Abstract: A vehicle includes one or more sensors arranged on at least one of a dashboard, a roof, and a center console of the vehicle, or one or more image capturing devices for capturing one or more images from a left side and a right side of the vehicle, an electronic control unit (ECU) configured to communicate with the one or more sensors or the one or more image capturing devices, and at least one of a morphing surface, a windshield display, and one or more displays configured to be controlled by the ECU, where the one or more sensors are arranged under a black panel surface.

Abstract: An unmanned aerial vehicle access method being used for a base station includes: receiving a random access request sent by an unmanned aerial vehicle, the random access request including a random access preamble; when it is determined to respond to the random access request of the unmanned aerial vehicle, sending a random access initial response to the unmanned aerial vehicle; receiving a first message sent by the unmanned aerial vehicle, the first message including terminal indication information, the terminal indication information being used for indicating that a terminal, which requests to access, is the unmanned aerial vehicle; and when it is determined to allow the access of the unmanned aerial vehicle, sending, to the unmanned aerial vehicle, a second message for indicating that access is allowed.

Abstract: The present invention discloses an unmanned aerial vehicle control system and an implementation method therefor, a ground control device and a relay station, where the unmanned aerial vehicle control system includes an unmanned aerial vehicle and a ground control device, and further includes one or more relay stations in communication connection with the ground control device through a local area network or the Internet, the ground control device being configured to send a control instruction to control the unmanned aerial vehicle, and the relay station being configured to: when satisfying a preset condition, receive the control instruction sent by the ground control device, and send the control instruction to the unmanned aerial vehicle.

Abstract: Systems and methods for controlling an autonomous vehicle to reduce idle data usage and vehicle downtime are provided. In one example embodiment, a computing system can obtain data associated with autonomous vehicle(s) that are online with a service entity. The computing system can obtain data indicative of the geographic area with an imbalance in a number of vehicles associated with the geographic area. The computing system can determine a first autonomous vehicle for re-positioning with respect to the geographic area based at least in part on the data associated with the one or more autonomous vehicles and the data indicative of the geographic. The computing system can communicating data indicative of a first re-positioning assignment associated with the first autonomous vehicle. In some implementations, the computing system can generate vehicle service incentive to entice a vehicle provider to re-position its autonomous vehicles with respect to the geographic area.

Abstract: Systems and methods are disclosed for periodic real-time reporting of in-flight turbulence experienced by aircraft using the existing Automatic Dependent Surveillance-Broadcast (ADS-B) messaging system. The invention includes both broadcast to and reception from other aircraft and ground stations using the ADS-B system. Systems and methods for displaying the received turbulence reports on a cockpit display system are also disclosed. The disclosed systems and methods accomplish the objectives of the invention on a non-interference basis with existing ADS-B system functionality by utilizing currently reserved message types and/or unused data fields for the turbulence reporting. The invention is applicable to both 1090ES and 978 UAT ADS-B systems.

Abstract: Various examples are directed to systems and methods for navigating an autonomous vehicle. Trip plan data may describe a plurality of candidate vehicle start points, a plurality of candidate waypoints, and a plurality of candidate vehicle end points. A plurality of candidate routes may be determined between an algorithm start point and an algorithm end point. Each candidate route of the plurality of candidate routes may include at least one of the plurality of candidate waypoints and at least one of the plurality of candidate vehicle end points. A best rate may be determined using the plurality of candidate routes. The best route may include a first candidate vehicle start point, a first candidate waypoint, and a first candidate vehicle end point. The autonomous vehicle may be controlled along the best route from the first candidate vehicle start point towards the first candidate vehicle end point.

Abstract: A landing gear system includes a landing gear collar and a strut assembly supported by the landing gear collar. The strut assembly includes a piston that is adjustable between a fully extended position and a fully compress position. The landing gear system further includes a rotary encoder and a controller. The rotary encoder rotates in response adjusting the piston and to outputs a data value in response to its rotation. The controller is in signal communication with the rotary encoder and determines a stroke of the piston based on the data value output from the rotary encoder.

