Abstract: A vehicle control apparatus that controls movement of a vehicle in response to an instruction from a remote control terminal located outside the vehicle, the apparatus comprising: an obtaining unit configured to obtain an image captured by an image capturing unit configured to capture an image of a periphery of the vehicle; an operator detecting unit configured to detect an operator of the remote control terminal on the basis of the image obtained by the obtaining unit; a terminal detecting unit configured to detect a position of the remote control terminal relative to the vehicle; and a control unit configured to control, on the basis of a detection result from the operator detecting unit and a detection result from the terminal detecting unit, whether to permit or prohibit a remote control operation of the vehicle performed through the remote control terminal.

Abstract: Techniques are disclosed herein for reconfiguring reprogrammable hardware in an autonomous vehicle system. According to an embodiment, an autonomous driving system includes sensors and a configurable circuit having physical logic units. The autonomous driving system aggregates data observed from each of the sensors. The autonomous driving system detects a trigger indicative of a defect in the configurable circuit. The defect is identified as a function of the aggregated data. The autonomous driving system performs, in response to the trigger, a reconfiguration action on the configurable circuit to repair the defect.

Abstract: Example aspects of the present disclosure describe a scene generator for simulating scenes in an environment. For example, snapshots of simulated traffic scenes can be generated by sampling a joint probability distribution trained on real-world traffic scenes. In some implementations, samples of the joint probability distribution can be obtained by sampling a plurality of factorized probability distributions for a plurality of objects for sequential insertion into the scene.

Abstract: The present disclosure provides an autonomous mobile apparatus and a control method thereof. The method includes: starting a SLAM mode; obtaining first image data captured by a first camera; extracting a first tag image of positioning tag(s) from the first image data; calculating a three-dimensional camera coordinate of feature points of the positioning tag(s) in a first camera coordinate system of the first camera based on the first tag image; calculating a three-dimensional world coordinate of the feature points of the positioning tag(s) in a world coordinate system based on a first camera pose of the first camera when obtaining the first image data in the world coordinate system and the three-dimensional camera coordinate; and generating a map file based on the three-dimensional world coordinate of the feature points of the positioning tag(s).

Abstract: An automated package retrieval system is provided. The automated package retrieval system includes a hub apparatus that includes multiple docking stations for multiple delivery devices, a power supply unit coupled to the hub apparatus, and a controller. The controller is configured to instruct at least one of the delivery devices to travel to a location to deliver an item ordered by a user. Once it is determined that the user has not retrieved the item from the delivery device, the delivery device is instructed to return to the hub apparatus. In response to detecting the user in proximity to the hub apparatus after the delivery device has returned to the hub apparatus, the user is provided with access to a storage compartment of the delivery device.

Abstract: Systems and methods are provided herein for managing a vehicle fleet based on compliance regulations, and, in some embodiments, involve blockchain or other distributed ledger technologies. Systems and methods for managing a vehicle fleet based on compliance regulations may include receiving a service request, wherein the service request is a request for a passenger ride or a request for a cargo transport, receiving first information for a first node, wherein the first information comprises a vehicle size, a vehicle weight, and/or a number of seats, receiving second information comprising a first threshold number of vehicle occupants and/or a first threshold vehicle occupant weight limit, determining that the first threshold number of vehicle occupants or the first threshold vehicle occupant weight limit are exceeded, and sending an indication that the first node has not accepted the service request.

Abstract: Systems and methods for perceiving a field around a mobile device include a sensor system has a distance sensor arranged on a mobile device. The sensor system captures distance measurements of a field of view. The distance measurements are captured at a unique set of angular scan positions per revolution of the distance sensor over a sequence of scan rotations. A perception system generates a three-dimensional point cloud representation of the field of view based on the distance measurements for each scan rotation in the sequence of scan rotations. The perception system generates a composite three-dimensional depth map of the field of view by compiling each of the three-dimensional point cloud representations for the sequence of scan rotations. Each of the three-dimensional point cloud representations has a resolution that is lower than a resolution of the composite three-dimensional depth map of the field of view.

Abstract: A method includes providing a virtual representation of an environment of a robot, the virtual representation including an object representation of an object in the environment. The method further includes receiving manipulation input from a user to teleoperate the robot for manipulation of the object. The method also includes alerting the user to an alignment dimension based upon the manipulation input, receiving confirmation input from the user to engage the alignment dimension, and constraining at least one dimension of movement of the object according to the alignment dimension.

Abstract: A mobile character control system includes a platform, a character assembly, a control system, and a transportation assembly. The platform is configured to support an operator. The character assembly is engaged with the platform and includes actuatable features configured to simulate movement of a creature. The control system is configured to control activation of the actuatable features of the character assembly in response to signals received from control features controlled by the operator. The transportation assembly is configured to direct movement of the platform and to support the character assembly.

Abstract: The height of a header of a self-propelled harvesting machine is controlled by a closed loop header position control system. A sensitivity control system receives parameters related to header position error (e.g., an accuracy parameter) and machine stability (e.g., a stability parameter) and automatically identifies a sensitivity metric indicative of a sensitivity with which the header position control system controls the header height, based upon the received parameters. The sensitivity metric is provided to the header position control system. The header position control system performs closed loop header position control with a sensitivity level based upon the sensitivity metric provided by the sensitivity control system.

