Abstract: Disclosed is a method for monitoring vehicle overload based on gravity anomaly, which includes the following steps of: setting a plurality of measuring positions on a single lane, arranging a gravimeter to acquire gravity anomaly values caused by a vehicle when the middle position along the length direction of the vehicle reaching each measuring position, using a monitoring camera to judge the category of the vehicle, acquire three geometric dimensions of the vehicle, and determine the position of the vehicle. The vehicle is simplified as a cuboid, and the mass density distribution is simplified as a piecewise constant function along the length direction of the vehicle. Calculating values of the piecewise constant function according to the gravity anomaly values, calculating a total weight of the vehicle according to the mass density distribution, comparing the total weight with the weight limit of the vehicle to judge whether the vehicle is overloaded.

Abstract: A vehicle control system includes: an on-board control module, configured to control driving of the vehicle according to a control instruction sent by a vehicle dispatching command platform; a vehicle state module, configured to collect state information of the vehicle; an environment sensing module, configured to collect first road environment information of the vehicle; a UAV scanning module, configured to collect second road environment information of the vehicle; a data center module, configured to generate fusion information according to the state information, the first road environment information, and the second road environment information; a map module, configured to generate a driving route map of the vehicle according to the fusion information; and a vehicle dispatching command platform, configured to generate the control instruction according to the fusion information and the driving route map.

Abstract: A remote control an automotive vehicle, the remote control including a 3-D shaped object, stretch sensors connecting the 3-D shaped object to a structure of the vehicle and a control unit configured to collect information from the stretch sensors, the remote control being configured to control a display of the automotive vehicle, the remote control being configured to be integrated in a stretchable fabric part of an interior of the vehicle.

Abstract: Techniques are provided for operating a vehicle in a first vehicle mode; operating at least one component of the vehicle in a first component mode; detecting a first change in operation of the vehicle from the first vehicle mode to a second vehicle mode different from the first vehicle mode; based on the detected the first change in the operation of the vehicle, predicting a second change in operation of the vehicle; in response to predicting the second change in operation of the vehicle, operating the at least one component in a second component mode different from the first component mode prior to the predicted second change in operation of the vehicle.

Abstract: Systems and methods for predicting a trajectory of a moving object are disclosed herein. One embodiment downloads, to a robot, a probabilistic hybrid discrete-continuous automaton (PHA) model learned as a deep neural network; uses the deep neural network to infer a sequence of high-level discrete modes and a set of associated low-level samples, wherein the high-level discrete modes correspond to candidate maneuvers for the moving object and the low-level samples are candidate trajectories; uses the sequence of high-level discrete modes and the set of associated low-level samples, via a learned proposal distribution in the deep neural network, to adaptively sample the sequence of high-level discrete modes to produce a reduced set of low-level samples; applies a sample selection technique to the reduced set of low-level samples to select a predicted trajectory for the moving object; and controls operation of the robot based, at least in part, on the predicted trajectory.

Abstract: Disclosed herein are methods and apparatus for controlling autonomous vehicles utilizing maps that include visibility information. A map is stored at a computing device associated with a vehicle. The vehicle is configured to operate in an autonomous mode that supports a plurality of driving behaviors. The map includes information about a plurality of roads, a plurality of features, and visibility information for at least a first feature in the plurality of features. The computing device queries the map for visibility information for the first feature at a first position. The computing device, in response to querying the map, receives the visibility information for the first feature at the first position. The computing device selects a driving behavior for the vehicle based on the visibility information. The computing device controls the vehicle in accordance with the selected driving behavior.

Abstract: A system for detection and reporting of data signal interference includes a plurality of vehicles. Each vehicle includes a global navigation satellite system (GNSS) receiver receiving a GNSS signal, a cellular modem configured for sending and receiving data over a wireless communication network, and a computerized detection and reporting controller. The controller includes programming to monitor the GNSS signal, analyze the GNSS signal to identify a presence of radio frequency (RF) interference, and generate a report of an RF interference event to a remote server device based upon the presence of the interference. The system further includes the server device including programming to monitor the report of the interference event from each of the vehicles, identify a threshold RF interference event based upon the report of the RF interference event from each of the plurality of vehicles, and command a remedial action based upon the threshold RF interference event.

Abstract: Embodiments relate to methods, systems, and devices for performing an inspection on a telecommunication structure and/or one or more objects that are attached on the telecommunication structure. The system includes an unmanned aerial vehicle that includes one or more of the following: an image capturing subsystem, an object identifying subsystem, a distance measuring subsystem, a navigation subsystem, an electromagnetic interference subsystem, and an onboard processor that is in communication with the image capturing subsystem, the object identifying subsystem, the distance measuring subsystem, the navigation subsystem, the electromagnetic interference subsystem. The system also includes a control processor that is in communication with the onboard processor. Further, the control processor may also be the onboard processor on the unmanned aerial vehicle.

Abstract: A method for controlling flight of an unmanned aerial vehicle, includes receiving a data file indicating flight restriction level and range of an airport area in an airport generated according to a risk level of the airport, parsing the data file to obtain the flight restriction level and the range of the airport area, and controlling the unmanned aerial vehicle to execute a flight restriction strategy according to the flight restriction level and the range of the airport area.

Abstract: A computer-implemented method for controlling a UAV includes identifying a set of target markers based on a plurality of images captured by an imaging device carried by the UAV, and controlling the UAV to fly based on the set of target markers. The set of target markers include at least two or more types of target markers and are in close proximity to be detected within a same field of view of the imaging device. Controlling the UAV to fly includes controlling the UAV to approach the set of target markers based at least in part on a spatial relationship between the UAV and a first type of target marker; and determining whether to control the UAV to land at or near the set of target markers based on information conveyed by a second type of target marker.

