Welcome to Todays T3e Webinar Modeling Dispatchers Managing

Welcome to Todays T3e Webinar Modeling Dispatchers Managing

Welcome to Todays T3e Webinar Modeling Dispatchers Managing Intelligent Transportation Systems Date: Wednesday, July 18, 2018 Time: 12:00pm EST Listen through your speakers/VoIP If audio cannot be heard 5 minutes prior to the start of this webinar, please dial into the webinar using the teleconference number provided onscreen. T3 Webinars are sponsored by ITS Professional Capacity Building Program, ITS Joint Program Office, U.S. Department of Transportation Modeling Dispatchers Managing Intelligent Transportation Systems 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 Host: Professor Missy Cummings, Ph.D. Director of Duke Robotics and Humans & Autonomy Lab neurosurgical robotics human and computer decisionmaking, sociotechnical systems motion planning and control, robotic systems Speaker: Victoria Chibuogu Nneji Ph.D. Candidate in Duke Robotics, Humans & Autonomy Lab networked and distributed control 3

Modeling Dispatchers Managing Intelligent Transportation Systems Victoria Chibuogu Nneji Ph.D. Candidate, Duke Robotics Volpe National Transportation Systems Center July 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? 5 1 2 3 4 What is a remote operations center (ROC)? Ports Customer Service Maintenance Personnel Safety/Security Agent Vehicles Traffic Controller Local Weather Tracker

Regional Network Managers Pilot Passenger 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). Environment ROC 6 5 1 2 3 4 Why do we need ROCs for intelligent transportation systems? 7 5 1 2 3

4 Why do we need ROCs for intelligent transportation systems? 8 5 1 2 3 4 Why do we need ROCs for intelligent transportation systems? ROC concept by Ehang (2016) Vehicle and vertiport concept by Lilium (2017) 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). Vehicle concept by Aurora (2017) Vehicle concept by Vahana (2016) 9 5 1 2 3 4

Why do we need ROCs for intelligent transportation systems? Dispatch operations center/call center/supervisory control center Energy requirements Passenger requirements Contingency requirements 10 5 1 2 3 4 How could heterogeneous levels of vehicle autonomy influence ROC requirements? Maintain Vehicle Safety Maintain Safe Separation Maintain Vehicle Control From other Participating Vehicles From Fixed and Dynamic Hazards Nominal and Contingency Limits Physical and Cyber Security Maintain Sufficient

Conditions to Complete Trip Ride Quality Energy Vehicle Performance Navigation Accuracy 11 5 1 2 3 4 A Concept of Operations for On-Demand Passenger Aircraft 1. Passenger requests flight 2. Passenger and pilot arrive to port 8. Aircraft is serviced 7. Pilot lands aircraft 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). 6. Pilot communicates with dispatch for clear landing port 3. Pilot completes pre-takeoff checks 4. Pilot maneuvers aircraft for takeoff 5. Enroute 12 5 1 Vehicle Autonomy 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). 2 Conventional Revolutionary Evolutionary* 1. Passenger requests flight

1. Passenger requests flight 1. Passenger requests flight 2. Passenger and pilot arrive to port 2. Passenger arrives to port 2. Passenger and pilot arrive to port 3. Pilot completes pretakeoff checks 3. System completes pre-takeoff checks 3. Pilot completes pretakeoff checks 4. Pilot maneuvers aircraft for takeoff 4. Aircraft maneuvers for takeoff 4. Pilot supervises aircraft takeoff 5. Enroute 5. Enroute 5. Enroute 6. Pilot communicates with dispatch for clear landing port 6. Aircraft communicates with dispatch for clear

landing port 6. Pilot communicates with dispatch for clear landing port 7. Pilot lands aircraft 7. Aircraft lands 7. 7. Pilot supervises aircraft landing 8. Aircraft is serviced 8. Aircraft is serviced 8. Aircraft is serviced 3 4 13 5 1 2 3 4 How could heterogeneous levels of vehicle autonomy influence ROC requirements? Function to Maintain: Safe Separation from traffic Safe separation from hazards

Vehicle control Physical and cyber security Energy management Conventional Plan flights within air traffic control (ATC) restrictions Plan flights to avoid obstructions Communicate with pilot-incommand (PIC) if rerouting Verify PIC, monitor Compute flight energy Navigation Follow flights Ride quality Communicate with PIC if disturbance Communicate with PIC in contingency Systems management Remote Operations Center Tasks Revolutionary Vehicle Evolutionary* Vehicle Autonomy Autonomy

