Abstract: A vehicle has a plurality of control apparatuses, a user input, a geographic position component, an object detection apparatus, memory, and a display. A processor is also included and is programmed to receive the destination information, identify a route, and determine the current geographic location of the vehicle. The processor is also programmed to identify an object and object type based on object information received from the object detection apparatus and to determine at least one warning characteristic of the identified object based on at least one of: the object type, a detected proximity of the detected object to the vehicle, the location of the detected object relative to predetermined peripheral areas of the vehicle, the current geographic location of the vehicle, and the route. The processor is also configured to select and display on the display an object warning image based on the at least one warning characteristic.

Abstract: Aspects and implementations of the present disclosure address shortcomings of existing technology by enabling autonomous vehicle simulations based on retro-reflection optical data. The subject matter of this specification can be implemented in, among other things, a method that involves initiating a simulation of an environment of an autonomous driving vehicle, the simulation including a plurality of simulated objects, each having an identification of a material type of the respective object. The method can further involve accessing simulated reflection data based on the plurality of simulated objects and retro-reflectivity data for the material types of the simulated objects, and determining, using an autonomous vehicle control system for the autonomous vehicle, a driving path relative to the simulated objects, the driving path based on the simulated reflection data.

Abstract: The technology relates to controlling a vehicle in an autonomous driving mode. For example, sensor data identifying a plurality of objects may be received. Pairs of objects of the plurality of objects may be identified. For each identified pair of objects of the plurality of objects, a similarity value which indicates whether the objects of that identified pair of objects can be responded to by the vehicle as a group may be determined. The objects of one of the identified pairs of objects may be clustered together based on the similarity score. The vehicle may be controlled in the autonomous mode by responding to each object in the cluster in a same way.

Abstract: The technology relates to identifying sensor occlusions due to the limits of the ranges of a vehicle's sensors and using this information to maneuver the vehicle. As an example, the vehicle is maneuvered along a route that includes traveling on a first roadway and crossing over a lane of a second roadway. A trajectory is identified from the lane that will cross with the route during the crossing at a first point. A second point beyond a range of the vehicle's sensors is selected. The second point corresponds to a hypothetical vehicle moving towards the route along the lane. A distance between the first point and the second point is determined. An amount of time that it would take the hypothetical vehicle to travel the distance is determined and compared to a threshold amount of time. The vehicle is maneuvered based on the comparison to complete the crossing.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, that obtain scene features in an environment that includes an autonomous vehicle and a target agent at a current time point; determine whether the target agent is an emergency vehicle that is active at the current time point; generate from the scene features, an input (i) that includes the scene features and (ii) that indicates whether the target vehicle is an emergency vehicle that is active at the current time point; and process the input using a machine learning model that is configured to generate a behavior prediction output for the target agent that characterizes predicted future behavior of the target agent after the current time point.

Abstract: Example embodiments relate to crosstalk reduction for light detection and ranging (lidar) devices using wavelength locking. An example embodiment includes a lidar device. The lidar device includes a first light emitter configured to emit a first light signal and a second light emitter configured to emit a second light signal. The lidar device also includes a first light guide and a second light guide. In addition, the lidar device includes a first light detector and a second light detector. Further, the lidar device includes a first wavelength-locking mechanism configured to use a portion of the first light signal to maintain a wavelength of the first light signal and a second wavelength-locking mechanism configured to use a portion of the second light signal to maintain a wavelength of the second light signal. The wavelengths of the first light signal and the second light signal are different.

Abstract: Aspects of the disclosure relate to evaluating pullovers for autonomous vehicles. In one instance, a set of potential pullover locations within a predetermined distance of a destination may be identified. Whether any of the potential pullover locations of the set include one or more of a plurality of predetermined types of regions of interest where a vehicle should not park for an extended period of time may be determined. A pullover location is identified based on the determination. The identified pullover location may be compared to a pullover location identified by autonomous vehicle control software in order to evaluate the pullover location identified by the autonomous vehicle control software.

