A method for autonomous vehicle driver situational awareness recovery training includes receiving an input parameter; determining a simulation; receiving user performance data; and analyzing the user performance data to determine a driving competency score. A method for autonomous vehicle driver situational awareness recovery training includes receiving an input parameter; executing a simulation; and recording user performance data. A system includes a processor a display device; and a non-transitory computer-readable medium storing instructions that, when executed by the one or more processors, cause the system to receive an input parameter; execute a simulation; and record user performance data.

A method for autonomous vehicle driver situational awareness recovery training includes receiving an input parameter; determining a simulation; receiving user performance data; and analyzing the user performance data to determine a driving competency score. A method for autonomous vehicle driver situational awareness recovery training includes receiving an input parameter; executing a simulation; and recording user performance data. A system includes a processor a display device; and a non-transitory computer-readable medium storing instructions that, when executed by the one or more processors, cause the system to receive an input parameter; execute a simulation; and record user performance data.

An evaluation device includes a first display control unit that displays imaging data acquired by an imaging device on a display screen of a display device, and a second display control unit that displays on the display screen a guide display based on work vehicle data on a work vehicle imaged, to determine an imaging position of the imaging device.

An evaluation apparatus includes a detected data acquisition unit that acquires photograph data of a bucket of a work equipment, a first display control unit that displays the photograph data on a display screen of a display device, a second display control unit that displays on the display screen a graphic representing at least part of an outline of the bucket as viewed from a side, and moves the graphic on the display screen by an operation of an input device, and an identification unit that distinguishes the bucket from excavated material above an opening end of the bucket on the display screen based on the graphic aligned with the bucket.

A driver assessment system includes at least one vehicle sensor on a vehicle gathering vehicle dynamics information. At least one occupant sensor gathers driver information. The at least one occupant sensor may be a wearable device or subdermal device on the driver. At least one computer receives the vehicle dynamics information and the driver information to determine a driver readiness.

Examples of the disclosure are directed to an automated vehicle that can function as a vehicle operator training system. A vehicle that combines sensors, actuators, infotainment, and/or communication functions may have the capability to provide a fully autonomous training experience in a real world environment. The vehicle can administer, monitor, and communicate with a vehicle operator for competency training and/or verification for a specific vehicle feature, sub-feature, situation maneuver, and/or objective.

Examples of the disclosure are directed to an automated vehicle that can function as a vehicle operator training system. A vehicle that combines sensors, actuators, infotainment, and/or communication functions may have the capability to provide a fully autonomous training experience in a real world environment. The vehicle can administer, monitor, and communicate with a vehicle operator for competency training and/or verification for a specific vehicle feature, sub-feature, situation maneuver, and/or objective.

In one or more embodiments, driver awareness may be calculated, inferred, or estimated utilizing a saliency model, a predictive model, or an operating environment model. An awareness model including one or more awareness scores for one or more objects may be constructed based on the saliency model or one or more saliency parameters associated therewith. A variety of sensors or components may detect one or more object attributes, saliency, operator attributes, operator behavior, operator responses, etc. and construct one or more models accordingly. Examples of object attributes associated with saliency or saliency parameters may include visual characteristics, visual stimuli, optical flow, velocity, movement, color, color differences, contrast, contrast differences, color saturation, brightness, edge strength, luminance, a quick transient (e.g., a flashing light, an abrupt onset of a change in intensity, brightness, etc.).

In one or more embodiments, driver awareness may be calculated, inferred, or estimated utilizing a saliency model, a predictive model, or an operating environment model. An awareness model including one or more awareness scores for one or more objects may be constructed based on the saliency model or one or more saliency parameters associated therewith. A variety of sensors or components may detect one or more object attributes, saliency, operator attributes, operator behavior, operator responses, etc. and construct one or more models accordingly. Examples of object attributes associated with saliency or saliency parameters may include visual characteristics, visual stimuli, optical flow, velocity, movement, color, color differences, contrast, contrast differences, color saturation, brightness, edge strength, luminance, a quick transient (e.g., a flashing light, an abrupt onset of a change in intensity, brightness, etc.).

An information presentation apparatus includes: a status estimation unit that identifies one or more tasks from input information which has been input, and task information indicative of one or more tasks indicating a status; a load estimation unit that identifies one or more subtasks based on each of the identified tasks and subtask information indicative of one or more subtracts, each indicating a work element possibly executed by an operator, in the status of each task, and acquires a required load amount, which is a mental load on each of the one or more subtasks, based on the identified subtask and required subtask amount information indicative of the required load amount; a margin estimation unit that acquires a margin based on the acquired required load amount; and a presented information selection unit that selects information to be presented based on the margin.