Abstract: An example method involves rotating a sensor that emits light pulses and detects reflections of the emitted light pulses based on a pointing direction of the sensor. The method also involves identifying a range of pointing directions of the sensor that are associated with a target region of an environment. The method also involves determining whether a current pointing direction of the sensor is within the identified range. The method also involves modulating the emitted light pulses according to a first modulation scheme in response to a determination that the current pointing direction is within the identified range. The method also involves modulating the emitted light pulses according to a second modulation scheme in response to a determination that the current pointing direction is outside the identified range. The second modulation scheme is different than the first modulation scheme.

Abstract: Techniques for controlling gear shifts based on future trajectory information are presented herein. A vehicle computing system may receive sensor data representing an environment of the vehicle during autonomous navigation of a path and determine, based on the sensor data, the vehicle is approaching a predefined situation positioned along the path. The computing system may then estimate a future acceleration or speed associated with navigation of the predefined situation by the vehicle. The computing system may determine, based on the future acceleration or speed and a current acceleration of the vehicle, whether to perform a gear shift prior to navigation of the predefined situation by the vehicle, and control the vehicle based on determining whether to perform the gear shift prior to navigation of the predefined situation.

Abstract: The technology relates to generating simulations in order to evaluate software used to control vehicles in an autonomous driving mode. In one example, an initial situation involving a certain kind of interaction between a vehicle operating in the autonomous driving mode and an object may be identified. A search of log data may be conducted in order to identify one or more similar situations based on characteristics of the initial situation. A new simulation may be generated using the identified one or more similar situations by inserting the object into the one or more similar situations. The new simulation may be run in order to evaluate the software.

Abstract: Aspects of the disclosure provide for automatically generating labels for sensor data. For instance, first sensor data for a vehicle may be identified. This first sensor data may have been captured by a first sensor of the vehicle at a first location during a first point in time and may be associated with a first label for an object. Second sensor data for the vehicle may be identified. The second sensor data may have been captured by a second sensor of the vehicle at a second location at a second point in time outside of the first point in time. The second location is different from the first location. A determination may be made as to whether the object is a static object. Based on the determination that the object is a static object, the first label may be used to automatically generate a second label for the second sensor data.

Abstract: Example embodiments relate to light detection and ranging (lidar) devices or other apparatus that incorporate laser light emitters capable of increased pulse energies and decreased pulse durations. These laser light emitters include a gain medium having two portions to which a pump electrode and a switch electrode, respectively, are coupled. The pump electrode is configured to apply a current through the gain medium that provides energy for lasing and the switch electrode is configured to apply a current through the gain medium that controls a transparency of the second portion of the gain medium. Thus the switch electrode, which controls the timing of emitted light pulses, can be driven by a lower current and thus have shorter pulse widths, rise time, and/or fall times, thereby allowing for shorter, higher-energy laser pulses to be emitted.

Abstract: Example methods and systems for detecting weather conditions using vehicle onboard sensors are provided. An example method includes receiving laser data collected for an environment of a vehicle, and the laser data includes a plurality of laser data points. The method also includes associating, by a computing device, laser data points of the plurality of laser data points with one or more objects in the environment, and determining given laser data points of the plurality of laser data points that are unassociated with the one or more objects in the environment as being representative of an untracked object. The method also includes based on one or more untracked objects being determined, identifying by the computing device an indication of a weather condition of the environment.

Abstract: The present disclosure relates to systems and methods that facilitate compliance of a laser device with a laser safety threshold. An example method includes receiving, from a sensing circuit, an operating voltage that is indicative of a charge of a capacitive element of a laser pulser circuit. The method also includes comparing a first voltage indicative of the operating voltage and a second voltage indicative of a reference voltage. The method additionally includes providing an output value based on the comparing. The method yet further includes evaluating compliance with the laser safety threshold based on the output value.

Abstract: The present disclosure relates to methods and systems that facilitate determination of a pose of a vehicle based on various combinations of map data and sensor data received from light detection and ranging (LIDAR) devices and/or camera devices. An example method includes receiving point cloud data from a (LIDAR) device and transforming the point cloud data to provide a top-down image. The method also includes comparing the top-down image to a reference image and determining, based on the comparison, a yaw error. An alternative method includes receiving camera image data from a camera and transforming the camera image data to provide a top-down image. The method also includes comparing the top-down image to a reference image and determining, based on the comparison, a yaw error.

Abstract: A method includes detecting, by a sensing system of an autonomous vehicle (AV) executing a trip from a first location to a second location, a signal to stop the AV, determining, in response to the signal, that the AV is to be stopped for a roadside inspection, and causing a vehicle control system of the AV to stop the AV at a third location to allow the roadside inspection to be performed.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for predicting agent response times. One of the methods includes continually updating, at each time step of a plurality of time steps, an accumulated measure of surprise for the agent due to the movements of another entity in the traffic environment. A distribution of previously predicted trajectories is obtained at a previous time step for the other entity in the environment. A measure of surprise is computed from the perspective of the agent. An accumulated measure of surprise is updated for the time step using the computed measure of surprise for the agent. If the accumulated measure of surprise crosses a threshold at a particular point in time, a predicted response time for the agent is generated based on the particular point in time that the accumulated measure of surprise crosses the threshold.

