Abstract: Example methods and systems for contingency landing a UAV are provided, comprising sensors configured to detect a position of the UAV and a command module. The command module receives a mission profile comprising a travel path mapped out in multidimensional space over time from an origin to a destination, a first boundary circumscribing at least a portion of the travel path, a second boundary circumscribing the first boundary, and contingent landing sites. The command module sends instructions to the UAV to fly according to the mission profile and determines a position of the UAV relative to the first and the second boundaries. Responsive to determining that the UAV is positioned at the first boundary, the command module sends instructions to land at a contingent landing site, and responsive to determining that the UAV is positioned at the second boundary, the command module sends instructions to land immediately.

Abstract: Techniques for procedurally generating scenarios that verify and validate predictions from or operation of a vehicle safety system are discussed herein. Sensors of a vehicle may detect one or more objects in the environment. A model may determine intersection values indicative of probabilities that the object will intersect with a trajectory of the vehicle. A vehicle may receive one or more intersection values from a model usable by a computing device to validate a prediction from a vehicle safety system.

Abstract: A method and arrangement for storing and recalling of setting values of a vehicle (10) and/or implement (14) that can be used for agricultural purposes comprising the following steps: inputting of at least one setting value for an actuator (49) of the vehicle (10) and/or implement (14) by a user interface; transmitting of a command based on the setting value to a control unit (32, 34, 38, 48) standing in operational connection with the actuator (49); and storing of setting data based on the setting value in a memory (72) in order to recall the setting value on the user interface or another user interface as required, wherein the setting value is converted into setting data using meta data regarding technical properties of the vehicle (10) and/or implement (14), the setting data representing a physical measure of the adjustment of the vehicle (10) and/or implement (14) achieved by the actuator (49) based on the setting value, the measure calculated based on the meta data and the setting value, and the setting

Abstract: Provided is a technique that when a traveling lane is determined by using a plurality of detection methods, can accurately estimate the traveling lane even if any of the detection methods is not sufficient in accuracy. A vehicle control device according to the present invention calculates integrated reliabilities by integrating reliabilities of lane estimation results by a first detector and reliabilities of lane estimation results by a second detector, and estimates a lane in which a vehicle is traveling by comparing the integrated reliabilities with each other.

Abstract: Lane configuration of a lane on which an ADV is driving and a current speed of the ADV are determined. A nudge space is determined based on the lane configuration, the current speed, and a vehicle width of the ADV. Path planning is performed to generate a path to nudge an obstacle, in response to the determining that the nudge space is greater than a predetermined threshold nudge space. The ADV is controlled to drive autonomously according to the planned path to nudge the obstacle.

Abstract: Aspects of the disclosure provide systems and methods for providing suggested locations for pick up and destination locations. Pick up locations may include locations where an autonomous vehicle can pick up a passenger, while destination locations may include locations where the vehicle can wait for an additional passenger, stop and wait for a passenger to perform some task and return to the vehicle, or for the vehicle to drop off a passenger. As such, a request for a vehicle may be received from a client computing device. The request may identify a first location. A set of one or more suggested locations may be selected by comparing the predetermined locations to the first location. The set may be provided to the client computing device.

Abstract: A control channel allocation method for a flying device comprises: allocating a task to a flying device, wherein the task comprises flight information for the flying device; determining a flight time of the flying device flying from a departure station to an arrival station according to the flight information; searching for one or more control channels at the departure station and the arrival station that are idle during the flight time based on one or more control channel occupation tables as one or more target control channels, wherein the one or more control channel occupation tables store idle states of a plurality of control channels at the departure station and the arrival station during a plurality of periods of time; and allocating the one or more target control channels for controlling the flying device to fly from the departure station to the arrival station.

Abstract: According to an aspect of the invention, the brake pedal of an agricultural vehicle can be used as an interface to control auto fold/unfold sequences on a sprayer. This can allow functionality to be moved from a button, such as on the armrest, to the brake pedal using a controller, sensing and software. In one aspect, a button on the armrest can start the sequence, as long as the brake pedal is depressed. After the button is pressed, the operator can remove his hand from the button, but maintain the brake pedal depressed. If the operator lifts his foot off of the pedal, or initiates a different boom control button press, the sequence can stop. This can advantageously allow the operator to have the use of his hands during the sequence.

Abstract: A method is provided for steering control of a vehicle by using lateral velocity of two know points (or lateral velocity of one known point and yaw rate), longitudinal velocity and steer angle information. These factors are used to calculate a target steer angle based on the track angle based on yaw decomposition to thus adjust a current steer angle of the vehicle based on the target steer angle.

Abstract: A flight control method for controlling an aircraft includes obtaining wind information of an operation region during a spread operation performed by the aircraft. The flight control method also includes controlling a flight location of the aircraft based on the wind information and an allowable deviation of the spread region in the spread operation.

Abstract: Systems and methods are disclosed for monitoring operations of an autonomous vehicle. For instance, a monitoring device receives sub-system data from one or more sub-systems of the autonomous vehicle. The device receives contextual data of the vehicle that comprises one or more situational circumstances of the vehicle during operation. The device receives control data from a control system of the vehicle and analyzes the sub-system data, the contextual data, and the control data to determine a control result. The device provides an assessment to a management system of the overall control status of the vehicle.

