LIDAR Phase Noise Cancellation System

Abstract

The present invention relates to a LIDAR phase noise cancellation system. A light detection and ranging (LIDAR) system includes a laser and a calibration unit. The laser is configured to generate a laser beam based on a specific laser waveform associated with at least one of a plurality of parameters. The calibration unit is configured to determine a specific value of at least one of the plurality of parameters to compensate for the distortion characteristics of the laser. The calibration unit is configured to determine the specific value based on the output frequency of the laser beam. The calibration unit is configured to update the specific laser waveform using the specific value of at least one of the plurality of parameters.

Description

[0001] This application is a divisional application of the application with PCT application number PCT / US2021 / 048923, international filing date of September 2, 2021, Chinese application number 202180067923.7, and invention title "LIDAR Phase Noise Cancellation System", which entered the Chinese national phase on April 3, 2023.

[0002] Cross - reference to related applications

[0003] This application claims the priority of U.S. non - provisional application No. 17 / 463,263, filed on August 31, 2021, and claims the priority of U.S. provisional application No. 63 / 074,832, filed on September 4, 2020. Application Nos. 17 / 463,263 and 63 / 074,832 are hereby incorporated by reference. Technical field

[0004] The present disclosure generally relates to light detection and ranging (LIDAR). Background art

[0005] Frequency - modulated continuous - wave (FMCW) LIDAR directly measures the distance and velocity of an object by aiming a frequency - modulated, collimated light beam at the target. Both the distance and velocity information of the target can be derived from the FMCW LIDAR signal. Designs and techniques for improving the accuracy of LIDAR signals are desired.

[0006] The automotive industry is currently developing autonomous features for controlling vehicles in certain situations. According to the SAE International Standard J3016, there are six levels of autonomy, ranging from level 0 (no autonomy) to level 5 (the vehicle can operate under all conditions without operator input). Vehicles with autonomous features use sensors to sense the environment through which the vehicle is navigating. Obtaining and processing data from the sensors allows the vehicle to navigate through its environment. An autonomous driving vehicle may include one or more FMCW LIDAR devices to sense its environment. Summary of the invention

[0007] Embodiments of the present disclosure include a light detection and ranging (LIDAR) system that includes a LIDAR measurement unit, a reference measurement unit, and a phase cancellation unit. The LIDAR measurement unit is configured to estimate the time it takes for a laser beam to travel between a laser source and a target. The reference measurement unit is configured to determine the phase of the laser source. The phase cancellation unit is configured to cancel phase noise from a signal representing the laser beam based at least in part on the phase of the laser source and the time the laser beam travels.

[0008] In an embodiment, the LIDAR system further includes a free-space interferometer and a fixed-length interferometer. The signal representing the laser beam is the first beat signal received from the free-space interferometer. The phase of the laser source is calculated based on the second beat signal received from the fixed-length interferometer. The laser source feeds the free-space interferometer and the fixed-length interferometer simultaneously.

[0009] In an embodiment, the free-space interferometer combines the first local oscillator signal with the target reflection signal to generate the first beat signal, and the fixed-length interferometer combines the second local oscillator signal with the fixed-length signal delayed by the fixed-length optical delay line to generate the second beat signal.

[0010] In an embodiment, the phase cancellation unit generates a delayed phase of the laser source using a delay operation configured to delay the phase of the laser source by the time the laser beam travels as estimated by the LIDAR measurement unit.

[0011] In an embodiment, the phase cancellation unit subtracts the delayed phase of the laser source from the phase of the laser source to generate a variable phase of the laser source. The variable phase of the laser source represents the phase noise within the signal representing the laser beam.

[0012] In an embodiment, the phase cancellation unit multiplies the complex conjugate of the variable phase by the signal representing the laser beam to cancel the phase noise.

[0013] In an embodiment, the LIDAR system further includes a distance calculation unit configured to calculate the distance between the laser source and the target based on the denoised signal, which is the signal representing the laser beam after canceling the phase noise.

[0014] In an embodiment, the distance calculation unit determines the frequency of the denoised signal, and the frequency of the denoised signal is determined based on the peak amplitude represented by the frequency of the denoised signal.

[0015] In an embodiment, the LIDAR system is a frequency-modulated continuous-wave (FMCW) LIDAR system.

[0016] In an embodiment, the reference measurement unit determines the phase of the laser source at least partially based on the in-phase signal and the quadrature signal from the fixed-length interferometer.

[0017] In an embodiment, to determine the phase of the laser source, the reference measurement unit is configured to apply an arctangent operation to the quadrature signal divided by the in-phase signal, and is configured to apply an integration operation to the output of the arctangent operation.

[0018] In an embodiment, to estimate the time of travel of the laser beam, the LIDAR measurement unit is configured to determine the frequency of the beat signal from the free-space interferometer, and the frequency of the beat signal is determined based on at least one peak amplitude represented by the frequency of the beat signal.

[0019] Embodiments of the present disclosure include an autonomous vehicle control system that includes an optical detection and ranging (LIDAR) system. The LIDAR system includes a LIDAR measurement unit, a reference measurement unit, and a phase cancellation unit. The LIDAR measurement unit is configured to estimate the time of travel of the laser beam between the laser source and the target. The reference measurement unit is configured to determine the phase of the laser source. The phase cancellation unit is configured to cancel phase noise from the signal representing the laser beam based at least in part on the phase of the laser source and the time of travel of the laser beam. The control system of the autonomous vehicle includes one or more processors to control the autonomous vehicle control system in response to the signal output by the phase cancellation unit.

[0020] In an embodiment, the LIDAR system further includes a free-space interferometer and a fixed-length interferometer. The signal representing the laser beam is the first beat signal received from the free-space interferometer. The phase of the laser source is calculated based on the second beat signal received from the fixed-length interferometer. The laser source feeds the free-space interferometer and the fixed-length interferometer simultaneously.

[0021] In an embodiment, the phase cancellation unit is configured to generate a delayed phase of the laser source using a delay operation that is configured to delay the phase of the laser source by the time of travel of the laser beam estimated by the LIDAR measurement unit.

[0022] In an embodiment, the phase cancellation unit subtracts the delayed phase of the laser source from the phase of the laser source to generate a variable phase of the laser source. The variable phase of the laser source represents the phase noise within the signal representing the laser beam. The phase cancellation unit is configured to multiply the complex conjugate of the variable phase by the signal representing the laser beam to cancel the phase noise.

[0023] Embodiments of the present disclosure include an autonomous vehicle system for an autonomous vehicle that includes an optical detection and ranging (LIDAR) system. The LIDAR system includes a LIDAR measurement unit, a reference measurement unit, and a phase cancellation unit. The LIDAR measurement unit is configured to estimate the time of travel of the laser beam between the laser source and the target. The reference measurement unit is configured to determine the phase of the laser source. The phase cancellation unit is configured to cancel phase noise from the signal representing the laser beam based at least in part on the phase of the laser source and the time of travel of the laser beam. The autonomous vehicle includes one or more processors to control the autonomous vehicle in response to the signal output by the phase cancellation unit.

[0024] In an embodiment, the LIDAR system further includes a free-space interferometer and a fixed-length interferometer. The signal representing the laser beam is the first beat frequency signal received from the free-space interferometer. The phase of the laser source is calculated based on the second beat frequency signal received from the fixed-length interferometer. The laser source feeds the free-space interferometer and the fixed-length interferometer simultaneously.

[0025] In an embodiment, the phase cancellation unit is configured to generate a delayed phase of the laser source using a delay operation configured to delay the phase of the laser source by the time the laser beam travels as estimated by the LIDAR measurement unit.

[0026] In an embodiment, the phase cancellation unit is configured to subtract the delayed phase of the laser source from the phase of the laser source to generate a variable phase of the laser source. The variable phase of the laser source represents the phase noise within the signal representing the laser beam. The phase cancellation unit is configured to multiply the complex conjugate of the variable phase by the signal representing the laser beam to cancel the phase noise.

[0027] Embodiments of the present disclosure include a light detection and ranging (LIDAR) system that includes a laser waveform function, a set of parameters, and a calibration unit. The laser waveform function defines a laser waveform. The set of parameters at least partially defines the laser waveform. The calibration unit is configured to estimate the partial derivative of the frequency response with respect to each parameter in the set of parameters. The frequency response is measured based on the laser output driven by the laser waveform. The calibration unit is configured to update the set of parameters so that the frequency response of the laser satisfies the conditions defined by the laser waveform function.

[0028] In an embodiment, the LIDAR system further includes a fixed-length interferometer. The calibration unit is configured to receive an in-phase signal and a quadrature signal from the fixed-length interferometer. The calibration unit is configured to determine the frequency response of the laser based on the in-phase signal and the quadrature signal.

[0029] In an embodiment, the calibration unit is configured to iteratively construct the laser waveform based on the laser waveform function and the set of parameters. The set of parameters includes an initial version of the set of parameters that is replaced by one or more updated versions of the set of parameters.

[0030] In an embodiment, the calibration unit is configured to iteratively evaluate the frequency response of the laser using the updated version of the set of parameters.

[0031] In an embodiment, to iteratively evaluate the frequency response of the laser, the calibration unit is configured to load the laser waveform into a digital-to-analog converter, wait for the laser to stabilize, measure the output of the interferometer, and calculate the frequency response based on the output of the interferometer.

[0032] In an embodiment, the calibration unit is configured to estimate the gradient of the laser waveform function.

[0033] In an embodiment, to estimate the gradient of the laser waveform function, the calibration unit is configured to compute a perturbed version of the laser waveform, load the perturbed version of the laser waveform into a digital-to-analog converter, measure the output of the laser, and evaluate the perturbed version of the laser waveform function.

