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: Aspects of the disclosure relate to arranging a pick up and drop off locations between a driverless vehicle and a passenger. As an example, a method of doing so may include receiving a request for a vehicle from a client computing device, wherein the request identifies a first location. Pre-stored map information and the first location are used to identify a recommended point according to a set of heuristics. Each heuristic of the set of heuristics has a ranking such that the recommended point corresponds to a location that satisfies at least one of the heuristics having a first rank and such that no other location satisfies any other heuristic of the set of heuristics having a higher rank than the first rank. The pre-stored map information identifying a plurality of pre-determined locations for the vehicle to stop, and the recommended point is one of the plurality of pre-determined locations.

Abstract: The technology includes a roof pod system for a vehicle configured to operate in one or more partly or fully autonomous self-driving modes. The roof pod system is arranged to sit above the roof of the vehicle, for instance at least 10-50 mm above the roof surface. The roof pod may be supported by a set of legs such as cross-rails. The roof pod system incorporates various sensors and related equipment to assist with self-driving operation. Some sensors may be arranged in the main housing of the roof pod, while others can be located in a dome-type structure extending above the main housing. A cabling harness assembly runs wiring and other links between the roof pod and the vehicle chassis.

Abstract: Example embodiments relate to GNSS time synchronization in redundant systems. A redundant system configured with two subsystems may initially synchronize clocks from both subsystems to GNSS time from a GNSS receiver. The synchronization of the first subsystem's clock may involve using a first communication link that enables communication between the first subsystem and the GNSS receiver while the synchronization of the second subsystem's clock may involve using both the first communication link and a second communication link that enables communication between the subsystems. The redundant system may then synchronize the first subsystem's clock to the second subsystem's clock while the second subsystem's clock is still synchronized to GNSS time from the GNSS receiver based on timepulses traversing a pair of wires that connect the subsystems and the GNSS receiver.

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: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for labeling point clouds using distance field data. One of the methods includes obtaining a point cloud characterizing a region of the environment, the point cloud comprising a plurality of points; obtaining distance field data specifying, for each of a plurality of locations in the region of the environment, a distance from the location to a nearest static object in the environment; determining, using the distance field data and for each of the plurality of points, a respective distance from the point to a nearest static object in the environment to the point; and identifying, based on the respective distances for the plurality of points in the point cloud, one or more of the points as candidate dynamic object points that are likely to be measurements of a dynamic object in the environment.

Abstract: Example implementations may relate to determining a strategy for a drop process associated with a light detection and ranging (LIDAR) device. In particular, the LIDAR device could emit light pulses and detect return light pulses, and could generate a set of data points representative of the detected return light pulses. The drop process could involve a computing system discarding data point(s) of the set and/or preventing emission of light pulse(s) by the LIDAR device. Accordingly, the computing system could detect a trigger to engage in the drop process, and may responsively (i) use information associated with the environment around the vehicle, operation of the vehicle, and/or operation of the LIDAR device as a basis to determine the strategy for the drop process, and (ii) engage in the drop process in accordance with the determined strategy.

Abstract: A method for preparing an optically transparent, superomniphobic coating on a substrate, such as an optical substrate, is disclosed. The method includes providing a glass layer disposed on a substrate, the glass layer having a first side adjacent the substrate and an opposed second side, the glass layer comprising 45-85 wt. % silicon oxide in a first glass phase and 10-40 wt. % boron oxide in a second glass phase, such that a glass layer has a composition in a spinodal decomposition region. The method further includes heating the second side of the glass layer to form a phase-separated portion of the layer, the phase-separated portion comprising an interpenetrating network of silicon oxide domains and boron oxide domains, and removing at least a portion of the boron oxide domains from the phase-separated portion to provide a graded layer disposed on the substrate. The graded layer has a first side disposed adjacent the substrate, the first side comprising 45-85 wt. % silicon oxide and 10-40 wt.

Abstract: The technology provides a lockout assembly that covers a latching mechanism, where the latching mechanism would enable a passenger to fold down the rear seat(s) of a vehicle. This can be particularly beneficial when the vehicle is operating in an autonomous driving mode, because certain areas within the vehicle may be restricted from passenger access. For instance, access to the trunk or cargo area may be limited to authorized service personnel. The lockout assembly includes a base unit and a cover. The cover is releasably secured to the base unit using one or more clip members. The base unit is fixedly secured to a receptacle of the latching mechanism using one or more fastener members, such as a screw. Once the cover is releasably secure, it may be completely or substantially flush with an inner edge of the receptacle.

Abstract: The technology provides a sign detection and classification methodology. A unified pipeline approach incorporates generic sign detection with a robust parallel classification strategy. Sensor information such as camera imagery and lidar depth, intensity and height (elevation) information are applied to a sign detector module. This enables the system to detect the presence of a sign in a vehicle's externa environment. A modular classification approach is applied to the detected sign. This includes selective application of one or more trained machine learning classifiers, as well as a text and symbol detector. Annotations help to tie the classification information together and to address any conflicts with different the outputs from different classifiers. Identification of where the sign is in the vehicle's surrounding environment can provide contextual details.

