Abstract: Example embodiments relate to using cleaning protocols to monitor defects associated with light detection and ranging (lidar) devices. An example embodiment includes a method. The method includes applying, using a cleaning device, a cleaning protocol to one or more optical components of a light detection and ranging (lidar) device. The method also includes emitting, from a light emitter of the lidar device, one or more light signals. Additionally, the method includes detecting, by a light detector of the lidar device, reflections of the one or more light signals. Further, the method includes determining, by a controller of the lidar device based on the detected reflections of the one or more light signals, that one or more defects are present within the one or more optical components or within the cleaning device.

Abstract: Example embodiments relate to methods and systems for automatic problematic maneuver detection and adapted motion planning. A computing device may obtain a route for navigation by a vehicle and a set of vehicle parameters corresponding to the vehicle. Each vehicle parameter can represent a physical attribute of the vehicle. The computing device may generate a virtual vehicle that represents the vehicle based on the set of vehicle parameters and perform a simulation that involves the virtual vehicle navigating the route. Based on the results of the simulation, the computing device may provide the original route or a modified route to the vehicle for the vehicle to subsequently navigate to its destination. In some cases, the simulation may further factor additional conditions, such as potential weather and traffic conditions that are likely to occur during the time when the vehicle plans on navigating the route.

Abstract: The subject matter of this specification relates to a light detection and ranging (LiDAR) system. In at least one implementation, the LiDAR system comprises a first signal source; a second signal source; a combiner to generate a hybrid transmission signal from signals generated by the first signal source and the second signal source; a first photodetector to measure a first component of a reflection signal related to range of a target; and a second photodetector to measure a second component of the reflection signal related to velocity of the target, wherein the system is configured to derive the range and velocity of the target from the first component and the second component, respectively.

Abstract: The subject matter of this specification can be implemented in, among other things, systems and methods of optical sensing that use destructive interference to suppress retro-reflected light during generation of sensing beams. Described, among other things, is a system that outputs a modulated transmitted (TX) beam and collects a received (RX) beam having one or more beams reflected from outside object(s). The system obtains, using the RX beam, a first phase information (PI) that includes a first feature and a second feature. The system obtains, using a copy of the modulated TX beam, a second PI, and reduces, using the second PI, the first feature of the first PI. The system then determines, using the second feature of the first PI, one or more characteristics of the outside object(s).

Abstract: Aspects of the disclosure provide systems and methods for providing suggested locations for pick up and destination locations. Pick up locations may include locations where an autonomous vehicle can pick up a passenger, while destination locations may include locations where the vehicle can wait for an additional passenger, stop and wait for a passenger to perform some task and return to the vehicle, or for the vehicle to drop off a passenger. As such, a request for a vehicle may be received from a client computing device. The request may identify a first location. A set of one or more suggested locations may be selected by comparing the predetermined locations to the first location. The set may be provided to the client computing device.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training a student machine learning model using a teacher machine learning model that has a pre-trained feature extractor.

Abstract: Aspects and implementations of the present disclosure relate, generally, to optimization of autonomous vehicle (AV) technology and, more specifically, to a transfer hub for autonomous trucking operations. In one example, the disclosed techniques include obtaining, at a first time, one or more first images of an outside environment of an AV and shutting down a data processing system of the AV. The techniques further include initiating a starting-up of the data processing system of the AV and obtaining, at a second time, one or more second images of the outside environment of the AV. The techniques further include determining, based on a comparison of the one or more first images to the one or more second images, that the AV has not moved between the first time and the second time and completing the starting-up of the data processing system of the AV.

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: The described aspects and implementations enable efficient object detection and tracking. In one implementation, disclosed is a method and a system to perform the method, the system including the sensing system configured to obtain sensing data characterizing an environment of the vehicle. The system further includes a data processing system operatively coupled to the sensing system and configured to process the sensing data using a first (second) set of neural network (NN) layers to obtain a first (second) set of features for a first (second) region of the environment, the first (second) set of features is associated with a first (second) spatial resolution. The data processing system is further to process the two sets of features using a second set of NN layers to detect a location of object(s) in the environment of the vehicle and a state of motion of the object(s).

Abstract: Methods and systems for the use of detected objects for image processing are described. A computing device autonomously controlling a vehicle may receive images of the environment surrounding the vehicle from an image-capture device coupled to the vehicle. In order to process the images, the computing device may receive information indicating characteristics of objects in the images from one or more sources coupled to the vehicle. Examples of sources may include RADAR, LIDAR, a map, sensors, a global positioning system (GPS), or other cameras. The computing device may use the information indicating characteristics of the objects to process received images, including determining the approximate locations of objects within the images. Further, while processing the image, the computing device may use information from sources to determine portions of the image to focus upon that may allow the computing device to determine a control strategy based on portions of the image.

Abstract: The technology involves identifying suitable pickup and drop-off locations based on detected pedestrian walking paths. Mapped areas have specific physical configurations, which may suggest places to pick up or drop off a rider (or a delivery). A walking path heatmap can be generated based on obtained historical and/or real-time pedestrian-related information, which can be obtained by autonomous vehicles driving in areas of interest. Incorporating heatmap information into the evaluation, the system identifies locations for optimized pickup or drop-off in accordance with where pedestrians would likely go. One aspect involves classifying different objects, for instance identifying one or more objects as people who may be walking versus riding a bicycle. Once classified, information about the paths is used to obtain a the heatmap associated with the walking paths.

Abstract: A system and method include scanning a light detection and ranging (LIDAR) device through a range of orientations corresponding to a scanning zone while emitting light pulses from the LIDAR device. The method also includes receiving returning light pulses corresponding to the light pulses emitted from the LIDAR device and determining initial point cloud data based on time delays between emitting the light pulses and receiving the corresponding returning light pulses and the orientations of the LIDAR device. The initial point cloud data has an initial angular resolution. The method includes identifying, based on the initial point cloud data, a reflective feature in the scanning zone and determining an enhancement region and an enhanced angular resolution for a subsequent scan to provide a higher spatial resolution in at least a portion of subsequent point cloud data from the subsequent scan corresponding to the reflective feature.