Metrics, Schmetrics! How The Heck Do You Determine A UAV’s 

Autonomy Anyway? 

Bruce T. Clough, Technical Area Leader, Air Force Research Laboratory 

Wright-Patterson AFB, OH, Bruce.Clough@wpafb.af.mil 

ABSTRACT So, they had to be simple, apply to a broad range of

systems, and yet exhibit good resolution. Obviously a 

simple task, but first let’s look at what it means to be

autonomous. 

The recently released DoD Unmanned Aerial Vehicles

Roadmap [9] discusses advancements in UAV autonomy in

terms of autonomous control levels (ACL). The ACL concept 

was pioneered by researchers in the Air Force Research

Laboratory’s Air Vehicles Directorate who are charged with

developing autonomous air vehicles. In the process of

developing intelligent autonomous agents for UAV control

systems we were constantly challenged to “tell us how 

autonomous a UAV is, and how do you think it can be

measured...” Usually we hand-waved away the argument and 

hoped the questioner will go away since this is a very subjective, 

and complicated, subject, but within the last year we’ve been

directed to develop national intelligent autonomous UAV control 

metrics - an IQ test for the flyborgs, if you will. The ACL chart 

is the result. We’ve done this via intense discussions with other

government labs and industry, and this paper covers the agreed

metrics (an extension of the OODA - observe, orient, decide, and

act - loop) as well as the precursors, “dead-ends”, and out-andout flops investigated to get there. 

2. Quick Difference Between Autonomous 

and Automatic (our definition) 

Many people don’t realize that there is a significant

difference between the words autonomous and automatic.

Many news and trade articles use these words 

interchangeably. Automatic means that a system will do

exactly as programmed, it has no choice. Autonomous

means that a system has a choice to make free of outside

influence, i.e., an autonomous system has free will. For 

instance, let’s compare functions of an automatic system

(autopilot) and an autonomous guidance system: 

• Autopilot: Stay on course chosen. 

• Autonomous Guidance: Decide which course to take,

then stay on it. Keywords: autonomy metrics, machine intelligence 

metrics, UAV, autonomous control

Example: a cruise missile is not autonomous, but

automatic since all choices have been made prior to 

launch. 1. Background 

At top levels of the US Department of Defense an effort

has been initiated to coordinate researchers across the 

Services and industry in meeting national goals in fixedwing vehicle development. The Fixed-Wing Vehicle

Initiative (FWV) has broad goals across numerous vehicle 

technologies. One of those areas is mission management

of UAVs. Our broad goal is to develop the technology

allowing UAVs to replace human piloted aircraft for any 

conceivable mission. This implies that we have to give

UAVs some level of autonomy to accomplish the

missions. One of the cornerstones of the FWV process is

the establishment of metrics so one know that a goal is 

reached, but what metrics were available for measuring 

UAV autonomy? Our research, in conjunction with

industry, determined that there was not any sort of metric

as we desired. Thus we set out to define our own [Note

1]. 

3. We Need To Measure Autonomy, Not 

Intelligence

For some reason people tend to equate autonomy to

intelligence. Looking through the proceedings of the last 

NIST Intelligent Systems Workshop there are several

papers which do this, and in fact, the entire conference

sets the tone that “intelligence is autonomy” [3]. They are

not the same. Many stupid things are quite autonomous 

(bacteria) and many very smart things are not (my 3 year

old daughter seemingly most of the time). Intelligence 

(one of a myriad of definitions) is the capability of

discovering knowledge and using it to do something.

Autonomy is: 

• the ability to generate one’s own purposes without

any instruction from outside (L. Fogel)

But what characteristics should these metrics have? We

decided that they needed to be: • having free will (B. Clough) 

What we want to know is how well a UAV will do a task, 

or better yet, develop tasks to reach goals, when we’re not 

around to do it for the UAV. We really don’t care how 

intelligent it is, just that it does the job assigned. 

Therefore, intelligence measures tell us little. So, although 

we could talk about the Turing Test [1] and other 

intelligence metrics, that is not what we wanted. 

• Easily visualized such that upper management could 

grasp the concepts in a couple of briefing slides. 

• Broad enough to measure past, present and future

autonomous system development.

