Abstract: A vehicle agnostic removable pod can be mounted on a vehicle using one or more legs of a pod mount. The removable pod can collect and time stamp a variety of environmental data as well as vehicle data. For example, environmental data can be collected using a sensor suite which can include an IMU, 3D positioning sensor, one or more cameras, and/or a LIDAR unit. As another example, vehicle data can be collected via a CAN bus attached to the vehicle. Environmental data and/or vehicle data can be time stamped and transmitted to a remote server for further processing by a computing device.

Abstract: Determining yaw parameter(s) (e.g., at least one yaw rate) of an additional vehicle that is in addition to a vehicle being autonomously controlled, and adapting autonomous control of the vehicle based on the determined yaw parameter(s) of the additional vehicle. For example, autonomous steering, acceleration, and/or deceleration of the vehicle can be adapted based on a determined yaw rate of the additional vehicle. In many implementations, the yaw parameter(s) of the additional vehicle are determined based on data from a phase coherent Light Detection and Ranging (LIDAR) component of the vehicle, such as a phase coherent LIDAR monopulse component and/or a frequency-modulated continuous wave (FMCW) LIDAR component.

Abstract: A teleoperations system may be used to select from among multiple scenarios generated by an autonomous vehicle based upon context data provided to the teleoperations system by the autonomous vehicle. Furthermore, an autonomous vehicle may validate a selected scenario prior to executing that scenario to confirm that the scenario does not violate any vehicle and environmental constraints for the autonomous vehicle. Further, a user interface may be presented to a teleoperations system operator to coordinate the display representations of different scenarios with those of the user interface controls used to select such scenarios.

Abstract: Sensor data collected from an autonomous vehicle data can be labeled using sensor data collected from an additional vehicle. The additional vehicle can include a non-autonomous vehicle mounted with a removable hardware pod. In many implementations, removable hardware pods can be vehicle agnostic. In many implementations, generated labels can be utilized to train a machine learning model which can generate one or more control signals for the autonomous vehicle.

Abstract: A method may include obtaining sensor data that include a plurality of sensor returns from an environment of an autonomous vehicle. A first set of features may be extracted from the sensor data. The first set of features may be processed with a machine learning model to generate, for at least a subset of the plurality of sensor returns, a first output that classifies a respective sensor return as corresponding to an object or non-object and a second output that indicates a property of the object. The sensor returns classified as corresponding to objects may be compared to a plurality of pre-classified objects to generate one or more generic object classifications. The autonomous vehicle may be controlled based at least in part on the one or more generic object classifications.

Abstract: A device may include an input optical path, a first optical path, a plurality of second optical paths, a first optical amplifier, a plurality of second optical amplifiers, and a control circuit. The input optical path may receive, at one end thereof, a beam from a laser source. The first optical path and the second optical paths may be respectively branched from at the other end of the input optical path. The first optical amplifier may be coupled to the first optical path. The second optical amplifiers may be respectively coupled to the second optical paths. The control circuit may selectively turn on one of the second optical amplifiers to output a modulated optical signal of the beam. The control circuit may turn on the first optical amplifier, in synchronization with turning on any one of the second optical amplifiers, to output a local oscillator (LO) signal.

Abstract: A teleoperations system may be used to selectively override conditions detected by an autonomous vehicle to enable the autonomous vehicle to effectively ignore detected conditions that are identified as false positives by the teleoperations system. Furthermore, a teleoperations system may be used to generate commands that an autonomous vehicle validates prior to executing to confirm that the commands do not violate any vehicle constraints for the autonomous vehicle. Still further, an autonomous vehicle may be capable of dynamically varying the video quality of one or more camera feeds that are streamed to a teleoperations system over a bandwidth-constrained wireless network based upon a current context of the autonomous vehicle.

Abstract: Implementations set forth herein relate to a camera calibration system for generating various types of calibration data by maneuvering a camera through a variety of different calibration test systems. The calibration data generated by the camera calibration system can be transmitted to the camera, which can locally store the calibration data. The calibration data can include spatial frequency response value data, which can be generated according to a spatial frequency response test that is performed by the camera calibration system. The calibration data can also include field of view values and distortion values that are generated according to a distortion test that is also performed by the camera calibration system. The camera calibration system can maneuver the camera through a variety of different calibration tests, as well as transmit any resulting calibration data to the camera for storage.

Abstract: A teleoperations system may be used to modify elements in the mapping data used by an autonomous vehicle to cause the autonomous vehicle to control its trajectory based on the modified elements. In addition, in some instances, a teleoperations system may be used to generate virtual paths of travel for an autonomous vehicle based upon teleoperations system virtual path suggestion inputs.

Abstract: Sensor data is received from an array of sensors configured to capture one or more objects in an external environment of an autonomous vehicle. A first sensor group is selected from the array of sensors based on proximity data or environmental contexts. First sensor data from the first sensor group is prioritized for transmission based on the proximity data or environmental contexts.