Monitor airspace status, Monitor airspace, communicate command aircraft to unmanned with pilots if adjusting aircraft system traffic separation management (UTM) Calibrate fleet maps with local Share new information w/ & infrastructure data streams between PIC to avoid hazards Monitor A/C (aircraft) sensorMonitor fleet, use AIDA if actuator status, use artificially rerouting & communicate w/ PIC intelligent decision aids (AIDA) if rerouting Monitor fleet network status, Verify PIC, communicate & maintain command authority maintain alertness Compute feasibility to land, Monitor fleet, provide PIC safe ensure sufficient between relanding alternatives if low charges energy Verify navigation of A/Cs on Verify navigation w/ PIC approach Monitor A/C sensors, Monitor & provide update communicate pertinent new info information for passenger with passengers comfort Monitor network, supervisory Monitor subsystem health, 14 control if A/C fails, redirect communicate w/ PIC if A/C fails resources w/ AIDA 5 1

2 3 4 How could heterogeneous levels of vehicle autonomy influence ROC requirements? Function to Maintain: Safe Separation from traffic Safe separation from hazards Vehicle control Physical and cyber security Energy management Conventional Plan flights within air traffic control (ATC) restrictions Plan flights to avoid obstructions Communicate with pilot-incommand (PIC) if rerouting Verify PIC, monitor Compute flight energy Navigation Follow flights Ride quality Communicate

with PIC if disturbance Communicate with PIC in contingency Systems management Remote Operations Center Tasks Revolutionary Vehicle Evolutionary* Vehicle Autonomy Autonomy Monitor airspace status, Monitor airspace, communicate command aircraft to unmanned with pilots if adjusting aircraft system traffic separation management (UTM) Calibrate fleet maps with local Share new information w/ & infrastructure data streams between PIC to avoid hazards Monitor A/C (aircraft) sensorMonitor fleet, use AIDA if actuator status, use artificially rerouting & communicate w/ PIC intelligent decision aids (AIDA) if rerouting Monitor fleet network status, Verify PIC, communicate & maintain command authority maintain alertness Compute feasibility to land, Monitor fleet, provide PIC safe ensure sufficient between relanding alternatives if low charges energy Verify navigation of A/Cs on Verify navigation w/ PIC approach Monitor A/C sensors, Monitor & provide update communicate pertinent new info

information for passenger with passengers comfort Monitor network, supervisory Monitor subsystem health, 15 control if A/C fails, redirect communicate w/ PIC if A/C fails resources w/ AIDA 5 1 2 3 4 How could heterogeneous levels of vehicle autonomy influence ROC requirements? Function to Maintain: Safe Separation from traffic Safe separation from hazards Vehicle control Physical and cyber security Energy management Conventional Plan flights within air traffic control (ATC) restrictions Plan flights to avoid obstructions Communicate

with pilot-incommand (PIC) if rerouting Verify PIC, monitor Compute flight energy Navigation Follow flights Ride quality Communicate with PIC if disturbance Communicate with PIC in contingency Systems management Remote Operations Center Tasks Revolutionary Vehicle Evolutionary* Vehicle Autonomy Autonomy Monitor airspace status, Monitor airspace, communicate command aircraft to unmanned with pilots if adjusting aircraft system traffic separation management (UTM) Calibrate fleet maps with local Share new information w/ & infrastructure data streams between PIC to avoid hazards Monitor A/C (aircraft) sensorMonitor fleet, use AIDA if actuator status, use artificially rerouting & communicate w/ PIC intelligent decision aids (AIDA) if rerouting Monitor fleet network status,

Verify PIC, communicate & maintain command authority maintain alertness Compute feasibility to land, Monitor fleet, provide PIC safe ensure sufficient between relanding alternatives if low charges energy Verify navigation of A/Cs on Verify navigation w/ PIC approach Monitor A/C sensors, Monitor & provide update communicate pertinent new info information for passenger with passengers comfort Monitor network, supervisory Monitor subsystem health, 16 control if A/C fails, redirect communicate w/ PIC if A/C fails resources w/ AIDA 5 1 2 3 4 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 networklevel Exception management Emergent behavior identification 17 5 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? As vehicles and ports are being designed, ROC concepts must also be investigated to support equivalent or better levels of performance on functional requirements. 18

1 2 3 4 5 How will remote operations centers need to innovate to support new ITS demands? 19 1 Collective Case Study Discrete Event Simulation 2 3 4 5 Team Expertise Attention Allocation Fleet Size Fleet Heterogeneity Fleet Autonomy Arrival Process Task Assignment

