Abstract: Aspects of the disclosure relate to providing information about a vehicle dispatched to pick up the user. In one example, a request for the vehicle to stop at a particular location is sent. In response, information identifying a current location of the vehicle is received. A map is generated. The map includes a first marker identifying the received location of the vehicle, a second marker identifying the particular location, and a shape defining an area around the second marker at which the vehicle may stop. The shape has an edge at least a minimum distance greater than zero from the second area. A route is displayed on the map between the first marker and the shape such that the route ends at the shape and does not reach the second marker. Progress of the vehicle towards the area along the route is displayed based on received updated location information for the vehicle.

Abstract: Systems, apparatus, and methods are presented for rendering color images. One method may include receiving first color values of a first image from a first image capture device. The first color values may represent colors in a first color space. The method also may include applying first color transformation information to the first color values to generate first transformed color values of the first image in a particular color space and applying desaturation information to the first transformed color values to generate first desaturated color values of the first image. Further, the method may include providing the first desaturated color values of the first image for rendering on a display device.

Abstract: Example embodiments relate to self-reflection filtering techniques within radar data. A computing device may use radar data to determine a first radar representation that conveys information about surfaces in a vehicle's environment. The computing device may use a predefined model to generate a second radar representation that assigns predicted self-reflection values to respective locations of the environment based on the information about the surfaces conveyed by the first radar representation. The predefined model can enable a predefined self-reflection value to be assigned to a first location based on information about a surface positioned at a second location and a relationship between the first location and the second location. The computing device may then modify the first radar representation based on the predicted self-reflection values in the second radar representation and provide instructions to a control system of the vehicle based on modifying the first radar representation.

Abstract: The technology relates to enhancing the operation of autonomous vehicles. Extendible sensors are deployed based on detected or predicted conditions around a vehicle while operating in a self-driving mode. When not needed, the sensors are fully retracted into the vehicle to reduce drag and increase fuel economy. When the onboard system determines that there is a need for a deployable sensor, such as to enhance the field of view of the perception system, the sensor is extended in a predetermined manner. The deployment may depend on one or more operating conditions and/or particular driving scenarios. These and other sensors of the vehicle may be protected with a rugged housing, for instance to protect against damage from the elements. And in other situations, deployable foils may extend from the vehicle's chassis to increase drag and enhance braking. This may be helpful for large trucks in steep descent situations.

Abstract: The present disclosure relates to methods and systems that improve the dynamic range of LIDAR systems. An example system includes a plurality of single-photon photodetectors and at least one additional photodetector monolithically integrated on a shared substrate. The plurality of single-photon photodetectors and the at least one additional photodetector are configured to detect light from a shared field of view. The system also includes a controller configured to carry out operations. The operations include: receiving respective photodetector signals from the plurality of single-photon photodetectors and the at least one additional photodetector; selecting a photodetector signal from at least two of: the two received photodetector signals and a combined photodetector signal formed by combining the two received photodetector signals; and determining an intensity of light in the field of view based on the selected photodetector signal.

Abstract: Aspects of the disclosure provide a method of identifying off-road entry lane waypoints. For instance, a polygon representative of a driveway or parking area may be identified from map information. A nearest lane may be identified based on the polygon. A plurality of lane waypoints may be identified. Each of the lane waypoints may correspond to a location within at least one lane. The polygon and the plurality of lane waypoints may be input into a model. A lane waypoint of the plurality of lane waypoints may be selected as an off-road entry lane waypoint. The off-road entry lane waypoint may be associated with the nearest lane. The association may be provided to an autonomous vehicle in order to allow the autonomous vehicle to use the association to control the autonomous vehicle in an autonomous driving mode.

Abstract: A computing system may operate a LIDAR device to emit light pulses in accordance with a time sequence including a time-varying dither. The system may then determine that the LIDAR detected return light pulses during corresponding detection periods for each of two or more emitted light pulses. Responsively, the system may determine that the detected return light pulses have (i) detection times relative to corresponding emission times of a plurality of first emitted light pulses that are indicative of a first set of ranges and (ii) detection times relative to corresponding emission times of a plurality of second emitted light pulses that are indicative of a second set of ranges. Given this, the system may select between using the first set of ranges as a basis for object detection and using the second set of ranges as a basis for object detection, and may then engage in object detection accordingly.

Abstract: The technology relates to using on-board sensor data, off-board information and a deep learning model to classify road wetness and/or to perform a regression analysis on road wetness based on a set of input information. Such information includes on-board and/or off-board signals obtained from one or more sources including on-board perception sensors, other on-board modules, external weather measurement, external weather services, etc. The ground truth includes measurements of water film thickness and/or ice coverage on road surfaces. The ground truth, on-board and off-board signals are used to build the model. The constructed model can be deployed in autonomous vehicles for classifying/regressing the road wetness with on-board and/or off-board signals as the input, without referring to the ground truth.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating a future occupancy prediction for a region of an environment. In one aspect, a method comprises: receiving sensor data generated by a sensor system of a vehicle that characterizes an environment in a vicinity of the vehicle as of a current time point, wherein the sensor data comprises a plurality of sensor samples characterizing the environment that were each captured at different time points; processing a network input comprising the sensor data using a neural network to generate an occupancy prediction output for a region of the environment, wherein: the occupancy prediction output characterizes, for one or more future intervals of time after the current time point, a respective likelihood that the region of the environment will be occupied by an agent in the environment during the future interval of time.

