Abstract: Devices, systems, and methods are provided for enhanced sensor cleaning validation. A device may determine a baseline performance measurement associated with a clean performance baseline of a sensor. The device may actuate a cleaning mechanism to remove at least a portion of an obstruction deposited on the sensor. The device may determine a first post-clean performance measurement associated with the sensor. The device may determine a degradation measurement between the baseline performance measurement and the first post-clean performance measurement, wherein the degradation measurement indicates an effectiveness of the cleaning mechanism.

Abstract: Devices, systems, and methods are provided for enhanced rider pairing of an autonomous vehicle (AV). A system may pair a first user profile of a first user located at a first location with a first autonomous vehicle (AV) to complete a trip to a destination selected by the first user. The system may detect a second AV at the first location, wherein the second AV is associated with a second user profile. The system may connect the second AV with the first user using a connection mechanism. The system may select a profile status of the first user profile based on the connection to the second AV. The system may pair the first user profile with the second AV based on the profile status.

Abstract: Systems and methods for operating radar systems. The methods comprise, by a processor: receiving point cloud information generated by radar devices; grouping data points of the point cloud to form at least one segment; computing possible true range-rate values for each data point in the at least one segment; identifying a scan window including possible true range-rate values for a largest number of data points; determining whether at least two modulus of the data points associated with the possible true range-rate values included in the identified scan window have moduli values that are different by a certain amount; determining a new range-rate value for each data point of the segment, when a determination is made that at least two modulus of the data points do have moduli values that are different by the certain amount; and modifying the point cloud information in accordance with the new range-rate value.

Abstract: Devices, systems, and methods are provided for compressive sensing using photodiode data. A device may identify light detecting and ranging (LIDAR) data detected by a sensor, the LIDAR data including a first time-of-flight (ToF) and a second ToF. The device may generate, based on the first ToF, a first frequency value. The device may generate, based on the second ToF, a second frequency value. The device may generate, based on the first value, a first sinusoid. The device may generate, based on the second value, a second sinusoid. The device may generate, based on the first sinusoid and the second sinusoid, a compressed signal in a domain incoherent with the time domain. The device may extract range information based on the compressed signal, and may control operation of a vehicle based on the range information.

Abstract: Devices, systems, and methods are provided for reducing interference of light detection and ranging (LIDAR) emissions. A vehicle may identify location information associated with a location of the vehicle. The vehicle may select, based on the location information, a modulation code associated with a LIDAR photodiode of the vehicle. The vehicle may emit, using the LIDAR photodiode, one or more LIDAR pulses based on the modulation code.

Abstract: Systems and methods for operating a vehicle. The methods comprise, by a processor: obtaining a pair of stereo images captured by a stereo camera; processing the pair of stereo images to generate a disparity map comprising a plurality of pixels defined by intensity values; converting each intensity value to a 3D position in a map (each 3D position defining a location of a data point in a point cloud); performing a hierarchical decision tree classification to determine a classification for each data point in the point cloud (the classification being a foreground classification or a background classification); and using the classifications to facilitate autonomous control of the vehicle.

Abstract: Systems and methods are provided for representing a traffic signal device. The method includes receiving a digital image of a traffic signal device that includes one or more traffic signal elements, representing the traffic signal device as a raster image, each traffic signal element of the traffic signal device being represented by a mask corresponding to a location of the traffic signal element on the traffic signal device, representing each mask in a channel in the raster image, providing the raster image as an input to a neural network to classify a state for each of the one or more traffic signal elements, and receiving, from the neural network, a classified raster image, in which the classified raster image includes a plurality of masks, each mask representing a state of one of the one or more traffic signal elements.

Abstract: Devices, systems, and methods are provided for testing and validation of a camera. For example, a system includes a camera that is configured to capture an image of a content object. A processor is programmed to: receive a signal indicative of the image of the content object during an electromagnetic event; generate an image quality score of the image; and generate a validation status of the camera based on a comparison of the image quality score to a validation threshold.

Abstract: A system of creating a simulation to simulate behavior of an autonomous vehicle includes a simulation system having an electronic device and a computer-readable storage medium having one or more programming instructions. When executed, the one or more programming instructions cause the electronic device to identify an event that is to be analyzed, receive from an autonomous vehicle system a first data stream that includes event information from one or more vehicle event log files that corresponds to the event, receive from a vehicle dynamics model a second data stream that includes synthetic event information that corresponds to the event, until a switch point is detected operate in a pure log execution stage, upon detection of the switch point operate in a play-forward execution stage, and cause the new simulation to be executed.

Abstract: Systems and methods for object detection. Object detection may be used to control autonomous vehicle(s). For example, the methods comprise: obtaining, by a computing device, a LiDAR dataset generated by a LiDAR system of autonomous vehicle; and using, by the computing device, the LiDAR dataset and image(s) to detect an object that is in proximity to the autonomous vehicle. The object is detected by performing the following operations: computing a distribution of object detections that each point of the LiDAR dataset is likely to be in; creating a plurality of segments of LiDAR data points using the distribution of object detections; merging the plurality of segments of LiDAR data points to generate merged segments; and detecting the object in a point cloud defined by the LiDAR dataset based on the merged segments. The object detection may be used by the computing device to facilitate at least one autonomous driving operation.

