Abstract: Devices, systems, and methods are provided for classifying detected objects as static or dynamic. A device may determine first light detection and ranging (LIDAR) data associated with a convex hull of an object at a first time, and determine second LIDAR data associated with the convex hull at a second time after the first time. The device may generate, based on the first LIDAR data and the second LIDAR data, a vector including values of features associated with the first convex hull and the second convex hull. The device may determine, based on the vector, a probability that the object is static. The device may operate a machine based on the probability that the object is static.

Abstract: An autonomous mobile device (AMD) may perform various tasks during operation. Some tasks, such as delivering a message to a particular user, may involve the AMD identifying the particular user. The AMD includes a camera to acquire an image, and image-based authentication techniques are used to determine a user's identity. A user may move within in a physical space, and the space may contain various obstructions which may occlude images. The AMD may move within the space to obtain a vantage point from which an image of the face of the user is obtained which is suitable for image-based authentication. In some situations, the AMD may present an attention signal, such as playing a sound from a speaker or flashing a light, to encourage the user to look at the AMD, providing an image for use in image-based authentication.

Abstract: Described is a system for competency assessment of an autonomous system. The system extracts semantic concepts representing a situation. Actions taken by the autonomous system are associated with semantic concepts that are activated when the actions are taken in the situation. The system measures an outcome of the actions taken in the situation and generates a reward metric. The semantic concepts representing the situation are stored as a memory with the actions taken in the situation and the reward metric as a memory. A prospective simulation is generated based on recall of the memory. A competency metric and an experience metric are determined. Competent operational control of the autonomous system is maintained when at least one of the competency metric and the experience metric is above a minimum value. An alert is generated when at least one of the competency metric and the experience metric falls below the minimum value.

Abstract: A vehicle system including a vehicle component and a controller is provided. The controller may selectively activate the vehicle component and communicate with a mobile unit including an interface. The controller may be programmed to interact with the mobile unit upon detection by accessing vehicle sleep mode instructions preprogrammed by a user via the interface in which the controller activates the vehicle component according to an escalation sequence schedule to disengage a vehicle sleep mode. The system may further include a sensor in communication with the vehicle component and the controller. The controller may be further programed to activate the vehicle component according to the escalation sequence schedule based on receipt of a signal from a sensor indicating a passenger is asleep.

Abstract: An information processing device includes a receiving unit configured to receive a signal sent from a wireless communication unit included in an autonomous vehicle, a failure determination unit configured to determine a failed vehicle in which an external monitoring camera has failed based on the signal from the wireless communication unit, a detection unit configured to detect a specific vehicle that is planned to travel on a route at least a section of which overlaps with a travel planned route of the failed vehicle beginning at a failure point of the failed vehicle that includes the exterior monitoring camera determined to be failed, and a sending unit configured to send travel control information to the failed vehicle to instruct the failed vehicle to travel in front of or behind the specific vehicle.

Abstract: A method for adjusting the position of a steered wheel of a vehicle includes detecting a steering position value of a steering control device of a vehicle such that the steering position value corresponds to an angular position of the steering control device; calculating a traction speed breakpoint at or above which steering desensitization may occur; and defining a maximum commencement steer angle at or below which steering desensitization may commence.

Abstract: Provided are methods for learning to identify safety-critical scenarios for autonomous vehicles. First state information representing a first state of a driving scenario is received. The information includes a state of a vehicle and a state of an agent in the vehicle's environment. The first state information is processed with a neural network to determine at least one action to be performed by the agent, including a perception degradation action causing misperception of the agent by a perception system of the vehicle. Second state information representing a second state of the driving scenario is received after performance of the at least one action. A reward for the action is determined. First and second distances between the vehicle and the agent are determined and compared to determine the reward for the at least one action. At least one weight of the neural network is adjusted based on the reward.

Abstract: A patient transport apparatus for transporting a patient over a surface. The patient transport apparatus has support wheels coupled to a base and swivelable about swivel axes. An auxiliary wheel assembly is coupled to the base and includes an auxiliary wheel configured to move between a plurality of wheel positions, and an actuator operably coupled to the auxiliary wheel to move the auxiliary wheel between the plurality of wheel positions. A sensing system with a sensor is provided to detect a motion condition of the patient transport apparatus. A controller is coupled to the sensing system and to the actuator, and is configured to drive the actuator to move the auxiliary wheel between the plurality of wheel positions based on the motion condition of the patient transport apparatus.

Abstract: Provided herein is a system comprising: one or more processors; and a memory storing instructions that, when executed by the one or more processors, causes the system to perform: identifying, in a map, one or more entities that change over time; predicting an amount of change of the identified one or more entities over time; and updating the map based on the predicted amount of change of the identified one or more entities over time.

Abstract: According to an aspect of an embodiment, operations may comprise accessing a set of vehicle poses of one or more vehicles; for each of the set of vehicle poses, accessing a high definition (HD) map of a geographical region surrounding the vehicle pose, with the HD map comprising a three-dimensional (3D) representation of the geographical region, determining a measure of constrainedness for the vehicle pose, with the measure of constrainedness representing a confidence for performing localization for the vehicle pose based on 3D structures surrounding the vehicle pose, and storing the measure of constrainedness for the vehicle pose; and for each of the geographical regions surrounding each of the set of vehicle poses, determining a measure of constrainedness for the geographical region based on measures of constrainedness of vehicle poses within the geographical region, and storing the measure of constrainedness for the geographical region.

