Abstract: A vehicle, and a system and method for operating the vehicle. The system includes a sensor and a processor. The sensor obtains a detection set related to an object at a range from the vehicle. The processor is configured to select a convolution path for the detection set based on a range of the object, wherein the convolution path includes one or more convolutional layers and a number of the one or more convolutional layers is dependent on the range of the object, apply the one or more convolutional layers of the selected convolution path to the detection set to generate a filtered data set, and operate the vehicle with respect to the object using the filtered data set.

Abstract: A vehicle control device includes a recognizer configured to recognize a surrounding situation of a vehicle, a driving controller configured to control steering and acceleration/deceleration of the vehicle independently of an operation of a driver of the vehicle; and a mode decider configured to decide on any one of a plurality of driving modes including a first driving mode and a second driving mode as a driving mode of the vehicle. The recognizer recognizes a sign existing within a reference range of a route along which the vehicle travels. The mode decider changes the driving mode of the vehicle from the second driving mode to the first driving mode when the driving mode of the vehicle is the second driving mode and when the number of signs recognized by the recognizer exceeds a first reference value.

Abstract: A present disclosure is a method of segmenting an abnormal robust for complex autonomous driving scenes and a system thereof, specifically relates to the technical field of an image segmenting system. The system includes: a segmentation module, configured to transmit an obtained input image to the segmentation network to obtain a segmentation prediction image, and then quantify the uncertainty of a segmentation prediction by means of calculating two different discrete metrics; a synthesis module, configured to match a generated data distribution with a data distribution of the input image by utilizing a conditional generative adversarial network; a difference module, configured to model and calculate the input image, an generated image, the semantic feature map and the uncertainty feature map based on an encoder, a fusion module and a decoder, to generate the segmentation prediction images for the abnormal objects; a model training module; and an integrated prediction module.

Abstract: Implementations process, using machine learning (ML) layer(s) of ML model(s), actor(s) from a past episode of locomotion of a vehicle and stream(s) in an environment of the vehicle during the past episode to forecast associated trajectories, for the vehicle and for each of the actor(s), with respect to a respective associated stream of the stream(s). Further, implementations process, using a stream connection function, the associated trajectories to forecast a plurality of associated trajectories, for the vehicle and each of the actor(s), with respect to each of the stream(s). Moreover, implementations iterate between using the ML layer(s) and the stream connection function to update the associated trajectories for the vehicle and each of the actor(s). Implementations subsequently use the ML layer(s) in controlling an AV.

Abstract: A lane change assistance method is provided. The method includes recognizing styles of two traffic lines corresponding to a lane where a vehicle is located. Whether the vehicle violates traffic rules is determined according to the styles of the two traffic lines when the vehicle is performing an operation of changing lanes. A prompt is issued when the vehicle violates traffic rules.

Abstract: Disclosed is a vehicle positioning method and apparatus, a storage medium and an electronic device. The method comprises: prelocating a target vehicle in a running state based on a global positioning system to obtain a priori position of the target vehicle in a map; acquiring running image data collected by a camera sensor in the target vehicle in a target area where the priori position is located; visually recognizing the running image data by using a vector map of the target area, so as to acquire lateral position information and longitudinal position information about a position where the target vehicle is located currently; determining a target lane in which a target vehicle is located according to the transverse position information, and positioning a travel position of the target vehicle in the target lane according to the longitudinal position information.

Abstract: A route selection device includes a processor configured to identify a position of a lane on which a vehicle is traveling; search for candidate partial routes leading from a current position of the vehicle to a waypoint on a route leading from a start point to a destination, the waypoint being located between the current position and the destination; determine a lane change location where a lane change will be made for each of the candidate partial routes found by searching; and select, as a partial route, a candidate partial route having a minimum total score regarding the lane change location from the candidate partial routes found by searching. The score is weighted depending on the position of the determined lane change location or whether a lane change at the lane change location can be controlled by a travel controller.

Abstract: In one embodiment, a computing system of a vehicle may access sensor data associated with a surrounding environment of a vehicle. The system may generate, based on the sensor data, a first trajectory having one or more first driving characteristics for navigating the vehicle in the surrounding environment. The system may generate a second trajectory having one or more second driving characteristics by modifying the one or more first driving characteristics of the first trajectory. The modifying may use adjustment parameters based on one or more human-driving characteristics of observed human-driven trajectories such that the one or more second driving characteristics satisfy a similarity threshold relative to the one or more human-driving characteristics. The system may determine, based on the second trajectory, vehicle operations to navigate the vehicle in the surrounding environment.

