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: System and method for responding to queries about objects in a cabin of a vehicle. The system detects a trigger that causes an in-cabin camera to capture video of the cabin, and the system generates a history of captions for at least selected frames of the video by a large multi-modal model (LMM). The system converts a spoken query received by a microphone to a text-based prompt and generates, by the LMM, a response to the prompt based on the history of captions. The response is converted to speech that is output to a speaker.

Abstract: System and method for determining a drive mode of a vehicle, such as an eco mode or a sport mode, and retrieving a human-machine interface (HMI) policy based on the drive mode. The HMI policy describes output parameters of various HMI systems of the vehicle, such as an audio system or a haptic system. The system and method further includes causing an HMI system of the vehicle to implement the HMI policy, where such implementation includes generating outputs, such as sounds, lighting patterns, and haptic vibrations, according to the output parameters described by the HMI policy. The system and method may optionally include retrieving a driver profile for a driver of the vehicle, such that the retrieved HMI policy is also based on the driver profile.

Abstract: Intelligent eco mode optimization in a battery electric vehicle (BEV) includes collecting data from one or more systems of a vehicle in which the vehicle includes a battery. A predicted route is generated based on the collected data. The collected data includes a navigation map for a portion of a vehicle transportation network. A state of the vehicle is determined based on the collected data and the predicted route. A drive mode is determined, using a decision-making model, for the vehicle based on the state of the vehicle and the predicted route. The drive mode is either a first drive mode having a first acceleration curve or a second drive mode have a second acceleration curve and the second drive mode reduces a rate of discharge of the battery as compared to the first drive mode. The vehicle is set to use the drive mode.

Abstract: Monitoring driving characteristics by a first sensor and environmental characteristics by a second sensor, and determining, based on data of the first sensor and data of the second sensor, a deviant driving characteristic and an associated environmental characteristic. Further determining a deviant driving behavior based on the deviant driving characteristic and the associated environmental characteristic, for example, based on repeated instances of the deviant driving characteristic and the associated environmental characteristic. Indicating, to a driver of the vehicle, a corrective action for correcting the deviant driving characteristic, where the indication may be via a user interface of the vehicle or of a mobile device. In some implementations, the driver can review determined deviant driving behaviors via an application and provide to the application a driving goal for focused correction of certain deviant driving behaviors.

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: 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.