Abstract: Systems, methods, and computer-readable media are disclosed for a systems and methods for intra-shot dynamic LIDAR detector gain. One example method my include receiving first image data associated with a first image of an object illuminated at a first wavelength and captured by a camera at the first wavelength, the first image data including first pixel data for a first pixel of the first image and second pixel data for a second pixel of the first image. The example method may also include calculating a first reflectance value for the first pixel using the first pixel data. The example method may also include calculating a second reflectance value for the second pixel using the second pixel data. The example method may also include generating, using first reflectance value and the second reflectance value, a first reflectance distribution of the object.

Abstract: A system includes one or more electronic devices of an autonomous vehicle and a computer-readable storage medium having one or more programming instructions. The system identifies an actor in an environment of the autonomous vehicle that is exhibiting unrecognized behavior or that has exhibited unrecognized behavior within a certain time period, and generates a circle associated with the actor, wherein the circle has a radius that is a function of a velocity of the actor. The system identifies one or more target points associated with the actor, where each target point represents a point along a possible route of the actor, and each target point is located along a circumference of the circle. The system assigns a score to each target point, selects the target point associated with the lowest score, and generates a reference path from the actor to the selected target point.

Abstract: Systems and methods are provided for detecting a flashing light on one or more traffic signal devices. The method includes capturing a series of images of one or more traffic signal elements in a traffic signal device over a length of time. The method further includes, for each traffic signal element, analyzing the series of images to determine one or more time periods at which the traffic signal element is in an on state or an off state, and analyzing the time periods to determine one or more distinct on states and one or more distinct off states. The method further includes identifying one or more cycles correlating to a distinct on state immediately followed by a distinct off state, or a distinct off state immediately followed by a distinct on state, and, upon identifying a threshold number adjacent cycles, classifying the traffic signal element as a flashing light.

Abstract: Systems and methods are described for measuring velocity of an object detected by a light detection and ranging (lidar) system. According to some aspects a method may include receiving a lidar dataset generated by the lidar system, transforming the lidar dataset into a first layer dataset and a second layer dataset, and converting the first layer dataset into a first image and the second layer dataset into a second image. The method may also include performing a feature detection operation that identifies at least one feature in the first image and the same feature in the second image, locating a first location of the feature in the first image and a second location of the feature in the second image, and generating a velocity estimate of the feature based on a difference between the first location and the second location and a difference between the different time intervals.

Abstract: A system and method for performing visual localization is disclosed. In aspects, the system implements methods to generate a global point cloud, the global point cloud representing a plurality of point clouds. The global point cloud can be mapped to a prior map information to locate a position of an autonomous vehicle, the prior map information representing pre-built geographic maps. The position of the autonomous vehicle can be estimated based on applying sensor information obtained from sensors and software of the autonomous vehicle to the mapped global point cloud.

Abstract: A system and method for transmitting data using an autonomous vehicle's LIDAR system. The autonomous vehicle may transmit the data by disengaging the LIDAR system's transmitters and receivers from operating to detect external objects. The autonomous vehicle may also rotate the LIDAR system to locate one of a plurality of receivers external to the autonomous vehicle. Data stored within the autonomous vehicle may then be transmitted to an external system using a light-based communication path established between at least one of the LIDAR system's transmitters and an external receiver. The LIDAR system's transmitters and receivers may then be re-engaged so as to be operable to detect external objects.

Abstract: Systems, methods, and computer-readable media are disclosed for a systems and methods for improved smart infrastructure data transfer. An example method may involve identifying that a software update is available for a smart infrastructure system. The example method may also involve determining, by a processor of the smart infrastructure system and using a signal strength between a first vehicle and the smart infrastructure system, that the first vehicle is within a threshold range of the smart infrastructure system. The example method may also involve establishing, by the smart infrastructure system, a first ad-hoc peer-to-peer communication link with the first vehicle. The example method may also involve sending, to the vehicle, a request for the software update. The example method may also involve receiving, from the vehicle, at least a first portion of the software update that is transferred using the first ad-hoc peer-to-peer communication link.

Abstract: A method for developing a map of objects in a region surrounding a location is disclosed. The method includes interrogating the region along a detection axis with a series of optical pulses and detecting reflections of the optical pulses that originate at objects located along the detection axis. A multi-dimensional map of the region is developed by scanning the detection axis about the location in at least one dimension. The reflections are detected via a single-photon detector that is armed using a sub-gating scheme such that the single-photon detector selectively detects photons of reflections that originate only within each of a plurality of zones that collectively define the detection field. In some embodiments, the optical pulses have a wavelength within the range of 1350 nm to 1390 nm, which is a spectral range having a relatively high eye-safety threshold and a relatively low solar background.

