Abstract: Example implementations are provided for an arrangement of co-aligned rotating sensors. One example device includes a light detection and ranging (LIDAR) transmitter that emits light pulses toward a scene according to a pointing direction of the device. The device also includes a LIDAR receiver that detects reflections of the emitted light pulses reflecting from the scene. The device also includes an image sensor that captures an image of the scene based on at least external light originating from one or more external light sources. The device also includes a platform that supports the LIDAR transmitter, the LIDAR receiver, and the image sensor in a particular relative arrangement. The device also includes an actuator that rotates the platform about an axis to adjust the pointing direction of the device.

Abstract: Example embodiments relate to techniques for providing incremental remote assistance to a vehicle. A computing device initially receives location information from a vehicle that is autonomously navigating to a destination. Based on the location information, the computing device displays a representation of an environment of the vehicle and receives a first input specifying a first waypoint and a second input specifying a second waypoint positioned on the representation of the environment. The computing device is configured to limit a location of the first waypoint based on a first location of the vehicle, and a location of the second waypoint based on a second location of the vehicle or the location of the first waypoint. The computing device can then provide route instructions to the vehicle based on at least the first waypoint or the second waypoint.

Abstract: Aspects of the disclosure provide for the generation of a driving difficulty heat map for autonomous vehicles. For instance, log data generated by a vehicle being driven in a manual driving mode for a segment of a route may be input into a disengage model in order to generate an output identifying a likelihood of a vehicle driving in an autonomous driving mode requiring a disengage from the autonomous driving mode along the segment of the route. The log data may have been collected within a geographic area. A grid for the geographic area may be generated. The grid may include a plurality of cells. The output is assigned to one of the plurality of cells. The plurality of cells and assigned output may be used to generate a driving difficulty heat map for the geographic area.

Abstract: A vehicle operable to pull a trailer comprising a payload is provided, that includes a plurality of sensors configured to capture sensor data related to the vehicle, the trailer, or both, and a controller configured to (i) receive the sensor data from the plurality of sensors, (ii) determine, based on the sensor data, one or more parameters associated with the trailer, the payload, or both, (iii) update, based on an analysis of the one or more parameters, a confidence level associated with an operation of the vehicle with the trailer and the payload, and (iv) based on the confidence level, responsively execute an autonomous control strategy comprising one or more adjustments to the operation of the vehicle.

Abstract: One example LIDAR device comprises a substrate and a waveguide disposed on the substrate. A first section of the waveguide extends lengthwise on the substrate in a first direction. A second section of the waveguide extends lengthwise on the substrate in a second direction different than the first direction. A third section of the waveguide extends lengthwise on the substrate in a third direction different than the second direction. The second section extends lengthwise between the first section and the second section. The LIDAR device also comprises a light emitter configured to emit light. The waveguide is configured to guide the light inside the first section toward the second section, inside the second section toward the third section, and inside the third section away from the second section.

Abstract: Example embodiments relate to using remote assistance to maneuver an autonomous vehicle to a location. A computing device used by a remote operator may receive a request for assistance from a vehicle that indicates the vehicle is stopped at a first location with one or more navigation options for enabling the vehicle to navigate from the first location to a second location. At least one navigation option includes a maneuver technique that requires operator approval prior to execution. The computing device may then display a graphical user interface (GUI) that conveys the one or more navigation options. Based on detecting a selection of a particular navigation option, the computing device may transmit instructions to the vehicle to perform the particular navigation option. The vehicle may configured to navigate from the first location to the second location by performing the particular navigation option while monitoring for changes in the environment.

Abstract: Aspects of the technology involve controlling a vehicle configured to operate in an autonomous driving mode. This includes receiving a set of environmental inputs including temperature information from different temperature sources, receiving initial steering information from a steering system of the vehicle, and obtaining an initial rack position command by a motion control module of the vehicle. The system determines, based on the environmental inputs, the initial steering information and an initial rack position, a likelihood that a steering actuator of the steering system of the vehicle is blocked or likely to become blocked. The system determines whether a threshold excitation amount has been applied to the steering system within a selected amount of time or a selected driving distance. When the threshold amount of excitation is not met, an excitation profile is applied to the steering system in order to modify the initial rack position.

Abstract: Example embodiments relate to enhanced depth of focus cameras using variable apertures and pixel binning. An example embodiment includes a device. The device includes an image sensor. The image sensor includes an array of light-sensitive pixels and a readout circuit. The device also includes a variable aperture. Additionally, the device includes a controller that is configured to cause: the variable aperture to adjust to a first aperture size when a high-light condition is present, the variable aperture to adjust to a second aperture size when a low-light condition is present, the readout circuit to perform a first level of pixel binning when the high-light condition is present, and the readout circuit to perform a second level of pixel binning when the low-light condition is present. The second aperture size is larger than the first aperture size. The second level of pixel binning is greater than the first level of pixel binning.

Abstract: The present disclosure relates to systems, methods, and vehicles that facilitate a light detection and ranging (LIDAR or lidar) system that may take advantage of “dead angles” where the lidar system is oriented toward support structure or another “uninteresting” feature. In such scenarios, light pulses may be redirected toward more-interesting features in the environment. An example system includes a lidar system configured to emit light pulses into an environment of the system so as to provide information indicative of objects within a default field of view. The system also includes a reflective surface optically coupled to the lidar system. The reflective surface is configured to reflect at least a portion of the emitted light pulses so as to provide an extended field of view. The lidar system is further configured to provide information indicative of objects within the extended field of view.

