Identification of Safety Critical Risks
Liberty University
AVIA 409 - Safety Management Systems
Professor: Andrew Walton
2022
Identification of Safety Critical Risks
The ISSA Concept of Operations addresses safety critical risks by examining the sources of
hazards that can challenge the viability of design and operations for UAM. These sources are the vehicle
itself, the environment, the operational context, and the aviation system. These sources reflect the
different types of hazards and their associated risk/safety impacts. The ConOps looks to define a set of
safety risk categories that IASMS services would work to resolve and/or mitigate. The risks stated must
indicate an overall risk category and the relevant agents of the system.
Later a discussion can be made under the architecture that identifies the interfaces between
the operators that are necessary in order to provide the monitoring and the assessment of data and also
identifies the agent(s) responsible for implementing the mitigating action. Our delineation of ISSA safety
critical risks was informed by an integration of multiple sources of expert reference. These sources of
expert reference represent different perspectives on UAM and IASMS. Some identified risks were
common across two or more sources, while in other instances a source because of its unique
perspective identified additional risks. Young (2018) classified these risks as safety risk outcomes or
causal/contributing factors to those outcomes.
Prioritizing Risks
The National Academies IASMS report underscored the importance of prioritizing risks, with the
risks having the most impact on system safety commensurate. Addressing higher priority risks balances
the safety benefit with the cost of risk mitigation while considering the complexity of the system.Safety
management systems use a traditional approach to risk assessment, based on the probability of
occurrence and the consequence of an event. This approach is viable for known risks in which it is
possible to leverage the historic data obtained from design and operation of conventional aircraft. This
approach does not work as effectively for the case of emerging risks with new entrants.
In particular, the National Academies report noted that new entrants can increase the level of
uncertainty for both the safety and efficiency of the NAS. This uncertainty builds from a paucity of data
on the effect of new entrants on NAS operations, the performance of human operators and their trust in
increasingly autonomous systems, and the prevalence of unauthorized UAS operations. Risk
prioritization is influenced by several factors such as: a.) how well the hazards that underlie risks are
understood and can be monitored and detected, b.) the types of data that can be used to identify
elevated risk states, and c.) societal risks. Conversely, it is unknown which risks do not warrant
monitoring due to high cost, low uncertainty, and minimal safety impact. Prioritization of risks changes
dynamically over time.
Significant changes in airspace operations, the emergence of new risks, the transition of new
technologies and advanced automated capabilities, and aggregation of new data on risks all contribute
to this dynamic risk prioritization. The National Academies report identified set of criteria for prioritizing
risks. For the purpose of this ISSA ConOps, uncertainty is used as a preliminary indication of risk. That is,
uncertainty represents the level of confidence that a risk is well understood and warrants only limited
future research that focuses on particular aspects of the risk. A risk could have low uncertainty, for
example, if understanding of the hazard is based on commercial or GA safety information that is
extensible to UAM, or if the risk has a minimal impact for safety assurance or risk management. A risk
could have high uncertainty, for example, if there is limited understanding of the hazard and no history
of its mitigation with commercial or GA. An actual rating of risk priority would depend on the
relationship of the underlying hazard with design, operational, and maintenance aspects.
Risk Discussion
Overall, the above tables respond to the recommendation within the National Academies report
that NASA identify and prioritize the risks assessed and mitigated by the ISSA system. Tables 1 and 2
identified the risks with UAM. Tables 3 provided an initial assessment of the priority of these risks based
on the levels of uncertainty with risk information and assessment. This information is important in
developing the ISSA concept of operations including the data and architecture necessary for the
functions comprising ISSA. With this information NASA can continue to collaborate with industry to
complete the definition of the ConOps for a scalable UAM IASMS. This provides a foundation for a
service-oriented architecture that can better focus safety investments in technological solutions with
emerging operations.
Key IASMS Services
Three service categories were identified by Young and others (2018) as key to an effective IASMS
consisting of monitor, assess and mitigate. These categories span the three functions comprising the
IASMS concept described in the National Academies IASMS report (2018).Several key IASMS capabilities
will need to exist to assure the safety of the vehicle, the airspace, and the overall NAS. Each IASMS
capability is envisioned to perform a safety service that affords each operation a reduction in risk by
providing in-time feedback of current state contrasted with expected and/or nominal state. To achieve
this, the monitoring of multiple sets of data is required and the analysis of that data will generate key
assessments of hazards (known and unknown) that threaten operational safety.
