Abstract: Aspects of the disclosure provide for arranging trips for autonomous vehicles. For instance, a request for a trip may be received by one or more processors of one or more server computing devices. The request may identify an initial location. A weather condition at the initial location may be identified. One or more internal vehicle state conditions and one or more priorities for pulling over may be determined based on the weather condition. A second location may be determined based on the one or more priorities and the initial location. Dispatch instructions may be provided to an autonomous vehicle, the dispatch instructions identifying the second location and the one or more internal vehicle state conditions in order to cause computing devices of the autonomous vehicle to control the autonomous vehicle to the second location and adjust internal vehicle state conditions based on the one or more internal vehicle state conditions.

Abstract: An in-vehicle communication network of a vehicle is monitored. An illicit signal is detected on the in-vehicle communication network. Whether the illicit signal satisfies a threshold severity condition is determined. A denial of service (DoS) operation with respect to at least part of the in-vehicle communication network is performed responsive to determining that the illicit signal satisfies the threshold severity condition.

Abstract: Aspects of the present disclosure relate generally to limiting the use of an autonomous or semi-autonomous vehicle by particular occupants based on permission data. More specifically, permission data may include destinations, routes, and/or other information that is predefined or set by a third party. The vehicle may then access the permission data in order to transport the particular occupant to the predefined destination, for example, without deviation from the predefined route. The vehicle may drop the particular occupant off at the destination and may wait until the passenger is ready to move to another predefined destination. The permission data may be used to limit the ability of the particular occupant to change the route of the vehicle completely or by some maximum deviation value. For example, the vehicle may be able to deviate from the route up to a particular distance from or along the route.

Abstract: Aspects and implementations of the present disclosure address shortcomings of the existing technology by enabling Doppler-assisted segmentation of points in a point cloud for efficient object identification and tracking in autonomous vehicle (AV) applications, by: obtaining, by a sensing system of the AV, a plurality of return points comprising one or more velocity values and one or more coordinates of a reflecting region that reflects a signal emitted by the sensing system, the one or more velocity values and the one or more coordinates obtained for the same instance of time, identifying that the set of the return points is associated with an object in an environment, and causing a driving path of the AV to be determined in view of the object.

Abstract: Computing devices, systems, and methods described in various embodiments herein may relate to light detection and ranging (LIDAR or lidar) systems. An example computing device could include a controller having at least one processor and at least one memory. The at least one processor is configured to execute program instructions stored in the at least one memory so as to carry out operations. The operations include receiving information indicative of transmit light emitted from a lidar system along a light-emission axis. The operations also include determining, based on the received information, a maximum instrumented distance. The maximum instrumented distance includes a known unobstructed region defined by a ray segment extending between the lidar system and a point along the light-emission axis.

Abstract: The described aspects and implementations enable efficient detection and classification of objects with machine learning models that deploy a bird's-eye view representation and are trained using depth ground truth data. In one implementation, disclosed are system and techniques that include obtaining images, generating, using a first neural network (NN), feature vectors (FVs) and depth distributions pixels of images, wherein the first NN is trained using training images and a depth ground truth data for the training images. The techniques further include obtaining a feature tensor (FT) in view of the FVs and the depth distributions, and processing the obtained FTs, using a second NN, to identify one or more objects depicted in the images.

Abstract: A method includes obtaining, by a processing device, radar sensor data from a driving environment of an autonomous vehicle (AV). The radar sensor data corresponds to a field-of-view (FOV) of a radar sensor of the AV. The method further includes identifying, by the processing device from the radar sensor data, a potential occlusion within the driving environment, obtaining, by the processing device, non-radar sensor data from the driving environment, the non-radar sensor data corresponding to a FOV of a non-radar sensor of the AV, determining, by the processing device from the non-radar sensor data, whether the potential occlusion is a false occlusion, and in response to determining that the potential occlusion is a false occlusion, removing, by the processing device, the false occlusion from the FOV of the radar sensor.

Abstract: Aspects of the disclosure relate to a method of managing a fleet of autonomous vehicles providing trip services. The method includes receiving information identifying an intermediate destination and a final destination for a trip. In this example, the intermediate destination is a destination where an autonomous vehicle will drop off and wait for a passenger in order to continue the trip, and the final destination is a destination where the trip ends. The method also includes determining an amount of waiting time the vehicle is likely to be waiting for the passenger at the intermediate destination, determining how a vehicle of the fleet of autonomous vehicles should spend the amount of waiting time, and sending an instruction to the vehicle, based on the determination of how the vehicle should spend the amount of waiting time.

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: Sensor data identifying an emergency vehicle approaching the autonomous vehicle may be received. A predicted trajectory for the emergency vehicle may be received. Whether the autonomous vehicle is impeding the emergency vehicle may be determined based on the predicted trajectory and map information identifying a road on which the autonomous vehicle is currently traveling. Based on a determination that the autonomous vehicle is impeding the emergency vehicle, the autonomous vehicle may be controlled in an autonomous driving mode in order to respond to the emergency vehicle.

Abstract: Systems and methods for monitoring and detecting partial or full mechanical failures of a spring contact configured to provide an electrical connection between two elements. In some examples, a spring contact may be arranged between a first element, such as a window or cover of an operational sensor, and a second element, such as a printed circuit board. The window or cover may have an electrical element with a first electrical contact, and the printed circuit board may be attached to the spring contact via a joint (e.g., solder, weld, adhesive). A processing system may be configured to determine a state of the joint based on vibrations sensed by one or more vibration sensors attached to the second element.

