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.

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 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 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: Sensor data collected from an autonomous vehicle can be labeled using sensor data collected from an additional vehicle. Labeled sensor data can generate targeted testing instances for a trained machine learning model, where the trained machine learning model is used in generating control signals for an autonomous vehicle. In many implementations, targeted training instances can generate an accuracy value for the trained neural network model. Additionally or alternatively, the sensor suite on the additional vehicle can include a removable hardware pod which can be mounted on a variety of vehicles.