Abstract: A method of utilizing basic safety messages (BSMs) to provide insight of traffic situations to a vehicle-to-everything (V2X) enabled vehicle includes one or more of the following: transmitting BSMs from on-road vehicles; forwarding V2X messages including BSMs to a multi-access edge computing (MEC) server; extracting data samples from the V2X messages including BSMs; generating statistical results from the extracted data samples; and visualizing the statistical results for a driver of the vehicle.

Abstract: Techniques for integrating sensor data into a scene or map based on statistical data of captured environmental data are discussed herein. The data may be stored as a multi-resolution voxel space and the techniques may comprise first applying a pre-alignment or localization technique prior to fully integrating the sensor data.

Abstract: The automated driving system includes a vehicle speed detection device for detecting a speed of a vehicle, a display device capable of displaying a running lane and an adjacent lane of the vehicle, and a processor configured to control display content of the display device, and execute an autonomous lane change of the vehicle. The processor is configured to prohibit the autonomous lane change of the vehicle when a speed of the vehicle is less than a predetermined value. The processor is configured to hide the adjacent lane when the autonomous lane change is prohibited and start displaying the adjacent lane before the speed of the vehicle reaches the predetermined value if it is planned for the vehicle to accelerate from a speed below the predetermined value to a speed equal to or greater than the predetermined value in order to carry out the autonomous lane change.

Abstract: Various technologies described herein pertain to routing an autonomous vehicle based upon risk of takeover of the autonomous vehicle by a human operator. A computing system receives an origin location and a destination location of the autonomous vehicle. The computing system identifies a route for the autonomous vehicle to follow from the origin location to the destination location based upon output of a computer-implemented model. The computer-implemented model is generated based upon labeled data indicative of instances in which autonomous vehicles are observed to transition from operating autonomously to operating based upon conduction by human operators while the autonomous vehicles are executing predefined maneuvers. The computer-implemented model takes, as input, an indication of a maneuver in the predefined maneuvers that is performed by the autonomous vehicle when the autonomous vehicle follows a candidate route.

Abstract: A travel assistance method for performing a lane change of a host vehicle includes: detecting an oncoming vehicle traveling in an opposite lane from which the host vehicle travels when the host vehicle starts the lane change; determining a crossing point between a virtual advancing route and a linear advancing route in which the oncoming vehicle advances linearly, the virtual advancing route being a virtual extension of an advancing route of the host vehicle during a period from start to end of the lane change; executing a lane change using the route and the vehicle speed in a case where a time difference between a first predicted time and a second predicted time is outside a predetermined range; and when within the predetermined range, executing a lane change by changing at least one of the route and the vehicle speed such that the time difference is outside the predetermined range.

Abstract: The present disclosure provides a system of an industrial Internet of Things (IoT) for an automated guided vehicle (AGV) control, a method, and a medium. The method includes: obtaining an AGV circuit layout and forming an AGV operation model; obtaining a feeding time, each AGV being fed for at least once within a preset period; the material end point being a node for receiving material in the AGV operation model; calculating an operation track of each AGV of the AGV operation model within the preset period based on the operation speed and the feeding time; adjusting the operation speed to recalculate the operation tracks until the operation process is qualified; and sending the operation track of the qualified operation process to a user platform through the service platform to display to a user.

Abstract: Methods, systems, and apparatus, including computer-readable storage devices, for robot navigation using 2D and 3D path planning. In the disclosed method, a robot accesses map data indicating two-dimensional layout of objects in a space and evaluates candidate paths for the robot to traverse. In response to determining that the candidate paths do not include a collision-free path across the space for a two-dimensional profile of the robot, the robot evaluates a three-dimensional shape of the robot with respect to a three-dimensional shape of an object in the space. Based on the evaluation of the three-dimensional shapes, the robot determines a collision-free path to traverse through the space.

Abstract: In one example, a method of operating a plurality of aerial vehicles in an environment includes receiving, at a first command module of a first aerial vehicle navigating along a first flight path, sensor data from one or more sensors on board the first aerial vehicle. The sensor data reflects one or more characteristics of the environment. The method further includes determining, via the first command module, a change from a predetermined formation to a different formation for a second aerial vehicle based at least in part on the sensor data, where the predetermined formation and the different formation are relative to the first aerial vehicle. The method also including generating, via the first command module, control signals reflecting the change from the predetermined formation to the different formation and sending the control signals from the first aerial vehicle to the second aerial vehicle.

