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 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: Aspects of the disclosure relate to generating simulated degraded sensor data. For instance, first sensor data collected by a sensor of a perception system of an autonomous vehicle may be received. The first sensor data may be inputted into simulated degraded sensor data for a particular degrading condition. The simulated degraded sensor data may be used to evaluate or train a model for detecting objects of the perception system.

Abstract: One example method involves rotating a housing of a light detection and ranging (LIDAR) device about a first axis. The housing includes a first optical window and a second optical window. The method also involves transmitting a first plurality of light pulses through the first optical window to obtain a first scan of a field-of-view (FOV) of the LIDAR device. The method also involves transmitting a second plurality of light pulses through the second optical window to obtain a second scan of the FOV. The method also involves identifying, based on the first scan and the second scan, a portion of the FOV that is at least partially occluded by an occlusion.

Abstract: The present disclosure relates to systems and methods for occlusion detection. One example method involves a light detection and ranging (LIDAR) device scanning at least a portion of an external structure within a field-of-view (FOV) of the LIDAR device. The LIDAR device is physically coupled to the external structure. The scanning comprises transmitting light pulses toward the external structure through an optical window, and receiving reflected light pulses through the optical window. The reflected light pulses comprise reflections of the transmitted light pulses returning back to the LIDAR device from the external structure. The method also involves detecting presence of an occlusion that at least partially occludes the LIDAR device from scanning the FOV based on at least the scan of the at least portion of the external structure.

Abstract: Aspects of the disclosure provide for automatically generating labels for sensor data. For instance first sensor data for a first vehicle is identified. The first sensor data is defined in both a global coordinate system and a local coordinate system for the first vehicle. A second vehicle is identified based on a second location of the second vehicle within a threshold distance of the first vehicle within the first timeframe. The second vehicle is associated with second sensor data that is further associated with a label identifying a location of an object, and the location of the object is defined in a local coordinate system of the second vehicle. A conversion from the local coordinate system of the second vehicle to the local coordinate system of the first vehicle may be determined and used to transfer the label from the second sensor data to the first sensor data.

Abstract: One example method involves rotating a housing of a light detection and ranging (LIDAR) device about a first axis. The housing includes a first optical window and a second optical window. The method also involves transmitting a first plurality of light pulses through the first optical window to obtain a first scan of a field-of-view (FOV) of the LIDAR device. The method also involves transmitting a second plurality of light pulses through the second optical window to obtain a second scan of the FOV. The method also involves identifying, based on the first scan and the second scan, a portion of the FOV that is at least partially occluded by an occlusion.

Abstract: Example implementations may relate to determining a strategy for a drop process associated with a light detection and ranging (LIDAR) device. In particular, the LIDAR device could emit light pulses and detect return light pulses, and could generate a set of data points representative of the detected return light pulses. The drop process could involve a computing system discarding data point(s) of the set and/or preventing emission of light pulse(s) by the LIDAR device. Accordingly, the computing system could detect a trigger to engage in the drop process, and may responsively (i) use information associated with the environment around the vehicle, operation of the vehicle, and/or operation of the LIDAR device as a basis to determine the strategy for the drop process, and (ii) engage in the drop process in accordance with the determined strategy.

Abstract: The technology relates to developing a highly accurate understanding of a vehicle's sensor fields of view in relation to the vehicle itself. A training phase is employed to gather sensor data in various situations and scenarios, and a modeling phase takes such information and identifies self-returns and other signals that should either be excluded from analysis during real-time driving or accounted for to avoid false positives. The result is a sensor field of view model for a particular vehicle, which can be extended to other similar makes and models of that vehicle. This approach enables a vehicle to determine when sensor data is of the vehicle or something else. As a result, the detailed modeling allowing the on-board computing system to make driving decisions and take other actions based on accurate sensor information.

Abstract: One example system includes a first light detection and ranging (LIDAR) device that scans a first field-of-view defined by a first range of pointing directions associated with the first LIDAR device. The system also includes a second LIDAR device that scans a second FOV defined by a second range of pointing directions associated with the second LIDAR device. The second FOV at least partially overlaps the first FOV. The system also includes a first controller that adjusts a first pointing direction of the first LIDAR device. The system also includes a second controller that adjusts a second pointing direction of the second LIDAR device synchronously with the adjustment of the first pointing direction of the first LIDAR device.

Abstract: One example method involves rotating a housing of a light detection and ranging (LIDAR) device about a first axis. The housing includes a first optical window and a second optical window. The method also involves transmitting a first plurality of light pulses through the first optical window to obtain a first scan of a field-of-view (FOV) of the LIDAR device. The method also involves transmitting a second plurality of light pulses through the second optical window to obtain a second scan of the FOV. The method also involves identifying, based on the first scan and the second scan, a portion of the FOV that is at least partially occluded by an occlusion.

Abstract: A system, program storage device, and method of optimizing data placement on a storage device, the method comprising establishing a specified time constraint for which the storage device is to delete data stored thereon; dividing a data object into a plurality of data bits; programming a block of data and the data bits with a logic operand if the storage device is incapable of deleting the data within the specified time constraint; creating an encoded block of data from the programmed block of data and the data bits; organizing the encoded block of data and the data bits in the storage device according to data deletion requirements; and removing the data bits from the storage device if the data bits are organized within a specified data deletion requirement, wherein the data bits are removed using a data shredding process, and wherein the logic operand comprises an exclusive-or (XOR) operator.