Abstract: Techniques are described for aligning optical components within a LIDAR assembly. The techniques may be performed to align the optical components during manufacturing or assembly of the LIDAR assembly. For example, a first optical element (e.g., one of a light source or light sensor) may be installed in the LIDAR assembly. An optimal alignment for a second optical element (e.g., the other of the light source or light sensor) may be determined and the second optical element may be installed at the optimal alignment. The optimal alignment for the second optical element may be determined based on detected signals, for example, which may correspond to an alignment resulting in a strongest return signal, highest quality return signal, and/or minimal interference. Additionally or alternatively, techniques may be used to align optical components at runtime by using an actuator to move one or more components of the LIDAR assembly during operation.

Abstract: A LIDAR system emits laser pulses, wherein each pulse is associated with a power level. A laser emitter is adjusted during operation of a LIDAR system using power profile data associated with the laser. The power profile data is obtained during a calibration procedure and includes information that associates charge duration for a laser power supply with the actual power output by laser. The power profiles can be used during operation of the LIDAR system. A laser pulse can be emitted, the reflected light from the pulse received and analyzed, and the power of the next pulse can be adjusted based on a lookup within the power profile for the laser. For instance, if the power returned from a pulse is too high (e.g., above some specified threshold), the power of the next pulse is reduced to a specific value based on the power profile.

Abstract: A system for detecting and/or mitigating the effects of an obstruction on a surface of a sensor may include a surface configured to receive the sensor, and comprising a sensor window configured to provide a path through which the sensor senses the environment. The system may also include a vibratory actuator configured to facilitate vibration of the sensor window and a heating element configured to heat the sensor window. The system may further include an obstruction detection system configured to detect an obstruction, such as moisture, and an obstruction mitigation controller configured to initiate activation of the vibratory actuator and/or the heating element to mitigate the obstruction. The obstruction detection system may be configured to receive a signal from one or more sensors and detect the obstruction based at least in part on the signal.

Abstract: A sensor pod system includes one or more sensor pods with a plurality of sensors configured to collect data from an environment. A sensor pod may include a housing and extend from a portion of a body of a vehicle. The sensor pod housing may have energy absorbing structures configured to absorb and dissipate energy during an impact in order to protect a pedestrian. The sensor pod may have deformable portions of the housing configured to absorb and dissipate energy during the impact. The sensor pod may have deformable fasteners coupling a sensor to the sensor pod configured to deform to absorb and dissipate energy during the impact.

Abstract: A sensor pod system includes one or more sensor pods with a plurality of sensors configured to collect data from an environment. A sensor pod may have an effective field of view created by individual sensors with overlapping fields of view. The sensor pod system may include sensors of different types and modalities. Sensor pods of the sensor pod system may be modularly disposed on a vehicle, for example, an autonomous vehicle to collect and provide data of the environment during operation of the vehicle. The sensor pods may be disposed at elevated locations around the vehicle to reduce obstacles within the sensor pods fields of view.

Abstract: A sensor pod system includes one or more sensor pods with a plurality of sensors configured to collect data from an environment. The sensor pod system may include a cleaning system to clean sensing surfaces of sensor pods during operation. The sensor pod system may include sensors of different types and modalities. Sensor pods of the sensor pod system may be modularly installed on a vehicle, for example, an autonomous vehicle and collect and provide data of the environment during operation of the vehicle.

Abstract: A sensor pod system includes one or more sensor pods with a plurality of sensors configured to collect data from an environment. A sensor pod may have an effective field of view created by individual sensors with overlapping fields of view. The sensor pod system may include sensors of different types and modalities. Sensor pods of the sensor pod system may be modularly installed on a vehicle, for example, an autonomous vehicle and collect and provide data of the environment during operation of the vehicle. A combination of techniques may be used to calibrate a sensor pod prior to installation in the vehicle and may be used to determine if the sensor pod is compatible with other sensor pods and may also be used to calibrate other sensors when integrating the sensor pod into the vehicle.

Abstract: A sensor pod system includes one or more sensor pods with a plurality of sensors configured to collect data from an environment. A sensor pod may have an effective field of view created by individual sensors with overlapping fields of view. The sensor pod system may include sensors of different types and modalities. Sensor pods of the sensor pod system may be modularly installed on a vehicle, for example, an autonomous vehicle and collect and provide data of the environment during operation of the vehicle.

Abstract: This disclosure describes methods, apparatuses, and systems for network training and testing for evaluating hardware characteristics and for hardware selection. For example, a sensor can capture a dataset, which may be transformed into a plurality of modified datasets to simulate changes to hardware. Each of the plurality of modified datasets may be used to individually train an untrained neural network, thereby producing a plurality of trained neural networks. In order to evaluate the trained neural networks, each neural network can be used to ingest an evaluation dataset to perform a variety of tasks, such as identifying various objects within the dataset. A performance of each neural network can be determined and compared. A performance curve can be determined for each characteristic under review, facilitating a selection of one or more hardware components and/or configurations.

Abstract: A LIDAR system emits laser bursts, wherein each burst has at least a pair of pulses. The pulses of each pair are spaced by a time interval having a variable duration to reduce effects of cross-talk. For example, certain embodiments may have multiple emitter/sensor channels that are used sequentially, and each channel may use a different duration for inter-pulse spacing to reduce the effects of cross-talk between channels. The durations may also be varied over time. The emitters and sensors are physically arranged in a two-dimensional array to achieve a relatively fine vertical pitch. The array has staggered rows that are packed using a hexagonal packing arrangement. The channels are used in a sequential order that is selected to maximize spacing between consecutively used channels, further reducing possibilities for inter-channel cross-talk.