[0034] In an embodiment, the perturbed version of the laser waveform includes the difference between a first parameter in a parameter set and a second parameter in the parameter set.

[0035] In an embodiment, the calibration unit is configured to update the parameter set based on the partial derivative of the frequency response with respect to each parameter in the parameter set.

[0036] In an embodiment, the calibration unit is configured to update the parameter set to compensate for the distortion characteristics of the laser.

[0037] Embodiments of the present disclosure include an autonomous vehicle control system that includes a light detection and ranging (LIDAR) system. The LIDAR system includes a laser waveform function that defines a laser waveform, a parameter set that at least partially defines the laser waveform, and a calibration unit configured to estimate the partial derivative of the frequency response with respect to each parameter in the parameter set. The frequency response is measured based on the laser output driven by the laser waveform. The calibration unit is configured to update the parameter set so that the frequency response of the laser satisfies the conditions defined by the laser waveform function. The autonomous vehicle control system includes one or more processors to control the autonomous vehicle control system in response to the laser waveform at least partially defined by the calibration unit.

[0038] In an embodiment, the autonomous vehicle control system further includes a fixed-length interferometer. The calibration unit is configured to receive an in-phase signal and a quadrature signal from the fixed-length interferometer. The calibration unit is configured to determine the frequency response of the laser based on the in-phase signal and the quadrature signal.

[0039] In an embodiment, the calibration unit is configured to iteratively construct the laser waveform based on the laser waveform function and the parameter set. The parameter set includes an initial version of the parameter set that is replaced by one or more updated versions of the parameter set.

[0040] In an embodiment, the calibration unit is configured to iteratively evaluate the frequency response of the laser using the updated version of the parameter set.

[0041] In an embodiment, to iteratively evaluate the frequency response of the laser, the calibration unit is configured to load the laser waveform into a digital-to-analog converter, wait for the laser to stabilize, measure the output of the interferometer, and compute the frequency response based on the output of the interferometer.

[0042] In an embodiment, the calibration unit is configured to estimate the gradient of the laser waveform function.

[0043] In an embodiment, to estimate the gradient of the laser waveform function, the calibration unit is configured to compute a perturbed version of the laser waveform, load the perturbed version of the laser waveform into a digital-to-analog converter, measure the output of the laser, and evaluate the perturbed version of the laser waveform function.

[0044] In an embodiment, an autonomous vehicle includes a Light Detection And Ranging (LIDAR) system. The LIDAR system includes a laser waveform function that defines a laser waveform, a set of parameters that at least partially defines the laser waveform, and a calibration unit that is configured to estimate the partial derivative of the frequency response with respect to each parameter in the set of parameters. The frequency response is measured based on the laser output driven by the laser waveform. The calibration unit is configured to update the set of parameters to cause the frequency response of the laser to satisfy the conditions defined by the laser waveform function. The autonomous vehicle includes one or more processors to control the autonomous vehicle in response to the laser waveform at least partially defined by the calibration unit.

[0045] In an embodiment, the calibration unit is configured to iteratively evaluate the frequency response of the laser using an updated version of the set of parameters.

[0046] In an embodiment, the calibration unit is configured to estimate the gradient of the laser waveform function.

[0047] Embodiments of the present disclosure include a Light Detection And Ranging (LIDAR) system that includes a reference measurement unit and a LIDAR measurement unit. The reference measurement unit is configured to determine the phase of a reference beat frequency signal from a fixed-length interferometer driven by a laser source. The LIDAR measurement unit is configured to range multiple targets at least in part based on pairing a first spectral peak from an upward frequency chirp from the laser source with a second spectral peak from a downward frequency chirp from the laser source. The LIDAR measurement unit is configured to range multiple targets at least in part based on the phase of the reference beat frequency signal.

[0048] In an embodiment, the LIDAR measurement unit is configured to estimate the travel time of a free-space laser signal to multiple targets using sequential paired peaks between the first spectral peak and the second spectral peak. The first peak pair includes the first of the first spectral peaks and the first of the second spectral peaks.

[0049] In an embodiment, the LIDAR measurement unit is configured to iteratively range multiple targets. Each of the multiple targets is associated with a travel time estimate determined based on a peak pair according to one of the first spectral peaks and one of the second spectral peaks.

[0050] In an embodiment, the LIDAR measurement unit is configured to delay the phase of the reference beat frequency signal by a duration equal to the travel time estimate to identify phase noise of the laser source.

[0051] In an embodiment, the LIDAR measurement unit is configured to multiply the complex conjugate of the phase noise by the free-space beat signal to eliminate the phase noise in the free-space beat signal to generate a denoised free-space beat signal.

[0052] In an embodiment, the LIDAR measurement unit is configured to eliminate the phase noise that appears during the upward frequency chirp. The reference measurement unit is configured to eliminate the phase noise that appears during the downward frequency chirp.

[0053] In an embodiment, the first spectral peak is generated from the first beat signal from the free-space interferometer. The second spectral peak is generated from the second beat signal from the free-space interferometer.

[0054] In an embodiment, the free-space interferometer combines the first local oscillator signal with the first target reflection signal to generate the first beat signal from the upward frequency chirp. The free-space interferometer combines the second local oscillator signal with the second target reflection signal to generate the second beat signal from the downward frequency chirp.

[0055] In an embodiment, the LIDAR system is a frequency-modulated continuous-wave (FMCW) LIDAR system.

[0056] In an embodiment, the reference measurement unit determines the phase of the reference beat signal at least in part based on the in-phase signal and the quadrature signal from the fixed-length interferometer.

[0057] In an embodiment, to determine the phase of the reference beat signal, the reference measurement unit is configured to apply an arctangent operation to the quadrature signal divided by the in-phase signal and is configured to apply an integration operation to the output of the arctangent operation.

[0058] In an embodiment, the reference beat signal is the first reference beat signal. The phase of the first reference beat signal is generated by the upward frequency chirp. The reference measurement unit is configured to estimate the phase of the second reference beat signal generated from the downward frequency chirp.

[0059] Embodiments of the present disclosure include an autonomous vehicle control system that includes a Light Detection and Ranging (LIDAR) system. The LIDAR system includes a LIDAR measurement unit and a reference measurement unit. The reference measurement unit is configured to determine the phase of a reference beat signal from a fixed-length interferometer driven by a laser source. The LIDAR measurement unit is configured to range multiple targets at least in part based on pairing a first spectral peak of an upward frequency chirp from the laser source with a second spectral peak of a downward frequency chirp from the laser source. The LIDAR measurement unit is configured to range multiple targets at least in part based on the phase of the reference beat signal. The autonomous vehicle control system includes one or more processors to control the autonomous vehicle control system in response to signals output by at least one of the LIDAR measurement unit and the reference measurement unit.

[0060] In an embodiment, the LIDAR measurement unit is configured to estimate the travel time of a free-space laser signal to multiple targets using sequential paired peaks between the first spectral peak and the second spectral peak. The first peak pair includes the first of the first spectral peaks and the first of the second spectral peaks.

[0061] In an embodiment, the LIDAR measurement unit is configured to iteratively range multiple targets. Each of the multiple targets is associated with a travel time estimate determined from a peak pair according to one of the first spectral peaks and one of the second spectral peaks.

[0062] In an embodiment, the LIDAR measurement unit is configured to delay the phase of the reference beat signal by a duration equal to the travel time estimate to identify the phase noise of the laser source. The LIDAR measurement unit is configured to multiply the complex conjugate of the phase noise with the free-space beat signal to cancel the phase noise in the free-space beat signal to generate a denoised free-space beat signal.

[0063] Embodiments of the present disclosure include an autonomous vehicle having a Light Detection and Ranging (LIDAR) system. The LIDAR system includes a LIDAR measurement unit and a reference measurement unit. The reference measurement unit is configured to determine the phase of a reference beat signal from a fixed-length interferometer driven by a laser source. The LIDAR measurement unit is configured to range multiple targets at least in part based on pairing a first spectral peak of an upward frequency chirp from the laser source with a second spectral peak of a downward frequency chirp from the laser source. The LIDAR measurement unit is configured to range multiple targets at least in part based on the phase of the reference beat signal. The autonomous vehicle includes one or more processors to control the autonomous vehicle in response to signals output by at least one of the LIDAR measurement unit or the reference measurement unit.

[0064] In an embodiment, the LIDAR measurement unit is configured to estimate the travel time of a free-space laser signal to multiple targets using sequential paired peaks between a first spectral peak and a second spectral peak. The first peak pair includes the first of the first spectral peaks and the first of the second spectral peaks.

[0065] In an embodiment, the LIDAR measurement unit is configured to iteratively range multiple targets. Each of the multiple targets is associated with a travel time estimate determined based on a peak pair of one of the first spectral peaks and one of the second spectral peaks.

[0066] In an embodiment, the LIDAR measurement unit is configured to delay the phase of a reference beat signal by a duration equal to the travel time estimate to identify the phase noise of the laser source. The LIDAR measurement unit is configured to multiply the complex conjugate of the phase noise with the free-space beat signal to cancel the phase noise in the free-space beat signal to generate a denoised free-space beat signal. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] Non-limiting and non-exhaustive embodiments of the present invention are described with reference to the following drawings, in which like reference numerals refer to like parts throughout the various views unless otherwise specified.

[0068] Figure 1 Illustrated is an optical measurement device that supports phase estimation, active phase cancellation, pre-distorted waveform generation, and peak pairing in a LIDAR system according to an embodiment of the present disclosure.

