Abstract: Methods and apparatus for automatically extending aircraft wing flaps in response to detecting an excess energy steep descent condition are described. An example control system of an aircraft includes one or more processors. The one or more processors determine whether the aircraft is experiencing an excess energy steep descent (EESD) condition. In response to determining that the aircraft is experiencing the EESD condition, the one or more processors command an actuator of the aircraft coupled to a flap of the aircraft to extend the flap from a current flap position to a subsequent flap position defined by a flap extension sequence.

Abstract: The present application provides a system for unmanned aerial vehicle (UAV) parachute landing. An exemplary system includes a detector configured to detect at least one of a flight speed, a wind speed, a wind direction, a position, a height, and a voltage of a UAV. The system also includes a memory storing instructions and a processor configured to execute the instructions to cause the system to: determine whether to open a parachute of the UAV in accordance with a criterion, responsive to the determination to open the parachute of the UAV, stop a motor of the UAV that spins a propeller of the UAV, and open the parachute of the UAV after stopping the motor of the UAV for a first period.

Abstract: An approach is provided for automatically coding cartographic feature(s), e.g., human settlement(s). The approach involves receiving data point(s) associated with point location(s) and indicative of a cartographic feature. The approach also involves retrieving or generating a cartographic feature polygon corresponding to the cartographic feature based on the data point(s). The approach further involves generating a plurality of polygon data points that replicate the cartographic feature polygon, and/or a spider web model that represents the point location(s). The approach further involves retrieving imagery data depicting the cartographic feature based on the plurality of polygon data points and/or the spider web model. The approach further involves merging the data point(s), the plurality of polygon data points, and/or the imagery data to generate new or updated polygon structure data point(s) to represent the cartographic feature.

Abstract: A travel assistance method for performing a lane change of a host vehicle includes: detecting an oncoming vehicle traveling in an opposite lane from which the host vehicle travels when the host vehicle starts the lane change; determining a crossing point between a virtual advancing route and a linear advancing route in which the oncoming vehicle advances linearly, the virtual advancing route being a virtual extension of an advancing route of the host vehicle during a period from start to end of the lane change; executing a lane change using the route and the vehicle speed in a case where a time difference between a first predicted time and a second predicted time is outside a predetermined range; and when within the predetermined range, executing a lane change by changing at least one of the route and the vehicle speed such that the time difference is outside the predetermined range.

Abstract: A computer includes a processor and a memory storing instructions executable by the processor to actuate movement of a vehicle at a first speed, identify a potentially available parking space in a vehicle direction of travel based on data received from at least one camera, then reduce a speed of the vehicle to a second speed that is less than the first speed while receiving data from at least one proximity sensor about the parking space, and actuate movement of the vehicle to a parking position in the parking space upon determining that the parking space is available based on the data from the at least one proximity sensor.

Abstract: Aspects of the disclosure relate to generating scouting objectives in order to update map information used to control a fleet of vehicles in an autonomous driving mode. For instance, a notification from a vehicle of the fleet identifying a feature and a location of the feature may be received. A first bound for a scouting area may be identified based on the location of the feature. A second bound for the scouting area may be identified based on a lane closest to the feature. A scouting objective may be generated for the feature based on the first bound and the second bound.

Abstract: Methods and systems for controlling navigation of an autonomous vehicle for traversing a drivable area are disclosed. The methods include receiving information relating to a drivable area that includes a plurality of polygons, identifying a plurality of logical edges that form a boundary of the drivable area, sequentially and repeatedly analyzing concavities of each the plurality of logical edges until identification of a first logical edge that has a concavity greater than a threshold, creating a first logical segment of the boundary of the drivable area. This segmentation may be repeated until each of the plurality of logical edges has been classified. The method may include creating and adding (to a map) a data representation of the drivable area that comprises an indication of the plurality of logical segments, and adding the data representation to a road network map comprising the drivable area.