• Have enough resolution to easily track impact of

technological program investments. 
4. Well, It Should Be Easy To Find Metrics, 

One Has The Web And Other Info Sources, 

Right? 

Well, one would think so, but after an exhaustive onemonth search involving the author, Air Force Research

Laboratory Library Staff, and several other search

organizations we found two. Two. Now if the goal was

“find machine intelligence metrics” we would have been

inundated with piles of paper. In addition to the 

aforementioned workshop, we would be looking through

hundreds of publications and papers. Maybe it was a good

thing we were looking for autonomy metrics! 

We had thought that maybe, just maybe, the folks working 

distributed autonomous robotic systems had looked at this 

problem, but our questions to experts in that field revealed

that they are just starting to ask those questions themselves 

[4]. The problem the researchers have in this area is that

the metrics they are coming up with are task specific - 

they don’t have general metrics quantitatively measuring 

higher-level characteristics of autonomous robot control 

architectures. 

Figure 1: MAP Survival Space in which autonomous 

systems can be measured 

These level metrics are fully described in Table 1 below. 

The space has three metrics has the three metrics outlines 

above, and six levels. 

M e tric

M o b ility A c q u is itio n P ro tectio n

L e v e l

" - "

M o tio n On ly Oc c u rs

U nder A pplic a tion 

O f An E x te rn a l

F o rc e

O pera te s fro m a nonreplentis hab le energ y

sourc e (batte ry, pow e r

line, etc .)

N egativ e 

D e fensiv e 

cap abilities 

(phys ically m o re

fra g ile th an the 

env ironm ent)

0

N o M o tion A b ilitie s Z e ro energ y

c onsum p tion or

deliv e ry

Z e ro defensiv e

abilitie s (s tructu ra l

s trength equa l to

env ironm ent

1

M o v e s D e lib e ra te ly

In O ne D im e n s io n

C an dire c tly

extract/app ly ex te rnal

energ y w h en av a ilable

F light or hide 

beha v io r against

hos tile s tim u lu s

2

M o v e s D e libera te ly

in T w o D im ens ions

C an effc iently

e x tra c t/s to re /u tiliz e

exte rnal energ y

F ight or flight

beha v io r against

hos tile s tim u lu s

3

M o v e s D e lib e ra te ly

In T h ree 

D im ens ions

U s e s fo cus ed ta c tic s to

e ffic iently ex tra c t, s to re 

and u tiliz e ex te rnal

energ y

T a c tic a l fig h t/flight

beha v io r against

hos tile s tim u lu s

4

C apab le O f dua lm ode m o tion w ith

tools , v ehic les, or

applic a tion of

s pecific d e s ign 

e le m ents

U s es planned ta c tic s 

to effic iently ex tract,

s to re and utilize 

exte rnal energ y

T oo, v ehic le , o r

m a te ria l use in

fight/flig h t ta c tic s

5 H u m a n H u m a n H u m a n Table 1: Level Descriptors For MAP Survival Space 

So what were the two that we found? Los Alamos 

National Laboratory’s “Mobility, Acquisition, and 

Protection” space [6], and Draper Laboratory’s “Three

Dimensional Intelligence Space”[7]. The following is a 

short discussion of each [Note 5].

1. Los Alamos National Lab: Mobility, Acquisition,

and Protection (MAP) 

MAP comes from the lab of Mark Tilden, who develops 

simple robots based on analog circuits [10]. He needed a

way to quantify the autonomous nature of his systems, and 

teamed up with LANL Physicist Brosl Hasslacher to

develop the “Mobility, Acquisition, and Protection” space, 

or MAP for short. Figure 1 is a diagram of MAP from [6] 

As one might expect from the name, this method uses

mobility, acquisition, and protection to measure the ability 

of an autonomous system to survive in the world. 

• Mobility relates to the capability of utilizing 

movement in the environment. M0 implies no motion 

abilities where as M3 can move in three dimensions, 

and M- means that external force must be used to 

move object. 

MAP is actually quite a versatile visual tool, allowing 

disparate items to be plotted on the same page. Since there

are three metrics, one can use a “radar chart” to display 

the measurements of a particular autonomous system, and 

this is excellent, since upper management likes radar 

charts! The Los Alamos researchers also realized this and 

included a MAP radar chart in their report. Showing this

versatility, one can plot an ant, human, and a toaster on 

the same chart as is done in Figure 2! Tilden and 

Hasslacher successfully use MAP to illustrate the survival 

capabilities of the robots they design. 