Abstract: According to one embodiment, a movement control system includes a receiver configured to receive start information for deciding a start order from which execution of an operation plan is started, the operation plan including a plurality of orders for controlling a movable object; and an operation plan executor configured to start the execution of the operation plan from the start order decided based on the start information.

Abstract: Arrangements described herein provide a reliability-aware method of determining a schedule for performing a set of tasks for agents. The arrangements determine the schedule based on an objective function that aims to provide a greater probability of completion of the schedule. This allows a more reliable schedule to be determined that takes into account the risk that one or more of the agents will fail during the operation of the schedule. This ensures that the schedule integrates sufficient fail-safes to avoid or at least reduce the need for rescheduling to account for agent failure.

Abstract: A system, apparatus and method of embedded ego vehicle speed estimation apparatus, including a car-mounted monocular camera for capturing a sequence of video frames of an outdoor scene from a moving car, where the outdoor scene includes a road, as a camera channel, and processing circuitry. The processing circuitry is configured with a single-shot network and a 3D convolutional neural network (3D-CNN), the single-shot network segments features of the road in the video frame sequence and generates a masked-attention map for the segmented road features, a concatenation circuit concatenates the masked-attention map as an additional channel to the camera channel to generate a masked-attention input, and the 3D-CNN network receives the masked-attention input and generates an estimated speed of the ego vehicle based on displacement of the segmented road features in the video sequences.

Abstract: A system, method and server for a controlling a relation between a vehicle and traffic flow over a road segment. The system includes a telemetric device of the vehicle and a processor of the server. The telemetric device obtains telemetric data related to the road segment being traversed by the vehicle. The processor determines a property of the road segment based on the telemetric data and a road profile for the road segment, determines a disruption score indicative of a level of disruption in the traffic flow for the road segment based on the property, and outputs a notification signal when the disruption score is above a selected disruption threshold. The notification signal is usable for controlling the relation between the vehicle and the traffic flow.

Abstract: There is disclosed a driverless transport system, DTS, (10) comprising: a travelling course (12), formed of routes (14), preferably travelled unidirectionally, which are defined respectively by one track (28), and of markers (16); a fleet (18) of at least two driverless transport vehicles, DTV, (20) travelling along the tracks (28) in a forcibly guided manner; and at least one station (26) defined by: at least one of the tracks (28), at least one of the markers (16), and an individualizing assigned station identifier; wherein each of the stations (26) can comprise a terminal (70) for allocation of travelling destination and wherein each of the stations (26) represents an area of the travelling course (12), within which the DTVs (20) can get assigned a new travelling destination and/or can be loaded, unloaded, energetically charged, and/or stopped; wherein the markers (16) include information, which is station-specific by being associating the respective information with the respectively assigned station ident

Abstract: In one embodiment, a lidar system includes a light source configured to emit pulses of light and a scanner configured to scan the emitted pulses of light along a high-resolution scan pattern located within a field of regard of the lidar system. The scanner includes one or more scan mirrors configured to (i) scan the emitted pulses of light along a first scan axis to produce multiple scan lines of the high-resolution scan pattern, where each scan line is associated with multiple pixels, each pixel corresponding to one of the emitted pulses of light and (ii) distribute the scan lines of the high-resolution scan pattern along a second scan axis. The high-resolution scan pattern includes one or more of: interlaced scan lines and interlaced pixels.

Abstract: Systems and methods for determining appropriate energy replenishment and controlling autonomous vehicles are provided. An example computer-implemented method can include obtaining one or more energy parameters associated with an autonomous vehicle. The method can include determining a refueling task for the autonomous vehicle based at least in part on the energy parameters associated with the autonomous vehicle. The refueling task comprises a first refueling task that is interruptible by a vehicle service assignment or a second refueling task that is not interruptible by the vehicle service assignment. The method can include communicating data indicative of the refueling task to the autonomous vehicle or to a second computing system that manages the autonomous vehicle. The method can include determining whether the refueling task for the autonomous vehicle has been accepted or rejected.

Abstract: A system for controlling an unmanned aerial vehicle may control to receive a departure point and a destination from a vehicle, and transmit information related to a shadow area between the departure point and the destination to the unmanned aerial vehicle to control the unmanned aerial vehicle to measure a communication sensitivity for each altitude in the shadow area.

Abstract: A method is described for the certification by a control unit of map elements for safety-critical driving functions. At least one observation variable of at least one mapping step of at least one map element is ascertained after an implementation of the mapping step via a monitoring function and is compared with a setpoint value of the observation variable. At least one result value is calculated based on a comparison of the observation variable with the setpoint value of the observation variable for the at least one mapping step via the monitoring function. The at least one result value is stored as a certificate and is linked to the at least one map element. A control unit, a computer program and a machine-readable memory medium are also described.