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 systems 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: The technology involves operation of a self-driving truck or other cargo vehicle when it is being inspected at a weigh station. This may include determining whether a weigh station is open for inspection. Once at the weigh station, the vehicle may follow instructions of an inspection officer or autonomous inspection system. The vehicle may perform predefined actions or operations so that various vehicle systems and safety issues can be evaluated, such as the brakes, lights, tires, connections between the tractor and trailer, exposed fuel tanks, leaks, etc. A visual inspection may be performed to ensure the load is secured, vehicle and cargo documents meet certain criteria, and the carrier's safety record meets any requirements. In addition, the weigh station itself may be operated in a partly or fully autonomous mode when dealing with autonomous and manually driven vehicles.

Abstract: Aspects of the disclosure relate to controlling a vehicle having an autonomous driving mode. For instance, that the vehicle is approaching a crosswalk may be determined. A set of segments may be identified for the crosswalk. A set of potential occluded pedestrians may be generated. Each potential occluded pedestrian of the set is assigned a speed characteristic and a segment. The segments of the set of potential occluded pedestrians may be updated over time using the assigned speed characteristics. Sensor data from a perception system of the vehicle is received, and one or more potential occluded pedestrians an having an updated assigned segment corresponding to a segment that is visible to a perception system of the vehicle may be removed from the set of potential occluded pedestrians. After the removing, the set may be used to control the vehicle in the autonomous driving mode.

Abstract: A method of determining a distance between a vehicle and a sound source includes detecting, at a microphone of the vehicle, sounds from a sound source external to the vehicle. The sounds have a first frequency component at a first frequency and a second frequency component at a second frequency. The method also includes determining, at a processor of the vehicle, a classification of the sound source based on audio properties of the sounds. The method further includes determining a first energy level associated with the first frequency component and a second energy level associated with the second frequency component. The method also includes determining a ratio between the first energy level and the second energy level. The method further includes determining the distance between the vehicle and the sound source based on the ratio and the classification of the sound source.

Abstract: Aspects of the disclosure relate to deployable structures, such as airbag systems, for vehicles. A system is disclosed for reducing likelihood of injury to a passenger in a collision. The system may include a seat configured to accommodate the passenger and an airbag system. At least a portion of the airbag system may be incorporated into a vehicle prior to deployment of the airbag system. The airbag system may include an airbag and a locking feature. The locking feature may be configured to lock the airbag with a locking portion of a vehicle when the airbag system has been deployed. The locking feature may be attached to the airbag. Aside from airbags, other confining structures, such as nets, shades, curtains, etc., may also be used as deployable structures.

Abstract: A method includes monitoring, at a computing device, outputs of a sensor validator. Each output is generated by the sensor validator based on corresponding sensor data from a sensor coupled to an autonomous vehicle, and each output indicates whether the corresponding sensor data is associated with an event. The method also includes mutating, at the computing device, particular sensor data to generate mutated sensor data that is associated with a particular event. The method further includes determining, at the computing device, a performance metric associated with the sensor validator based on a particular output generated by the sensor validator. The particular output is based on the mutated sensor data.

Abstract: Aspects of the disclosure provide for localizing a vehicle. In one instance, a weather condition in which the vehicle is currently driving may be identified. A plurality of sensor inputs including intensity information, elevation information, and radar sensor information may be received. For each of the plurality of sensor inputs, an alignment score is determined by comparing the intensity information, elevation information, and radar sensor information to a corresponding pre-stored image for each of the intensity information, the elevation information, and the radar sensor information. A set of weights for the plurality of sensor inputs may be determined based on the identified weather condition. The alignment scores may then be combined using the set of weights in order to localize the vehicle.

Abstract: The technology relates to autonomous vehicles that use a perception system to detect objects and features in the vehicle's surroundings. A camera assembly having a ling-type structure is provided that gives the perception system an overall 360° field of view around the vehicle. Image sensors are arranged in camera modules around the assembly to provide a seamless panoramic field of view. One subsystem has multiple pairs of image sensors positioned to provide the overall 360° field of view, while another subsystem provides a set of image sensors generally facing toward the front of the vehicle to provide enhanced object identification. The camera assembly may be arranged in a housing located on top of the vehicle. The housing may include other sensors such as LIDAR and radar. The assembly includes a chassis and top and base plates, which may provide EMI protection from other sensors disposed in the housing.

Abstract: Porous solid members are provided that include, disposed on at least a portion of the internal surfaces of a plurality of channels thereof, a plurality of superhydrophobic particles. These particles inhibit ingress of water or other aqueous fluids into the internal spaces of the porous solid member, reducing the occurrence of corrosion, biological growth, or other unwanted effects of fluids present in the internal spaces. The porous solid member could be fabricated using 3D printing methods, e.g., the member could be a 3D printed aluminum or other metal(s). The plurality of superhydrophobic particles can be disposed on the internal surfaces of the channels within the porous solid member via a number of processes, e.g., by delivering the particles into the channels while disposed in a payload fluid that later evaporates.

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: 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.