Service Process Shift Team Size AI Support Team Coordination Environment Model Input Parameters Data Recorded from Case Study Service time of Duration of task dispatchers performance Arrival process of fleet Arrival times of planning, condition- and team calls, and issue resolutions coordination-generated tasks during shift and events Multinomial distribution Count of each type of task event type arriving during shift Dispatch er Dispatch er Attention Allocation Model Output Measures Human Workload System Delays System Throughput Human Errors 20

1 2 3 4 5 21 1 Workload Delays 2 3 4 5 Throughput 22 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 23 organizers! Thank you Lets get coffee: [email protected] linkedin.com/in/victorian 24 References [1] 204208. D. J. Garland, C. M. Mitchell, S. Caisse, L. Sandusky, and P. J. Smith, Air Traffic Management: The View from the Ground, in Proceedings of the Human Factors and Ergonomics Society 43rd Annual Meeting, 1999, pp. [2] E. M. Roth, N. Malsch, and J. Multer, Understanding How Train Dispatchers Manage and Control Trains: Results of a Cognitive Task Analysis, Washington, D.C., 2001. [3] M. S. Young and N. A. Stanton, Mental Workload, in Handbook of Human Factors and Ergonomics Methods, CRC Press LLC, 2005, pp. 391399. [4] C. A. Bowers and F. Jentsch, Team Workload, in Handbook of Human Factors and Ergonomics Methods, CRC Press LLC, 2005, pp. 571573. [5] W. W. Wierwille and F. T. Eggemeier, Recommendations for Mental Workload Measurement in a Test and Evaluation Environment, Hum. Factors, vol. 35, no. 2, pp. 263281, 1993.

[6] D. K. Schmidt, A queuing analysis of the air traffic controllers work load, IEEE Transactions on Systems, Man and Cybernetics, vol. 8, no. 6. pp. 492498, 1978. [7] W. B. Rouse, Systems engineering models of human-machine interaction, vol. 6. North-Holland, 1980. [8] M. L. Cummings and S. Guerlain, Developing operator capacity estimates for supervisory control of autonomous vehicles., Hum. Factors, vol. 49, no. 1, pp. 115, 2007. [9] Office of Railroad Safety, Collaborative Incident Analysis and Human Performance Handbook. Washington, D.C.: Federal Railroad Administration, 2014. [10] V. C. Nneji, A. Stimpson, M. (Missy) Cummings, and K. H. Goodrich, Exploring Concepts of Operations for On-Demand Passenger Air Transportation, in 17th AIAA Aviation Technology, Integration, and Operations Conference, 2017, pp. 112. [11] J. A. Davies, The use of autonomous systems in emergency medical services: bridging human intelligence and technology, Naval Postgraduate School, 2015. [12] Arup Group, Future of Rail 2050, London, 2015. [13] M. Alonso Raposo, B. Ciuffo, M. Makridis, and C. Thiel, The r-evolution of driving: from Connected Vehicles to Coordinated Automated Road Transport (C-ART) Part I: Framework for a safe & efficient Coordinated

Automated Road Transport (C-ART) system, EUR 28575 EN, 2017. [14] P. Green, Driver distraction, telematics design, and workload managers: safety issues and solutions, Ann Arbor, MI, 2004. [15] W. Whitt, Dynamic staffing in a telephone call center aiming to immediately answer all calls, Oper. Res. Lett., vol. 24, no. 5, pp. 205212, 1999. [16] A. K. Thorsen, E. Saeverhagen, and J.-O. Dagestad, Remote Operations Center - An Efficient And Highly Competent Environment To Optimize Operational Performance And Reduce Risk, in Society of Petroleum Engineers/IADC Drilling Conference, 2013. [17] [18] A. Carrel, R. G. Mishalani, N. H. M. Wilson, and J. P. Attanucci, A framework for evaluating operations control on a metro line: Integrating multiple perspectives and automatically collected train and passenger movement data, Public Transp., vol. 5, no. 3, pp. 149176, 2013. [19] Union Pacific, Harriman Dispatching Center. [Online]. Available: https://up.jobs/harriman-dispatch-center.html. [Accessed: 23-Jul-2017]. [20] Airline Dispatchers Federation, Aircraft Dispatcher Job Description, Airline Dispatchers Federation, 2017. [Online]. Available: https://www.dispatcher.org/dispatcher/job-description. [Accessed: 16-May-2017]. J. Booth, Real-Time Drilling Operations Centers: A History of Functionality and Organizational Purpose - The Second Generation, Soc. Pet. Eng., vol. 26, no. 2, 2011.

