Abstract: A cruise control apparatus controls travel of an own vehicle based on a predicted course which is a future travel course of the own vehicle. The cruise control apparatus includes a first predicted course calculating unit and a second predicted course calculating unit, as a plurality of course prediction means for calculating a predicted course, and is provided with a course change determination unit for determining whether a change in the course is to be performed and a prediction switching unit which performs switching to enable one of a first predicted course calculated by the first predicted course calculating unit and a second predicted course calculated by the second predicted course calculation unit, the switching being based on a result of determination made by the course change determination unit as to whether a change in the course is to be performed.

Abstract: A method for a data processing installation for obtaining an operating state of a first autonomous vehicle. The method includes determining a current state of the first autonomous vehicle from a received measurement value of a sensor of a second vehicle. When the current state of the first autonomous vehicle deviates from a setpoint state, the method includes sending a first message to the first autonomous vehicle, wherein the first message contains a command to travel autonomously to a service location. Alternatively, the method includes sending a second message to a person responsible for the first autonomous vehicle, wherein the second message includes information about the deviation of the current state of the first autonomous vehicle from the setpoint state. Alternatively, the method includes sending a third message to service personnel, wherein the third message contains an instruction for the service personnel to set the vehicle to the setpoint state.

Abstract: A mining machine management system includes a detection unit mounted on a mining machine that travels in a mine in which a plurality of landmarks is installed, and which detects a position of the landmark with respect to the mining machine in a non-contact manner, and a traveling control unit which corrects a current position of the mining machine based on a position of the landmark, the position having been obtained in advance, and the position of the landmark obtained by the detection unit and causes the mining machine to travel by dead reckoning navigation, and which does not use at least the position of the landmark detected by the detection unit in causing the mining machine to travel by the dead reckoning navigation, when a vehicle traveling in the mine exists around the position of the landmark detected by the detection unit.

Abstract: Systems and methods are described for determining an optimal path and/or route to a destination for a vehicle. Embodiments of the systems and methods disclosed herein facilitate intelligent route planning to a desired destination by a vehicle. In certain embodiments, a path and/or route to a desired destination is determined that accounts for vehicle charging and/or refueling requirements. Disclosed systems and methods may further generate and distribute reservation information ensuring availability of vehicle charging and/or refueling stations along a selected route. Further embodiments disclosed herein may implement information targeting services in connection with intelligent route planning.

Abstract: Systems and methods for an imaging system having a memory with a historical localization dictionary database having geo-located driving image sequences, such that each reference image is applied to a threshold to produce a binary representation. A sensor to acquire a sequence of input images of a dynamic scene. An encoder to determine, for each input image in the sequence, a histogram of each input image indicating a number of vertical edges at each bin of the input image and to threshold the histogram to produce a binary representation of the input image. A visual odometer to compare the binary representations of each input image and each reference image, by matching an input image against a reference image. Wherein the visual odometer determines a location of the input image based on a match between the input image and the reference image.

Abstract: According to some embodiments, a system determines a number of boundary areas having predetermined dimensions centered around each of a number of control points of a first reference line. The system selects a number of two-dimensional polynomials each representing a segment of an optimal reference line between adjacent control points. The system defines a set of constraints to the two-dimensional polynomials to at least ensure the two-dimensional polynomials passes through each of the boundary areas. The system performs a quadratic programming (QP) optimization on a target function such that a total cost of the target function reaches minimum while the set of constraints are satisfied. The system generates a second reference line representing the optimal reference line based on the QP optimization to control the ADV autonomously according to the second reference line.

Abstract: The example embodiments are directed to a device and method for determining a root cause of equipment failure. In one example, the method includes storing a plurality of root causes of previous equipment failures, receiving textual data associated with a current equipment failure, determining a root cause for the current equipment failure by determining a similarity of keywords of each root cause with respect to the received textual data of the current equipment failure and selecting at least one root cause based on the determined similarities of the plurality of root causes, and displaying the at least one determined root cause for the current equipment failure via a display device. The example embodiments provide a system and method that automatically determine a root cause of equipment failure rather than rely on a subject matter expert.

Abstract: A system and method for indicating the drive state of an autonomous vehicle. The system includes a human machine interface and an electronic controller electrically coupled to the human machine interface and which is configured to display a drive state indicator. The drive state indicator includes a mode indicator based on a current operating mode for the autonomous vehicle, a duration indicator based on a duration for the current operating mode and a descriptor based on the duration. The electronic controller is further configured to update the duration indicator and the descriptor based on a remainder of the duration. In an alternative embodiment, the electronic controller is further configured to determine whether a second operating mode is available and display a second mode indicator based on the second operating mode when a second operating mode is available.

Abstract: A dynamic gap at a vehicle interface can be actively managed using an interface seal. The interface can be defined between a first vehicle component and a second vehicle component. A gap can be defined between the first vehicle component and the second vehicle component. The gap can have an associated width. The width of the gap can vary during vehicle operation. An interface seal can be provided at least partially within the first vehicle component. The interface seal can be movable between a stowed position and a deployed position. In the stowed position, the interface seal can be located substantially entirely within the first vehicle component. In the deployed position, the interface seal can extend at least partially out of the first vehicle component in a direction toward the second vehicle component. As a result, the width of the gap can be reduced.

