Abstract: Aspects of the disclosure relate to generating scouting objectives in order to update map information used to control a fleet of vehicles in an autonomous driving mode. For instance, a notification from a vehicle of the fleet identifying a feature and a location of the feature may be received. A first bound for a scouting area may be identified based on the location of the feature. A second bound for the scouting area may be identified based on a lane closest to the feature. A scouting objective may be generated for the feature based on the first bound and the second bound.

Abstract: Recent location and control information received from “lead” vehicles that traveled over a segment of land, sea, or air is captured to inform, via aggregated data, subsequent “trailing” vehicles that travel over that same segment of land, sea, or air. The aggregated data may provide the trailing vehicles with annotated road information that identifies obstacles. In some embodiments, at least some sensor control data may be provided to the subsequent vehicles to assist those vehicles in identifying the obstacles and/or performing other tasks. Besides, obstacles, the location and control information may enable determining areas traveled by vehicles that are not included in conventional maps, as well as vehicle actions associated with particular locations, such as places where vehicles park or make other maneuvers.

Abstract: The present invention is related to a vehicle positioning device being connected to a first sensor outputting satellite positioning data, and a second sensor detecting a state amount of a vehicle and outputting the state amount as state amount data, and being connected to at least one of a third sensor detecting a terrestrial object and outputting data of a relative relationship between the terrestrial object and the vehicle, and a fourth sensor detecting a road line shape and outputting road line shape data. The vehicle positioning device includes: observed value processing circuitry configured to generate an actual observed value; sensor correction circuitry; inertial positioning circuitry; and observed value prediction circuitry configured to predict an observed value and output the observed value as a predicted observed value. Positioning calculation is performed by using the predicted observed value and the actual observed value and results are output as positioning results.

Abstract: A method and system provide the ability to autonomously operate an unmanned aerial system (UAS) over long durations of time. The UAS vehicle autonomously takes off from a take-off landing-charging station and autonomously executes a mission. The mission includes data acquisition instructions in a defined observation area. Upon mission completion, the UAS autonomously travels to a target landing-charging station and performs an autonomous precision landing on the target landing-charging station. The UAS autonomously re-charges via the target landing-charging station. Once re-charged, the UAS is ready to execute a next sortie. When landed, the UAS autonomously transmits mission data to the landing-charging station for in situ or cloud-based data processing.

Abstract: A reliability map is generated by superimposing or associating populations or other intrinsic data is superimposed or associated with a geographic map of a region, which is divided into a grid having cells of uniform size. Densities of towns, cities or other geospatial areas are determined and assigned to cells of the grid, which have sizes corresponding to minimum dimensions of a corridor required for travel by an aerial vehicle. When a mission requiring travel from an origin to a destination within the region is identified, one or more paths of a safe route between the origin and the destination are selected based on the reliability map. The safe route is selected to avoid areas of high population density or locations of critical infrastructure such as schools, hospitals or public safety buildings.

Abstract: In one embodiment, a method includes determining a fleet-level objective for a vehicle associated with instructing the vehicle to travel a route according to route criteria based on the fleet-level objective. The method includes receiving a ride request from a ride requestor associated with ride criteria including a pick-up location and a drop-off location. The method includes determining that the ride requestor is a passenger for the vehicle to satisfy the fleet-level objective contingent on modifications to the ride criteria. The method includes providing incentives to the ride requestor contingent on acceptance of the modifications to the ride criteria. The method includes, after receiving the acceptance of the modifications to the ride criteria, modifying the ride criteria in accordance with the route criteria. The method includes instructing the vehicle to transport the ride requestor based on the modified ride criteria so as to fulfill the fleet-level objective.

Abstract: Embodiments of the present disclosure are directed to systems and methods for autonomously performing and/or facilitating drone diagnostic functions. Prior to a mission of a UAV, an inspection station comprising at least one imaging sensor and at least one directional force sensor may be used to perform a plurality of air worthiness inspections and/or maintenance checks with little to no human intervention. Once the UAV has been determined to be air worthy, it is approved for a subsequent mission.