Abstract: An electrical method for centering telehandler rear wheels preferably includes an electronic control module (ECM), a rear steering cylinder, a pair of rear centering valves, a front steering cylinder, a steer mode valve, at least one steering position sensor, a steering control unit and a mode selection switch. The front and rear steering cylinders are connected to the steer mode valve. The steering control unit directs hydraulic fluid from a hydraulic pump to flow into the front and rear steering cylinders to turn the wheels. A 2W steering mode requires that the rear wheels be straight before going from a 4W steering mode into the 2W steering mode. The ECM monitors a position of the rear wheels through the at least one steering position sensor. If the wheels are not straight, the ECM will open a centering valve to straighten the rear wheels, before going into the 2W steering mode.

Abstract: A method for controlling wheel slip of a vehicle, which comprises a plurality of driving devices for generating driving force for driving the vehicle, includes: obtaining, by a controller, equivalent inertia information for each driving device based on operation information of each driving system during traveling of the vehicle; calculating, by the controller, a calibration amount for calibrating a driving force command or a braking force command for each wheel in real time by using the equivalent inertia information obtained; calibrating, by the controller, the driving force command or the braking force command for each wheel by using the calculated calibration amount; and controlling, by the controller, the driving force according to the calibrated driving force command or the braking force according to the calibrated braking force command.

Abstract: A path providing device configured to provide a path information to a vehicle includes a communication unit configured to receive, from a server, map information including a plurality of layers of data, an interface unit configured to receive sensing information from one or more sensors disposed at the vehicle, and a processor. The processor is configured to determine an optimal path for guiding the vehicle from an identified lane, generate autonomous driving visibility information based on the sensing information and the determined optimal path, update the optimal path based on dynamic information related to a movable object located on the optimal path and the autonomous driving visibility information, receive different types of sensor data from a plurality of sensors, and update at least one of the autonomous driving visibility information or the optimal path based on information generated by combining at least two types of sensor data.

Abstract: First to third inertial sensors (16, 17, 18) are respectively mounted on a boom (5A), an arm (5B) and a bucket (5C) to rotate in coordinate axes different from each other at the time of operating the boom (5A). In a case where the boom (5A) is operated in a state where a traveling operation pressure Pa and a revolving operation pressure Pb are equal to or less than respective preset operation pressure threshold values, a controller (20) makes a determination on which movable part of the boom (5A), the arm (5B) and the bucket (5C) each of the inertial sensors (16, 17, 18) is mounted, based upon sensor outputs outputted from the inertial sensors (16, 17, 18). The controller (20) sets a corresponding relation between each of the boom (5A), the arm (5B) and the bucket (5C) and each of the inertial sensors (16, 17, 18) based upon the determination result.

Abstract: A vehicle control system includes an imaging device, a map generating unit, and an own lane identifying unit. The own lane identifying unit is configured to identify one lane as an own lane on a map in a case where the one lane is an only lane present in a specific area on the map, compare a type of a delimiting line of a captured own lane with types of delimiting lines of a plurality of lanes on the map in a case where the plurality of lanes are present in the specific area on the map, and identify one of the plurality of lanes on the map as the own lane on the map in a case where the type of the delimiting line of the captured own lane matches only a type of a delimiting line of the one of the plurality of lanes on the map.

Abstract: A time master and sensor data collection module for a robotic system such as an autonomous vehicle is disclosed. The module includes a processing device, one or more sensors, and programming instructions that are configured to cause the processing device to operate as a timer that generates a vehicle time, receive data from the one or more sensors contained within the housing, and synchronize the data from the one or more sensors contained within the housing with the vehicle time. The integrated sensors may include sensors such as a global positioning system (GPS) unit and/or an inertial measurement unit (IMU). The module may interface with external sensors such as a LiDAR system and/or cameras.

