Material-Sensing Light Imaging, Detection, And Ranging (LIDAR) systems optionally include a laser configured to generate a light pulse, a beam steerer configured to produce a polarization-adjusted light pulse emitted towards an object, at least one polarizer configured to polarize reflected, scattered, or emitted light returned from the object, and a processor configured to detect at least one material of the object based on an intensity and polarization of the polarized reflected, scattered or emitted light from the object. The beam steerer may include a kirigami nanocomposite. Methods are also provided, including, for example, generating a light pulse, adjusting a polarization of the light pulse to produce a polarization-adjusted light pulse emitted towards an object, polarizing reflected, scattered, or emitted light returned from the object, and detecting at least one material of the object based on an intensity and polarization of the polarized reflected, scattered or emitted light from the object.

FIELD

The present disclosure relates to Light Imaging, Detection, And Ranging (LIDAR) systems and, more particularly, to materials-sensing LIDAR systems and methods for making and using the same.

BACKGROUND

LIDAR is a surveying method that measures distance to an object by illuminating the object with a pulsed laser light, and measuring the reflected pulses with a sensor. Differences in laser return times and wavelengths can then be used to make digital 3D-representations of the detected object. LIDAR may be used to produce high-resolution maps, with applications in geodesy, geomatics, archaeology, geography, geology, geomorphology, seismology, forestry, atmospheric physics, laser guidance, airborne laser swath mapping (ALSM), and laser altimetry. LIDAR technology may also be used for the control and navigation of autonomous cars.

A conventional LIDAR device may operate as follows. A laser source produces a pulse of polarized or unpolarized light at a specific wavelength. When the light is first emitted, a time-of-flight sensor records the initial time. The time-of-flight is used to determine the total distance the light travels from source to detector by using the speed at which light travels.

The emitted light is then “steered” in a given angle. This “steering” can also include the splitting of a light pulse into multiple pulse components aimed at various angles. The steering angle(s) will change over time in order to obtain a specific field of view for a comprehensive mapping of the environment. After it has been aimed, the light may pass through linear polarization optics before and after the emission. These types of LIDARs are known as polarization LIDARs, and may use polarization optics at a registration step.

Conventional LIDAR devices typically employ optical lenses that are bulky and expensive. Moreover, the optical lenses utilized in conventional LIDAR devices require extensive protective packaging due to their sensitivity to moisture, which increases the weight, size, and complexity of the LIDAR devices in which they are employed. One well-known problem with implementation of LIDAR systems with rotational optics (e.g., the Velodyne-HDL64™ model) in autonomous vehicles and robots is their large size and high cost. The rotation of the entire device to steer laser beams reduces reliability, restricts miniaturization, and increases energy consumption. LIDAR systems based on solid-state beam steering address this problem, but their implementation is impeded by insufficient accuracy and range. Another issue is the performance of LIDARs and all the other sensors in inclement weather. Currently utilized laser beams with wavelengths around 900-940 nm can be strongly scattered by rain, fog, and snow, so that their read-outs can become highly uncertain under such conditions.

In addition, conventional LIDAR devices and their appurtenant analysis systems have proven limited in their ability to accurately to perform object recognition. For example, LIDAR point clouds are known to be based solely on a distance read-out from the laser source to the object. In this representation of the human world, a person resting on a bench and a statue of the same are identical. The problem is also true for a sleeping baby and a similarly sized plastic doll lying next to it, or when attempting to distinguish a black car in the distance from the pavement. The burden of distinguishing these objects and deciphering the surroundings is carried by the computational processing of these 3D maps.

Adequate classification of objects based on their geometries is not a trivial problem, requiring complex algorithms and large computational power, especially considering the highly dynamic nature of various environments. Furthermore, typical LIDAR hardware makes adequate object recognition and classification even more difficult because the current beam steering methods cause clustering and banding of points in LIDAR clouds, which results in ambiguous interpretation of 3D images and their individual points. Consequently, the geometry-based perception of surroundings demands high computational costs, large energy consumption, and long processing times.

Accordingly, improved LIDAR systems and methods are desired, especially LIDAR systems and methods providing an ability to identify the material from which an object is formed.

SUMMARY

In certain aspects, the present disclosure provides a system comprising a laser configured to generate a light pulse, a beam steerer configured to produce a polarization-adjusted light pulse emitted towards an object, at least one polarizer configured to polarize reflected, scattered, or emitted light returned from the object, and a processor configured to detect at least one material of the object based on an intensity and polarization of the polarized reflected, scattered or emitted light from the object.