Abstract: Provided is a self-position estimation apparatus capable of appropriately estimating a self-position of a mobile object regardless of an environment around the mobile object. The self-position estimation apparatus is for estimating a self-position of a mobile object, and includes: N types of sensors (where N is a natural number equal to or greater than two) that detect information with different contents from each other regarding a moving status of the mobile object; an environment determiner that determines an environment around the mobile object; a selector that selects information detected by one or more but less than the N types of sensors based on a determination result of the environment determiner; and an estimator that estimates the self-position of the mobile object based on the information selected by the selector.

Abstract: A system receives a road network map that corresponds to a road network that is in an environment of an autonomous vehicle. For each of the one or more lane segments, the system identifies one or more conflicting lane segments from the plurality of lane segments, each of which conflicts with the lane segment, and adds conflict data pertaining to a conflict between the lane segment and the one or more conflicting lane segments to a set of conflict data. The system analyzes the conflict data to identify a conflict cluster that is representative of an intersection. The system groups predecessor lane segments and the successor lane segments as inlets or outlets of the intersection, generates an outer geometric boundary of the intersection, generates an inner geometric boundary of the intersection, creates a data representation of the intersection and adds the data representation to the road network map.

Abstract: A domestic robotic system includes a robot and data storage operable to store data defining a boundary of a working area, the robot includes a payload actuable to perform work on a portion of the working area adjacent the robot, at least one processor, a first positioning system, one or more sensors operable to sense directly the boundary of the working area and a current distance of the robot thereto, a second positioning system, which uses data from the sensors. The processor is programmed to operate in (i) an area coverage mode, wherein the processor, using the first positioning system and the stored data defining the boundary of the working area, navigates the robot around the working area, with the payload active, and (ii) a boundary proximity mode, wherein the processor, using the second positioning system, navigates the robot around the working area, in proximity to the boundary.

Abstract: A method for creating a radar localization map. The method includes: a) sensing a defined region using a surround-field sensor of a mapping vehicle; b) providing photographic satellite data of the defined region with the aid of a satellite, c) determining matching detected objects of the region in the surround-field sensor data and in the photographic satellite data; d) generating a transfer model from the matching detected objects, the photographic satellite data being transferable reciprocally into the surround-field sensor data utilizing the transfer model; and e) creating the radar localization map by use of the transfer model using photographic satellite data, the photographic satellite data being converted into corresponding data of the radar localization map.

Abstract: A mobile vehicle and a method of controlling the mobile vehicle are provided. The method of controlling the mobile vehicle includes defining a first line between the mobile vehicle and the user, determining a position and a size of each of at least one obstacle having a predetermined relationship with the first line, defining a second line based on the first line, the position of the at least one obstacle, and the size of the at least one obstacle, and controlling the mobile vehicle according to a direction and a size of an external force on the second line.

Abstract: In some embodiments, an electronic device displays suggested routes based on the characteristics of respective vehicles. In some embodiments, an electronic device receives data for respective vehicles from a source external to the electronic device. In some embodiments, an electronic device anonymizes a vehicle identifier and determines customized suggested routes using the anonymized vehicle identifier.

Abstract: A mobile robot platooning system includes a plurality of mobile robots operated for transfer of objects in a factory, and a central server connected to the plurality of mobile robots through wireless communication, and configured to set a path of the plurality of mobile robots to control autonomous driving of the mobile robots, wherein the central server is configured to group a platoon of the mobile robots required for processing a work, dispose the platoon of the mobile robots into a predetermined platoon form, collect state information of the platoon of the mobile robots, and control the platoon of the mobile robots to move in a synchronized form based on the state information.

Abstract: A method of managing a moving object in a fleet system includes: transmitting, by a user device, a moving object request message including a departure point and use time information to a server; receiving, by the user device, a list of available moving objects based on the moving object request message from the server: selecting, by the user device, a moving object from the list of available moving objects; transmitting, by the user device, reservation information based on the moving object selection information and the use time information to the server; and allocating the moving object to the user device based on the reservation information. The server may update allocation and placement information of a plurality of moving objects based on reservation information of a plurality of user devices.

