Abstract: Systems and methods of training vehicles to improve performance, reliability and autonomy of vehicles. Sensors capture a vehicle driver's eye movements, hand grip and contact area on steering wheel, distance of the accelerator and brake pedals from the foot, depression of these pedals by the foot, and ambient sounds. Data from these sensors is used as training data to improve autonomy of vehicles. Detection of events occurring outside a vehicle is made by comparing human sensor data with an associated road map to determine correlation. Signatures corresponding to human reactions and actions are extracted, which also incorporate data acquired using vehicle and outside environment sensors. By scoring driver responses to events outside the vehicle as well as during non-events, expert drivers are identified, whose responses are used as a part of vehicle training data.

Abstract: Systems and methods of training vehicles to improve performance, reliability and autonomy of vehicles. Sensors capture a vehicle driver's eye movements, hand grip and contact area on steering wheel, distance of the accelerator and brake pedals from the foot, depression of these pedals by the foot, and ambient sounds. Data from these sensors is used as training data to improve autonomy of vehicles. Detection of events occurring outside a vehicle is made by comparing human sensor data with an associated road map to determine correlation. Signatures corresponding to human reactions and actions are extracted, which also incorporate data acquired using vehicle and outside environment sensors. By scoring driver responses to events outside the vehicle as well as during non-events, expert drivers are identified, whose responses are used as a part of vehicle training data.

Abstract: Systems and methods of training vehicles to improve performance, reliability and autonomy of vehicles. Sensors capture a vehicle driver's eye movements, hand grip and contact area on steering wheel, distance of the accelerator and brake pedals from the foot, depression of these pedals by the foot, and ambient sounds. Data from these sensors is used as training data to improve autonomy of vehicles. Detection of events occurring outside a vehicle is made by comparing human sensor data with an associated road map to determine correlation. Signatures corresponding to human reactions and actions are extracted, which also incorporate data acquired using vehicle and outside environment sensors. By scoring driver responses to events outside the vehicle as well as during non-events, expert drivers are identified, whose responses are used as a part of vehicle training data.

Abstract: Systems and methods to improve performance, reliability and learning to enhance autonomy of vehicles are disclosed. Responses of subjects to non-events and outside events when operating vehicles under various conditions are scored partly based on vehicle location and an associated map. Scoring for non-events is based on performance and mental functioning on selected categories, while scoring for outside events is based on success and failure and mental functioning when responding to these events. Subjects are ranked and expert drivers identified depending on their raw or scaled scores, or a combination score, or a threshold score. Responses of expert drivers are then used as vehicle training data to enable or improve vehicle autonomy. Response data can include data from vehicle sensors, outside environment sensors and human sensors.

Abstract: Systems and methods to improve performance, reliability and learning to enhance autonomy of vehicles. Sensors capture human eye movements, hearing, hand grip and contact area on steering wheel, the positions of accelerator and brake pedals from the wall behind them as well as from the foot. Outside event signatures corresponding to human reactions and actions are then extracted form these sensors and correlated to events, status and situations acquired using vehicle and outside environment sensors. These outside event signatures are then used to train vehicles to improve their autonomous capabilities.

Abstract: Systems and methods to improve performance, reliability and learning to enhance autonomy of vehicles are disclosed. Responses of subjects to non-events and outside events when operating vehicles under various conditions are scored partly based on vehicle location and an associated map. Scoring for non-events is based on performance and mental functioning on selected categories, while scoring for outside events is based on success and failure and mental functioning when responding to these events. Subjects are ranked and expert drivers identified depending on their raw or scaled scores, or a combination score, or a threshold score. Responses of expert drivers are then used as vehicle training data to enable or improve vehicle autonomy. Response data can include data from vehicle sensors, outside environment sensors and human sensors.

Abstract: Systems and methods to improve performance, reliability and learning to enhance autonomy of vehicles. Sensors capture human eye movements, hearing, hand grip and contact area on steering wheel, the positions of accelerator and brake pedals from the wall behind them as well as from the foot. Outside event signatures corresponding to human reactions and actions are then extracted form these sensors and correlated to events, status and situations acquired using vehicle and outside environment sensors. These outside event signatures are then used to train vehicles to improve their autonomous capabilities.