• Acquisition relates to the ability to gather, store, and 

utilize energy. A0 implies zero energy consumption 

or delivery, A4 means planned tactics used to

efficiently extract, store, and utilize external energy, 

while A- indicates object uses a non-replenishable

energy store 

• Protection indicates the capability to defend oneself. 

P- indicates one is physically more fragile than the 

environment while P1 means one executes flight/hide

behaviors against hostile stimuli, and P3 demonstrates 

tactical fight/flight behaviors. 
MAP

Space

Multi-UAV Autono AV Autonomous Neural Net Neural Net SW

M e tric

L e ve l M o b ility C ontro l T a s k Pla nnin g S itu a tio n a l A w a re n e ss

1 N o n e, RPA O n ly N on e, R P A O n ly N o n e, R P A on ly, or

s en s or as c onduit

2 O p erator A s s is ted W ayp oin t or

fea tu re orien ted

L o w -level s en s or

p roc es s in g , e.g . vis u al

s ervoin g (tem p late 

trac k ing)

3

G et to w a yp oin t, d o

on e featu re-b as ed

c omman d

In terp ret g oals

in to a c tion

S ingle-S en s or m od el

m a tc h in g

4 In teg rate m u ltip le

action s

M u lti-A g en t

C olla b oration an d

C 2

In teg rated , m u lti-s en s or

fu s io n

Figure 2: Various Objects plotted in MAP survival

Space . Figure 3: Autonomous Control System Plotted On 

MAP 

Can this be used to measure the autonomy of UAVs? 

Possibly. Figure 3 shows a plot of a multi-UAV neural

net-based autonomous control system. This plot illustrates 

the limitations of MAP for our use. All UAVs would 

score M3 and A- - they move in three dimensions but

require stored fuel. Protection ranges between P0

(structural strength to absorb damage) to P4 (groups of

UAVs deliberately take out SAM sites). So, is this really

useful for FWV autonomy measurement? No. 

These metrics were described in a paper [7] written by the 

Draper Lab researchers last year. This paper contained 

several different options to measure both intelligence and

autonomy. Here we focus on the 3D Intelligence Space

outlined in Table 2.

One can see this metric space has a couple desireable 

attributes: 

• It has three metrics,so we can still use three-axis radar 

charts to represent the results, which will keep 

management happy(Figure CSD1). 

• Only one axis shows any variability, the others are 

fixed. 

• It has metrics which can be directly related to 

operational issues. 

• The metrics just do not address operational 

characteristics of UAVs. They do not relate the 

autonomy present in the vehicle to the capability to

perform useful missions. 

• The metrics do not address interaction between UAVs 

(teams, swarms, etc.) 

• The metrics do not allow us to adequately

discriminate between different levels of autonomy.

For instance, an RPV and an UAV with autonomy 

doing the same mission would score the same. 

So although using MAP seems to make sense for simple

robots, as a UAV autonomy measurement it isn’t

particularly useful. 

Table 2: 3D Intelligence Space 

Note that we made the distance between levels in Figure 4 

increasing exponentially to represent the difficulties 

So, the first metric space we examined could not fulfill our technicallly in going between steps. 

autonomous control system metric search, so we went on

to investigate the other candidate we found – the “3D 

Intelligence Space” of Charles Stark Draper Laboratory. 

We went ahead an plotted the same multi-UAV

autonomous control systems used earlier in evaluation 

MAP space on the Draper radar chart. The results of this

are in Figure 5. Note that this simple multi-UAV 

autonomous control system managed to “max-out” the 

metric space on all three axes, and highlight the fact that 

the resolution needs to be better. Other drawbacks 

include: 

2. Draper 3D Intelligence Space 

Charles Stark Draper Laboratory (Cambridge, MA) has

been developing robotic systems for military and other

Federal customers for a number of years. They saw the

same need to measure how well their systems could 

perform various tasks, and developed metrics under the

sponsorship of the Office of Naval Research.

