Abstract: Aspects of the technology relate to providing service area maps for an autonomous vehicle transportation service having a fleet of vehicles. For instance, each vehicle of the fleet is associated with a polygon corresponding to a service area for that vehicle. A first location may be received from a client computing device, and a set of vehicles of the fleet of vehicles that are currently available to provide transportation services may be identified based on the first location. The polygons associated with each of the set of vehicles may be used to determine a first polygon having a geographic area. A first portion of map information corresponding to the geographic area of the first polygon may be identified, and the first portion may be provided to the client computing device for display to a user such that the portion represents a currently available service area for the user.

Abstract: Example embodiments relate to vehicle sensor modules with external audio receivers. An example sensor module may include sensors and can be coupled to a vehicle's roof with a first microphone positioned proximate to the front of the sensor module. The sensor module can also include a second microphone extending into a first side of the sensor module such that the second microphone is configured to detect audio originating from an environment located relative to a first side of the vehicle and a third microphone extending into a second side of the sensor module such that the third microphone is configured to detect audio originating from the environment located relative to a second side of the vehicle, wherein the second side is opposite of the first side.

Abstract: Aspects of the disclosure relate to detecting sidewalks adjacent to roads. In this regard, a set of potential sidewalk areas adjacent to one or more roads in a vehicle's vicinity may be determined based on map data. Topology data for the set of potential sidewalk areas may be generated based on sensor data received from a perception system of the vehicle. The set of potential sidewalks may be filtered to remove areas unlikely to include a sidewalk. The vehicle may be operated based on the filtered set of potential sidewalk areas, which may include taking precautionary measures when within a predetermined distance from any of the filtered set of potential sidewalks.

Abstract: The technology relates to controlling a vehicle in an autonomous driving mode, the method. For instance, a vehicle may be maneuvered in the autonomous driving mode using pre-stored map information identifying traffic flow directions. Data may be received from a perception system of the vehicle identifying objects in an external environment of the vehicle related to a traffic redirection not identified the map information. The received data may be used to identify one or more corridors of a traffic redirection. One of the one or more corridors may be selected based on a direction of traffic flow through the selected corridor. The vehicle may then be controlled in the autonomous driving mode to enter and follow the selected one of the one or more corridors based on the determined direction of flow of traffic through each of the one or more corridors.

Abstract: Aspects of the disclosure provide for the determining whether to assign a driver to a trip. For instance, a route for a trip between a pickup location and a destination location for a passenger may be determined. Branch routes for the route may be determined based on one or more lane changes of the route. A probability of following each determined branch route may be determined. A probability of requiring assistance for each determined branch route may be determined. Whether to assign a driver to the trip may be determined based on the probabilities for each determined branch route. A vehicle of a fleet of vehicles may be assigned to the trip based on the determine of whether to assign a driver.

Abstract: One example method involves obtaining a plurality of images using a camera located at a given position relative to a light detection and ranging device (LIDAR). A first image of the plurality may be indicative of a view, via one or more optical elements of the LIDAR, of a receiver of the LIDAR. The method also involves determining simulated detector positions for intercepting reflections of simulated light beams associated with a plurality of light sources in a transmitter of the LIDAR. The method also involves determining one or more alignment offsets between the transmitter and the receiver based on at least the simulated detector positions and the plurality of images.

Abstract: An example method includes setting an initial pickup location. The method includes causing an autonomous vehicle to navigate towards the initial pickup location. The method includes periodically generating a plurality of candidate updated pickup locations. The method includes selecting a suggested pickup location from the plurality of candidate pickup locations that achieves a benchmark for reducing one or more of a time and a distance associated with the initial pickup location for one or more of the autonomous vehicle and the client device. The method includes determining that the suggested pickup location satisfies a set of pre-update checks for limiting a number of pickup location updates. The method includes responsive to (i) selecting the suggested pickup location, and (ii) determining that the suggested pickup location satisfies the set of pre-update checks, causing the autonomous vehicle to navigate to the suggested pickup location.

Abstract: Aspects of the disclosure provide for controlling an autonomous vehicle to enter a driveway. As an example, an instruction to pickup or drop off a passenger at a location may be received. It may be determined that the vehicle is arriving in a lane on an opposite side of a street as the location, the lane having a traffic direction opposite to a traffic direction of a lane adjacent to the location. A difficulty score to maneuver the vehicle to the lane adjacent to the location may be determined, and the difficulty score may be compared to a predetermined difficulty score. Based on the comparison, it may be determined to an available driveway on the same side of the street as the location. The vehicle may be controlled to enter the available driveway.

Abstract: The technology relates to an exterior sensor system for a vehicle configured to operate in an autonomous driving mode. The technology includes a close-in sensing (CIS) camera system to address blind spots around the vehicle. The CIS system is used to detect objects within a few meters of the vehicle. Based on object classification, the system is able to make real-time driving decisions. Classification is enhanced by employing cameras in conjunction with lidar sensors. The specific arrangement of multiple sensors in a single sensor housing is also important to object detection and classification. Thus, the positioning of the sensors and support components are selected to avoid occlusion and to otherwise prevent interference between the various sensor housing elements.

