Implicit intent information for conflict detection and alerting

Victor Carreño, NASA Langley Research Center, Hampton, Virginia

César Muñoz, National Institute of Aerospace, Hampton, Virginia

# Abstract

Conflict detection algorithms can be broadly classified as state based
and intent based algorithms. State based algorithms predict the path of
aircraft by projecting their current position and velocity vectors. The
path predictions are then used to determine if the aircraft are in
conflict within a look ahead time. That is, if loss of separation will
occur in the future. Intent based algorithm use flight plans and other
information, which usually resides in a flight management computer, to
predict the path of aircraft. Algorithms that use intent information for
conflict detection obtain the intent of other aircraft and broadcast
their own intent via some type of communication link. Intent information
that is exchanged among aircraft is defined as explicit intent. A
conflict detection algorithm can also make use of intent information
based on aircraft nominal routes, corridors, published approaches, etc.,
and in this case, there is no information exchange between aircraft.
This kind of intent is defined as implicit intent. Implicit intent can
be used very effectively in conflict detection without the added cost
and complexity of communication links. The use of implicit intent, in
combination with state information, reduces false alarms over a state
based conflict detection algorithm and therefore increases the
effectiveness of the alerting system. In this paper, conflict detection
using implicit intent information is described and its performance is
compared with a state based conflict detection algorithm.

# Introduction

Conflict detection, alerting, and resolution is a critical part of
on-board navigation tools. The trend from ground based towards airborne
based navigation has sparked strong research and development in methods
for conflict prediction, characterization, and analysis. The ultimate
goal of conflict detection, alerting, and resolution is to provide safe
separation for aerial vehicles in the national and international
airspace.

Depending on the time range and distance, tools to maintain separation
are called collision avoidance and conflict avoidance. Collision
avoidance refers to short look ahead times and small protected zones: in
the order of 20 to 30 seconds and half of a nautical mile respectively.
Conflict avoidance includes times in the order of 1 minute to the
duration of the mission and several miles to several thousand miles.
Conflict avoidance can also be classified into tactical and strategic
conflict avoidance. Tactical conflict avoidance is mainly used to
prevent loss of separation. Strategic conflict avoidance uses other
criteria such as routing, arrival flows, and scheduling to improve the
performance of the airspace system.

Conflict detection is based on predicting the locations where aircraft
are going to be for a given look ahead time. Various methods of
predicting the location of an aircraft are used. These include state
projection, probabilistic, worst case, and intended trajectory. State
projection is the simplest method and uses the aircraft current location
and velocity vector to calculate a future location \[1\]\[2\].
Probabilistic methods calculate possible aircraft maneuvers based on
aircraft performance and assigns probabilistic values to future location
an aircraft could be \[3\]. Worst cases estimate the aircraft maneuvers
that will cause the shortest separation between aircraft \[4\]. Intended
trajectory creates the route of the aircraft using aircraft flight plans
\[5\].

Conflict detection algorithms, which use intent information for
trajectory prediction, must exchange flight plans, trajectory change
points, and other information. The strategic intent based conflict
detection and resolution proposed in \[5\] uses aircraft to aircraft
data link for information exchange. We refer to these algorithms, which
exchange their intended path, as *explicit* intent conflict detection.

The intent based conflict detection method described in this paper does
not make use of information exchange to calculate the intended aircraft
trajectory. It infers the aircraft trajectory from established data such
as published routes and published approaches. We have called this
*implicit* intent conflict detection. Obviously, this method can only be
used where published data is available and loaded in the on-board
navigation equipment. Implicit intent eliminates the need and associated
cost, complexity, and communication bandwidth of the data link used in
explicit intent conflict detection.

Intent based conflict detection has advantages over other conflict
detection algorithms in moderately to highly constrained environments.
In the terminal area, for example, where published approach data is
readily available, intent based conflict detection significantly reduces
the number of false alarms over state based conflict detection.

In the following sections, the implicit intent based algorithm is
described and results of simulations, to compare the performance of
intent and state based algorithms, are presented.

