Abstract: Systems for vehicles are provided. A system includes a brain-computer interface, an artificial intelligence system, and a vehicle control system. The brain-computer interface is configured to interact with the vehicle, to interact with a rider of the vehicle, and to generate output data. The artificial intelligence system is configured to receive the output data and to analyze an emotional state of the rider based on the output data. The vehicle control system is configured to optimize at least one operating parameter of the vehicle based on the emotional state of the rider as analyzed by the artificial intelligence system.

Abstract: A device for treating a diseased native valve in a patient is provided, the device including a frame structure and a plurality of leaflets. The device can further include a spiral anchor configured to extend around an outer circumference of the frame structure. The anchor can be configured to deliver a contrast agent into or near a target tissue. Other embodiments and methods of use are also provided.

Abstract: A driving support method and a driving support device set a timing to start an own vehicle based on an elapsed time from a stop of the own vehicle to a start of a preceding vehicle existing in front of the own vehicle, wherein the timing in case that the elapsed time is long is later than the timing in case that the elapsed time is short, and start the own vehicle at the set timing by following the preceding vehicle.

Abstract: The system 100 can include: a fleet management system, a motion planner, a vehicle controller, and a track data system. The system can optionally include a user interface. However, the system 100 can additionally or alternatively include any other suitable set of components. The system functions to facilitate waypoint-based command, navigation, and/or control of rail vehicles within a rail network. Additionally or alternatively, the system can function to facilitate execution of the method S100 and/or sub-elements thereof.

Abstract: Computer-implemented method for determining coordinates of navigation key points indicative of road sign locations and/or turn points, the method comprising: collecting, as a first training data subset, one or more first images of a first camera comprised in a mobile device; obtaining, as a second training data subset, image-related coordinates of navigation key points related to the images of the first training data subset; supplying the first training data subset and the second training data subset to an artificial neural network as a training dataset; training the artificial neural network on the training dataset to predict image-related coordinates of navigation key points indicative of road sign locations and/or turn points; capturing a second image of a second camera as an input dataset, processing the input dataset by the artificial neural network to predict image-related coordinates of navigation key points indicative of road sign locations and/or turn points.

Abstract: Proposed is an apparatus, software program, and a corresponding method of controlling driving of an electrified vehicle. The method includes increasing a level of regenerative braking on the basis of a remaining distance from the vehicle to a stop-event point when a brake control mode is entered, determining whether or not braking needs to be performed, on the basis of a stop approval signal when the remaining distance is equal to or less than a preset first distance in the brake control mode, and decreasing the level of regenerative braking when a distance of traveling of the vehicle from the stop-event point is equal or greater than a preset second distance in the brake control mode.

Abstract: An apparatus includes a first layer, wherein the first layer comprises a scaffold structure forming an inner lumen along a length of the scaffold structure, and wherein the first layer comprises a bioresorbable material; a second layer on the first layer, wherein the second layer comprises a bioresorbable material configured to expand, wherein the second layer is positioned on the first layer such that ends of the first layer extend beyond ends of the second layer; and an expandable balloon located within a distal end of the inner lumen of the first layer.

Abstract: In one embodiment, a microcontroller unit (MCU) receives an expected state of an autonomous driving vehicle (ADV) from a controller of the ADV, where the controller controls motions of the ADV using a control algorithm. The MCU receives sensor data from one or more sensors of the ADV. The MCU determine an actual state of the ADV based on the sensor data. The MCU determines a performance metric of the control algorithm based on the expected state and the actual state. In response to determining the performance metric has satisfied a predetermined condition, the MCU determines a plurality of weight values for the control algorithm. The MCU sends the plurality of weight values to the control system to tune one or more weight parameters of the control algorithm using the plurality of weight values, where the controller controls the ADV using the tuned control algorithm.

Abstract: A vehicle may include: a camera configured to obtain a surrounding image of the vehicle; and a controller configured to derive spatial recognition data by learning the surrounding image of the vehicle as an input value, derive object recognition data including wheel area data of surrounding vehicles by learning the surrounding image of the vehicle as an input value, determine a ground clearance between a bottom surface of a vehicle body of the surrounding vehicle and a ground by use of the spatial recognition data and the wheel area data, and control the vehicle to park the vehicle by reflecting the ground clearance.

Abstract: In some examples, one or more processors of an unmanned aerial vehicle (UAV), control a propulsion mechanism of the UAV to cause the UAV to navigate to a plurality of positions in relation to a scan target. Using one or more image sensors of the UAV, a first image of the scan target is captured from a first position of the plurality of positions, and a second image of the scan target is captured from a second position of the plurality of positions. A disparity is determined between the first image captured at the first position and the second image captured at the second position. A three-dimensional model corresponding to the scan target is determined based in part on the disparity determined between the first image and the second image.

