Abstract: An apparatus includes an optical emitter configured to emit a first optical signal along an optical path towards a target object in an outdoor environment. The apparatus includes an optical detector positioned collinearly with respect to the optical emitter. The optical detector is configured to detect a second optical signal that is retro-reflected from the target object. The apparatus includes a lock-in amplifier coupled to the optical detector. The lock-in amplifier is configured to generate, based on the first optical signal and the second optical signal, a signal indicative of a retro-reflectivity of the target object in the outdoor environment.

Abstract: Aspects and implementations of the present disclosure relate, generally, to optimization of the autonomous vehicle technology and, more specifically, to a transfer hub for autonomous trucking operations. The transfer hub supports automated trailer delivery, trailer-tractor matching, trailer-tractor hitching, automated post-hitching visual inspection, automated refueling, parking spot calibration, load verification, load distribution assessment, automated brake inspection, and other improvements to the existing technology.

Abstract: One example device comprises a plurality of emitters including at least a first emitter and a second emitter. The first emitter emits light that illuminates a first portion of a field-of-view (FOV) of the device. The second emitter emits light that illuminates a second portion of the FOV. The device also comprises a controller that obtains a scan of the FOV. The controller causes each emitter of the plurality of emitters to emit a respective light pulse during an emission time period associated with the scan. The controller causes the first emitter to emit a first-emitter light pulse at a first-emitter time offset from a start time of the emission time period. The controller causes the second emitter to emit a second-emitter light pulse at a second-emitter time offset from the start time of the emission time period.

Abstract: Aspects of the disclosure provide for controlling an autonomous vehicle. For instance, a trajectory for the autonomous vehicle to traverse in order to follow a route to a destination may be generated. A first error value for a boundary of an object, a second error value for a location of the autonomous vehicle, a third error value for a predicted future location of the object may be received. An uncertainty value for the object may be determined by combining the first error value, the second error value, and the third error value. A lateral gap threshold for the object may be determined based on the uncertainty value. The autonomous vehicle may be controlled in an autonomous driving mode based on the lateral gap threshold for the object.

Abstract: Aspects of the disclosure relate to controlling a vehicle in an autonomous driving mode where vehicle has a drive by wire braking system. For instance, while the vehicle is being controlled in the autonomous driving mode, a signal corresponding to input at a brake pedal of the drive by wire braking system may be received. An amount of braking may be determined based on the received signal. The amount of braking may be used to determine a trajectory for the vehicle to follow. The vehicle may be controlled in the autonomous driving mode using the trajectory.

Abstract: Aspects of the disclosure provide for the selection of a route for a vehicle having an autonomous driving mode. For instance, an initial location of the vehicle may be identified. This location may be used to determine a set of possible routes to a destination location. A cost for each route of the plurality is determined by inputting time of day information, map information, and details of that route into one or more models in order to determine whether the vehicle is likely to be stranded along that route and assessing the cost based at least in part on the determination of whether the vehicle is likely to be stranded along that route. One of the routes of the set of possible routes may be selected based on any determined costs. The vehicle may be controlled in the autonomous driving mode using the selected one.

Abstract: Example embodiments relate to methods and systems for automated generation of radar interference reduction training data for autonomous vehicles. In an example, a computing device causes a radar unit to transmit radar signals in an environment of a vehicle. The computing device may include a model trained based on a labeled interferer dataset that represents interferer signals generated by an emitter located remote from the vehicle. The interferer signals are based on one or more radar signal parameter models. The computing device may use the model to determine whether received electromagnetic energy corresponds to transmitted radar signals or an interferer signal. Based on determining that the electromagnetic energy corresponds to the transmitted radar signals, the computing device may generate a representation of the environment of the vehicle using the electromagnetic energy.

Abstract: The present disclosure relates to limitation of noise on light detectors using an aperture. One example implementation includes a system. The system includes a lens that focuses light from a scene toward a focal plane. The system also includes an aperture defined within an opaque material. The system also includes a plurality of waveguides. A given waveguide of the plurality has an input end that receives a portion of light transmitted through the aperture, and guides the received portion toward an output end of the given waveguide. A cross-sectional area of the guided portion at the output end is greater than a cross-sectional area of the received portion at the input end. The system also includes an array of light detectors that detects the guided light transmitted through the output end.

Abstract: The disclosure relates to running simulations in order to test software used to control a vehicle in an autonomous driving mode. For instance, logged data may be identified for a simulation. The logged data may have been collected by a first vehicle and may identifying an agent that is a road user. The logged data may be analyzed to identify one or more signals of intent of the agent including a logged path of the agent. One or more characteristics may be identified based on the one or more signals. The simulation may be run using the logged data by replacing the agent with an interactive agent having the one or more characteristics. The interactive agent may be capable of responding to actions performed by a simulated vehicle in the simulation using software for controlling a vehicle in an autonomous driving mode.