[0069] Figure 2 Illustrated is a LIDAR system that can incorporate phase estimation, active phase cancellation, pre-distorted waveform generation, and peak pairing according to an embodiment of the present disclosure.

[0070] Figure 3A and Figure 3B Illustrated is an example phase noise cancellation system according to an embodiment of the present disclosure.

[0071] Figure 4A and 4B Illustrated is an example of a pre-distorted waveform generator according to an embodiment of the present disclosure.

[0072] Figure 5A and Figure 5B Illustrated is an example of a multi-target recognition system for an FMCW LIDAR system according to an embodiment of the present disclosure.

[0073] Figure 6A and Figure 6B Illustrated is an example of an operating cycle of a LIDAR system that incorporates phase estimation, active phase cancellation, pre-distorted waveform generation, and peak pairing according to various embodiments of the present disclosure.

[0074] Figure 7A An autonomous vehicle including an example sensor array according to an embodiment of the present disclosure is illustrated.

[0075] Figure 7B A top view of an autonomous vehicle including an example sensor array according to an embodiment of the present disclosure is illustrated.

[0076] Figure 7C An example vehicle control system including sensors, a powertrain, and a control system according to an embodiment of the present disclosure is illustrated. Detailed Description

[0077] Embodiments for phase estimation, active phase cancellation, pre-distorted waveform generation, and peak pairing for a light detection and ranging (LIDAR) system are described herein. In the following description, numerous specific details are set forth to provide a thorough understanding of the embodiments. However, those of ordinary skill in the relevant art will recognize that the techniques described herein can be practiced without one or more of the specific details, or with other methods, components, or materials. In other instances, well-known structures, materials, or operations are not shown or described in detail to avoid obscuring certain aspects.

[0078] References to "one embodiment" or "an embodiment" throughout this specification mean that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the present invention. Thus, the appearances of the phrases "in one embodiment" or "in an embodiment" throughout this specification are not necessarily all referring to the same embodiment. Additionally, the particular features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.

[0079] Throughout this specification, several technical terms are used. These terms have their ordinary meanings in the fields from which they are derived, unless specifically defined herein or the context of their use clearly implies otherwise. For the purposes of this disclosure, the term "autonomous vehicle" includes a vehicle having autonomous features at any autonomous level of the SAE International standard J3016.

[0080] In aspects of the present disclosure, visible light can be defined as having a wavelength range of approximately 380 nm - 700 nm. Invisible light can be defined as light having a wavelength outside the visible light range, such as ultraviolet and infrared light. Infrared light having a wavelength range of approximately 700 nm - 1 mm includes near-infrared light. In aspects of the present disclosure, near-infrared light can be defined as having a wavelength range of approximately 700 nm - 1.6 μm.

[0081] In various aspects of the present disclosure, the term "transparent" can be defined as having a light transmittance greater than 90%. In some embodiments, the term "transparent" can be defined as a material having a visible light transmittance greater than 90%.

[0082] A coherent LIDAR system directly measures the distance and velocity of an object by aligning a modulated, collimated beam of light at the object. The light reflected from the object is combined with a tapped version of the beam. Once the Doppler shift is corrected, the frequency of the resulting beat is proportional to the distance between the object and the LIDAR system, which can be measured using a second measurement. These two measurements, which can be performed simultaneously or separately, provide both distance and velocity information. In the present application, frequency-modulated continuous-wave (FMCW) LIDAR is described as an example of coherent LIDAR. However, the embodiments and examples described in the present application can be applied to any type of coherent LIDAR.

[0083] In some embodiments, FMCW LIDAR, which is a type of coherent LIDAR, can be used. Specifically, FMCW LIDAR modulates the frequency of the beam from a laser source. FMCW LIDAR can utilize integrated photonics to improve manufacturability and performance. The integrated photon system can use micron-scale waveguide devices to manipulate individual optical modes.

[0084] An integrated FMCW LIDAR system relies on one or more laser sources that provide optical power to the system. The optical fields generated by such lasers typically exhibit both deterministic and random phase fluctuations, which can cause the returned FMCW beat signal to broaden, thereby degrading system performance.

[0085] The FMCW LIDAR system emits light that can be reflected from more than one object in a scene before returning to the unit. These multiple echoes result in a beat signal with a spectrum having multiple peaks. To determine the distance and velocity of each of these echoes, two measurements can be performed on the beat signal. To correctly determine multiple distances and velocities, the peaks in the two spectra are correctly paired.

[0086] The FMCW LIDAR system uses a linear frequency chirp to achieve good performance. This linear frequency chirp can be achieved by driving the laser using a laser drive waveform. To compensate for the distortion characteristics of the laser, the laser drive waveform can be defined to compensate for the characteristics of the emitted laser. As the system ages, the linearity of the frequency chirp may degrade. Some embodiments of the present disclosure provide in-situ recalibration of the laser drive waveform to support system degradation or changes.

[0087] A system for directly measuring the frequency offset of a laser in an integrated FMCW LIDAR system is described. The measured frequency offset can be integrated to determine the phase optical signal generated by the laser.

[0088] The system includes a short (fixed-length) integrated interferometer that is connected in parallel or coupled to a main free-space interferometer that at least partially defines an FMCW LIDAR system. A single laser source feeds signals to both interferometers.

[0089] Using an initial estimate of the target distance from the main free-space interferometer, unwanted optical phase fluctuations (phase noise) in the beat frequency signal can be estimated and subtracted from the beat frequency signal, thereby improving the measurement capabilities of the main free-space interferometer.

[0090] When there are multiple echoes (multiple targets), the estimated beat frequency signal phase can additionally be used to correctly pair the spectral peaks.

[0091] The measured frequency offset can also be used for in-situ generation and calibration of a pre-distorted waveform to improve the linearity of the laser frequency chirp.

[0092] Systems and methods for identifying and eliminating optical phase fluctuations (“phase noise”), for identifying multiple targets, and for generating the pre-distorted waveforms described in this disclosure can (collectively or individually) be used to support autonomous operation of a vehicle. In combination Figures 1 - 7C These and other embodiments are described in more detail.

[0093] Figure 1 An optical phase measurement device 100 that supports phase estimation, active phase cancellation, pre-distorted waveform generation, and peak pairing in an FMCW LIDAR system in accordance with an embodiment of the present disclosure is illustrated. The optical phase measurement device 100 includes a laser source 101, a beam splitter 102, a free-space interferometer 103, and a fixed-length interferometer 104 that support phase estimation, phase noise cancellation, pre-distorted waveform generation, and peak pairing in the LIDAR system.

[0094] Light emitted by the laser source 101 enters a beam splitter 102 that divides the optical power of the laser source 101 into two separate optical channels. The splitting ratio achieved by the beam splitter 102 can be equal (50:50) or some other ratio (e.g., 80:20). In practice, most of the optical power is divided and routed to the free-space interferometer 103, while the remaining small portion of the optical power is routed to the fixed-length interferometer 104.

[0095] The fixed-length interferometer 104 is configured to measure or approximate the instantaneous laser frequency of the laser source 101. The fixed-length interferometer 104 may include a beam splitter 105, a fixed-length optical delay line 106, an optical mixer 107, a balanced photodiode pair 108, and a balanced photodiode pair 109. The fixed-length interferometer 104 may incorporate a short optical delay in the fixed-length optical delay line 106, for example, in the range of 10 cm - 30 cm. The fixed-length interferometer 104 may thus be referred to as a short fixed-length interferometer or a reference interferometer.

[0096] The light entering the fixed-length interferometer 104 passes through the beam splitter 105, which can have equal or unequal splitting ratios. The top output of the beam splitter 105 is connected to the fixed-length optical delay line 106. The fixed-length optical delay line 106 delays the optical signal by a short amount of time compared to the light leaving the bottom output of the beam splitter 105. These two optical paths (i.e., the top optical path and the bottom optical path) are connected to the optical mixer 107, which can be implemented as a 2x4 optical mixer. The optical mixer 107 mixes the bottom signal with the top signal that has been delayed by the fixed-length optical delay line 106. Since the laser source 101 can be driven to output a linearly time-varying frequency (i.e., chirp), the frequency of the top signal arriving at the optical mixer 107 is slightly different (e.g., faster or slower) from the frequency of the bottom signal arriving at the optical mixer 107. When signals of different frequencies are mixed or combined, they produce a beat tone or beat frequency signal whose beat frequency is equal to the difference between the two frequencies.

[0097] The beat frequency signal from the optical mixer 107 is measured using the balanced photodiode pair 108 and the balanced photodiode pair 109 and converted into an electrical signal. The balanced photodiode pair 108 produces an electrical signal corresponding to the in-phase signal I ref while the balanced photodiode pair 109 produces an electrical signal corresponding to the quadrature signal Q ref For an embodiment of the fixed-length optical delay line 106 that is sufficiently short (e.g., 20 cm), the measured phase of the in-phase signal I ref and the quadrature signal Q ref is proportional to the instantaneous frequency of the laser source 101. The instantaneous frequency of the laser source 101 can be integrated to calculate the instantaneous phase of the laser source 101. The instantaneous phase of the laser source 101 can be used to isolate the phase noise of the laser source 101, which can be defined by deterministic and random phase fluctuations.

[0098] The free-space interferometer 103 is configured to measure or estimate the distance between the laser source 101 and a target. The free-space interferometer 103 may include a beam splitter 110, a variable-distance optical delay line 111, an optical mixer 112, a balanced photodiode pair 113, and a balanced photodiode pair 114.

