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Welcome to Today’s T3e Webinar

Modeling Dispatchers Managing Intelligent Transportation Systems

Date: Wednesday, July 18, 2018Time: 12:00pm EST

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Mary (Missy) Cummings, Ph.D., Director of Duke Robotics and Humans & Autonomy Lab Professor of Engineering, Computer Science, and Brain Sciences

Victoria Chibuogu Nneji, Ph.D. Candidate in Duke Robotics, Researcher in the Humans & Autonomy Lab

neurosurgical robotics

human and computer decision-making,

sociotechnical systems

motion planning and control, robotic systems

networked and distributed control

Speaker: Victoria Chibuogu NnejiPh.D. Candidate in Duke Robotics, Humans & Autonomy Lab

Host: Professor Missy Cummings, Ph.D.Director of Duke Robotics and Humans & Autonomy Lab


Victoria Chibuogu NnejiPh.D. Candidate, Duke Robotics

Volpe National Transportation Systems CenterJuly 18, 2018

Host: Professor Mary (Missy) Cummings, Ph.D.Director of Duke Robotics

Outline

1. What is a remote operations center (ROC)?2. Why do we need ROCs for intelligent transportation systems?3. How could heterogeneous levels of vehicle autonomy influence ROC

requirements?4. What should we consider when staffing and designing ROCs for ITS?5. Where do we need to focus ROC efforts for future ITS concepts to

become operational?

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What is a remote operations center (ROC)?

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ROC

Vehicles•Pilot•Passenger

Ports•Customer Service•Maintenance Personnel•Safety/Security Agent

Environment•Traffic Controller•Local Weather Tracker•Regional Network Managers

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Nneji, V. C., Cummings, M. L., Stimpson, A. J., & Goodrich, K. H. (2018). Functional Requirements for Remotely Managing Fleets of On-Demand Passenger Aircraft. In 2018 AIAA Aerospace Sciences Meeting (p. 2007).

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Why do we need ROCs for intelligent transportation systems?

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Vehicle concept by Aurora (2017)

Vehicle concept by Vahana (2016)

Vehicle and vertiport concept by Lilium (2017)

ROC concept by Ehang (2016)ROC concept by Ehang (2016)


Nneji, Stimpson, Cummings, & Goodrich (2017). Exploring Concepts of Operations for On-Demand Passenger Air Transportation. In 17th AIAA Aviation Technology, Integration, and Operations Conference (p. 3085).

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• Dispatch operations center/call center/supervisory control center• Energy requirements• Passenger requirements• Contingency requirements


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How could heterogeneous levels of vehicle autonomy influence ROC requirements?

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Maintain Vehicle SafetyMaintain Safe Separation• From other Participating

Vehicles• From Fixed and Dynamic

Hazards

Maintain Vehicle Control• Nominal and Contingency

Limits• Physical and Cyber

Security

Maintain Sufficient Conditions to Complete Trip• Ride Quality• Energy• Vehicle Performance• Navigation Accuracy

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A Concept of Operations for On-Demand Passenger Aircraft

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1. Passenger requests flight

2. Passenger and pilot arrive to port

3. Pilot completes pre-takeoff checks

4. Pilot maneuvers aircraft for takeoff

5. Enroute

6. Pilot communicates with dispatch for clear landing port

7. Pilot lands aircraft

8. Aircraft is serviced


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2. Passenger arrives to port

3. System completes pre-takeoff checks

4. Aircraft maneuvers for takeoff

5. Enroute

6. Aircraft communicates with dispatch for clear landing port

7. Aircraft lands

8. Aircraft is serviced13




4. Pilot maneuvers aircraft for takeoff

5. Enroute


7. Pilot lands aircraft


ConventionalVehicle Autonomy Revolutionary Evolutionary*

7. Aircraft lands




4. Pilot supervises aircraft takeoff

5. Enroute


7. Pilot supervises aircraft landing



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Function to Maintain: Remote Operations Center TasksConventional Revolutionary Vehicle Autonomy Evolutionary* Vehicle Autonomy

Safe Separation from traffic

Plan flights within air traffic control (ATC) restrictions

Monitor airspace status, command aircraft to unmanned aircraft system

traffic management (UTM)

