Abstract: A removable brush head for a personal groom device, the removable brush head having a stationary portion, wherein the stationary portion is annular and includes a plurality of outwardly-extending brush bristles. The removable brush head also includes a movable portion positioned within the stationary portion, wherein the movable portion is configured to at least partially rotate relative to the stationary portion, and further wherein the movable portion includes a plurality of outwardly-extending brush bristles. Additionally, the removable brush head includes a locking collar, wherein the locking collar is slidably couplable to the stationary portion so as to axially retain the movable portion within the stationary portion while allowing the movable portion to at least partially rotate relative to the stationary portion.

Abstract: Disclosed herein are system, method, and computer program product embodiments for automated autonomous vehicle pose validation. An embodiment operates by generating a range image from a point cloud solution comprising a pose estimate for an autonomous vehicle. The embodiment queries the range image for predicted ranges and predicted class labels corresponding to lidar beams projected into the range image. The embodiment generates a vector of features from the range image. The embodiment compares a plurality of values to the vector of features using a binary classifier.

Abstract: An automated bootstrap process implemented as a simple state machine generates an initial pose for an autonomous vehicle, without reliance on human intervention. To trigger initiation of the bootstrap process automatically, the autonomous vehicle remains stationary. A GPS-derived position estimate, combined with lidar sweep data and HD map reference point cloud data, can be used to generate a pose using an iterative closest point algorithm. The bootstrap solution can then be automatically validated by a machine learning-based binary classifier trained with appropriate features. Full automation of the bootstrap process may facilitate launching a fleet service of autonomous vehicles.

Abstract: Devices, systems, and methods are provided for enhanced pointing angle validation. A device may generate a collimated beam using a light source emitting a light beam through a fiducial target in an optical instrument. The device may capture an image fiducial target using a camera, wherein the camera is mounted on a mounting datum that is orthogonal to the collimated beam. The device may determine a pointing angle associated with camera based on the captured image of the fiducial target. The device may compare a location of the fiducial target in the image to an optical center of the camera. The device may determine a validation status of camera based on the location of the fiducial target in the image.

Abstract: A system detects multiple instances of an object in a digital image by receiving a two-dimensional (2D) image that includes a plurality of instances of an object in an environment. For example, the system may receive the 2D image from a camera or other sensing modality of an autonomous vehicle (AV). The system uses a first object detection network to generate a plurality of predicted object instances in the image. The system then receives a data set that comprises depth information corresponding to the plurality of instances of the object in the environment. The data set may be received, for example, from a stereo camera of an AV, and the depth information may be in the form of a disparity map. The system may use the depth information to identify an individual instance from the plurality of predicted object instances in the image.

Abstract: Systems and methods for on-board selection of data logs for training a machine learning model are provided. The system includes an autonomous vehicle having a plurality of sensors and a processor. The processor receives a plurality of unlabeled images from the plurality of sensors, a machine learning model, and a loss function corresponding to the machine learning model. For each of the plurality of images, the processor then determines one or more predictions using the machine learning model, compute an importance function based on the loss function and the one or more predictions, and transmit that image to a remote server for updating the machine learning model when a value of the importance function is greater than a threshold.

Abstract: A method of verifying execution sequence integrity of an execution flow includes receiving, by a local monitor of an automated device monitoring system from one or more sensors of an automated device, a unique identifier for each function in a subset of an execution flow for which the local monitor is responsible for monitoring. The method includes combining the received unique identifiers to generate a combination value, applying a hashing algorithm to the combination value to generate a temporary hash value, retrieving, from a data store, a true hash value, determining whether the temporary hash value matches the true hash value, and in response to the temporary hash value not matching the true hash value, generating a fault notification. The true hash value represents a result of applying the hashing algorithm to a combination of actual unique identifiers associated with each function in the subset.

Abstract: This document describes methods by which a system determines a pickup/drop-off zone (PDZ) to which a vehicle will navigate to perform a ride service request. The system will define a PDZ that is a geometric interval that is within a lane of a road at the requested destination of the ride service request by: (i) accessing map data that includes the geometric interval; (ii) using the vehicle's length and the road's speed limit at the destination to calculate a minimum allowable length for the PDZ; (iii) setting, start point and end point boundaries for the PDZ having an intervening distance that is equal to or greater than the minimum allowable length; and (iv) positioning the PDZ in the lane at or within a threshold distance from the requested destination. The system will then generate a path to guide the vehicle to the PDZ.

Abstract: Systems and methods for generating a map. The methods comprise: performing, by a computing device, ray-casting operations to generate a 3D point cloud with a reduced number of data points associated with moving objects; generating, by the computing device, a 2D binary mask for at least one semantic label class of the 3D point cloud; determining, by the computing device, x-coordinates and y-coordinates for a 2D volume defining an object of the at least one semantic label class; identifying, by the computing device, data points in the 3D point cloud based on the 2D volume; comparing, by the computing device, z-coordinates of the identified data points to at least one threshold value selected for the at least one semantic label class; and generating, by the computing device, the map by removing data points from the 3D point cloud based on results of the comparing.

