Abstract: A following target identification system is used for identifying a following target of a moving object configured to move autonomously. The following target identification system includes: a portable information generation terminal having a wireless communication function and configured to generate following target information about a movement of the following target in a state where the information generation terminal is carried by the following target; a communication unit included in the moving object and configured to receive the following target information sent from the information generation terminal via wireless communication with the information generation terminal; and a following control unit included in the moving object and configured to determine a position of the following target based on the following target information.

Abstract: An unmanned aerial vehicle may include: a sensor part configured to acquire inertia information or position information of the unmanned aerial vehicle; and a controller. The controller is configured to estimate the position of the unmanned aerial vehicle by applying the information acquired by the sensor part to an extended Kalman filter and control movement of the unmanned aerial vehicle, based on the estimated position of the unmanned aerial vehicle. The sensor part includes: an inertia sensor configured to acquire the inertia information of the unmanned aerial vehicle; a tag recognition sensor configured to recognize a tag attached to a rack and acquire absolute position information of the unmanned aerial vehicle; and an image sensor attached to the unmanned aerial vehicle so as to acquire an image of the movement environment of the unmanned aerial vehicle.

Abstract: A method of path planning for a host vehicle includes: receiving host vehicle, environmental and obstacle information; calculating one or more projected host vehicle locations; computing a projected obstacle location for each obstacle; and determining a collision potential between each projected host vehicle location and each projected obstacle location. Until a maximum number of steps is reached, and while at least one projected host vehicle location has an associated collision potential below a collision threshold, the method further includes repeating the calculating, computing and determining steps.

Abstract: A method includes receiving one or more images from a camera positioned on a back portion of the vehicle and receiving a driver planned path from a user interface. The driver planned path includes a plurality of waypoints. The method also includes transmitting one or more commands to a drive system causing the vehicle to autonomously maneuver along the driver planned path. The method also includes determining a current vehicle position and an estimated subsequent vehicle position. The method also includes determining a path adjustment from the current vehicle position to the estimated subsequent vehicle position. The method also includes transmitting instructions to the drive system causing the vehicle to autonomously maneuver towards the estimated subsequent vehicle position based on the path adjustment.

Abstract: An autonomous vehicle computing system can include a primary perception system configured to receive a plurality of sensor data points as input generate primary perception data representing a plurality of classifiable objects and a plurality of paths representing tracked motion of the plurality of classifiable objects. The autonomous vehicle computing system can include a secondary perception system configured to receive the plurality of sensor data points as input, cluster a subset of the plurality of sensor data points of the sensor data to generate one or more sensor data point clusters representing one or more unclassifiable objects that are not classifiable by the primary perception system, and generate secondary path data representing tracked motion of the one or more unclassifiable objects. The autonomous vehicle computing system can generate fused perception data based on the primary perception data and the one or more unclassifiable objects.

Abstract: A system, method, and computer-readable medium having instructions stored thereon to enable an ego vehicle having an autonomous driving function to estimate and traverse a curved segment of highway utilizing radar sensor data. The radar sensor data may comprise stationary reflections and moving reflections. The ego vehicle may utilize other data, such as global positioning system data, for the estimation and traversal. The estimation of the curvature may be refined based upon a lookup table or a deep neural network.

Abstract: An optical apparatus includes a deflector configured to deflect illumination light from a light source to scan an object, and configured to deflect reflected light from the object, a light guide configured to guide the illumination light form the light source to the deflector, and configured to guide the reflected light from the deflector to a light receiving element, an optical member having a reflective area that makes first light which is part of the illumination light from the deflector incident on the deflector by reflection, and a controller configured to obtain information regarding the deflector on the basis of information of the first light from the reflective area. In a cross-section including the optical path from the reflective area to the light guide, a width of the reflective area is smaller than a width of the illumination light on the reflective area.

Abstract: A system and method for controlling deployment of a vehicle air dam that include receiving vehicle data associated with a vehicle operating condition. The system and method also include analyzing the vehicle data to determine if an elevated engine load condition is present to implement a normal air dam deployment mode or a prohibitive air dam deployment mode. The system and method further include controlling an actuator associated with the vehicle air dam to deploy or retract the vehicle air dam based on the implementation of the normal air dam deployment mode or the prohibitive air dam deployment mode.

Abstract: Systems, methods, and apparatus for acquiring surface profile information (e.g., depths at multiple points) from limited-access structures and objects using an autonomous or remotely operated flying platform (such as an unmanned aerial vehicle). The systems proposed herein use a profilometer to measure the profile of an area on a surface where visual inspection has indicated that the surface has a potential anomaly. After the system has gathered data representing the surface profile in the area containing the potential anomaly, a determination may be made whether the collected image data indicates that the structure or object should be repaired or may be used as is.

