Displays and automation to improve train drivers' situational awareness
Many train incidents involve a loss of situational awareness by the train drivers. These problems can be exacerbated by weather, fatigue, poor display design, and unexpected events that require rapid replanning. Currently, no mechanisms exist to assist drivers in rapidly regaining situational awareness. The MIILO project is focused on developing displays and automation which help drivers rapidly regain situational awareness in order to prevent drivers from committing many errors, or allow them to rapidly correct their mistakes.
Research sponsored by Alstom.
The Deck operations Course of Action Planner (DCAP) is designed to aid in planning on aircraft carrier decks. This environment is highly complex, with myriad failures and highly variable processing times, failure rates, and failure durations. The DCAP system serves as a decision support tool for a supervisor overseeing people, vehicles, and unmanned vehicles working in this environment.
The DCAP interface provides both up-to-the-minute information about the current state of operations – the location of all entities in the environment, their upcoming tasks, and the current usage of deck resources – and the ability for a User to work with a planning algorithm to request new schedules on deck. The DCAP interface provides the User the ability change scheduling priorities, change the designation of priority aircraft, and manipulate the schedules of these priority aircraft in real time. The planning algorithm then takes these User specifications for use in creating a new schedule, which is returned to the User for review.
Flying an unmanned helicopter with an iPhone:
Unmanned Aerial Vehicles (UAVs), such as the military's Predator drone, currently require sophisticated and intensive training to operate. For example, an Air Force Predator UAV requires two full-time operators, both certified pilots who have attended a 10-week training course. Advances in UAV design have led to personal, micro aerial vehicles (MAVs) which can range in size from a fly to a basketball. MAVs are even more difficult to fly than their larger cousins due to a lack of conventional control surfaces like rudders and flaps. These MAVs are intended for use by soldiers or other highly trained first-responder personnel for real-time scouting and exploration. It is therefore impractical to demand the additional specialized training required to operate such a complex craft with a traditional joystick or remote control.
David Pitman's Master's thesis, Collaborative Micro Aerial Vehicle Exploration of Outdoor Environments, created and analyzed a controller, MAV-VUE, for flying these MAVs which uses an iPhone. MAV-VUE allows users to control a MAV via high-level waypoint commands, or through fine-grained nudge controls. This innovation, which can work on any hand-held device, can be extended to operate any type of robot such as a bomb disposal robot.
New Use for Your iPhone: Controlling Drones by Wired.com
Research sponsored by ONR and The Boeing Company
Replan Understanding for Heterogeneous Unmanned Vehicle Teams
In order for a single operator to supervise multiple unmanned vehicles remotely, the operator must interact with the vehicles via a mission manager, with lower level cognitive tasks like actually flying and navigating the vehicles relegated to the automation. Automation is required because of the large amount of information that needs to be processed under time pressure, but human judgment and experience is needed because of the significant uncertainty inherent in the system. In order to allow a single operator the ability to manage multiple vehicles, we have developed a display design that processes the plethora of sensor data coming from the multiple vehicles, and presents it in an easy-to-understand format, which engages the user in high-level tasking decisions as well as contingency planning.
To complete a high level system acquisition decision, decision makers must process a large amount of data to determine which system best fits a given mission or purpose. This project investigates what type of decision support tool could provide decision makers the greatest understanding of the system acquisition trade space, thus allowing them to make more informed decisions. As part of this project, a new configural display named Fan Visualization (FanVis) has been conceived, designed and developed. FanVis takes the system acquisition trade space data, and by using emergent features, naturally maps the data for the decision maker. An experiment has shown improvement in performance using FanVis over a set of Excel bar and line charts, which represent the tools used currently in system acquisition decisions.
Massie, A. E., (2009), Designing a Graphical Decision Support Tool to Improve System Acquisition Decision, S. M. Thesis, MIT Aeronautics and Astronautics, Cambridge, MA.