Abstract: A computer-implemented method, system, and/or computer program product controls a driving mode of a self-driving vehicle (SDV). One or more processors compare a control processor competence level of an on-board SDV control processor in controlling the SDV to a human driver competence level of a human driver in controlling the SDV while the SDV encounters a current roadway condition which is a result of current weather conditions of the roadway on which the SDV is currently traveling. One or more processors then selectively assign control of the SDV to the SDV control processor or to the human driver while the SDV encounters the current roadway condition based on which of the control processor competence level and the human driver competence level is relatively higher to one another.

Abstract: A computer-implemented method, system, and/or computer program product controls a driving mode of a self-driving vehicle (SDV). One or more processors compare a control processor competence level of an on-board SDV control processor in controlling the SDV to a human driver competence level of a human driver in controlling the SDV while the SDV encounters a current roadway condition which is a result of current weather conditions of the roadway on which the SDV is currently traveling. One or more processors then selectively assign control of the SDV to the SDV control processor or to the human driver while the SDV encounters the current roadway condition based on which of the control processor competence level and the human driver competence level is relatively higher to one another.

Abstract: A computer-implemented method, system, and/or computer program product controls a driving mode of a self-driving vehicle (SDV). One or more processors compare a control processor competence level of an on-board SDV control processor in controlling the SDV to a human driver competence level of a human driver in controlling the SDV while the SDV encounters a current roadway condition which is a result of current weather conditions of the roadway on which the SDV is currently traveling. One or more processors then selectively assign control of the SDV to the SDV control processor or to the human driver while the SDV encounters the current roadway condition based on which of the control processor competence level and the human driver competence level is relatively higher to one another.

Abstract: A computer-implemented method, system, and/or computer program product controls self-driving vehicles (SDVs). An emergency message is transmitted to a receiver within a self-driving vehicle (SDV). The emergency message describes an emergency state of an emergency vehicle and an identified future route of the emergency vehicle, where the identified future route is a planned route to an emergency destination for the emergency vehicle that includes a first pathway. In response to the SDV receiving the emergency message, the SDV is redirected, via an auto-control hardware system on the SDV, to drive to a second pathway that does not conflict with the identified future route of the emergency vehicle.

Abstract: Systems and methods for dynamically managing figments are disclosed. A computer-implemented method includes: receiving, by a computing device, a question from a user; answering, by the computing device, the question using a first degree figment; classifying, by the computing device, the question based on topics; forwarding, by the computing device, the question to a set of second degree figments; receiving, by the computing device, answers to the question from the set of second degree figments; ranking, by the computing device, the answers received from the set of second degree figments; and providing, by the computing device, the ranked answers to the user.

Abstract: A computer-implemented method, system, and/or computer program product controls a driving mode of a self-driving vehicle (SDV). One or more processors compare a control processor competence level of an on-board SDV control processor in controlling the SDV to a human driver competence level of a human driver in controlling the SDV while the SDV encounters a current roadway condition which is a result of current weather conditions of the roadway on which the SDV is currently traveling. One or more processors then selectively assign control of the SDV to the SDV control processor or to the human driver while the SDV encounters the current roadway condition based on which of the control processor competence level and the human driver competence level is relatively higher to one another.

Abstract: A method, system, and/or computer program product controls a self-driving vehicle (SDV) based on a purchase transaction at a point of sale (POS) device. One or more processors detects a purchase transaction at a device for a product. In response to detecting the purchase transaction at the POS device, the processors(s) transmits instructions to the SDV to transport a passenger associated with the purchase transaction from a location of the POS device to a predetermined location.

Abstract: A method, system, and/or computer program product controls operations of an aerial drone within a predetermined airspace. One or more processors detects that the aerial drone has entered a predetermined airspace, and also determines a physical size of the aerial drone. In response to detecting that the aerial drone has entered the predetermined airspace, one or more processors directs the aerial drone to alter a velocity of the aerial drone based on its physical size.

Abstract: A computer-implemented method, system, and/or computer program product controls a driving mode of a self-driving vehicle (SDV). One or more processors compare a control processor competence level of an on-board SDV control processor in controlling the SDV to a human driver competence level of a human driver in controlling the SDV while the SDV encounters a current roadway condition which is a result of current weather conditions of the roadway on which the SDV is currently traveling. One or more processors then selectively assign control of the SDV to the SDV control processor or to the human driver while the SDV encounters the current roadway condition based on which of the control processor competence level and the human driver competence level is relatively higher to one another.