Monitor airspace, communicate with pilots if adjusting separation

Safe separation from hazards

Plan flights to avoid obstructions

Calibrate fleet maps with local infrastructure data streams

Share new information w/ & between PIC to avoid hazards

Vehicle control Communicate with pilot-in-command (PIC) if rerouting

Monitor A/C (aircraft) sensor-actuatorstatus, use artificially intelligent decision aids (AIDA) if rerouting

Monitor fleet, use AIDA if rerouting & communicate w/ PIC

Physical and cybersecurity

Verify PIC, monitor Monitor fleet network status, maintain command authority

Verify PIC, communicate & maintain alertness

Energy management Compute flight energy

Compute feasibility to land, ensure sufficient between re-charges

Monitor fleet, provide PIC safe landing alternatives if low energy

Navigation Follow flights Verify navigation of A/Cs on approach Verify navigation w/ PICRide quality Communicate with

PIC if disturbanceMonitor A/C sensors, communicate pertinent new info with passengers

Monitor & provide update information for passenger comfort

Systems management Communicate withPIC in contingency

Monitor network, supervisory control if A/C fails, redirect resources w/ AIDA

Monitor subsystem health, communicate w/ PIC if A/C fails

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What should we consider when staffing and designing ROCs for ITS?

• Customer service• Port service• Resource scheduling• Vehicle command authority

• Teams of human and AI agents• Path planning• Scheduling• Resource allocation

• Remote operator tactical interface

• Monitor• Command

• Scaling up to network-level• Exception management• Emergent behavior identification

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1 2 3 4 5

Where do we need to focus ROC efforts for future ITS concepts to become operational?

• Metrics for ROC operator performance, system safety and efficiency

• How many more or less ROC operators should be staffed to manage vehicles with revolutionary autonomy?

• Which types of artificial intelligence decision aids should be designed for ROC operators?

• How many vehicles can be managed at a time with heterogenous levels of network autonomy?

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As vehicles and ports are being designed, ROC concepts must also be investigated to support equivalent or better levels of performance on functional requirements.

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How will remote operations centers need to innovate to support new ITS

demands?

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Dispatcher

Dispatcher

Task Assignment

Service Process

ShiftTeam SizeAI Support

Team Coordination

Attention Allocation

Team Expertise

Attention Allocation

Environment

Fleet SizeFleet HeterogeneityFleet Autonomy

Arrival Process 𝜆𝜆

Model Input Parameters Data Recorded from Case StudyService time of dispatchers Duration of task performanceArrival process of fleet condition-and team coordination-generated and events

Arrival times of planning, calls, and issue resolutions tasks during shift

Multinomial distribution event type

Count of each type of task arriving during shift

Collective Case Study

Model Output Measures

Human Workload

System Delays

System Throughput

Human Errors

Discrete Event Simulation

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Workload Delays Throughput

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Acknowledgements

• American Airlines, Delta Airlines, Forward Air, Horizon Air, Rio Grande Pacific Company, Southwest Airlines, UPS

• Airbus A3, Ehang, FAA, Gryphon Sensors, Kairos, Lilium Aviation, Lockheed Martin-Sikorsky, NASA Ames, NUAIR, Uber

• Federal Railroad Administration, US Department of Transportation• National Institute of Aerospace and NASA Langley Research Center• Missy Cummings, Alfredo Garcia, Jeffrey Glass, Michael Zavlanos• Comrades in Duke Robotics and AIAA• Talking Technology and Transportation (T3e) Webinar organizers!

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Thank youLet’s get coffee: [email protected]

linkedin.com/in/victorian

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mailto:[email protected]

https://www.linkedin.com/in/victorian

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Questions and AnswersPlease Type your questions in the Q & A pod and we will answer as time allows.

Contacts:

Mary (Missy) Cummings, Ph.D., Director of Duke Robotics and Humans & Autonomy Lab Professor of Engineering, Computer Science, and Brain Sciences

Victoria Chibuogu Nneji, Ph.D. Candidate in Duke Robotics, Researcher in the Humans & Autonomy Lab

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