Abstract: The present disclosure relates to optical systems and methods for their manufacture. An example optical system includes a first substrate having a mounting surface and a spacer structure having at least one cavity. The spacer structure is coupled to the mounting surface of the first substrate. The optical system also includes a light-emitter device that is coupled to the spacer structure and a detector device coupled to the first substrate such that the at least one detector device is disposed within the at least one cavity of the spacer structure. The optical system also includes a second substrate that mounts a lens and a waveguide and is coupled to the spacer structure. The optical system also includes a shim coupled between the second surface of the spacer structure and a mounting surface of the second substrate.

Abstract: One example system includes a first light detection and ranging (LIDAR) device that scans a first field-of-view defined by a first range of pointing directions associated with the first LIDAR device. The system also includes a second LIDAR device that scans a second FOV defined by a second range of pointing directions associated with the second LIDAR device. The second FOV at least partially overlaps the first FOV. The system also includes a first controller that adjusts a first pointing direction of the first LIDAR device. The system also includes a second controller that adjusts a second pointing direction of the second LIDAR device synchronously with the adjustment of the first pointing direction of the first LIDAR device.

Abstract: Aspects of the disclosure relate to systems for cleaning a sensor. For example, the sensor may include a housing as well as internal sensor components housed within the housing. The housing may include a sensor input surface through which signals may pass. The system may also include a motor configured to rotate the internal sensor components relative to a mount as well as the mount to which the motor is fixed. The system may also include a wiper including a wiper blade. The wiper may be attached to the mount such that rotating the housing causes the wiper to contact the sensor input surface in order to clean the sensor.

Abstract: Aspects and implementations of the present disclosure address shortcomings of the existing technology by enabling lidar-assisted segmentation and identification of particulate matter in autonomous vehicle (AV) applications, by: obtaining, by a sensing system of the AV, a plurality of return points, each return point having one or more velocity values and one or more coordinates of a reflecting region that reflects a signal emitted by the sensing system, identifying, in view of the one or more velocity values of each of a first set of the return points of the plurality of return points, that the first set of the return points is associated with a particulate matter in an environment of the AV, and causing a driving path of the AV to be determined in view of the particulate matter.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating a spatio-temporal embedding of a sequence of point clouds. One of the methods includes obtaining a temporal sequence comprising a respective point cloud input corresponding to each of a plurality of time points, each point cloud input comprising point cloud data generated from sensor data captured by one or more sensors of a vehicle at the respective time point; processing each point cloud input using a first neural network to generate a respective spatial embedding that characterizes the point cloud input; and processing the spatial embeddings of the point cloud inputs using a second neural network to generate a spatio-temporal embedding that characterizes the point cloud inputs in the temporal sequence.

Abstract: The disclosure relates to training models for identification of events likely to cause discomfort to passengers of autonomous vehicles and for assessment of overall ride quality of autonomous vehicle rides. For instance, ride data may be associated with a ride quality value indicative of a level of discomfort and/or a first overall ride quality value indicating an overall ride quality provided by the passenger for the first ride. This ride data may be used to train a model such that the model is configured to, in response to receiving ride data for a second ride as input, output a list of events likely to cause discomfort to a passenger during the second ride and/or such that the model is configured to, in response to receiving second ride data for a second ride as input, output a second overall ride quality value for the second ride.

Abstract: Example systems and methods enable an autonomous vehicle to request assistance from a remote operator in certain predetermined situations. One example method includes determining a representation of an environment of an autonomous vehicle based on sensor data of the environment. Based on the representation, the method may also include identifying a situation from a predetermined set of situations for which the autonomous vehicle will request remote assistance. The method may further include sending a request for assistance to a remote assistor, the request including the representation of the environment and the identified situation. The method may additionally include receiving a response from the remote assistor indicating an autonomous operation. The method may also include causing the autonomous vehicle to perform the autonomous operation.

Abstract: The technology relates to determining general weather conditions affecting the roadway around a vehicle, and how such conditions may impact driving and route planning for the vehicle when operating in an autonomous mode. For instance, the on-board sensor system may detect whether the road is generally icy as opposed to a small ice patch on a specific portion of the road surface. The system may also evaluate specific driving actions taken by the vehicle and/or other nearby vehicles. Based on such information, the vehicle's control system is able to use the resultant information to select an appropriate braking level or braking strategy. As a result, the system can detect and respond to different levels of adverse weather conditions. The on-board computer system may share road condition information with nearby vehicles and with remote assistance, so that it may be employed with broader fleet planning operations.