• Task Planning axis needs to be renamed. Many

successful autonomous systems are based on pure

reactive behaviors (such as insects). Task planning

isn’t a prerequisite to autonomy, it just allows better 

reactions to complicated situations. 
Autonomy Radar my Radar Chart

Draper Metrics Mobility

Control Situation

Awareness

Task Planning

• Situational awareness is based on the number of 

sensors and how they are fused, not on whether or not

the autonomous system understands what’s going on

around it. In other words, this should be a 

measurement on how well the “big picture” is

comprehended and understood. 

Autonomy Radar Chart

Draper Metrics Mobility

Control Situation

Awareness

Task Planning

Multi-UAV Au -UAV Autonomous Neural Net SW

Figure 5: Autonomous Control System Plotted Using 

Draper Metrics 

5. Initial Autonomous Control Level (ACL) 

Chart 

We decided that since no existing metric space existed that

could be directly used, we would integrate the best

features of the ones we found with what we already used

internally to represent where our technology was going. 

Table 3 is that first cut at an Autonomous Control Level

(ACL) chart [Note 2]. 

Figure 4: Radar Chart Of Draper Metrics

The drawbacks not withstanding, the Draper metrics 

provided us another good way of looking at the world. 

Level Level Descriptor Perception/Situational Awareness Analysis/Decision Making Communication/Cooperation

10 Human-Like

9 Multi-Vehicle Detection & tracking of other air vehicles within Full decision making capability on-board Distributed cooperation with other air vehicles

Tactical airspace Dynamically optimize multi-ship group for tactical On-board deconfliction and collision avoidance

Performance situation Fully independent of supervision/control if

Optimization desired; No centralized control within multi-UAV group

8 Multi-Vehicle Detection & tracking of other air vehicles within Continuous mission/trajectory evaluation & replan - External supervision - abort/recall or new overall goal

Mission local airspace optimize for current mission situation On-board deconfliction & collision avoidance

Performance OK to operate in controlled airspace w/o external Avoid collisions and replan/optimize trajectory to meet Distributed cooperation with other A/V's

Optimization control goals, etc

7 Real-Time Detection of other A/V's in local airspace Continuous flight path evaluation & replan On-board collision avoidance

Multi-Vehicle Multi-threat detection/analysis on-board Compensate for anticipated system malfunctions, Uses off-board data sources for deconfliction & tracking

Cooperation weather, etc - optimize trajectory to meet goals, Hierarchical cooperation with other A/V's

manage resources, avoid threats, etc

6 Real-Time Detection of other A/V's in local airspace Event-driven on-board, RT flight path replan - goal On-board collision avoidance

Multi-Vehicle Single threat detection/analysis on-board driven & avoid threats Uses off-board data sources for deconfliction & tracking

Coordination RT Health Diagnosis; Ability to compensate for most Assumed acceptance of replan; External supervision -

failures and flight conditions - inner loop changes rejection of plan is exception

reflected in outer loop performance Possible close air space separation (1-100 yds)

5 Fault/Event Automated Aerial Refueling & Formation sensing Event-driven on-board, RT traj replan to new destination On-board derived vehicle trajectory "corridors"

Adaptive Situational awareness supplemented by off-board data RT Health Diagnosis; Ability to compensate for most Uses off-board data sources for deconfliction & tracking

Vehicle (threats, other A/Vs, etc) failures and flight conditions; Ability to predict onset of External supervision - accept/reject of replan

failures (e.g. Prognostic Health Mgmt) Possible close air space separation (1-100 yds) for

On-board assessment of status vs trajectory AAR, formation in non-threat conditions

4 Robust Response Threat sensing on-board RT Health Diagnosis (Can I continue with these Secure, within LOS electronic tether to nearby friendlies

to problems?); Ability to compensate for most Offboard derived vehicle "corridors"; Medium vehicle

Anticipated failures and flight conditions (e.g. Adaptive inner loop airspace separation (100's of yds)

Faults/Events control); Automatic trajectory execution; On-board Threat analysis off-board

assessment of status vs mission completion

3 Limited Response RT Health Diag (What is the extent of the problems?) Health Status monitored by external supervision

to Real Time Ability to compensate for limited failures (e.g. Off-board replan; Waypoint plan upload

Faults/Events Reconfigurable Control) Wide airspace separation requirements (miles)