[0099] Light entering the free-space interferometer 103 enters the beam splitter 110. The beam splitter 110 separates the "local oscillator" field (i.e., the bottom path shown, "bottom signal" and / or "local oscillator signal") from the "signal" field (i.e., the top path shown, "top signal", and / or "delayed signal"). The top signal power is coupled to free space. This light travels a different or variable distance before hitting the target and reflecting back to the LIDAR unit (e.g., free-space interferometer 103). This light is received by the free-space interferometer 103, effectively forming a variable-distance optical delay line 111. The delayed signal and the local oscillator signal are mixed together by the optical mixer 112. The output of the optical mixer 112 is converted into an electrical signal using balanced photodiode pair 113 and balanced photodiode pair 114. The resulting electrical signal corresponds to the in-phase signal I FS and the quadrature signal Q FS , which are components of the FMCW LIDAR beat frequency signal, respectively. The phase fluctuations in the measured beat frequency signal are time-correlated with the phase fluctuations in the beat frequency signal measured by the fixed-length interferometer 104 because the interferometers are fed by the laser source 101 simultaneously.

[0100] Figure 2 FIG. illustrates an example of an FMCW LIDAR system 200 according to an embodiment of the present disclosure, which may be configured to incorporate phase estimation, active phase cancellation, pre-distorted waveform generation, and peak pairing. The FMCW LIDAR system 200 includes a LIDAR processing engine 201 and a focal plane array (FPA) system 202. In other embodiments, different forms of beam steering may be used.

[0101] The LIDAR processing engine 201 includes a microcomputer 203 configured to drive a digital-to-analog converter (DAC) 204, and the digital-to-analog converter (DAC) 204 generates a modulation signal for the laser controller 205. The laser controller 205 modulates the frequency of the Q-channel laser array 206. The optical power emitted by the laser array 206 is divided and routed to the switchable coherent pixel array 208 and the laser phase reference interferometer 207 (which may include Figure 1 the fixed-length interferometer 104). The light entering the switchable coherent pixel array 208 is controlled by the FPA driver 209. The light emitted from different positions in the switchable coherent pixel array 208 is collimated at different angles by the lens 210 and emitted into free space 211.

[0102] The light emitted into free space 211 is reflected by the target, travels back through the lens 210, and is coupled back into the switchable coherent pixel array 208. The received light is measured using the N-channel receiver 212, which may incorporate the optical mixer 107 and / or the optical mixer 112 (as Figure 1As shown). The resulting current is digitized using one or more M-channel analog-to-digital converters (ADCs) 213, and these signals are processed by the microcomputer 203.

[0103] In parallel with the free-space measurement, the optical field passing through the laser phase reference interferometer 207 is measured using the P-channel receiver 214, which generates a current that is converted to a digital signal using the R-channel analog-to-digital converter (ADC) 215. The resulting digital signal is processed by the microcomputer 203 to estimate the phase fluctuations (phase noise) of the laser. The estimated or determined phase noise can then be used to eliminate the phase noise ("denoise") from the free-space distance measurement signal.

[0104] Figure 3A and Figure 3B illustrates an example phase noise cancellation system according to an embodiment of the present disclosure, the optical phase measurement device 100 ( Figure 1 as shown) and the FMCW LIDAR system 200 (as Figure 2 shown) through which can be used to actively cancel unwanted phase fluctuations ("phase noise") to support FMCW LIDAR distance and velocity measurements.

[0105] Figure 3A illustrates a phase noise cancellation system 300 according to an embodiment of the present disclosure. The phase noise cancellation system 300 may include a LIDAR measurement unit 301, a reference measurement unit 302, a phase cancellation unit 303, and a distance calculation unit 304. The LIDAR measurement unit 301 may be configured to perform FMCW distance measurement by estimating the time it takes for light to travel between the light source and the target (i.e., the travel time). In this application, the time it takes for light to travel between the light source (e.g., a laser source) and the target (e.g., an object in the environment where the LIDAR system is located) is defined as the time of flight. At the same time, the reference measurement unit 302 may be configured to determine an estimate of the phase of light (e.g., laser light) by using a reference or fixed-length interferometer. The time-of-flight estimate τ est from the LIDAR measurement unit 301 and the phase φ ex of the light from the reference measurement unit 302 est are provided to the phase cancellation unit 303. The phase cancellation unit 303 uses the time-of-flight estimate τ ex(t) and the phase φ FS of the light to estimate and eliminate the phase noise from the signal representing the light (e.g., the in-phase signal I FS and / or the quadrature signal Q dn, the distance calculation unit 304 uses this signal to estimate the distance to the target. The distance calculation unit 304 can also be configured to calculate the velocity of the target by, for example, performing Doppler shift calculations or measurements.

[0106] Figure 3B Illustrates an example of a phase noise cancellation system 340 according to an embodiment of the present disclosure. The phase noise cancellation system 340 is an example embodiment of the phase noise cancellation system 300. One or more components or operations within the phase noise cancellation system 340 can be implemented in a photonic integrated circuit and / or an FMCW LIDAR system.

[0107] The LIDAR measurement unit 301 is configured to receive a signal 305 (e.g., a voltage signal) and generate a time-of-flight estimate τ est . The signal 305 is a signal representing light that has traveled to and from at least one target in free space. The signal 305 can be a beat signal that is a combination of a local oscillator signal and a free space optical signal. The signal 305 can include an in-phase signal I FS and / or a quadrature signal Q FS and can be received from the free space interferometer 103 ( Figure 1 as shown).

[0108] The LIDAR measurement unit 301 includes a frequency conversion block 306, a filter block 307, and a peak search block 308. The frequency conversion block 306 converts the signal 305 into a frequency representation of the signal 305. The LIDAR measurement unit 301 can perform this operation using a Fourier transform (e.g., a fast Fourier transform (FFT)). The frequency conversion block 306 can digitize the signal 305 and can use a Fourier transform to calculate the power spectral density (PSD) of the signal 305. The filter block 307 filters the output of the frequency conversion block 306 to improve the signal-to-noise ratio. The peak search block 308 can identify the highest peak in the filtered spectrum of the signal 305. Based on system parameters, this peak position can be converted into an estimate of the time-of-flight τ est of the optical signal.

[0109] While the LIDAR measurement unit 301 is operating, the reference measurement unit 302 is configured to determine the phase φ ex (t) of the light. The reference measurement unit 302 can include a division block 311, an arctangent block 312, a phase unwrapping block 313, and an integration block 314. The reference measurement unit 302 receives the in-phase signal 310 and the quadrature signal 309 as inputs from a fixed-length interferometer. In one embodiment, the in-phase signal 310 and the quadrature signal 309 are the in-phase signal I ref and the quadrature signal Q ref。The division block 311 includes dividing the quadrature signal 309 by the in-phase signal 310. The arctangent block 312 performs the arctangent of the output of the division block 311 to estimate the phase of the beat frequency signal represented by at least one of the in-phase signal 310 and / or the quadrature signal 309. The phase unwrapping block 313 applies phase unwrapping to the output of the arctangent block 312. The output of the phase unwrapping block 313 is integrated (with respect to time) at the integration block 314 to estimate the phase fluctuations of the system laser with respect to time.

[0110] The phase cancellation unit 303 is configured to cancel phase noise from the signal 305 based at least in part on the phase φ ex of the light and the time of flight τ est The phase cancellation unit 303 includes a delay block 315, a subtraction block 316, an exponentiation block 317, and a multiplication block 318. The delay block 315 generates a delayed phase φ ex (t - τ est ), which is an estimate of the time delay of the phase φ ex of the light. The delay can be a digital delay, and the duration of the delay is the duration of the time of flight τ est . By delaying the phase φ ex (t) of the light by the duration of the time of flight τ est , the phase cancellation unit 303 identifies a portion of the phase φ ex (t) of the light associated with the optical transmission defining the signal 305. The subtraction block 316 subtracts the phase φ ex (t - τ est ) from the phase φ ex (t) of the light to isolate the variable phase Δφ ex (τ est ) defining the phase fluctuations or phase noise of the laser source when transmitting the signal 305. The exponentiation block 317 constructs a conjugate phasor from the variable phase Δφ ex (τ est ). The multiplication block 318 multiplies the conjugate phasor by the signal 305 to create a denoised signal V(t) dn . The denoised signal V(t) dn is the signal 305 with the unwanted phase fluctuations removed or denoised. The denoised signal V(t) dn is the resulting "clean" beat frequency signal that can be passed to the distance calculation unit 304.

[0111] The distance calculation unit 304 is configured to use the denoised signal V(t) dn to determine the distance between the light source and the target. The distance calculation unit 304 includes a frequency conversion block 319, a filter block 320, and a peak finding block 321. The frequency conversion block 319 converts the denoised signal V(t) dn into a denoised signal V(t) dnFrequency representation. The distance calculation unit 304 can perform this operation using a Fourier transform (e.g., fast Fourier transform (FFT)). The frequency conversion block 319 can digitize the denoised signal V(t) dn and can use a Fourier transform to calculate the power spectral density (PSD) of the denoised signal V(t) dn . The filter block 320 filters the output of the frequency conversion block 319 to improve the signal-to-noise ratio. The peak finding block 321 can identify one or more peaks in the filtered spectrum of the denoised signal V(t) dn . Then this peak information is used to estimate the position and velocity of the target. At block 322, the phase noise cancellation system 340 ends the operation.