Abstract: Aspects of the disclosure relate to generating scouting objectives in order to update map information used to control a fleet of vehicles in an autonomous driving mode. For instance, a notification from a vehicle of the fleet identifying a feature and a location of the feature may be received. A first bound for a scouting area may be identified based on the location of the feature. A second bound for the scouting area may be identified based on a lane closest to the feature. A scouting objective may be generated for the feature based on the first bound and the second bound.

Abstract: Aspects of the disclosure relate to controlling a vehicle in an autonomous driving mode using trajectories. For instance, a trajectory may be received by one or more first computing devices from one or more second computing devices. While the first computing devices are controlling the vehicle in the autonomous driving mode based on the trajectory, an error may be generated by second computing devices. Whether the error is a recoverable error may be determined, and if so, the second computing devices attempt to generate a new trajectory. When the second computing devices generate the new trajectory, the vehicle may be controlled by the first computing devices according to the new trajectory.

Abstract: A system includes multiple microphone arrays positioned at different locations on a roof of an autonomous vehicle. Each microphone array includes two or more microphones. Internal clocks of each microphone array are synchronized by a processor and used to generate timestamps indicating when microphones capture a sound. Based on the timestamps, the processor is configured to localize a source of the sound.

Abstract: Example systems and methods are disclosed for determining vehicle pose data for an autonomous vehicle. The vehicle computer system may receive pose data from multiple pose measurement systems of the autonomous vehicle. Each pose measurement system may include one or more corresponding sensors of the autonomous vehicle. The vehicle computer system may determine a pose data quality for the received pose data for each pose measurement system. The vehicle computer system may set the vehicle pose data to the pose data of the pose measurement system with the highest pose data quality. The vehicle computer system may control the autonomous vehicle based on the vehicle pose data.

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.

Abstract: Disclosed herein are neural networks for generating target classifications for an object from a set of input sequences. Each input sequence includes a respective input at each of multiple time steps, and each input sequence corresponds to a different sensing subsystem of multiple sensing subsystems. For each time step in the multiple time steps and for each input sequence in the set of input sequences, a respective feature representation is generated for the input sequence by processing the respective input from the input sequence at the time step using a respective encoder recurrent neural network (RNN) subsystem for the sensing subsystem that corresponds to the input sequence. For each time step in at least a subset of the multiple time steps, the respective feature representations are processed using a classification neural network subsystem to select a respective target classification for the object at the time step.

Abstract: The technology relates to an exterior sensor system for a vehicle configured to operate in an autonomous driving mode. The technology includes a close-in sensing (CIS) camera system to address blind spots around the vehicle. The CIS system is used to detect objects within a few meters of the vehicle. Based on object classification, the system is able to make real-time driving decisions. Classification is enhanced by employing cameras in conjunction with lidar sensors. The specific arrangement of multiple sensors in a single sensor housing is also important to object detection and classification. Thus, the positioning of the sensors and support components are selected to avoid occlusion and to otherwise prevent interference between the various sensor housing elements.

Abstract: The present disclosure relates to optical systems, specifically light detection and ranging (LIDAR) systems. An example optical system includes a laser light source operable to emit laser light along a first axis and a mirror element with a plurality of reflective surfaces. The mirror element is configured to rotate about a second axis. The plurality of reflective surfaces is disposed about the second axis. The mirror element and the laser light source are coupled to a base structure, which is configured to rotate about a third axis. While the rotational angle of the mirror element is within an angular range, the emitted laser light interacts with both a first reflective surface and a second reflective surface of the plurality of reflective surfaces and is reflected into the environment by the first and second reflective surfaces.

Abstract: A method for determining a pullover spot for a vehicle is described. The method includes using a computing device to detect information related to a system of the vehicle or an environment surrounding the vehicle using a sensor of a vehicle and determine a local failure of the vehicle based on the information. The computing device may then be used to determine that the vehicle should pullover before completing a current trip related to transporting a passenger or good by comparing vehicle requirements for the trip with the local failure and determine a pullover spot by identifying a first area for the vehicle to park in part based on a second area being available for a second vehicle to pick up the passenger or good. The computing device may operate the vehicle to the pullover spot and transmit a request for a second vehicle.

Abstract: Aspects of the disclosure relate to reducing the likelihood of injury to a passenger in a collision. In one example, a computing device may determine that an impact between a vehicle and an object external to the vehicle is imminent. The computing device may determine a protection range for a vehicle's airbag based on characteristics of the passenger, including the passenger's seating location within the vehicle. An airbag extension system may position an airbag package, including the vehicle's airbag, such that the vehicle's airbag is within the protection range of the passenger's seating location.