Abstract: A method for remediating developmentally delayed plants within a field of crops using at least one work vehicle during a field operation. The method comprises identifying delayed plants within the field with a sensor on the work vehicle and generating, with a processor, location data associated with the location of the delayed plant with the field. Upon arriving at the location of the delayed plant with the work vehicle, the delayed plant is remediated.

Abstract: An information processing apparatus includes a control section. The control section predicts an amount of electricity demanded for charging of vehicles that a plurality of users use, respectively, and determines a time slot in which the amount of electricity demanded exceeds a threshold value, and when the control section determines that one user of the plurality of users will refrain from use of the relevant vehicle on the following day, makes charging of the one user's vehicle wait in the time slot and provides an incentive to the one user.

Abstract: A LIDAR-to-vehicle alignment system includes a memory and alignment and autonomous driving modules. The memory stores points of data provided based on an output of one or more LIDAR sensors and localization data. The alignment module performs an alignment process including: based on the localization data; determining whether a host vehicle is turning; in response to the host vehicle turning; selecting a portion of the points of data; aggregating the selected portion to provide aggregated data; selecting targets based on the aggregated data; and based on the selected targets, iteratively reducing a loss value of a loss function to provide a resultant LIDAR-to-vehicle transformation matrix. The autonomous driving module: based on the resultant LIDAR-to-vehicle transformation matrix, converts at least the selected portion to at least one of vehicle coordinates or world coordinates to provide resultant data; and performs one or more autonomous driving operations based on the resultant data.

Abstract: A power and data center (PDC) can serve as a combined data concentrator and power distributor that delivers scalable and affordable network/power redundancy into an automotive electrical/electronic architecture (E/EA) that supports partially or fully autonomous vehicles. In some embodiments, a vehicle that includes the smart E/EA is divided into zones, where each zone includes one or more PDCs and one or more sensors, actuators, controllers, loudspeakers or other devices that are coupled to and powered by their zone PDC(s). Each PDC collects and processes (or passes through) raw or pre-processed sensor data from the one or more sensors in its zone. The sensors provide their data to the PDC by way of cost efficient, short-range data links. In some embodiments, one or more actuators in each zone are coupled to their respective zone PDC, and receive their control data from the PDC over a high-speed data bus or data link.

Abstract: Various technologies described herein pertain to causing presentation on a user interface of an immediate portion of a navigation route of an autonomous vehicle. A computing system of the autonomous vehicle determines whether an object detected by sensor(s) of the autonomous vehicle proximate to the immediate portion of the navigation route are of a type and relative position defined as one of consequential and inconsequential for a human passenger. In response to determining that an object has both a type and relative position defined as consequential, the computing system causes presentation on the user interface a representation of the object relative to the immediate portion of the navigation route to provide a confidence engendering indication that the autonomous vehicle has detected the object. Otherwise if inconsequential, presentation on the user interface of any representation of the object is not caused by the computing system to avoid creating a confusing presentation.

Abstract: A navigation system includes a server apparatus connected to vehicles via a network and a navigation apparatus connected to the server apparatus via the network. The server apparatus receives, from a first vehicle, traffic-related information related to snow on a road on which the first vehicle has traveled or slipperiness of the road, and location information of the road; generates difficulty information for the road related to a travel difficulty caused by the snow or the slipperiness; and transmits the difficulty information and the location information to the navigation apparatus movable together with a second vehicle identical to or different from the first vehicle. The navigation apparatus receives the difficulty information and the location information; sets a traffic regulation for passage of the second vehicle through the road; and sets, for the second vehicle, a travel route based on the traffic regulation.

Abstract: A method of conducting a diagnostic test to determine leakage within a brake system includes first providing a brake system having a plunger assembly including a housing defining a bore therein. The plunger assembly includes a piston slidably disposed therein such that movement of the piston pressurizes a pressure chamber when the piston is moved in a first direction. The pressure chamber of the plunger assembly is in fluid communication with an output, and wherein the plunger assembly further includes an electrically operated linear actuator for moving the piston within the bore. The linear actuator of the plunger assembly is actuated to provide pressure at the output of the plunger assembly at a first predetermined level. The pressure at the output of the plunger assembly is held for a predetermined length of time. The method further includes determining whether conditional criteria has been met indicating a leakage within the brake system.

Abstract: Methods, apparatus, systems, and articles of manufacture to select track paths for one or more vehicles in a field are disclosed. An example apparatus includes input interface circuitry to obtain a request for a first track path for a first vehicle, path instruction generation circuitry to, in response to the request, obtain information defining a plurality of track paths in a field, and select, based on the request, the first track path from the plurality of track paths, and update circuitry to update the information to indicate that the first track path is claimed by the first vehicle.

Abstract: A computer implemented method for controlling a vehicle includes obtaining a value of the mass of the vehicle, receiving a plurality of time sequential measured first values of one or more further state parameters, calculating a first plurality of time sequential values of the vehicle mass, including a first calculated mass value, using the plurality of measured first values of the one or more further state parameters, the non-linear model, and an extended Kalman filter with a first filter tuning, with the obtained mass value as a start value, receiving a plurality of time sequential measured second values of the one or more of the further state parameters, and calculating a second plurality of time sequential values of the vehicle mass, including a second calculated mass value, using the plurality of measured second values of the one or more further state parameters, the non-linear model, and an extended Kalman filter with a second filter tuning, with the first calculated mass value as a start value, wherein

Abstract: The Automated Wayside Asset Monitoring system utilizes a camera-based optical imaging device and an image database to provide intelligence, surveillance and reconnaissance of environmental geographical information pertaining to railway transportation. Various components of the Automated Wayside Asset Monitoring system can provide features and functions that can facilitate the operation and improve the safety of transportation via a railway vehicle.