[0112] Figure 4A and 4B illustrates an example of a predistortion waveform generator according to an embodiment of the present disclosure. The predistortion waveform generator can use the optical phase measurement device 100 (as Figure 1 shown) and the FMCW LIDAR system 200 (as Figure 2 shown) to generate a predistortion waveform for driving the laser of the LIDAR system, or to refine ("calibrate") an existing predistortion waveform. Due to the distortion characteristics of the laser, predistortion waveform generation can be beneficial in LIDAR systems. For example, to linearly increase and / or decrease the frequency of the laser, the LIDAR system can be configured to drive the frequency of the laser using a waveform such as a triangular waveform. The value of the triangular waveform increases linearly and decreases linearly. However, the distortion characteristics of the laser may cause the frequency response of the laser to produce an output that does not have a linearly increasing frequency and a linearly decreasing frequency. Since FMCW LIDAR systems rely on frequency modulation (e.g., chirping), such systems may benefit from a predistortion waveform that compensates for the distortion characteristics of the laser incorporated into a particular LIDAR system. In-situ or in-place adjustment, refinement, or calibration of the predistortion waveform provides the advantage of compensating for the minor unique operating characteristics of each laser.

[0113] Figure 4A illustrates a predistortion waveform generator 400 according to an embodiment of the present disclosure. The predistortion waveform generator 400 includes a function definition block 401, a parameter set block 402, and a calibration unit 403. Before the operation of the LIDAR system, the function definition block 401 defines a cost function F(f). The cost function F(f) defines or quantifies the linearity (or shape) of the laser frequency for the chirp used to drive the laser. Similarly, before the operation of the LIDAR system, the parameter set block 402 defines a parameter p, which includes a set of numbers that define the shape / behavior of the laser drive waveform. The cost function F(f) can explicitly be a function of the time-dependent frequency for chirping the LIDAR laser and can explicitly or implicitly depend on the parameter p.

[0114] The calibration unit 403 is applied to the LIDAR system to find the parameter p that minimizes the cost function F(f). The calibration unit 403 is configured to generate a pre-distorted waveform to compensate for the distortion characteristics of the laser. The calibration unit 403 generates the pre-distorted waveform by applying the partial derivative to the cost function F(f) with respect to each parameter p in the set of parameters p. By iteratively identifying the distortion characteristics of the laser, the calibration unit 403 redefines the set of parameters p and saves the set of these parameters p to define the laser drive waveform for future use.

[0115] The pre-distortion waveform generator 400 ends its operation at block 419.

[0116] Figure 4B An example of a pre-distortion waveform generator 430 according to an embodiment of the present disclosure is illustrated. The pre-distortion waveform generator 430 is an example embodiment of the pre-distortion waveform generator 400 ( Figure 4A shown in).

[0117] The calibration unit 403 includes a plurality of operation or process blocks for supporting the generation of the pre-distortion waveform. The calibration unit 403 includes a waveform construction block 404, an evaluation function block 405, an estimated gradient block 411, and an update block 417. In the waveform construction block 404, the calibration unit 403 constructs an initial drive waveform V(t,p) according to the cost function F(f) and the parameter p defined in the function definition block 401 and the parameter set block 402.

[0118] Next, the value of the cost function F(f) is evaluated in the evaluation function block 405. The evaluation function block 405 may include several sub-operations. In block 406, the current version of the drive waveform V(t,p) is loaded into the digital-to-analog converter (DAC) 406. At block 407, the laser driven by the drive waveform V(t,p) is allowed to stabilize to steady-state operation. At block 408, the in-phase signal I ref and the quadrature signal Q ref are measured at the output of a short reference interferometer (e.g., Figure 1 the fixed-length interferometer 104 shown). At block 409, the in-phase signal I ref and the quadrature signal Q ref are used to calculate an estimate of the time-dependent laser frequency f, for example, as described for the Figure 3B reference measurement unit 302. At block 409, by dividing the quadrature signal Q ref by the in-phase signal I ref , taking the arctangent of the division result, expanding the arctangent result, and dividing the quantity by the relative delay τ of the fixed-length interferometer, the in-phase signal I ref and the quadrature signal Q refFor calculating an estimate of the time - dependent laser frequency f. At block 410, the time - dependent frequency from block 409 is used to calculate the current value of the cost function F(f).

[0119] After the current value of the cost function F(f) is evaluated in the evaluation function block 405, the estimate gradient block 411 is configured to estimate the gradient of the cost function F(f). The estimate gradient block 411 is configured to determine the gradient by calculating the partial derivative of the cost function F(f) with respect to each parameter p. Block 412 includes the j - th element of the perturbed parameter p, calculates the perturbed version of the drive waveform V(p i +Δp j ), and uploads the perturbed version of the drive waveform V(p i +Δp j ) to the DAC. Block 413 includes evaluating the corresponding (perturbed) value of the cost function F(p i +Δp j ), e.g., using a sub - operation of the evaluation function block 405. At block 414, the partial derivative ∂F / ∂p i +Δp j ) of the cost function F(p j ) with respect to the j - th element of the parameter p is estimated. The partial derivative ∂F / ∂p j is approximated using finite differences. At block 415, it is determined whether there are additional parameters p for perturbation. If there are more elements in the parameter p, block 415 advances to block 416, where the value of j is incremented and the estimate gradient block 411 is repeated. If each element in the parameter p has been evaluated, block 415 advances to block 417.

[0120] In the update block 417, the calibration unit 403 updates the parameter p based on the evaluated cost function F(f) (from the evaluation function block 405) and the estimate of the gradient of the cost function F(f) (from the estimate gradient block 411). At block 418, the calibration unit 403 performs a convergence check. The convergence check is an assessment of how well the frequency response of the laser matches the cost function F(f) defined when driven with the parameter p. If the cost function F(f) has converged, the final version of the parameter p is selected and thus the optimized drive signal V(t,p) is selected, and the calibration unit 403 advances to block 419 to end.

[0121] Figure 5A and Figure 5B illustrates an example of a multi - target recognition system that uses reference phase measurements to identify multiple targets in an FMCW LIDAR system. The multi - target recognition system applies phase measurements to the pairing of multiple spectral echo peaks in an FMCW LIDAR beat signal.

[0122] Figure 5AIllustrated is an example of a multi-target recognition system 500 that uses reference phase measurements to identify multiple targets in an FMCW LIDAR system. The multi-target recognition system 500 includes a LIDAR measurement unit 551 and a reference measurement unit 552. According to one embodiment, the LIDAR measurement unit 551 includes some features of the LIDAR measurement unit 301 ( Figure 3A and 3B as shown), and the reference measurement unit 552 includes some features of the reference measurement unit 302 ( Figure 3A and 3B as shown in).

[0123] The LIDAR measurement unit 551 is configured to range multiple targets. The LIDAR measurement unit 551 is configured to range multiple targets by identifying a first set of spectral peaks that have been generated from an upward frequency chirp of a laser source. The LIDAR measurement unit 551 is configured to range multiple targets by identifying a second set of spectral peaks that have been generated from a downward frequency chirp of the laser source. The LIDAR measurement unit 551 is configured to pair the peaks from the first set of spectral peaks with the peaks from the second set of spectral peaks to confirm the presence of each of the multiple targets and estimate the time-of-flight to each of the multiple targets. The LIDAR measurement unit 551 is configured to denoise the free-space beat signal from which the spectral peaks are derived using the phase φ ex (t) of a reference beat signal.

[0124] The reference measurement unit 552 is configured to provide a phase measurement of the laser source using a fixed-length interferometer. The reference measurement unit 552 is configured to determine the phase φ ex (t) of a reference beat signal from the fixed-length interferometer and provide the phase φ ex (t) of the reference beat signal to the LIDAR measurement unit 551 to enable the LIDAR measurement unit 551 to eliminate phase noise. The reference measurement unit 552 may calculate a first phase based on a first reference beat signal created by an upward frequency chirp. The reference measurement unit 552 may calculate a second phase based on a second reference beat signal created by a downward frequency chirp. The reference measurement unit 552 is configured to provide the first phase from the first reference beat to the LIDAR measurement unit 551 to enable phase noise to be eliminated from the free-space beat signal from the upward frequency chirp. The reference measurement unit 552 is configured to use the second phase from the second reference beat signal to eliminate phase noise from the free-space beat signal from the downward frequency chirp.

[0125] The operation of the multi-target recognition system 500 ends at block 553.

[0126] Figure 5BIllustrated is an example of a multi-target recognition system 570 that uses reference phase measurements to identify multiple targets in an FMCW LIDAR system. The multi-target recognition system 570 is an example implementation of the multi-target recognition system 500.

[0127] Initially, a beat signal is generated from a laser source. At block 501, a free-space beat signal is generated from a free-space interferometer using an upward frequency chirp (rising ramp). At block 502, a free-space beat signal is generated from the free-space interferometer using a downward frequency chirp (falling ramp). Both free-space beat signals are collected using an FMCW LIDAR system. These beat signals correspond to distance and velocity measurements through free space. At block 503, the power spectral density (PSD) of the rising ramp beat signal is calculated, and the positions (frequencies) of the highest N peaks are located in the spectrum of the rising ramp. At block 504, the power spectral density (PSD) of the falling ramp beat signal is calculated, and the positions (frequencies) of the highest N peaks are located in the spectrum of the falling ramp.

[0128] In parallel with the free-space interferometer measurements, at blocks 505 and 506, a reference beat signal is generated from the same laser source. At block 505, a reference beat signal is generated from a reference (fixed-length) interferometer using an upward frequency chirp (rising ramp). At block 506, a reference beat signal is generated from the reference interferometer using a downward frequency chirp (falling ramp). At block 507, the phase φ ex (t) of the rising ramp beat signal is calculated. At block 508, the phase φ ex (t) of the falling ramp beat signal is calculated.

