Abstract: The technology relates to localizing a vehicle. As one approach, a first LIDAR sensor scan data of an environment of the vehicle and localization data for the vehicle at a location where the first LIDAR sensor scan was captured are stored. Thereafter, the computing device is suspended and subsequently unsuspended. After the computing device is unsuspended, second LIDAR sensor scan data of the vehicle's environment is received. The first LIDAR sensor scan data is compared to the second LIDAR sensor scan data to determine whether the vehicle has moved. Based on the determination of whether the vehicle has moved from the location, the stored localization data is used to localize the vehicle. Other approaches are also described.

Abstract: Example embodiments relate to identification of proxy calibration targets for a fleet of sensors. An example method includes collecting, using a sensor coupled to a vehicle, data about one or more objects within an environment of the vehicle. The sensor has been calibrated using a ground-truth calibration target. The method also includes identifying, based on the collected data, at least one candidate object, from among the one or more objects, to be used as a proxy calibration target for other sensors coupled to vehicles within a fleet of vehicles. Further, the method includes providing, by the vehicle, data about the candidate object for use by one or more vehicles within the fleet of vehicles.

Abstract: An example method involves detecting a sensor-testing trigger. Detecting the sensor-testing trigger may comprise determining that a vehicle is within a threshold distance to a target in an environment of the vehicle. The method also involves obtaining sensor data collected by a sensor of the vehicle after the detection of the sensor-testing trigger. The sensor data is indicative of a scan of a region of the environment that includes the target. The method also involves comparing the sensor data with previously-collected sensor data indicating detection of the target by one or more sensors during one or more previous scans of the environment. The method also involves generating performance metrics related to the sensor of the vehicle based on the comparison.

Abstract: One example method involves obtaining a plurality of scans of a field-of-view (FOV) of a light detection and ranging (LIDAR) device disposed inside a housing. Obtaining each scan of the plurality of scans comprises: transmitting, through a plurality of sections of the housing, a plurality of light pulses emitted from the LIDAR device in different directions toward the housing; and detecting a plurality of returning light pulses comprising reflected portions of the transmitted plurality of light pulses that are reflected back toward the LIDAR device. The method also involves detecting an obstruction that at least partially occludes the LIDAR device from scanning the FOV through the housing based on the plurality of scans.

Abstract: An example method involves detecting a sensor-testing trigger. Detecting the sensor-testing trigger may comprise determining that a vehicle is within a threshold distance to a target in an environment of the vehicle. The method also involves obtaining sensor data collected by a sensor of the vehicle after the detection of the sensor-testing trigger. The sensor data is indicative of a scan of a region of the environment that includes the target. The method also involves comparing the sensor data with previously-collected sensor data indicating detection of the target by one or more sensors during one or more previous scans of the environment. The method also involves generating performance metrics related to the sensor of the vehicle based on the comparison.

Abstract: Example embodiments relate to identification of proxy calibration targets for a fleet of sensors. An example method includes collecting, using a sensor coupled to a vehicle, data about one or more objects within an environment of the vehicle. The sensor has been calibrated using a ground-truth calibration target. The method also includes identifying, based on the collected data, at least one candidate object, from among the one or more objects, to be used as a proxy calibration target for other sensors coupled to vehicles within a fleet of vehicles. Further, the method includes providing, by the vehicle, data about the candidate object for use by one or more vehicles within the fleet of vehicles.

Abstract: Example embodiments relate to taking images at certain predetermined angles in order to have consistent exposure throughout the images. An example embodiment includes a method. The method includes determining, using a lidar device, light intensity information of a surrounding environment of the lidar device. The light intensity information includes a plurality of angles within a threshold range of light exposure. The method also includes determining rotation times associated with each of the angles within the threshold range of light exposure. Further, the method includes based on the rotation times associated with each of the angles within the threshold range of light exposure, determining a plurality of target image times. In addition, the method includes capturing, by a camera system, a plurality of images at the plurality of target image times.

Abstract: An example method includes receiving, from a light detector, information indicative of a light intensity of a field of view of an optical system. The optical system includes one or more optical components and a light detector configured to receive light from a field of view of an environment of the optical system by way of the one or more optical components. The optical system also includes an occlusion-detection camera configured to capture images of the one or more optical components. The example method also includes adjusting, based on the received information, at least one operating parameter of the occlusion-detection camera. The example method also includes causing the occlusion-detection camera to capture at least one image of the one or more optical components according to the at least one adjusted operating parameter.

Abstract: Example embodiments relate to identification of proxy calibration targets for a fleet of sensors. An example method includes collecting, using a sensor coupled to a vehicle, data about one or more objects within an environment of the vehicle. The sensor has been calibrated using a ground-truth calibration target. The method also includes identifying, based on the collected data, at least one candidate object, from among the one or more objects, to be used as a proxy calibration target for other sensors coupled to vehicles within a fleet of vehicles. Further, the method includes providing, by the vehicle, data about the candidate object for use by one or more vehicles within the fleet of vehicles.

Abstract: A system includes a light detection and ranging device configured to generate, for each respective point of a plurality of points in an environment, a corresponding waveform that represents physical characteristics of the respective point. The system also includes a signal processor configured to determine, based on the corresponding waveform of each respective point, a map of the environment that includes a representation of a corresponding position of the respective point. The system additionally includes an embedding model configured to determine, for each respective point and based on the corresponding waveform, a corresponding vector comprising a plurality of values representative of the physical characteristics of the respective point. The system further includes a feature detector configured to detect or classify a physical feature based on (i) the corresponding positions of one or more points of the plurality of points and (ii) the corresponding vectors of the one or more points.

