Abstract: Aspects of the disclosure relate to determining and responding to an internal state of a self-driving vehicle. For instance, an image of an interior of the vehicle captured by a camera mounted in the vehicle is received. The image is processed in order to identify one or more visible markers at predetermined locations within the vehicle. The internal state of the vehicle is determined based on the identified one or more visible markers. A responsive action is identified action using the determined internal state, and the vehicle is controlled in order to perform the responsive action.

Abstract: Aspects of the disclosure relate to determining whether a vehicle should continue through an intersection. For example, the one or more of the vehicle's computers may identify a time when the traffic signal light will turn from yellow to red. The one or more computers may also estimate a location of a vehicle at the time when the traffic signal light will turn from yellow to red. A starting point of the intersection may be identified. Based on whether the estimated location of the vehicle is at least a threshold distance past the starting point at the time when the traffic signal light will turn from yellow to red, the computers can determine whether the vehicle should continue through the intersection.

Abstract: The present disclosure is directed to an autonomous vehicle having a vehicle control system. The vehicle control system includes an image processing system. The image processing system receives an image that includes a plurality of image portions. The image processing system also calculates a score for each image portion. The score indicates a level of confidence that a given image portion represents an illuminated component of a traffic light. The image processing system further identifies one or more candidate portions from among the plurality of image portions. Additionally, the image processing system determines that a particular candidate portion represents an illuminated component of a traffic light using a classifier. Further, the image processing system provides instructions to control the autonomous vehicle based on the particular candidate portion representing an illuminated component of a traffic light.

Abstract: An optical system for a vehicle may be configured with a plurality of camera sensors. Each camera sensor may be configured to create respective image data of a respective field of view. The optical system is further configured with a plurality of image processing units coupled to the plurality of camera sensors. The image processing units are configured to compress the image data captured by the camera sensors. A computing system is configured to store the compressed image data in a memory. The computing system is further configured with a vehicle-control processor configured to control the vehicle based on the compressed image data. The optical system and the computing system can be communicatively coupled by a data bus.

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: 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 implementations may relate to use of a light-control feature to control extent of light encountered by an image capture device of a self-driving vehicle. In particular, a computing system of the vehicle may make a determination that quality of image data generated by an image capture device is or is expected to be lower than a threshold quality due to external light encountered or expected to be encountered by the image capture device. In response to the determination, the computing system may make an adjustment to the light-control feature to control the extent of external light encountered or expected to be encountered by the image capture device. This adjustment may ultimately help improve quality of image data generated by the image capture device. As such, the computing system may operate the vehicle based at least on image data generated by the image capture device.

Abstract: Example implementations may relate to sun-aware vehicle routing. In particular, a computing system of a vehicle may determine an expected position of the sun relative to a geographic area. Based on the expected position, the computing system may make a determination that travel of the vehicle through certain location(s) within the geographic area is expected to result in the sun being proximate to an object within a field of view of the vehicle's image capture device. Responsively, the computing system may generate a route for the vehicle in the geographic area based at least on the route avoiding travel of the vehicle through these certain location(s), and may then operate the vehicle to travel in accordance with the generated route. Ultimately, this may help reduce or prevent situations where quality of image(s) degrades due to sunlight, which may allow for use of these image(s) as basis for operating the vehicle.

Abstract: The present disclosure relates to a target, a method, and a system for calibrating a camera. One example embodiment includes a target. The target includes a first pattern of fiducial markers. The target also includes a second pattern of fiducial markers. The first pattern of fiducial markers is a scaled version of the second pattern of fiducial markers, such that a calibration image captured of the target simulates multiple images of a single pattern captured at multiple calibration perspectives.

Abstract: One example system comprises a LIDAR sensor that rotates about an axis to scan an environment of the LIDAR sensor. The system also comprises one or more cameras that detect external light originating from one or more external light sources. The one or more cameras together provide a plurality of rows of sensing elements. The rows of sensing elements are aligned with the axis of rotation of the LIDAR sensor. The system also comprises a controller that operates the one or more cameras to obtain a sequence of image pixel rows. A first image pixel row in the sequence is indicative of external light detected by a first row of sensing elements during a first exposure time period. A second image pixel row in the sequence is indicative of external light detected by a second row of sensing elements during a second exposure time period.

Abstract: Aspects of the disclosure relate to maneuvering a vehicle. As an example, sensor information identifying a set of objects as well as a set of characteristics for each object of the set of objects is received from a perception system of a vehicle. The set of objects is filtered to remove objects corresponding to vehicles, bicycles, and pedestrians. An object within an expected future path of the vehicle is selected from the filtered set of objects. The object is classified as drivable or not drivable based on the set of characteristics. Drivable indicates that the vehicle can drive over the object without causing damage to the vehicle. The vehicle is maneuvered based on the classification such that when the object is classified as drivable, maneuvering the vehicle includes driving the vehicle over the object by not altering the expected future path of the vehicle.

