Abstract: An information processing device of: generating first routes satisfying a first condition; calculating a first index of each node based on a cost of each edge; generating a second route arriving at each node from a starting point and satisfying a second condition; generating a third route satisfying the second condition by adding the edge to the second route; calculating a second index being a difference between a total value of the first index and the cost of the second and third routes; updating the second route when the second index of the third route is smaller than the second index of the second route; excluding the edge having not contributed to the updating more than a predetermined number of times; and outputting a route with the smallest second index, the first index representing a degree of reduction of the cost in a linearly relaxed problem of the routing problem.

Abstract: A network apparatus, for example, obtains probe data corresponding to a respective vehicle making a late lane change while traversing a TME of a traversable network. Location information indicating a location of the late lane change is extracted from the probe data. Map, weather, and/or traffic data for the location is obtained. A late lane change feature description is generated based on information extracted from the probe data and the map, weather, and/or traffic data. A model is trained using a machine learning technique and training data comprising the late lane change feature description. The model is executed to generate a late lane change prediction corresponding to a TME of a digital map. The network apparatus causes at least one of (a) the digital map to be updated, (b) traffic data corresponding to the TME to be updated, or (c) a navigation-related function to be performed based on the prediction.

Abstract: An ADS performs processing including setting an acceleration command to V1 when an autonomous state is set to an autonomous mode, when the acceleration command has a value indicating deceleration, when a vehicle velocity is zero, and when a standstill command is set to “Applied” and setting an immobilization command to “Applied” when a traveling direction of a vehicle indicates a standstill status, when there is a wheellock request, and when predetermined time has elapsed since the vehicle came to a standstill.

Abstract: A vehicle control system (500) controls a vehicle whereon a plurality of ECUs (30) and a vehicle integrated control device (10) to control the plurality of ECUs (30) are mounted. The vehicle integrated control device (10) includes a control target value operation unit to calculate a control target value to control the plurality of ECUs (30). Further, the vehicle integrated control device (10) includes a prediction control value operation unit to estimate a state of the vehicle in the future, and to calculate a prediction control value to control the plurality of ECUs (30). The vehicle integrated control device (10) includes an instruction signal generation unit to generate an instruction signal including an operation instruction and a prediction control instruction. Each of the plurality of ECUs (30) includes an actuator control unit to control an actuator (50) based on the prediction control instruction.

Abstract: System, methods, and computer-readable media for configuring an autonomous vehicle based on safety scores determined by a safety score prediction algorithm, and an associated training technique, to output a safety score for a predicted and/or actual path. The safety score is a value indicating a likelihood of risky events per distance. The safety score prediction algorithm is trained with historical human driving datasets associated with paths (predicted or actual) taken by one or more AVs during human driving.

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: An embodiment is an autonomous driving system in a heterogeneous SD map and precise map environment, which can provide an autonomous driving service using a heterogeneous SD map having properties that do not match those of a precise map. The autonomous driving system in a heterogeneous SD map and HD map environment includes a service platform and an autonomous vehicle, and the operation and operation method of the system depend on whether a user can input TBT in addition to a start point and a destination.

Abstract: A plurality of vehicle charging stations and a plurality of stationary sensors with respective fields of view can be disposed within an area. A stationary node computer including a first processor and a first memory and communicatively can be coupled to the charging station and the stationary sensors, the first memory storing instructions executable by the first processor such that the stationary computer is programmed to authorize a request from a vehicle to access the charging station; identify an object, detected in the area in data from one or more of the stationary sensors, to be tracked with respect to the vehicle; wherein the object to be tracked with respect to the vehicle is identified based on a type of the object, an attribute of the object and an attribute of the vehicle; and transmit data about the object to the vehicle.

Abstract: Systems and techniques are provided for providing a preorder delivery in an autonomous vehicle (AV). An example method includes receiving, from a computing device associated with a user, a first user request for a ride in an AV from a user location to a user destination; receiving, from the computing device associated with the user, a second user request for the AV to pick up a product at a pick up location associated with the product, prior to picking up the user; determining, based on the first and second user requests, a route for the AV to navigate; and sending, from a computer associated with the AV to a locker of the AV, an instruction to open the locker where the product is placed.