Abstract: A method for dynamic airspace planning is performed by one or more processors, and includes identifying, in an airspace model that includes an array of nodes, a set of path elements. Each path element connects a pair of adjacent nodes in the array. The method includes obtaining, for each aircraft in a set of aircraft, a respective current position on a path element. The set of aircraft may include thousands of manned and/or unmanned aircraft. The method includes obtaining, for each aircraft, a respective final position on a path element; and enumerating, for each aircraft, a respective set of flight paths. Each flight path includes one or more path elements, and extends from at least the current position of a respective aircraft to the final position of the respective aircraft. The method includes determining, for each aircraft, a respective optimal flight path based on the respective set of flight paths.

Abstract: A vehicle control system includes a memory, a processor connected to the memory, and a functional section. The functional section is configured by a first device and a second device having equivalent functionality to each other, and the functional section is connected in a communicable manner to the processor which serves as a driving assistance control section configured to perform driving assistance including at least braking control of a vehicle, and services as an autonomous driving control section configured to perform autonomous driving of the vehicle. The processor is configured to use the first device at least during execution of driving assistance of the vehicle, and to use the second device at least during execution of autonomous driving of the vehicle.

Abstract: In various examples, a sequential deep neural network (DNN) may be trained using ground truth data generated by correlating (e.g., by cross-sensor fusion) sensor data with image data representative of a sequences of images. In deployment, the sequential DNN may leverage the sensor correlation to compute various predictions using image data alone. The predictions may include velocities, in world space, of objects in fields of view of an ego-vehicle, current and future locations of the objects in image space, and/or a time-to-collision (TTC) between the objects and the ego-vehicle. These predictions may be used as part of a perception system for understanding and reacting to a current physical environment of the ego-vehicle.

Abstract: A method and an apparatus for recognition a position and generating a path for autonomous driving of a mobile robot in an orchard environment are provided. The method converts three-dimensional (3D) point cloud data for the orchard environment into a 2D grid map, obtains a position of the mobile robot by performing local scan matching and global scan matching on the 2D grid map, and extracts a tree from the 2D grid map based on occupancy of each grid of the 2D grid map. Then, an effective line based on center points of extracted trees is obtained, a destination based on the obtained effective line is generated, and a driving path according to the generated destination and the position of the mobile robot is generated.

Abstract: A method for generating at least one non-human animal crossing indicator, the method may include receiving by a vehicle computerized system, non-human animal crossing indicators; obtaining sensed information regarding an environment of the vehicle; processing the sensed information, wherein the processing comprises searching for one or more non-human animal crossing indicators of the non-human animal crossing indicators; wherein the non-human animal crossing element is selected out of (i) a non-human animal crossing object and (ii) a non-human animal crossing situation; autonomously determining, when finding at least one of the one or more non-human animal crossing identifiers, that the vehicle is driving towards a non-human animal crossing or is within the non-human animal crossing; and generating an alert when determining that the vehicle is driving towards the non-human animal crossing or is within the non-human animal crossing.

Abstract: A method for controlling the electrical power supply of injectors for a hybrid automotive vehicle, including an internal combustion engine and an electric motor. A first electrical network, having a first DC voltage, supplies power to a motor control of the engine. A second electrical network having a second DC voltage, higher than the first DC voltage, supplies power to the electric motor. The method includes connecting the second DC voltage to the injectors; reading the value of the second DC voltage; adapting control parameters of the injectors based on the value of engine speed, engine temperature and injection pressure upstream of the injectors; and controlling the injectors using the second DC voltage. Wherein there is no change in the control parameters when the value is higher than a threshold value; and changing at least one of the control parameters when the value is lower than the threshold value.

Abstract: A method for partially or fully autonomously guiding a motor vehicle includes setting a target steering angle for guiding along a target trajectory. It is checked whether a zero point shift exists between a current position of the motor vehicle with respect to the target trajectory. The set target steering angle is adjusted on the basis of a correction constant in the event that a determined zero point shift exceeds a predefined threshold.