Abstract: A wind data estimating apparatus includes one or more processors configured to collect vehicle information including a first acceleration, an amount of driving operation performed by a driver of a vehicle, and position information, which are obtained by sensors installed in the vehicle; classify the collected vehicle information by an area of a plurality of areas according to the position information; and estimate a wind velocity and a wind direction for the area and for a time range when the vehicle information is obtained, on the basis of an acceleration obtained from subtracting a second acceleration caused by the amount of driving operation from the first acceleration included in the vehicle information classified by the area.

Abstract: Provided is an Artificial Intelligence (AI) system for simulating a human brain's functions, such as recognition, decision, etc., by using a machine learning algorithm such as deep learning, etc. and applications of the AI system. Provided is an electronic device including: a camera configured to capture an outside image of a vehicle, and a processor configured to execute one or more instructions stored in a memory, wherein the processor executes the one or more instructions to: determine, from the captured image, at least one object for estimating lane information; estimate, from the image, lane information of a road on which the vehicle is traveling, based on a distance between the determined at least one object and the vehicle and a vanishing point of the image; and output guide information for guiding driving of the vehicle based on the estimated lane information.

Abstract: Methods and systems of crowd-sourced magnetic fingerprinting for mobile device indoor navigation with neural network re-training are described. In an example, at multiple locations, magnetic input parameters in accordance with a magnetic infrastructure profile of an indoor area are determined. Further, from a mobile device positioned at a first location, measured magnetic parameters at respective ones of multiple locations are received. A route segment similarity factor for the mobile device based on the measured magnetic parameters of a corresponding route segment accessible from the magnetic fingerprint dataset is computed. If the similarity factor exceeds a threshold, the magnetic measured parameters are added to the magnetic fingerprint dataset. For at least a second location along the route, another set of magnetic measured parameters is received from the mobile device.

Abstract: A georeferenced trajectory system for vehicles receives trajectory data generated by a plurality of vehicle sensors and scaffolds of previously generated maps and aligns geometry data for a geographic region and trajectory data from the received data from different map builds. A scaffold of a geographic region to be mapped during an initial map build is generated, and the trajectory data from respective map builds is aligned with the scaffold of previously generated maps to generate a map of the geographic region. The resulting map expands the coverage of the existing map such that old and new map data is in a common consistent reference frame whereby the map may be built incrementally by merging or expanding local scaffolds and filling in the merged or expanded scaffold while ensuring global consistency.

Abstract: A driving control apparatus for a vehicle having a function for generating a target path to a predetermined range as a target and performing automated lane change to a neighboring lane when no other vehicle is recognized in the predetermined range of the neighboring lane by a surrounding recognition function and a merging support function for generating a target path using other vehicle information obtained by a communication function, performing acceleration/deceleration control and steering control, and automatically merging to a merged lane, wherein the driving control apparatus has a function for altering override threshold values serving as a determination criterion of operation intervention for stopping the acceleration/deceleration control and the steering control to a value greater than during normal operation of the communication function when failure occurs in the communication function during the automated merging.

Abstract: Systems and methods described herein relate to generating a task offloading strategy for a vehicular edge-computing environment. One embodiment simulates a vehicular edge-computing environment in which one or more vehicles perform computational tasks whose data is partitioned into segments and performs, for each of a plurality of segments, a Deep Reinforcement Learning (DRL) training procedure that includes receiving state-space information regarding the one or more vehicles and one or more intermediate network nodes; inputting the state-space information to a policy network; generating, from the policy network, an action concerning a current segment; and assigning a reward to the policy network for the action in accordance with a predetermined reward function. This embodiment produces, via the DRL training procedure, a trained policy network embodying an offloading strategy for segmentation offloading of computational tasks from vehicles to one or more of an edge server and a cloud server.