Abstract: A method of controlling driving of a vehicle using an in-wheel system includes determining whether the vehicle enters a steering avoidance section, based on driving information of the vehicle, verifying a detailed or specific section in the steering avoidance section in which the vehicle is located when the vehicle enters the steering avoidance section, and controlling torque of a motor mounted in each wheel to satisfy a yaw moment required in the verified detailed section.

Abstract: Elevation data obtained from a terrain database is a measurement in an inertial navigation system for land vehicles.

Abstract: Aerial vehicles may be configured to control their attitudes by changing one or more physical attributes. For example, an aerial vehicle may be outfitted with propulsion motors having repositionable mounts by which the motors may be rotated about one or more axes, in order to redirect forces generated by the motors during operation. An aerial vehicle may also be outfitted with one or more other movable objects such as landing gear, antenna and/or engaged payloads, and one or more of such objects may be translated in one or more directions in order to adjust a center of gravity of the aerial vehicle. By varying angles by which forces are supplied to the aerial vehicle, or locations of the center of gravity of the aerial vehicle, a desired attitude of the aerial vehicle may be maintained irrespective of velocity, altitude and/or forces of thrust, lift, weight or drag acting upon the aerial vehicle.

Abstract: A method for determining a neutral position of a MDPS (Motor Driven Power Steering) system may include: determining, by a controller, whether a vehicle is driving; determining, by the controller, whether a steering torque is smaller than a preset break point on a boost curve, when the vehicle is driving; and determining, by the controller, that the vehicle is in a neutral state, when the steering torque is smaller than the preset break point on the boost curve.

Abstract: An agricultural row unit for planting seeds in a furrow includes an opening tool that cuts a furrow in the soil to be planted, and a gauge wheel that engages the soil to control elevation. A depth control actuator applies a controllable down force to the gauge wheel, and a sidewall compaction sensor extends into sidewalls of the furrow and produces a signal representing the compaction of the soil. A controller supplies the depth control actuator with a control signal representing a down pressure set point to form a furrow having a desired depth. The controller uses the signal representing the compaction of the soil in the sidewalls to determine whether the down pressure set point should be increased or decreased, and supplies the depth control actuator with a control signal when it is determined that the down pressure set point should be increased or decreased.

Abstract: A system includes an agricultural vehicle having a first storage container configured to store an agricultural product, a support vehicle having a second storage container, and an aerial vehicle having one or more sensors configured to monitor a fullness of the second storage container. The aerial vehicle is configured to provide a first signal indicative of the fullness of the second storage container to the agricultural vehicle.

Abstract: A method of generating a horizon for use by an ADAS of a vehicle involves using digital location-based data, driver data and/or vehicle data to determine the likelihood that different outgoing paths are taken at a decision point along a currently traversed road segment, and deriving a probability that each path may be taken. The probability may be based on one or more of: an angle of the path relative to the incoming path, the road class of the path, a speed profile of the path, historical paths taken by vehicles at the decision point, and historical paths taken at the decision point by the individual driver or vehicle.

Abstract: A system and method for remotely programming a vehicle including a vehicle connector with a plurality of pins in communication with one or more vehicle sub-systems or modules, a vehicle communication device connected to the vehicle connector; a bi-directional communication link between the vehicle communication device and a remote communication device, and a computer system connected to the remote communication device. The vehicle communication device is configured to receive signals from the pins, convert the signals to a network compatible data packet which can then be transmitted to the remote communication device, which re-coverts the signals to the pin signals, which can be read by a computing system, such as a vehicle scan tool. Programming instructions can be sent from the scan tool to the vehicle, over the bi-directional communication link between the remote communication device and the vehicle communication device.

Abstract: A vehicle control system includes: a surroundings information acquisition section configured to acquire vehicle surroundings information; an automated driving controller configured to execute automated driving in which at least one of speed control or steering control is controlled automatically based on the vehicle surroundings information acquired by the surroundings information acquisition section; a communication section configured to directly or indirectly communicate with another device; and a communication controller configured to transmit the vehicle surroundings information and information indicating an execution status of the automated driving to the other device using the communication section.

Abstract: An apparatus comprising a sensor, an interface and a processor. The sensor may be configured to generate a video signal based on a targeted view of a driver. The interface may be configured to receive status information about one or more components of a vehicle. The processor may be configured to generate a control signal in response to a determined field of view of the driver. The control signal may be used to adjust one or more mirrors of the vehicle. The field of view may be determined based on (i) the video signal and (ii) the status information.

Abstract: Described is a system and method for the classification of agents based on agent movement patterns. In operation, the system receives position data of a moving agent from a camera or sensor. Motion data of the moving agent is then extracted and used to generate a predicted future motion of the moving agent using a set of pre-calculated Echo State Networks (ESN). Each ESN represents an agent classification and generates a predicted future motion. A prediction error is generated for each ESN by comparing the predicted future motion for each ESN with actual motion data. Finally, the agent is classified based on the ESN having the smallest prediction error.