Abstract: The technology includes communicating the current status of a self-driving vehicle to users, such as passengers within the vehicle and other users awaiting pickup. Certain information about the trip and vehicle status is communicated depending on where the passenger is sitting within the vehicle or where the person awaiting pickup is located outside the vehicle. This includes disseminating the “monologue” of a vehicle operating in an autonomous driving mode to a user via an app on the user's device (e.g., mobile phone, tablet or laptop PC, wearable, or other computing device) and/or an in-vehicle user interface. The monologue includes current status information regarding driving decisions and related operations or actions. This alerts the user as to why the vehicle is taking (or not taking) a certain action, which reduces confusion and allows the user to focus on other matters.

Abstract: A system includes a microphone unit coupled to a roof of an autonomous vehicle. The microphone unit includes a microphone board having a first opening. The microphone unit also includes a first microphone positioned over the first opening and coupled to the microphone board. The microphone unit further includes an accelerometer. The system also includes a processor coupled to the microphone unit.

Abstract: A method and system are provided for controlling braking a vehicle in an autonomous driving mode. For instance, the vehicle is controlled in the autonomous driving mode according to a first braking control mode using a first model to adjust the position of a vehicle relative to an expected position of a current trajectory of the vehicle. Using a second model how close to a maximum deviation threshold the vehicle would come if a maximum braking strength for the vehicle was applied is predicted. The maximum deviation threshold provides an allowed forward deviation from the current trajectory. Based on the prediction, the vehicle is controlled in the autonomous driving mode according to a second braking control mode by automatically applying the maximum braking strength.

Abstract: Examples described may enable provision of remote assistance for an autonomous vehicle. An example method includes a computing system operating by default in a first mode and periodically transitioning from operation in the first mode to operation in a second mode. In the first mode, the system may receive environment data provided by the vehicle and representing object(s) having a detection confidence below a threshold, where the detection confidence is indicative of a likelihood of correct identification of the object(s), and responsive to the object(s) having a confidence below the threshold, provide remote assistance data comprising an instruction to control the vehicle and/or a correct identification of the object(s). In the second mode, the system may trigger user interface display of remote assistor alertness data based on pre-stored data related to an environment in which the pre-stored data was acquired, and receive a response relating to the alertness data.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for using a surfel map to generate long range localization. One of the methods includes obtaining, for a particular location of a vehicle having a camera and a detection sensor, surfel data including a plurality of surfels. Each surfel in the surfel data has a respective location and corresponds to a different respective detected surface in an environment. Image data captured by the camera is obtained. It is determined that a region of interest for detecting objects for a vehicle planning process is outside a detectable region for the detection sensor. In response, it is determined that the image data for the region of interest matches surfel color data for the surfels corresponding to the region of interest. In response, the vehicle planning process is performed with the region of interest designated as having no unexpected objects.

Abstract: Jaywalking behaviors of vulnerable road users (VRUs) such as cyclists or pedestrians can be predicted. Location data is obtained that identifies a location of a VRU within a vicinity of a vehicle. Environmental data is obtained that describes an environment of the VRU, where the environmental data identifies a set of environmental features in the environment of the VRU. The system can determine a nominal heading of the VRU, and generate a set of predictive inputs that indicate, for each of at least a subset of the set of environmental features, a physical relationship between the VRU and the environmental feature. The physical relationship can be determined with respect to the nominal heading of the VRU and the location of the VRU. The set of predictive inputs can be processed with a heading estimation model to generate a predicted heading offset (e.g., a target heading offset) for the VRU.

Abstract: The disclosure relates to assessing operation of two or more cameras. These cameras may be a group of cameras of a perception system of a vehicle having an autonomous driving mode. A first image captured by a first camera and a second image captured by a second camera may be received. A first feature vector for the first image and a second feature vector for the second image may be generated. A similarity score may be determined using the first feature vector and the second feature vector. This similarity score may be used to assess the operation of the two cameras and an appropriate action may be taken.

Abstract: Example embodiments described herein involve determining three dimensional data representative of an environment for an autonomous vehicle using radar. An example embodiment involves receiving radar reflection signals at a radar unit coupled to a vehicle and determining an azimuth angle and a distance for surfaces in the environment causing the radar reflection signals. The embodiment further involves determining an elevation angle for the surfaces causing the radar reflection signals based on phase information of the radar reflection signals and controlling the vehicle based at least in part on the azimuth angle, the distance, and the elevation angle for the surfaces causing the plurality of radar reflection signals. In some instances, the radar unit is configured to receive radar reflection signals using a staggered linear array with one or multiple radiating elements offset in the array.

Abstract: The disclosure relates to assessing and responding to wheel slippage and estimating road friction for a road surface. For instance, a vehicle may be controlled in an autonomous driving mode in order to follow a trajectory. A wheel of the vehicle may be determined to be slipping such that the vehicle has limited steering control. In response to determining that the wheel is slipping, steering of one or more wheels may be controlled in order to orient the one or more wheels towards the trajectory in order to allow the vehicle to proceed towards the trajectory when the wheel is no longer slipping. In addition, the road friction may be estimated based on the determination that the wheel is slipping. The vehicle may be controlled in the autonomous driving mode based on the estimated road friction.

Abstract: A system includes an image sensor having a plurality of pixels that form a plurality of regions of interest (ROIs), image processing resources, and a scheduler configured to perform operations including determining a priority level for a particular ROI of the plurality of ROIs based on a feature detected by one or more image processing resources of the image processing resources within initial image data associated with the particular ROI. The operations also include selecting, based on the feature detected within the initial image data, a particular image processing resource of the image processing resources by which subsequent image data generated by the particular ROI is to be processed. The operations further include inserting, based on the priority level, the subsequent image data into a processing queue of the particular image processing resource to schedule the subsequent image data for processing by the particular image processing resource.

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: This technology relates to system for clearing a sensor cover. The system may include a sprayer, a support, and a positioning device. The sprayer may be mounted to the support and direct a flow of fluid to the sensor cover. The positioning device may be configured to adjust the position of the sprayer on the support. The system may include a nozzle and the nozzle may be positioned to direct the flow of fluid to an area of the sensor cover. The system may include additional sprayers.

Abstract: Aspects of the disclosure relate generally to generating and providing route options for an autonomous vehicle. For example, a user may identify a destination, and in response the vehicle's computer may provide routing options to the user. The routing options may be based on typical navigating considerations such as the total travel time, travel distance, fuel economy, etc. Each routing option may include not only an estimated total time, but also information regarding whether and which portions of the route may be maneuvered under the control of the vehicle alone (fully autonomous), a combination of the vehicle and the driver (semiautonomous), or the driver alone. The time of the longest stretch of driving associated with the autonomous mode as well as map information indicating portions of the routes associated with the type of maneuvering control may also be provided.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating cut-in probabilities of agents surrounding a vehicle. One of the methods includes obtaining agent trajectory data for one or more agents in an environment; obtaining vehicle trajectory data of a vehicle in the environment; and processing a network input generated from the agent trajectory data and vehicle trajectory data using a neural network to generate a cut-in output, wherein the cut-in output comprises respective cut-in probabilities for each of a plurality of locations in the environment, wherein the respective cut-in probability for each location that is a current location of one of the one or more agents characterizes a likelihood that the agent in the current location will intersect with a planned future location of the vehicle within a predetermined amount of time.