Abstract: An object of the present invention is to provide a method capable of calibrating a sensor function required in a safety design of a radar safety sensor in real time. A calibration station (11) is provided on a traveling route of an unmanned vehicle (1) on which a safety sensor (3) for detecting an obstacle (2) ahead is mounted, and a standard reflection is provided at a position of a maximum measurement distance (L) of the safety sensor (3) at the calibration station (11). Prior to normal traveling of the unmanned trolley 1, the unmanned trolley 1 is moved to the calibration station 11 in advance, and the reference value obtained by measuring the standard reflector 12 with the safety sensor 3 is taught, During normal operation of the unmanned trolley 1, every time the unmanned trolley 1 reaches the calibration station 11, the measured value obtained by measuring the standard reflector 12 by the safety sensor 3 is compared with a reference value. Calibrate the sensor function of FIG. 1.

Abstract: A method and a system for generating a mesh representation of a surface. The method includes receiving a three-dimensional (3D) point cloud representing the surface, generating a reconstruction dataset having a higher resolution than the 3D point cloud in one or more regions corresponding to the surface from the 3D point cloud, and generate a polygon mesh representation of the surface by using a fine-to-coarse hash map for building polygons at a highest resolution first followed by progressively coarser resolution polygons, using the reconstruction dataset.

Abstract: This document describes methods by which an autonomous vehicle deploys a simulation scenario while operating in a real-world environment. The vehicle's sensors collect perception data. During a run of the vehicle in a real-world environment, the vehicle's on-board computing system will: (i) receive the perception data; (ii) publish the perception data to a muxing tool of the on-board computing system; (iii) generate simulation data that identifies and labels one or more simulated objects in the environment; (iv) publish the simulation data to the muxing tool; (v) use the muxing tool to add at least a portion of the simulation data to the perception data to yield augmented perception data; and (v) use at least a portion of the augmented perception data to make one or more navigation decisions based for the autonomous vehicle.

Abstract: Disclosed herein are system, method, and computer program product embodiments for localizing three-dimensional objects relative to a vehicle. The system includes: at least one sensor for generating two-dimensional (2D) data and a three-dimensional (3D) point cloud of an environment external to a vehicle. The 3D point cloud includes object points associated with a stationary traffic control object. The localization system also includes a memory and at least one processor coupled to the memory. The processor is programmed to: select a bounding box associated with the object from the memory based on the 2D data; arrange the bounding box proximate to the object points in the 3D point cloud; assign a weight to each point of the 3D point cloud based on a position of the point relative to the bounding box; filter the weighted points; and generate a 3D location of the object based on the filtered points.

Abstract: A system of creating a simulation scenario definition to simulate behavior of an autonomous vehicle includes a computing device and a computer-readable storage medium having one or more programming instructions. The system identifies an event on which a simulation scenario definition is to be based, identifies one or more log files associated with the event, parses the one or more log files in a time-sequential order and populates the simulation scenario definition with information from the identified log files until an event trigger is detected, identifies an actor from one or more of the log files, infer a shape of the actor, generates one or more simulated tracks that includes the inferred shape for the actor, and adds the simulated tracks to the simulation scenario definition.

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 and method embodiments to implement a validation of an SfM map. An embodiment operates by receiving a motion-generated map corresponding to a digital image, generating a first depth map, wherein the first depth map comprises depth information for one or more triangulated points located within the motion generated image. The embodiment further receives a light detection and ranging (lidar) generated point cloud including at least a portion of the one or more triangulated points, splats the lidar point cloud proximate to the portion of the one or more triangulated points and generates a second depth map for the portion and identifies an incorrect triangulated point, of the one or more triangulated points, based on comparing the first depth information to the second depth information. The incorrect triangulated points may be removed from the SfM map or marked with a low degree of confidence.

Abstract: Systems and methods for operating an autonomous vehicle. The methods comprising: obtaining, by a computing device, a LiDAR dataset; plotting, by a computing device, the LiDAR dataset on a 3D graph to define a 3D point cloud; using, by a computing device, the LiDAR dataset and contents of a vector map to define a cuboid on the 3D graph that encompasses points of the 3D point cloud that are associated with an object in proximity to the vehicle, where the vector map comprises lane information; and using the cuboid to facilitate driving-related operations of the autonomous vehicle.

Abstract: A kind of electromagnetic vehicle pulse effects various dimensions remote online monitoring system of the present invention belongs to technical field of electromagnetic compatibility measurement, its structure has CAN bus to diagnose subsystem (1), audio-video monitoring subsystem (2), induced voltage measurement subsystem (3), fiber distribution network (4) and remote control subsystem (5), wherein, remote control subsystem 5 is connected respectively to CAN bus by fiber distribution network (4) and diagnoses subsystem (1), audio-video monitoring subsystem (2) and induced voltage measurement subsystem (3).

Abstract: Devices, systems, and methods are provided for mitigating vehicle power loss. A vehicle charging system may include a power supply, and a voltage control device associated with receiving first voltage from the power supply, providing the first voltage to a hybrid vehicle or a battery electric vehicle, and blocking a second voltage from the hybrid vehicle or the battery electric vehicle, wherein the vehicle charging system is external to the hybrid vehicle or the battery electric vehicle.

Abstract: Systems and methods are provided herein for improved short range object detection in LiDAR systems. The associated systems may include a first portion and a second portion configured to rotate relative to one another. The system may also include a first magnet located on the second portion and arranged with a north pole of the first magnet facing a first direction. The system may also include a second magnet located on the second portion and arranged with a south pole of the second magnet facing the first direction. The system may also include a first sensor located on the first portion, wherein the first sensor is further configured to measure a first magnetic field of the first magnet and a second magnetic field of the second magnet as the first portion and second portion rotate relative to one another.