The ConOps provides a list of key service categories, generated from multiple publications that
are necessary to assure safe and scalable transformation of the NAS. These services are divided into
Monitor services, Assessment services, and Mitigation services, all of which when combined form an
IASMS capability. Several relevant information classes exist that are necessary to provide the data
necessary to enable the IASMS capabilities. Figure 2 below identifies the information classes available;
data classes either singularly or in combination can be used to generate an IASMS capability. These
services and capabilities are described in greater detail below as part of the Monitor, Assess, and
Mitigate functional services. The monitoring function is comprised of information services that provide
data from the classes listed below in Figure 2. The assess function leverages tools and techniques to
create models that can judge changes to operational safety margins. Applied to the monitored data
based on the overall system requirements and data architecture.
The mitigate function is the method for multiple agents or environment. This capability requires
power health data at a minimum to perform its function. All three service categories are capable of
interacting independently but function more effectively through interconnectivity of shared information.
SDS-S type services include post-flight data analytics that range from services that exist today such as
Flight Operations Quality Assurance (FOQA) and the Aviation Safety Reporting System (ASRS), to future
prognostic capabilities. Future capabilities may evolve to evaluating system-wide operational trends in
increasingly near-real time, as well as validate performance models that leverage increased levels of
autonomy. Inclusion of multiple information classes offers an opportunity for innovative developments
in enhanced scalability and efficiency when dealing with safety related issues. For example, a service
capability that ingests power health information as well as aircraft model data and population density
can leverage all sets of information to generate a time-or distance-remaining metric and generate a list
of options to safely land the aircraft with minimal harm to the vehicle and the surrounding environment.
To account for safety assurance amidst the growing scale and complexity of operations, IASMS
service capabilities must at a minimum communicate between other IASMS service capabilities or
include multiple information classes to take informed mitigation responses. Therefore, it is envisioned
that the risk reduction of an IASMS capability is on a continuum that corresponds with the information it
ingests and the possible mitigation responses it can generate.
References
Federal Aviation Administration (2012). Helicopter Flying Handbook. FAA-H-8083-21H.
Federal Aviation Administration (2018). Unmanned aircraft systems (UAS) traffic management (UTM)
concept of operations v. 1.0. Retrieved fromhttps://utm.arc.nasa.gov/docs/2018-UTM-ConOps-
v1.0.pdfSeptember 18, 2018.
National Academies of Sciences, Engineering, and Medicine 2018. In-Time Aviation Safety Management:
Challenges and Research for an Evolving Aviation System. Washington, DC: The National
Academies Press. https://doi.org/10.17226/24962.
National Aeronautics and Space Administration (2017). NASA Aeronautics Strategic Implementation
Plan. Retrieved from https://www.nasa.gov/sites/default/files/atoms/files/sip-2017-03-23-17-
high.pdf.maspa
National Institute of Standards and Technology, Cognition and Collaboration Systems Group. Autonomy
Levels For Unmanned Systems. Retrieved from https://www.nist.gov/el/intelligent-systems-
division-73500/cognition-and-collaboration-systems/autonomy-levels-unmanned September 30,
2019.
Radio Technical Commission for Aeronautics (2016). DO-364, Minimum Aviation System Performance
Standards for Aeronautical Information/Meteorological Data Link Services Services. Retrieved
from https://global.ihs.com/doc_detail.cfm?document_name=RTCA%20DO-
364&item_s_key=00701504.
Radio Technical Commission for Aeronautics (2015). DO-200B, Standards for Processing Aeronautical
Data. Retrieved from https://standards.globalspec.com/std/9950777/rtca-do-200.
Rios, J. (2018). UAS Service Suppliers: Development of specifications, tests, and implementations in
parallel. Presentation at the FAA V&V Summit, September 16, 2018. Retrieved from
https://www.faa.gov/about/office_org/headquarters_offices/ang/offices/tc/library/
v&vsummit/v&vsummit2018/presentations/3%20Joseph%20Rios%202018%20V+V%20Summit
%20v20180916.pdf
Uber Elevate (2016). Fast-Forwarding to a Future of On-Demand Urban Air Transportation. October 27,
2016. Retrieved from https://www.uber.com/elevate.pdf
Unmanned Aircraft System Traffic Management (UTM) Research Transition Team, Concept Working
Group. (2018a). Concept & Use Cases Package #2: Technical Capability Level 3, Version 1.0. FAA
and NASA