Abstract: An example method includes (a) obtaining sensor data descriptive of an environment of an autonomous vehicle; (b) obtaining a plurality of travel way markers from map data descriptive of the environment; (c) determining, using a machine-learned object detection model and based on the sensor data, an association between one or more travel way markers of the plurality of travel way markers and an object in the environment; and (d) generating, using the machine-learned object detection model, an offset with respect to the one or more travel way markers of a spatial region of the environment associated with the object.

Abstract: This document describes techniques for localizing a vehicle to map data, for example, to enable assisted driving and automated driving within a road. A road model builder is described that uses vehicle location data to determine its position relative to map data obtained from a provider, which can be different than the location data's source. Using the vehicle position, the road model builder maintains a current lane segment for the vehicle and its relative position within the current lane segment. Based on its relative position, the road model builder determines whether a transition to another lane segment is appropriate before setting the current lane segment to be the other lane segment. The current lane segment, when provided as an input to an assisted-driving or automated-driving system, can enable safer operation of the vehicle and use fewer computing resources than other systems.

Abstract: An apparatus for controlling an autonomous vehicle disclosure may include a processor and a memory configured to be operatively connected to the processor and to store at least one code performed in the processor, wherein the memory may store a code that, when executed by the processor, causes the processor to control the autonomous vehicle to travel on the basis of a distance from a preceding vehicle in a travel lane in which the autonomous vehicle travels or a preset speed, determine a risk level of a lane change on the basis of a speed of the autonomous vehicle, a speed of a side vehicle traveling in a target lane of a lane change, and a distance between the autonomous vehicle and the side vehicle upon occurrence of a lane change request, and perform longitudinal or lateral control for the lane change on the basis of the risk level.

Abstract: A system and a method for controlling movement of a robotic vehicle. The robotic vehicle comprises a computer system, a chassis controller, and a plurality of sensors. Data measured by the plurality of sensors is received by the computer system. A base trajectory and an emergency trajectory for the robotic vehicle are generated based on the data. During normal operations of the robotic vehicle the chassis controller causes the robotic vehicle to travel along the base trajectory. After determining that an error has occurred, the chassis controller causes the robotic vehicle to travel along the emergency trajectory.

Abstract: This application provides a method for controlling vehicles driving as a platoon performed by an electronic device. The method includes: transmitting a first vehicle clearing message to a target in-platoon vehicle, and a second vehicle clearing message to following vehicles behind the target in-platoon vehicle, the first vehicle clearing message used for instructing the target in-platoon vehicle to leave the platoon, the second vehicle clearing message used for instructing temporary clearing of the following vehicles behind the target in-platoon vehicle; receiving a platoon joining message returned from the following vehicles behind the target in-platoon vehicle; and reorganizing the following vehicles behind the target in-platoon vehicle according to the platoon joining message to form a new vehicle platoon, the new vehicle platoon not including the target in-platoon vehicle and following vehicles having not transmitted the platoon joining message to the leading vehicle.

Abstract: A number of variations may include a method or system using human driving characteristics in autonomous vehicle driving method or system.

Abstract: Techniques for determining a vehicle trajectory that causes a vehicle to navigate in an environment relative to one or more objects are described herein. For example, the techniques may include a computing device determining a decision tree having nodes to represent different object intents and/or nodes to represent vehicle actions at a future time. A tree search algorithm can search the decision tree to evaluate potential interactions between the vehicle and the one or more objects over a time period, and output a vehicle trajectory for the vehicle. The vehicle trajectory can be sent to a vehicle computing device for consideration during vehicle planning, which may include simulation.

Abstract: An autonomous mobile robot uses a capability-aware pathfinding algorithm to traverse from a start pose to an end pose efficiently and effectively. The robot receives a start pose and an end pose, and determines a primary path from the start pose to the end pose based on a primary pathfinding algorithm. The robot may smooth the primary path using Bezier curves. The robot may identify a conflict point on the primary path where the robot cannot traverse, and may determine a secondary path from a first point before the conflict point to a second point after the conflict point. The secondary path may use a secondary pathfinding algorithm that uses motion primitives of the robot to generate the secondary path based on the motion capabilities of the robot. The robot may then traverse from the start pose to the end pose based on the primary path and the secondary path.