**Implicit Intent Conflict Detection**

Implicit intent conflict detection makes use of published data to
predict aircraft future locations. An example of a generic FAA RNAV
terminal area T approach is shown in Figure 1. An aircraft making an
instrument approach to runway 18 will firsts proceed to one of the
Initial Arrival Fixes (IAF) and then follow the pertinent segments of
the approach.

Aircraft approaching from the upper quadrants (north side of the runway)
will fly directly to waypoint TALIA and proceed to the intermediate and
final segment. Aircraft approaching from the southeast quadrant will fly
to waypoint BETSY and then proceed to base, intermediate, and final
segments. Aircraft from the southwest will fly to waypoint GRACE, base,
intermediate, and final segments. This information is used by the
conflict detection logic to predict future aircraft locations.

![](media/image1.jpeg){width="3.1944444444444446in"
height="5.314583333333333in"}

Figure 1. Terminal Area RNAV T Approach

Figures 2 and 3 show an aircraft on the base segment of the T approach
and depict how the future location of the aircraft will be predicted.
Figure 2 uses state based projection and Figure 3 uses implicit intent
based projection. The intent projection is approximated by a sequence of
straight segments. The length of the projection is determined by the
look ahead time and the speed of the aircraft. The location of the
aircraft at the look ahead time is represented by the "X" at the end of
the projected segment.

![](media/image2.jpeg){width="3.1979166666666665in"
height="2.0229166666666667in"}

Figure 2. State Based Projection

![](media/image3.jpeg){width="3.1944444444444446in"
height="1.9326388888888888in"}

Figure 3. Explicit Intent Projection

The implicit intent conflict detection algorithm uses intent projection
when the aircraft is flying a nominal path. When the aircraft diverts
from its nominal path by more than a predetermined margin, then the
implicit intent conflict detection algorithm reverts to state
projection. In this sense, the algorithm is a *hybrid* conflict
detection algorithm. The nominal path margin is illustrated in Figure 3.

A volume surrounding each aircraft is assigned to all aircraft in the
airspace. This volume is called the protected zone. The conflict
detection algorithm projects the protected zone of its own aircraft and
all aircraft in the vicinity along the projected path. If the protected
zones of two aircraft overlap along the predicted trajectories, then a
conflict is detected.

The sensitivity of a conflict detection algorithm and as a result its
safety and effectiveness is determined by the time values selected for
its look ahead times and dimensions of the protected zone. In the
experiment described in the next section, look ahead times and
dimensions are parameters for the experiment.

# Experiment

In order to compare different logics for conflict detection and
alerting, a low fidelity simulation environment called ISABELLE (Intent
and StAte Batch Experiments in Low fideLity Environment) was developed.
ISABELLE has been written in Scilab[^1] which is a free scientific
software package for numerical computations similar to Matlab.

The ISABELLE simulation environment provides:

-   A low fidelity model of aircraft kinematics, where aircraft are
    represented as points in a 3-D space with a velocity vector.

-   Two types of aircraft performances with speeds ranging from 60 to
    170 knots.

-   Path deviations, modeled by random uniform errors in speed, heading,
    and altitude.

-   Pseudo-randomly generated nominal and non-nominal traffic.

-   An interface for batch and interactive simulation that enables
    different settings of configurable variables.

The experiment covers 4 periods of 8 hours of operations. The airspace
is defined by a 40 by 40 nautical miles square. Nominal traffic in this
area flies a maximum deviation of 0.5 nautical miles horizontally and
200 feet vertically from a predefined trajectory configuration. The
experiment considers two kinds of configurations:

1.  A Y configuration where two flows of aircraft come from different
    directions, merge in a common point, and then fly a shared segment,
    Figure 4.

2.  A T configuration where two flows of aircraft come from opposite
    directions, fly toward each other, and then fly parallel segments,
    Figure 5.

Note that the Y and T configuration not necessarily pertain to a
terminal area. They could be the merging of routes at a waypoint. For
the experiment, both aircraft flying the routes and non-nominal (off
route) aircraft are simulated. Non-nominal traffic aircraft, such as
aircraft AC1 and AC2 in Figure 4, fly randomly generated trajectories.

