Abstract: Example methods and systems for detecting weather conditions using vehicle onboard sensors are provided. An example method includes receiving laser data collected for an environment of a vehicle, and the laser data includes a plurality of laser data points. The method also includes associating, by a computing device, laser data points of the plurality of laser data points with one or more objects in the environment, and determining given laser data points of the plurality of laser data points that are unassociated with the one or more objects in the environment as being representative of an untracked object. The method also includes based on one or more untracked objects being determined, identifying by the computing device an indication of a weather condition of the environment.

Abstract: Example methods and systems for detecting weather conditions using vehicle onboard sensors are provided. An example method includes receiving laser data collected for an environment of a vehicle, and the laser data includes a plurality of laser data points. The method also includes associating, by a computing device, laser data points of the plurality of laser data points with one or more objects in the environment, and determining given laser data points of the plurality of laser data points that are unassociated with the one or more objects in the environment as being representative of an untracked object. The method also includes based on one or more untracked objects being determined, identifying by the computing device an indication of a weather condition of the environment.

Abstract: An autonomous vehicle is configured to detect an active turn signal indicator on another vehicle. An image-capture device of the autonomous vehicle captures an image of a field of view of the autonomous vehicle. The autonomous vehicle captures the image with a short exposure to emphasize objects having brightness above a threshold. Additionally, a bounding area for a second vehicle located within the image is determined. The autonomous vehicle identifies a group of pixels within the bounding area based on a first color of the group of pixels. The autonomous vehicle also calculates an oscillation of an intensity of the group of pixels. Based on the oscillation of the intensity, the autonomous vehicle determines a likelihood that the second vehicle has a first active turn signal. Additionally, the autonomous vehicle is controlled based at least on the likelihood that the second vehicle has a first active turn signal.

Abstract: Methods and systems for predictive reasoning for controlling speed of a vehicle are described. A computing device may be configured to identify a first and second vehicle travelling ahead of an autonomous vehicle and in a same lane as the autonomous vehicle. The computing device may also be configured to determine a first buffer distance behind the first vehicle at which the autonomous vehicle will substantially reach a speed of the first vehicle and a second buffer distance behind the second vehicle at which the first vehicle will substantially reach a speed of the second vehicle. The computing device may further be configured to determine a distance at which to adjust a speed of the autonomous vehicle based on the first and second buffer distances and the speed of the autonomous vehicle, and then provide instructions to adjust the speed of the autonomous vehicle based on the distance.

Abstract: A vehicle configured to operate in an autonomous mode may engage in a reverse-parallax analysis that includes a vehicle system detecting an object, capturing via a camera located at a first location a first image of the detected object, retrieving location data specifying (i) a location of a target object, (ii) the first location, and (iii) a direction of the camera, and based on the location data and the position of the detected object in the first image, predicting where in a second image captured from a second location the detected object would appear if the detected object is the target object.

Abstract: Methods and systems for predictive reasoning for controlling speed of a vehicle are described. A computing device may be configured to identify a first and second vehicle travelling ahead of an autonomous vehicle and in a same lane as the autonomous vehicle. The computing device may also be configured to determine a first buffer distance behind the first vehicle at which the autonomous vehicle will substantially reach a speed of the first vehicle and a second buffer distance behind the second vehicle at which the first vehicle will substantially reach a speed of the second vehicle. The computing device may further be configured to determine a distance at which to adjust a speed of the autonomous vehicle based on the first and second buffer distances and the speed of the autonomous vehicle, and then provide instructions to adjust the speed of the autonomous vehicle based on the distance.

Abstract: Methods and systems for predictive reasoning for controlling speed of a vehicle are described. A computing device may be configured to identify a first and second vehicle travelling ahead of an autonomous vehicle and in a same lane as the autonomous vehicle. The computing device may also be configured to determine a first buffer distance behind the first vehicle at which the autonomous vehicle will substantially reach a speed of the first vehicle and a second buffer distance behind the second vehicle at which the first vehicle will substantially reach a speed of the second vehicle. The computing device may further be configured to determine a distance at which to adjust a speed of the autonomous vehicle based on the first and second buffer distances and the speed of the autonomous vehicle, and then provide instructions to adjust the speed of the autonomous vehicle based on the distance.

Abstract: A vehicle configured to operate in an autonomous mode may engage in a reverse-parallax analysis that includes a vehicle system detecting an object, capturing via a camera located at a first location a first image of the detected object, retrieving location data specifying (i) a location of a target object, (ii) the first location, and (iii) a direction of the camera, and based on the location data and the position of the detected object in the first image, predicting where in a second image captured from a second location the detected object would appear if the detected object is the target object.

Abstract: Methods and systems for predictive reasoning for controlling speed of a vehicle are described. A computing device may be configured to identify a first and second vehicle travelling ahead of an autonomous vehicle and in a same lane as the autonomous vehicle. The computing device may also be configured to determine a first buffer distance behind the first vehicle at which the autonomous vehicle will substantially reach a speed of the first vehicle and a second buffer distance behind the second vehicle at which the first vehicle will substantially reach a speed of the second vehicle. The computing device may further be configured to determine a distance at which to adjust a speed of the autonomous vehicle based on the first and second buffer distances and the speed of the autonomous vehicle, and then provide instructions to adjust the speed of the autonomous vehicle based on the distance.