Abstract: An approach is provided for using a passenger-based driving profile. The approach involves detecting an identity of a user of a vehicle. The approach also involves retrieving a passenger profile of the user based on the identity. The passenger profile is generated based on sensor data indicating a reaction of the user to at least one vehicle driving behavior previously experienced by the user as a vehicle passenger. The approach further involves configuring the vehicle based on the passenger profile.

Abstract: Method and system for determining a route for a vehicle. The method associates a navigation module to a vehicle fitted with tires and a tire monitoring unit to at least one tire fitted to the vehicle. The monitoring unit has a sensing element to generate a sensing signal descriptive of deformations undergone by the tire. The deformations form a contact area between the tire and a rolling surface on which the tire rotates. During rotation of the tire, the sensing signal, including the sensing signal generated in correspondence of passages of the sensing element through the contact area, is undersampled for a number of passages sufficient to obtain an estimated length of the contact area. The weight of the vehicle is then estimated based on such estimated length, and at least one route among two or more routes is selected, based on such estimated weight of the vehicle.

Abstract: A model aggregation device includes a communication device able to communicate with a plurality of vehicles in which neural network models are learned, a storage device storing a part of the neural network models sent from the plurality of vehicles, and a control device. The neural network model outputs at least one output parameter from a plurality of input parameters. The control device is configured to, if receiving a new neural network model from one vehicle among the plurality of vehicles through the communication device, compare ranges of the plurality of input parameters which were used for learning the new neural network model and ranges of the plurality of input parameters which were used for learning a current neural network model stored in the storage device to thereby determine whether to replace the current neural network model with the new neural network model.

Abstract: A method, system, and computer program product provide navigation guidance around various object types in a vicinity of a guidance assistance device for a user by analyzing received data regarding an environment around the guidance assistance device to identify one or more entities Ei and by applying an artificial intelligence machine learning analysis to group the one or more entities Ei into corresponding categories Cj and to determine a minimum spacing distance Dmin for each of the one or more entities Ei, wherein the minimum spacing distance Dmin is a minimum distance between the guidance assistance device and the entity Ei based on the categorization Ci specified by the user, and then providing feedback to the user when any of the one or more entities Ei is within than minimum spacing distance Dmin corresponding to said entity.

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: A method of mapping and localization is disclosed that includes, reconstructing a point cloud and a camera pose based on VSLAM, synchronizing the camera pose and a GPS timestamp at a first set of GPS coordinate points and transforming the first set of GPS coordinate points corresponding to the GPS timestamp into a first set of ECEF coordinate points. The method also includes determining a translation and a rotation between the camera pose and the first set of ECEF coordinate points, transforming the point cloud and the camera pose into a second set of ECEF coordinates based on the translation and the rotation and transforming the point cloud and the camera pose into a second set of GPS coordinate points. The method further includes constructing and storing a key-frame image, a key-frame timestamp and a key-frame GPS based on the second set of GPS coordinate points.

Abstract: A vehicle control device includes at least one electronic control unit configured to recognize at least one object, calculate a time to collision, operate first driving assistance, when the time to collision is equal to or less than a first threshold value, operate second driving assistance for avoiding the collision between the at least one object and the host vehicle or reducing damage of the collision, when the time to collision is equal to or less than a second threshold value smaller than the first threshold value, and set, while the first driving assistance is operated, the second threshold value to a second setting value smaller than a first setting value set when the first driving assistance is not operated, when a second target object causing the second driving assistance to operate is the same object as a first target object causing the first driving assistance to operate.

Abstract: A method for positioning a movably arranged panel comprises the steps of determining a panel braking distance based on a physical property of the moveable panel and a physical property of at least one of a drive assembly and a drive motor. The panel braking distance corresponds to a distance covered by the moveable panel in a period of time starting from a decision to switch off the drive motor until stand-still of the moveable panel. The drive motor is switched off when an expected stop position is within a predetermined range around a desired stop position.

Abstract: The subject disclosure relates to techniques for positioning an autonomous vehicle for sensor calibration. A process of the disclosed technology can include steps for positioning an autonomous vehicle along a first axis on a platform in a predetermined environment using guide rails, positioning the autonomous vehicle along a second axis on the platform using one or more elevated platform features, inserting one or more lifting alignment pins of a lifting mechanism into one or more sockets located on an underbody of the autonomous vehicle, and positioning the autonomous vehicle along a third axis using the lifting mechanism. Systems and machine-readable media are also provided.

Abstract: A training system for training a trained model for use by a navigating robot to perform visual navigation includes memory including N base virtual training environments, each of the N base virtual training environments including a field of view at a location within an indoor space, where N is an integer greater than 1. A randomization module is configured to generate N varied virtual training environments based on the N base virtual training environments, respectively, by varying at least one characteristic of the respective N base virtual training environments. A training module is configured to train the trained model for use by the navigating robot to perform visual navigation based on a training set including: the N base virtual training environments; and the N varied virtual training environments.