Abstract: A proximity sensing system on a marine vessel includes a main IMU positioned on the vessel at a main installation attitude and a main location, a first proximity sensor configured to measure the proximity of objects from a first sensor location on the vessel, and a first sensor IMU positioned on the vessel at the first sensor location and at a first installation attitude. A control system is configured to receive main IMU data from the main IMU and first IMU data from the first sensor IMU, wherein the main location of the main IMU on the marine vessel is known and at least one of the first sensor location and the first installation attitude of the first sensor IMU on the marine vessel are initially unknown, and calibrate the proximity measurements from the first proximity sensor based on the main IMU data and the first IMU data.

Abstract: A method includes receiving sensed vehicle-state data, actuation-command data, and surface-coefficient data from a plurality of remote vehicles, inputting the sensed vehicle-state data, the actuation-command data, and the surface-coefficient data into a self-supervised recurrent neural network (RNN) to predict vehicle states of a host vehicle in a plurality of driving scenarios, and commanding the host vehicle to move autonomously according to a trajectory determined using the vehicle states predicted using the self-supervised RNN.

Abstract: A method and an apparatus for generating sensor data for controlling an autonomous vehicle in an environment is provided, such as driverless transport vehicles in a factory for example. Sensor positions of static sensors and the sensors of autonomous vehicles are defined in a global coordinate system on the basis of an environment model, such as a BIM model for example. Sensor data is centrally generated in this global coordinate system for all sensors as a function of these sensor positions. The sensor data is then transformed into a local coordinate system of an autonomous vehicle and transferred for controlling the autonomous vehicle.

Abstract: A system for heating a battery in a vehicle supplying driving power by a motor as a vehicle-driving source is provided. The system includes a big-data server configured to receive driving information of the vehicle and to determine an estimated driving start time of the vehicle and required output required at an initial driving stage of the vehicle based on the received driving information, and a controller, installed in the vehicle and configured to provide the driving information to the big-data server, to receive the estimated driving start time and the required output provided from the big-data server, and to derive a heating time of the battery, required to ensure the required output, based on the a temperature and an SoC of the battery installed in the vehicle.

Abstract: One or more information maps are obtained by an agricultural work machine. The one or more information maps map one or more agricultural characteristic values at different geographic locations of a field. An in-situ sensor on the agricultural work machine senses an agricultural characteristic as the agricultural work machine moves through the field. A predictive map generator generates a predictive map that predicts a predictive agricultural characteristic at different locations in the field based on a relationship between the values in the one or more information maps and the agricultural characteristic sensed by the in-situ sensor. The predictive map can be output and used in automated machine control.

Abstract: An object-collection system is disclosed. The system including a vehicle connected to a bucket, a camera connected to the vehicle, and an object picking assembly configured to pick up objects off of ground. The system further includes a processor that obtains object information for identified objects, guides the object-collection system over a target geographical area toward the identified objects based on the object information, captures images of the ground relative to the object picker as the object-collection system is guided towards the identified objects, identifies a target object in the images, tracks movement of the target object across the images as the object-collection system is guided towards the identified objects, and employs the tracked movement of the target object to instruct the object picker to pick up the target object.

Abstract: A parking assistance method includes: detecting, from an image of surroundings of a vehicle, a first target object position; detecting a first image capturing situation when the image is captured; with respect to a second target object position of a target object detected from a past image of surroundings of a target parking position in a past, retrieving one or more combinations of a relative positional relationship between the second target object position and the target parking position and a second image capturing situation when the past image is captured; selecting, the relative positional relationship combined with the second image capturing situation having a difference from the first image capturing situation less than or equal to a predetermined difference; and calculating, based on the selected relative positional relationship and the first target object position, a relative position between a current position of the vehicle and the target parking position.

Abstract: A steering control device includes a base axial force calculator, a limiting axial force calculator, and a final axial force calculator. The limiting axial force calculator includes a steering angle holder, a reference angle calculator, a final difference calculator, and an axial force calculator. The steering angle holder is configured to hold a steering angle at the time of limiting a turning operation of the turning wheels when the turning operation is limited. The reference angle calculator is configured to calculate a reference angle. The final difference calculator is configured to calculate a final difference which is a final difference between the reference angle and the current steering angle. The axial force calculator is configured to calculate a limiting axial force based on a value of the final difference.

Abstract: A method for operating a vehicle seat device for motor vehicles, which has a vehicle seat with a seat part and a backrest, associated with the seat part, and at least one actuator which is associated with the seat part and/or the backrest and by means of which the vehicle seat can be brought automatically into a sitting position and into a reclining position, wherein the movement takes place as a function of a desired position that can be specified by a user and of at least one detectable vehicle seat state. It is provided that a current temperature is monitored as the vehicle seat state and that if the monitored temperature falls below a limit temperature, the vehicle seat is brought into a sitting position or left in a current sitting position regardless of the desired position.

Abstract: An electric or hybrid working machine includes a ground engaging structure, an electrical source of power, an electric motor arrangement, one or more hydraulically actuated devices, and a hydraulic pump configured to supply hydraulic fluid to the one or more hydraulically actuated devices. The working machine also includes a transmission operable to transmit drive from the electric motor arrangement to the ground engaging structure and the hydraulic pump. The transmission has a first input member connected to a first electric motor, a drive train comprising a first input gear connected to the first input member, and a coupling device connected between the first input member and the first input gear to selectively adjust transmission of drive to the hydraulic pump and the ground engaging structure.

Abstract: A computer is programmed to, in response to a navigation feature being active for a route, prompt an operator of the vehicle to activate a driver-assist feature of the vehicle. For example, the computer can output a message via a user interface indicating that the driver-assist feature is available to be used. The computer is further programmed to receive an input indicating a destination, determine the route for the vehicle from a current location of the vehicle to the destination, and activate the navigation feature to output guidance instructing the operator to follow the route.