Abstract: A vehicle computing system may implement techniques to determine relevance of objects detected in an environment to a vehicle operating in the environment (e.g., whether an object could potentially impact the vehicle's ability to safely travel). The techniques may include determining an initial location and trajectory associated with a detected object and determining whether the detected object may intersect a path polygon (e.g., planned path with a safety buffer) of the vehicle. A path intersection may include the detected object intersecting the path polygon or sharing a road segment with the vehicle at a substantially similar height as the vehicle (e.g., on a similar plane). Based on a determination that the detected object does not intersect the planned path of the vehicle, the vehicle computing system may determine that the detected object is irrelevant to the vehicle and may disregard the detected object in vehicle control planning considerations.

Abstract: A vehicle for predicting a risk of collision includes a controller configured to: calculate distances between the vehicle and left and right lines of a first lane, respectively, using a position of the vehicle and first lane width information of the first lane, calculate distances between the surrounding vehicle and left and right lines of a second lane, respectively, using a position of the surrounding vehicle and second lane width information of the second lane, calculate a second distance between the vehicle and the surrounding vehicle by reflecting the calculated distances between the vehicle and the left and right lines of the first lane or the calculated distances between the surrounding vehicle and the left and right lines of the second lane to a first distance, and predict a risk of collision between the vehicle and the surrounding vehicle based on the second distance.

Abstract: Determining head orientation and/or position of a vehicle occupant includes determining a first detection range for head orientations and/or positions of a first imagining sensor located in the vehicle interior based on various head orientations and/or positions in relation to the location of the first sensor, determining the second detection range of the second imaging sensor for head orientations and/or positions based on various head orientations and/or positions in relation to the position of the second sensor, and, based on the head orientation and/or position of the vehicle occupant, determining the head orientation and/or position with that sensor that has a detection range in which the head orientation and/or position can be better determined than in the detection range of another sensor.

Abstract: A control system controls a vehicle using a probabilistic motion planner and an adaptive predictive controller. The probabilistic motion planner produces a sequence of parametric probability distributions over a sequence of target states for the vehicle with parameters defining a first and higher order moments. The adaptive predictive controller optimizes a cost function over a prediction horizon to produce a sequence of control commands to one or multiple actuators of the vehicle. The cost function balances a cost of tracking of different state variables in the sequence of the target states defined by the first moments. The balancing is performed by weighting different state variables using one or multiple of the higher order moments of the probability distribution.

Abstract: A computer-implemented method for implementing electronic control unit (ECU) testing optimization includes capturing, within a neural network model, input-output relationships of a plurality of ECUs operatively coupled to a controller area network (CAN) bus within a CAN bus framework, including generating the neural network model by pruning a fully-connected neural network model based on comparisons of maximum values of neuron weights to a threshold, reducing signal connections of a plurality of collected input signals and a plurality of collected output signals based on connection weight importance, ranking importance of the plurality of collected input signals based on the neural network model, generating, based on the ranking, a test case execution sequence for testing a system including the plurality of ECUs to identify flaws in the system, and initiating the test case execution sequence for testing the system.

Abstract: The present teaching relates to method, system, medium, and implementations for detecting a need for sensor adjustment. Information is received from an inertial measurement unit (IMU) attached to a sensor, including one or more measurements associated with the IMU, where the sensor is deployed on a vehicle for sensing surrounding information to facilitate autonomous driving. The one or more measurements are analyzed with respect to one or more corresponding known measurements of the IMU. The discrepancy between the one or more measurements and the one or more corresponding known measurements is used to determine whether an adjustment to the sensor is needed.

Abstract: Disclosed are an anti-drone method using a GPS spoofing signal and a system thereof. According to an embodiment of the inventive concept, an anti-drone method may include injecting a GPS spoofing signal to analyze a drone feature of a target drone and hijacking the target drone by injecting a GPS spoofing signal into the target drone based on a drone hijacking strategy corresponding to the analyzed drone feature among predefined drone hijacking strategies. The analyzing of the drone feature may include injecting the GPS spoofing signal to analyze a safety device mechanism (GPS fail-safe) and a path-following algorithm of the target drone.

Abstract: A zone mobility system and method for an autonomous device includes a work area within which the autonomous device is intended to operate, one or more electrically separated boundary conductors, each boundary conductor surrounding a work zone within the work area, a transmitter base electrically connected to at least one of the boundary conductors, and a removably coupled transmitter configured to transmit a signal for directing movement of the autonomous device on the boundary conductor wire. The transmitted signal can be adjusted to optimize the power consumption of the transmitter, and a work operation can be commenced utilizing a transmitter interface, and/or utilizing an autonomous device user interface.

Abstract: An obstacle recognition device, a vehicle system including the same, and a method thereof are provided. The obstacle recognition device includes a storage storing data and an algorithm for calculating a risk probability and a processor configured to execute the algorithm to generate an occupancy grid map based on a sensing value of at least one ultrasonic sensor, calculate the risk probability of each cell on the occupancy grid map, and determine a shape and location of an obstacle based on the risk probability of each cell.