Abstract: An example method includes using a plurality of switches corresponding to a plurality of capacitors to select a first set of capacitors for charging at a first time. Charging the first set of capacitors corresponds to sampling from a first set of adjacent light detectors. The method includes using the plurality of switches to select a second set of capacitors from the plurality of capacitors for discharging at a second time. The method includes using a sampling switch to sample an output of the second set of capacitors as they discharge. The output of the second set of capacitors corresponds to the first set of adjacent light detectors. The method includes determining, based on sampling the output of the second set of capacitors, a collective intensity of light received by the first set of adjacent light detectors.

Abstract: An example method includes using a plurality of switches corresponding to a plurality of capacitors to select a first set of capacitors for charging at a first time. Charging the first set of capacitors corresponds to sampling from a first set of adjacent light detectors. The method includes using the plurality of switches to select a second set of capacitors from the plurality of capacitors for discharging at a second time. The method includes using a sampling switch to sample an output of the second set of capacitors as they discharge. The output of the second set of capacitors corresponds to the first set of adjacent light detectors. The method includes determining, based on sampling the output of the second set of capacitors, a collective intensity of light received by the first set of adjacent light detectors.

Abstract: The technology relates to determining whether a camera is occluded. For instance, an image may be captured using a camera having red, green, and blue pixels each including a photosensor. Output values for the photosensors of each of the red pixels, green pixels, and blue pixels may be determined for the images. The output values of the green pixels may be compared to one or more of the output values of the red pixels or the output values of the blue pixels. That the camera is occluded is determined based on the comparison.

Abstract: Disclosed are devices and methods that may be used for the calibration of sensors of a vehicle. A calibration system disclosed herein includes a trigger device configured to detect a first sensing event. The calibration system further includes a plurality of sensor targets. Additionally, the calibration system includes a processor configured to reveal the plurality of sensor targets within a given region in response to the first sensing event. The calibration system may be configured to detect light from a LIDAR striking a light sensors. In response to detecting the light from the LIDAR, a plurality of lights, both visible and infrared, may be illuminated to correspond with a position and timing of the LIDAR pulse.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for a computing system that receives images that each have a predefined exposure attribute. For each image, a first set of features of the image are extracted. The first set of features are associated with a label indicating no modification of the image is required. A luminosity characteristic of the image is adjusted to form an adjusted image. A second set of features of the adjusted image are extracted. A neural network is trained to adjust luminosity characteristics of images using the first set of features and the second set of features of the adjusted image. An exposure adjustment model adjusts luminosity characteristics of images based on correction values determined using the trained neural network.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for a computing system that receives images that each have a predefined exposure attribute. For each image, a first set of features of the image are extracted. The first set of features are associated with a label indicating no modification of the image is required. A luminosity characteristic of the image is adjusted to form an adjusted image. A second set of features of the adjusted image are extracted. A neural network is trained to adjust luminosity characteristics of images using the first set of features and the second set of features of the adjusted image. An exposure adjustment model adjusts luminosity characteristics of images based on correction values determined using the trained neural network.

Abstract: Techniques and mechanisms for determining a configuration of the lens system. In an embodiment, respective distances from a reference are determined for each of a plurality of objects that are observable via the lens system. Based on the object distances, counts of in-focus objects are determined, each for a corresponding focal configuration of the lens system. Each such count of in-focus objects represents a total number of objects that are (or would be) in focus during the corresponding focal configuration, wherein a respective one of the plurality of objects is at a near depth of field of the corresponding focal configuration. In another embodiment, a preference of one focal configuration over another focal configuration is determined based on the counts of in-focus objects.

Abstract: Techniques and mechanisms for determining a configuration of the lens system. In an embodiment, respective distances from a reference are determined for each of a plurality of objects that are observable via the lens system. Based on the object distances, counts of in-focus objects are determined, each for a corresponding focal configuration of the lens system. Each such count of in-focus objects represents a total number of objects that are (or would be) in focus during the corresponding focal configuration, wherein a respective one of the plurality of objects is at a near depth of field of the corresponding focal configuration. In another embodiment, a preference of one focal configuration over another focal configuration is determined based on the counts of in-focus objects.