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 and methods for monitoring the lane of an object in an environment of an autonomous vehicle are disclosed. The methods include receiving sensor data corresponding to the object, and assigning an instantaneous probability to each of a plurality of lanes based on the sensor data as a measure of likelihood that the object is in that lane at a current time. The methods also include generating a transition matrix for each of the plurality of lanes that encode one or more probabilities that the object transitioned to that lane from another lane in the environment or from that lane to another lane in the environment at the current time. The methods then include determining an assigned probability associated with each of the plurality of lanes based on the instantaneous probability and the transition matrix as a measure of likelihood of the object occupying that lane at the current time.

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: Disclosed are Geiger-mode avalanche-photodiode-based LiDAR systems and methods that interrogate a detection region with a periodic series of optical pulses whose reflections are detected via a receiver comprising multiple Geiger-mode avalanche-photodiode-based pixels. The pixels of the receiver are configured to asynchronously disarm and rearm after absorption of a reflection. As a result, each pixel can detect multiple reflections of the same optical pulse during a single detection frame whose duration is defined by the periodicity of the series of optical pulses. Furthermore, each pixel can store time-of-flight data for each of multiple reflections detected during a detection frame. Each individual pixel of the receiver, therefore, is not blinded and inoperative for the remainder of a detection frame once it detects a first reflection.

Abstract: Systems, methods, and computer-readable media are disclosed for identifying light flares in images. An example method may involve receiving an image from an imaging device, the image including data indicative of a flare artifact originating from a region of the image. The example method may also involve determining, based on the image data, a first array of pixels extending radially outwards from the region and a second array of pixels extending radially outwards from the region. The example method may also involve creating, based on the image data, a flare array, the flare array including the first array of pixels and the second array of pixels. The example method may also involve determining, based on the flare array, a peak flare artifact value indicative of a size of the flare artifact; and determining, based on the peak flare artifact value, a flare artifact score for the imaging device.

Abstract: Systems/methods for operating an autonomous vehicle. The methods comprise: detecting an object in proximity to the autonomous vehicle; determining a path of travel for the object that comprises a number of data points that is equal to a given number of vehicle locations selected based on a geometry of a lane in which the object; generating cost curves respectively associated with the data points, each cost curve representing a displacement cost to be at a particular location along a given cross line that (i) passes through a respective data point of said data points and (ii) extends perpendicular to and between boundary lines of the lane; determining a polyline representing displacements of the cost curves from a center of the lane; defining a predicted path of travel for the object based on the polyline; and using the predicted path of travel for the object to facilitate autonomous driving operation(s).

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: A system for communicating with multiple vehicles or other electronic devices that share a common media access control (MAC) or other address is disclosed. Upon receiving a certificate signing request (CSR) from a connected device and determining that the device does not have a unique address, the system will generate a unique address for the device and embedding the unique addresses in a certificate, sign the certificate, and transfer the certificate to the device. Then, when the system communicates with the device, the system may use that unique address to identify the device.

Abstract: A system of verifying execution sequence integrity of an execution flow includes a monitoring system in communication with one or more sensors of a system being monitored, where the monitoring system includes one or more electronic devices, and a computer-readable storage medium having one or more programming instructions. When executed, the one or more programming instructions cause at least one of the electronic devices to receive from the sensors, a parameter value for each of one or more parameters that pertain to an operational state of the system, combine the received parameters to generate a combination value, apply a hashing algorithm to the combination value to generate a temporary hash value, search a data store for a result code associated with the temporary hash value, and in response to the result code associated with the temporary hash value indicating that the temporary hash value is incorrect, generate a fault notification.

Abstract: Systems and methods are provided for operating a vehicle, is provided. The method includes, by a vehicle control system of the vehicle, identifying map data for a present location of the vehicle using a location of the vehicle and pose and trajectory data for the vehicle, identifying a field of view of a camera of the vehicle, and analyzing the map data to identify an object that is expected to be in the field of view of the camera. The method further includes, based on (a) a class of the object, (b) characteristics of a region of interest in the field of view of the vehicle, or (c) both, selecting an automatic exposure (AE) setting for the camera. The method additionally includes causing the camera to use the AE setting when capturing images of the object, and using the camera, capturing the images of the object.

Abstract: Systems and methods for operating an autonomous vehicle. The methods comprising: obtaining, by a computing device, loose-fit cuboids overlaid on 3D graphs so as to each encompass LiDAR data points associated with a given object; defining, by the computing device, an amodal cuboid based on the loose-fit cuboids; using, by the computing device, the amodal cuboid to train a machine learning algorithm to detect objects of a given class using sensor data generated by sensors of the autonomous vehicle or another vehicle; and causing, by the computing device, operations of the autonomous vehicle to be controlled using the machine learning algorithm.