Abstract: Systems and methods are provided for vehicle navigation. In one implementation, a system may comprise an interface to obtain sensing data of an environment of the host vehicle. A processing device may be configured to determine a planned navigational action for the host vehicle; identify a target vehicle in the environment of the host vehicle; predict a resulting distance between the host and target vehicles if the planned action were taken; determine a host vehicle stopping distance based on a braking rate, maximum acceleration capability, and current speed of the host vehicle; determine a target vehicle stopping distance based on a braking rate and current speed of the target vehicle; and continue with the planned navigational action while the predicted distance is greater than a minimum safe longitudinal distance calculated based on the host vehicle stopping distance and the target vehicle stopping distance.

Abstract: Provided is a robot, including: a main brush; a peripheral brush; a first actuator; a first sensor; one or more processors; and memory storing instructions that when executed by at least some of the one or more processors effectuate operations including: determining a first location of the robot; obtaining first data indicative of an environmental characteristic of the first location; adjusting a first operational parameter of the first actuator based on the sensed first data; and forming or updating a debris map of the working environment based on data output by the first sensor or another sensor configured to collect data indicative of an existence of debris on a floor, wherein the debris map at least indicates areas covered by the robot and with a high level of debris accumulation; and an application of a communication device paired with the robot and configured to at least display the debris map.

Abstract: Operating an autonomous grounds maintenance machine includes determining a travel path for the machine to reach a destination waypoint in a zone of a work region. The travel path is analyzed for problem areas based on a predetermined terrain map. Rotations of the machine may be planned even before the machine begins to travel down the path to proactively prevent the machine from becoming trapped.

Abstract: Systems, methods, tangible non-transitory computer-readable media, and devices for operating an autonomous vehicle are provided. For example, the disclosed technology can include receiving state data that includes information associated with states of an autonomous vehicle and an environment external to the autonomous vehicle. Responsive to the state data satisfying vehicle stoppage criteria, vehicle stoppage conditions can be determined to have occurred. A severity level of the vehicle stoppage conditions can be selected from a plurality of available severity levels respectively associated with a plurality of different sets of constraints. A motion plan can be generated based on the state data. The motion plan can include information associated with locations for the autonomous vehicle to traverse at time intervals corresponding to the locations. Further, the locations can include a current location of the autonomous vehicle and a destination location at which the autonomous vehicle stops traveling.

Abstract: A parking assistance device according to the present disclosure performs automatic travel of a vehicle based on teaching travel by a driver. The parking assistance device includes a memory and a hardware processor coupled to the memory. The hardware processor is configured to: learn a travel route for parking the vehicle in a parking position and a speed, a steering angle, and braking operation of the vehicle during the teaching travel, based on the teaching travel; and modify the speed, the steering angle, or the braking operation in the automatic travel of the vehicle from the speed, the steering angle, or the braking operation in the teaching travel, based on any of a regulation for the automatic travel of the vehicle, environmental information relating to an environment around the vehicle, or geographic information relating to a road condition around the vehicle.

Abstract: Disclosed is a method and apparatus for extracting data in a deep learning model. The method includes receiving an input query, determining a first decision boundary set being a subset of a decision boundary set corresponding to a target layer of the deep learning model, extracting a decision region including the input query based on the first decision boundary set, and extracting data included in the decision region.

Abstract: A parking assist system is configured to autonomously move a vehicle to a target parking position. The parking assist system includes: a distance acquiring unit configured to acquire a distance between the vehicle and a vehicle stopper provided in the target parking position; and a control unit configured to control a movement of the vehicle to the target parking position based on the distance acquired by the distance acquiring unit.

