Automated Crew Support in a Naval Vessel CC · H.F.R. Arciszewski, J.H. van Delft ICCRTS June 2005...

ICCRTS June 2005H.F.R. Arciszewski, J.H. van Delft

TNO Defence, Safety and Security

Automated Crew Support in a Naval Vessel CC


People (Crew Support)

• The Hague (C2 + Information Management)• H. Arciszewski• B. van Dam• H. Kluiver• L. Prins

• Soesterberg (Human Factors)• J. van Delft• B. Bierman• K. Houttuin


Outline

• C2 Study• Outline of the Study

• Crew Support• Situation Assessment• Information Management• Human-Machine Interface• Evaluation• Conclusions

• C2 Study• Conclusions


C2 Study Programme Goal

CIC MCIC M--FFFF

InformationOverload

OldTechnology

NewMissions

WorkloadCommand Team

Problem Definition

Future RNLN Ships

Future CICFuture CIC

IncreasedAutomation

ImprovedInformationPresentation

New TeamConcepts

Programme Goal

Technology Human

Organization“Clean Slate”approach

- Workload Decrease- Efficiency Increase- Manning Reduction


Phased Approach

2002 200320001998

Hot-Spot Analysis

Observation OrientationDecision Making Action

IntegrationCIC concept

Teammodel

AutomationPresentation

Live

Simulation

Partial Solutions

Organization

Technology Human

Moreautomation Better

training

Betterhuman-machine

support

Taak-verdeling


Observation Orientation Decision Making Action

Phase 1: AnalysisHot Spots:- Time- Volume- Complexity

Hot Spot:

- Situation Assessment


Phase 2: Partial Solutions

Organization

Technology Human

Automation BeterTraining

TeamOrganisation

BeterSupport

Workload

Data

Model variants: Basic model Evolutionairy model Revolutionairy model

AAW;AsuW;MTScenario’s: Basic Test

WorkloadProfiles

Workloadvisualisationinstrument

IPME Modelling andsimulation environment

Teammodel

Team modelling

Better Training

Interpretation support

SuperVisionWorkstation

Automatische Beeldvorming

Radar TV/IR IFF

InformatieManagement

Attenties

Taak delegatie

tracksTactische signalenID’s/Classificaties.ID’s/Classificaties

Systeem

Gebruiker

=Doelsinfo

IDCRITS

Gedrag

Sensor Modellen

Identificatie

GedragsanalyseHerkenning

Track fusie

CMSfunctionality

Decisionsupport


Baseline CIC Concept:- Team Concept- Automated Support

IntegralC2 Concept

Phase 3: Integration

Automation &Presentation

(Command team)

Team Model

Empirical(Live experiments)

Analytic(Simulation)

Advanced WorkstationAutomated Supportcurrent roles

ComparisonTeam Conceptscurrent technology

Automation &Presentation

(Command team)


Workstation + Automation

Improved MMI Concept:- 3D ‘Awareness’- Information Presentation- Information Management- Decision Support

“Basic T”• Judgement Automation and

Presentation• Determination of Workload

5 Scenarios:- AAW: Basis and Test- ASuW: Basis and Test- AWW Threat

RMPTactical informationTactical alerts

Tactical decisionsTask delegation

Automated RMP Support:- Classification & Identification- Signaling of tactically relevant events- Comparison human/system RMP- Task delegation

“Future CMS”

SuperVisionWorkstation

Automatische Beeldvorming

Radar TV/IR IFF

InformatieManagement

Attenties

Taak delegatie

tracksTactische signalenID’s/Classificaties.ID’s/Classificaties

Systeem

Gebruiker

=Doelsinfo

IDCRITS

Gedrag

Sensor Modellen

Identificatie

GedragsanalyseHerkenning

Track fusie


Automation Concept

Basic TWorkstation

Situation Assessment

InformationManagement

Tactical Alerts

Task delegation

System RMP

User RMP

System

Users

BackgroundInformationSensor Track

FusionSensor

Management

Sensors

Advice


Situation Assessment

• Autonomous Sensor Track Fusion

• Autonomous Platform Classification• Based on radar (RCS, NCTR), ESM (emitter), VISID, ...

