Abstract: Determining an instantaneous vehicle characteristic (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 instantaneous vehicle characteristic of the additional vehicle. For example, autonomous steering, acceleration, and/or deceleration of the vehicle can be adapted based on a determined instantaneous vehicle characteristic of the additional vehicle. In many implementations, the instantaneous vehicle characteristics 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 LIDAR sensor system for a vehicle can include a light source configured to generate a beam; at least one optical amplifier configured to amplify the beam to produce an amplified beam; an optical power distribution network; a transmitter configured to receive the plurality of distributed beams; and one or more optics configured to emit the plurality of distributed beams. The optical power distribution network can include at least one input port configured to receive the amplified beam; one or more optical splitters configured to split the amplified beam into a plurality of distributed beams; a plurality of output ports respectively configured to provide the plurality of distributed beams; and one or more optical isolators configured to attenuate reflected signals at the plurality of output ports by coherently interfering the reflected signals.

Abstract: A light detection and ranging (LIDAR) system includes a laser and a calibration unit. The laser is configured to generate a laser beam based on a particular laser waveform that is associated with at least one parameter of a plurality of parameters. The calibration unit is configured to determine a particular value for the at least one parameter of the plurality of parameters to compensate for distortion characteristics of the laser. The calibration unit is configured to determine the particular value based on an output frequency of the laser beam. The calibration unit is configured to update the particular laser waveform with the particular value of the at least one parameter of the plurality of parameters.

Abstract: A phase shifter includes a substrate layer, a cladding layer, and a waveguide. The phase shifter includes a waveguide and a heating element. The phase shifter includes a thermally conductive structure disposed on the cladding layer to disperse heat from the waveguide. The thermally conductive structure may include a metal strip disposed longitudinally along the beam, may include thermally conductive pads, and/or may include thermally conductive vias coupled between the cladding layer and the substrate layer. The phase shifter may be incorporated into light detection and ranging (LIDAR) devices, telecommunications devices, and/or computing devices.

Abstract: Implementations process, using machine learning (ML) layer(s) of ML model(s), actor(s) from a past episode of locomotion of a vehicle and stream(s) in an environment of the vehicle during the past episode to forecast associated trajectories, for the vehicle and for each of the actor(s), with respect to a respective associated stream of the stream(s). Further, implementations process, using a stream connection function, the associated trajectories to forecast a plurality of associated trajectories, for the vehicle and each of the actor(s), with respect to each of the stream(s). Moreover, implementations iterate between using the ML layer(s) and the stream connection function to update the associated trajectories for the vehicle and each of the actor(s). Implementations subsequently use the ML layer(s) in controlling an AV.

Abstract: An apparatus include a motor, a first scanner, and a second scanner. The first scanner is coupled to the motor, and the motor is configured to rotate the first scanner at a first angular velocity about a rotation axis to deflect a first beam incident in a third plane on the first scanner into a first plane different from the third plane. The second scanner is coupled to the motor, and the motor is configured to rotate the second scanner at a second angular velocity different from the first angular velocity about the rotation axis to deflect a second beam incident in the third plane on the second scanner into a second plane different from the third plane.

Abstract: A light detection and ranging (LIDAR) sensor system for a vehicle includes a transmitter, a receiver, and a scanner. The transmitter is configured to output a transmit beam. The transmitter includes a first grating coupler. The receiver includes a plurality of second grating couplers spaced apart from the first grating coupler.

Abstract: Techniques for controlling an autonomous vehicle with a processor that controls operation, includes operating a Doppler LIDAR system to collect point cloud data that indicates for each point at least four dimensions including an inclination angle, an azimuthal angle, a range, and relative speed between the point and the LIDAR system. A value of a property of an object in the point cloud is determined based on only three or fewer of the at least four dimensions. In some of embodiments, determining the value of the property of the object includes isolating multiple points in the point cloud data which have high value Doppler components. A moving object within the plurality of points is determined based on a cluster by azimuth and Doppler component values.

