Systems, apparatus and methods may be configured to implement actively-controlled light emission from a robotic vehicle. A light emitter(s) of the robotic vehicle may be configurable to indicate a direction of travel of the robotic vehicle and/or display information (e.g., a greeting, a notice, a message, a graphic, passenger/customer/client content, vehicle livery, customized livery) using one or more colors of emitted light (e.g., orange for a first direction and purple for a second direction), one or more sequences of emitted light (e.g., a moving image/graphic), or positions of light emitter(s) on the robotic vehicle (e.g., symmetrically positioned light emitters). The robotic vehicle may not have a front or a back (e.g., a trunk/a hood) and may be configured to travel bi-directionally, in a first direction or a second direction (e.g., opposite the first direction), with the direction of travel being indicated by one or more of the light emitters.

A behavior prediction device comprising: a moving object behavior detection unit configured to detect moving object behavior, a behavior prediction model database that stores a behavior prediction model, a behavior prediction calculation unit configured to calculate a behavior prediction of the moving object using the behavior prediction model, a prediction deviation determination unit configured to determine whether a prediction deviation occurs based on the behavior prediction and a detection result of the moving object behavior corresponding to the behavior prediction, a deviation occurrence reason estimation unit configured to estimate a deviation occurrence reason when determination is made that the prediction deviation occurs, and an update necessity determination unit configured to determine a necessity of an update of the behavior prediction model database based on the deviation occurrence reason when the determination is made that the prediction deviation occurs.

A collision avoidance system may validate, reject, or replace a trajectory generated to control a vehicle. The collision avoidance system may comprise a secondary perception component that may receive sensor data, receive and/or determine a corridor associated with operation of a vehicle, classify a portion of the sensor data associated with the corridor as either ground or an object, determine a position and/or velocity of at least the nearest object, determine a threshold distance associated with the vehicle, and control the vehicle based at least in part on the position and/or velocity of the nearest object and the threshold distance.

According to an aspect of the present inventive concept there is provided an autonomous mobile robot comprising: a set of radar sensors, the sensors being arranged at spatially different positions on the mobile robot, the set including at least a first radar sensor having a first main detection lobe extending in front of the robot and a second radar sensor having a second main detection lobe extending in front of the robot, wherein the first radar sensor and the second radar sensor are arranged such that the first main detection lobe and the second main detection lobe intersect in front of the mobile robot.

The present disclosure relates generally to the operation of autonomous machinery for performing various tasks at various industrial work sites, and more particularly to the volumetric estimation and dimensional estimation of a pile of material or other object, and the use of multiple sensors for the volumetric estimation and dimensional estimation of a pile of material or other object at such work sites. An application and a framework is disclosed for volumetric estimation and dimensional estimation of a pile of material or other object using at least one sensor, preferably a plurality of sensors, on an autonomous machine (e.g., robotic machines or autonomous vehicles) in various work-site environments applicable to various industries such as, construction, mining, manufacturing, warehousing, logistics, sorting, packaging, agriculture, etc.

A vehicle system includes an electronic control unit. The electronic control unit is configured to execute a first program, a second program, and a third program. The first program is configured to recognize an object present around a vehicle, the second program is configured to store information related to the recognized object as time-series map data, and the third program is configured to predict a future position of the object based on the stored time-series map data. The first program and the third program are configured to be (i) first, individually optimized based on first training data corresponding to output of the first program and second training data corresponding to output of the third program, and (ii) then, collectively optimized based on the second training data corresponding to the output of the third program.

Disclosed herein is an intelligent robot cleaner. The intelligent robot cleaner primarily senses foreign matter sucked through a suction unit under the control of a control unit, and secondarily senses an article collected in a collection unit, if articles other than the foreign matter are sensed, thus allowing a use to recognize accurate information about the article collected in the collection unit and preventing valuables or small articles from being lost. The intelligent robot device may be associated with an artificial intelligence module, a unmanned aerial vehicle (UAV), a robot, an augmented reality (AR) device, a virtual reality (VR) device, devices related to 5G services, and the like.

Sensor systems for communications between heavy equipment machines during tree felling operations. A system includes a first heavy equipment comprising a first winch and a second heavy equipment comprising a second winch. The system includes a first cable attached to the first winch and a fulcrum roller and a second cable attached to the second winch and the fulcrum roller. The system is such that the first heavy equipment communicates with the second heavy equipment by way of long-range radio signals.

Technologies disclosed herein facilitate identification of a trip route from an origin of a user to a desired destination of the user, wherein the trip route includes multiple transportation modalities, at least one of which is an autonomous vehicle (AV), and dispatching of the AV to a pickup location for the user in connection with providing transportation to the user along a portion of the identified trip route.

A vehicle computing system validates location data received from a Global Navigation Satellite System receiver with other sensor data. In one embodiment, the system calculates velocities with the location data and the other sensor data. The system generates a probabilistic model for velocity with a velocity calculated with location data and variance associated with the location data. The system determines a confidence score by applying the probabilistic model to one or more of the velocities calculated with other sensor data. In another embodiment, the system implements a machine learning model that considers features extracted from the sensor data. The system generates a feature vector for the location data and determines a confidence score for the location data by applying the machine learning model to the feature vector. Based on the confidence score, the system can validate the location data. The validated location data is useful for navigation and map updates.