Automatic trajectory execution

2 Pre-loaded RT Health diagnosis (Do I have problems?) External commands - alternative plans, approvals,

Alternative Plans Automatic trajectory execution (via waypoints) aborts

Preloaded alternative plans (e.g. abort) Reports status on request or on schedule

Wide airspace separation requirements (miles)

1 Execute Situational awareness via Remote Operator Robotic/Preprogrammed External control via low level commands

Preplanned Flight Control and Navigation Sensing Pre/Post Flight BIT Reports status on request

Mission Wide airspace separation requirements (miles)

No on-board knowledge of other air vehicles - all

actions are preplanned

0 Remotely Flight Control (attitude, rates) sensing N/A Remotely Piloted

Piloted Nose camera Vehicle status data via telemetry

Vehicle Situational awareness via Remote Pilot

Table 3: Initial ACL Metrics Chart 
We kept three metrics since we liked the idea of 

representing systems as areas on a radar chart when 

briefing management. We added ten levels for better

resolution between remotely piloted aircraft and fully

autonomous UAVs. The metrics related to operational 

issues while still being attached somewhat to 

technological systems. Populating the levels was a group

endeavor, with a team of researchers, program managers, 

tech area leads, and contractor experts determining the

meaning of each level: “1” was simple – the traditional

remotely piloted aircraft (RC-type) while “10” was 

“human”. The trick was populating the eight levels 

between. As with the Draper metric space in Figure 4 we 

represented the radar chart as having levels which are

exponentially more difficult. We then plotted the same

multi-UAV autonomous control system as before, and the 

result is in Figure 6. We recovered some of the resolution

lost in the Draper metrics; however, we still had some

issues with the metrics: 

• The metrics weren’t broad enough to cover UAVs

acting on strategic knowledge to achieve strategic

results. Our chart limited them to tactical thinking

only.

• One metric mixed cooperation with communication – 

mixing a “what” with a “how”. 

In general, we tried to stuff as much into three metrics as

possible to retain simplicity in briefing presentation and in 

the process lost the capability to split out issues of multiUAV control and human-UAV interaction, to name two.

We were going to press ahead and use this metric space 

when one of the autonomous control system development

engineers came up with a good idea [Note 3]. 

6. If You’re Replacing A Human, Why Not 

Measure Like One? 

The great insight was this: we are designing algorithms, 

agents if you will, to replace pilot decision functions. 

Machines replace human – so why not look to the human 

effectiveness community for metrics? Modify the OODA 

(observe, orient, decide & act) loop - originally developed

to show how to get inside your enemy’s decision loops - 

[8, & Note 4] for our use, and populate the levels with 

modifications of the qualifiers of the initial ACL chart. 

Table 4 is what we developed using this insight. The

same team of experts that developed the initial ACL chart 

also worked on the new ACL chart to ensure consistency

with earlier thoughts. We lost the three axes 

representation, which means we lost the ability to generate 

the “simple” radar charts which makes management

happy; however, we gained better resolution between

metrics which, at least in our thoughts, more than made up

for that. 

In itia l A CL C h a r t - Ex a m p l e

Comm/Coop

Perception

Analyze/Decide

Figure 6: Autonomous Control System Plotted On Initial 

ACL Radar Chart

Since we have developed the ACL chart, we’ve used it to

both assess the current UAV efforts, and to extract from 

that where our own technical efforts must go. Nationally,

we’ve developed time-phased autonomous system goals to

put our autonomous systems roadmap together. The ACL

has been published as part of the DoD UAV Roadmap [9],

and other DoD Labs use it to measure their autonomy

development. Locally we’ve developed technical area

roadmaps putting programs together to meet the timephased ACL goals. The ACL chart also acts as a program 

advocacy tool, allowing us to show management how each 

program fits into increasing ACL capability for each

metric, and also how each program investment integrates 

into the national strategy. Our experience from using it

for one budget planning cycle has been very positive: 

Multi-UAV Autonomous Neural Net SW

• Once management was briefed on the chart and it’s 

development (and some in management had

ownership in it’s development) it was accepted as the

tool to measure program goals. 

• It provided clear indications of where the technology 

was targeted and what national goal it met, allowing

better informed budget planning decisions. 

• We have common ground for talking amongst other

Federal technology development organizations, 

universities, and industry. Each of us has a much

clearer picture as to where technological programs fit. 

