Abstract: A planar sensor array, like an imaging sensor array, can be mounted to a ceramic circuit board to provide improved mechanical stability and heat dissipation. Embodiments herein provide improvements to such assemblies by providing two or more recesses in the ceramic circuit board opposite the sensor array. Electronic components can be placed in these recesses, reducing the overall size and moment of inertia of the sensor assembly relative to placement on the same side of the ceramic circuit board as the sensor array. Placing the electronics in the two or more recesses allows for simplified mounting of the assembly to heat sinks, flexible circuit boards, or other elements. The two or more recesses are separated by a stiffening cross-brace portion of the ceramic circuit board that enhances the mechanical strength of the ceramic circuit board.

Abstract: The technology relates to detection of a nearby occluded object based on illumination emitted from that object. Illumination by the occluded object of one or more areas in the surrounding area, for instance by headlights of an occluded vehicle, is detected by a perception system of a self-driving vehicle. The self-driving vehicle can classify the detected object to determine whether the illumination is caused by a vehicle or other road user, or from objects in the surrounding environment. Illumination data and other information can be evaluated by the self-driving vehicle, for instance to identify a type of the object, a location of the object along a roadway, to disambiguate the direction of travel of the other object, etc. As a result, the self-driving vehicle may infer the behavior of the other object and modify its own driving operations to account for the other object's presence and likely behavior.

Abstract: The technology relates to determining a future heading of an object. In order to do so, sensor data, including information identifying a bounding box representing an object in a vehicle's environment and locations of sensor data points corresponding to the object, may be received. Based on dimensions of the bounding box, an area corresponding to a wheel of the object may be identified. An orientation of the wheel may then be estimated based on the sensor data points having locations within the area. The estimation may then be used to determine a future heading of the object.

Abstract: The present disclosure relates to limitation of noise on light detectors using an aperture. One example embodiment includes a system. The system includes a lens disposed relative to a scene and configured to focus light from the scene onto a focal plane. The system also includes an aperture defined within an opaque material disposed at the focal plane of the lens. The aperture has a cross-sectional area. In addition, the system includes an array of light detectors disposed on a side of the focal plane opposite the lens and configured to intercept and detect diverging light focused by the lens and transmitted through the aperture. A cross-sectional area of the array of light detectors that intercepts the diverging light is greater than the cross-sectional area of the aperture.

Abstract: Aspects of the disclosure relate to arranging a pick up and drop off locations between a driverless vehicle and a passenger. As an example, a method of doing so may include receiving a request for a vehicle from a client computing device, wherein the request identifies a first location. Pre-stored map information and the first location are used to identify a recommended point according to a set of heuristics. Each heuristic of the set of heuristics has a ranking such that the recommended point corresponds to a location that satisfies at least one of the heuristics having a first rank and such that no other location satisfies any other heuristic of the set of heuristics having a higher rank than the first rank. The pre-stored map information identifying a plurality of pre-determined locations for the vehicle to stop, and the recommended point is one of the plurality of pre-determined locations.

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: A first driving solution for a vehicle along a portion of a route is determined based on an agent detected in the vehicle's environment following a first trajectory of a plurality of possible trajectories. A switching time is determined for the vehicle to deviate from the first driving solution for a situation in which the agent is following a second trajectory of the plurality of possible trajectories. The first driving solution is revised such that the vehicle will be able to switch from the revised first driving solution to another driving solution at the switching time in case if the detected agent is following the second trajectory.

Abstract: Aspects of the disclosure provide controlling an autonomous vehicle. For example, a first trajectory generated by a first planning system may be received. A portion of the first trajectory may be input into a second planning system. A second trajectory generated by the second planning system based on the portion of the first trajectory may be received. The second trajectory may be used to determine whether to validate the first trajectory, and when the second trajectory is determined to be validated, the autonomous vehicle may be enabled to be controlled based on the first trajectory.

Abstract: Systems and methods for performing operations based on LIDAR communications are described. An example device may include one or more processors and a memory coupled to the one or more processors. The memory includes instructions that, when executed by the one or more processors, cause the device to receive data associated with a modulated optical signal emitted by a transmitter of a first LIDAR device and received by a receiver of a second LIDAR device coupled to a vehicle and the device, generate a rendering of an environment of the vehicle based on information from one or more LIDAR devices coupled to the vehicle, and update the rendering based on the received data. Updating the rendering includes updating an object rendering of an object in the environment of the vehicle. The instructions further cause the device to provide the updated rendering for display on a display coupled to the vehicle.

Abstract: The technology relates to determining a source of a horn honk or other noise in an environment around one or more self-driving vehicles. Aspects of the technology leverage real-time information from a group of self-driving vehicles regarding received acoustical information. The location and pose of each self-driving vehicle in the group, along with the precise arrangement of acoustical sensors on each vehicle, can be used to triangulate or otherwise identify the actual location in the environment for the origin of the horn honk or other sound. Other sensor information, map data, and additional data can be used narrow down or refine the location of a likely noise source. Once the location and source of the noise is known, each self-driving vehicle can use that information to modify current driving operations and/or use it as part of a reinforcement learning approach for future driving situations.

Abstract: Example embodiments relate to low elevation side lobe antennas with fan-shaped beams. An example radar unit may include a radiating plate having a first side and a second side with an illuminator, a waveguide horn, a waveguide opening, and a radiating sleeve extending into the first side of the radiating plate. The waveguide opening is positioned on the first end of the first side and the radiating sleeve is positioned on the second end of the first side. The radar unit also includes a metallic cover coupled to the first side of the radiating plate such that the metallic cover and the radiating plate form waveguide structures.

Abstract: The described aspects and implementations enable fast and accurate verification of radar detection of objects in autonomous vehicle (AV) applications using combined processing of radar data and camera images. In one implementation, disclosed is a method and a system to perform the method that includes obtaining a radar data characterizing intensity of radar reflections from an environment of the AV, identifying, based on the radar data, a candidate object, obtaining a camera image depicting a region where the candidate object is located, and processing the radar data and the camera image using one or more machine-learning models to obtain a classification measure representing a likelihood that the candidate object is a real object.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for processing inputs using slice-based dynamic neural networks. One of the methods includes receiving a new input for processing by a neural network that includes a first conditional neural network layer that has a set of shared parameters and a respective set of slice parameters for each of a plurality of slices. One or more slices to which the new input belongs are identified. The new input is processed to generate a network output, including: receiving a layer input to the first conditional neural network layer; and processing the layer input using the set of shared parameters, the respective one or more sets of slice parameters for the identified one or more slices, but not the respective sets of slice parameters for any other slices to which the new input does not belong.

Abstract: Example embodiments relate to identification of proxy calibration targets for a fleet of sensors. An example method includes collecting, using a sensor coupled to a vehicle, data about one or more objects within an environment of the vehicle. The sensor has been calibrated using a ground-truth calibration target. The method also includes identifying, based on the collected data, at least one candidate object, from among the one or more objects, to be used as a proxy calibration target for other sensors coupled to vehicles within a fleet of vehicles. Further, the method includes providing, by the vehicle, data about the candidate object for use by one or more vehicles within the fleet of vehicles.

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: 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: 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: 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.