Abstract: Provided is a method for an autonomous vehicle to handle goods in collaboration with an unmanned aerial vehicle. The method comprises recognizing an unmanned aerial vehicle loading goods by the autonomous vehicle, capturing an image of the recognized unmanned aerial vehicle by the autonomous vehicle, analyzing the captured image to recognize a marker by the autonomous vehicle, adjusting a relative position of the autonomous vehicle and the unmanned aerial vehicle by moving the autonomous vehicle based on a recognition result of the marker, and taking over the goods from the unmanned aerial vehicle by the autonomous vehicle after position adjustment is completed.

Abstract: Aspects of the present disclosure relate to automated vehicle condition grading. In examples, a set of images for a vehicle are processed using a machine learning engine to generate an optical vehicle condition grade for each image of the set. The resulting set of optical vehicle condition grades may be aggregated to generate an aggregate optical vehicle condition grade for the vehicle. In some examples, additional information associated with the vehicle is processed, which may be used to generate an adjustment grade. Accordingly, the adjustment grade and the aggregate optical vehicle condition grade are used to generate a final vehicle condition grade for the vehicle. The final vehicle condition grade is associated with the vehicle in a vehicle condition grade data store for subsequent use by the vehicle grading service and/or by an associated client device, among other examples.

Abstract: A system for digital twinning a refuse vehicle includes a refuse vehicle and a controller. The refuse vehicle includes a sensor, a device, and an electronic control system. The controller is configured to receive multiple datasets from at least one of the sensor, the device, and the electronic control system. The controller is also configured to generate a virtual refuse vehicle based on the plurality of datasets. The virtual refuse vehicle includes a visual representation of the refuse vehicle and the multiple datasets. Each of the multiple datasets are mapped to a corresponding one of the sensors or one of the electronic control systems. The controller is configured to operate a display of a user device to provide the visual representation of the refuse vehicle and one or more of the multiple datasets to a user.

Abstract: A drone includes a propulsion system, a processing unit, and a power system having a battery and a power connection port. The power system is configured to receive packetized electrical power over a tether including an air-to-ground power feed attachable to the power connection port. The power system is also configured to supply electrical power to the processing unit, the battery, and the propulsion system. The processing unit is configured to acknowledge receipt of the packetized electrical power. A power delivery system includes a packetized electrical power transmitter and a processing unit. The processing unit is configured to operate the packetized electrical power transmitter to transmit packets of electrical power over a power feed within a tether attached to a drone. The processing unit is also configured to monitor a communication channel within the tether for acknowledgements indicating the drone has received the transmitted packets of electrical power.

Abstract: A method for detecting non-approved parts in a vehicle includes generating a plurality of component identifiers (IDs) for a plurality of components, wherein each component ID identifies a unique component of the plurality of components. The component IDs are stored to a decentralized database. A vehicle identifier (ID) that uniquely identifies a vehicle is generated and stored to the decentralized database. Component IDs associated with components of the vehicle that are stored in the decentralized data base are associated with the vehicle ID. The method includes subsequently, determining that a component of the vehicle has been replaced with a replacement component and searching the decentralized database for a component ID associated with the replacement component.

Abstract: A remote fuel monitoring system includes a vehicle-mounted device and a server for monitoring a remaining fuel amount of the vehicle or the machine based on vehicle information received from the vehicle-mounted device, calculating a refueling plan including refueling time and a refueling amount for the vehicle or the machine, and outputting a refueling command based on the refueling plan for the vehicle or the machine to a refueling device. The server includes an input device, a storage, an analyzing device analyzing information regarding the remaining fuel amount of the vehicle or the machine based on the vehicle information and information stored in the storage, a refueling plan calculation device calculating the refueling plan for the vehicle or the machine by using the analyzed information regarding the remaining fuel amount, and an output device outputting the refueling plan for the vehicle or the machine to the refueling device.

Abstract: An on-vehicle communication device capable of relaying data between a plurality of function units mounted on a vehicle includes: a log notification acquisition unit configured to receive, from an external device outside the vehicle, a log notification indicating a log target function unit which is the function unit that is a target of log collection, a start condition for log collection, and a trigger function unit which is the function unit capable of determination for the start condition; a notification unit configured to notify the trigger function unit of the start condition indicated by the log notification; and a command unit configured to give a command for log collection to the log target function unit, in response to a notification that the start condition is satisfied from the trigger function unit.

Abstract: In a method for data collection by means of a vehicle fleet and a control center, the control center produces data collection tasks for data collection and transmits at least one of the data collection tasks (6) to at least one vehicle in the vehicle fleet, the vehicles in the vehicle fleet being identifiable by means of vehicle attributes. The control center links each data collection task with an objective, wherein the vehicle attributes of the vehicles logged in to the control center are compared with the objectives of the data collection tasks, after which, if the objective of one of the data collection tasks matches the vehicle attributes of one of the vehicles, the corresponding data collection task is transmitted to the corresponding vehicle, after which a data collection device in the vehicle acquires task-specific data and transmits it to the control center.

Abstract: A method for transmitting sensing information of an autonomous vehicle for remote driving in automated vehicle & highway systems includes: receiving information on a driving route of the autonomous vehicle from a server; acquiring sensing information of a surrounding environment; setting priority values of sensors for transmission of the sensing information on the basis of a driving direction determined by the information on the driving route; setting a value for a degree of danger of a sensed object on the basis of the sensing information; and setting a transmission period of the sensing information on the basis of the value for the degree of danger. Accordingly, sensing information required for a driving situation can be efficiently transmitted.