Abstract: The present disclosure provides a method, a device and a system for guiding an unmanned aerial vehicle to land. A navigation equipment on the unmanned aerial vehicle sends a landing instruction to the ground, so that a landing platform receiving the landing instruction displays a landing pattern. The navigation equipment scans image of the ground to identify the landing pattern, and guides the unmanned aerial vehicle to land at the landing pattern and land on the landing platform eventually. The landing platform displays a landing pattern according to the received instruction, and the unmanned aerial vehicle lands at the landing pattern, so that the unmanned aerial vehicle lands quickly and conveniently without extensive calculation.

Abstract: An autonomous vehicle is described herein. The autonomous vehicle comprises a first sensor and a second sensor having limited fields of view, an articulation system, and a computing system. The computing system determines a first region and a second region external to the autonomous vehicle based on a sensor prioritization scheme comprising a ranking of regions surrounding the autonomous vehicle. The computing system then causes the articulation system to orient the first sensor towards the first region and the second region towards the second region. Responsive to receiving a sensor signal from the first sensor indicating that an object has entered a field of view of the first sensor, the computing system determines a third region having a higher ranking than the second region within the sensor prioritization scheme. The computing system then causes the articulation system to orient the second sensor towards the third region.

Abstract: A system and method for learning naturalistic driving behavior based on vehicle dynamic data that include receiving vehicle dynamic data and image data and analyzing the vehicle dynamic data and the image data to detect a plurality of behavioral events. The system and method also include classifying at least one behavioral event as a stimulus-driven action and predicting at least one behavioral event as a goal-oriented action based on the stimulus-driven action. The system and method additionally include building a naturalistic driving behavior data set that includes annotations that are based on the at least one behavioral event that is classified as the stimulus-driven action. The system and method further include controlling a vehicle to be autonomously driven based on the naturalistic driving behavior data set.

Abstract: A vehicle control system includes: a terminal configured to be carried by a user; and a control device configured to execute remote autonomous moving processing to move a vehicle from an initial position to a stop position. The terminal includes: a touch panel configured to display an acceptance area that accepts a prescribed repetitive operation performed for continuing traveling of the vehicle and to display a suspension icon that accepts an operation performed for stopping the vehicle; and a processing unit configured to make the touch panel display the suspension icon at a part where the repetitive operation has been performed when the repetitive operation is stopped. When the control device or the processing unit determines that the suspension icon is operated or that the repetitive operation is not resumed within a prescribed period after the repetitive operation has been stopped, the control device stops the vehicle.

Abstract: In one embodiment, a method for temporal smoothness in localization results for an autonomous driving vehicle includes: creating a probability offset volume that represents an overall matching cost between a first set of keypoints from the online point cloud and a second set of keypoints from a pre-built point cloud map for each of a series of sequential light detection and ranging (LiDAR) frames in an online point cloud. The method also includes compressing the probability offset volume into multiple probability vectors across a X dimension, a Y dimension and a yaw dimension; providing each probability vector of the probability offset volume to a number of recurrent neural networks (RNNs); and generating, by the RNNs, a trajectory of location results across the plurality of sequential LiDAR frames.

Abstract: A system and method for autonomous vehicle control to minimize energy cost are disclosed. A particular embodiment includes: generating a plurality of potential routings and related vehicle motion control operations for an autonomous vehicle to cause the autonomous vehicle to transit from a current position to a desired destination; generating predicted energy consumption rates for each of the potential routings and related vehicle motion control operations using a vehicle energy consumption model; scoring each of the plurality of potential routings and related vehicle motion control operations based on the corresponding predicted energy consumption rates; selecting one of the plurality of potential routings and related vehicle motion control operations having a score within an acceptable range; and outputting a vehicle motion control output representing the selected one of the plurality of potential routings and related vehicle motion control operations.

Abstract: An autonomous vehicle uses a secondary vehicle control system to supplement a primary vehicle control system to perform a controlled stop if an adverse event is detected in the primary vehicle control system. The secondary vehicle control system may use a redundant lateral velocity determined by a different sensor from that used by the primary vehicle control system to determine lateral velocity for use in controlling the autonomous vehicle to perform the controlled stop.

Abstract: There are disclosed various methods and apparatuses for multi-vehicle maneuver and impact analyses.

Abstract: Aspects relate to methods and systems for reversionary flight control for an electrical vertical take-off and landing (eVTOL) aircraft. An exemplary system includes a pilot control, a sensor configured to sense and transmit analog control data associated with a pilot interaction with the pilot control, a pilot interface module configured to receive the analog control data, convert the analog control data to digital control data, and transmit digital control, an actuator, and a flight controller. The flight controller may be configured to receive the digital control data, determine a primary command datum as a function of the digital control data, transmit the primary command datum to the actuator, determine that the digital control signal is non-functional, receive the analog control data, determine a reversionary command datum as a function of the analog control data, and transmit the reversionary command datum to the actuator.

Abstract: In some implementations, the EMV uses a calibration to inform autonomous control over the EMV. To calibrate an EMV, the system first selects a calibration action comprising a control signal for actuating a control surface of the EMV. Then, using a calibration model comprising a machine learning model trained based on one or more previous calibration actions taken by the EMV, the system predicts a response of the control surface to the control signal of the calibration action. After the EMV executes the control signal to perform the calibration action, the EMV system monitors the actual response of the control signal and uses that to update the calibration model based on a comparison between the predicted and monitored states of the control surface.