Abstract: A vehicle is provided that includes one or more wheels positioned at a bottom side of the vehicle. The vehicle also includes a first light detection and ranging device (LIDAR) positioned at a top side of the vehicle opposite to the bottom side. The first LIDAR is configured to scan an environment around the vehicle based on rotation of the first LIDAR about an axis. The first LIDAR has a first resolution. The vehicle also includes a second LIDAR configured to scan a field-of-view of the environment that extends away from the vehicle along a viewing direction of the second LIDAR. The second LIDAR has a second resolution. The vehicle also includes a controller configured to operate the vehicle based on the scans of the environment by the first LIDAR and the second LIDAR.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for performing data compression and data decompression on lidar range images.

Abstract: Aspects of the disclosure relate to providing transportation services with autonomous vehicles. For instance, a first route to a first destination may be determined. The first route may have a first cost. Weather information for the first destination may be received. A characteristic is determined based on the weather information. A second destination having the characteristic may be selected. The second destination may be different from the first destination. A second route to the second destination may be determined. The second route may have a second cost. The first cost may be compared to the second cost, and the vehicle may use the comparison to set one of the first destination or the second destination as a current destination for a vehicle to cause the vehicle to control itself in an autonomous driving mode to the current destination.

Abstract: The disclosure describes a system that includes a self-driving system for operating a vehicle autonomously, one or more optical transmitters mounted on the vehicle, and one or more computing devices in communication with the self-driving system and the one or more optical transmitters. The one or more computing devices are configured to operate the self-driving system to cause the vehicle to approach a designated location in proximity of a structure on which one or more receivers are mounted and determine that the one or more optical transmitters have an alignment with the one or more receivers. Then, the one or more computing devices are configured to operate the one or more optical transmitters to establish an optical communication link with the one or more receivers and transmit data to the one or more receivers over the optical communication link.

Abstract: Models can be generated of a vehicle's view of its environment and used to maneuver the vehicle. This view need not include what objects or features the vehicle is actually seeing, but rather those areas that the vehicle is able to observe using its sensors if the sensors were completely un-occluded. For example, for each of a plurality of sensors of the object detection component, a computer may generate an individual 3D model of that sensor's field of view. Weather information is received and used to adjust one or more of the models. After this adjusting, the models may be aggregated into a comprehensive 3D model. The comprehensive model may be combined with detailed map information indicating the probability of detecting objects at different locations. The model of the vehicle's environment may be computed based on the combined comprehensive 3D model and detailed map information.

Abstract: Example embodiments relate to triple redundant brake systems for trucks and other types of vehicles. Disclosed systems offer additional redundancy for braking applications by incorporating a third service brake actuator (e.g., a third ECU), which may be installed parallel to the second controller (e.g., a second ECU). In some examples, the third service brake actuator is an electronically activatable pressure valve and can be implemented using pneumatic select-high valves. These valves can be used to perform a mechanical max arbitration between pressure provided by the second controller and the third controller.

Abstract: A system includes a memory device and a processing device, operatively coupled to the memory device, to identify one or more erroneous predictions of behavior for one or more objects in an environment of an autonomous vehicle traveling along a planned trajectory, and initiate, based on the one or more erroneous predictions, one or more operations to adjust the planned trajectory of the autonomous vehicle. Each erroneous prediction of the one or more erroneous predictions is determined based on a comparison between a corresponding observed spatial overlap between the autonomous vehicle and a corresponding object, and a corresponding predicted spatial overlap between the autonomous vehicle and the corresponding object.

Abstract: Methods and systems for predicting a trajectory an autonomous vehicle (AV) are disclosed. A method includes generating, based on sensor data from a sensing system of the AV, one or more embeddings, generating, using a machine learning model (MLM) and the one or more embeddings, one or more predicted future trajectories for the AV, and causing, using the one or more predicted future trajectories, a planning system of the AV to generate an update to a current trajectory of the AV.

Abstract: Described examples relate to an apparatus comprising one or more image sensors coupled to a vehicle and at least one processor. The at least one processor may be configured to capture, in a burst sequence using the one or more image sensors, multiple frames of an image of a scene, the multiple frames having respective, relative offsets of the image across the multiple frames and perform super-resolution computations using the captured, multiple frames of the image of the scene. The at least one processor may also be configured to accumulate, based on the super-resolution computations, color planes and combine, using the one or more processors, the accumulated color planes to create a super-resolution image of the scene.