Abstract: Systems and methods for controlling the motion of an autonomous are provided. In one example embodiment, a computer-implemented method includes obtaining data associated with an object within a surrounding environment of an autonomous vehicle. The data associated with the object is indicative of a predicted motion trajectory of the object. The method includes determining a vehicle action sequence based at least in part on the predicted motion trajectory of the object. The vehicle action sequence is indicative of a plurality of vehicle actions for the autonomous vehicle at a plurality of respective time steps associated with the predicted motion trajectory. The method includes determining a motion plan for the autonomous vehicle based at least in part on the vehicle action sequence. The method includes causing the autonomous vehicle to initiate motion control in accordance with at least a portion of the motion plan.

Abstract: A light detection and ranging (LIDAR) system may include one or more processors configured to transmit an optical signal to an environment. The one or more processors may receive, from the environment, a returned optical signal in response to transmitting the optical signal. The one or more processors may generate, based on the returned optical signal, an electrical signal. The one or more processors may determine a Doppler frequency shift in the returned optical signal based on the electrical signal. The one or more processors may adjust the electrical signal using the Doppler frequency shift. The one or more processors may divide the adjusted electrical signal into a plurality of electrical signals. The one or more processors may determine a range of the returned optical signal by performing an operation on the plurality of electrical signals in parallel.

Abstract: Systems and methods for improved vehicle-to-vehicle communications are provided. A system can obtain sensor data depicting its surrounding environment and input the sensor data (or processed sensor data) to a machine-learned model to perceive its surrounding environment based on its location within the environment. The machine-learned model can generate an intermediate environmental representation that encodes features within the surrounding environment. The system can receive a number of different intermediate environmental representations and corresponding locations from various other systems, aggregate the representations based on the corresponding locations, and perceive its surrounding environment based on the aggregated representations. The system can determine relative poses between the each of the systems and an absolute pose for each system based on the representations.

Abstract: Various examples are directed to systems and methods for controlling an autonomous vehicle. For example, a navigator system at an autonomous vehicle may generate a plurality of local routes beginning at a vehicle location and extending to a plurality of local route end points. The navigator system may access general route cost data, the general route cost data describing general route costs from the plurality of local route end points to a trip end point. The navigator system may select the first local route of the plurality of routes based at least in part on the general route cost data. A vehicle autonomy system at the autonomous vehicle may begin to control the autonomous vehicle along the first local route.

Abstract: Example aspects of the present disclosure relate to an example computer-implemented method for predicting the intent of actors within an environment. The example method includes obtaining state data associated with a plurality of actors within the environment and map data indicating a plurality of lanes of the environment. The method includes determining a plurality of potential goals each actor based on the state data and the map data. The method includes processing the state data, the map data, and the plurality of potential goals with a machine-learned forecasting model to determine (i) a forecasted goal for a respective actor of the plurality of actors, (ii) a forecasted interaction between the respective actor and a different actor of the plurality of actors based on the forecasted goal, and (iii) a continuous trajectory for the respective actor based on the forecasted goal.

Abstract: The present disclosure is directed to altering vehicle paths. In particular, a computing system can access map data for a geographic area. The computing system can obtain target zone data describing a target zone within the geographic area. The computing system can determine an altered nominal path to traverse the target zone. The computing system can designate a portion of the altered nominal path as a designated action region associated with the target zone. The computing system can generate a longitudinal plan for an autonomous vehicle through the geographic area based on the altered nominal path. The computing system can generate a target velocity for one or more portions of the nominal path within the designated action region. The computing system can generate a trajectory for the autonomous vehicle based on the target velocity and the altered nominal path.

Abstract: A light detection and ranging (LIDAR) device may include a local oscillator network and one or more LIDAR pixels coupled to the local oscillator network. At least one of the one or more LIDAR pixels may include a transmit optical antenna, a receive optical antenna, and at least one receiver. The transmit optical antenna may be configured to emit a transmit beam. The receive optical antenna may be configured to detect a first polarization orientation of a return beam and a second polarization orientation of the return beam. The at least one receiver can be configured to receive at least one local oscillator signal from the local oscillator network. The at least one receiver can be configured to generate a signal based on the local oscillator signal and the return beam.

Abstract: Systems and methods for controlling the motion of an autonomous are provided. In one example embodiment, a computer implemented method includes obtaining, by one or more computing devices on-board an autonomous vehicle, data associated with one or more objects that are proximate to the autonomous vehicle. The data includes a predicted path of each respective object. The method includes identifying at least one object as an object of interest based at least in part on the data associated with the object of interest. The method includes generating cost data associated with the object of interest. The method includes determining a motion plan for the autonomous vehicle based at least in part on the cost data associated with the object of interest. The method includes providing data indicative of the motion plan to one or more vehicle control systems to implement the motion plan for the autonomous vehicle.

Abstract: A light detection and ranging (LIDAR) system may include a laser configured to output a beam, an amplifier and a plurality of splitters. The amplifier may generate, based on the beam, a plurality of amplified optical signals that are respectively associated with a plurality of phases. The plurality of splitters, coupled between the amplifier and a plurality of outputs, may receive the plurality of amplified optical signals, generate, at a first point of time, a first combined optical signal of the plurality of amplified optical signals at a first output of the plurality of outputs with no optical signal at remaining outputs of the plurality of outputs, and generate, at a second point of time, a second combined optical signal of the plurality of amplified optical signals at a second output of the plurality of outputs with no optical signal at remaining outputs of the plurality of outputs.