Abstract: Systems and methods for determining a current state of at least one traffic light (“TL”) for use in controlling an autonomous vehicle are described herein. Implementations can generate a corresponding numerical measure, for each of multiple candidate states of the TL, based on processing an image capturing a region that is predicted to include the TL using machine learning classifier(s). Implementations further generate a corresponding final measure, for each of the candidate states, based on processing each of the corresponding numerical measures using TL model(s), select one of the candidate states as a current state of the TL based on the final measures, and control the autonomous vehicle based on the current state of the TL. Notably, each of the corresponding final measures can be generated as a function of the corresponding numerical measures of the candidate states, and the corresponding numerical measures of the other candidate states.

Abstract: A light detection and ranging (LIDAR) sensor system mounted to a vehicle includes a first device and a second device coupled to the first device. The first device includes a laser source and one or more optical components. The first device is configured to output an optical signal associated with a local oscillator (LO) signal. The second device includes an optical amplifier array device and a transceiver device. The optical amplifier array device includes an integrated optical component and is configured to amplify the optical signal. The transceiver device is configured to transmit the amplified optical signal to an environment and receive a returned optical signal that is reflected from an object in the environment.

Abstract: A live map system may be used to propagate observations collected by autonomous vehicles operating in an environment to other autonomous vehicles and thereby supplement a digital map used in the control of the autonomous vehicles. In addition, a live map system in some instances may be used to propagate location-based teleassist triggers to autonomous vehicles operating within an environment. A location-based teleassist trigger may be generated, for example, in association with a teleassist session conducted between an autonomous vehicle and a remote teleassist system proximate a particular location, and may be used to automatically trigger a teleassist session for another autonomous vehicle proximate that location and/or to propagate a suggested action to that other autonomous vehicle.

Abstract: A light detection and ranging (lidar) system for a vehicle may include a receiver layer, a transmitter layer coupled to the receiver layer through an adhesive layer in a first direction, and one or more optics. The transmitter layer may receive, at a first side of the transmitter layer, a transmit signal from a laser source, and transmit the transmit signal through the one or more optics. The receiver layer may receive, through the one or more optics, a return signal reflected by an object in an environment of the vehicle, and output the return signal at a first side of the receiver layer. The first side of the transmitter layer and the first side of the receiver layer may be apart from and parallel to each other in a second direction crossing the first direction.

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 light detection and ranging (lidar) system for a vehicle may include a receiver layer, a transmitter layer coupled to the receiver layer through an adhesive layer in a first direction, and one or more optics. The transmitter layer may receive, at a first side of the transmitter layer, a transmit signal from a laser source, and transmit the transmit signal through the one or more optics. The receiver layer may receive, through the one or more optics, a return signal reflected by an object in an environment of the vehicle, and output the return signal at a first side of the receiver layer. The first side of the transmitter layer and the first side of the receiver layer may be apart from and parallel to each other in a second direction crossing the first direction.

Abstract: A light detection and ranging (LIDAR) sensor system for a vehicle includes one or more LIDAR pixels. At least one of the one or more LIDAR pixels includes a dual-polarization optical antenna, a second optical antenna, and one or more receivers. The dual-polarization optical antenna is configured to emit a transmit beam having a first polarization orientation, and detect a return beam having a second polarization orientation. The second optical antenna is offset from the dual-polarization optical antenna by a particular distance, and is configured to detect the return beam having the second polarization orientation. The one or more receivers are configured to generate one or more signals in response to detecting the second polarization orientation of the return beam.

Abstract: A method for classifying an object in a point cloud includes computing first and second classification statistics for one or more points in the point cloud. Closest matches are determined between the first and second classification statistics and a respective one of a set of first and second classification statistics corresponding to a set of N classes of a respective first and second classifier, to estimate the object is in a respective first and second class. If the first class does not correspond to the second class, a closest fit is performed between the point cloud and model point clouds for only the first and second classes of a third classifier. The object is assigned to the first or second class, based on the closest fit within near real time of receiving the 3D point cloud. A device is operated based on the assigned object class.

Abstract: A system and method for combining multiple functions of a light detection and ranging (LIDAR) system includes receiving a second optical beam generated by the laser source or a second laser source, wherein the second optical beam is associated with a second local oscillator (LO); splitting the second optical beam into a third split optical beam and a fourth split optical beam; transmitting, to the optical device, the third split optical beam and the fourth split optical beam; receiving, from the optical device, a third reflected beam that is associated with the third split optical beam and a fourth reflected beam that is associated with the fourth split optical beam; and pairing the third reflected beam with the second LO signal and the fourth reflected beam with the second LO signal.