Abstract: The technology relates to camera systems for vehicles having an autonomous driving mode. An example system includes a first camera mounted on a vehicle in order to capture images of the vehicle's environment. The first camera has a first exposure time and being without an ND filter. The system also includes a second camera mounted on the vehicle in order to capture images of the vehicle's environment and having an ND filter. The system also includes one or more processors configured to capture images using the first camera and the first exposure time, capture images using the second camera and the second exposure time, use the images captured using the second camera to identify illuminated objects, use the images captured using the first camera to identify the locations of objects, and use the identified illuminated objects and identified locations of objects to control the vehicle in an autonomous driving mode.

Abstract: The present disclosure is directed to an autonomous vehicle having a vehicle control system. The vehicle control system includes an image processing system. The image processing system receives an image that includes a plurality of image portions. The image processing system also calculates a score for each image portion. The score indicates a level of confidence that a given image portion represents an illuminated component of a traffic light. The image processing system further identifies one or more candidate portions from among the plurality of image portions. Additionally, the image processing system determines that a particular candidate portion represents an illuminated component of a traffic light using a classifier. Further, the image processing system provides instructions to control the autonomous vehicle based on the particular candidate portion representing an illuminated component of a traffic light.

Abstract: Aspects of the disclosure relate to determining whether a vehicle should continue through an intersection. For example, the one or more of the vehicle's computers may identify a time when the traffic signal light will turn from yellow to red. The one or more computers may also estimate a location of a vehicle at the time when the traffic signal light will turn from yellow to red. A starting point of the intersection may be identified. Based on whether the estimated location of the vehicle is at least a threshold distance past the starting point at the time when the traffic signal light will turn from yellow to red, the computers can determine whether the vehicle should continue through the intersection.

Abstract: Aspects of the disclosure relate to determining whether a vehicle should continue through an intersection. For example, the one or more of the vehicle's computers may identify a time when the traffic signal light will turn from yellow to red. The one or more computers may also estimate a location of a vehicle at the time when the traffic signal light will turn from yellow to red. A starting point of the intersection may be identified. Based on whether the estimated location of the vehicle is at least a threshold distance past the starting point at the time when the traffic signal light will turn from yellow to red, the computers can determine whether the vehicle should continue through the intersection.

Abstract: The present disclosure is directed to an autonomous vehicle having a vehicle control system. The vehicle control system includes an image processing system. The image processing system receives an image that includes a plurality of image portions. The image processing system also calculates a score for each image portion. The score indicates a level of confidence that a given image portion represents an illuminated component of a traffic light. The image processing system further identifies one or more candidate portions from among the plurality of image portions. Additionally, the image processing system determines that a particular candidate portion represents an illuminated component of a traffic light using a classifier. Further, the image processing system provides instructions to control the autonomous vehicle based on the particular candidate portion representing an illuminated component of a traffic light.

Abstract: Aspects of the disclosure relate to maneuvering a vehicle. As an example, sensor information identifying a set of objects as well as a set of characteristics for each object of the set of objects is received from a perception system of a vehicle. The set of objects is filtered to remove objects corresponding to vehicles, bicycles, and pedestrians. An object within an expected future path of the vehicle is selected from the filtered set of objects. The object is classified as drivable or not drivable based on the set of characteristics. Drivable indicates that the vehicle can drive over the object without causing damage to the vehicle. The vehicle is maneuvered based on the classification such that when the object is classified as drivable, maneuvering the vehicle includes driving the vehicle over the object by not altering the expected future path of the vehicle.

Abstract: Aspects of the disclosure relate to determining whether a vehicle should continue through an intersection. For example, the one or more of the vehicle's computers may identify a time when the traffic signal light will turn from yellow to red. The one or more computers may also estimate a location of a vehicle at the time when the traffic signal light will turn from yellow to red. A starting point of the intersection may be identified. Based on whether the estimated location of the vehicle is at least a threshold distance past the starting point at the time when the traffic signal light will turn from yellow to red, the computers can determine whether the vehicle should continue through the intersection.

Abstract: The present disclosure is directed to an autonomous vehicle having a vehicle control system. The vehicle control system includes an image processing system. The image processing system receives an image that includes a plurality of image portions. The image processing system also calculates a score for each image portion. The score indicates a level of confidence that a given image portion represents an illuminated component of a traffic light. The image processing system further identifies one or more candidate portions from among the plurality of image portions. Additionally, the image processing system determines that a particular candidate portion represents an illuminated component of a traffic light using a classifier. Further, the image processing system provides instructions to control the autonomous vehicle based on the particular candidate portion representing an illuminated component of a traffic light.

Abstract: Aspects of the disclosure relate to determining whether a vehicle should continue through an intersection. For example, the one or more of the vehicle's computers may identify a time when the traffic signal light will turn from yellow to red. The one or more computers may also estimate a location of a vehicle at the time when the traffic signal light will turn from yellow to red. A starting point of the intersection may be identified. Based on whether the estimated location of the vehicle is at least a threshold distance past the starting point at the time when the traffic signal light will turn from yellow to red, the computers can determine whether the vehicle should continue through the intersection.