Abstract: A method includes receiving detection data and Inertial Measurement Unit (IMU) data from a vehicle as the vehicle traverses a testing lane, wherein the detection data corresponds to lidar detections, camera detections, and radar detections, by one or more sensors of the vehicle, and the IMU data corresponds to a determination of one or more motion constraints from the one or more sensors and an IMU and a determination of one or more constraints between the IMU and the vehicle to generate. A plurality of targets are arranged with respect to the testing lane and have a plurality of calibration elements configured to allow detection by the one or more sensors of the vehicle. The method further includes processing the received detection data and IMU data using a calibration algorithm, and generating one or more calibration parameters for the one or more sensors of the vehicle based on the processing.

Abstract: A system, method, and computer program for updating calibration lookup tables within an autonomous vehicle or transmitting roadway marking changes between online and offline mapping files is disclosed. A LIDAR sensor may be used for generating an online (rasterized) mapping file with online intensity values which are compared against a correlated offline (rasterized) mapping file having offline intensity values. The online intensity value may be used to acquire a lookup table having a normal distribution that is compared against the offline intensity value. The lookup table may be updated when the offline intensity value is within the normal distribution. Or the vehicle may transmit a roadway marking change when the offline intensity value is outside the normal distribution.

Abstract: Disclosed herein are system, method, and computer program product embodiments for detecting a change in a drivable area. For example, the method includes determining a ground height associated with a cell of a tile map based on sensor data from a sensor of a vehicle and updating a base map based on a determination that there is a change in a drivable area of the vehicle based on at least the ground height.

Abstract: Disclosed herein are system, method, and computer program product embodiments for handling changes in road markings. For example, the method includes identifying a road marking in a road trajectory of a vehicle using an artificial intelligence (AI) model and sensor data from a sensor of the vehicle and performing a map update based on a determination that there is a change in road markings in the road trajectory. The determination is based on at least on-board data generated when the vehicle is traversing the road trajectory and off-board data generated using stored data. The sensor data includes two or more sensor modalities.

Abstract: A system, method, and computer program for updating calibration lookup tables within an autonomous vehicle or transmitting roadway marking changes between online and offline mapping files is disclosed. A LIDAR sensor may be used for generating an online (rasterized) mapping file with online intensity values which are compared against a correlated offline (rasterized) mapping file having offline intensity values. The online intensity value may be used to acquire a lookup table having a normal distribution that is compared against the offline intensity value. The lookup table may be updated when the offline intensity value is within the normal distribution. Or the vehicle may transmit a roadway marking change when the offline intensity value is outside the normal distribution.

Abstract: A system for reconstructing a three-dimensional (3D) model of a scene including a point cloud having points identified by 3D coordinates includes at least one sensor to acquire a set of images of the scene from different poses defining viewpoints of the images and a memory to store the set of images and the 3D model of the scene. The system also includes a processor operatively connected to the memory and coupled with stored instructions to transform the images from the set of images to produce a set of virtual images of the scene viewed from virtual viewpoints; compare at least some features from the images and the virtual images to determine the viewpoint of each image in the set of images; and update 3D coordinates of at least one point in the model of the scene to match coordinates of intersections of ray back-projections from pixels of at least two images corresponding to the point according to the viewpoints of the two images.

Abstract: A system for reconstructing a three-dimensional (3D) model of a scene including a point cloud having points identified by 3D coordinates includes at least one sensor to acquire a set of images of the scene from different poses defining viewpoints of the images and a memory to store the set of images and the 3D model of the scene. The system also includes a processor operatively connected to the memory and coupled with stored instructions to transform the images from the set of images to produce a set of virtual images of the scene viewed from virtual viewpoints; compare at least some features from the images and the virtual images to determine the viewpoint of each image in the set of images; and update 3D coordinates of at least one point in the model of the scene to match coordinates of intersections of ray back-projections from pixels of at least two images corresponding to the point according to the viewpoints of the two images.