Abstract: A laser range finder projects light while changing a direction in a horizontal direction at a predetermined angle with respect to a vertical direction to also receive reflected light of the light, and detects a direction and a distance in which the light is reflected from an obstacle or the like, according to a difference time between a time of light projection and a time of light reception. A normal direction of a flat plane forming a road surface is detected on the basis of a polarized image. The laser range finder projects light such that the light has a predetermined angle with respect to the vertical direction so as to be orthogonal to the normal direction of the flat plane forming the road surface. The laser range finder can be applied to in-vehicle systems.

Abstract: In one embodiment, a set of LIDAR images representing LIDAR point cloud data captured by a LIDAR device of an ADV at different points in time is received. For each of the LIDAR images, a perception method is utilized to determine a location of an obstacle captured in the LIDAR image in a local coordinate system. The LIDAR image is transformed using a coordinate converter (e.g., a LIDAR to GPS coordinate conversion logic or function) from the local coordinate system to a global coordinate system. The coordinate converter is optimized based on the transformed LIDAR images by adjusting one or more parameters of the coordinate converter and the above operations are iteratively performed to obtain a set of optimal parameters. The optimized coordinate converter can then be utilized to process subsequent LIDAR images during autonomous driving at real-time.

Abstract: An autonomous driving control device includes a map information input unit, a new map information storage unit, and a calculation unit. The map information input unit includes several input means and several processing means. The calculation unit performs the autonomous driving using the new map information obtained by accessing the new map information storage unit.

Abstract: A method for the remote control of at least one function of a motor vehicle by means of a mobile controller involves a remote control command transmitted to the motor vehicle and executed by the motor vehicle if an approval has been issued. The method provides a remote control system that has a particularly high level of reliability, which is achieved by detecting an image of the motor vehicle by an optical detection device in the mobile controller is transmitted to the motor vehicle, and the approval is then issued by a checking device in the motor vehicle if the motor vehicle has been identified in the transmitted image by the checking device.

Abstract: Described herein are embodiments of a neural network-based task planner (TaskNet) for autonomous vehicle. Given a high-level task, the TaskNet planner decomposes it into a sequence of sub-tasks, each of which is further decomposed into task primitives with specifications. TaskNet comprises a first model for predicating the global sequence of working area to cover large terrain, and a second model for determining local operation order and specifications for each operation. The neural models may include convolutional layers for extracting features from grid map-based environment representation, and fully connected layers to combine extracted features with past sequences and predict the next sub-task or task primitive. Embodiments of the TaskNet are trained using an excavation trace generator and evaluate its performance using a 3D physically-based terrain and excavator simulator.

Abstract: An other lane monitoring device of the present disclosure includes an other vehicle acquisition section, a path acquisition section, a lane estimation section, and a position estimation section. The other vehicle acquisition section is configured to acquire other vehicle position information that indicates the current position of an other vehicle near the own vehicle. The path acquisition section is configured to acquire an own vehicle travel path that indicates a path along which the own vehicle has traveled. The lane estimation section is configured to use the own vehicle travel path as a basis for estimating an other lane area that is a lane where the other lane is present. The position estimation section is configured to estimate a future position of the other vehicle, assuming that the other vehicle moves along the own vehicle travel path or in the other lane area.

Abstract: A travelling vehicle system includes travelling vehicles and a controller. The controller includes a storage that stores a last permitted travelling vehicle, to which a passage permission is transmitted lastly and the passage permission for which is not canceled, for each direction in a branching section or a merging section included in a blocking area, stores a last canceled travelling vehicle, the passage permission for which is canceled lastly, for each direction in the blocking area, and stores the travelling vehicle, to which the passage permission in a same direction in the blocking area is transmitted lastly, as a forward travelling vehicle of a travelling vehicle waiting for the permission to pass through the blocking area at the time of transmission of the passage permission to the passage-permission waiting travelling vehicle, and a determiner that determines whether to give passage permission to the travelling vehicle waiting for the permission.