Abstract: Techniques for controlling an autonomous vehicle with a processor that controls operation, includes operating a Doppler LIDAR system to collect point cloud data that indicates for each point at least four dimensions including an inclination angle, an azimuthal angle, a range, and relative speed between the point and the LIDAR system. A value of a property of an object in the point cloud is determined based on only three or fewer of the at least four dimensions. In some of embodiments, determining the value of the property of the object includes isolating multiple points in the point cloud data which have high value Doppler components. A moving object within the plurality of points is determined based on a cluster by azimuth and Doppler component values.

Abstract: A longitudinally guiding driver assistance system in a motor vehicle has a first detection system for detecting currently applying events or relevant events lying ahead, which require a change of the permissible maximum speed, a second detection system for detecting the course of the route, and a function unit which, when detecting a relevant event while taking into account the location of the relevant event, determines a location-dependent point in time, whose reaching causes the function unit to initiate an automatic adaptation of the currently permissible maximum speed or an output of a prompt information for permitting an automatic adaptation of the currently permissible maximum speed to anew permissible maximum speed. The function unit is designed to take into account available information concerning the course of the route when determining the location-dependent point in time.

Abstract: Systems and methods are disclosed for data collection from landing gear components and providing predictive analytics. Example methods include sending a signal to excite wheel assembly sensors located on a vehicle, and receiving a return signal encoded with measurement data. The measurement data is then stored on an RFID card, on an edge node, and/or on a remote server. Upon receipt of the measurement data by a remote server, the remote server analyzes the data to generate predictive maintenance analytics data.

Abstract: An autonomous driving control device includes: a plurality of control devices that each have a first storage unit and a processing unit; a load control unit; a second storage unit; and a failure detection unit. The programs include autonomous driving programs and other programs. When a failure is detected, the load control unit unloads at least some of the other programs from the first storage unit of a normal control device, and loads the autonomous driving program corresponding to that loaded in the first storage unit of a failed control device from the second storage unit into the first storage unit of the normal control device.

Abstract: A takeoff and landing control method of a multimodal air-ground amphibious vehicle includes: receiving dynamic parameters of the multimodal air-ground amphibious vehicle; processing the dynamic parameters by a coupled dynamic model of the multimodal air-ground amphibious vehicle to obtain dynamic control parameters of the multimodal air-ground amphibious vehicle, wherein the coupled dynamic model of the multimodal air-ground amphibious vehicle comprises a motion equation of the multimodal air-ground amphibious vehicle in a touchdown state; and the motion equation of the multimodal air-ground amphibious vehicle in a touchdown state is determined by a two-degree-of-freedom suspension dynamic equation and a six-degree-of-freedom motion equation of the multimodal air-ground amphibious vehicle in the touchdown state; and controlling takeoff and landing of the multimodal air-ground amphibious vehicle according to the dynamic control parameters of the multimodal air-ground amphibious vehicle.

Abstract: The communication apparatus (1100) configured to be installed in a first aircraft (1b) comprises a controller (1110) and a transmitter (1106). The controller (1110) is configured to acquire resource information and determine a communication condition based on the resource information, the resource information being related to a second aircraft (1a) different from the first aircraft (1b) or to a radio altimeter installed in the second aircraft (1a). The transmitter (1106) is configured to transmit transmission data to one other communication apparatus installed in the first aircraft (1b), according to the communication condition.

Abstract: Sensor data captured by one or more sensors coupled to a first entity are received by an apparatus. The sensor data comprises a location of the first entity or representation of surroundings of the first entity. Based at least in part on the sensor data, a second entity is identified. Based at least in part on trajectories along which the first and second entities are respectively traveling, it is expected that the second entity will enter a zone defined around the first entity. Based at least in part on the sensor data, an amount of time until the second entity will enter the zone is determined. The amount of time until the second entity will enter the zone is provided as (a) input to a navigation function and/or (b) to be displayed as a countdown via a display element and/or speaker element of the first entity.

Abstract: A method of assisting in piloting an aircraft having a fuselage, the aircraft having a first side arrangement and a second side arrangement, which arrangements are disposed laterally on either side of the fuselage, and each of them participates in the movement of the aircraft. During an assistance phase, the method includes the following steps: measuring a value of a first ground clearance of the first side arrangement and a value of a second ground clearance of the second side arrangement; and displaying on a screen at least one symbol that varies as a function of the variation in the value of the first ground clearance and/or of the variation in the value of the second ground clearance.

Abstract: An autonomous driving system mounted on a vehicle determines a target path based on necessary information and performs vehicle travel control such that the vehicle follows the target path. A first coordinate system is a vehicle coordinate system at a first timing when the necessary information is acquired. A second coordinate system is a vehicle coordinate system at a second timing later than the first timing. The autonomous driving system calculates, based on the necessary information acquired at the first timing, a first target path defined in the first coordinate system. Then, the autonomous driving system corrects the first target path to a second target path defined in the second coordinate system by performing coordinate transformation from the first coordinate system to the second coordinate system. The autonomous driving system uses the second target path as the target path to perform the vehicle travel control.

Abstract: A Internet protocol-based unmanned aerial vehicle (UAV) management system and method is disclosed that includes UAVs including a communication chip, a user computer, a cloud-based service for performing a virtual UAV. The method includes storing a plurality of pre-planned missions, controlling communication between the UAV and the user computer, mapping the UAV to the virtual UAV, assigning a mission to include multiple waypoints, and allocating the task at the multiple waypoints. Dynamic mission planning of the assigned mission is performed to generate planned paths for performing the task. Operation of the UAV is controlled by way of the corresponding virtual UAV including receiving messages and commands for the mission from the user computer, sending control commands to the UAV, receiving data signals from the UAV, and transmitting location and status of performance of the task for the UAV. The cloud-based service performs image processing using the received data signals.