Abstract: A work vehicle includes an engine; at least one continuously variable power source (CVP) configured to operate according to a commanded torque; an output shaft; a transmission positioned operatively between the output shaft and the engine and the CVP such that the output shaft selectively receives power from one or both of the engine and the at least one CVP; and a prognostics system configured to monitor a respective component associated with at least one of the output shaft or the transmission. The prognostics system includes a prognostics controller having a processor and memory architecture, configured to: receive input data, including the commanded torque; generate a usage value for the respective component for a time period as a function of the commanded torque; aggregate the usage value with previous usage values to generate a prognostics value over a life of the respective component; and store the prognostics value.

Abstract: Disclosed is an obstacle avoiding method and apparatus for an unmanned aerial vehicle based on a multi-signal acquisition and route planning model.

Abstract: A centralized event data management system for collecting event data from a plurality of vehicles and managing thereof is disclosed. The centralized system liberates the vehicle onboard event data recorder (EDR) from the constraints of data storage space. Free of the constraints of data storage space, the EDR allows for more flexible data collection than is mandated by law or regulation.

Abstract: A utility vehicle that includes a vehicle control system having one or more real time clocks (RTC). The RTC can be embedded in the vehicle control system, or in components or subsystems of the vehicle control system, and can be either dedicated electronics or software based. Information provided by the RTC can be used to synchronize components and subsystems of the vehicle control system. Further, such inclusion of the RTC can enable the vehicle control system to initiate a number of time based functions, including, for example, time based functions relating to battery charging, wake-up and shut down of components, status reporting, periodic vehicle level events and maintenance, and management of time based operation or use of the utility vehicle or components thereof, including vehicle cameras.

Abstract: The present technology provides systems, methods, and devices that can dynamically augment aspects of a map as an autonomous vehicle navigates a route, and therefore avoids the need for dispatching a special purpose mapping vehicle to keep navigating a route. As the autonomous vehicle navigates a route, the autonomous vehicle can determine that current data captured by at least one sensor of the autonomous vehicle describing a location is inconsistent with the primary map of the location. The autonomous vehicle can determine that a portion of the current data describes a second feature that is distinct from a first feature described by a primary map. The second feature can be added to an augmented map that is based on the primary map, and the position of the autonomous vehicle can be located with respect to the first feature rather than the second feature on the augmented map.

Abstract: A guidance system provides a driver of a vehicle with guidance on a driving operation, and includes a server device that accumulates past traveling records on a traveling road on which the vehicle travels, an acquisition device that acquires a current state of the vehicle, and a notification device that notifies the driver, while driving, of the operation content determined based on the current state of the vehicle and the past traveling records.

Abstract: Provided is a method for an autonomous vehicle to handle goods in collaboration with an unmanned aerial vehicle. The method comprises recognizing an unmanned aerial vehicle loading goods by the autonomous vehicle, capturing an image of the recognized unmanned aerial vehicle by the autonomous vehicle, analyzing the captured image to recognize a marker by the autonomous vehicle, adjusting a relative position of the autonomous vehicle and the unmanned aerial vehicle by moving the autonomous vehicle based on a recognition result of the marker, and taking over the goods from the unmanned aerial vehicle by the autonomous vehicle after position adjustment is completed.

Abstract: A terminal device according to one aspect of an embodiment includes a storage, an acquisition unit, a selection unit, and a transmitting unit. The storage stores therein imaging data captured from a moving body. The acquisition unit acquires a transmitting request for imaging data transmitted from an external device on the basis of positional information on the moving body. The selection unit selects, from among the imaging data stored in the storage, target data that is imaging data corresponding to the transmitting request acquired by the acquisition unit. The transmitting unit transmits the target data selected by the selection unit.

Abstract: An acquisition unit acquires vehicle information related to a vehicle. An in-vehicle storage unit is provided in the vehicle and stores therein the vehicle information acquired by the acquisition unit. A transmission unit transmits the vehicle information acquired by the acquisition unit to an off-vehicle server located at an outside of the vehicle. A classification unit classifies the vehicle information acquired by the acquisition unit into information for transmission to be transmitted to the off-vehicle server or information for storage to be stored in the in-vehicle storage unit.

Abstract: A virtual assistance system for a vehicle is provided. The virtual assistance system includes one or more processors, one or more memory modules communicatively coupled to the one or more processors, an input device communicatively coupled to the one or more processors, an output device communicatively coupled to the one or more processors, and machine readable instructions stored in the one or more memory modules. The virtual assistance system detects a duration of a vehicle stop. When the duration of the stop is greater than a predetermined threshold, the system requests a reason for the stop, receives the reason for the stop, determines a location, date, and time of the stop, and stores a record comprising data associated with the stop.

Abstract: Transportation systems have artificial intelligence including neural networks for recognition and classification of objects and behavior including natural language processing and computer vision systems. The transportation systems involve sets of complex chemical processes, mechanical systems, and interactions with behaviors of operators. System-level interactions and behaviors are classified, predicted and optimized using neural networks and other artificial intelligence systems through selective deployment, as well as hybrids and combinations of the artificial intelligence systems, neural networks, expert systems, cognitive systems, genetic algorithms and deep learning.