Abstract: The offline map generation process may collect multiple point cloud data of the same area. A perception algorithm may operate on the point cloud data to detect static objects, which may be fixed road features that do not change among the point cloud data, allowing the perception algorithm to more accurately detect the static objects. During online operation of the ADV through the area, the ADV may trim regions-of-interest (ROI) of the area to exclude the predefined static objects. The perception algorithm may execute the sensor data of the ROI in real-time to detect objects in the ROI. The may be added back to the output of the perception algorithm to complete the perception output.

Abstract: Provided are an action plan making system and method for an unmanned aerial vehicle, and a storage medium. The action plan making system (1) includes: an unmanned aerial vehicle (10), provided with an aerial shooting device; and a computer (30). A control unit of the computer (30) executes a making module (324), which makes an action plan including a flight plan and a shooting plan of the unmanned aerial vehicle (10) according to a region accepted by executing a region acceptance module (322) and a purpose accepted by executing a purpose acceptance module (323). A control unit (14) of the unmanned aerial vehicle (10) executes a control module (141), and controls the aerial shooting of a camera (17) and flying of the unmanned aerial vehicle (10) based on the action plan made by executing the making module (324) by the control unit (32) of the computer (30).

Abstract: A system for detecting and tracking objects using lidar can include one or more processors configured to receive lidar data. The one or more processors can determine shape data from the lidar data. The shape data can be indicative of an object. The one or more processors can determine a plurality of extents of the object based on the shape data. The one or more processors can update a state of the object based on the plurality of extents, the state including a boundary of the object. The one or more processors can provide the state of the object to an autonomous vehicle controller to cause the autonomous vehicle controller to control an autonomous vehicle responsive to the state of the object.

Abstract: Vehicle having dangerous situation notification function and control method, where the method includes monitoring a vicinity of a host vehicle, detecting a movable object in the vicinity of the host vehicle, predicting a dangerous situation in which a collision between the host vehicle and the object is expected, and displaying a warning mark on at least one of a roadway or a wall surface in a projection mapping method in response to the dangerous situation. One or more of an autonomous vehicle, a user terminal, and a server may be in conjunction with an Artificial Intelligence (AI) module, an Unmanned Aerial Vehicle (UAV), an Augmented Reality (AR) device, a Virtual Reality (VR) device, and a device related to a 5G service, etc.

Abstract: A method for detecting an obstacle along a known path of a machine can include relating the location of the machine with a map. The map can include a worksite with a known path or known paths that the machine can travel on. The stopping distance of the machine can be determined and based on these identified known path and its characteristics along with a traveling speed and a weight of the machine. A LIDAR region of interest can be determined based on the stopping distance, the position and orientation of the machine, and characteristics of the known path. The machine can include a LIDAR system that can be configured to be oriented with respect to the LIDAR region of interest. A concentrated LIDAR scan can be performed to detect if an obstacle is present within the LIDAR region of interest.

Abstract: A system of a first vehicle includes sensors and a processor that performs generating of an initial trajectory along a travel route of the first vehicle, acquiring trajectories of second vehicles along the route, adjusting the initial trajectory based on the acquired trajectories of the second vehicles, and navigating the first vehicle based on the adjusted initial trajectory.

Abstract: A system of linearizing a trajectory of an autonomous vehicle about a reference path includes a computing device and a computer-readable storage medium.

Abstract: A vehicle includes a controller that may be configured to, responsive to receiving a delivery request associated with a drone, periodically transmit a current location, trip route information, and acceleration data of the vehicle to guide the drone to a rendezvous location, and responsive to receiving a proximity notification associated with the drone, open a delivery opening of the vehicle.

Abstract: A method for a follower vehicle following a lead vehicle, comprising establishing, in a first control mode of the follower vehicle, a path for the follower vehicle to follow the lead vehicle, characterized by generating environmental data which is related to the environment of the lead vehicle, determining, based on the generated environmental data, an expected behaviour of an operational parameter of the lead vehicle, determining an actual behaviour of the lead vehicle operational parameter, comparing the determined expected behaviour of the lead vehicle operational parameter and the determined actual behaviour of the lead vehicle operational parameter, determining based on said comparison whether to continue in first control mode of the follower vehicle, or in a second control mode of the follower vehicle, differing from the first control mode.

Abstract: An active impact control system includes: at least one actuator couplable to a crush structure of a vehicle and couplable to a portion of a structure of the vehicle; at least one sensor configured to sense impact with an object; and a controller configured to receive information from the at least one sensor and to determine a location and angle of impact based on the information received from the sensor, the controller being further configured to selectively signal the actuator to cause the crush structure to move relative to the structure of the vehicle.

Abstract: An agent management device includes a processor including hardware, the processor being configured to: generate a response to an inquiry from an occupant of a moving body; determine whether or not a change in a control content of the moving body to correspond to the response is possible; output a question whether or not to execute the change to the control content to the occupant when determining that the change to the control content is possible; and execute the change to the control content when the occupant approves the change to the control content.