Abstract: A computer implemented method for determining a location of an object comprises the following steps carried out by computer hardware components: determining a pre-stored map of a vicinity of the object; acquiring sensor data related to the vicinity of the object; determining an actual map based on the acquired sensor data; carrying out image registration based on the pre-stored map and the actual map; carrying out image registration based on the image retrieval; and determining a location of the object based on the image registration.

Abstract: A method for determining driving resistance in an insufficient warm-up state includes measuring driving data including at least first rolling resistance when a vehicle travels to include at least one constant-speed driving section in an insufficient warm-up state and determining a second rolling resistance based on the first rolling resistance measured in the constant-speed driving section, wherein the second rolling resistance is a rolling resistance predicted in the sufficient warm-up state.

Abstract: Implementations claimed and described herein provide systems and methods for generating instructions for a mobile electric vehicle (EV) charging station to meet an EV at a particular time and place. In one implementation, EV trip data including a remaining range of the EV and an intended route of the EV is collected to determine a range of locations that the EV can stop at along its route without running out of power. Instructions to one of the locations are generated for a mobile EV charging station that is a best fit for arriving at the particular location and for the EV to reach the same location.

Abstract: An obstacle information management device includes a vehicle behavior acquisition unit that is configured to acquire vehicle behavior data indicative of behaviors of a plurality of vehicles in association with position information; an appearance determination unit that is configured to specify a point, as an obstacle registration point, where an obstacle has appeared based on the vehicle behavior data acquired by the vehicle behavior acquisition unit; and a disappearance determination unit that is configured to determine, based on the vehicle behavior data acquired by the vehicle behavior acquisition unit, whether the obstacle remains or has disappeared at the obstacle registration point where the obstacle was determined to exist by the appearance determination unit.

Abstract: A monitoring system is described for a plurality of homogeneous devices, of at least one railway vehicle, wherein a functional state of each device is represented by a respective value of at least one operating quantity common to the devices. The monitoring system comprises: a control means which acquires, in a succession of acquisition instants, values of the operating quantity; each value is representative of the functional state of a respective device in an acquisition instant; diagnostic means which receive the values acquired in an acquisition instant and detect an operating anomaly or a maintenance request from at least one device as a function of a comparison, for each acquisition instant, of the values received with a range of reference values comprising a reference value determined as a function of at least two values acquired in an acquisition instant.

Abstract: A terrestrial positioning system includes a mobile terrestrial transmitter including a local clock source and an inertial measurement unit, and a synchronization master including a memory configured to store initially known positioning information of the terrestrial transmitter. The terrestrial transmitter determines altered positioning information of the terrestrial transmitter using the first IMU and broadcasting a positioning signal including a timestamp. The synchronization master calculates an expected time of flight of the received positioning signal based on the determined altered positioning information and the stored initially known positioning information of the terrestrial transmitter, to determine a first deviation of the actual time of flight determined from the timestamp and the actual reception time of the received positioning signal with respect to the calculated expected time of flight and to instruct the terrestrial transmitter to delay or prepone the broadcast of the positioning signal.

Abstract: A method of automatically switching between manners of answering calls by vehicle occupants in a vehicle connects a vehicle-mounted system with terminal device of occupant. Information as to persons in the vehicle is detected. Information as to different occupants allows method to recommend one of a first manner, a second manner, and a third manner of responding to an incoming call, the driver being able to select his preferred manner by pushbutton mounted on steering wheel. Driving safety is improved, and privacy of a personal conversation can be maintained. A vehicle-mounted system applying the method is also disclosed.

Abstract: Provided is a driver assistance apparatus, including: a camera mounted on a vehicle and configured to have a front field of view of the vehicle and acquire image data; and a controller including a processor configured to process the image data, wherein the controller is configured to: determine a pair of boundary lines based on the processing of the image data, control a steering device of the vehicle so that the vehicle travels within a zone defined by the pair of boundary lines, based on the vehicle being deviated from the zone, and change the pair of boundary lines to extend the zone, based on a driver not holding a steering wheel.

Abstract: A remote computer system may receive data representative of a state of an autonomous vehicle traversing an environment. A representation of the environment and the state may be input to a first machine learned model, which may output a vector representation of the environment at a first time. The vector representation may be input to a second machine learned model, which may output a second vector representation of the environment at a second time. A third machine learned model may receive the second vector representation and may output a mean velocity and a mean steering angle of the vehicle at the first time and associated uncertainty variables. Based on the mean velocity, the mean steering angle, and the uncertainty variables, a predicted vehicle state at the first time and an associated uncertainty may be determined and the vehicle may be controlled based on the predicted vehicle state and the uncertainty.

Abstract: The present application relates to an electric vehicle energy consumption prediction method and apparatus, a computer device, a computer-readable storage medium, and a computer program product. The method includes: acquiring discharge duration data of an electric vehicle; acquiring driving position characteristic data of the electric vehicle; and inputting the discharge duration data and the driving position characteristic data into an energy consumption prediction model to obtain energy consumption prediction data of the electric vehicle. The energy consumption prediction model is obtained based on a machine learning algorithm.