Abstract: Systems and methods are provided that may to cause autonomous navigation of autonomous vehicles in the case of non-standard traffic flows such as police stops, emergency vehicle passing, construction sites, vehicle collision sites, and other non-standard road conditions. An entity associated with the non-standard traffic flow (e.g., an emergency vehicle, road sign, barrier, etc.) may transmit or broadcast a control signal to be received (or otherwise detected) at one or more autonomous vehicles. Each autonomous vehicle, upon receiving the control signal, may autonomously navigate in accordance with the control signal, thus mitigating or eliminating dangers associated with non-standard traffic flows.

Abstract: Dynamically adjusting, using artificial intelligence (AI), sensors and models of an autonomous roaming robotic device, which includes receiving data regarding an asset at a computer of a roaming robotic device from sensors on the robotic device. The robotic device identifies an asset at a location using the sensors, and the robotic device has instructions, received from a control system, to inspect the location or items at the location. The data is analyzed using the computer of the robotic device, and the analysis includes using historical data for the asset. An AI model is loaded using the computer of the robotic device, based on the identification of the asset. A sensor is selected using the computer of the robotic device, for conducting an inspection of the asset based on the analysis of the data and the AI model.

Abstract: Provided is a vehicle traveling assist device capable of suppressing deterioration of ride comfort of an own vehicle due to vehicle traveling assist. A target determination unit calculates a target speed and a target distance based on target vehicle speed information. A plan generation unit generates a distance plan and a speed plan based on the target speed and the target distance through use of a three-stage moving average filter. The three-stage moving average filter is a filter including a first moving average filter, a second moving average filter, and a third moving average filter which are sequentially and serially connected to each other. A vehicle control unit generates an acceleration command for an own vehicle based on the distance plan and the speed plan.

Abstract: The present invention provides an object recognition system for recognising objects within an environment, including: tags, each tag being associated with a respective object, each tag including: a tag memory to store an object model being a computational model indicative of a relationship between the respective object and a shape encoding layer; a sensing system including: a sensing device to sense the environment; a sensing system memory to store a sensing device model being a computational model indicative of a relationship between sensor data captured using the sensing device and the shape encoding layer; sensing system processing devices to: receive object models from a transceiver; retrieve the sensing device model from the sensing system memory; acquire sensor data indicative of the environment from the sensing device; and analyse the sensor data using the sensing device model and the object models to thereby recognise the objects within the environment.

Abstract: The present application discloses a method, system, and computer system for providing a unified map and performing an active measure for re-routing transport based on one or more hazards along a current route. The method includes obtaining context information for a managed vehicle, obtaining map data, determining, based at least in part on the context information and the map data, whether the managed vehicle is parked in an unsafe location, in response to determining that the managed vehicle is parked in the unsafe location, determining whether to perform an active measure with respect to the managed vehicle, and in response to determining to perform the active measure, performing the active measure.

Abstract: Systems and methods for process tending with a robot arm are presented. The system comprises a robot arm and robot arm control system mounted on a self-driving vehicle, and a server in communication with the vehicle and/or robot arm control system. The vehicle has a vehicle control system for storing a map and receiving a waypoint based on a process location provided by the server. The robot arm control system stores at programs that is executable by the robot arm. The vehicle control system autonomously navigates the vehicle to the waypoint based on the map, and the robot arm control system selects a target program from the stored programs based on the process location and/or a process identifier.

Abstract: Disclosed is a vehicle path control apparatus including a memory provided to store danger driving vehicle data, a communication device provided to receive road information from an external server, and a processor configured to identify a danger driving vehicle driving on a road based on the road information and the danger driving vehicle data, select an unmanned vehicle driving within a preset distance from the danger driving vehicle based on the road information, generate a driving path of the unmanned vehicle for obstructing a course of the danger driving vehicle based on an expected path of the danger driving vehicle, and control the communication device to transmit information on the driving path to the unmanned vehicle.

Abstract: A mobile turf sprayer operates to spray a product onto a turf site. In some embodiments the mobile turf sprayer identifies a plurality of regions of the turf site to be sprayed and automatically sprays product onto those regions as the mobile turf sprayer moves about the turf site.

Abstract: Example data processing methods and apparatus are provided. One example method includes obtaining an image captured by an in-vehicle camera. A to-be-detected target in the image is determined. A feature region corresponding to the to-be-detected target in the image is further determined based on a location of the to-be-detected target in the image. A first parking state is determined based on the image and wheel speedometer information. A first homography matrix corresponding to the first parking state is determined from a prestored homography matrix set, where different parking states correspond to different homography matrices. Image information of the feature region is processed based on the first homography matrix to obtain a detection result.