In one aspect, the beam steerer comprises a kirigami nanocomposite.

In one aspect, the at least one polarizer comprises a kirigami nanocomposite.

In one aspect, the processor is further configured to classify the object based on the detected at least one material of the object.

In a further aspect, the processor is configured to classify the object based on the detected at least one material of the object by applying a machine-learning algorithm.

In a further aspect, the machine-learning algorithm comprises an artificial neural network algorithm.

In one aspect, the beam steerer is configured to adjust a polarization of the light pulse to produce the polarization-adjusted light pulse.

In one aspect, the beam steerer is configured to adjust a polarization of the light pulse by at least one of imparting a polarization to an unpolarized light pulse and changing a polarization of a polarized light pulse.

In one aspect, the beam steerer is configured to adjust a polarization of the light pulse by applying at least one of the following types of polarization: linear polarization, circular polarization, and elliptical polarization.

In a further aspect, applying linear polarization comprises applying at least one of s-type linear polarization and p-type linear polarization.

In one aspect, the at least one polarizer is configured to polarize the reflected, scattered, or emitted light returned from the object by applying at least one of the following types of polarization: linear polarization, circular polarization, and elliptical polarization.

In a further aspect, the applying is applying linear polarization that comprises applying at least one of s-type linear polarization and p-type linear polarization.

In one aspect, the at least one polarizer comprises a plurality of polarizers.

In one aspect, the system further comprises at least one polarization detector connected to the at least one polarizer and the processor, wherein the at least one polarization detector is configured to detect the intensity of the polarized reflected, scattered or emitted light from the object.

In a further aspect, the at least one polarization detector comprises a plurality of polarization detectors.

In a further aspect, the at least one polarization detector is configured to detect an angle of incidence associated with the polarized reflected, scattered or emitted light from the object.

In a further aspect, the processor is further configured to detect the at least one material of the object based on the angle of incidence associated with the polarized reflected, scattered or emitted light from the object.

In yet other variations, the present disclosure provides a method comprising generating a light pulse, adjusting a polarization of the light pulse to produce a polarization-adjusted light pulse emitted towards an object, polarizing reflected, scattered, or emitted light returned from the object, and detecting at least one material of the object based on an intensity and polarization of the polarized reflected, scattered or emitted light from the object.

In one aspect, adjusting the polarization of the light pulse is performed by a beam steerer comprising a kirigami nanocomposite.

In one aspect, the kirigami nanocomposite is manufactured via a vacuum-assisted filtration (VAF) process.

In one aspect, the kirigami nanocomposite is manufactured via a layer-by-layer (LBL) deposition process.

In one aspect, the method further comprises classifying the object based on the detected at least one material of the object.

In one aspect, classifying the object comprises classifying the object by applying a machine-learning algorithm.

In one aspect, the machine-learning algorithm comprises an artificial neural network algorithm.

DETAILED DESCRIPTION

The present disclosure provides LIDAR systems and methods configured to detect not only an object's distance, but also the object's material composition. According to some examples, material composition classification may be achieved by virtue of polarization analysis processed using machine learning algorithms.

The optical elements of the system described herein may be configured to bring all of the light emitted from a light source (e.g., a laser) to a known polarization state, such that the shift in polarization can be accurately measured later on. This light then travels until it reaches an object interface (the object being composed of one or more materials), at which point a portion of the light will be diffusely reflected back.

This disclosure describes, among other things, a new method of perception of surroundings by creating semantic maps of 3D space with the addition of materials and surface texture (MST) classification at each point of the LIDAR cloud. The MST classification inferred from the polarization signature of the returned photons may reduce the ambiguity of 3D point clouds and facilitate the recognition of various objects (metal points, glass points, rough dielectric points, etc.). The polarization classification may precede the surface tangent plane estimation and, therefore, may pre-identify the objects by grouping the points with similar polarization signatures. LIDARs that are equipped with MST classification will be referred to herein as M-LIDARs.

According to one example of the instant disclosure, the M-LIDAR technology may be configured to be lightweight and conformable through the use of kirigami optics, rather than conventional, bulky optics, such as near-infrared optics and the like. According to one example, the M-LIDAR systems and methods described herein may be used in the detection of black ice for vehicles with different degree of automation.