![](media/image4.jpeg){width="3.19375in" height="2.4868055555555557in"}

Figure 4. Y Configuration

![](media/image5.jpeg){width="3.1951388888888888in"
height="2.4652777777777777in"}

Figure 5. T Configuration

Traffic data, which include non-nominal traffic, are generated for each
simulation period using simple uniform distributions. These
distributions were chosen to exercise the detection logics and they do
not correspond to actual distributions of traffic. In average, there
were 50 nominal operations and 2 non-nominal operations per period.
Figure 6 shows a typical distribution of traffic for a period of 8 hours
of simulation (from 8:00h to 16:00h).

![](media/image6.jpeg){width="3.19375in" height="2.527083333333333in"}

Figure 6. Eight Hours Traffic Distribution

On top of the aircraft kinematics model, ISABELLE introduces path
deviations that model small navigation errors. In the case of nominal
traffic, these errors are bounded by the maximum horizontal and vertical
deviations for the predefined trajectory configuration.

Two different conflict detection and alerting logics are implemented in
ISABELLE.

-   State based: Trajectories for all traffic are predicted using linear
    projections of their current state.

-   Hybrid: Trajectories of nominal traffic are predicted using the
    predefined trajectory configuration. Trajectories of non-nominal
    traffic are predicted using linear projections of their current
    state.

Conflicts that are time-wise short, i.e., about 2 seconds, are filtered
out by the alerting logic. In a real system, this filtering mechanism is
an effective way to cope with corrupted data. Finally, to be able to
count false alarms, ISABELLE does not implement resolution maneuvers for
aircraft in conflict. In case of violations, ISABELLE arbitrarily
excludes one of the aircraft from the simulation.

# Results

In the context of this experiment, the following concepts are defined.

-   *Protected zone:* Cylindrical volume of diameter D and altitude H
    surrounding an aircraft.

-   *Violation:* Loss of separation, i.e., overlap of the protected
    zones of two aircraft.

-   *Conflict:* Predicted overlap of the protected zones of two
    aircraft.

-   *Caution:* Conflict within a look ahead time Tc.

-   *Warning:* Conflict within a look ahead time Tw.

-   *Alerts:* Cautions and warnings issued by the alerting logic.

-   *False Alarm*: Alerts without subsequent violation.

-   *Missed Alarm:* Violation without preceding alert.

State and hybrid conflict detection logics are exercised and compared
over the same data sets for different values of D, H, Tc, and Tw, and

different trajectory configurations. In total, the experiment consists
of 288 simulations, each one of them covering 8 hours of operations for
a specific configuration.

Figures 7 through 9 summarize the results for a Y configuration, while
Figures 10 through 12 summarize the results for a T configuration. In
all figures, the horizontal axis corresponds to the diameter of the
protected zone, which varies from 0.5 to 3 nautical miles (actual data
points are 0.5, 1, 2, and 3). The vertical axis corresponds to the ratio
of false alarms with respect to the total number of alarms issued by the
alerting logic. The smaller this ratio, the more effective the logic to
avoid false alarms.

For each configuration, three different look ahead times are considered
(a) Tc = 180 seconds and Tw = 60 seconds (Figures 7 and 10), (b) Tc = 60
seconds and Tw = 20 seconds (Figures 8 and 11), and (c) Tc = 20 seconds
and Tw = 10 seconds (Figures 9 and 12).

![](media/image7.jpeg){width="3.1993055555555556in"
height="3.0208333333333335in"}

Figure 7. Y Configuration, Tc = 180 s, Tw = 60 s

![](media/image8.jpeg){width="3.1993055555555556in"
height="3.0097222222222224in"}

Figure 8. Y Configuration, Tc = 60 s, Tw = 20 s

![](media/image9.jpeg){width="3.1993055555555556in"
height="2.998611111111111in"}

Figure 9. Y Configuration, Tc = 20 s, Tw = 10 s

![](media/image10.jpeg){width="3.1993055555555556in" height="3.0in"}

Figure 10.T Configuration, Tc = 180 s, Tw = 60 s

The figures show that the hybrid approach performs better than state
only conflict detection. Furthermore, the ratio of false alarms to
alarms of the hybrid algorithm is below 25% in all but one case. In
contrast, the state based conflict detection algorithm may have a ratio
as high as 55%. That is, in some cases, more than half of the alerts
issued by the state based algorithm are false alarms.

