Abstract: A light detection and ranging (LIDAR) system includes one or more components that include at least one of an electrical circuit, an electro-optical component, or an optical component. The one or more components are configured to receive an optical beam generated by a laser source, split the optical beam into a plurality of optical beams, transmit the plurality of optical beams through a first subset of optical paths. The one or more components are configured to in response to transmitting the plurality of optical beams, receive a reflected beam through a second subset of the optical paths, generate a first output signal based on a first local oscillator (LO) signal and the reflected beam, and generate a second output signal based on a second local oscillator (LO) signal and the reflected beam.

Abstract: In techniques disclosed herein, machine learning models can be utilized in the control of autonomous vehicle(s), where the machine learning models are trained using automatically generated training instances. In some such implementations, a label corresponding to an object in a labeled instance of training data can be mapped to the corresponding instance of unlabeled training data. For example, an instance of sensor data can be captured using one or more sensors of a first sensor suite of a first vehicle can be labeled. The label(s) can be mapped to an instance of data captured using one or more sensors of a second sensor suite of a second vehicle.

Abstract: A system for an autonomous vehicle includes sensors, a time sensitive network switch, and a sensor interface. The sensors are configured to generate sensor data. The time sensitive network switch is configured to receive the sensor data and an elevated voltage.

Abstract: Various implementations described herein generate training instances that each include corresponding training instance input that is based on corresponding sensor data of a corresponding autonomous vehicle, and that include corresponding training instance output that is based on corresponding sensor data of a corresponding additional vehicle, where the corresponding additional vehicle is captured at least in part by the corresponding sensor data of the corresponding autonomous vehicle. Various implementations train a machine learning model based on such training instances. Once trained, the machine learning model can enable processing, using the machine learning model, of sensor data from a given autonomous vehicle to predict one or more properties of a given additional vehicle that is captured at least in part by the sensor data.

Abstract: An autonomous vehicle uses a secondary vehicle control system to supplement a primary vehicle control system to perform a controlled stop if an adverse event is detected in the primary vehicle control system. The secondary vehicle control system may use a redundant lateral velocity determined by a different sensor from that used by the primary vehicle control system to determine lateral velocity for use in controlling the autonomous vehicle to perform the controlled stop.

Abstract: Logged data from an autonomous vehicle is processed to generate augmented data. The augmented data describes an actor in an environment of the autonomous vehicle, the actor having an associated actor type and an actor motion behavior characteristic. The augmented data may be varied to create different sets of augmented data. The sets of augmented data can be used to create one or more simulation scenarios that in turn are used to produce machine learning models to control the operation of autonomous vehicles.

Abstract: A method and system for synthetic data generation and analysis includes generating a synthetic dataset. A set of parameters is determined and scenarios are generated from the parameters that represent three-dimensional scenes. Synthetic images are rendered for the scenarios. A synthetic dataset may be formed to have a controlled variation in attributes of synthetic images over a synthetic dataset. The synthetic dataset may be used for training or evaluating a machine learning model.

Abstract: A LIDAR system includes a laser source, a first scanner, and a second scanner. The first scanner receives a first beam from the laser source and applies a first angle modulation to the first beam to output a second beam at a first angle. The second scanner receives the second beam and applies a second angle modulation to the second beam to output a third beam at a second angle.

Abstract: Time-division quadrature sampling may be used in a pulse-modulated continuous wave (PMCW) radar receiver circuit, e.g., as may be employed in various types of radar sensors used in automotive and other applications, to enable a quadrature sampling circuit to sequence between digitally sampling different complex components of a received radar signal at different times.

Abstract: A light detection and ranging (LIDAR) system includes a transceiver, a laser source coupled to the transceiver, a time-division multiplexing (TDM) circuit, and one or more processors. The TDM circuit is configured to generate a plurality of first signals. The one or more processors are configured to control the laser source to provide an optical beam to a first input optical channel of a plurality of input optical channels of the transceiver during a first time slot, based on the optical beam provided to the first input optical channel, control the transceiver to send a first reflected beam and a second reflected beam to the TDM circuit through a first output optical channel of a plurality of output optical channels of the transceiver, and based on a control signal provided to the TDM circuit, control the TDM circuit to select the plurality of first signals during the first time slot.

Abstract: A LIDAR system includes a laser source, a first scanner, and a second scanner. The first scanner receives a first beam from the laser source and applies a first angle modulation to the first beam to output a second beam at a first angle. The second scanner receives the second beam and applies a second angle modulation to the second beam to output a third beam at a second angle.

Abstract: Sensor data collected via an autonomous vehicle can be labeled using sensor data collected via an additional vehicle, such as a non-autonomous vehicle mounted with a vehicle agnostic removable hardware pod. A training instance can include an instance of data collected by an autonomous vehicle sensor suite and one or more corresponding labels.

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: A relative atlas may be used to lay out elements in a digital map used in the control of an autonomous vehicle. A vehicle pose for the autonomous vehicle within a geographical area may be determined, and the relative atlas may be accessed to identify elements in the geographical area and to determine relative poses between those elements. The elements may then be laid out within the digital map using the determined relative poses, e.g., for use in planning vehicle trajectories, for estimating the states of traffic controls, or for tracking and/or identifying dynamic objects, among other purposes.

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: A LIDAR system includes a laser source configured to output a first beam and a polygon scanner. The polygon scanner includes a plurality of facets. Each facet of the plurality of facets is configured to transmit a second beam responsive to the first beam. The plurality of facets include a first facet having a first field of view over which the first facet transmits the second beam and a second facet having a second field of view over which the second facet transmits the second beam. The first field of view is greater than the second field of view.

Abstract: An autonomous vehicle control system includes one or more processors. The one or more processors are configured to cause a transmitter to transmit a transmit signal from a laser source. The one or more processors are configured to cause a receiver to receive a return signal reflected by an object. The one or more processors are configured to cause one or more optics to generate a first polarized signal of the return signal with a first polarization, and generate a second polarized signal of the return signal with a second polarization. The one or more processors are configured to calculate a value of reflectivity based on a signal-to-noise ratio (SNR) value of the first polarized signal and an SNR value of the second polarized signal. The one or more processors are configured to operate a vehicle based on the value of reflectivity.

Abstract: A control system interface for a vehicle includes a primary processing unit and a secondary processing unit. The primary processing unit is configured to receive and process a trajectory generated by a trajectory computing unit for autonomous control of the vehicle. The secondary processing unit is configured to receive the trajectory concurrently with the primary processing unit, and to process the trajectory. Autonomous control of the vehicle is passed from the primary processing unit to the secondary processing unit in response to a fault condition with the primary processing unit.

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: The present disclosure is directed to a coherent signal generator comprising an amplifier configured to receive a plurality of optical signals that are respectively associated with a plurality of phases, and generate a plurality of amplified optical signals using the plurality of optical signals; and a splitter network that is coupled to the amplifier. The splitter network is configured to receive the plurality of amplified optical signals, and generate a combined optical signal at an output of a plurality of outputs using the plurality of amplified optical signals.