Referring now toFIG. 1, a representative simplified M-LIDAR system100is provided. The M-LIDAR system100may include a laser102, a beam steerer106, a first polarizer114, a second polarizer116, a first polarization detector122, a second polarization detector124, and a processor126. AlthoughFIG. 1illustrates first and second polarizers114,116and first and second polarization detectors122,124, according to some implementations, only a single polarizer (e.g., the first polarizer114) and a single polarization detector (e.g., the first polarization detector122) may be included as part of the system100without departing from the teachings of the present disclosure. Furthermore, according to certain examples, more than two polarizers and/or more than two polarization detectors may be included as part of the system100without departing from the teachings herein.

For purposes of simplicity and illustration, throughout the remainder of this disclosure the first polarizer114will be treated as a s-polarization linear polarizer114. Similarly, for purposes of simplicity and illustration, the second polarizer116will be treated as a p-polarization linear polarizer116. Further, the first polarization detector122will be treated as a p-polarization detector122and the second polarization detector will be treated as a s-polarization detector124.

However, as will be appreciated by those having ordinary skill in the art, the polarizers114,116may be configured for a variety of different types of polarization without deviating from the teachings herein. For example, a given polarizer may be configured to perform linear polarization (e.g., s or p type linear polarization), right-circular polarization, left-circular polarization, elliptical polarization, or any other suitable type of polarization known in the art. Similarly, a given detector may be configured to detect linearly-polarized light (e.g., s or p type linearly polarized light), right-circular polarized light, left-circular polarized light, elliptically-polarized light, or any other type of polarized light known in the art. According to some examples, the polarization of a light beam (i.e., a combination of two or more light pulses) may be modulated from pulse to pulse to obtain additional information about one or more objects under consideration.

As discussed in additional detail below, the system100may be configured to detect one or more materials making up an object110, and classify the object110based, at least in part, on the detected materials. According to some examples, the object classification may be performed using one or more artificial intelligence algorithms including, but not limited to, neural network-based artificial intelligence.

In operation, the system100may function as follows. The laser102may be configured to generate (i.e., emit) one or more polarized or unpolarized light pulses, the one or more polarized or unpolarized light pulses collectively forming a polarized/unpolarized light beam104. According to the example shown inFIG. 1, each pulse includes an s-polarization component (represented by the dots along the beam104inFIG. 1) and a transverse p-polarization component (represented by the double-sided arrows running in a perpendicular direction through the beam104inFIG. 1). Alternatively (and in conjunction with the preceding discussion on different types of polarized light), the pulses may include, for example, left and right circularly polarized sequences, elliptically polarized sequences, any combination of the foregoing, or any other suitably polarized light sequences.

According to some examples, the laser102may be configured to generate anywhere from one, to over one million, pulses a second. Furthermore, according to some implementations, the laser102may constitute a 550 nanometer (nm), 808 nm, 905 nm, or 1550 nm pulsed laser—or any other suitable wavelength of laser—without deviating from the teachings of the instant disclosure. For example, implementations for home robotics, autonomous vehicles, and machine vision may employ lasers having eye-safe frequencies above 800 nm. For outdoor applications, light beams in the water transparencies windows—around 900 nm to 1550 nm, for example—may be suitably employed. According to some implementations, upon a given pulse being generated by the laser102, the processor126—executing executable instructions—may record the initial time that the pulse is generated. This “time-of-flight” information may be subsequently utilized to calculate a distance to the object110by using the speed of light.

The beam104may be directed by the laser102through the beam steerer106. The beam steerer106may be configured to produce a polarization-adjusted light pulse. In certain aspects, a polarization of each polarized/unpolarized pulse of the polarized/unpolarized light beam104is adjusted by the beam steerer106. As used herein, adjusting a polarization may include imparting a polarization or changing a polarization. Thus, the beam steerer106may adjust a polarization of each polarized/unpolarized pulse of the polarized/unpolarized light beam104to produce one or more linearly polarized light pulses (the linearly polarized light pulses collectively forming a linearly polarized light beam108). While the foregoing example contemplates linear polarization, the beam steerer106may, according to some examples, circularly (e.g., left or right) or elliptically polarize the beam104. According to another example, the beam steerer106may not apply any polarization to the beam at all. For example, if the beam104is already polarized as it enters the beam steerer106, the beam steerer106may further modify the properties of the polarization-adjusted light pulse produced (e.g., split or modulate the pulses), but may not need to adjust the polarity of the previously polarized light pulse. Further still, according to some examples, the beam steerer106may polarize a first pulse of a beam according to a first type of polarization and polarize a second pulse of the same beam according to a second, different type of polarization. In addition to, or as an alternative to, performing polarization of the beam104, the beam steerer106may also control the direction of any beam (e.g., beam108) emitted therefrom. Further still, the beam steerer106may split a beam (e.g., beam104) into several different beams, whereby one or more of the beams are emitted a defined angles, to steer multiple beams at a time. This concept is illustrated inFIG. 1with regard to the many diverging arrows emanating from the beam steerer106.

