Abstract: A cross reality system that provides an immersive user experience by storing persistent spatial information about the physical world that one or multiple user devices can access to determine position within the physical world and that applications can access to specify the position of virtual objects within the physical world. Persistent spatial information enables users to have a shared virtual, as well as physical, experience when interacting with the cross reality system. Further, persistent spatial information may be used in maps of the physical world, enabling one or multiple devices to access and localize into previously stored maps, reducing the need to map a physical space before using the cross reality system in it. Persistent spatial information may be stored as persistent coordinate frames, which may include a transformation relative to a reference orientation and information derived from images in a location corresponding to the persistent coordinate frame.

Abstract: A computer-implemented method for controlling a vehicle includes receiving, via a processor, from two or more IX control devices disposed at a two or more stationary positions having known latitudes longitudes and orientations, first sensory data identifying the position and dimensions of a feature in a mapped region. The processor generates a plurality of IX nodes based on the first sensory data received from the IX control devices, and receives LiDAR point cloud that includes LiDAR and other vehicle sensory device data such as Inertial Measurement Unit (IMU) data received from a Vehicle (AV) driving in the mapped region. The LiDAR point cloud includes a simultaneous localization and mapping (SLAM) map having second dimension information and second position information associated with the feature in the mapped region. The processor generates, without GPS and/or real-time kinematics information, an optimized High-Definition (HD) map having Absolute accuracy using batch optimization and map smoothing.

Abstract: An apparatus includes at least one camera configured to capture a series of image frames for traffic lanes in front of an ego vehicle, where each of the series of image frames is captured at a different one of a plurality of times. A target object detection and tracking controller is configured to process each of the image frames using pixel measurements extracted from the respective image frame to determine, from the pixel measurements, a predicted time to line crossing for a target vehicle detected in the respective image frame at a time corresponding to capture of the respective image frame.

Abstract: A method includes obtaining sensor data associated with a target object at an ego vehicle, where the sensor data includes image data within an image frame. The method also includes identifying a first longitudinal distance of a first point of the target object from the ego vehicle within a world frame. The method further includes identifying a first lateral distance of the first point of the target object from the ego vehicle within the world frame using a first normalization ratio that is based on the image data. The method also includes predicting a future path of the target object based on the first longitudinal distance and the first lateral distance. In addition, the method includes controlling at least one operation of the ego vehicle based on the predicted future path of the target object.

Abstract: An apparatus includes at least one camera configured to capture at least one image of a traffic lane, an inertial measurement unit (IMU) configured to detect motion characteristics, and at least one processor. The at least one processor is configured to obtain a vehicle motion trajectory using the IMU and based on one or more vehicle path prediction parameters, obtain a vehicle vision trajectory based on the at least one image, wherein the vehicle vision trajectory includes at least one lane boundary, determine distances between one or more points on the vehicle and one or more intersection points of the at least one lane boundary based on the obtained vehicle motion trajectory, determine at least one time to line crossing (TTLC) based on the determined distances and a speed of the vehicle, and activate a lane departure warning indicator based on the determined at least one TTLC.

Abstract: A system and method for operating a vehicle operating on a route that will pass within a predetermined distance of a smart infrastructure node (IX Node) includes: retrieving, prior to receiving a message from the IX Node, data about the IX Node including (a) an IX Node assist zone based on an effective broadcast range of the IX Node, (b) an IX Node field of view area that is within the IX Node assist zone and that is defined by a field of view of one or more IX Node sensors, and (c) available services from the IX Node; and selecting a vehicle application for operation on the route while in the IX Node assist zone based on the IX Node field of view area, and the available services from the IX Node, and/or a type and accuracy of one or more IX Node sensors.

Abstract: A system includes a computer including a processor and a memory storing instructions executable by the processor to identify a location and an orientation of a vehicle on a map. The instructions include instructions to determine a location of an infrastructure sensor on the map based on the location and the orientation of the vehicle, data from a vehicle sensor, and data from the infrastructure sensor.

Abstract: An apparatus includes an inertial measurement unit (IMU) configured to detect motion characteristics of a vehicle. The apparatus also includes an odometry system configured to detect a wheel speed of each wheel of the vehicle. The apparatus further includes at least one processor communicatively connected to the IMU and the odometry system, the at least one processor configured to determine first parameters for predicting a path of the vehicle, determine second parameters for predicting the path of the vehicle, and predict the path of the vehicle using a combination of the first parameters and the second parameters, wherein the combination is weighted based on a longitudinal acceleration of the vehicle obtained using the IMU.

Abstract: A method includes obtaining sensor data associated with a target object at an ego vehicle, where the sensor data includes image data within an image frame. The method also includes identifying a first longitudinal distance of a first point of the target object from the ego vehicle within a world frame. The method further includes identifying a first lateral distance of the first point of the target object from the ego vehicle within the world frame using a first normalization ratio that is based on the image data. The method also includes predicting a future path of the target object based on the first longitudinal distance and the first lateral distance. In addition, the method includes controlling at least one operation of the ego vehicle based on the predicted future path of the target object.

Abstract: Present disclosure provides a method and system for package movement visibility in warehouse operations. The method includes identifying, by the package management system (1000), an object entering AOE and moving in a predetermined direction and recording, by the package management system (1000), image frame of the object. The method also includes determining, by the package management system (1000), that the object in the image frame is a package and determining, by the package management system (1000), a label on the package from the image frame. Further, the method also includes determining, by the package management system (1000), a match to the label in a cloud platform (400) and sending, by the package management system (1000), tracking details associated with the package based on the match to the label in the cloud platform (400), to a client device in real-time.