Abstract: A light detection and ranging (LIDAR) sensor system includes a transmitter, one or more scanning optics, an optical module, and a receiver. The transmitter is configured to output a beam having a linear polarization. The optical module is configured to provide the beam to the one or more scanning optics. The one or more scanning optics are configured to output the beam received from the optical module. The receiver is spaced from the transmitter and receiver configured to receive a return beam from reflection of the beam by an object.

Abstract: A localization system can be validated by: obtaining log data descriptive of a plurality of environmental and operational conditions associated with operation of the autonomous vehicle in an environment; augmenting the log data with one or more simulated anomalies; simulating operation of the localization system using the augmented data as input; obtaining simulation result data descriptive of a state of the localization system subsequent simulating the operation of the localization system; and determining a metric associated with the localization system based on the simulation result data.

Abstract: A method includes generating, by a first object detection model of a perception system of an autonomous vehicle and based on a first portion of sensor data describing a first portion of an environment of the autonomous vehicle, a first object detection output that comprises a first predicted class for an object; generating, by a second object detection model of the perception system and based on a second portion of sensor data describing a second portion of the environment, a second object detection output that comprises a predicted distribution over candidate classes for the object; adapting at least a portion of the first object detection output into an adapted representation of the first object detection output; providing the adapted representation in a shared prediction output space with the predicted distribution from the second object detection output; and generating a resolved object detection output based on the adapted representation and the predicted distribution.

Abstract: Aspects of the present disclosure involve a vehicle computer system comprising a computer-readable storage medium storing a set of instructions, and a method for online light detection and ranging (Lidar) intensity normalization. Consistent with some embodiments, the method may include accumulating point data output by a channel of a Lidar unit during operation of an autonomous or semi-autonomous vehicle. The accumulated point data includes raw intensity values that correspond to a particular surface type. The method further includes calculating a median intensity value based on the raw intensity values and generating an intensity normalization multiplier for the channel based on the median intensity value. The intensity normalization multiplier, when applied to the median intensity value, results in a reflectivity value that corresponds to the particular surface type.

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: Systems and methods for improved tracking of articulated vehicles can obtain, through one or more sensor systems onboard a vehicle, sensor data descriptive of an environment of the vehicle, the environment including an articulated vehicle having at least a first portion and a second portion; generate based on the sensor data, first state data for the first portion of the articulated vehicle and second state data for the second portion of the articulated vehicle; determine an articulated vehicle relationship between the first portion and the second portion based on the first state data for the first portion and the second state data for the second portion; generate, using an articulated vehicle motion model, synthetic motion data for the second portion based on the first state data for the first portion; and update the second state data of the second portion based on the synthetic motion data.

Abstract: Systems and methods are directed to generating behavioral predictions in reaction to autonomous vehicle movement. In one example, a computer-implemented method includes obtaining, by a computing system, local scene data associated with an environment external to an autonomous vehicle, the local scene data including actor data for an actor in the environment external to the autonomous vehicle. The method includes extracting, by the computing system and from the local scene data, one or more actor prediction parameters for the actor using a machine-learned parameter extraction model. The method includes determining, by the computing system, a candidate motion plan for the autonomous vehicle. The method includes generating, by the computing system and using a machine-learned prediction model, a reactive prediction for the actor based at least in part on the one or more actor prediction parameters and the candidate motion plan.

Abstract: A light detection and ranging (LIDAR) system includes a laser source and a polygon scanner. The laser source is configured to generate a first beam. The polygon scanner includes a frame and a plurality of mirrors coupled to the frame, each mirror comprising a glass material.

Abstract: Techniques for improving the performance of an autonomous vehicle (AV) by automatically annotating objects surrounding the AV are described herein. A system can obtain sensor data from a sensor coupled to the AV and generate an initial object trajectory for an object using the sensor data. Additionally, the system can determine a fixed value for the object size of the object based on the initial object trajectory. Moreover, the system can generate an updated initial object trajectory, wherein the object size corresponds to the fixed value. Furthermore, the system can determine, based on the sensor data and the updated initial object trajectory, a refined object trajectory. Subsequently, the system can generate a multi-dimensional label for the object based on the refined object trajectory. A motion plan for controlling the AV can be generated based on the multi-dimensional label.

Abstract: Systems and methods of the present disclosure are directed to a method for editing a machine-learned model to facilitate distributed processing. The method can include obtaining a machine-learned graph model comprising a plurality of connected nodes. The method can include determining a plurality of processing capabilities for a respective plurality of computation resources. The method can include determining a plurality of portions from the machine-learned graph model, wherein each of the plurality of portions comprises a respective subset of the plurality of nodes and a minimum processing capability. The method can include assigning each of the plurality of portions to a respective computation resource of the plurality of computation resources based at least in part on the minimum processing capability of a respective portion and the processing capability of the respective computation resource.