Abstract: The disclosure provides for a method of calibrating a detection system that is mounted on a vehicle. The method includes detecting characteristics of the mirror and characteristics of a vehicle portion using the detection system. The mirror reflects the vehicle portion to be detected using the detection system. The method also includes determining a first transform based on the detected one or more of mirror characteristics, determining a second transform based on the one or more vehicle portion characteristics, and determining a third transform based on a known position of the vehicle portion in relation to the vehicle. Further, the method includes determining a position of the detection system relative to the vehicle based on the first, second, and third transforms and then calibrating the detection system using the determined position of the detection system relative to the vehicle.

Abstract: Example embodiments relate to calibration and localization of a light detection and ranging (lidar) device using a previously calibrated and localized lidar device. An example embodiment includes a method. The method includes receiving, by a computing device associated with a second vehicle, a first point cloud captured by a first lidar device of a first vehicle. The first point cloud includes points representing the second vehicle. The method also includes receiving, by the computing device, pose information indicative of a pose of the first vehicle. In addition, the method includes capturing, using a second lidar device of the second vehicle, a second point cloud. Further, the method includes receiving, by the computing device, a third point cloud representing the first vehicle. Yet further, the method includes calibrating and localizing, by the computing device, the second lidar device.

Abstract: Aspects of the disclosure provide for a method of calibrating a vehicle's plurality of detection systems. To calibrate a first detection system, orientation data is collected using the first detection device when the vehicle faces a first direction and a second direction opposite the first direction. To calibrate a second detection system, data is collected using the second detection device while the vehicle moves in a repeatable pattern near a first object. To calibrate a third detection system, light reflected off a second object is detected using the third detection system as the vehicle is moved towards the second object. To calibrate a fourth detection system, data collected using the second detection system and the fourth detection system is compared. To calibrate a fifth detection system, radar signals reflected off a third object is received using the fifth detection system while the vehicle moves relative to the third object.

Abstract: One example method involves obtaining a plurality of scans of a field-of-view (FOV) of a light detection and ranging (LIDAR) device disposed inside a housing. Obtaining each scan of the plurality of scans comprises: transmitting, through a plurality of sections of the housing, a plurality of light pulses emitted from the LIDAR device in different directions toward the housing; and detecting a plurality of returning light pulses comprising reflected portions of the transmitted plurality of light pulses that are reflected back toward the LIDAR device. The method also involves detecting an obstruction that at least partially occludes the LIDAR device from scanning the FOV through the housing based on the plurality of scans.

Abstract: The disclosure provides for a method of calibrating a detection system that is mounted on a vehicle. The method includes detecting characteristics of the mirror and characteristics of a vehicle portion using the detection system. The mirror reflects the vehicle portion to be detected using the detection system. The method also includes determining a first transform based on the detected one or more of mirror characteristics, determining a second transform based on the one or more vehicle portion characteristics, and determining a third transform based on a known position of the vehicle portion in relation to the vehicle. Further, the method includes determining a position of the detection system relative to the vehicle based on the first, second, and third transforms and then calibrating the detection system using the determined position of the detection system relative to the vehicle.

Abstract: An example method involves detecting a sensor-testing trigger. Detecting the sensor-testing trigger may comprise determining that a vehicle is within a threshold distance to a target in an environment of the vehicle. The method also involves obtaining sensor data collected by a sensor of the vehicle after the detection of the sensor-testing trigger. The sensor data is indicative of a scan of a region of the environment that includes the target. The method also involves comparing the sensor data with previously-collected sensor data indicating detection of the target by one or more sensors during one or more previous scans of the environment. The method also involves generating performance metrics related to the sensor of the vehicle based on the comparison.

Abstract: The technology relates to localizing a vehicle. As one approach, a first LIDAR sensor scan data of an environment of the vehicle and localization data for the vehicle at a location where the first LIDAR sensor scan was captured are stored. Thereafter, the computing device is suspended and subsequently unsuspended. After the computing device is unsuspended, second LIDAR sensor scan data of the vehicle's environment is received. The first LIDAR sensor scan data is compared to the second LIDAR sensor scan data to determine whether the vehicle has moved. Based on the determination of whether the vehicle has moved from the location, the stored localization data is used to localize the vehicle. Other approaches are also described.

Abstract: An example method involves detecting a sensor-testing trigger. Detecting the sensor-testing trigger may comprise determining that a vehicle is within a threshold distance to a target in an environment of the vehicle. The method also involves obtaining sensor data collected by a sensor of the vehicle after the detection of the sensor-testing trigger. The sensor data is indicative of a scan of a region of the environment that includes the target. The method also involves comparing the sensor data with previously-collected sensor data indicating detection of the target by one or more sensors during one or more previous scans of the environment. The method also involves generating performance metrics related to the sensor of the vehicle based on the comparison.

Abstract: The technology relates to localizing a vehicle. As one approach, a first LIDAR sensor scan data of an environment of the vehicle and localization data for the vehicle at a location where the first LIDAR sensor scan was captured are stored. Thereafter, the computing device is suspended and subsequently unsuspended. After the computing device is unsuspended, second LIDAR sensor scan data of the vehicle's environment is received. The first LIDAR sensor scan data is compared to the second LIDAR sensor scan data to determine whether the vehicle has moved. Based on the determination of whether the vehicle has moved from the location, the stored localization data is used to localize the vehicle. Other approaches are also described.

Abstract: Aspects of the disclosure provides for a method for performing checks for a vehicle. In this regard, a plurality of performance checks may be identified including a first check for a detection system of a plurality of detection systems of the vehicle and a second check for map data. A test route for the vehicle may be determined based on a location of the vehicle and the plurality of performance checks. The vehicle may be controlled along the test route in an autonomous driving mode, while sensor data may be received from the plurality of detection systems of the vehicle. An operation mode may be selected based on results of the plurality of performance checks, and the vehicle may be operated in the selected operation mode.