Abstract: Techniques for determining a degraded state associated with a sensor are discussed herein. For example, a sensor associated with vehicle may captured data of an environment. A portion of the data may represent a portion of the vehicle. Data associated with a region of interest can be determined based on a calibration associated with the sensor. For example, in the context of image data, image coordinates may be used to determine a region of interest, while in the context of lidar data, a beam and/or azimuth can be used to determine a region of interest. A data metric can be determined for data in the region of interest, and an action can be determined based on the data metric. For example, the action can include cleaning a sensor, scheduling maintenance, reducing a confidence associated with the data, or slowing or stopping the vehicle.

Abstract: Sensors, including time-of-flight sensors, may be used to detect objects in an environment. In an example, a vehicle may include a time-of-flight sensor that images objects around the vehicle, e.g., so the vehicle can navigate relative to the objects. Sensor data generated by the time-of-flight sensor can be impacted by glare. In some examples, corrected data is generated by quantifying glare. A glare region including pixels that are not associated with an object in a range of the time-of-flight sensor may provide glare intensity and glare depth values used to quantify the glare. The glare intensity and glare depth may be used to correct measured data.

Abstract: A LIDAR system emits laser bursts, wherein each burst has at least a pair of pulses. The pulses of each pair are spaced by a time interval having a variable duration to reduce effects of cross-talk. For example, certain embodiments may have multiple emitter/sensor channels that are used sequentially, and each channel may use a different duration for inter-pulse spacing to reduce the effects of cross-talk between channels. The durations may also be varied over time. The emitters and sensors are physically arranged in a two-dimensional array to achieve a relatively fine vertical pitch. The array has staggered rows that are packed using a hexagonal packing arrangement. The channels are used in a sequential order that is selected to maximize spacing between consecutively used channels, further reducing possibilities for inter-channel cross-talk.

Abstract: Sensors, including time-of-flight sensors, may be used to detect objects in an environment. In an example, a vehicle may include a time-of-flight sensor that images objects around the vehicle, e.g., so the vehicle can navigate relative to the objects. The sensor may generate first image data at a first configuration and second image data at a second configuration. The first image data and the second image data may be combined to provide disambiguated depth and improved intensity values for imaging the environment. In some examples, the first and second configurations may have different modulation frequencies, different integration times, and/or different illumination intensities. In some examples, configurations may be dynamically altered based on depth and/or intensity information of a previous frame.

Abstract: A LIDAR system emits laser pulses, wherein each pulse is associated with a power level. A laser emitter is adjusted during operation of a LIDAR system using power profile data associated with the laser. The power profile data is obtained during a calibration procedure and includes information that associates charge duration for a laser power supply with the actual power output by laser. The power profiles can be used during operation of the LIDAR system. A laser pulse can be emitted, the reflected light from the pulse received and analyzed, and the power of the next pulse can be adjusted based on a lookup within the power profile for the laser. For instance, if the power returned from a pulse is too high (e.g., above some specified threshold), the power of the next pulse is reduced to a specific value based on the power profile.

Abstract: Systems and processes for controlling a autonomous vehicle when the autonomous vehicle detects a disaster may include receiving a sensor signal from a sensor of a autonomous vehicle and determining that the sensor signal corresponds to a disaster definition accessible to the autonomous vehicle. The systems and processes may further include receiving a corroboration of the detected disaster and altering a drive mode of the autonomous vehicle or receiving an indication that the detected disaster was a false positive and returning to a nominal drive mode of the autonomous vehicle.

Abstract: Particulate matter, such as dust, steam, smoke, rain, etc. may cause one or more sensor types to generate false positive detections. In particular, various depth measurements may be impeded by particulate matter. Identifying a false return and/or removing a false detection based at least in part on a sensor output may comprise determining a similarity of a portion of a return signal to an emitted light pulse or an expected return signal, determining a variance of the signal portion over time, determining a difference between a power spectrum of the return relative to an expected power spectrum, and/or determining that a duration associated with the signal portion meets or exceeds a threshold duration.

Abstract: A machine-learned (ML) model for detecting that depth data (e.g., lidar data, radar data) comprises a false positive attributable to particulate matter, such as dust, steam, smoke, rain, etc. The ML model may be trained based at least in part on simulated depth data generated by a fluid dynamics model and/or by collecting depth data during operation of a device (e.g., an autonomous vehicle. In some examples, an autonomous vehicle may identify depth data that may be associated with particulate matter based at least in part on an outlier region in a thermal image. For example, the outlier region may be associated with steam.

Abstract: A LIDAR system that identifies, from a channel output, a false positive return and/or suppressing a corresponding false positive detection caused, in some cases, a strong reflection by a highly reflective surface that caused light to leak from a first channel to a second channel. The LIDAR system described herein may identify, as a false return, a return detected in the second channel that has an intensity that is much less than a return in the first channel and indicates a distance that is the same or very close to a distance indicated the return in the first channel. Based at least in part on identifying a return as a false return, the LIDAR system may suppress a false detection associated with the false return by modifying a detection threshold.

Abstract: A LIDAR system emits laser pulses, wherein each pulse is associated with a power level. A laser emitter is adjusted during operation of a LIDAR system using power profile data associated with the laser. The power profile data is obtained during a calibration procedure and includes information that associates charge duration for a laser power supply with the actual power output by laser. The power profiles can be used during operation of the LIDAR system. A laser pulse can be emitted, the reflected light from the pulse received and analyzed, and the power of the next pulse can be adjusted based on a lookup within the power profile for the laser. For instance, if the power returned from a pulse is too high (e.g., above some specified threshold), the power of the next pulse is reduced to a specific value based on the power profile.