Abstract: Vehicle forgotten item detection using depth aided image background removal includes determining a first image of an interior of a vehicle for a start of a time-period and a second image for the interior of the vehicle for an end of the time-period. The first image and the second image include a depth metric. One or more elements present in both the first image and the second image are removed from the second image. One or more remaining elements are determined to exist within the second image after removal, which are categorized. An occupant of the vehicle is selectively notified based on the categorization.

Abstract: A video of a driver of a vehicle is obtained. Based on subset of frames of the video, a series of body poses are identified. The series of body poses are identified by detecting landmark points associated with respective body parts of the driver. The landmark points correspond to coordinates of locations of pixels that represent at least one or more joints of the respective body parts of the driver in the subset of frames. A driver behavior is identified based on the series of the body poses. An assistive vehicle control action for the vehicle is output based on the driver behavior.

Abstract: A system may maintain historical vehicle information, collected from multiple vehicles in a vehicle transportation network, in a historical database. The historical vehicle information for a vehicle may include historical data elements corresponding to data types. The system may receive a user request via a communications network. The system may determine, based on the user request, to respond with a historical data element from the historical database or a current data element from current vehicle information collected from a current vehicle that is active in the vehicle transportation network. The system may return a response to the user request via the communications network. The response may include the historical data element or the current data element.

Abstract: A method of delivering data to a vehicle including transmitting a request for the data from a vehicle to a database. The data stored in the database is tagged with location information. A location of the vehicle is determined. The data in the database tagged with location information within a predetermined distance of the determined location of the vehicle is determined. The data is delivered to the vehicle based on the determined location of the vehicle. The delivered data is displayed on a display disposed in the vehicle.

Abstract: Software aggregates and integrates credit exposure and credit data across accounting, trading and operational systems within an organization and generates views of available credit in light of the exposure and credit limits. A comprehensive model of exposure to all counterparties, across all of their divisions and subsidiaries, is assembled, enabling the creation of a hierarchical view of each counterparty that models its real-world parent-child relationships. Credit limits are set across the enterprise, supporting the organization's unique methodology and business process, and on a granular basis, incorporating factors such as external credit ratings, internal credit scores, commodity, geographic region, deal duration, and security instruments. Credit, transactions, and risk are then determined at any level in the hierarchy.

Abstract: Software aggregates and integrates credit exposure and credit data across accounting, trading and operational systems within an organization and generates views of available credit in light of the exposure and credit limits. A comprehensive model of exposure to all counterparties, across all of their divisions and subsidiaries, is assembled, enabling the creation of a hierarchical view of each counterparty that models its real-world parent-child relationships. Credit limits are set across the enterprise, supporting the organization's unique methodology and business process, and on a granular basis, incorporating factors such as external credit ratings, internal credit scores, commodity, geographic region, deal duration, and security instruments. Credit, transactions, and risk are then determined at any level in the hierarchy.

Abstract: Software aggregates and integrates credit exposure and credit data across accounting, trading and operational systems within an organization and generates views of available credit in light of the exposure and credit limits. A comprehensive model of exposure to all counterparties, across all of their divisions and subsidiaries, is assembled, enabling the creation of a hierarchical view of each counterparty that models its real-world parent-child relationships. Credit limits are set across the enterprise, supporting the organization's unique methodology and business process, and on a granular basis, incorporating factors such as external credit ratings, internal credit scores, commodity, geographic region, deal duration, and security instruments. Credit, transactions, and risk are then determined at any level in the hierarchy.

Abstract: Software aggregates and integrates credit exposure data across accounting, trading and operational systems within an energy trading organization. A comprehensive model of exposure to all counterparties, across all of their divisions and subsidiaries, is then assembled, enabling the creation of a hierarchical view of each counterparty that models its real-world parent-child relationships, and taking into account netting, setoff, and margin requirements, collateral requirements and contract terms, internal and external views of exposure and liquidity, and risk concentrations based on both system and user-defined risk categories. After aggregating the exposure information, credit, transactions, risk and other properties are determined at any level in the hierarchy and then the system presents a comprehensive, detailed, real-time, enterprise-wide view of current exposure, collateral requirements and outlays for both a company and its counterparties.

Abstract: Software aggregates and integrates credit exposure and credit data across accounting, trading and operational systems within an organization and generates views of available credit in light of the exposure and credit limits. A comprehensive model of exposure to all counterparties, across all of their divisions and subsidiaries, is assembled, enabling the creation of a hierarchical view of each counterparty that models its real-world parent-child relationships. Credit limits are set across the enterprise, supporting the organization's unique methodology and business process, and on a granular basis, incorporating factors such as external credit ratings, internal credit scores, commodity, geographic region, deal duration, and security instruments. Credit, transactions, and risk are then determined at any level in the hierarchy.