[0129] The N spectral peaks in the rising ramp PSD and the falling ramp PSD correspond to N different echo paths in free space. By correctly pairing each peak in the rising ramp PSD with each peak in the falling ramp PSD, the lengths of these paths and the rate of change of these path lengths (i.e., the relative velocity of the target) can be calculated. At block 509, the first peak in the rising ramp PSD is paired with the first peak in the falling ramp PSD. This pairing yields an estimate of the target distance, velocity, and flight time τ est . At block 510, the time-dependent phase φ ex (t) of the laser rising ramp obtained from the reference interferometer is delayed. The delay applied to the phase φ ex (t) is the duration of the estimated flight time τ est , which generates a delayed phase φ ex (t - τ est ). At block 511, the delayed phase φ ex (t - τ est)Subtract from the undelayed time - related phase φ ex (t) of the laser ramp - up to produce the variable phase Δφ(t). The variable phase Δφ(t) is an estimate of the contribution of phase noise and nonlinearity to the free - space beat - frequency signal generated at block 501. At block 512, the conjugate phasor is formed from the variable phase Δφ(t). At block 513, the conjugate phasor is multiplied by the free - space ramp - up beat - frequency signal to cancel the phase noise and produce a denoised beat - frequency signal. At block 514, the PSD of the denoised beat - frequency signal is calculated, and the resulting peak is located.

[0130] Evaluate each set of peaks identified at block 509. At block 515, check peak pairs to determine if there are more (unevaluated) peak pairs remaining in the ramp - up and ramp - down PSDs. If peak pairs remain, repeat blocks 509 - 515 for each remaining pair. If all pairs have been tested, block 515 advances to block 516. At block 516, compare the peaks of the PSDs calculated for each pair to determine which pairings are correct (correct pairings occur when the PSD peak is maximized in the ramp - up). At block 517, select the peak pairings after verifying the pairings are correct.

[0131] After the correct peak pairings are selected in block 517, the multi - target recognition system 570 can advance to block 553 to end the operation. Alternatively, after block 517, the multi - target recognition system 570 can repeat the removal of phase noise to improve the signal - to - noise ratio (SNR) of the down - ramp signal. Using the peak pairs, estimate the distances of all measured path lengths. Based on these path lengths or based on the estimated time - of - flight τ est , at block 518, produce the delayed phase φ ex (t) by delaying the time - related phase φ ex (t) estimated for the down - ramp by est τ ex . At block 519, subtract the delayed phase φ ex (t - τ est)To generate a variable phase Δφ(t). The variable phase Δφ(t) is an estimate of the contributions of phase noise and nonlinearity to the free - space beat frequency signal generated by the down - ramp at block 502. At block 520, a conjugate phasor is constructed from the variable phase Δφ(t). At block 521, the conjugate phasor is multiplied by the free - space down - ramp beat frequency signal to cancel the phase noise and generate a denoised beat frequency signal. At block 522, the PSD of the denoised beat frequency signal is calculated, and the resulting peak is located. At block 523, a check is made to determine if the peak pair is retained. If so, blocks 518 - 523 are repeated. If all peaks have been processed, the final distances and velocities of multiple targets can be calculated, and block 523 advances to block 553 to end the operation of the multi - target recognition system 570.

[0132] Figure 6A and Figure 6B illustrates an example of an operating cycle 600 of an FMCW LIDAR incorporating phase estimation, active phase cancellation, pre - distortion waveform generation, and peak pairing fitting according to various embodiments of the present disclosure.

[0133] At block 601, the system and the laser are powered on. At block 602, using the data generated by the temperature sensor 603, the temperature of the laser is allowed to stabilize. Once the laser temperature has stabilized, at block 605, the system loads an existing laser drive waveform 604 to modulate the laser frequency. Depending on the wear of the system, changes in the environmental state, etc., the loaded laser drive waveform 604 may not be optimal. At block 606, a check can be performed to determine if the frequency characteristics of the laser (e.g., chirp rate and chirp nonlinearity) satisfy the specifications satisfactorily. If the laser characteristics meet the specifications, block 606 advances to block 609 (as Figure 6B shown). If the laser characteristics do not meet the specifications, block 606 advances to block 607. In block 607, in - situ refinement of the laser drive waveform 604 is performed. The in - situ refinement can be performed according to the pre - distortion waveform generator 400 and / or the pre - distortion waveform generator 430 (shown in Figure 4A and 4B ). Block 607 advances to block 608 and block 609 (as Figure 6B shown). At block 608, the updated waveform is saved for the next power cycle.

[0134] Turn to Figure 6B, if the existing version of the laser drive waveform 604 meets the specifications, or alternatively, if in-situ refinement has been completed, block 609 begins the process of capturing frame data. Block 609 can include multiple sub-operations. As the laser frequency is modulated, at block 610, the LIDAR system waits for a trigger indicating that the rising ramp (increasing frequency chirp) has started. In response, at block 611, the performance of the free-space FMCW LIDAR measurement is triggered, and in parallel, at block 612, the measurement of the time-correlated laser phase fluctuations (noise and non-linearity) is triggered. The operations associated with blocks 611 and 612 can correspond to the optical phase measurement device 100 ( Figure 1 as shown). The measurement results of blocks 611 and 612 are combined at block 613. The operation at block 613 can represent the operation of the phase noise cancellation system 300 ( Figure 3A as shown), the phase noise cancellation system 340 ( Figure 3B as shown), the multi-target recognition system 500 ( Figure 5A as shown), and / or the multi-target recognition system 570 ( Figure 5B as shown). The operation of block 613 can improve the fidelity of the free-space LIDAR measurement. At block 614, the beat frequency signal spectrum is calculated. Blocks 610 - 614 are repeated for the falling ramp. At block 615, based on the resulting filtered PSD, the distance and velocity of the points in the scene can be calculated.

[0135] Typically, a LIDAR frame includes more than one point. At block 616, the LIDAR system determines whether more points remain in the frame. If more points remain in the frame, block 616 advances to block 617, where the position of the beam emitted by the FMCW LIDAR system is modified, and the rising / falling ramp capture process of blocks 610 - 615 is repeated. Once all the points in the frame have been captured, block 616 advances to block 618, where the point cloud can be assembled, which completes the operation loop 600.

[0136] The order in which some or all of the process blocks appear in systems and processes 300, 340, 400, 430, 500, 570, and / or 600 should not be considered restrictive. Instead, those of ordinary skill in the art, benefiting from this disclosure, will understand that some process blocks can be performed in various orders not shown or even in parallel.

[0137] Figure 7A illustrates an example autonomous vehicle 700 that can include a LIDAR design according to aspects of the present disclosure and can include Figure 1 - Figure 6. The illustrated autonomous vehicle 700 includes a sensor array configured to capture one or more objects in the external environment of the autonomous vehicle and generate sensor data related to the captured one or more objects for controlling the operation of the autonomous vehicle 700. Figure 7AShow sensors 733A, 733B, 733C, 733D, and 733E. Figure 7B FIG. illustrates a top view of an autonomous vehicle 700 that includes sensors 733F, 733G, 733H, and 733I in addition to sensors 733A, 733B, 733C, 733D, and 733E. Any one of sensors 733A, 733B, 733C, 733D, 733E, 733F, 733G, 733H, and / or 733I may include a LIDAR device having a design that includes Figure 1 -6. Figure 7C FIG. illustrates a block diagram of an example system 799 for an autonomous vehicle 700. For example, autonomous vehicle 700 may include a powertrain 702 that includes a prime mover 704 powered by an energy source 706 and capable of providing power to a power system 708. Autonomous vehicle 700 may also include a control system 710 that includes a direction control 712, a powertrain control 714, and a brake control 716. Autonomous vehicle 700 may be implemented as any number of different vehicles, including vehicles capable of transporting people and / or cargo and capable of traveling in a variety of different environments. It should be understood that the above components 702-716 can vary widely depending on the type of vehicle in which these components are used.

[0138] For example, the embodiments discussed below will focus on wheeled land vehicles such as cars, vans, trucks, or buses. In such embodiments, prime mover 704 may include one or more electric motors and / or internal combustion engines (among others). The energy source may include, for example, a fuel system (e.g., providing gasoline, diesel, hydrogen), a battery system, a solar panel or other renewable energy source, and / or a fuel cell system. Power system 708 may include wheels and / or tires along with a transmission and / or any other mechanical drive components suitable for converting the output of prime mover 704 into vehicle motion, as well as one or more brakes configured to controllably stop or slow down autonomous vehicle 700 and a direction or steering component suitable for controlling the trajectory of autonomous vehicle 700 (e.g., a rack and pinion steering linkage that enables one or more wheels of autonomous vehicle 700 to pivot about a generally vertical axis to change the angle of the rotational plane of the wheels relative to the longitudinal axis of the vehicle). In some embodiments, a combination of a powertrain and an energy source may be used (e.g., in the case of an electric / gas hybrid vehicle). In some embodiments, multiple electric motors (e.g., dedicated to individual wheels or axles) may be used as prime movers.

[0139] The direction control 712 can include one or more actuators and / or sensors for controlling and receiving feedback from a direction or steering component to enable the autonomous vehicle 700 to follow a desired trajectory. The powertrain control 714 can be configured to control the output of the powertrain 702, such as controlling the output power of the prime mover 704 and controlling the gear position of the transmission in the power train 708, thereby controlling the speed and / or direction of the autonomous vehicle 700. The brake control 716 can be configured to control one or more brakes that decelerate or stop the autonomous vehicle 700, such as disc or drum brakes coupled to the wheels of the vehicle.