Abstract: Some embodiments include a robot, including: a chassis; a set of wheels coupled to the chassis; at least one encoder coupled to a wheel with a resolution of at least one count for every ninety degree rotation of the wheel; a trailing arm suspension coupled to each drive wheel for overcoming surface transitions and obstacles, wherein a first suspension arm is positioned on a right side of a right drive wheel and a second suspension arm is positioned on a left side of a left drive wheel; a roller brush; a collection bin; a fan with multiple blades for creating a negative pressure resulting in suction of dust and debris; a network card for wireless communication with at least one of: a computing device, a charging station, and another robot; a plurality of sensors; a processor; and a media storing instructions that when executed by the processor effectuates robotic operations.

Abstract: A perception system includes a perception module configured to capture first sensor data that includes data from at least one of an external sensor and a camera captured in a first period, a prediction module configured to receive the first sensor data, generate, based on the first sensor data, predicted sensor data for a second period subsequent to the first period, receive second sensor data for the second period, and output results of a comparison between the predicted sensor data and the second sensor data, and a diagnostic module configured to selectively identify a fault in the perception system based on the results of the comparison.

Abstract: A path planning system includes a path planning algorithm operable to receive a desired harvest width for each pass of a harvester implement, and a boundary of a harvest area to be harvested. The path planning algorithm determine a surface elevation of the harvest area within the boundary, which includes at least one elevation contour establishing a line of constant elevation. The path planning algorithm then defines a harvest path for the harvester implement to follow while harvesting the crop material. The harvest path is defined to substantially parallel the at least one elevation contour, and is incremented in elevation in a parallel manner based on the desired harvest width.

Abstract: To provide personalized data for display on a map, a server device obtains location data for a user and identifies locations that are familiar to the user based on the frequency and recency in which the user visits the locations. The server device then provides the familiar locations in search results/suggestions and annotates the familiar locations with a description of a relationship between the familiar location and the user. The server device also includes the familiar locations as landmarks for performing maneuvers in a set of navigation instructions. Furthermore, the server device provides a familiar location as a frame of reference on a map display when a user selects another location nearby the familiar location. Moreover, the server device includes a familiar location as an intermediate destination when the user request navigation directions to a final destination.

Abstract: A cleaning control method and device including a cleaning robot are provided. The cleaning robot includes a sweeping member and a mopping member which cooperate with each other, and an operating parameter is obtained. A sweeping area formed in real time by the sweeping member, a mopping area formed in real time by the mopping member and a mopped area formed on a lateral side of the cleaning robot are controlled to meet a first preset relationship according to the operating parameter. The first preset relationship includes that a first distance formed between a boundary of the sweeping area closest to the mopped area and a boundary of the mopped area closest to the sweeping area is greater than or equal to a second distance between a boundary of the mopping area closest to the mopped area and a boundary of the mopped area closest to the mopping area.

Abstract: The present disclosure provides autonomous vehicle systems and methods that include or otherwise leverage a machine-learned yield model. In particular, the machine-learned yield model can be trained or otherwise configured to receive and process feature data descriptive of objects perceived by the autonomous vehicle and/or the surrounding environment and, in response to receipt of the feature data, provide yield decisions for the autonomous vehicle relative to the objects. For example, a yield decision for a first object can describe a yield behavior for the autonomous vehicle relative to the first object (e.g., yield to the first object or do not yield to the first object). Example objects include traffic signals, additional vehicles, or other objects. The motion of the autonomous vehicle can be controlled in accordance with the yield decisions provided by the machine-learned yield model.

Abstract: A map database stores data describing a set of connected roadways, each having one or more lanes. A coverage system selects lanes from the map database along which AVs can stop, such as parking lanes. The coverage system generates buffer regions for each of the stopping lanes, where each buffer region includes an area within a given distance of the respective lane. The coverage system combines the buffer regions to generate a coverage area, and provides a display of the coverage area overlayed over a map of roads corresponding to the coverage area.