Abstract: Aspects of the disclosure relate to a system that includes a memory storing a queue for arranging tasks, a plurality of self-driving systems for controlling an autonomous vehicle, and one or more processors. The one or more processors may receive a non-passenger task request with a priority level of the non-passenger task request. When the non-passenger task request is accepted, the one or more processors may insert the task in the queue based on the priority level of the task request. Then, the one or more processors may provide instructions to one or more self-driving systems according to the non-passenger task request. Having received updates of the status of the autonomous vehicle, the one or more processors may determine that the task is completed based on the updates. After determining that the task is completed, the one or more processors may remove the task from the queue.

Abstract: A first driving solution for a vehicle along a portion of a route is determined based on an agent detected in the vehicle's environment following a first trajectory of a plurality of possible trajectories. A switching time is determined for the vehicle to deviate from the first driving solution for a situation in which the agent is following a second trajectory of the plurality of possible trajectories. The first driving solution is revised such that the vehicle will be able to switch from the revised first driving solution to another driving solution at the switching time in case if the detected agent is following the second trajectory.

Abstract: Example embodiments relate to identification of proxy calibration targets for a fleet of sensors. An example method includes collecting, using a sensor coupled to a vehicle, data about one or more objects within an environment of the vehicle. The sensor has been calibrated using a ground-truth calibration target. The method also includes identifying, based on the collected data, at least one candidate object, from among the one or more objects, to be used as a proxy calibration target for other sensors coupled to vehicles within a fleet of vehicles. Further, the method includes providing, by the vehicle, data about the candidate object for use by one or more vehicles within the fleet of vehicles.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for determining occupancies of surrounding agents. One of the methods includes obtaining scene data characterizing an environment at a current time point; processing a first network input generated from the scene data using a first neural network to generate an intermediate output; obtaining an identification of a future time point that is after the current time point; and generating, from the intermediate output and the future time point, an occupancy output, wherein the occupancy output comprises respective occupancy probabilities for each of a plurality of locations in the environment, wherein the respective occupancy probability for each location characterizes a likelihood that one or more agents will occupy the location at the future time point.

Abstract: Methods, computer systems, and apparatus, including computer programs encoded on computer storage media, for processing a perspective view range image generated from sensor measurements of an environment. The perspective view range image includes a plurality of pixels arranged in a two-dimensional grid and including, for each pixel, (i) features of one or more sensor measurements at a location in the environment corresponding to the pixel and (ii) geometry information comprising range features characterizing a range of the location in the environment corresponding to the pixel relative to the one or more sensors. The system processes the perspective view range image using a first neural network to generate an output feature representation. The first neural network comprises a first perspective point-set aggregation layer comprising a geometry-dependent kernel.

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: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for processing inputs using slice-based dynamic neural networks. One of the methods includes receiving a new input for processing by a neural network that includes a first conditional neural network layer that has a set of shared parameters and a respective set of slice parameters for each of a plurality of slices. One or more slices to which the new input belongs are identified. The new input is processed to generate a network output, including: receiving a layer input to the first conditional neural network layer; and processing the layer input using the set of shared parameters, the respective one or more sets of slice parameters for the identified one or more slices, but not the respective sets of slice parameters for any other slices to which the new input does not belong.

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: 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: Aspects of the disclosure relate to controlling a vehicle having an autonomous driving mode or an autonomous vehicle. For instance, a polygon representative of the shape and location of a first object may be received. A polyline contour representation of a portion of a polygon representative of the shape and location of a second object may be received. The polyline contour representation may be in half-plane coordinates and including a plurality of vertices and line segments. Coordinates of the polygon representative of the first object may be converted to the half-plane coordinate system. A collision location between the polyline contour representation and the polygon representative of the first object may be determined using the converted coordinates. The autonomous vehicle may be controlled in the autonomous driving mode to avoid a collision based on the collision location.

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.