In addition, or alternatively, in some examples, the beam steerer106may be configured to modulate the linearly polarized light beam108. In one example, the beam steerer106may include a kirigami nanocomposite beam steerer or the like. According to this example, and as discussed in additional detail below, the beam steerer106may be configured to linearly polarize and/or modulate the linearly polarized light beam108by increasing or decreasing an amount of strain applied to the kirigami nanocomposite beam steerer.

Furthermore, according to one example, the beam steerer106may be configured to linearly polarize each unpolarized pulse of the unpolarized light beam104by linearly polarizing each unpolarized pulse of the unpolarized light beam104for p-polarization. This example is illustrated inFIG. 1where it can be seen that the beam104, after passing through the beam steerer106, no longer includes any s-polarization components (i.e., “dot” components shown in beam104are absent in linearly polarized light beam108). In alternative aspects, the linearly polarized light beam108may instead be p-polarized. Further, in certain aspects, the beam steerer106may modify, control, and steer the linearly polarized light beam108emitted towards object110, as will be discussed further herein. The beam steerer106may enable dynamic, wavelength-dependent beam steering and amplitude modulation of electromagnetic waves.

Continuing withFIG. 1, the linearly polarized light beam108may be diffusively reflected off the object110. One or more pulses of light collectively form a beam112that constitutes a reflected version of the linearly polarized light beam108. According to some examples, the reflected linearly polarized light beam112may have a different polarization than the linearly polarized light beam108(i.e., the beam pre-reflection off of the object110). This difference in status is illustrated by virtue of the beam112including both p-polarization and s-polarization components (reflected, respectively, by the dots and double-sided arrows along the path of the beam112), whereas the beam108only is shown to include p-polarization components. Further, the object110may include any suitable object (or target) made up of one or more different materials of which detection is desired. Although discussed above and in the sections that follow as being a reflected “linearly” polarized light beam112, according to certain examples, the reflected beam112may be polarized in a variety of different ways, including circularly or elliptically, without deviating from the teachings herein.

The reflected linearly polarized light beam112diffusively reflected off, scattered off of, or otherwise emitted by the object112may pass through the s-polarization linear polarizer114and/or the p-polarization linear polarizer116of the system100. In certain aspects, respective portions of the reflected, scattered, or otherwise emitted linearly polarized light beam112passes through both the s-polarization linear polarizer114and/or the p-polarization linear polarizer116of the system100. The s-polarization linear polarizer114is configured to linearly polarize the one or more light pulses making up the beam112for s-polarization to produce one or more reflected s-polarization light pulses (the one or more reflected s-polarization light pulses collectively forming a reflected s-polarization light beam118). Similarly, the p-polarization linear polarizer116is configured to linearly polarize the one or more light pulses making up the beam112for p-polarization to produce one or more reflected p-polarization light pulses (the one or more reflected p-polarization light pulses collectively forming a reflected p-polarization light beam120). According to some examples, the s-polarization linear polarizer114and/or the p-polarization linear polarizer116may include a kirigami nanocomposite or the like, such as kirigami nanocomposites of the types discussed above with regard to the beam steerer106and/or below with regard toFIGS. 4a-4dand 5a-5c. However, those having ordinary skill in the art will recognize that non-kirigami nanocomposites or other optic devices may be employed as part of the system100, according to some examples, without deviating from the teachings herein.

Similar arrangements of polarizers114,116may be utilized, according to some examples, for the polarization of left and right circularly polarized light or elliptically polarized light reflected, scattered or otherwise emitted off/from the object110.

An s-polarization detector122may be configured to detect an intensity of each of the one or more reflected s-polarization light pulses forming the reflected s-polarization light beam118. In addition, according to some implementations, the s-polarization detector122may be configured to detect an angle of incidence associated with the reflected s-polarization light beam118. The detected intensity of the one or more reflected s-polarization light pulses forming the reflected s-polarization light beam118and/or the detected angle of incidence associated with the reflected s-polarization light beam118may be utilized by the processor126to perform material type detection (using, for example, MST classification), as discussed in additional detail below.