Abstract: A delivery system includes a cooking station, a meal serving station, a drone, a meal conveying device configured to convey a meal from the cooking station to the meal serving station, and a controlling portion. A character of a theme park is displayed on the drone. The controlling portion causes the drone to fly from the cooking station to the meal serving station and causes the meal conveying device to convey the meal from the cooking station to the meal serving station such that, just after the drone has arrived at the meal serving station, the meal conveyed by the meal conveying device is served to a visitor from the meal serving station.

Abstract: A control apparatus is configured to control advertisement data presented to an occupant of a movable object having an automated driving function, and includes a travel road specification unit for specifying a road scheduled to be travelled by the movable object corresponding to the destination, a setting unit for setting, according to the road scheduled to be travelled, a switching recommendation point for switching a driving mode of the movable object from automated drive to driver-led manual drive, a notification plan generation unit for setting a degree of importance of the advertisement data to be presented according to the driving mode for the road scheduled to be travelled, and generating a notification plan for presenting the advertisement data to the occupant according to the set degree of importance, and a notification control unit for presenting notification data including the advertisement data to the occupant according to the notification plan.

Abstract: A vehicular control system determines a planned path of travel for a vehicle along a traffic lane in which the vehicle is traveling on a road. The system determines a respective target speed for waypoints along the planned path that represents a speed the vehicle should travel when passing through the respective waypoint. The system determines a speed profile for the vehicle to travel at as the vehicle travels along the planned path, with at least two different speeds being based on a difference in target speeds of at least two consecutive respective waypoints of the plurality of waypoints. The system determines an acceleration profile for the vehicle to follow as it changes from one speed to another speed of the speed profile. The system controls the vehicle to maneuver the vehicle along the planned path in accordance with the determined speed and acceleration profiles.

Abstract: An apparatus including a processor unit configured to provide a first high-definition map, a sensor unit configured for providing sensor data representing an environmental condition in a periphery of the apparatus and a receiver unit configured to receive data representing a second high-definition map. The processor unit is configured to fuse the second high-definition map and the sensor data so as to provide the first high-definition map. The apparatus further includes a transmitter unit configured for transmitting a result of fusing the second high-definition map and the sensor data, and includes a command generator unit configured to generate a command signal representing a vehicle control command for a vehicle carrying the apparatus based on the first high-definition map.

Abstract: Example embodiments relate to using remote assistance to maneuver an autonomous vehicle to a location. A computing device used by a remote operator may receive a request for assistance from a vehicle that indicates the vehicle is stopped at a first location with one or more navigation options for enabling the vehicle to navigate from the first location to a second location. At least one navigation option includes a maneuver technique that requires operator approval prior to execution. The computing device may then display a graphical user interface (GUI) that conveys the one or more navigation options. Based on detecting a selection of a particular navigation option, the computing device may transmit instructions to the vehicle to perform the particular navigation option. The vehicle may configured to navigate from the first location to the second location by performing the particular navigation option while monitoring for changes in the environment.

Abstract: A grabbing method for an industrial robot is disclosed. The method includes obtaining an object information file. The object information file includes numbers and/or positions of detected objects. The method further includes determining collision boundary lines and collision representative objects according to the object information file. In addition, the method includes determining a collision-free grabbing path of a gripper of the industrial robot based on the determined collision boundary lines and the collision representative objects. The collision-free grabbing path is a linear path that satisfies joint limits of the industrial robot. The disclosure further relates to a grabbing device for an industrial robot, a computer storage medium, and an industrial robot.

Abstract: Techniques for determining error models for use in simulations are discussed herein. Ground truth perception data and vehicle perception data can be determined from vehicle log data. Further, objects in the log data can be identified as relevant objects by signals output by a planner system or based on the object being located in a driving corridor. Differences between the ground truth perception data and the vehicle perception data can be determined and used to generate error models for the relevant objects. The error models can be applied to objects during simulation to increase realism and test vehicle components.

