Abstract: A management device includes a circuit. The circuit generates driving data of each of a plurality of areas, the driving data indicating a driving path in the area and causing a vehicle to autonomously drive along the driving path, while the vehicle is driving in each area, transmits driving data of an area next to the area to the vehicle via a base station of the area, and when driving data of a second area that is next to a first area is not transmitted to the vehicle via a base station of the first area while the vehicle is autonomously driving in the first area, after the vehicle has driven back to a previous area, transmits driving data of a different area that is located at a position from the previous area to a target spot to the vehicle via a base station of the previous area.

Abstract: In various examples, at least partial control of a vehicle may be transferred to a control system remote from the vehicle. Sensor data may be received from a sensor(s) of the vehicle and the sensor data may be encoded to generate encoded sensor data. The encoded sensor data may be transmitted to the control system for display on a virtual reality headset of the control system. Control data may be received by the vehicle and from the control system that may be representative of a control input(s) from the control system, and actuation by an actuation component(s) of the vehicle may be caused based on the control input.

Abstract: A system comprises a computer including a processor and a memory. The memory storing instructions executable by the processor to cause the processor to detect a roadside device via at least one vehicle sensor of a plurality of vehicle sensors; determine a location of a vehicle based on a fixed location of the roadside device; determine a location correction adjustment, wherein the location correction adjustment comprises a difference between an assumed location of the vehicle and the determined location of the vehicle, wherein the assumed location is obtained from a navigation system of the vehicle; and adjust the assumed location based on the location correction adjustment.

Abstract: A steering control device includes an electronic control unit. The electronic control unit detects an absolute steering angle. The electronic control unit controls driving of a motor. The electronic control unit determines whether movement of a turning shaft to one of right and left sides has been limited by an end contact or execution of end contact relaxation control. The electronic control unit acquires a plurality of limit position determination angles corresponding to the absolute steering angle. The electronic control unit permits update of an end-position-corresponding angle. The electronic control unit updates the end-position-corresponding angle stored in the electronic control unit.

Abstract: A system for monitoring wear on ground engaging tools of an agricultural implement includes a ground engaging tool supported relative to a frame of the agricultural implement, the ground engaging tool configured to rotate with engagement of the ground during operation of the agricultural implement, a non-contact sensor configured to detect a parameter indicative of wear on the ground engaging tool, and a controller communicatively coupled to the non-contact sensor. The controller is configured to determine a status of the wear on the ground engaging tool based on sensor data received from the non-contact sensor.

Abstract: Disclosed are a driving guide system and method. The driving guide system includes an input unit configured to receive signal information from a signal controller, a memory in which a driving guide program using the signal information is embedded, and a processor configured to execute the program. When it is not possible to recognize a traffic light using a camera, the processor determines a driving behavior using the signal information and then performs autonomous driving according to the driving behavior or provides a notification to a driver.

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: 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: The present disclosure relates to a method for generating control signals to assist occupants in a vehicle, wherein a context of the vehicle is determined, a rule of a rule-based data system is selected depending on the determined context, wherein the rule-based data system comprises a plurality of rules, wherein each rule has a condition part and a result part, wherein the condition part comprises conditions for the context of the vehicle, a confidence value associated with the selected rule is determined, wherein the confidence value indicates the probability with which the result of the rule corresponds with the preference of the user, a result of the selected rule is generated, a control signal is generated and output depending on the generated rule result, wherein the control signal automates a vehicle function with a degree of automation, wherein the degree of automation depends on the confidence value of the selected rule. The disclosure likewise relates to a device for executing this method.

Abstract: Techniques are discussed for controlling a vehicle, such as an autonomous vehicle, based on occluded areas in an environment. An occluded area can represent areas where sensors of the vehicle are unable to sense portions of the environment due to obstruction by another object. An occlusion grid representing the occluded area can be stored as map data or can be dynamically generated. An occlusion grid can include occlusion fields, which represent discrete two- or three-dimensional areas of driveable environment. An occlusion field can indicate an occlusion state and an occupancy state, determined using LIDAR data and/or image data captured by the vehicle. An occupancy state of an occlusion field can be determined by ray casting LIDAR data or by projecting an occlusion field into segmented image data. The vehicle can be controlled to traverse the environment when a sufficient portion of the occlusion grid is visible and unoccupied.