Abstract: Determining classification(s) for object(s) in an environment of autonomous vehicle, and controlling the vehicle based on the determined classification(s). For example, autonomous steering, acceleration, and/or deceleration of the vehicle can be controlled based on determined pose(s) and/or classification(s) for objects in the environment. The control can be based on the pose(s) and/or classification(s) directly, and/or based on movement parameter(s), for the object(s), determined based on the pose(s) and/or classification(s). In many implementations, pose(s) and/or classification(s) of environmental object(s) are determined based on data from a phase coherent Light Detection and Ranging (LIDAR) component of the vehicle, such as a phase coherent LIDAR monopulse component and/or a frequency-modulated continuous wave (FMCW) LIDAR component.

Abstract: A vehicle radar sensor utilizes Frequency Modulated Continuous Wave (FMCW) radar signals that incorporate non-uniform FMCW chirps having chirp profiles that differ from one another to sense one or more parameters of one or more objects in a field of view of the radar sensor. The chirp profiles may differ from one another in various manners, e.g., based on starting frequency, repetition interval, duration and/or slope, and among other advantages, may be used to enhance sensing of various parameters such as range, Doppler/velocity and/or angle.

Abstract: A system for detecting and tracking objects using lidar can include one or more processors configured to receive lidar data. The one or more processors can determine shape data from the lidar data. The shape data can be indicative of an object. The one or more processors can determine a plurality of extents of the object based on the shape data. The one or more processors can update a state of the object based on the plurality of extents, the state including a boundary of the object. The one or more processors can provide the state of the object to an autonomous vehicle controller to cause the autonomous vehicle controller to control an autonomous vehicle responsive to the state of the object.

Abstract: A relative atlas graph maintains mapping data used by an autonomous vehicle. The relative atlas graph may be generated for a geographical area based on observations collected from the geographical area, and may include element nodes corresponding to elements detected from the observations along with edges that connect pairs of element nodes and define relative poses between the elements for connected pairs of element nodes, as well as relations that connect multiple element nodes to define logical relationships therebetween.

Abstract: Sensor data is received from an array of sensors configured to capture one or more objects of an external environment of an autonomous vehicle. A first sensor group is selected from the array of sensors based on vehicle operation data representative of a state of the autonomous vehicle. First sensor data from the first group is prioritized for transmission based on the vehicle operation data.

Abstract: In some implementations, a light detection and ranging (LIDAR) system includes a laser source configured to provide an optical signal at a first signal power, an amplifier having a plurality of gain levels, at which the amplifier is configured to amplify the optical signal, and one or more processors. The one or more processors are configured to, based on the first signal power and a duty cycle of the optical signal, vary a gain level of the amplifier from the plurality of gain levels to generate a pulse signal, transmit the pulse signal from the amplifier to an environment, receive a reflected signal that is reflected from an object, responsive to transmitting the pulse signal, and determine a range to the object based on an electrical signal associated with the reflected signal.

Abstract: A structure of a silicon photonics device for LIDAR includes a first insulating structure and a second insulating structure disposed above one or more etched silicon structures overlying a substrate member. A metal layer is disposed above the first insulating structure without a prior deposition of a diffusion barrier and adhesion layer. A thin insulating structure is disposed above the second insulating structure. A first configuration of the metal layer, the first insulating structure and the one or more etched silicon structures forms a free-space coupler. A second configuration of the thin insulating structure above the second insulating structure forms an edge coupler.

Abstract: A relative atlas graph is generated to store mapping data used by an autonomous vehicle. The relative atlas graph may be generated for a geographical area based on observations collected from the geographical area, and may include element nodes corresponding to elements detected from the observations along with edges that connect pairs of element nodes and define relative poses between the elements for connected pairs of element nodes.

Abstract: In some implementations, a light detection and ranging (LIDAR) system includes a laser source configured to provide an optical signal at a first signal power, an amplifier having a plurality of gain levels, at which the amplifier is configured to amplify the optical signal, and one or more processors. The one or more processors are configured to, based on the first signal power and a duty cycle of the optical signal, vary a gain level of the amplifier from the plurality of gain levels to generate a pulse signal, transmit the pulse signal from the amplifier to an environment, receive a reflected signal that is reflected from an object, responsive to transmitting the pulse signal, and determine a range to the object based on an electrical signal associated with the reflected signal.

Abstract: A structure of a silicon photonics device for LIDAR includes a first insulating structure and a second insulating structure disposed above one or more etched silicon structures overlying a substrate member. A metal layer is disposed above the first insulating structure without a prior deposition of a diffusion barrier and adhesion layer. A thin insulating structure is disposed above the second insulating structure. A first configuration of the metal layer, the first insulating structure and the one or more etched silicon structures forms a free-space coupler. A second configuration of the thin insulating structure above the second insulating structure forms an edge coupler.