Abstract: Broadcasting geolocation information of an Unmanned Aerial Vehicle (UAV) from the UAV by determining current geolocation of the UAV by communicating with a geolocation service and utilizing the geolocation service to geolocate the UAV. Then the UAV prepares a radio frame that includes geolocation information identifying the current geolocation of the UAV and other information associated with the UAV using a radio protocol of one of a 3rd Generation Partnership Project (3GPP) radio protocol, a WiFi radio protocol, a wireless personal area network protocol and a low-power wide-area network protocol and transmits the radio frame to broadcast the current geolocation of the UAV.

Abstract: An apparatus for a motor vehicle driver assistance system for an ego vehicle is provided. The apparatus implements a state estimator configured to use a first state of the ego vehicle to calculate a subsequent second state of the ego vehicle, wherein calculating the second state from the first state includes a prediction element and an update element, wherein calculating the second state from the first state includes using an artificial neural network (“ANN”).

Abstract: A driving system includes a vehicle computing device on a vehicle and a cloud computing device at a location physically separated from the vehicle. The vehicle computing device transmits navigation information to the cloud computing device, including destination information, location information of the vehicle, and high-definition map information regarding a surrounding environment of the vehicle. The cloud computing device generates, based on the navigation information, path information including primary path information and backup path information and transmits the path information to the vehicle computing device. The vehicle computing device controls a trajectory of the vehicle based on the received path information.

Abstract: Systems and methods for dynamic predictive control of autonomous vehicles are disclosed. In one aspect, an in-vehicle control system for a semi-truck includes one or more control mechanisms configured to control movement of the semi-truck and a processor. The system further includes computer-readable memory in communication with the processor and having stored thereon computer-executable instructions to cause the processor to receive a desired trajectory and a vehicle status of the semi-truck, determine a dynamic model of the semi-truck based on the desired trajectory and the vehicle status, determine at least one quadratic program (QP) problem based on the dynamic model, generate at least one control command for controlling the semi-truck by solving the at least one QP problem, and provide the at least one control command to the one or more control mechanisms.

Abstract: A method for controlling sensors or components of a vehicle, using a control device. Measurement data of at least one sensor of the vehicle being received and evaluated. A traffic situation, in which the vehicle finds itself, is ascertained by evaluating the measurement data; and the at least one sensor and/or at least one component of the vehicle being activated, deactivated and/or set to a standby mode as a function of the ascertained traffic situation. A control device and a computer program are also described.

Abstract: Embodiments of the present invention include a collaborative manufacturing system utilizing a plurality of mobile agents. The mobile agents operate dual robotic arms to improve single and multi-material builds' efficiency. In some embodiments, the dual robotic arms work together in the same area to create multi-functional components. In addition, the mobile agents can change tool heads on the arms to allow for hybrid manufacturing such as pick and place, additive, and subtractive manufacturing. One or more mobile agents interact with other mobile agents, thereby increasing the end product's efficiency and quality. Mobile agents utilize swarm manufacturing techniques to improve the manufactured product's time efficiency further and use machine learning to adjust and re-assign mobile agents constantly.

Abstract: A cross-regional travel recommendation method and apparatus, an electronic device and a storage medium are provided, which relates to the fields of intelligent transportation and deep learning. A specific implementation solution is: acquiring a travel request of a user, the travel request comprising a start point and an end point which are located in different regions; extracting user features according to the travel request of the user; and recommending at least one travel plan to the user according to the user features and a pre-trained cross-regional travel recommendation model. The technical solutions can make up for the deficiency of the existing technology, provide a cross-regional travel plan recommendation under a large-space scale and a multimodal environment through a pre-trained cross-regional travel recommendation model and user features extracted based on a travel request of a user, and can satisfy a cross-regional travel request of the user, and is highly practical.

Abstract: Provided are a cleaning robot and a method of controlling the same, and more specifically, a cleaning robot provided to detect an obstacle in various directions and a method of controlling the same. The cleaning robot includes a light emitter configured to radiate light, a plurality of light receivers configured to receive a radiation of the light in a predetermined direction among radiations of the light reflected from an obstacle when the radiated light is reflected from the obstacle, a support plate to which the light emitter and the light receiver are fixed and which is rotatably provided, and a controller configured to detect the obstacle on the basis of output signals transmitted from the light emitter and the plurality of light receivers and rotation information of the support plate.