Abstract: In an exemplary embodiment, a vehicle is provided that includes an antenna and a telematics system. The telematics system is configured to be coupled to the antenna, and includes a processor that is configured to at least facilitate: determining whether power to an antenna of the vehicle would result in radiated power emissions that would exceed a regulatory requirement; and reducing power to the antenna when it is determined that the power to the antenna would result in radiated power emissions that would exceed the regulatory requirement.

Abstract: Among other things, techniques are described for conditional motion predictions. The techniques include generating, by a planning circuit, a set of candidate trajectories for a vehicle based on possible macro actions by the vehicle, predicting, by the planning circuit and for at least some trajectories in the set of candidate trajectories, a response by a target vehicle to the respective trajectory and a probability of the response by the target vehicle, selecting, by the planning circuit, a trajectory from the set of candidate trajectories based at least in part on the response by the target vehicle to the trajectory, the probability of the response by the target vehicle, and characteristics of the trajectory, and operating, by a control circuit, the vehicle based on the selected trajectory.

Abstract: A parking control method includes: detecting a first parking space based on the ambient image of the vehicle captured by an imaging device; controlling at least a steering actuator of the vehicle to cause the vehicle to be parked in the first parking space; detecting a second parking space based on the ambient image of the vehicle captured by the imaging device, while the vehicle is travelling into the first parking space, the second parking space being adjacent to the first parking space in a travelling direction of the vehicle, the second parking space being farther than the first parking space from the vehicle; and controlling at least the steering actuator of the vehicle to cause the vehicle to be parked in the second parking space.

Abstract: An autonomous work system includes a first work machine, a second work machine, and a setting apparatus, in which the setting apparatus sets a part of an entire work region as a first work region in which the first work machine performs work, and calculates a first work time required for the first work machine to perform the work in the first work region, sets a region other than the first work region in the entire work region as a second work region in which the second work machine performs work, and calculates a second work time required for the second work machine to perform the work in the second work region, changes the first work region and the second work region such that a difference between the first work time and the second work time is within a predetermined value, transmits information regarding the changed first work region to the first work machine, and transmits information regarding the changed second work region to the second work machine.

Abstract: An unmanned conveying system includes: plural unmanned conveying vehicles that can convey cargo; a cargo priority order deciding section that decides upon a priority order in which the cargo are to be conveyed, in accordance with conditions of the cargo; and a traveling route setting section that decides upon traveling routes of the unmanned conveying vehicles on the basis of the priority order.

Abstract: Provided are a method and apparatus for creating a driving route of an autonomous vehicle and a computer program therefor. A method of creating a driving route of an autonomous vehicle, the method being performed by a computing device, includes obtaining information about a start point and an end point, setting an intermediate point between the start point and the end point, and creating a driving route by connecting the start point, the set intermediate point, and the end point, wherein the driving route includes a set of a curve connecting the start point and the set intermediate point and a curve connecting the set intermediate point and the end point, is expressed by a polynomial function for an angle of movement of the autonomous vehicle, and satisfies one or more continuity conditions related to a curvature.

Abstract: A method, apparatus and server for real-time learning of a travelling strategy of a driverless vehicle are provided. The method includes: when a first travelling strategy of the driverless vehicle is unable to be generated, recording travelling trajectories of other vehicles on a road; when a number of vehicles on a same travelling trajectory is greater than a preset first number threshold, generating a second travelling strategy using the same travelling trajectory; and controlling the driverless vehicle to travel using the second travelling strategy. A situation in which a driverless vehicle is unable to normally generate a travelling strategy can be improved effectively.

Abstract: Techniques associated with predicting behaviors and states of objects in a physical environment using thermal data.. In some cases, the system may be configured to determine heat signatures of individual features of an object and based on a combination of heat signatures determine a predicted behavior and/or a state of the object. The system may also utilize the thermal data to determine a confidence associated with predicted behavior and/or states of the object.

Abstract: An apparatus for identifying the state of an object inputs time series images into a first classifier to detect an object region including a predetermined object from each image, determines whether the region of each image is in a mixed state in which the region includes another object other than the object, chronologically inputs characteristics obtained from pixel values of the region of each image into a second classifier having a recursive structure, and applies a recursively used internal state of the second classifier stored in a memory to the second classifier, identifying the state of the object involving time-varying changes in outward appearance. The apparatus rejects the latest internal state when the region of each image is in the mixed state. The apparatus updates the internal state stored in the memory with this latest internal state when the region is not in the mixed state.