25 References [21] K. A. Berry and J. J. Pace, Examining the Actors and Functions of an Airline Operations Center, in Proceedings of the Human Factors and Ergonomics Society 55th Annual Meeting, 2011, pp. 14121416. [22] J. B. de Guzman, C. C. de Ritz, and R. G. Ado, Mobile Emergency Response Application Using Geolocation for Command Centers, Int. J. Comput. Commun. Eng., vol. 3, no. 4, pp. 235238, 2014. [23] S. Alarie and M. Gamache, Overview of Solution Strategies Used in Truck Dispatching Systems for Open Pit Mines, Int. J. Surf. Mining, Reclam. Environ., vol. 16, no. 1, pp. 5976, 2002. [24] Akamai Technologies, Network Operations Command Center. USA, 2017. [25] R. Hackett, Inside AT&Ts Global Network Operations Center, Fortune, Bedminster, NJ, Apr-2016. [26] M. L. Cummings and C. E. Nehme, Modeling the impact of workload in network centric supervisory control settings, Neurocognitive Physiol. factors Dur. high-tempo Oper., pp. 2340, 2010. [27] M. Sivak and B. Schoettle, Report No. SWT-2017-8: A Survey of Public Opinion About Flying Cars, University of Michigan, Sustainable Worldwide Transportation, 2017.

[28] S. Burgstaller, D. Flowers, D. Tamberrino, H. P. Terry, and Y. Yang, Rethinking Mobility, 2017. [29] B. M. Joerss, F. Neuhaus, and J. Schroder, How customer demands are reshaping last-mile delivery, 2016. [30] AT&T Intellectual Property, History of Network Management, 2017. [Online]. Available: http://www.corp.att.com/history/nethistory/management.html. [Accessed: 29-Jul-2017]. [31] Jul-2017]. Bureau of Transportation Statistics, National Transportation Statistics, U.S. Department of Transportation, 2016. [Online]. Available: http://www.bts.gov/publications/national_transportation_statistics/. [Accessed: 26- [32] J. Bierstedt, A. Gooze, C. Gray, J. Peterman, L. Raykin, and J. Walters, Effects of Next-Generation Vehicles on Travel Demand & Highway Capacity, 2014. [33] International Association of Public Transport, World Report on Metro Automation, 2016. [34] B. Vlasic, G.M. Wants to Drive the Future of Cars That Drive Themselves, The New York Times, Detroit, MI, 04-Jun-2017.

[35] National Science and Technology Council, Preparing for the Future of Artificial Intelligence, Washington, D.C., 2016. [36] US Department of Transportation, Secretary Foxx Unveils President Obamas FY17 Budget Proposal of Nearly $4 Billion for Automated Vehicles and Announces DOT Initiatives to Accelerate Vehicle Safety Innovations. Washington, D.C., 2016. [37] M. Sivak and B. Schoettle, Road Safety with Self-Driving Vehicles: General Limitations and Road Sharing with Conventional Vehicles, Ann Arbor, MI, 2015. [38] National Highway Traffic Safety Administration (NHTSA), Federal Motor Vehicle Safety Standards; V2V Communications. U.S.A.: Department of Transportation (DOT), 2016, pp. 1392. [39] D. Pomerantz, Brains For Trains: How Software Is Making Trains Smarter, GE Reports, 2016. [Online]. Available: http://www.ge.com/reports/brains-for-trains-how-software-is-making-trains-smarter/. [Accessed: 08Aug-2017]. [40] International Transport Forum / OECD, Automated and Autonomous Driving. Regulation under uncertainty, Interational Transp. Forum, pp. 132, 2015. 26 References [41] M.-V. Florin, Risk and Opportunity Governance of Autonomous Cars, Zrich, Switzerland, 2016.