Abstract: An apparatus includes at least one camera configured to capture at least one image of a traffic lane, an inertial measurement unit (IMU) configured to detect motion characteristics, and at least one processor. The at least one processor is configured to obtain a vehicle motion trajectory using the IMU and based on one or more vehicle path prediction parameters, obtain a vehicle vision trajectory based on the at least one image, wherein the vehicle vision trajectory includes at least one lane boundary, determine distances between one or more points on the vehicle and one or more intersection points of the at least one lane boundary based on the obtained vehicle motion trajectory, determine at least one time to line crossing (TTLC) based on the determined distances and a speed of the vehicle, and activate a lane departure warning indicator based on the determined at least one TTLC.

Abstract: A method includes identifying, using at least one processor, a first point associated with an uncertain location of an object in a space and a polynomial curve associated with an uncertain location of a feature in the space. The method also includes determining, using the at least one processor, a probabilistic proximity of the object and the feature. The probabilistic proximity is determined by identifying a second point on the polynomial curve, transforming an uncertainty associated with the polynomial curve into an uncertainty associated with the second point, and identifying the probabilistic proximity of the object and the feature using the first and second points and the uncertainty associated with the second point.

Abstract: An apparatus includes at least one camera configured to capture a series of image frames for traffic lanes in front of an ego vehicle, where each of the series of image frames is captured at a different one of a plurality of times. A target object detection and tracking controller is configured to process each of the image frames using pixel measurements extracted from the respective image frame to determine, from the pixel measurements, a predicted time to line crossing for a target vehicle detected in the respective image frame at a time corresponding to capture of the respective image frame.

Abstract: An apparatus includes at least one camera configured to capture an image of a traffic lane in front of a vehicle. The apparatus also includes a radar transceiver configured to detect one or more target vehicles proximate to the vehicle. The apparatus further includes a path prediction and vehicle detection controller configured to determine first parameters for predicting a path of the vehicle; determine second parameters for predicting the path of the vehicle; predict the path of the vehicle using a combination of the first parameters and the second parameters, where the combination is weighted based on a speed of the vehicle; identify one of the one or more target vehicles as a closest in path vehicle based on the predicted path of the vehicle; and activate at least one of a braking control and a steering control based on a proximity of the identified closest in path vehicle.

Abstract: A computer-implemented method for controlling a vehicle includes receiving, via a processor, from two or more IX control devices disposed at a two or more stationary positions having known latitudes longitudes and orientations, first sensory data identifying the position and dimensions of a feature in a mapped region. The processor generates a plurality of IX nodes based on the first sensory data received from the IX control devices, and receives LiDAR point cloud that includes LiDAR and other vehicle sensory device data such as Inertial Measurement Unit (IMU) data received from a Vehicle (AV) driving in the mapped region. The LiDAR point cloud includes a simultaneous localization and mapping (SLAM) map having second dimension information and second position information associated with the feature in the mapped region. The processor generates, without GPS and/or real-time kinematics information, an optimized High-Definition (HD) map having Absolute accuracy using batch optimization and map smoothing.

Abstract: A reference pose of an object in a coordinate system of a map of an area is determined. The reference pose is based on a three-dimensional (3D) reference model representing the object. A first pose of the object is determined as the object moves with respect to the coordinate system. The first pose is determined based on the reference pose and sensor data collected by the sensor at a first time. A second pose of the object is determined as the object continues to move with respect to the coordinate system. The second pose is determined based on the reference pose, the first pose, and sensor data collected by the sensor at a second time consecutive to the first time.

Abstract: A system includes a computer for a vehicle including a processor and a memory. The memory stores instructions executable by the processor to determine a first location of the vehicle, to send the first location to a stationary infrastructure element, to receive, from the stationary infrastructure element, a second location of the vehicle determined from (a) infrastructure sensor data upon identifying the vehicle from a plurality of vehicles, and (b) the first location sent by the vehicle, and to determine a third location of the vehicle based on the infrastructure-determined second location and the first location.

Abstract: A calibration system for a radar sensor and a method of using the system are disclosed. The method may comprise (a) receiving, from a first sensor in a vehicle, a plurality of global navigation satellite system (GNSS) parameters, wherein the plurality of GNSS parameters define a unique terrestrial position of the first sensor; (b) receiving, from a radar sensor in the vehicle, a plurality of radar parameters, wherein the plurality of radar parameters define a position of a calibration target relative to the radar sensor; (c) repeating the receiving of (a) and (b) at additional unique terrestrial positions of the first sensor; (d) using the plurality of GNSS parameters received in (a) and (c) and the plurality of radar parameters received in (b) and (c), determining corresponding positions of the calibration target; and (e) using the corresponding positions of the calibration target, determining radar calibration parameters.

Abstract: A system includes a processor and a memory. The memory stores instructions executable by the processor to generate, from LIDAR data, a first intensity map of an area around a vehicle, and to estimate a vehicle location based on comparing the first intensity map to a grayscale top view image.

Abstract: A calibration system for a radar sensor and a method of using the system are disclosed. The method may comprise (a) (a) receiving, from a first sensor in a vehicle, a plurality of global navigation satellite system (GNSS) parameters, wherein the plurality of GNSS parameters define a unique terrestrial position of the first sensor; (b) receiving, from a radar sensor in the vehicle, a plurality of radar parameters, wherein the plurality of radar parameters define a position of a calibration target relative to the radar sensor; (c) repeating the receiving of (a) and (b) at additional unique terrestrial positions of the first sensor; (d) using the plurality of GNSS parameters received in (a) and (c) and the plurality of radar parameters received in (b) and (c), determining corresponding positions of the calibration target; and (e) using the corresponding positions of the calibration target, determining radar calibration parameters.