Abstract: A pedal system for a vehicle that is configured to be driven in an at least part-automated manner. The pedal system includes a driving pedal and a brake pedal. The pedal system is allocated a representation function between a pedal actuation and control parameters for the longitudinal control of the vehicle. The pedal system is configured so as, when the vehicle is being operated, to vary the representation function between the pedal actuation and the control parameters for the longitudinal control of the vehicle in dependence upon a variable that represents the degree of automation of the driving mode of the vehicle and at least a first condition for restricting the representation function of the pedal actuation and/or at least a second condition for eliminating the restriction of the representation unction of the pedal actuation.

Abstract: Apparatuses, methods, systems, and program products are disclosed for dynamic selection of an aeronautical data provider. An apparatus includes a processor and a memory that stores code executable by the processor to receive a first stream of real-time aeronautical data from a first aeronautical data provider, receive at least one second stream of real-time aeronautical data from at least one second aeronautical data provider simultaneously with the first stream, provide data from the first stream and the at least one second stream to a decision model for predicting which data has a higher accuracy, and switch using data from the first stream to data from one of the at least one second streams in response to the one of the at least one second streams having a higher predicted accuracy than the first stream.

Abstract: A method for controlling rights of an autonomous vehicle includes acquiring, via a network, identification information for identifying a user or a terminal of the user, and dispatching request information that indicates a dispatch request for the autonomous vehicle issued by the user. The method further includes selecting the autonomous vehicle to be dispatched to the user from among multiple autonomous vehicles based on the dispatch request information, and assigning a control right for the selected autonomous vehicle, to the user or the terminal, based on the identification information.

Abstract: Methods and systems for jointly estimating a pose and a shape of an object perceived by an autonomous vehicle are described. The system includes data and program code collectively defining a neural network which has been trained to jointly estimate a pose and a shape of a plurality of objects from incomplete point cloud data. The neural network includes a trained shared encoder neural network, a trained pose decoder neural network, and a trained shape decoder neural network. The method includes receiving an incomplete point cloud representation of an object, inputting the point cloud data into the trained shared encoder, outputting a code representative of the point cloud data. The method also includes generating an estimated pose and shape of the object based on the code. The pose includes at least a heading or a translation and the shape includes a denser point cloud representation of the object.

Abstract: A mobile body control device includes: an observation trajectory acquisition unit that acquires, a trajectory along which the subject walks; a basic trajectory acquisition unit that acquires, a basic trajectory that matches the observation trajectory; a subject predicted trajectory acquisition unit that acquires a trajectory along which the subject walks after the current time point based on the specific basic trajectory; a prediction reliability calculation unit that calculates a prediction reliability based on uncertainty added along the subject predicted trajectory; a target trajectory acquisition unit that acquires, a trajectory formed by a target position of a mobile body set ahead of the subject in a traveling direction of the subject; and a control-command-sequence-generating unit that generates a control command sequence using an evaluation function that uses as input the subject predicted trajectory, prediction reliability, target trajectory, and a control trajectory.

Abstract: Disclosed herein are methods for determining an estimated weight of a vehicle. The methods comprise operating at least one processor to: receive vehicle data associated with the vehicle, the vehicle data comprising a plurality of vehicle parameters collected during operation of the vehicle; identify one or more vehicle maneuvers based on the vehicle data, each vehicle maneuver being associated with a portion of the vehicle data; and use at least one machine learning model to determine the estimated weight of the vehicle based on the portion of the vehicle data associated with each of the one or more vehicle maneuvers, the at least one machine learning model trained using training data associated with a plurality of previous vehicle maneuvers. Also disclosed are systems for implementing methods of the present disclosure.

Abstract: A system and corresponding method for autonomous driving of a vehicle are provided. The system comprises at least one neural network (NN) that generates at least one output for controlling the autonomous driving. The system further comprises a main data path that routes bulk sensor data to the at least one NN and a low-latency data path with reduced latency relative to the main data path. The low-latency data path routes limited sensor data to the at least one NN which, in turn, employs the limited sensor data to improve performance of the at least one NN's processing of the bulk sensor data for generating the at least one output. Improving performance of the at least one NN's processing of the bulk sensor data enables the system to, for example, identify a safety hazard sooner, enabling the autonomous driving to divert the vehicle and avoid contact with the safety hazard.

Abstract: According to one embodiment, an unmanned transport vehicle includes: a moving body capable of autonomously traveling on a floor surface and having a docking portion connecting to the object; a detection sensor capable of detecting an approach side support leg, wherein, of the plurality of support legs in the object, a pair of support legs located on a docking side of the moving body are designated as approach side support legs, and a pair of support legs located on a side opposite to a docking side in a traveling direction of the moving body is used as depth side support legs; and a control device connected to the moving body and a first detection sensor. The moving body has a height capable of entering a bottom space surrounded by a lower surface of the load plate of the object, the plurality of support legs, and the floor surface.

Abstract: A method of implementing distributed AI or ML learning for autonomous vehicles is disclosed. An AI or ML model specific to a location or a type of the location is generated at a vehicle. In response to a detection that the vehicle is within a proximity to a road side unit (RSU) associated with the location or the type of the location or the vehicle is within a proximity to an additional vehicle that is present or anticipated to be present at the location or the type of the location, causing an AI or ML model transmission to the additional vehicle or the RSU. Based on the causing the AI or ML model reception, causing deployment of an additional AI or ML model in the vehicle to optimize the vehicle for the location or the type of the location.