[42] A. Blandford and B. L. W. Wong, Situation Awareness in Emergency Medical Dispatch, Int. J. Hum. Comput. Stud., vol. 61, no. 4, pp. 421452, 2004. [43] D. Van Der Linden, M. Frese, and T. F. Meijman, Mental Fatigue and Cognitive Control, Acta Psychol. (Amst)., pp. 138, 2006. [44] G. D. Roach, A. Fletcher, and D. Dawson, A Model to Predict work-related fatigue based on hours of work, Aviat. Sp. Environ. Med., vol. 75, no. SUPPL.1, pp. 6169, 2004. [45] J. Dorrian, G. D. Roach, A. Fletcher, and D. Dawson, Simulated train driving: Fatigue, self-awareness and cognitive disengagement, Appl. Ergon., vol. 38, no. 2, pp. 155166, 2007. [46] M. R. Endsley, Toward a Theory of Situation Awareness in Dynamic Systems, Hum. Factors J. Hum. Factors Ergon. Soc., vol. 37, no. 1, pp. 3264, 1995. [47] C. E. Nehme, Modeling human supervisory control in heterogeneous unmanned vehicle systems, MIT, 2009. [48] R. M. Yerkes and J. D. Dodson, The Relation of Strength of Stimulus to Rapidity of Habit-Formation, J. Comp. Neurol. Psychol., vol. 18, no. 5, pp. 459482, 1908.

[49] J. D. Sterman, Systems Thinking and Modeling for a Complex World, vol. 6, no. 1. 2000. [50] J. Coyle, J. Holt, and D. Exelby, System Dynamics in defence analysis: some case studies, J. Oper. Res. Soc., vol. 50, no. 4, pp. 372382, 1999. [51] J. C. Ryan and M. L. Cummings, A Systems Analysis of the Introduction of Unmanned Aircraft into Aircraft Carrier Operations, IEEE Trans. Human-Machine Syst., vol. 99, no. 1, pp. 112, 2014. [52] A. R. Pritchett, S. Y. Kim, and K. M. Feigh, Modeling Human-Automation Function Allocation (Requirements), J. Cogn. Eng. Decis. Mak., vol. 8, no. 1, pp. 3351, 2014. [53] M. C. Aubert, W. Ross, S. Mazzari, A. Stimpson, and M. L. Cummings, Interaction Design Considerations for an Aircraft Carrier Deck Agent-based Simulation, in IEEE Aerospace Conference, 2016. [54] B. Donmez, C. Nehme, and M. L. Cummings, Modeling Workload Impact in Multiple Unmanned Vehicle Supervisory Control, IEEE Trans. Syst. Man. Cybern., vol. 40, no. 6, pp. 11801190, 2010. [55] C. Nehme, B. Mekdeci, J. Crandall, and M. Cummings, The Impact of Heterogeneity on Operator Performance in Future Unmanned Vehicle Systems, Int. C2 J., vol. 2, no. 2, pp. 130, 2009. [56]

T. A. Mazzuchi and R. B. Wallace, Analyzing skill-based routing call centers using discrete-event simulation and design experiment, in 36th Conference on Winter Simulation, 2004, pp. 18121820. [57] K. Lam and R. S. M. Lau, A simulation approach to restructuring call centers, Bus. Process Manag. J., vol. 10, no. 4, pp. 481494, 2004. [58] F. Gao and M. Cummings, Using Discrete Event Simulation to Model Multi-Robot Multi-Operator Teamwork, Proc. Hum. Factors Ergon. Soc. Annu. Meet., vol. 56, pp. 20932097, 2012. [59] review Nneji, V. C., Stimpson, A., & Cummings, M. L. (2017). Predicting Locomotive Crew Performance in Rail Operations with Onboard Human and Automated Assistance, IEEE Transactions on Human Machine Systems, in [60] V. L. Stouffer and K. H. Goodrich, State of the Art of Autonomous Platforms and Human-Machine Systems: Only a Fool Would Stand In the Way of Progress, 15th AIAA Aviat. Technol. Integr. Oper. Conf., no. June, pp. 115, 2015. 27 References [61] B. Mekdeci and M. L. Cummings, Modeling multiple human operators in the supervisory control of heterogeneous unmanned vehicles, Proc. 9th Work. Perform. Metrics Intell. Syst., pp. 18, 2009. [62] S.-Y. Chien, M. Lewis, S. Mehrotra, N. Brooks, and K. Sycara, Scheduling Operator Attention for Multi-Robot Control.