Abstract: Example embodiments relate to corrugated radomes for protecting and concealing components of radar units. An example radar system may include a radar unit that includes at least one antenna having a radiation pattern. The radar unit is configured to transmit a radar signal based on the radiation pattern and receive radar signals. In addition, the radar system further includes a radome located in a direction of transmission of the radiation pattern. Particularly, the radome includes a stepped surface having at least one step that includes a height equal to one-quarter of a wavelength at a frequency of operation of the radar unit. The least one step is positioned on the radome such that the at least one step causes deconstructive interference of reflections of the transmitted radar signal caused by the radome.

Abstract: Aspects of the disclosure provide for a method of controlling an autonomous vehicle in an autonomous driving mode. For instance, a predicted future trajectory for an object detected in a driving environment of the autonomous vehicle may be received. A routing intent for a planned trajectory for the autonomous vehicle may be received. The predicted future trajectory and the routing intent intersect with one another may be determined. When the predicted future trajectory and the routing intent are determined to intersect with one another, a time gap may be applied to a predicted future state of the object defined in the predicted future trajectory. A planned trajectory may be determined for the autonomous vehicle based on the applied time gap. The autonomous vehicle may be controlled in the autonomous driving mode based on the planned trajectory.

Abstract: The present disclosure relates to optical systems, vehicles, and methods for providing improved mechanical performance of a camera and corresponding optical elements. An example optical system includes an outer housing and an inner support member. The optical system also includes an optical window coupled to the outer housing and the inner support member. The optical window is configured to be temperature-controllable. The optical system also includes a camera coupled to the inner support member. The camera is optically coupled to the optical window. Additionally, the outer housing, the optical window, and the camera are configured to be impact resistant.

Abstract: The technology relates to autonomous vehicles suffering a breakdown along a roadway. Onboard systems may utilize various proactive operations to alert specific vehicles or other objects on or near the roadway about the breakdown. This can be done alternatively or in addition to turning on the hazard lights or calling for remote assistance. The disabled vehicle is able to detect nearby and approaching objects. The detection may be performed in combination with a determination of the type of object or predicted behavior for that object, enables the vehicle to generate a targeted alert that can be transmitted or otherwise presented to that particular object. This approach provides the other object, such as a vehicle, bicyclist or pedestrian, sufficient time and information about the breakdown to take appropriate corrective action. Different communication options are available and may be selected based on the particular object, environmental conditions and other factors.

Abstract: A computing system may operate a LIDAR device to emit and detect light pulses in accordance with a time sequence including standard detection period(s) that establish a nominal detection range for the LIDAR device and extended detection period(s) having durations longer than those of the standard detection period(s). The system may then make a determination that the LIDAR detected return light pulse(s) during extended detection period(s) that correspond to particular emitted light pulse(s). Responsively, the computing system may determine that the detected return light pulse(s) have detection times relative to corresponding emission times of particular emitted light pulse(s) that are indicative of one or more ranges. Given this, the computing system may make a further determination of whether or not the one or more ranges indicate that an object is positioned outside of the nominal detection range, and may then engage in object detection in accordance with the further determination.

Abstract: Examples described may related to an imaging sensor used by a vehicle, including a light sensor. The light sensor comprises a plurality of cells aligned in a plurality of horizontal rows and a plurality of vertical columns. The apparatus further includes an optical system configured to provide the light sensor with a field of view of an external environment of the apparatus. Additionally, the system includes a processing unit configured to: divide the plurality of horizontal rows of the light sensor into one or more enabled rows and one or more disabled rows; obtain image data from the light sensor by sampling one or more cells in the one or more enabled rows; and store the received image data in a memory.

Abstract: Example embodiments relate to multiple operating modes to expand dynamic range. An example embodiment includes a camera system. The camera system may include a first image sensor having a first dynamic range corresponding to a first range of luminance levels in a scene. The system may also include a second image sensor having a second dynamic range corresponding to a second range of luminance levels in the scene. The camera system may further include a processor coupled to the first image sensor and the second image sensor. The processor may be configured to execute instructions to identify objects of a first type in a first image of the scene captured by the first image sensor and identify objects of a second object type in a second image of the scene captured by the second image sensor.

Abstract: The technology relates to autonomous vehicles for transporting cargo and/or people between locations. Distributed sensor arrangements may not be suitable for vehicles such as large trucks, busses or construction vehicles. Side view mirror assemblies are provided that include a sensor suite of different types of sensors, including LIDAR, radar, cameras, etc. Each side assembly is rigidly secured to the vehicle by a mounting element. The sensors within the assembly may be aligned or arranged relative to a common axis or physical point of the housing. This enables self-referenced calibration of all sensors in the housing. Vehicle-level calibration can also be performed between the sensors on the left and right sides of the vehicle. Each side view mirror assembly may include a conduit that provides one or more of power, data and cooling to the sensors in the housing.

Abstract: A LIDAR device may transmit light pulses originating from one or more light sources and may receive reflected light pulses that are then detected by one or more detectors. The LIDAR device may include a lens that both (i) collimates the light from the one or more light sources to provide collimated light for transmission into an environment of the LIDAR device and (ii) focuses the reflected light onto the one or more detectors. The lens may define a curved focal surface in a transmit path of the light from the one or more light sources and a curved focal surface in a receive path of the one or more detectors. The one or more light sources may be arranged along the curved focal surface in the transmit path. The one or more detectors may be arranged along the curved focal surface in the receive path.