7. Summary 

Our work with autonomous UAVs indicated to us that we

needed metrics to measure the progress of our programs

building that autonomy. The same issues existed on a

national level, so we decided to develop metrics for the 

national-level effort, then apply those to our local program

planning process. 

Our literature search for autonomous system metrics only 

returned two references for metrics. Both we examined

and used on an example problem. Although each wasn’t 

directly applicable, concepts of each, integrated with our 

own existing ideas, formed an initial ACL chart. This 

chart was modified based on concepts human dynamists 

had developed - specifically the OODA loop. The 
resulting set of metrics captured our original intent. [Note 

4] 

Since development, the ACL metrics have been used 

successfully at the Air Force Research Laboratory in 

developing plans and programs in autonomous UAV 

controls research. The ACL chart is in current review at 

DOD levels to be applied across the services as part of the 

FVW initiative. With this development we are pressing 

ahead in the assessment of possible sub-metrics to better 

hone our program planning. 

8. References 

1. Turing, A. M. “Computing Machinery And 

Intelligence”, Journal of the Mind Association, Vol. 

LIX, No. 236. Oxford Press. UK. October 1950. 

2. Clough, B. Autonomous UAV Control System Safety – 

What Should It Be, How Do We Reach It, And What 

Should We Call It? NAECON 2000 paper. October 

2000 

3. A. Meystal et al., “Measuring Performance of 

Systems with Autonomy: Metrics for Intelligence of 

Constructed Systems,” NIST Workshop, 

“Performance Metrics for Intelligent Systems,” 

August 14-16, 2000 

4. Parker, L.E. “Evaluating Success in Autonomous 

Multi-Robot Teams: Experiences from ALLIANCE 

Architecture Implementations”. Unpublished (as of 

the time of this writing) paper. Center for 

Engineering Science Advanced Research, Oak Ridge 

National Laboratory. Fall 2000. 

5. “Measuring the Performance of Intelligent Systems” 

Web publication available from 

http://www.isd.cme.nist.gov/conferences/performance

_metrics/Measuring.html. Intelligent Systems 

Division, National Institute for Science and 

Technology, Spring 2000 

6. Hasslacher, B., Tilden, M. W. “Living Machines” Los 

Alamos National Laboratory. 1995. 

7. Cleary M., Abramson M., Adams M.B., Kolitz S. 

“Metrics For Embedded Collaborative Intelligent 

Systems” Charles Stark Draper Laboratory, Inc. 2000 

8. Boyd, J.R. Discourse on Winning and Losing. Found 

on the web at http://www.defense-andsociety.org/FCS_Folder/boyd.htm 

9. Unmanned Aerial Vehicles Roadmap 2000-2025. 

Office Of The Secretary Of Defense, Washington DC. 

April 2001 

10. Trachtman, P. “Redefining Robots”. Smithsonian 

Magazine. Smithsonian, Washington D.C. February 

2000. 

11. Sheridan, T., Parasuramam, R. “A Model for Types 

and Levels of Human Interaction with Automation”, 

IEEE Transactions on Systems, Man, and Cybernetics 

– Part A: Systems and Humans, VOL. 30, No. 3. 

IEEE May 2000.

9. Notes 

1. Not that we didn’t have any. We had already split 

autonomy into four levels depending on the amount of 

human interaction and where it occurred. These are: 

• Remotely Piloted: The UAV is simply a remotely 

piloted aircraft with the human operator making all 

decisions. 

• Remotely Operated: The human allows the UAV to 

do the piloting, but outer loop decisions are made by 

the human (like where to go and what to do once 

there). The UAV is a “mother-may-I” system, asking 

the human permission to do tasks. 

• Remotely Supervised: The human allows the UAV to 

execute its own tasks, only taking command if the 

UAV fails to properly execute them. 

• Fully Autonomous: The UAV receives goals from the 

humans and translates that into tasks which it does 

without human intervention. The UAV has authority 

to make all decisions. 

2. Most of the grunt work in putting the chart together 

was done by Dan Thompson, AFRL/VACC, and Dr. 

Alan Burkhard, AFRL/VAC - the rest of us got to 

snipe at it. 

3. The researcher’s name is Bob Smith, and besides 

coming up with decent ideas he also has developed a 

formation flight agent for UAV formations which 

uses a blend of deliberate and emergent behavior. 