![](media/image11.jpeg){width="3.1993055555555556in"
height="2.988888888888889in"}

Figure 11. T Configuration, Tc = 60 s, Tw = 20 s

![](media/image12.jpeg){width="3.1972222222222224in"
height="3.017361111111111in"}

Figure 12. T Configuration, Tc = 20 s, Tw = 10 s

The configuration of the nominal trajectories seems to be a significant
factor in the performance of an algorithm with respect to false alarms.
Indeed, the difference in performance between the two conflict detection
algorithms is less significant in the Y configuration than in the T
configuration.

This is most likely due to the head on segments in the T configuration,
which will cause alarms to be issued when an aircraft merges before
another aircraft, but separation is not compromised. Furthermore, in
parallel segments, small navigation errors could lead to state
projections that intersect the opposite segment causing false alarms. In
contrast, intent projections take into account that the paths are
parallel.

The experiment also shows that, as expected, the shorter the look ahead
time the smaller the ratio of false alarms. It is important to note that
neither algorithm had missed alerts. All conflicts that lead to a loss
of separation were detected by the hybrid and the state based
algorithms.

# Conclusions

This paper presented an innovative approach to conflict detection based
on implicit intent combined with state projection. Implicit intent
minimizes the communication exchange between aircraft. A preliminary
experiment in a low fidelity simulation environment shows that the
proposed hybrid approach is effective in reducing the number of false
alarms. Implicit intent is an important component of the Conflict
Detection and Alerting concept of NASA\'s Small Aircraft Transportation
System (SATS) \[6\] project. The study presented in this paper is
consistent with preliminary batch studies of the SATS concept \[7\].
Both studies show that a hybrid approach to conflict detection compares
positively to state based approach with respect to false alarms.
However, the results of these experiments should be considered as
relative indicators rather than actual performances to be expected in a
real deployment of a hybrid CD&A algorithm. High fidelity simulations
and pilot-in-the-loop experiments are necessary to assess technical
feasibility and safety issues of the proposed approach.

# References

\[1\] K. Bilimoria; A Geometric Optimization Approach to Aircraft
Conflict Resolution, AIAA Guidance, Navigation, and Control Conference &
Exhibit, Denver, Colorado, AIAA 2000-4265, August 2000.

\[2\] G. Dowek, A. Geser, and C. Muñoz; Tactical Conflict Detection and
Resolution in a 3-D Airspace, 4^th^ USA/Europe Air Traffic Management
R&D Seminar, New Mexico, Oct 2001.

\[3\] H. Kremer, B. Bakker, and H. Blom; Probabilistic versus Geometric
Conflict Probing, Air Traffic Management Research and Development
Seminar, Saclay, France, 1997.

\[4\] L. Rine, T. Abbott, G. Lohr, D. Elliott, M. Waller, and R. Perry;
The Fllight Deck Perspective of the NASA Langley AILS Concept,
NASA/TM-2000-209841, January, 2000.

\[5\] M. Ballin, V. Sharma, R. Vivona, E. Johnson, and E. Ramiscal; A
Flight Deck Decision Support Tool for Autonomous Airborne Operations,
AIAA Guidance, Navigation, and Control Conference & Exhibit, Monterey,
California, August 2002.

\[6\] T. Abbott, K. Jones, M. Consiglio, D. Williams, C. Adams, Small
Aircraft Transportation System, High Volume Operations Concept: Normal
Operations, NASA/TM-2004-213022, 2004.

\[7\] M. Consiglio, C. Muñoz, and V. Carreño, Conflict Detection and
Alerting in a Self Controlled Terminal Aerospace, 24^th^ International
Congress of the Aeronautical Sciences, Yokohama, Japan, 2004.

[^1]: Scilab is available from http://scilabsoft.inria.fr.