Abstract: A computer-implemented method, system, and/or computer program product controls a driving mode of a self-driving vehicle (SDV). One or more processors compare a control processor competence level of an on-board SDV control processor in controlling the SDV to a human driver competence level of a human driver in controlling the SDV while the SDV encounters a current roadway condition which is a result of current weather conditions of the roadway on which the SDV is currently traveling. One or more processors then selectively assign control of the SDV to the SDV control processor or to the human driver while the SDV encounters the current roadway condition based on which of the control processor competence level and the human driver competence level is relatively higher to one another.

Abstract: A computer-implemented method, system, and/or computer program product controls a driving mode of a self-driving vehicle (SDV). One or more processors compare a control processor competence level of an on-board SDV control processor in controlling the SDV to a human driver competence level of a human driver in controlling the SDV while the SDV encounters a current roadway condition which is a result of current weather conditions of the roadway on which the SDV is currently traveling. One or more processors then selectively assign control of the SDV to the SDV control processor or to the human driver while the SDV encounters the current roadway condition based on which of the control processor competence level and the human driver competence level is relatively higher to one another.

Abstract: An unmanned aerial vehicle for determining geolocation exclusion zones of animals. The unmanned aerial vehicle includes a processor-based monitoring device to track geolocation information associated with an animal from the unmanned aerial vehicle, an identification device mounted on the unmanned aerial vehicle to identify the animal and to track a position of the animal over time, and a mapping device coupled to the monitoring device to determine locations where the animal has traversed and to identify where an encounter with the animal is reduced.

Abstract: A method, system, and/or computer program product controls operations of an aerial drone within a predetermined airspace. One or more processors detects that the aerial drone has entered a predetermined airspace, and also determines a physical size of the aerial drone. In response to detecting that the aerial drone has entered the predetermined airspace, one or more processors directs the aerial drone to alter a velocity of the aerial drone based on its physical size.

Abstract: Detecting propensity profile for a person may comprise receiving artifacts associated with the person; detecting profile characteristics for the person based on the artifacts; receiving a plurality of predefined profiles comprising a plurality of characteristics and relationships between the characteristics over time, each of the plurality of predefined profiles specifying an indication of propensity; matching the profile characteristics for the person with one or more of the plurality of predefined profiles; and outputting one or more propensity indicators based on the matching, the propensity indicators comprising at least an expressed strength of a given propensity in the person at a given time.

Abstract: A computer-implemented method, system, and/or computer program product controls a driving mode of a self-driving vehicle (SDV). One or more processors compare a control processor competence level of an on-board SDV control processor in controlling the SDV to a human driver competence level of a human driver in controlling the SDV while the SDV encounters a current roadway condition which is a result of current weather conditions of the roadway on which the SDV is currently traveling. One or more processors then selectively assign control of the SDV to the SDV control processor or to the human driver while the SDV encounters the current roadway condition based on which of the control processor competence level and the human driver competence level is relatively higher to one another.

Abstract: A method, system, and/or computer program product controls a self-driving vehicle (SDV) based on a purchase transaction at a point of sale (POS) device. One or more processors detects a purchase transaction at a device for a product. In response to detecting the purchase transaction at the POS device, the processors(s) transmits instructions to the SDV to transport a passenger associated with the purchase transaction from a location of the POS device to a predetermined location.

Abstract: A method, system, and/or computer program product controls a self-driving vehicle based on a purchase transaction at a point of sale (POS) device. One or more processors detect a purchase transaction at the POS device. In response to detecting the purchase transaction at the POS device, the processor(s) transmit instructions to the SDV to pick up a passenger associated with the purchase transaction at a predetermined location.

Abstract: An unmanned aerial vehicle for determining geolocation exclusion zones of animals. The unmanned aerial vehicle includes a processor-based monitoring device to track geolocation information associated with an animal from the unmanned aerial vehicle, an identification device mounted on the unmanned aerial vehicle to identify the animal and to track a position of the animal over time, and a mapping device coupled to the monitoring device to determine locations where the animal has traversed and to identify where an encounter with the animal is reduced.

Abstract: A method, system, and/or computer program product controls operations of an aerial drone within a predetermined airspace. A drone controller device detects a presence of an aerial drone. The drone controller device and the aerial drone negotiate permission to fly within a predetermined airspace under a predefined aerial drone state. In response to successfully negotiating the permission, the drone controller device enables a drone on-board computer to operate the aerial drone within the predetermined airspace in accordance with the predefined aerial drone state.

Abstract: An unmanned aerial vehicle for determining geolocation exclusion zones of animals. The unmanned aerial vehicle includes a processor-based monitoring device to track geolocation information associated with an animal from the unmanned aerial vehicle, an identification device mounted on the unmanned aerial vehicle to identify the animal and to track a position of the animal over time, and a mapping device coupled to the monitoring device to determine locations where the animal has traversed and to identify where an encounter with the animal is reduced.