Abstract: Techniques for accurately predicting vehicle state errors for avoiding collisions with objects detected in an environment of a vehicle are discussed herein. A vehicle safety system can implement a model determine a representation of the vehicle usable in a scenario. The model may dynamically determine a size and/or a heading of the vehicle representation based on differences between a candidate trajectory and a current trajectory of the vehicle. The safety system can identify a potential collision between the vehicle and the object based at least in part on an overlap of the vehicle representation and an object representation.

Abstract: Techniques for determining unified futures of objects in an environment are discussed herein. Techniques may include determining a first feature associated with an object in an environment and a second feature associated with the environment and based on a position of the object in the environment, updating a graph neural network (GNN) to encode the first feature and second feature into a graph node representing the object and encode relative positions of additional objects in the environment into one or more edges attached to the node. The GNN may be decoded to determine a first predicted position of the object. The first predicted position may be determined to be outside of a bounded area of the environment. Based on this determination, a second predicted position of the object may be determined using map data associated with the object.

Abstract: A method for determining a position of a vehicle is provided. First and second signals having first and second frequencies are transmitted towards a target. First and second reflected signals corresponding to the first and second signals reflected from the target are received at first and second antennae, respectively. A first frequency difference between the first signal and the first reflected signal is determined. The first frequency difference corresponds to a first range between the vehicle and target. A second frequency difference between the second pulsed signal and the second reflected signal is determined. The second frequency difference corresponds to a second range between the vehicle and target. A vehicle velocity is based on the first range and the second range. A position of the vehicle is determined based on the velocity.

Abstract: A vehicle driving control system includes a road map information updating unit, a control information calculation unit, and a driving control execution unit. Upon determining that a stopped vehicle is present ahead of a target vehicle and that the target vehicle is able to pass by the stopped vehicle, the control information calculation unit calculates control information to be used to restrict a vehicle speed to a first set vehicle speed or less until the target vehicle passes by the stopped vehicle, and calculates, if a door of the stopped vehicle has been opened, the control information to be used to restrict the vehicle speed to a second set vehicle speed or less until a set time period elapses from the opening of the door or until the target vehicle passes by the stopped vehicle. The second set vehicle speed is lower than the first set vehicle speed.

Abstract: The present disclosure relates to a method for forecasting a motion trajectory, a computer-readable storage medium, and a computer device. The method includes: obtaining an observed past trajectory of an object; obtaining a spatial pointwise feature of each trajectory point in the observed past trajectory; obtaining a temporal pointwise feature of the trajectory point according to the spatial pointwise feature of the trajectory points within a preset observation time interval; and performing motion trajectory prediction on the object according to the spatial pointwise feature and the temporal pointwise feature of the trajectory points. The present disclosure promotes the accuracy of the motion trajectory prediction.

Abstract: Disclosed herein are systems, methods, and computer program products for controlling acceleration of a vehicle. The methods comprising: detecting a lateral distance from a point on a trajectory of the vehicle to a first object the vehicle is expected to pass when following the trajectory; selecting whether acceleration limiting is to be performed by the vehicle based on the lateral distance; obtaining a margin of the vehicle defined by a sequence of points; obtaining an amount by which the acceleration of the vehicle is to be limited based on the trajectory and the margin of the vehicle, when a selection is made that acceleration limiting is to be performed by the vehicle; and causing the vehicle to perform operations for autonomous driving with limiting of acceleration by the obtained amount.

Abstract: A driving assistance method for performing a lane change of an own vehicle by autonomous travel control includes: calculating a target route along which the own vehicle is caused to travel to a destination; determining whether or not multiple lane changes are required to be performed successively for the own vehicle to travel in accordance with the target route; determining whether or not the lane change is performed by the autonomous travel control; and when a determination that, in a second or later lane changes among the multiple lane changes, a lane change is not performed by the autonomous travel control is included, not performing a first lane change by the autonomous travel control.

Abstract: In a method for operating a brake system of a vehicle during autonomous driving of the vehicle, a presence of status information relating to a driving command unit which controls the autonomous driving is checked by a brake control unit of the brake system. If the brake control unit detects that the status information is missing or detects status information which indicates an error of the driving command unit, a control signal which controls the autonomous driving in the longitudinal and/or transverse direction is generated by the brake control unit from current environmental data and/or driving parameters.

