the past two decades. The UTC Aerospace Systems Optical Ice Detector (OID) is a prototype laser sensor intended to discriminate cloud phase, to quantify LWC and IWC, and to detect SLD and differentiate SLD conditions from those of Appendix C. Phase discrimination is achieved through depolarization scattering measurements of a circularly polarized laser beam transmitted into the cloud. Optical extinction measurements indicate the liquid and ice water contents, while the differential backscatter from two distinct probe laser wavelengths implies an effective droplet size. The OID is designed to be flush-mounted with the aircraft skin and to sample the air stream beyond the boundary layer of the aircraft.

Recent years have seen a rise in the number of air crashes and on board fatalities. Statistics reveal that human error constitutes upto 56% of these incidents. This can be attributed to the ever growing air traffic and technological advancements in the field of aviation, leading to an increase in the electronic and mechanical controls in the cockpit. Accidents occur when pilots misinterpret gauges, weather conditions, fail to spot mechanical faults or carry out inappropriate actions. Currently, pilots rely on flight manuals (hard copies or an electronic tablet) to respond to an emergency. This is prone to human error or misinterpretation. Also, a considerable amount of time is spent in seeking, reading, interpreting and implementing the corrective action. The proposed augmented head mount virtual assist for the pilot eliminates flight manuals, by virtually guiding the pilot in responding to in-flight necessities.

Aircraft icing has been a focus of the aviation industry for many years. While regulations existed for the certification of aircraft and engine ice protection systems (IPS), no FAA or EASA regulations pertaining to certification of ice detection systems existed for much of this time. Interim policy on ice detection systems has been issued through the form of AC 20-73A as well as FAA Issue Papers and EASA Certification Review Items to deal mainly with Primary Ice Detection Systems. A few years ago, the FAA released an update to 14 CFR 25.1419 through Amendment 25-129 which provided the framework for the usage of ice detection systems on aircraft. As a result of the ATR-72 crash in Roselawn, Indiana due to Supercooled Large Droplets (SLD) along with the Air France Flight 447 accident and numerous engine flame-outs due to ice crystals, both the FAA and EASA have developed new regulations to address these concerns.

Turbulence is by far the number one concern of anxious passengers and a cause for airline injuries. Apart from causing discomfort to passengers, it also results in unplanned downtime of aircrafts. Currently the Air Traffic Control (ATC) and the meteorological weather charts aid the pilot in devising flight paths that avoid turbulent regions. Even with such tailored flight paths, pilots report constant encounters with turbulence. The probability of turbulence avoidance can be increased by the use of predictive models on historical and transactional data. This paper proposes the use of predictive analytics on meteorological data over the geographical area where the aircraft is intended to fly. The weather predictions are then relayed to the cloud server which can be accessed by the aircraft planned to fly in the same region. Predictive algorithms that use Time series forecasting models are discussed and their comparative performance is documented.