Abstract: Aspects of the disclosure provide a method of identifying off-road entry lane waypoints. For instance, a polygon representative of a driveway or parking area may be identified from map information. A nearest lane may be identified based on the polygon. A plurality of lane waypoints may be identified. Each of the lane waypoints may correspond to a location within at least one lane. The polygon and the plurality of lane waypoints may be input into a model. A lane waypoint of the plurality of lane waypoints may be selected as an off-road entry lane waypoint. The off-road entry lane waypoint may be associated with the nearest lane. The association may be provided to an autonomous vehicle in order to allow the autonomous vehicle to use the association to control the autonomous vehicle in an autonomous driving mode.

Abstract: A system comprising a computer including a processor and a memory, the memory including instructions such that the processor is programmed to: process vehicle sensor data with a deep neural network to generate a prediction indicative of one or more objects based on the data and determine an object uncertainty corresponding to the prediction and when the object uncertainty is greater than an uncertainty threshold, segment the vehicle sensor data into a foreground portion and a background portion. Classify the foreground portion as including an unseen object class when a foreground uncertainty is greater than a foreground uncertainty threshold; classify the background portion as including unseen background when a background uncertainty is greater than a background uncertainty threshold; and transmit the data and a data classification to a server.

Abstract: Provided is a lidar sensor having a driving motor of which the entire portion is covered under an inclined surface of a scan mirror having an opened bottom surface and an A-shaped cross-section. A rotating shaft of the driving motor may be connected to the bottom of a backing surface disposed under the inclined surface, a magnet may be disposed at the bottom of one or more of the backing surface and a support surface which are connected to the inclined surface of the scan mirror, and a hall sensor may be disposed at the bottom surface of the lidar sensor corresponding to the position where the magnet is disposed.

Abstract: The present disclosure discloses a method for generating a parking model, an electronic device and a storage medium, and relates a field of autonomous parking technologies. The detailed implementing solution includes: obtaining multiple sample sets; constructing a parking cruise space for the target vehicle based on each sample set, and extracting a first parking trajectory corresponding to each sample set from each parking cruise space; recognizing an abnormal position on each first parking trajectory, and deleting driving data corresponding to the abnormal position from a sample set corresponding to each first parking trajectory to obtain target sample data in each sample set; and performing model training based on the target sample data in each sample set to generate a target parking model.

Abstract: Various examples are directed to routing autonomous vehicles. A processor unit accesses first routing graph modification data and second routing graph modification data. The first routing graph modification data based at least in part on first vehicle capability data describing a first type of autonomous vehicle and the second routing graph modification data based at least in part on second vehicle capability data describing a second type of autonomous vehicle. The processor unit accesses routing graph data describing a plurality of graph elements and generates a first route for a first autonomous vehicle of the first type based at least in part on the first routing graph modification data and the routing graph data. The processor unit also generates a second route for a second autonomous vehicle of the second type based at least in part on the second routing graph modification data and the routing graph data.

Abstract: A robotic vehicle can seek markers in one or more zones and take action if the marker so indicates. Systems, methods, and computer-readable media can cause a robotic vehicle to seek and identify a marker, then determine whether action should be taken based on a characteristic of the marker. If an action should be performed, the robotic vehicle can perform an action, and if no action should be performed, a determination can be made whether to seek another marker, move to another zone, or terminate the robotic vehicle's routine.

Abstract: Aspects and implementations of the present disclosure relate to filtering of return points from a point cloud based on radial velocity measurements. An example method includes: receiving, by a sensing system of an autonomous vehicle (AV), data representative of a point cloud comprising a plurality of return points, each return point comprising a radial velocity value and position coordinates representative of a reflecting region that reflects a transmission signal emitted by the sensing system; applying, to each of the plurality of return points, at least one threshold condition related to the radial velocity value of a given return point to identify a subset of return points within the plurality of return points; removing the subset of return points from the point cloud to generate a filtered point cloud; and identifying objects represented by the remaining return points in the filtered point cloud.

Abstract: A mobile robot of the present disclosure includes: a traveling unit configured to move a main body; a cleaning unit configured to perform a cleaning function; a sensing unit configured to sense a surrounding environment; an image acquiring unit configured to acquire an image outside the main body; and a controller configured to generate a distance map indicating distance information from an obstacle for a cleaning area based on information detected and the image through the sensing unit and the image acquiring unit, divide the cleaning area into a plurality of detailed areas according to the distance information of the distance map and control to perform cleaning independently for each of the detailed areas. Therefore, the area division is optimized for the mobile robot traveling in a straight line by dividing the area in a map showing a cleaning area.