Abstract: Systems and methods for providing lockless access to a buffer ring are disclosed. The systems include a shared memory comprising the buffer ring. The buffer ring includes a plurality of sequentially arranged fixed size buffers configured to store messages, and a global header comprising a counter and a lock. The systems further include a publisher configured to write a plurality of messages to the plurality of fixed sized buffers in the buffer ring, and a subscriber configured to read one or more of the plurality of messages written by the publisher. The counter provides information relating to a fixed size buffer in the buffer ring to which the publisher will next write a message.

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.

Abstract: Devices, systems, and methods are provided for indoor localization. A self-driving vehicle may detect a first fiducial marker located at a first location within a building, wherein the first fiducial marker comprises first fiducial marker information associated with the first location. The self-driving vehicle may retrieve the first fiducial marker information from the first fiducial marker. The self-driving vehicle may generate localization information of the self-driving vehicle based on the first fiducial marker information. The self-driving vehicle may utilize the localization information to transition to a second location within the building, wherein the second location comprises a second fiducial marker.

Abstract: Devices, systems, and methods are provided for classifying detected objects as static or dynamic. A device may determine first light detection and ranging (LIDAR) data associated with a convex hull of an object at a first time, and determine second LIDAR data associated with the convex hull at a second time after the first time. The device may generate, based on the first LIDAR data and the second LIDAR data, a vector including values of features associated with the first convex hull and the second convex hull. The device may determine, based on the vector, a probability that the object is static. The device may operate a machine based on the probability that the object is static.

Abstract: Systems, methods, and computer-readable media are disclosed for a systems and methods for improved LIDAR return light capture efficiency. One example method may include comparing, by a controller including a processor and at a first time, a first temperature of a first computing element to a first threshold temperature and a second temperature of a second computing element to a second threshold temperature. The example method may also include sending, based on a determination that the first temperature is below the first threshold temperature and the second temperature is above the second threshold temperature, a first signal to a switch to activate a data output corresponding to the second computing element. The example method may also include sending, to the second computing element, a second signal to cause a third computing element to increase heat dissipation from the third computing element to the first computing element.

Abstract: An assembly includes a housing arranged to house a sensor. The assembly includes a ring-shaped first shield supported by the housing. The assembly includes a second shield supported by the housing above the first shield and defining an outer perimeter greater than an inner perimeter, and less than an outer perimeter, of the first shield.

Abstract: Systems, methods, and computer-readable media are disclosed for characterizing LIDAR point cloud quality. An example method may involve capturing a first point cloud for a test target including a retroreflective object, the first point cloud including a first region. The example method may also involve capturing, by a LIDAR system, a second point cloud for the test target. The example method may also involve applying a penalty function to a first point in the second point cloud, wherein the first point is within an acceptable error threshold based on the first region. The example method may also involve applying a penalty function to a second point in the second point cloud, wherein the second point is outside of the acceptable error threshold based on the first region. The example method may also involve generating a first score for the first point and a second score for the second point based on the penalty function.

Abstract: A method and a system for maneuvering an autonomous vehicle is disclosed. The system includes an autonomous vehicle including one or more sensors and a processor. The processor is configured to generate a nominal route from a start position toward a destination with reference to a road network map. The nominal route includes a plurality of consecutive lane segments from the start position to the destination. The processor is further configured to use the road network map to identify at least one candidate lane segment corresponding to one or more of the plurality of consecutive lane segments to generate an expanded route representation, generate a multi-corridor representation of a local region around the autonomous vehicle while travelling on the nominal path, and generate a trajectory for the autonomous vehicle to traverse the local region using the multi-corridor representation and perception data corresponding to the autonomous vehicle.

Abstract: Devices, systems, and methods are provided for communications between autonomous and emergency vehicles. A method may include identifying, by an autonomous vehicle (AV), a first message received from a first vehicle, and identifying, by the AV, in the first message, information associated with identifying the AV, a security key associated with identifying the first vehicle, and an instruction associated with causing the AV to perform an action. The method may include authenticating, by the AV, based on the security key, the first vehicle, and controlling operation, based on the instruction and the information associated with identifying the AV, of the AV to perform the action.

Abstract: A node is provided for capturing information about moving objects at an intersection. The node includes a plurality of first cameras that are positioned to capture first digital images of an intersection from different fields of view and a second camera positioned to capture second digital images in a field of view that is wider than that of each first camera. The node includes a processor that detects in the first and second digital images a set of objects of interest of the intersection, determines motion of each detected object of interest in the set from consecutive images of the first digital images or the second digital images. The node generates, for each object of interest of the set, augmented perception data that includes location data in the global coordinate system and the determined motion of each object of interest in the set.