• Advice on Identification only• Rule-based Identification (IDCRITS)

e.g. if (“In Airlane” and “IFF-3”) or (“In Airlane” and “Big”) ⇒ Neutral

• Track Monitoring• Continuously Monitor all Tracks• Signal Tactical Events (deviations from “expected” behaviour)


BuildingInformation


Information Management

• Worldview Comparison• Signaling of Discrepancies or Counter Evidence

• Information Aggregation and Filtering• All events are equal but some events are more equal than others• Collation of Tactical Signals (significant changes in State Vector)• Track Relevance Indication

• Task Delegation• If the system generates a good interpretation why bother the operator?• Delegation of “easy” and “safe” Identification cases to CMS:

robustness, trust• Leave “complicated” situations (and more time) to operator


Fine-grained Identification Delegation

NANAH

NANAS

NANAN

NANAAF

NANAF

NANAU

NAPUserIdentity

HSNAFFUP

System Identity


Workstation Concept: “Basic-T”

Bird’s eye View

3DTactical Space

InformationSupport

IdentificationSupport

TrackProfiles

Side view

Track Buttons

StereoscopicTactical Space

TacticalEvaluation

AssetPlanning

(Harpoon)

AssessmentAssessment tools Planning toolsTactical InsightTactical Insight


Basic-T


Evaluation

• Evaluation• Air Warfare and Surface Warfare Officers • Five scenarios: two AAW, two ASuW, one AWW; increasing complexity• No ‘null measurement’ – interviews and expert opinion

0

2

4

6

8

10

basic test combined

Scenario

Diffi

culty AAW

ASuW


Evaluation - Interviews

• Positive reactions• Fast and consistent picture compilation thanks to automation• Reduction of routine work• System advice and tactical events aid situational awareness• 3D Tactical Space aids situational awareness


User/System Agreement

0.0

1.0

2.0

3.0

basicASuW

testASuW

AWW basicAAW

testAAW

AWW

Scenario

N T

rack

s

System Errors

User Errors

Differences


Suppression of Alerts

0.0

5.0

10.0

15.0

20.0

25.0

30.0

35.0

basic test combined

Scenario

Eve

nts/

min ASuW events

AAW eventsASuW alertsAAW alerts


Difficulty vs.Workload

0

2

4

6

8

10

basic test combined

Scenario

Diffi

culty AAW

ASuW

1

2

3

4

5

basic test combined

ScenarioSu

bjec

tive

wor

kloa

d

AAWASuW


Conclusions Crew Support

• Workload Reduction and Increase of Effectiveness

• Based on General Principles• Separate system and user world views• Robust rules and algorithms for simple cases (trust)• Warning aggregation (tactical signals)• Delegation of simple cases


Baseline CIC Concept:- Team Concept- Automated Support

IntegralC2 Concept

Results C2 Study

Automation &Presentation(Command team)

Team Model

Empirical(Live experiments)

Analytic(Simulation)

Advanced WorkstationAutomated Supportcurrent roles

Comparison 3Team Conceptscurrent technology

New Automated Support reduces Workload of Operator

One WFO is capable of handling AWW Scenario

Crew Reduction through Team Reorganisation is possible

Integration of AWO/SuWO to WFO is possible


Overall Conclusions C2 Study

Results• A significantly smaller team seems feasible

under the assumptions and constraints• The crew support concepts are usable now or in the

near future (ADCF)

Constraints• The team concept has not been tested in an experimental

environment (CC)• Simplifications (no ASW, limited communication)

Automated Crew Support in a Naval Vessel CC · H.F.R. Arciszewski, J.H. van Delft ICCRTS June 2005...

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Transcript of Automated Crew Support in a Naval Vessel CC · H.F.R. Arciszewski, J.H. van Delft ICCRTS June 2005...