Abstract: A system includes a near-infrared (NIR) illuminator, an NIR image sensor, a light detection and ranging (LIDAR) device, and control circuitry configured to perform operations. The operations include causing the NIR illuminator to illuminate a portion of an environment, and obtaining, from the NIR image sensor, NIR image data representing the portion of the environment illuminated by the NIR illuminator. The operations also include detecting a retroreflector within the NIR image data and, based on detecting the retroreflector within the NIR image, determining a position of the retroreflector within the environment. The operations further include, based on the position of the retroreflector within the environment, adjusting at least one parameter of the LIDAR device in connection with scanning the retroreflector.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for predicting scene flow. One of the methods includes obtaining a current point cloud representing an observed scene at a current time point; obtaining object label data that identifies a first three-dimensional region in the observed scene; determining, for each current three-dimensional point that is within the first three-dimensional region and using the object label data, a respective preceding position of the current three-dimensional point at a preceding time point in a reference frame of the sensor at the current time point; and generating, using the preceding positions, a scene flow label for the current point cloud that comprises a respective ground truth motion vector for each of a plurality of the current three-dimensional points.

Abstract: Aspects of the disclosure provide for enabling autonomous vehicles to pull over into driveways when picking up or dropping off passengers or goods. For instance, a request for a trip identifying a first location and a second location may be received. The first location may be a location of a client computing device, and the second location may be a starting location or a destination for the trip. A user preference for the trip indicating that a pickup for the trip be in a driveway may be identified. That the first location corresponds with the second location may be identified. Based on the determination that the first location corresponds with the second location, dispatch instructions may be to an autonomous vehicle. The dispatch instructions may identify a polygon for a driveway at the second location in order to cause the autonomous vehicle to pull over into the driveway.

Abstract: A vehicle configured to operate in an autonomous mode may operate a sensor to determine an environment of the vehicle. The sensor may be configured to obtain sensor data of a sensed portion of the environment. The sensed portion may be defined by a sensor parameter. Based on the environment of the vehicle, the vehicle may select at least one parameter value for the at least one sensor parameter such that the sensed portion of the environment corresponds to a region of interest. The vehicle may operate the sensor, using the selected at least one parameter value for the at least one sensor parameter, to obtain sensor data of the region of interest, and control the vehicle in the autonomous mode based on the sensor data of the region of interest.

Abstract: Aspects of the disclosure relate to determining and responding to an internal state of a self-driving vehicle. For instance, an image of an interior of the vehicle captured by a camera mounted in the vehicle is received. The image is processed in order to identify one or more visible markers at predetermined locations within the vehicle. The internal state of the vehicle is determined based on the identified one or more visible markers. A responsive action is identified action using the determined internal state, and the vehicle is controlled in order to perform the responsive action.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for agent trajectory prediction using anchor trajectories.

Abstract: Aspects of the technology relate to exception handling for a vehicle. For instance, a current trajectory for the vehicle and sensor data corresponding to one or more objects may be received. Based on the received sensor data, projected trajectories of the one or more objects may be determined. Potential collisions with the one or more objects may be determined based on the projected trajectories and the current trajectory. One of the potential collisions that is earliest in time may be identified. Based on the one of the potential collisions, a safety-time-horizon (STH) may be identified. When a runtime exception occurs, before performing a precautionary maneuver to avoid a collision, waiting no longer than the STH for the runtime exception to resolve.

Abstract: A control system for an autonomous vehicle is configured to pick up a passenger at a pickup location. The autonomous vehicle includes a self-driving system and one or more computing devices in communication with the self-driving system. The one or more computing devices are configured to receive a trip request including a pickup location and a destination location, the trip request being associated with a client device; cause the self-driving system to navigate the autonomous vehicle to the pickup location; send a prompt to the client device to collect semantic information; determine a specified location based on the semantic information received in response to the prompt; identify one or more semantic markers for the specified location from the semantic information; and send a message to the client device identifying the specified location using the one or more semantic markers.

Abstract: Aspects of the disclosure relate to detecting and responding to objects in a vehicle's environment. For example, an object may be identified in a vehicle's environment, the object having a heading and location. A set of possible actions for the object may be generated using map information describing the vehicle's environment and the heading and location of the object. A set of possible future trajectories of the object may be generated based on the set of possible actions. A likelihood value of each trajectory of the set of possible future trajectories may be determined based on contextual information including a status of the detected object. A final future trajectory is determined based on the determined likelihood value for each trajectory of the set of possible future trajectories. The vehicle is then maneuvered in order to avoid the final future trajectory and the object.

Abstract: A system and method include scanning a light detection and ranging (LIDAR) device through a range of orientations corresponding to a scanning zone while emitting light pulses from the LIDAR device. The method also includes receiving returning light pulses corresponding to the light pulses emitted from the LIDAR device and determining initial point cloud data based on time delays between emitting the light pulses and receiving the corresponding returning light pulses and the orientations of the LIDAR device. The initial point cloud data has an initial angular resolution. The method includes identifying, based on the initial point cloud data, a reflective feature in the scanning zone and determining an enhancement region and an enhanced angular resolution for a subsequent scan to provide a higher spatial resolution in at least a portion of subsequent point cloud data from the subsequent scan corresponding to the reflective feature.