Abstract: A system and method for safely terminating navigation of an unmanned aerial vehicle (UAV). A method includes generating a navigation plan for the UAV, the UAV including a propulsion system, wherein the navigation plan includes at least a start point, an end point, and a virtual three-dimensional (3D) tunnel connecting the start and end points; and configuring the UAV to execute the navigation plan by navigating from the start point to the end point, wherein the UAV is configured such that the UAV executes the navigation plan by navigating from the start point to the end point, wherein the UAV is further configured such that the UAV terminates navigation by terminating power to the propulsion system of the UAV and deploying a failsafe, wherein the UAV is configured to terminate navigation when the UAV is outside of the 3D tunnel.

Abstract: A vehicle driving system that controls switching between automatic driving by a vehicle and manual driving by a driver of the vehicle includes a temperature sensor configured to monitor a temperature of an automatic driving ECU which controls the automatic driving; and a microcontroller configured to: (i) set a threshold temperature based on an estimated gradient of increase in the temperature of the automatic driving ECU, the estimated gradient of increase being estimated based on a drive state of an equipment installed on the vehicle, and (ii) in a case where the temperature of the automatic driving ECU that is sensed by the temperature sensor becomes equal to or greater than the threshold temperature, perform an operation for prompting the driver to switch to the manual driving during the automatic driving.

Abstract: Navigation devices and methods of operation are provided. The navigation device includes a communication unit; a display; an input unit for receiving an input of data; a communication unit; and a controller for controlling operation of the display and the input unit. The controller connects to a data server through the communication unit, requests path setting information to the data server, receives the path setting information from the data server, acquires present position information of the navigation device, acquires a user moving path by reflecting the acquired position information and the received path setting information, and sets the user moving path as a guidance path. In this case, the path setting information is generated in another electronic device or the data server based on user input information input from the another electronic device.

Abstract: Apparatuses and methods for predicting a crash using estimated vehicle speed. A set of sensor measurements are received from a mobile device disposed within a vehicle. A set of contiguous windows based on the sensor measurements may be defined. Each contiguous window represents a contiguous portion of the sensor measurements. A set of sensor measurements may be defined for each contiguous window. A trained neural network may execute, using the set of features, to generate one or more speed predictions. A vehicle crash prediction may be generated using the speed prediction. The vehicle crash prediction may then be transmitted to a remote device.

Abstract: A system and method for implementing reward based strategies for promoting exploration that include receiving data associated with an agent environment of an ego agent and a target agent and receiving data associated with a dynamic operation of the ego agent and the target agent within the agent environment. The system and method also include implementing a reward function that is associated with exploration of at least one agent state within the agent environment. The system and method further include training a neural network with a novel unexplored agent state.

Abstract: A method of controlling a travel path of an agricultural baling system including a baler and a tow vehicle includes: receiving a first bale density signal indicative of a first bale density of a forming bale at a first location in a baling chamber; receiving a second bale density signal indicative of a second bale density of the forming bale at a second location; receiving a windrow signal indicative of a width of a windrow; comparing the first bale density to the second bale density; determining a difference between the first bale density and the second bale density exceeds a threshold difference; and outputting a steering signal to a steering mechanism of the tow vehicle to adjust at least one steerable wheel of the tow vehicle by an adjustment when the determined difference exceeds the threshold difference, the adjustment being based at least partially on the received windrow signal.

Abstract: A detection system detects malfunctions in an autonomous farming vehicle during an autonomous routine using one or more models and data from sensors coupled to the autonomous farming vehicle. The models may include machine-learned models trained on the sensor data and configured to identify objects indicative of an operational or malfunctioning component within a tilling assembly such as a tilling shank or sweep. Additionally, a machine-learned model may be trained on sensor data to detect whether debris has plugged the tilling assembly of the autonomous farming vehicle. In response to detecting a malfunction or a plug, the detection system may modify the autonomous routine (e.g., pausing operation) or provide information for the malfunction to be addressed (e.g., the likely location of a malfunctioning sweep that has detached from the tilling assembly).

Abstract: An electronic device configured to control a host vehicle includes: an image sensor configured to photograph a surrounding environment of the host vehicle; and a processor configured to perform an image processing operation based on a first image captured by the image sensor, and control the host vehicle based on the processing result, wherein the processor determines whether to use a high speed performance of the image processing operation based on a speed of the host vehicle, and the electronic device is configured such that when the high speed performance is not used, the processor performs the image processing operation by using a first image processing module, and when the high speed performance is used, the processor performs the image processing operation by using a second image processing module having less data throughput than the first image processing module.

Abstract: The present disclosure provides a method comprising receiving a rideshare request from a user, the rideshare request including a destination; transporting the passenger to the destination using an autonomous vehicle (AV) having a plurality of windows comprising electrically switchable smart glass; during the transporting, monitoring a metric related to arrival of the AV at the destination; and untinting the windows when the monitored metric has a prescribed relationship to a threshold value.

Abstract: An example method may include i) determining a first distance between a pair of feet of a robot at a first time, where the pair of feet is in contact with a ground surface; ii) determining a second distance between the pair of feet of the robot at a second time, where the pair of feet remains in contact with the ground surface from the first time to the second time; iii) comparing a difference between the determined first and second distances to a threshold difference; iv) determining that the difference between determined first and second distances exceeds the threshold difference; and v) based on the determination that the difference between the determined first and second distances exceeds the threshold difference, causing the robot to react.