[63] F. Sasangohar, S. D. Scott, and M. L. Cummings, Supervisory-level interruption recovery in time-critical control tasks, Appl. Ergon., vol. 45, no. 4, pp. 11481156, Jul. 2014. [64] M. L. Cummings, Operator Interaction with Centralized Versus Decentralized UAV Architectures, pp. 114. [65] J. R. Peters, Coordination Strategies for Human Supervisory Control of Robotic Teams by, no. June 2017. [66] V. C. Nneji, M. Cummings, A. Stimpson, and K. H. Goodrich, Functional Requirements for Remotely Managing Fleets of Personal On-Demand Aircraft, in 2018 AIAA Aerospace Sciences Meeting, Science and Technology Forum. [67] A. Davies, Nissans Path to Self-Driving Cars? Humans in Call Centers, WIRED, 2017. [Online]. Available: https://www.wired.com/2017/01/nissans-self-driving-teleoperation/. [Accessed: 08-Aug-2017]. [68] Nissan Motor Corporation, Putting lessons from Mars to work on Earth insights on autonomous vehicle research from Nissans Dr. Maarten Sierhuis. pp. 15, 2017. [69] I. Rust and GM Global Technology Operations LLC, Expert Mode for Vehicles. US Patent and Trademark Office, Detroit, MI, pp. 120, 2017. [70]

F. Gao, M. L. Cummings, and E. T. Solovey, Modeling Teamwork in Supervisory Control of Multiple Robots. [71] M. L. Cummings, Human Supervisory Control of Swarming Networks, Cambridge, MA, 2004. 28 Questions and Answers Please 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 Feedback A feedback form will emailed to all participants following the webinar. Please take a few minutes to fill it out we value your input. The form contains information for those requesting Professional Development Hours (PDHs). To receive notifications of upcoming T3s, send an email to [email protected] with Add to mailing list in the subject line. Thank you! Contact us at: [email protected] ITS PCB: http://www.pcb.its.dot.gov Thank you!

Recently Viewed Presentations

  • Dioxin Toxicity - CLU-IN

    Dioxin Toxicity - CLU-IN

    Bruce Duncan, Senior Ecologist with EPA Region 10's Office of Environmental Assessment ([email protected]) Jim Shine, Associate Professor of Aquatic Chemistry, SRP Grantee ([email protected]) ... Oregon State University Celia Chen,* Research Associate Professor, Dartmouth ...
  • Glencoe Biology - taylor.k12.ky.us

    Glencoe Biology - taylor.k12.ky.us

    Section 3: Sponges and Cnidarians. 24.2 Animal Body Plans. Introduction to Animals. Evolution of Animal Body Plans . Anatomical features in animals' body plans mark the branching points on the evolutionary tree. ... Glencoe Biology Last modified by:
  • Elements of Non-Fiction - Copley

    Elements of Non-Fiction - Copley

    Elements of Non-Fiction By Mr. Antal NONFICTION Nonfiction is writing about real people, places, and events. Mainly written to convey factual information. Information may be shaped by the author's own purpose and attitudes.
  • Mechanical Structural Electrical Optical Thermal Materials Electronic Bio

    Mechanical Structural Electrical Optical Thermal Materials Electronic Bio

    Examples: Pneumatic Tools Hydraulic Lift Brake systems Help What are the Core Technologies? A technology system is a group of resources (subsystems) working together to solve problems and extend human capabilities. The core technologies are the "building blocks" of all...
  • Retrospective Study of Volume Changes in Two Pathological ...

    Retrospective Study of Volume Changes in Two Pathological ...

    Dickie C, Griffin A, Parent A, Sharpe M, Chung P, Catton C, Wunder J, Ferguson P, O'Sullivan B. ... Griffin Charles Catton Peter Chung Jay Wunder Peter Ferguson Ben Deheshi Amy Parent Oncology Physics Therapy Graham Wilson Mike Sharpe Patient...
  • Poetry - 7th Grade Language Arts

    Poetry - 7th Grade Language Arts

    POETRY TERMS and DEFINITIONS TERMS Anthology - collection of poems grouped together Assonance - vowel rhyme - repetition of a pattern of similar sounds Consonance - repetition of the same consonant Chorus - part of poem repeated after each verse...
  • HANTAVIRUS: VIRAL EVOLUTION Chelsea, Zuania, Keerthana, Eduardo Transmission

    HANTAVIRUS: VIRAL EVOLUTION Chelsea, Zuania, Keerthana, Eduardo Transmission

    Uses nsRNA genome as a cap snatching device. steals end of host mRNA genome. migration of newly formed hantaviral proteins. Deterministic Model (ODEs) Conceptual Diagram. Stochastic Model (SDEs) Adds Brownian motion. Quasispecies (viral evolution)
  • MICROSOFT WORD 2007 - Algonquin College

    MICROSOFT WORD 2007 - Algonquin College

    MICROSOFT WORD 2007. INTERMEDIATE/ADVANCED. CREATE A NEW STYLE BASED ON A SELECTED TEXT. ... Ensure that you have correct spacing and punctuation around the Fill-in field. A CURRENT MAIL MERGE LETTER TO A PRINTER.