Abstract: The technology relates to maneuvering a vehicle prior to making a turn from a current lane of the vehicle. As an example, a route that includes making the turn from the lane is identified. An area of the lane prior to the turn having a lane width of at least a predetermined size is also identified. Sensor data identifying an object within the lane is received from a perception system of a vehicle. Characteristics of the object, including a location of the object relative to the vehicle, are identified from the sensor data. The vehicle is then maneuvered through the area prior to making the turn using the identified characteristics.

Abstract: The present disclosure relates to optical systems and methods for their manufacture. An example method includes coupling a first surface of a light-emitter substrate to a reference surface of a carrier substrate. The method also includes registering a mold structure with respect to the reference surface of the carrier substrate. Furthermore, the method includes using the mold structure to form an optical material over at least a portion of the light-emitter substrate. The optical material is shaped according to a shape of the mold structure and includes at least one registration feature. The method also includes coupling an optical lens element to the optical material such that the optical lens element is registered to the at least one registration feature.

Abstract: Aspects of the disclosure relate to training a machine learning model on a distributed computing system. The model can be trained using selected processors of the training platform. The distributed system automatically modifies the model for instantiation on each processor, adjusts an input pipeline to accommodate the capabilities of selected processors, and coordinates the training between those processors. Simultaneous processing at each stage can be scaled to reduce or eliminate bottlenecks in the distributed system. In addition, autonomous monitoring and re-allocating of resources can further reduce or eliminate bottlenecks. The training results may be aggregated by the distributed system, and a final model may then be transmitted to a user device.

Abstract: A system may include a first sensor of a first type and a second sensor of a second different type and having a detector. A field of view of the second sensor may be formed by a plurality of regions of interest (ROIs) defined by the detector. Control circuitry of the system may be configured to perform operations including obtaining, from the first sensor, first sensor data representing an environment, and determining, based on the first sensor data, information associated with a feature of interest within the environment. The operations may also include determining, based on the information, a particular ROI that corresponds to an expected position of the feature at a later time, obtaining a plurality of ROI sensor data from the particular ROI instead of obtaining full-resolution sensor data, and analyzing the plurality of ROI sensor data to determine one or more attributes of the feature.

Abstract: The present disclosure relates to contaminant detection systems and related optical systems and methods. An example contaminant detection system includes an optical coupler configured to couple light into and/or out of an optical element. The contaminant detection system also includes a plurality of light-emitter devices configured to emit emission light toward the optical coupler. The contaminant detection system additionally includes a plurality of detector devices configured to detect at least a portion of the emission light by way of the optical element and the optical coupler. The plurality of detector devices is also configured to provide detector signals indicative of a presence of a contaminant on the optical element.

Abstract: The technology relates to a seamless interface between an autonomous vehicle service provider and one or more ridesharing, ride-hailing or delivery partner companies, in order to provide timely, efficient rider or delivery services and support. A partner trip application programming interface (API) provides robust features to the ridesharing or ride-hailing partner companies and also supports delivery of meals, groceries, packages or other cargo. For instance, the API may be employed with delivery partners (e.g., food, package or bulk cargo delivery), and/or for concierge services (e.g., a hotel or medical provider or an e-commerce specialist that coordinates deliveries). The technology supports both ad hoc and scheduled trips. Agents for different partners may schedule trips to or from specific stores or other (geofenced) locations, and observe and manage trips for an entire enterprise. Permissions can be tailored for each agent.

Abstract: One example method involves obtaining a plurality of scans of a field-of-view (FOV) of a light detection and ranging (LIDAR) device disposed inside a housing. Obtaining each scan of the plurality of scans comprises: transmitting, through a plurality of sections of the housing, a plurality of light pulses emitted from the LIDAR device in different directions toward the housing; and detecting a plurality of returning light pulses comprising reflected portions of the transmitted plurality of light pulses that are reflected back toward the LIDAR device. The method also involves detecting an obstruction that at least partially occludes the LIDAR device from scanning the FOV through the housing based on the plurality of scans.

Abstract: Methods, systems, and apparatus, for a stray-light testing station. In one aspect, the stray-light testing station includes an illumination assembly including a spatially extended light source and one or more optical elements arranged to direct a beam of light from the spatially extended light source along an optical path to an optical receiver assembly including a lens receptacle configured to receive a lens module and position the lens module in the optical path downstream from the parabolic mirror so that the lens module focuses the beam of light from the spatially extended light source to an image plane, and a moveable frame supporting the optical receiver assembly including one or more adjustable alignment stages to position the optical receiver assembly relative to the illumination assembly such that the optical path of the illumination assembly is within a field of view of the optical receiver assembly.

Abstract: The technology relates to controlling a vehicle in an autonomous driving mode. In one instance, sensor data identifying an object in an environment of the vehicle may be received. A first path of a first trajectory where the vehicle will pass the object may be determined. A function is used to determining a first maximum speed of the vehicle based on a predetermined minimum lateral clearance between the object and the vehicle. The first maximum speed may be used to determine whether an actual lateral clearance between the object and the vehicle will meet the predetermined minimum lateral clearance. The determination of whether the actual lateral clearance will meet the predetermined minimum lateral clearance may be used to generate a first speed plan for the first trajectory. The vehicle may be controlled in the autonomous driving mode according to the first trajectory including the first speed plan and the first path.