Abstract: The technology relates to developing a highly accurate understanding of a vehicle's sensor fields of view in relation to the vehicle itself. A training phase is employed to gather sensor data in various situations and scenarios, and a modeling phase takes such information and identifies self-returns and other signals that should either be excluded from analysis during real-time driving or accounted for to avoid false positives. The result is a sensor field of view model for a particular vehicle, which can be extended to other similar makes and models of that vehicle. This approach enables a vehicle to determine when sensor data is of the vehicle or something else. As a result, the detailed modeling allowing the on-board computing system to make driving decisions and take other actions based on accurate sensor information.

Abstract: The technology relates to a system for cleating a sensor cover. The system may comprise a wiper comprising a wiper support, a wiper blade, and a sensor cover. The wiper blade may be configured to clear the sensor cover of debris, and the sensor cover may be configured to house one or more sensors. A wiper motor may rotate the wiper and a sensor motor may rotate the sensor cover. The system wiper blade may comprise a first edge attached to the wiper support and a second edge which may be configured to be in contact with the sensor cover. The wiper blade may extend in a corkscrew shape around the wiper support. The wiper motor may be configured to rotate the wiper in a first direction and the sensor motor may be configured to rotate the sensor cover in a second direction opposite the first direction.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for predicting near-curb driving behavior. One of the methods includes obtaining agent trajectory data for an agent in an environment, the agent trajectory data comprising a current location and current values for a predetermined set of motion parameters of the agent; processing a model input generated from the agent trajectory data using a trained machine learning model to generate a model output comprising a prediction of whether the agent will exhibit near-curb driving behavior within a predetermined timeframe, wherein an agent exhibits near-curb driving behavior when the agent operates within a particular distance of an edge of a road in the environment; and using the prediction to generate a planned path for a vehicle in the environment.

Abstract: One example system for preventing data loss during memory blackout events comprises a memory device, a sensor, and a controller operably coupled to the memory device and the sensor. The controller is configured to perform one or more operations that coordinate at least one memory blackout event of the memory device and at least one data transmission of the sensor.

Abstract: The technology involves to pickups performed by autonomous vehicles. In particular, it includes identifying one or more potential pullover locations adjacent to an area of interest that an autonomous vehicle is approaching. The vehicle detects that a given one of the potential pullover locations is occupied by another vehicle and determines whether the other vehicle will be vacating the given pullover location within a selected amount of time. Upon determining that the other vehicle will be vacating the given potential pullover location within the timeframe, the vehicle determines whether to wait for the other vehicle to vacate the given pullover location. Then a driving system of the vehicle either performs a first action in order to wait for the other vehicle to vacate the given pullover location or performs a second action that is different from the first action when it is determined to not wait.

Abstract: Aspects of the disclosure relate to controlling a vehicle. For instance, using a camera, a first camera image including a first object may be captured. A first bounding box for the first object and a distance to the first object may be identified. A second camera image including a second object may be captured. A second bounding box for the second image and a distance to the second object may be identified. Whether the first object is the second object may be determined using a plurality of models to compare visual similarity of the two bounding boxes, to compare a three-dimensional location based on the distance to the first object and a three-dimensional location based on the distance to the second object, and to compare results from the first and second models. The vehicle may be controlled in an autonomous driving mode based on a result of the third model.

Abstract: The described aspects and implementations enable efficient identification of real and image signs in autonomous vehicle (AV) applications. In one implementation, disclosed is a method and a system to perform the method that includes obtaining, using a sensing system of the AV, a combined image that includes a camera image and a depth information for a region of an environment of the AV, classifying a first sign in the combined image as an image-true sign, performing a spatial validation of the first sign, which includes evaluation of a spatial relationship of the first sign and one or more objects in the region of the environment of the AV, and identifying, based on the performed spatial validation, the first sign as a real sign.

Abstract: The technology employs a contrasting color scheme on different surfaces for sensor housing assemblies mounted on exterior parts of a vehicle that is configured to operate in an autonomous driving mode. Lighter and darker colors may be chosen on different surfaces according to a thermal budget for a given sensor housing assembly, due to the different types of sensors arranged along particular surfaces, or to provide color contrast for different regions of the assembly. For instance, differing colors such as black/white or blue/white, and different finishes such as matte or glossy, may be selected to enhance certain attributes or to minimize issues associated with a sensor housing assembly.

Abstract: Described examples relate to an apparatus comprising a memory for storing a sequence of image frames and at least one processor. The at least one processor may be configured to receive a first image frame of a sequence of image frames from an image capture device and select a first portion of a first image frame. The at least one processor may also be configured to obtain alignment information and determine a first portion and a second portion of a second image frame based on the alignment information. Further, the at least one processor may be configured to determine a bounding region within the second image frame and fetch image data corresponding to the bounding region of the second image frame from memory. In some examples, the first image frame may comprise a base image and the second image frame may comprise an alternative image frame. Further, the first image frame may comprise any one of the image frames of the sequence of image frames.