Abstract: Systems and methods for controlling an autonomous vehicle are provided. In one example embodiment, a computer-implemented method includes receiving data indicative of an operating mode of the vehicle, wherein the vehicle is configured to operate in a plurality of operating modes. The method includes determining one or more response characteristics of the vehicle based at least in part on the operating mode of the vehicle, each response characteristic indicating how the vehicle responds to a potential collision. The method includes controlling the vehicle based at least in part on the one or more response characteristics.

Abstract: Methods and apparatus related to generating a model for an object encountered by a robot in its environment, where the object is one that the robot is unable to recognize utilizing existing models associated with the robot. The model is generated based on vision sensor data that captures the object from multiple vantages and that is captured by a vision sensor associated with the robot, such as a vision sensor coupled to the robot. The model may be provided for use by the robot in detecting the object and/or for use in estimating the pose of the object.

Abstract: Methods and systems for jointly estimating a pose and a shape of an object perceived by an autonomous vehicle are described. The system includes data and program code collectively defining a neural network which has been trained to jointly estimate a pose and a shape of a plurality of objects from incomplete point cloud data. The neural network includes a trained shared encoder neural network, a trained pose decoder neural network, and a trained shape decoder neural network. The method includes receiving an incomplete point cloud representation of an object, inputting the point cloud data into the trained shared encoder, outputting a code representative of the point cloud data. The method also includes generating an estimated pose and shape of the object based on the code. The pose includes at least a heading or a translation and the shape includes a denser point cloud representation of the object.

Abstract: Systems and methods are directed to determining autonomous vehicle scenarios based on autonomous vehicle operation data. In one example, a computer-implemented method for determining operating scenarios for an autonomous vehicle includes obtaining, by a computing system comprising one or more computing devices, log data representing autonomous vehicle operations. The method further includes extracting, by the computing system, a plurality of attributes from the log data. The method further includes determining, by the computing system, one or more scenarios based on a combination of the attributes, wherein each scenario includes multiple scenario variations and each scenario variation comprises multiple features. The method further includes providing, by the computing system, the one or more scenarios for generating autonomous vehicle operation analytics.

Abstract: In one embodiment, an ADV is routed by executing a first driving scenario that is active. The first driving scenario is one of a plurality of driving scenario types, each driving scenario type being associated with one or more stages to be executed while a corresponding driving scenario type is active. Based on an environmental condition around the ADV, a second driving scenario is set as active. The ADV is routed by executing the second driving scenario. When the second driving scenario exits, execution of the first driving scenario resumes at the one or more stages of the first driving scenario that remains to be executed.

Abstract: A vehicle control method includes recognizing an object, generating a target trajectory of a vehicle, and automatically controlling driving of the vehicle on the basis of the target trajectory, calculating a region between a first virtual line, which passes through a reference point using the vehicle as a reference and a first point present in the vicinity of an outer edge of the object, and a second virtual line, which passes through the reference point and a second point present in the vicinity of the outer edge of the object, as a region through which the vehicle should avoid to travel, and excluding the target trajectory present in the traveling avoidance region, and automatically controlling the driving of the vehicle on the basis of a remaining target trajectory remained without being excluded.

Abstract: A system for autonomous or semi-autonomous operation of a vehicle is disclosed. The system includes a machine automation portal (MAP) application configured to enable a computing device to (a) display a map of a work site and (b) provide a graphical user interface that enables a user to (i) define a boundary of an autonomous operating zone on the map and (ii) define a boundary of one or more exclusion zones. The system also includes a robotics processing unit configured to (a) receive the boundary of the autonomous operating zone and the boundary of each exclusion zone from the computing device, (b) generate a planned command path that the vehicle will travel to perform a task within the autonomous operating zone while avoiding each exclusion zone, and (c) control operation of the vehicle so that the vehicle travels the planned command path to perform the task.

Abstract: An information processing apparatus according to an aspect of the present technology includes an estimation unit, a generation unit, and a frequency control unit. The estimation unit estimates at least one of a location or a posture of a mobile object. The generation unit generates a movement plan for moving the mobile object. The frequency control unit controls frequency of update of the movement plan to be performed by the generation unit, on the basis of load index information serving as an index of a load on the estimation unit.