Abstract: Methods and systems for detecting weather conditions including fog using vehicle onboard sensors are provided. An example method includes receiving laser data collected from scans of an environment of a vehicle, and associating, by a computing device, laser data points of with one or more objects in the environment. The method also includes comparing laser data points that are unassociated with the one or more objects in the environment with stored laser data points representative of a pattern due to fog, and based on the comparison, identifying by the computing device an indication that a weather condition of the environment of the vehicle includes fog.

Abstract: Methods and systems for predictive reasoning for controlling speed of a vehicle are described. A computing device may be configured to identify a first and second vehicle travelling ahead of an autonomous vehicle and in a same lane as the autonomous vehicle. The computing device may also be configured to determine a first buffer distance behind the first vehicle at which the autonomous vehicle will substantially reach a speed of the first vehicle and a second buffer distance behind the second vehicle at which the first vehicle will substantially reach a speed of the second vehicle. The computing device may further be configured to determine a distance at which to adjust a speed of the autonomous vehicle based on the first and second buffer distances and the speed of the autonomous vehicle, and then provide instructions to adjust the speed of the autonomous vehicle based on the distance.

Abstract: Example methods and systems for detecting weather conditions including wet surfaces using vehicle onboard sensors are provided. An example method includes receiving laser data collected for an environment of a vehicle. The method also includes determining laser data points that are associated with one or more objects in the environment, and based on laser data points being unassociated with the one or more objects in the environment, identifying an indication that a surface on which the vehicle travels is wet. The method may further include receiving radar data collected for the environment of the vehicle that is indicative of a presence of the one or more objects in the environment of the vehicle, and identifying the indication that the surface on which the vehicle travels is wet further based on laser data points being unassociated with the one or more objects in the environment indicated by the radar data.

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: Methods and systems for detecting weather conditions including fog using vehicle onboard sensors are provided. An example method includes receiving laser data collected from scans of an environment of a vehicle, and associating, by a computing device, laser data points of with one or more objects in the environment. The method also includes comparing laser data points that are unassociated with the one or more objects in the environment with stored laser data points representative of a pattern due to fog, and based on the comparison, identifying by the computing device an indication that a weather condition of the environment of the vehicle includes fog.

Abstract: Methods and systems for detecting weather conditions including sunlight using onboard vehicle sensors are described. In one example, a method is provided that includes receiving laser data collected for an environment of a vehicle. The method also includes associating laser data points with one or more objects in the environment, and determining given laser data points that are unassociated with the one or more objects in the environment as being representative of an untracked object at a given position with respect to the vehicle. The method also includes determining that the untracked object remains at a substantially same relative position with respect to the vehicle as the vehicle moves, and identifying by the computing device an indication that a weather condition of the environment of the vehicle is sunny.

Abstract: Methods and apparatus are disclosed related to classifying illuminated objects. A computing device can receive a sequence of images including first and second images. The first image can be of an environment of a vehicle taken at a first time. The second image can be of the environment of the vehicle taken at a second time. The vehicle can include a light source that illuminates a portion of the environment. The first and second times can differ. The computing device can detect an object having a first size and a first brightness in the first image. The computing device can detect the object having a second size and a second brightness in the second image. The computing device can classify the object based on a brightness difference between the first brightness and second brightness and a size difference between the first size and second size.

Abstract: Methods and systems for detecting weather conditions including fog using vehicle onboard sensors are provided. An example method includes receiving laser data collected from scans of an environment of a vehicle, and associating, by a computing device, laser data points of with one or more objects in the environment. The method also includes comparing laser data points that are unassociated with the one or more objects in the environment with stored laser data points representative of a pattern due to fog, and based on the comparison, identifying by the computing device an indication that a weather condition of the environment of the vehicle includes fog.

Abstract: Methods and apparatus are disclosed related to autonomous vehicle applications for selecting destinations. A control system of an autonomous vehicle can determine a status of the autonomous vehicle. The control system can determine a possible destination of the autonomous vehicle. The control system can generate and provide a hint related to the possible destination based on the status of the autonomous vehicle. The control system can receive input related to the hint. Based on the input, the control system can determine whether to navigate the autonomous vehicle to the possible destination. After determining to navigate the autonomous vehicle to the possible destination, the control system can direct the autonomous vehicle to travel to the possible destination.

Abstract: Disclosed herein are methods and apparatus for controlling autonomous vehicles utilizing maps that include visibility information. A map is stored at a computing device associated with a vehicle. The vehicle is configured to operate in an autonomous mode that supports a plurality of driving behaviors. The map includes information about a plurality of roads, a plurality of features, and visibility information for at least a first feature in the plurality of features. The computing device queries the map for visibility information for the first feature at a first position. The computing device, in response to querying the map, receives the visibility information for the first feature at the first position. The computing device selects a driving behavior for the vehicle based on the visibility information. The computing device controls the vehicle in accordance with the selected driving behavior.

Abstract: Postprocessing of medical images (e.g., MRI and CT images) can be facilitated by a variety of techniques, including training methods which include modular organization and/or online presentation, postprocessing protocols which can be used to specify activities which can be predictably and consistently performed by technologists, and deployment of thin client image processing technology. Additional beneficial results relative to what is possible with the prior art can be obtained by combining one or more of the above approaches.