Abstract: Aspects of the disclosed technology provide solutions for enabling vehicle-to-vehicle communications and in particular, for providing optical communication capabilities using Light Detection and Ranging (LiDAR) sensors. In some aspects, the disclosed technology encompasses solutions for utilizing LiDAR sensors to engage in Vehicle-to-Everything (V2X) communications. A LiDAR sensor of the disclosed technology can include a processing unit, an image-sensor coupled to the processing unit, and a first array of photodetectors coupled to the processing unit, wherein the first array of photodetectors is disposed in an optical field of the optical lens and coupled to a top-edge of the image sensor, and wherein an integration rate for light signals received by the first array of photodetectors is faster than an integration rate for light signals received by the image-sensor.

Abstract: Systems and techniques are provided for determining range and range-rate in time-of-flight sensors. An example method includes determining a first depth map that is based on a first image frame and a second image frame, wherein the first image frame and the second image frame correspond to a first set of image frames received from a time-of-flight sensor; calculating, based on the first depth map, a first set of three-dimensional optical flow data between the first image frame and the second image frame; performing three-dimensional warping of at least one image frame from the first set of image frames using the first set of three-dimensional optical flow data to yield a first realigned image frame; and determining a second depth map that is based on the first realigned image frame.

Abstract: Systems and methods of simulating an effect of fog on a Light Detection and Ranging (LiDAR) sensor are disclosed. The method includes the steps of determining whether a target is present within the field-of-view (FOV) of the LiDAR sensor, determining a fog probability density function (PDFfog) vs range, modifying, if a target is present within the FOV, the PDFfog to account for the target, calculating a cumulative density function (CDF) for the PDFfog, randomly sampling the CDF to determine a plurality of ranges and additively plotting a predetermined Gaussian distribution centered on each range, and identifying a peak value of the additive plot and reporting the range associated with the peak value as the strongest return of the LiDAR unit.

Abstract: The systems and methods disclosed herein address simulating the effect of fog on a photon. One method defines a target at a position in a 3D environment and includes the steps of selecting a starting position of the photon in the 3D environment, selecting a propagation vector directed from the starting position toward the target, selecting a propagation distance, determining a new position of the photon based in part on the starting position of the photon and the propagation vector and the propagation distance, determining whether the photon is absorbed before reaching the new position and determining, if the photon has not been absorbed, whether the photon intersects the target before reaching the new position.

Abstract: A method is provided for offloading autonomous vehicle (AV) data to a download pole. The AV may include a transceiver. The download pole may include a transceiver. The method may include identifying, by the AV, the download pole in a parking spot that is close to the AV. The method may also include establishing short range wireless link between the AV and the download pole. The method may also include positioning the AV so that the transceiver of the AV is aligned with the transceiver of the download pole based on instructions received over the Bluetooth connection from the download pole. The method may further include establishing a first link between the transceiver of the AV and the transceiver of the download pole.

Abstract: Systems and techniques are provided for determining range and range-rate in time-of-flight sensors. An example method includes determining a first depth map that is based on a first image frame and a second image frame, wherein the first image frame and the second image frame correspond to a first set of image frames received from a time-of-flight sensor; calculating, based on the first depth map, a first set of three-dimensional optical flow data between the first image frame and the second image frame; performing three-dimensional warping of at least one image frame from the first set of image frames using the first set of three-dimensional optical flow data to yield a first realigned image frame; and determining a second depth map that is based on the first realigned image frame.

Abstract: Various technologies described herein pertain to mitigating motion misalignment of a time-of-flight sensor system and/or generating transverse velocity estimate data utilizing the time-of-flight sensor system. A stream of frames outputted by a sensor of the time-of-flight sensor system is received. A pair of non-adjacent frames in the stream of frames is identified. Computed optical flow data is calculated based on the pair of non-adjacent frames in the stream of frames. Estimated optical flow data for at least one differing frame can be generated based on the computed optical flow data, and the at least one differing frame can be realigned based on the estimated optical flow data. Moreover, transverse velocity estimate data for an object can be generated based on the computed optical flow data.

Abstract: Various technologies described herein pertain to mitigating motion misalignment of a time-of-flight sensor system and/or generating transverse velocity estimate data utilizing the time-of-flight sensor system. A stream of frames outputted by a sensor of the time-of-flight sensor system is received. A pair of non-adjacent frames in the stream of frames is identified. Computed optical flow data is calculated based on the pair of non-adjacent frames in the stream of frames. Estimated optical flow data for at least one differing frame can be generated based on the computed optical flow data, and the at least one differing frame can be realigned based on the estimated optical flow data. Moreover, transverse velocity estimate data for an object can be generated based on the computed optical flow data.