4. Boyd’s OODA loop, originally developed to illustrate 

how to take advantage of an enemy, has been grasped 

wholeheartedly by business management folks. The 

observe, assess, design, and act (OADA) loop 

organizational dynamists use to explain how decisions 

are made is a direct descendant. 

5. I know, you’re wondering about Sheridan’s 

Autonomy Levels. Truth of the matter is that if you 

search for “autonomy”, “metrics”, “measuring 

autonomy”, etc., you don’t run into Sheridan. Had I 

searched using “teleoperation” I would have found 

the Sheridan Autonomy Levels [11]. 
ATR

7 Short track awareness - History and predictive battlespaTactical group goals assigned

Battlespace data in limited range, timeframe, and numbers Enemy trajectory estimated

Knowledge Limited inference supplemented by off-board data

6 Real Time Tactical group goals assigned

Multi-Vehicle Ranged awareness - on-board sensing for long range, Enemy location sensed/estimated

Cooperation supplemented by off-board data

5 Real Time Sensed awareness - Local sensors to detect others, Tactical group plan assigned

Multi-Vehicle Fused with off-board data RT Health Diagnosis; Ability to compensate for most

Coordination failures and flight conditions; Ability to predict onset of

failures (e.g. Prognostic Health Mgmt)

Group diagnosis and resource management

4 Deliberate awareness - allies communicate data Tactical plan assigned

Fault/Event Assigned Rules of Engagement

Adaptive RT Health Diagnosis; Ability to compensate for most

Vehicle failures and flight conditions - inner loop changes

reflected in outer loop performance

3 Robust Response Health/status history & models Tactical plan assigned

to Real Time RT Health Diag (What is the extent of the problems?)

Faults/Events Ability to compensate for most control failures and

flight conditions (i.e. adaptive inner-loop control)

2 Health/status sensors RT Health diagnosis (Do I have problems?)

Changeable Off-board replan (as required)

Mission

1 Execute Preloaded mission data

Preplanned Flight Control and Navigation Sensing Pre/Post Flight BIT

Mission Report status

0 Remotely Flight Control (attitude, rates) sensing Telemetered data

Piloted Nose camera Remote pilot commands

Vehicle

Individual task planning/execution to meet goals Group accomplishment of tactical goal with

minimal supervisory assistance

Coordinated trajectory planning and execution to meet Group accomplishment of tactical goal with

goals - group optimization minimal supervisory assistance

Possible close air space separation (1-100 yds)

On-board trajectory replanning - optimizes for Group accomplishment of tactical plan as external

current and predictive conditions assigned

Collision avoidance Air collision avoidance

Possible close air space separation (1-100 yds) fo

AAR, formation in non-threat conditions

On-board trajectory replanning - event driven Self accomplishment of tactical plan as externally

Self resource management assigned

Deconfliction

Medium vehicle airspace separation (100's of yds)

Evaluate status vs required mission capabilities Self accomplishment of tactical plan as externally

Abort/RTB if insufficient assigned

Execute preprogrammed or uploaded plans Self accomplishment of tactical plan as externally

in response to mission and health conditions assigned

Preprogrammed mission and abort plans Wide airspace separation requirements (miles)

N/A Control by remote pilot

Table 4: Final ACL Chart 

Level Level Descriptor Observe Orient

Perception/Situational Awareness Analysis/Coordination

10 Fully Autonomous Cognizant of all within Battlespace Coordinates as necessary

Battlespace inference - Intent of self and others Strategic group goals assigned

9 Battlespace (allies and foes).

Swarm Complex/Intense environment - on-board tracking Enemy strategy inferred

Cognizance

8 Proximity inference - Intent of self and others Strategic group goals assigned

Battlespace (allies and foes)

Cognizance Reduced dependance upon off-board data Enemy tactics inferred

Decide Act

Decision Making Capability

Capable of total indepenance Requires little guidance to do job

Distributed tactical group planning Group accomplishment of strategic goal with

Individual determination of tactical goal no supervisory assistance

Individual task planning/execution

Choose tactical targets

Coordinated tactical group planning Group accomplishment of strategic goal with

Individual task planning/execution minimal supervisory assistance

Choose targets of opportunity (example: go SCUD hunting)

ly

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