[0140] Other vehicle types, including but not limited to off-road vehicles, all-terrain or tracked vehicles, or construction equipment will necessarily use different powertrains, power trains, energy sources, direction controls, powertrain controls, and brake controls, as will be understood by one of ordinary skill in the art benefiting from this disclosure. Additionally, in some embodiments, some components can be combined, for example, where the direction control of the vehicle is primarily handled by changing the output of one or more prime movers. Accordingly, the embodiments disclosed herein are not limited to the specific application of the techniques described herein to autonomous wheeled land vehicles.

[0141] In the illustrated embodiment, the autonomous control of the autonomous vehicle 700 is implemented in a vehicle control system 720, which can include one or more processors in a processing logic 722 and one or more memories 724, where the processing logic 722 is configured to execute program code (e.g., instructions 726) stored in the memory 724. The processing logic 722 can include, for example, one or more graphics processing units (GPUs) and / or one or more central processing units (CPUs). The vehicle control system 720 can be configured to control the powertrain 702 of the autonomous vehicle 700 in response to the output of a light mixer of LIDAR pixels. The vehicle control system 720 can be configured to control the powertrain 702 of the autonomous vehicle 700 in response to the output from a plurality of LIDAR pixels. The vehicle control system 720 can be configured to control the powertrain 702 of the autonomous vehicle 700 in response to the output from a microcomputer 203 generated based on a signal received from the FPA system 202.

[0142] The sensors 733A - 733I can include various sensors suitable for collecting data from the surrounding environment of the autonomous vehicle for controlling the operation of the autonomous vehicle. For example, the sensors 733A - 733I can include a RADAR unit 734, a LIDAR unit 736, one or more 3D positioning sensors 738, such as satellite navigation systems, such as GPS, GLONASS, BeiDou, Galileo, or Compass. Figure 1- The LIDAR design of FIG. 6 can be included in the LIDAR unit 736. The LIDAR unit 736 can include, for example, a plurality of LIDAR sensors distributed around the autonomous vehicle 700. In some embodiments, the (multiple) 3D positioning sensors 738 can use satellite signals to determine the position of the vehicle on the earth. The sensors 733A - 733I can optionally include one or more ultrasonic sensors, one or more cameras 740, and / or an inertial measurement unit (IMU) 742. In some embodiments, the camera 740 can be a single-image or stereo camera and can record still images and / or video images. The camera 740 can include a complementary metal oxide semiconductor (CMOS) image sensor configured to capture images of one or more objects in the external environment of the autonomous vehicle 700. The IMU 742 can include a plurality of gyroscopes and accelerometers capable of detecting the linear and rotational motion of the autonomous vehicle 700 in three directions. One or more encoders (not shown), such as wheel encoders, can be used to monitor the rotation of one or more wheels of the autonomous vehicle 700.

[0143] The outputs of the sensors 733A - 733I can be provided to the control subsystem 750, which includes a positioning subsystem 752, a trajectory subsystem 756, a perception subsystem 754, and a control system interface 758. The positioning subsystem 752 is configured to determine the position and orientation (sometimes also referred to as "pose") of the autonomous vehicle 700 in its surrounding environment, and typically within a specific geographical area. The position of the autonomous vehicle can be compared with the positions of other vehicles in the same environment as part of generating labeled autonomous vehicle data. The perception subsystem 754 can be configured to detect, track, classify, and / or determine objects within the surrounding environment of the autonomous vehicle 700. The trajectory subsystem 756 is configured to generate a trajectory of the autonomous vehicle 700 within a given desired destination and a specific time range of static and moving objects within the environment. Machine learning models according to several embodiments can be used to generate vehicle trajectories. The control system interface 758 is configured to communicate with the control system 710 to implement the trajectory of the autonomous vehicle 700. In some embodiments, machine learning models can be utilized to control the autonomous vehicle to execute the planned trajectory.

[0144] It should be understood that Figure 7C the set of components of the vehicle control system 720 illustrated in Figure 7CThe different types of sensors illustrated herein can be used for redundancy and / or for covering different areas in the environment surrounding the autonomous vehicle. In some embodiments, different types and / or combinations of control subsystems can be used. Additionally, although subsystems 752 - 758 are illustrated as separate from processing logic 722 and memory 724, it should be understood that in some embodiments, some or all of the functionality of subsystems 752 - 758 can be implemented using program code, such as instructions 726 residing in memory 724 and executed by processing logic 722, and these subsystems 752–758 can in some cases use the same processor(s) and / or memory for implementation. The subsystems in some embodiments can be implemented at least in part using various dedicated circuit logics, various processors, various field programmable gate arrays (“FPGAs”), various application specific integrated circuits (“ASICs”), various real-time controllers, etc., and as described above, multiple subsystems can utilize circuits, processors, sensors, and / or other components. Additionally, the various components in vehicle control system 720 can be networked in various ways.

[0145] In some embodiments, autonomous vehicle 700 can also include an auxiliary vehicle control system (not illustrated), which can serve as a redundant or backup control system for autonomous vehicle 700. In some embodiments, the auxiliary vehicle control system is capable of operating autonomous vehicle 700 in response to a specific event. In response to a specific event detected in the primary vehicle control system 720, the auxiliary vehicle control system may have only limited functionality. In other embodiments, the auxiliary vehicle control system can be omitted.

[0146] In some embodiments, different architectures including various combinations of software, hardware, circuit logic, sensors, and networks can be used to implement Figure 7C the various components illustrated herein. For example, each processor can be implemented as a microprocessor, and each memory can represent a random access memory (“RAM”) device including main storage, as well as any supplementary levels of memory, such as cache memory, non-volatile or backup memory (e.g., programmable memory or flash memory), or read-only memory. Additionally, each memory can be considered to include memory physically located elsewhere in autonomous vehicle 700, such as any cache memory in the processor, and any storage capacity used as virtual memory, such as that stored on a mass storage device or other computer controller. Figure 7C The processing logic 722 illustrated herein, or completely separate processing logic, can be used to implement additional functionality in autonomous vehicle 700 beyond the purpose of autonomous control, e.g., controlling an entertainment system, operating doors, lights, or convenience features.

[0147] Additionally, for additional storage, the autonomous vehicle 700 may also include one or more mass storage devices, such as, for example, removable disk drives, hard disk drives, direct access storage devices ("DASD"), optical drives (e.g., CD drives, DVD drives), solid state storage drives ("SSD"), network attached storage, storage area networks, and / or tape drives, etc. Further, the autonomous vehicle 700 may include a user interface 764 to enable the autonomous vehicle 700 to receive multiple inputs from passengers and generate outputs for passengers, such as, for example, one or more displays, touchscreens, voice and / or gesture interfaces, buttons, and other tactile controls. In some embodiments, inputs from passengers may be received via another computer or electronic device, such as via an application on a mobile device or via a network interface.

[0148] In some embodiments, the autonomous vehicle 700 may include one or more network interfaces, such as, for example, network interface 762, which is adapted to communicate with one or more networks 770 (e.g., local area network ("LAN"), wide area network ("WAN"), wireless network, and / or the Internet, etc.) to permit information communication with other computers and electronic devices, including, for example, central services, such as cloud services, from which the autonomous vehicle 700 receives environmental and other data for its autonomous control. In some embodiments, data collected by one or more of the sensors 733A - 733I can be uploaded via the network 770 to a computing system 772 for additional processing. In such embodiments, a timestamp can be associated with each instance of the vehicle data prior to upload.

[0149] Figure 7C The processing logic 722 illustrated therein and the various additional controllers and subsystems disclosed herein generally operate under the control of an operating system and execute or otherwise rely on various computer software applications, components, programs, objects, modules, or data structures, as described in more detail below. Additionally, various applications, components, programs, objects, or modules may also be executed on one or more processors in another computer coupled to the autonomous vehicle 700 via the network 770, such as in a distributed, cloud - based, or client - server computing environment, whereby the processing required to implement the computer program functionality can be distributed across multiple computers and / or services via the network.

[0150] Routines that are executed to implement the various embodiments described herein, whether implemented as part of an operating system or as a specific application, component, program, object, module, or sequence of instructions, or even a subset thereof, will be referred to herein as "program code". Program code typically includes one or more instructions that reside at various times in various memories and storage devices and, when read and executed by one or more processors, perform the steps necessary to execute the steps or elements embodying aspects of the present invention. Additionally, while embodiments have been and may hereinafter be described in the context of full-featured computers and systems, it should be understood that the various embodiments described herein can be distributed as a program product in a variety of forms and can be implemented as an embodiment regardless of the specific type of computer-readable medium used to actually effect the distribution. Examples of computer-readable media include tangible, non-transitory media such as volatile and non-volatile storage devices, floppy disks and other removable disks, solid state drives, hard disk drives, magnetic tape, and optical discs (e.g., CD-ROM, DVD), and the like.

[0151] In addition, the various program codes described hereinafter can be identified based on the application programs in which they are implemented in a particular embodiment. However, it should be understood that any specific program naming used hereinafter is for convenience only, and thus, the present invention should not be limited to use only in any particular application identified and / or implied by such nomenclature. Additionally, considering the numerous ways in which computer programs can typically be organized into routines, procedures, methods, modules, objects, etc., and the various ways in which program functionality can be distributed among the various software layers (e.g., operating system, libraries, APIs, application programs, applets) that can reside in a typical computer, it should be understood that the present invention is not limited to the particular organization and distribution of program functionality described herein.

[0152] Those skilled in the art who benefit from this disclosure will recognize that Figure 7C the exemplary environments illustrated herein are not intended to limit the embodiments disclosed herein. In fact, those skilled in the art will recognize that other alternative hardware and / or software environments can be used without departing from the scope of the embodiments disclosed herein.

