Abstract: Edge devices utilizing personalized machine learning and methods of operating the same are disclosed. An example edge device includes a model accessor to access a first machine learning model from a cloud service provider. A local data interface is to collect local user data. A model trainer is to train the first machine learning model to create a second machine learning model using the local user data. A local permissions data store is to store permissions indicating constraints on the local user data with respect to sharing outside of the edge device. A permissions enforcer is to apply permissions to the local user data to create a sub-set of the local user data to be shared outside of the edge device. A transmitter is to provide the sub-set of the local user data to a public data repository.

Abstract: Apparatuses and methods for evaluating the risk factors of a proposed vehicle maneuver using remote data are disclosed. In embodiments, a computer-assisted/autonomous driving vehicle communicates with one or more remote data sources to obtain remote sensor data, and process such remote sensor data to determine the risk of a proposed vehicle maneuver. A remote data source may be authenticated and validated, such as by correlation with other remote data sources and/or local sensor data. Correlation may include performing object recognition upon the remote data sources and local sensor data. Risk evaluation is performed on the validated data, and the results of the risk evaluation presented to a vehicle operator or to an autonomous vehicle navigation system.

Abstract: Technologies for managing a world model of a monitored area includes a roadway server configured to receive LIDAR sensing data from a LIDAR sensing system positioned to monitor the monitored area and generate a world map of the monitored area based on the LIDAR sensing data. The world model includes data that identifies objects located in the monitored area. The roadway server may distribute the world model to automated vehicles traveling through the monitored area via a stream or in response to directed requests. The roadway server may also receive sensor data from the automated vehicles, which may be used to generate the world model. The roadway server may distribute the world model to other interested devices located in or near the monitored area.

Abstract: Apparatuses, methods and storage medium associated with computer-assisted or autonomous vehicle incident management, are disclosed herein. In some embodiments, a vehicle incident management system includes a main system controller to determine whether a vehicle hosting the apparatus is involved in a vehicle incident; if so, whether another vehicle is involved; and if so, whether the other vehicle is equipped to exchange incident information; and an inter-vehicle communication subsystem to exchange incident information with the other vehicle, on determination that the vehicle is involved in a vehicle incident involving the other vehicle, and the other vehicle is equipped to exchange incident information. Other embodiments are also described and claimed.

Abstract: Technologies for managing a world model of a monitored area includes a roadway server configured to receive LIDAR sensing data from a LIDAR sensing system positioned to monitor the monitored area and generate a world map of the monitored area based on the LIDAR sensing data. The world model includes data that identifies objects located in the monitored area. The roadway server may distribute the world model to automated vehicles traveling through the monitored area via a stream or in response to directed requests. The roadway server may also receive sensor data from the automated vehicles, which may be used to generate the world model. The roadway server may distribute the world model to other interested devices located in or near the monitored area.

Abstract: Edge devices utilizing personalized machine learning and methods of operating the same are disclosed. An example edge device includes a model accessor to access a first machine learning model from a cloud service provider. A local data interface is to collect local user data. A model trainer is to train the first machine learning model to create a second machine learning model using the local user data. A local permissions data store is to store permissions indicating constraints on the local user data with respect to sharing outside of the edge device. A permissions enforcer is to apply permissions to the local user data to create a sub-set of the local user data to be shared outside of the edge device. A transmitter is to provide the sub-set of the local user data to a public data repository.

Abstract: Apparatuses and methods for evaluating the risk factors of a proposed vehicle maneuver using remote data are disclosed. In embodiments, a computer-assisted/autonomous driving vehicle communicates with one or more remote data sources to obtain remote sensor data, and process such remote sensor data to determine the risk of a proposed vehicle maneuver. A remote data source may be authenticated and validated, such as by correlation with other remote data sources and/or local sensor data. Correlation may include performing object recognition upon the remote data sources and local sensor data. Risk evaluation is performed on the validated data, and the results of the risk evaluation presented to a vehicle operator or to an autonomous vehicle navigation system.

Abstract: Apparatuses, methods and storage medium associated with computer-assisted or autonomous vehicle incident management, are disclosed herein. In some embodiments, a vehicle incident management system includes a main system controller to determine whether a vehicle hosting the apparatus is involved in a vehicle incident; if so, whether another vehicle is involved; and if so, whether the other vehicle is equipped to exchange incident information; and an inter-vehicle communication subsystem to exchange incident information with the other vehicle, on determination that the vehicle is involved in a vehicle incident involving the other vehicle, and the other vehicle is equipped to exchange incident information. Other embodiments are also described and claimed.

Abstract: Example blockchain-based digital data exchanges including example data publisher endpoint devices and example data subscriber endpoint systems are disclosed herein. Example data publisher endpoint devices disclosed herein include a datamart publisher client to transmit a message to a data exchange to publish availability of data to be accessed from data storage associated with the data publisher endpoint device, and in response to a request from a data subscriber endpoint system, initiate a transaction to provide the data subscriber endpoint system with access to the data. Disclosed example data publisher endpoint devices also include a blockchain client to publish a record of the transaction to a blockchain network when the transaction is validated by the datamart publisher client, the record to be included in a blockchain implemented by the blockchain network.

Abstract: Various embodiments are generally directed to the provision and use of multiple person biometric authentication systems. An apparatus including a processor element and logic executable by the processor component is disclosed. The logic is configured to cause the apparatus to receive information including an indication of a plurality of biometric measurements and generate a combined biometric indicator based in part on the plurality of biometric measurements. The combined biometric indicator can be generated using fuzzy hashing techniques.

Abstract: Various embodiments are generally directed to the provision and use of multiple person biometric authentication systems. An apparatus including a processor element and logic executable by the processor component is disclosed. The logic is configured to cause the apparatus to receive information including an indication of a plurality of biometric measurements and generate a combined biometric indicator based in part on the plurality of biometric measurements. The combined biometric indicator can be generated using fuzzy hashing techniques.