Abstract: Example systems and methods allow for reporting and sharing of information reports relating to driving conditions within a fleet of autonomous vehicles. One example method includes receiving information reports relating to driving conditions from a plurality of autonomous vehicles within a fleet of autonomous vehicles. The method may also include receiving sensor data from a plurality of autonomous vehicles within the fleet of autonomous vehicles. The method may further include validating some of the information reports based at least in part on the sensor data. The method may additionally include combining validated information reports into a driving information map. The method may also include periodically filtering the driving information map to remove outdated information reports. The method may further include providing portions of the driving information map to autonomous vehicles within the fleet of autonomous vehicles.

Abstract: Examples described may related to an imaging sensor used by a vehicle, including a light sensor. The light sensor comprises a plurality of cells aligned in a plurality of horizontal rows and a plurality of vertical columns. The apparatus further includes an optical system configured to provide the light sensor with a field of view of an external environment of the apparatus. Additionally, the system includes a processing unit configured to: divide the plurality of horizontal rows of the light sensor into one or more enabled rows and one or more disabled rows; obtain image data from the light sensor by sampling one or more cells in the one or more enabled rows; and store the received image data in a memory.

Abstract: Aspects of the present disclosure relate switching between autonomous and manual driving modes. In order to do so, the vehicle's computer may conduct a series of environmental, system, and driver checks to identify certain conditions. The computer may correct some of these conditions and also provide a driver with a checklist of tasks for completion. Once the tasks have been completed and the conditions are changed, the computer may allow the driver to switch from the manual to the autonomous driving mode. The computer may also make a determination, under certain conditions, that it would be detrimental to the driver's safety or comfort to make a switch from the autonomous driving mode to the manual driving mode.

Abstract: Aspects of the disclosure relate to methods for controlling a vehicle having an autonomous driving mode. For instance, sensor data may be received from one or more sensors of the perception system of the vehicle, the sensor data identifying characteristics of an object perceived by the perception system. When it is determined that the object is no longer being perceived by the one or more sensors of the perception system, predicted characteristics for the object may be generated based on one or more of the identified characteristics. The predicted characteristics of the object may be used to control the vehicle in the autonomous driving mode such that the vehicle is able to respond to the object when it is determined that the object is no longer being perceived by the one or more sensors of the perception system.

Abstract: Aspects of the disclosure relate to validating autonomous control software for operating a vehicle autonomously. For instance, the autonomous control software is run through a driving scenario to observe an outcome for the autonomous control software. A validation model is run through the driving scenario a plurality of times to observe an outcome for the model for each of the plurality of times. Whether the software passed the driving scenario is determined based on whether the outcome for the software indicates that a virtual vehicle under control of the software collided with another object during the single time. Whether the validation model passed the driving scenario is determined based on whether the outcome for the model indicates that a virtual vehicle under control of the model collided with another object in any one of the plurality of times. The software is validated based on the determinations.

Abstract: Example embodiments present radar units capable of operating in multiple polarizations. An example radar unit may include a set of transmission antennas and a set of reception antennas. Particularly, the transmission antennas may each be configured to transmit radar signals that radiate in one or more of four potential polarizations. The four polarizations can correspond to horizontal linear, vertical linear and slanted polarizations at approximately positive forty-five degrees and negative forty-five degrees from the horizontal plane. As such, the reception antennas of the radar unit may each be configured to receive reflected radar signals that are radiating in one of the four potential polarizations. The radar unit may further include an amplifier configured to cause one or multiple transmission antennas to selectively transmit between two or more of the four polarization channels.

Abstract: The present disclosure relates to systems, vehicles, and methods that include adjusting a position of an image sensor with respect to a lens assembly. An example optical system includes a lens assembly having at least one lens. The at least one lens defines an optical axis, a focal distance, and a focal plane. The optical system also includes a lens holder coupled to the lens assembly and a substrate having a first surface. The optical system additionally includes an image sensor attached to the first surface of the substrate. Furthermore, the optical system includes an active compensation system having a piezoelectric structure coupled between the lens holder and the first surface of the substrate. The active compensation system is configured to maintain the image sensor at the focal plane over a predetermined temperature range.