[0153] The term "processing logic" (e.g., processing logic 722) in this disclosure can include one or more processors, microprocessors, multi-core processors, application-specific integrated circuits (ASICs), and / or field-programmable gate arrays (FPGAs) to perform the operations disclosed herein. In some embodiments, a memory (not shown) is integrated into the processing logic to store instructions to perform operations and / or store data. The processing logic can also include analog or digital circuits for performing the operations in accordance with the embodiments of this disclosure.

[0154] The "units" in the present disclosure can be constructed using hardware components (e.g., AND, OR, NOR, XOR gates), can be implemented as circuits embedded in one or more processors, ASICs, FPGAs, or photonic integrated circuits (PICs), and / or can be partially defined as software instructions stored in one or more memories within the LIDAR system. As an example, according to embodiments of the present disclosure, the various units disclosed herein can be at least partially implemented in the LIDAR processing engine 201, the microcomputer 203, the laser controller 205, and / or the FPA driver 209 ( Figure 2 as shown).

[0155] One or more "memories" described in the present disclosure can include one or more volatile or non-volatile memory architectures. The "memory" or "memories" can be removable and non-removable media implemented by any method or technology for storing information such as computer-readable instructions, data structures, program modules, or other data. Example memory technologies can include RAM, ROM, EEPROM, flash memory, CD-ROM, digital versatile disks (DVDs), high-definition multimedia / data storage disks, or other optical storage, cassette tapes, magnetic tapes, disk storage, or other magnetic storage devices, or any other non-transitory medium capable of storing information for access by a computing device.

[0156] The network can include any network or network system, such as but not limited to the following: peer-to-peer networks; local area networks (LANs); wide area networks (WANs); public networks such as the Internet; private networks; cellular networks; wireless networks; wired networks; wireless and wired combined networks; and satellite networks.

[0157] The communication channel can include or be routed through one or more wired or wireless communications that utilize the IEEE802.11 protocol, SPI (Serial Peripheral Interface), I 2 C (Inter-Integrated Circuit), USB (Universal Serial Port), CAN (Controller Area Network), cellular data protocols (e.g., 3G, 4G, LTE, 5G), optical communication networks, Internet service providers (ISPs), peer-to-peer networks, local area networks (LANs), wide area networks (WANs), public networks (e.g., the "Internet"), private networks, satellite networks, etc.

[0158] The computing device can include a desktop computer, a laptop computer, a tablet computer, a phablet, a smartphone, a feature phone, a server computer, etc. The server computer can be remotely located in a data center or stored locally.

[0159] The processes explained above are described in terms of computer software and hardware. The techniques described can constitute machine-executable instructions embodied in a tangible or non-transitory machine (e.g., a computer) readable storage medium, which when executed by a machine will cause the machine to perform the described operations. Additionally, these processes can be embodied in hardware, such as a special purpose integrated circuit (“ASIC”) or others.

[0160] A tangible non-transitory machine readable storage medium includes any mechanism that provides (i.e., stores) information in a form accessible by a machine (e.g., a computer, a network device, a personal digital assistant, a manufacturing tool, any device having one or more processor sets, etc.). For example, a machine readable storage medium includes recordable / non-recordable media (e.g., read only memory (ROM), random access memory (RAM), magnetic disk storage media, optical storage media, flash memory devices, etc.).

[0161] The above description of the illustrated embodiments of the invention, including what is described in the abstract, is not intended to be exhaustive or to limit the invention to the precise forms disclosed. While specific embodiments and examples of the invention have been described herein for illustrative purposes, various modifications can be made within the scope of the invention, as will be recognized by those skilled in the relevant art.

[0162] These modifications to the invention can be made in light of the above detailed description. The terms used in the appended claims should not be construed as limiting the invention to the specific embodiments disclosed in the specification. Rather, the scope of the invention will be determined entirely by the appended claims, which are to be construed in accordance with established principles of claim interpretation.

Claims

1. A light detection and ranging LIDAR system, comprising: A laser configured to generate a laser beam based on a specific laser waveform associated with at least one of a plurality of parameters, the at least one parameter defining at least one of the shape or linearity of the specific laser waveform; And A calibration unit configured to determine a specific value of at least one of the plurality of parameters based on the output frequency of the laser beam to compensate for distortion characteristics of the laser, Wherein determining the specific value of the at least one parameter includes minimizing the value of a cost function that depends on the at least one parameter and quantifies at least one of the linearity or shape of the specific laser waveform, Wherein the calibration unit is further configured to update the specific laser waveform with the specific value of at least one of the plurality of parameters.

2. The LIDAR system according to claim 1, further comprising: A digital-to-analog converter configured to drive the laser with the specific laser waveform to determine the output frequency of the laser beam.

3. The LIDAR system according to claim 1, further comprising: An interferometer configured to output a reference signal, wherein the reference signal represents a fixed-distance propagation of the laser beam, and wherein the calibration unit is configured to calculate the output frequency of the laser beam based on the reference signal from the interferometer.

4. The LIDAR system according to claim 1, wherein, Compensation for distortion characteristics includes reducing the distortion characteristics of the laser, wherein the distortion characteristics of the laser are based on the difference between the specific laser waveform and the output frequency of the laser.

5. The LIDAR system according to claim 1, wherein, The plurality of parameters have initial values, and wherein the calibration unit is configured to replace one or more initial values with one or more updated values.

6. The LIDAR system according to claim 1, further comprising: An interferometer configured to provide in-phase and quadrature signals representing a fixed-distance propagation of the laser beam, Wherein the calibration unit is configured to receive the in-phase and quadrature signals from the interferometer, Wherein the calibration unit is configured to determine the output frequency of the laser beam based on the in-phase and quadrature signals.

7. An autonomous vehicle control system, comprising: A light detection and ranging LIDAR system, the LIDAR system comprising: A laser configured to generate a laser beam based on a specific laser waveform associated with at least one of a plurality of parameters, the at least one parameter defining at least one of the shape or linearity of the specific laser waveform; and A calibration unit configured to determine a specific value of at least one of the plurality of parameters based on the output frequency of the laser beam to compensate for distortion characteristics of the laser, Wherein determining the specific value of the at least one parameter includes minimizing the value of a cost function that depends on the at least one parameter and quantifies at least one of the linearity or shape of the specific laser waveform, wherein the calibration unit is further configured to update the specific laser waveform using a specific value of at least one of the plurality of parameters; and one or more processors, the one or more processors controlling the autonomous vehicle control system in response to the specific laser waveform updated at least in part by the calibration unit.

8. The autonomous vehicle control system according to claim 7, further comprising: a digital-to-analog converter configured to drive the laser using the specific laser waveform to determine an output frequency of the laser beam.

9. The autonomous vehicle control system according to claim 7, further comprising: an interferometer configured to output a reference signal, wherein the reference signal represents a fixed-distance propagation of the laser beam, and wherein the calibration unit is configured to calculate the output frequency of the laser beam based on the reference signal from the interferometer.

10. The autonomous vehicle control system according to claim 7, wherein, Compensation for the distortion characteristic includes reducing the distortion characteristic of the laser, wherein the distortion characteristic of the laser is based on a difference between the specific laser waveform and the output frequency of the laser.

11. The autonomous vehicle control system according to claim 7, wherein, The plurality of parameters have initial values, and wherein the calibration unit is configured to replace one or more of the initial values with one or more updated values.

12. The autonomous vehicle control system according to claim 11, further comprising: a memory, wherein the one or more processors are configured to store the one or more updated values in the memory to support retrieval of an update to the specific laser waveform.

13. The autonomous vehicle control system according to claim 7, further comprising: an interferometer configured to provide an in-phase signal and a quadrature signal representing a fixed-distance propagation of the laser beam, wherein the calibration unit is configured to receive the in-phase signal and the quadrature signal from the interferometer, wherein the calibration unit is configured to determine the output frequency of the laser beam based on the in-phase signal and the quadrature signal.

14. An autonomous vehicle, comprising: a light detection and ranging LIDAR system, the LIDAR system comprising: a laser configured to generate a laser beam based on a specific laser waveform associated with at least one of a plurality of parameters, the at least one parameter defining at least one of a shape or a linearity of the specific laser waveform; and a calibration unit configured to determine a specific value of at least one of the plurality of parameters based on an output frequency of the laser beam to compensate for a distortion characteristic of the laser, wherein determining the specific value of the at least one parameter includes minimizing a value of a cost function that depends on the at least one parameter and quantifies at least one of the linearity or the shape of the specific laser waveform, wherein the calibration unit is further configured to update the specific laser waveform using the specific value of at least one of the plurality of parameters; and one or more processors, the one or more processors controlling the autonomous vehicle in response to the specific laser waveform updated at least in part by the calibration unit.

15. The autonomous vehicle according to claim 14, wherein, The calibration unit is configured to calibrate the laser when integrated in the autonomous vehicle.

16. The autonomous vehicle according to claim 14, wherein, Compensation for distortion characteristics includes reducing the distortion characteristics of the laser, wherein the distortion characteristics of the laser are based on the difference between the specific laser waveform and the output frequency of the laser.

17. The autonomous vehicle according to claim 14, further comprising: A digital-to-analog converter configured to drive the laser with the specific laser waveform to determine the output frequency of the laser beam.

18. The autonomous vehicle according to claim 14, further comprising: An interferometer configured to output a reference signal, wherein the reference signal represents the propagation of the laser beam over a fixed distance, and wherein the calibration unit is configured to calculate the output frequency of the laser beam based on the reference signal from the interferometer.

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