Abstract: An autonomous mobile device (AMD) may move around a physical space performing various tasks. Most of the sensors on the AMD may be directed towards the front and sides of the AMD to facilitate autonomous navigation, interactions with users, and so forth. The AMD may return to a dock to recharge batteries, replenish consumables, transfer payload, and so forth. The AMD uses the sensors to approach the vicinity of the dock. Once in the vicinity, the AMD turns around and backs into the dock. This orientation allows the AMD to continue to operate the forward-facing sensors and interact with users while docked. The AMD uses inductive sensors to detect metallic targets located at particular positions in the dock. Output from the inductive sensors provides information about the relative position of the AMD with respect to the dock and is used to steer the AMD into a desired docked position.

Abstract: A control system including a drive system and a blade system; a collision sensor; a main board having a main processor and in communication with the collision sensor, the drive system, and the blade system; the collision sensor, upon sensing a collision, capable of transmitting a collision signal to the main processor; the main board capable of transmitting an adjustment command to the drive system or the blade system; a vision sensor; a vision processor in communication with the vision sensor and the main processor, and capable of determining whether the image data represent an obstacle; the vision sensor capable of transmitting image data to the vision processor; when the vision processor determines the image data represent an obstacle, the vision processor capable of transmitting a collision-replicating signal to the main processor, prompting the main board to transmit the adjustment command to the drive system and/or the blade system.

Abstract: A work machine loads materials onto a conveyance vehicle. A system for controlling the work machine includes a detection device that detects an operation of the conveyance vehicle, and a controller that controls the work machine. The work machine includes a work implement attached to a rotating body. The controller acquires operation data indicative of an operation of the conveyance vehicle from the detection device. The controller causes the rotating body to rotate toward a predetermined unloading position. The controller determines whether the conveyance vehicle is stopped at a predetermined loading position based on the operation data. The controller causes the work implement to wait on standby at the unloading position upon determining that the conveyance vehicle is operating. The controller controls the work machine to unload materials at the unloading position upon determining that the conveyance vehicle is stopped at the loading position.

Abstract: An autonomous mobile device (AMD) may move around a physical space while performing tasks. Sensor data is used to determine an occupancy map of the physical space. Some objects within the physical space may be difficult to detect because of characteristics that result in lower confidence in sensor data, such as transparent or reflective objects. To include difficult-to-detect objects in the occupancy map, image data is processed to identify portions of the image that includes features associated with difficult-to-detect objects. Given the portion that possibly includes difficult-to-detect objects, the AMD attempts to determine where in the physical space that portion corresponds to. For example, the AMD may use stereovision to determine the physical area associated with the features depicted in the portion. Objects in that area are included in an occupancy map annotated as objects that should persist unless confirmed to not be within the physical space.

Abstract: The steering control apparatus determines whether or not a deviation degree of a current traveling route of a vehicle with respect to a target traveling route is equal to or more than a predetermined threshold, when steering control is switched from manual steering control to automatic steering control. In the automatic steering control, a command steering angle is calculated so that a difference between a real traveling route of the vehicle and the target traveling route is eliminated. However, when the deviation degree is equal to or more than the predetermined threshold, the steering control apparatus recalculates the command steering angle used in the command steering angle tracking control based on a steering angle at the time of starting the automatic steering control and the command steering angle so that a real steering angle after switching to the automatic steering control changes gradually to the command steering angle.

Abstract: An information processing system includes: a first server associated with a first region, the first server including a first processor configured to output information concerning a predetermined service to a vehicle traveling in the first region; and the vehicle including a second processor, wherein the first processor is configured to perform handover processing for outputting information concerning the vehicle held by the first server to a second server associated with a second region adjacent to the first region when the vehicle moves from the first region to the second region in a case where the vehicle has moved to an outside of the first region and to an outside of an extended region obtained by extending the first region.