Abstract: A system for estimating a spacing profile for a road agent includes a first module and a second module. The first module includes instructions that cause one or more processors to receive data related to characteristics of the road agent and road agent behavior detected in an environment of an autonomous vehicle, initiate an analysis of the road agent behavior, and estimate the spacing profile of the road agent as part of the analysis. The spacing profile includes a lateral gap preference and predicted behaviors of the road agent related to changes in lateral gap. The second module includes instructions that cause the one or more processors to determine one or more components of autonomous vehicle maneuver based on the estimated spacing profile and send control instructions for performing the autonomous vehicle maneuver.

Abstract: Aspects of the disclosure provide for a method for identifying speed limit signs and controlling an autonomous vehicle in response to detected speed limit signs. The autonomous vehicle's computing devices identifies a speed limit sign in a vehicle's environment and a location and orientation corresponding to the speed limit sign. Then, the and orientation location of the speed limit sign is determined to not correspond to a pre-stored location and a pre-stored orientation of a speed limit sign that is pre-stored in map information. An effect zone of the speed limit sign is determined based on the location and orientation of the speed limit sign and characteristics of surrounding areas or other detected object before or after the speed limit sign. The autonomous vehicle's computing devices determines a response of the vehicle based on the determined effect zone, and controls the autonomous vehicle based on the determined response.

Abstract: The present disclosure relates to systems, vehicles, and methods for adjusting a pointing direction and/or a scanning region of a lidar. An example method includes determining a plurality of points of interest within an environment of a vehicle. The method also includes assigning, to each point of interest of the plurality of points of interest, a respective priority score. The method additionally includes partitioning at least a portion of the environment of the vehicle into a plurality of sectors. Each sector of the plurality of sectors includes at least one point of interest. For each sector of the plurality of sectors, the method includes adjusting a scanning region of a lidar unit based on the respective sector and causing the lidar unit to scan the respective sector.

Abstract: Aspects of the disclosure relate to determining perceptive range of a vehicle in real time. For instance, a static object defined in pre-stored map information may be identified. Sensor data generated by a sensor of the vehicle may be received. The sensor data may be processed to determine when the static object is first detected in an environment of the vehicle. A distance between the object and a location of the vehicle when the static object was first detected may be determined. This distance may correspond to a perceptive range of the vehicle with respect to the sensor. The vehicle may be controlled in an autonomous driving mode based on the distance.

Abstract: Example embodiments relate to controlling detection time in photodetectors. An example embodiment includes a device. The device includes a substrate. The device also includes a photodetector coupled to the substrate. The photodetector is arranged to detect light emitted from a light source that irradiates a top surface of the device. A depth of the substrate is at most 100 times a diffusion length of a minority carrier within the substrate so as to mitigate dark current arising from minority carriers photoexcited in the substrate based on the light emitted from the light source.

Abstract: Aspects of the disclosure provide for generating a visualization of a three-dimensional (3D) world view from the perspective of a camera of a vehicle. For example, images of a scene captured by a camera of the vehicle and 3D content for the scene may be received. A virtual camera model for the camera of the vehicle may be identified. A set of matrices may be generated using the virtual camera model. The set of matrices may be applied to the 3D content to create a 3D world view. The visualization may be generated using the 3D world view as an overlay with the image, and the visualization provides a real-world image from the perspective of the camera of the vehicle with one or more graphical overlays of the 3D content.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for predicting how likely it is that a target agent in an environment will yield to another agent when the pair of agents are predicted to have overlapping future paths. In one aspect, a method comprises obtaining a first trajectory prediction specifying a predicted future path for a target agent in an environment; obtaining a second trajectory prediction specifying a predicted future path for another agent in the environment; determining that, at an overlapping region, the predicted future path for the target agent overlaps with the predicted future path for the other agent; and in response: providing as input to a machine learning model respective features for the target agent and the other agent; and obtaining the likelihood score as output from the machine learning model.

Abstract: Methods and systems for laser point clouds are described herein. The method and system may include receiving, at a computing device, lidar data indicative of an environment of a vehicle from a first lidar data source, where the lidar data includes a first plurality of data points indicative of locations of reflections from the environment and further includes a respective intensity for each data point. The method and system also include determining a first surface normal for at least a first data point of the first plurality of data points. The method and system further includes determining a first angle of incidence for the first data point based on the surface normal. Additionally, the method and system includes adjusting the intensity of the first data point based on the first angle of incidence to create a first adjusted intensity for the first data point.

Abstract: Systems and methods for automated vehicle sensor calibration and verification are provided. One example method involves monitoring a vehicle using one or more external sensors of a vehicle calibration facility. The sensor data may be indicative of a relative position of the vehicle in the vehicle calibration facility. The method also involves causing the vehicle to navigate in an autonomous driving mode, based on the sensor data, from a current position of the vehicle to a first calibration position in the vehicle calibration facility. The method also involves causing a first sensor of the vehicle to perform a first calibration measurement while the vehicle is at the first calibration position. The method also involves calibrating the first sensor based on at least the first calibration measurement.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training a neural network having a plurality of sub neural networks to assign respective confidence scores to one or more candidate future trajectories for an agent. Each confidence score indicates a predicted likelihood that the agent will move along the corresponding candidate future trajectory in the future. In one aspect, a method includes using the first sub neural network to generate a training intermediate representation; using the second sub neural network to generate respective training confidence scores; using a trajectory generation neural network to generate a training trajectory generation output; computing a first loss and a second loss; and determining an update to the current values of the parameters of the first and second sub neural networks.