Abstract: According to one aspect, cooperative multi-goal, multi-agent, multi-stage (CM3) reinforcement learning may include training a first agent using a first policy gradient and a first critic using a first loss function to learn goals in a single-agent environment using a Markov decision process, training a number of agents based on the first policy gradient and a second policy gradient and a second critic based on the first loss function and a second loss function to learn cooperation between the agents in a multi-agent environment using a Markov game to instantiate a second agent neural network, each of the agents instantiated with the first agent neural network in a pre-trained fashion, and generating a CM3 network policy based on the first agent neural network and the second agent neural network. The CM3 network policy may be implemented in a CM3 based autonomous vehicle to facilitate autonomous driving.

Abstract: A periphery monitoring device includes a coupling determiner that determines whether a towed vehicle is coupled to a towing vehicle to which the towed vehicle can be coupled; a target setter that sets a target moving position to be a target for moving at least the towed vehicle coupled to the towing vehicle; a storing controller that stores, as a moving target image, an image, including the target moving position, of a peripheral image generated by as imager provided at the towing vehicle; and an image controller that displays the stored moving target image in association with the towing vehicle or the towed vehicle included in a current image generated by the imager and currently displayed on a display device.

Abstract: An autonomous driving device 100 is configured to set a target route to merge into a first main line L11 from a branch line L20 based on waypoints P set in a lane. If it is determined by the merge determination unit 14 that the merge is not easy, the target route setting unit 15 is configured to select the waypoint P on the branch line L20 set at the position on an end edge E side of the branch line L20 as the end edge waypoint Pa closest to the end edge E side on the branch line L20 to be selected for setting the target route, compared to a case where it is determined that the merge is easy.

Abstract: A method for controlling emergency stop of an autonomous vehicle is provided. The method includes: controlling emergency stop of an autonomous vehicle capable of recognizing a stop request from an emergency vehicle, determining whether a current situation is an emergency situation or a general situation, and performing a procedure corresponding to each situation to stop the autonomous vehicle based on each situation.

Abstract: Disclosed is an electronic device for determining the angular position of a shaft of a motor vehicle, the device including a printed circuit board and a magnetic guide including at least two fastening tabs for fastening to the printed circuit board, the printed circuit board including a base substrate and at least two fastening areas for fastening the magnetic guide, each designed to receive a fastening tab of the magnetic guide, the fastening tab defining a fastening orifice. Each fastening area is defined on the base substrate of the printed circuit board and includes a pad fastened to the base substrate. Each fastening tab is joined to the pad of the corresponding fastening area by way of an adhesive that is applied in its fastening orifice.

Abstract: Smart car method for autonomous navigation by creating a 3D model based on outputs of the camera and sensor; accessing a high definition map database and generating a trip with travel segments from origin to destination; detecting a freeway entrance or an exit lane based on a road marking using a camera and a sensor; if the travel segment passes the freeway entrance or exit, then follow the current lane without exiting; and otherwise following the freeway entrance or exit.

Abstract: An autonomous driving vehicle includes a user detection monitoring device and a start control device. The user detection monitoring device detects a user who got out of the autonomous driving vehicle after the autonomous driving vehicle stopped at a destination as an alighted user and monitors the alighted user. The start control device maintains a stopped state of the autonomous driving vehicle after the alighted user was detected until a start condition is satisfied and, if the start condition is satisfied, permits a start of the autonomous driving vehicle. The start condition is one of a condition indicating that the alighted user at least moves out of a movement determination area around the autonomous driving vehicle and a condition indicating that the alighted user is present in the movement determination area but remains at the same position for a certain period of time or longer.

Abstract: A vehicle control device in an embodiment includes a recognition unit that recognizes a surrounding situation of a vehicle and a driving control unit that controls one or both of steering and acceleration or deceleration of the vehicle on the basis of the surrounding situation recognized by the recognition unit, and in a case where the vehicle merges into a second lane from a first lane in which the vehicle travels, and a section of the second lane before merging recognized by the recognition unit is downhill, the driving control unit makes a speed or acceleration of the vehicle higher than in a case where the section before merging is not downhill.

Abstract: Navigation devices and methods of operation are provided. The navigation device includes a communication unit; a display; an input unit for receiving an input of data; a communication unit; and a controller for controlling operation of the display and the input unit. The controller connects to a data server through the communication unit, requests path setting information to the data server, receives the path setting information from the data server, acquires present position information of the navigation device, acquires a user moving path by reflecting the acquired position information and the received path setting information, and sets the user moving path as a guidance path. In this case, the path setting information is generated in another electronic device or the data server based on user input information input from the another electronic device.

Abstract: The present disclosure relates to a concept for machine-learning-based prediction of a destination for a user. Based on historic search data associated with the user, at least one candidate destination is determined based on the user and a given context. A plurality of embedding vectors are determined from an embedding matrix, wherein the embedding vectors are associated with the at least one candidate destination, the user, and the given context. The embedding matrix comprising embedding vectors for different components of the historic search data. The plurality of embedding vectors are fed into one or more first neural network layers to generate a semantic embedding for the candidate destination. The semantic embedding is into one or more second neural network layers to generate a probability score for the candidate destination.