Abstract: A mobile object control device acquires an image obtained by imaging an outside space of a mobile object, determines whether the mobile object is in a merging lane closer to a merged lane between two merging lanes, determines whether another mobile object is in a merging lane farther from the merged lane between the merging lanes in front of the mobile object when it is determined that the mobile object is in the closer merging lane, sets one or more merging position candidates at which merging of the mobile object to the merged lane is completed and which are positions between mobile objects in the merged lane, and selects a merging position at which the merging of the mobile object to the merged lane is completed from the one or more merging position candidates.

Abstract: Machine learning model optimization systems and methods are disclosed. A system receives sensor data captured by one or more sensors of a vehicle during a first time period. The vehicle uses a first trained machine learning (ML) model for one or more decisions of a first decision type during the first time period. The system generates a second trained ML model at least in part by using the sensor data to train the second trained ML model. The system identifies an optimal trained ML model from a plurality of trained ML models. The plurality of trained ML models includes the first trained ML model and the second trained ML model. The system causes the vehicle to use the optimal trained ML model for one or more further decisions of the first decision type during a second time period after the first time period.

Abstract: The present disclosure provides a route processing method and apparatus. The solution includes: acquiring an initial traveling route, which includes a plurality of track points, corresponding to a vehicle; determining a vehicle traveling area, which includes an area where the vehicle is located when traveling to the track point, corresponding to each track point; determining at least one target track point in the plurality of track points according to the vehicle traveling area, where a first obstacle exists in the vehicle traveling area; performing updating processing on a position of each target track point in the initial traveling route respectively according to the position of the each target track point and a position of the first obstacle, and obtaining a target traveling route according to the target track point for which the updating processing has been performed; and controlling the vehicle to travel according to the target traveling route.

Abstract: A method of controlling a movable body includes: a step of causing a sensor provided on the movable body to detect an obstacle; a step of determining the position and attitude of a front surface, of the obstacle, opposite to the travel direction of the movable body based on the detection result of the obstacle; a step of generating an avoidance path that avoids the obstacle while heading toward a side of a first direction intersecting the travel direction based on the position and attitude of the front surface; a step of causing the movable body to move along the avoidance path; a step of detecting the obstacle while the movable body is moving along the avoidance path; a step of determining the position and attitude of the side surface of the obstacle on the side of the first direction based on the detection result obtained during movement along the avoidance path; and a step of updating the avoidance path so as to return to a side of a second direction opposite to the first direction while avoiding the obsta

Abstract: Aspects of the disclosure provide a method of generating and following planned trajectories for an autonomous vehicle. For instance, a baseline for a planned trajectory that the autonomous vehicle can use to follow a route to a destination may be determined. A stopping point corresponding to a traffic control that will cause the autonomous vehicle to come to a stop using the baseline may be determined. Sensor data identifying objects and their locations may be received. A plurality of constraints may be generated based on the sensor data. A planned trajectory may be generated using the baseline, the stopping point, and the plurality of constraints, wherein constraints beyond the stopping point are ignored.

Abstract: A method, an apparatus and a computer program product for enhancing training of autonomous movement models based on map-based annotations. The method comprises augmenting an original training set with map based annotated modified scenarios at least one of which affects a path and at least one of which does not affect a path. The method comprises generating modified driving scenarios introducing a synthetic path altering object into the functional map representing the original movement or driving scenario, that induces a driving path that is different than the original path; and other modified driving scenarios that introduce a path non-altering object into the functional map of the original scenario, that the original path is applicable therein. An autonomous driving model for predicting driving path within a road segment based on a functional map representation is trained using the modified driving scenarios.

Abstract: A system of controlling operation of a vehicle includes one or more sensors operatively connected to the vehicle. The sensors are configured to obtain respective data of a scene and include a radar unit. A command unit is adapted to receive the respective data and includes a processor and tangible, non-transitory memory on which instructions are recorded. The command unit is configured to determine an orientation angle of a pedestrian in the scene, a Doppler frequency of the pedestrian and a distance of the pedestrian from a border of a road, based in part on the respective data. The orientation angle is based on a heading of the pedestrian relative to a direction of a road. The command unit is configured to designate a status of the pedestrian as either crossing or not crossing based on the distance, the orientation angle and the Doppler frequency of the pedestrian.