Abstract: Systems and methods for improved transitioning and updating of navigations modes for multi-modal transportation routes are presented. In one embodiment, a method is provided that includes receiving a transportation route, which may include a first segment and a second segment. A first interface associated with the first segment may be displayed and may include a visual indicator of a rate of progress. A predicted travel time may be predicted, based on the rate of progress, to a starting location of the second segment. The visual indicator may be updated based on a comparison of the predicted travel time to a start time of the second segment.

Abstract: Techniques are described for cognitive analysis for directed control transfer for autonomous vehicles. In-vehicle sensors are used to collect cognitive state data for an individual within a vehicle which has an autonomous mode of operation. The cognitive state data includes infrared, facial, audio, or biosensor data. One or more processors analyze the cognitive state data collected from the individual to produce cognitive state information. The cognitive state information includes a subset or summary of cognitive state data, or an analysis of the cognitive state data. The individual is scored based on the cognitive state information to produce a cognitive scoring metric. A state of operation is determined for the vehicle. A condition of the individual is evaluated based on the cognitive scoring metric. Control is transferred between the vehicle and the individual based on the state of operation of the vehicle and the condition of the individual.

Abstract: A monitoring system and a monitoring method for a planter includes a computer having an unit speed fusion module, a seeding flow rate monitoring module, and a decision module for theoretical rotation speed of a drive motor, a rotation speed deviation inference module, a controlling parameter tuning module, an adjusting module for rotation speed of a seeding shaft, a controlling module for rotation speed of seeding shaft and a positioning signal receiver fixed on a top of a cab for receiving a geographic position signal. The system monitors operating state parameters of the planter and accurately controls seed amount.

Abstract: A method includes receiving sensor data from a sensor associated with a vehicle and determining a likelihood that the vehicle is experiencing an emergency condition based on the sensor data. The method also includes sending an alert indicative of the likelihood, the alert comprising at least a portion of the sensor data.

Abstract: A system and method include scanning a light detection and ranging (LIDAR) device through a range of orientations corresponding to a scanning zone while emitting light pulses from the LIDAR device. The method also includes receiving returning light pulses corresponding to the light pulses emitted from the LIDAR device and determining initial point cloud data based on time delays between emitting the light pulses and receiving the corresponding returning light pulses and the orientations of the LIDAR device. The initial point cloud data has an initial angular resolution. The method includes identifying, based on the initial point cloud data, a reflective feature in the scanning zone and determining an enhancement region and an enhanced angular resolution for a subsequent scan to provide a higher spatial resolution in at least a portion of subsequent point cloud data from the subsequent scan corresponding to the reflective feature.

Abstract: A method for operating an unmanned aerial vehicle, which is flight-controlled by a flight control unit to follow a given trajectory, includes an optimization of the trajectory based on a simulation model of the unmanned aerial vehicle and a communication link to a ground station to form a trajectory along which a certain connection quality to the ground station can be maintained.

Abstract: The present disclosure provides a control system for controlling unmanned aircraft systems (UAS). The control system comprises of an application user system 102 to operate the UAS, an operating system 103, a virtual road system (VRS) 109 and a virtual packet 501. The virtual packet 501 created as a boundary around the UAS defined by application user system 102 or VRS 109. The operating system 103 includes a machine learning processing unit (MLPU) 104 configured for positioning the UAS, detecting collision within path of the virtual packet 901. The VRS 109 configured to generate a virtual roadway 902 using architecture similar to Internet service provider architecture and modules for routing the UAS. The routing and controlling of UAS by the VRS 109 is based on request received from the MLPU 104, application zone packet parameters and actual position co-ordinates received from the MLPU 104.

Abstract: A method of controlling an unmanned aerial vehicle (UAV) in an environment includes detecting, with aid of a sensor coupled to the UAV and while the UAV is in flight, a signal that is emitted from and uniquely identifying a locating marker; determining, with aid of a processor, a sequence of actions to control the UAV in response to a plurality of instructions encoded in the locating marker and communicated by the signal; and controlling, with aid of the processor, the UAV to effect the sequence of actions according to a specified time interval included in the plurality of instructions. The specified time interval indicates a wait time before the UAV effects the sequence of actions.

Abstract: A bionic visual navigation control system for autonomous aerial refueling docking includes: a tanker/receiver bottom layer control module, a multi-wind disturbances hose-drogue stable control module, an enable and select module, a close-range bionic vision relative navigation module, and a receiver relative position precise control module. A bionic visual navigation control method for autonomous aerial refueling docking is also provided. The present invention aims at improving the reliability, anti-interference and accuracy of the close-range relative navigation in the autonomous air refueling docking stage, and designs a matching relative position accurate control method with control switch, thereby improving the accuracy of close-range navigation and control, thereby promoting the successful realization of probe-and-drogue autonomous aerial refueling and improving the autonomy of UAVs.

Abstract: Systems and methods are provided for vehicle navigation. In one implementation, a system may comprise an interface to obtain sensing data of an environment of the host vehicle. The processing device may be configured to determine a planned navigational action; identify, a target vehicle in the environment of the host vehicle; predict a distance between the host vehicle and the target vehicle if the planned navigational action was taken; determine a current host vehicle stopping distance based on a braking capability, acceleration capability, and speed of the host vehicle; determine a current target vehicle braking distance based on a speed and braking capability of the target vehicle; and implement the planned navigational action when the predicted distance of the planned navigational action is greater than a minimum safe longitudinal distance calculated based on the current host vehicle stopping distance and the current target vehicle braking distance.