Abstract: Systems and methods to improve performance, reliability, learning and safety and thereby enhance autonomy of vehicles. Human sensors are used to capture human eye movement, hearing, hand grip and contact area, and foot positions. Event signatures corresponding to human actions, reactions and responses are extracted form these sensor values and correlated to events, status and situations acquired using vehicle and outside environment sensors. These event signatures are then used to train vehicles to improve their autonomous capabilities. Human sensors are vehicle mounted or frame mounted. Signatures obtained from events are classified and stored.

Abstract: Systems and methods to improve performance, reliability, learning and safety and thereby enhance autonomy of vehicles. Human sensors are used to capture human eye movement, hearing, hand grip and contact area, and foot positions. Event signatures corresponding to human actions, reactions and responses are extracted form these sensor values and correlated to events, status and situations acquired using vehicle and outside environment sensors. These event signatures are then used to train vehicles to improve their autonomous capabilities. Human sensors are vehicle mounted or frame mounted. Signatures obtained from events are classified and stored.

Abstract: Systems and methods to improve performance, reliability, learning and safety and thereby enhance autonomy of vehicles. Human sensors are used to capture human eye movement, hearing, hand grip and contact area, and foot positions. Event signatures corresponding to human actions, reactions and responses are extracted form these sensor values and correlated to events, status and situations acquired using vehicle and outside environment sensors. These event signatures are then used to train vehicles to improve their autonomous capabilities. Human sensors are vehicle mounted or frame mounted. Signatures obtained from events are classified and stored.

Abstract: A cycle counter for a wheeled tractor scraper is disclosed. The cycle counter may have an ejector configured to push material from a bowl of the wheeled tractor scraper, a sensor configured to generate a pressure signal indicative of a position of the ejector, and a controller in communication with the sensor. The controller may be configured to determine movement of the ejector toward a full dump position based on the pressure signal, and to record completion of a cycle for the wheeled tractor scraper after the ejector has reached the full dump position only if a value of the pressure signal exceeded a threshold value during movement of the ejector toward the full dump position.

Abstract: A cycle counter for a wheeled tractor scraper is disclosed. The cycle counter may have an ejector configured to push material from a bowl of the wheeled tractor scraper, a sensor configured to generate a pressure signal indicative of a position of the ejector, and a controller in communication with the sensor. The controller may be configured to determine movement of the ejector toward a full dump position based on the pressure signal, and to record completion of a cycle for the wheeled tractor scraper after the ejector has reached the full dump position only if a value of the pressure signal exceeded a threshold value during movement of the ejector toward the full dump position.

Abstract: A tool control system for a scraper is disclosed. The tool control system may have a tool, a first tool actuator configured to move the tool, and a first sensor configured to detect a position parameter of at least one of the tool and the first tool actuator and to generate a corresponding first signal. The tool control system may also have a tool member operatively connected to the tool, a second tool actuator connectable to the tool member in a plurality of configurations to move the tool member, and a second sensor configured to detect a position parameter of at least one of the tool member and the second tool actuator and to generate a corresponding second signal. The tool control system may further have a controller configured to make a comparison of the first and second signals, and to determine a current connection configuration based on the comparison.

Abstract: A tool control system for a scraper is disclosed. The tool control system may have a tool, a first tool actuator configured to move the tool, and a first sensor configured to detect a position parameter of at least one of the tool and the first tool actuator and to generate a corresponding first signal. The tool control system may also have a tool member operatively connected to the tool, a second tool actuator connectable to the tool member in a plurality of configurations to move the tool member, and a second sensor configured to detect a position parameter of at least one of the tool member and the second tool actuator and to generate a corresponding second signal. The tool control system may further have a controller configured to make a comparison of the first and second signals, and to determine a current connection configuration based on the comparison.