Abstract: A method of mobile automation apparatus localization in a navigation controller includes: controlling a depth sensor to capture a plurality of depth measurements corresponding to an area containing a navigational structure; selecting a primary subset of the depth measurements; selecting, from the primary subset, a corner candidate subset of the depth measurements; generating, from the corner candidate subset, a corner edge corresponding to the navigational structure; selecting an aisle subset of the depth measurements from the primary subset, according to the corner edge; selecting, from the aisle subset, a local minimum depth measurement for each of a plurality of sampling planes extending from the depth sensor; generating a shelf plane from the local minimum depth measurements; and updating a localization of the mobile automation apparatus based on the corner edge and the shelf plane.

Abstract: Systems and methods for providing a user control of an autonomous vehicle to enable the user to assist the autonomous vehicle. In one example embodiment, a computer implemented method includes identifying, by a computing system comprising one or more computing devices, an occurrence of an event associated with an autonomous vehicle. The event can hinder an ability of the autonomous vehicle to operate in an autonomous operating mode. In response to the occurrence of the event associated with the autonomous vehicle, the method includes determining, by the computing system, a user to operate the autonomous vehicle based at least in part on a profile associated with the user. The method includes providing, by the computing system, one or more control signals to cause the autonomous vehicle to enter into an operating mode that allows the user to operate the autonomous vehicle.

Abstract: A computer-implemented method, apparatus, and system for receiving and calibrating lateral deviation values and for controlling an autonomous vehicle to correct the lateral deviation is described. In every perception and planning cycle, a single lateral deviation value representative of an estimated autonomous vehicle lateral deviation from a reference line (e.g., corresponding to a center of the lane) is generated based on camera detection. The deviation value for a present cycle is received. A calibrated deviation value can be updated for the present cycle based on the received deviation value and a Gaussian distribution model. Control signals for the present cycle are generated to drive the autonomous vehicle to at least partially correct the autonomous vehicle lateral deviation based on the updated calibrated deviation value.

Abstract: The driving support system extracts edge points based on luminance of pixels, in which up edge point is the edge point where the luminance of the inner pixel is smaller than that of the outer pixel, down edge point is the edge point where the luminance of the outer pixel is smaller than the luminance of inner pixel. The system determines, among a plurality of line candidates acquired in accordance with the edge points location, a lane candidate excluding the line candidate that satisfies a predetermined exclusion condition, to be the lane marking, the line candidate determined as the lane marking being located most closely to the vehicle position. The exclusion condition includes a condition where the number of edge points of the up edge line is larger than or equal to a predetermined point threshold, compared to the number of edge points of the down edge line.

Abstract: A method of dynamic path generation in a navigational controller includes: generating (i) a plurality of paths extending from a starting location to a goal location, and (ii) a cost for each of the paths; selecting, based on the costs, an optimal path for execution from the plurality of paths; storing a subset of the costs in a cost history queue; responsive to detecting an obstacle during execution of the optimal path, retrieving one of the costs from the cost history queue; determining an initial re-planning search space based on the cost retrieved from the cost history queue; and initiating generation of a re-planned path within the initial re-planning search space.

Abstract: Systems and methods are provided for navigating an autonomous vehicle using reinforcement learning techniques.

Abstract: Various systems and methods for drone path planning are described herein. A system for drone path planning for a drone, the system to perform the operations: storing a current location of the drone as a current waypoint in a set of waypoints, the set of waypoints to include the current waypoint and a previous waypoint; detecting a current set of frontiers from the current location; storing the current set of frontiers in a list of active goals, the current set of frontiers associated with the current location; determining whether a target is discovered in the current set of frontiers; exploring the current set of frontiers to attempt to find the target; and exploring a previous set of frontiers from the list of active goals, when exploring the current set of frontiers does not, discover the target, the previous set of frontiers associated with the previous waypoint.