Abstract: Disclosed is a method of providing a proximity warning using a head-worn device. A distance is determined between the head worn device and a relevant object, such as a cyclist, jogger or vehicle, using image-processing techniques. A speed of the head-worn device is determined using a GPS receiver or other position components located in the head-worn device or an associated user-device. A braking distance for the head-worn device is determined based on the speed of the head-worn device, and compared to the distance to the relevant object. A warning notification is provided by the head-worn device if the distance to the relevant object is less than the braking distance.

Abstract: A comfort determination model learning unit 81 learns a comfort determination model, by using comfortable activity data where a comfort indicator, which is an indicator measuring whether an individual is comfortable or not when an activity classified as a comfortable activity is performed, is associated with a teacher label indicating comfort, and uncomfortable activity data where the comfort indicator when an activity classified as an uncomfortable activity is performed, is associated with a teacher label indicating discomfort, as first training data, taking an objective variable for a comfort value indicating a degree of comfort, and taking an explanatory variable for each of the comfort indicators. An individual data generation unit 82 generates individual data including explanatory variables, which are used in the comfort determination model, generated based on the comfort indicators of the subject during riding on a vehicle, and driving situations of the vehicle when the comfort indicators are obtained.

Abstract: When an occupant has performed a specific operation that is an operation different to an input operation on a state switching section while in a state in which a display section capable of displaying information related to an input section is not displaying the information, a processor causes the information to be displayed on the display section and places the input section in an input enabled state in which the input operation is receivable. Moreover, when the occupant has not performed the specific operation on the state switching section while in state in which the information is not being displayed on the display section, the processor does not cause the information to be displayed on the display section and places the input section in an input disabled state in which the input operation is not receivable.

Abstract: Disclosed is a system for providing a big data-based AI automatic allocation matching service using taxi demand prediction. The system comprises a user terminal, a taxi terminal and a matching service providing server including a big datafication unit, a prediction unit, a transmission unit, a matching unit, and an alarm unit.

Abstract: A system for determining a type of a road upon which a vehicle is traveling includes a processor and a memory in communication with the processor. The memory has a road type determination module having has instructions that cause the processor to determine, using sensor data having information about at least one of a vehicle and a road upon which the vehicle is traveling, that the vehicle previously traveled on a ramp leading to a limited access highway. The road type determination module also has instructions that cause the processor to determine, using the sensor data, that the road is a limited access highway when the vehicle is traveling at or below a first predetermined speed for a first predetermined amount of time sufficiently immediately after determining that the vehicle was traveling on a ramp, and the vehicle is behind one or more preceding slow-moving vehicles.

Abstract: A system and a method of operating an automated guided vehicle includes: an Automated Guided Vehicle (AGV) which is loaded with a component and transfers the component along a set travelling path in a vehicle production factory; a Programmable Logic Controller (PLC) which is provided in a process line and each of a plurality of nodes existing on the travelling path and controls a peripheral automation facility; and an operation server which is configured to control an operation of the AGV and each automation facility through the PLC, and sets a PLC control condition for controlling each automation facility for each section by collecting PLC memory data from the PLC and inquiring the PLC memory data based on a movement position of the AGV when the travelling path is set.

Abstract: Techniques for determining unified futures of objects in an environment are discussed herein. Techniques may include determining a first feature associated with an object in an environment and a second feature associated with the environment and based on a position of the object in the environment, updating a graph neural network (GNN) to encode the first feature and second feature into a graph node representing the object and encode relative positions of additional objects in the environment into one or more edges attached to the node. The GNN may be decoded to determine a distribution of predicted positions for the object in the future. A predicted position of the object at a subsequent timestep may be determined by sampling from the distribution of predicted positions according to various sampling strategies. Alternatively, the predicted position of the object may be overwritten using a candidate position of the object.

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: The present disclosure generally pertains to time-of-flight simulation data training circuitry, configured to: obtain time-of-flight camera model data based on a time-of-flight camera model modelling a real time-of-flight camera of a real time-of-flight camera type; obtain real time-of-flight data from at least one real time-of-flight camera of the real time-of-flight camera type; determine a difference between the real time-of-flight data and the time-of-flight camera model data; and update, based on the determined difference, the time-of-flight camera model for generating simulated time-of-flight data representing the real time-of-flight camera.