Abstract: An apparatus includes a lens assembly that includes at least one lens that defines an optical axis. The apparatus also includes a substrate, an image sensor disposed on the substrate, and an actuator coupled to the substrate and configured to adjust a position of the substrate relative to the lens assembly to move the image sensor along the optical axis. The apparatus additionally includes a capacitive position sensor that includes a first capacitive plate coupled to the substrate and a second capacitive plate coupled to the lens assembly. The capacitive position sensor may be configured to generate a capacitance measurement indicative of the position of the substrate relative to the lens assembly. The apparatus further includes circuitry configured to control the actuator based on (i) the capacitance measurement and (ii) a target position of the image sensor relative to the lens assembly.

Abstract: Aspects of the disclosure provide for controlling an autonomous vehicle. For instance, a first probability distribution may be generated for the vehicle at a first future point in time using a generative model for predicting expected behaviors of objects and a set of characteristics for the vehicle at an initial time expected to be perceived by an observer. Planning system software of the vehicle may be used to generate a trajectory for the vehicle to follow. A second probability distribution may be generated for a second future point in time using the generative model based on the trajectory and a set of characteristics for the vehicle at the first future point expected to be perceived by the observer. A surprise assessment may be generated by comparing the first probability distribution to the second probability distribution. The vehicle may be controlled based on the surprise assessment.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for selecting locations in an environment of a vehicle where objects are likely centered and determining properties of those objects. One of the methods includes receiving an input characterizing an environment external to a vehicle. For each of a plurality of locations in the environment, a respective first object score that represents a likelihood that a center of an object is located at the location is determined. Based on the first object scores, one or more locations from the plurality of locations are selected as locations in the environment at which respective objects are likely centered. Object properties of the objects that are likely centered at the selected locations are also determined.

Abstract: A computing system may operate a LIDAR device to emit and detect light pulses in accordance with a time sequence including standard detection period(s) that establish a nominal detection range for the LIDAR device and extended detection period(s) having durations longer than those of the standard detection period(s). The system may then make a determination that the LIDAR detected return light pulse(s) during extended detection period(s) that correspond to particular emitted light pulse(s). Responsively, the computing system may determine that the detected return light pulse(s) have detection times relative to corresponding emission times of particular emitted light pulse(s) that are indicative of one or more ranges. Given this, the computing system may make a further determination of whether or not the one or more ranges indicate that an object is positioned outside of the nominal detection range, and may then engage in object detection in accordance with the further determination.

Abstract: The technology relates to assisting large self-driving vehicles, such as cargo vehicles, as they maneuver towards and/or park at a destination facility. This may include a given vehicle transitioning between different autonomous driving modes. Such a vehicles may be permitted to drive in a fully autonomous mode on certain roadways for the majority of a trip, but may need to change to a partially autonomous mode on other roadways or when entering or leaving a destination facility such as a warehouse, depot or service center. Large vehicles such as cargo truck may have limited room to maneuver in and park at the destination, which may also prevent operation in a fully autonomous mode. Here, information from the destination facility and/or a remote assistance service can be employed to aid in real-time semi-autonomous maneuvering.

Abstract: Methods, computer systems, and apparatus, including computer programs encoded on computer storage media, for performing a conditional behavior prediction for one or more agents. The system obtains context data characterizing an environment. The context data includes data characterizing a plurality of agents, including a query agent and one or more target agents, in the environment at a current time point. The system further obtains data identifying a planned future trajectory for the query agent after the current time point, and for each target agent in the set, processes the context data and the data identifying the planned future trajectory using a first neural network to generate a conditional trajectory prediction output that defines a conditional probability distribution over possible future trajectories of the target agent after the current time point given that the query agent follows the planned future trajectory for the query agent after the current time point.

Abstract: The technology relates to assisting large self-driving vehicles, such as cargo vehicles, as they maneuver towards and/or park at a destination facility. This may include a given vehicle transitioning between different autonomous driving modes. Such a vehicles may be permitted to drive in a fully autonomous mode on certain roadways for the majority of a trip, but may need to change to a partially autonomous mode on other roadways or when entering or leaving a destination facility such as a warehouse, depot or service center. Large vehicles such as cargo truck may have limited room to maneuver in and park at the destination, which may also prevent operation in a fully autonomous mode. Here, information from the destination facility and/or a remote assistance service can be employed to aid in real-time semi-autonomous maneuvering.

Abstract: This technology relates to a display mounted messaging system. The display mounted messaging system may include a light emitting diode (LED) display attached to a housing of a sensor. The housing of the sensor may rotate. The display mounted messaging system may also include an LED controller which is configured to selectively activate and deactivate at least one LED in the LED display, to provide a message in the direction of an intended recipient.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating realistic full-scene point clouds. One of the methods includes obtaining an initial scene point cloud characterizing an initial scene in an environment; obtaining, for each of one or more objects, an object point cloud that characterizes the object; and processing a first input comprising the initial scene point cloud and the one or more object point clouds using a first neural network that is configured to process the first input to generate a final scene point cloud that characterizes a transformed scene that has the one or more objects added to the initial scene.