Abstract: Systems and techniques of the present disclosure may access data from a time-of-flight (TOF) sensor of an autonomous vehicle (AV). The TOF sensor may have light signals and received reflections of those transmitted signals such that a set of simulation data can be generated. This set of simulation data may identify a distance to associate with an object that is different from a calibration distance. Equations may be used to identify a light signal amplitude, a signal to noise ratio (SNR), and a range inaccuracy due to noise from the accessed data. The identified the light signal amplitude, the SNR, and the range inaccuracy due to noise may have been identified using equations. Once the set of simulation data is generated, it may be saved for later access by a processor executing a simulation program used to train devices used to control the driving of an AV.

Abstract: Various technologies described herein pertain to mitigating motion misalignment of a time-of-flight sensor system and/or generating transverse velocity estimate data utilizing the time-of-flight sensor system. A stream of frames outputted by a sensor of the time-of-flight sensor system is received. A pair of non-adjacent frames in the stream of frames is identified. Computed optical flow data is calculated based on the pair of non-adjacent frames in the stream of frames. Estimated optical flow data for at least one differing frame can be generated based on the computed optical flow data, and the at least one differing frame can be realigned based on the estimated optical flow data. Moreover, transverse velocity estimate data for an object can be generated based on the computed optical flow data.

Abstract: Systems and techniques of the present disclosure may access data from a time-of-flight (TOF) sensor of an autonomous vehicle (AV). The TOF sensor may have light signals and received reflections of those transmitted signals such that a set of simulation data can be generated. This set of simulation data may identify a distance to associate with an object that is different from a calibration distance. Equations may be used to identify a light signal amplitude, a signal to noise ratio (SNR), and a range inaccuracy due to noise from the accessed data. The identified the light signal amplitude, the SNR, and the range inaccuracy due to noise may have been identified using equations. Once the set of simulation data is generated, it may be saved for later access by a processor executing a simulation program used to train devices used to control the driving of an AV.

Abstract: Various technologies described herein pertain to mitigating motion misalignment of a time-of-flight sensor system and/or generating transverse velocity estimate data utilizing the time-of-flight sensor system. A stream of frames outputted by a sensor of the time-of-flight sensor system is received. A pair of non-adjacent frames in the stream of frames is identified. Computed optical flow data is calculated based on the pair of non-adjacent frames in the stream of frames. Estimated optical flow data for at least one differing frame can be generated based on the computed optical flow data, and the at least one differing frame can be realigned based on the estimated optical flow data. Moreover, transverse velocity estimate data for an object can be generated based on the computed optical flow data.

Abstract: Aspects of disclosed technology provide solutions for reducing interference between optical sensors and in particular, for eliminating crosstalk interference between proximally positioned Light Detection and Ranging (LiDAR) sensors (e.g., flash LiDAR sensors or full-field LiDAR sensors). A process of the disclosed technology can include steps for determining a center modulation frequency for a first Light Detection and Ranging (LiDAR) sensor, scheduling a first capture sequence for the first LiDAR sensor to occur at a first time, determining a center modulation frequency for a second LiDAR sensor, and scheduling a second capture sequence for the second LiDAR sensor to occur at a second time. Systems and machine-readable media are also provided.

Abstract: Aspects of the disclosed technology provide solutions for enabling vehicle-to-vehicle communications and in particular, for providing optical communication capabilities using Light Detection and Ranging (LiDAR) sensors. In some aspects, the disclosed technology encompasses solutions for utilizing LiDAR sensors to engage in Vehicle-to-Everything (V2X) communications. A LiDAR sensor of the disclosed technology can include a processing unit, an image-sensor coupled to the processing unit, and a first array of photodetectors coupled to the processing unit, wherein the first array of photodetectors is disposed in an optical field of the optical lens and coupled to a top-edge of the image sensor, and wherein an integration rate for light signals received by the first array of photodetectors is faster than an integration rate for light signals received by the image-sensor.

Abstract: A method is provided for offloading autonomous vehicle (AV) data to a download pole. The AV may include a transceiver. The download pole may include a transceiver. The method may include identifying, by the AV, the download pole in a parking spot that is close to the AV. The method may also include establishing short range wireless link between the AV and the download pole. The method may also include positioning the AV so that the transceiver of the AV is aligned with the transceiver of the download pole based on instructions received over the Bluetooth connection from the download pole. The method may further include establishing a first link between the transceiver of the AV and the transceiver of the download pole.