Similarly, a p-polarization detector124may be configured to detect an intensity of each of the one or more reflected p-polarization light pulses forming the reflected p-polarization light beam120. In addition, according to some implementations, the p-polarization detector124may be configured to detect an angle of incidence associated with the reflected p-polarization light beam120. The detected intensity of the one or more reflected p-polarization light pulses forming the reflected p-polarization light beam120and/or the detected angle of incidence associated with the reflected p-polarization light beam120may also be utilized by the processor126to perform material type detection, as discussed in additional detail below.

The processor126is configured to detect at least one material of the object110based on (i) the detected intensities of the one or more light pulses forming beams118and/or120and/or (ii) the detected angles of incidence associated with the reflected s-polarization light beam118and/or the reflected p-polarization light beam120. More specifically, according to some examples, the processor126is configured to apply machine-learning algorithms to detect the one or more materials making up the object110. As used herein, “applying a machine-learning learning algorithm” may include, but is not limited to, executing executable instructions stored in memory and accessible by the processor. In addition, according to one example, the specific machine-learning algorithm used for material detection may include an artificial neural network. However, other machine-learning algorithms known in the art may be suitably employed without deviating from the teachings of the instant disclosure.

Furthermore, according to some examples, the processor126may be configured to classify the object110based on the detected material(s) of the object110by applying a machine-learning algorithm. Again, the machine learning algorithm used for object classification may include an artificial neural network. However, other machine-learning algorithms known in the art may be suitably employed without deviating from the teachings of the instant disclosure.

Before turning toFIG. 2, the following reflects an overview of the process for detecting the material(s) of an object utilizing a M-LIDAR system, such as the system100shown inFIG. 1

As noted above, one aim of the instant disclosure is to enable the detection of object materials and to reduce the data processing necessary for modern LIDAR devices by obtaining more data at each point in the point cloud. This additional polarization data, when combined with machine learning algorithms, enables material detection, which simplifies object recognition for a variety of applications including, but not limited to, autonomous vehicles, machine vision, medical applications (e.g., devices to assist the blind), and advanced robotics.

An M-LIDAR system according to example implementations of the instance disclosure may operate as follows. The pair of detectors (e.g., detectors122,124) with perpendicularly oriented linear polarizers (e.g., polarizers114,116) may be used to measure the return light (e.g., the one or more pulses of light constituting the reflected version of the linearly polarized light beam112). Some of the diffusely backscattered light (e.g., reflected light112) may be directed at the detectors (e.g., detectors122,124) and pass through the narrow band interference filters (e.g., linear polarizers114,116) placed in front of each detector pair (e.g., detector pair122/124). The narrow band interference filters may only allow a small range of wavelengths to pass through (e.g., 1-2 nm), which may reduce undesired noise from ambient lighting or external sources.

According to other examples of the foregoing system, the system may be configured to detect, and perform machine-learning processing upon, circularly and/or elliptically polarized light reflected, scattered, or otherwise emitted by an object.

Due to this selectivity, an M-LIDAR system in accordance with the present disclosure (e.g., system100) may be configured to simultaneously measure multiple wavelengths, completely independently. The coherent light may then be polarized using, for example, a co-polarizer and/or cross-polarizer. The intensity of light may decrease by some amount as it travels through the polarizers, depending on the shift in polarization upon reflecting off of the object (e.g., object110). Beam focusing optics (e.g., polarizers114,116) may direct the coherent, polarized light (e.g., beams118,120) towards the detection surface (e.g., the surfaces of detectors122,124), and the angle from which the returned light traveled (i.e., the angle(s) of incidence) can be detected based on the location the light strikes on the detection screen.

Once the detectors identify light, the time-of-flight sensor (e.g., the time of flight sensor implemented in the processor126) may record the travel time of that light pulse. Each light pulse may have its intensity measured for both the co-polarized and cross-polarized detectors, and these two values in combination allow the polarization effects caused during the reflection to be quantified.

Following this process, the following parameters may be detected: (i) initial angle(s) at which the beam was steered; (ii) angle(s) from which the backscattered light returns; (iii) the time-of-flight from emission to detection; and (iv) the intensity at each detector.

Note that due to the detectors being at different locations, a single pulse of light may take a slightly different amount of time to reach each detector. By understanding the system geometry as well as the relationship between intensity and distance, this difference can be compensated for and the intensity at one detector precisely adjusted. The time-of-flight data may be used to determine the distance between the source (e.g., the laser102) and the object (e.g., the object110), and—in conjunction with the initial and return angle—the specific location of that point in space, relative to the M-LIDAR system, can be determined. These compensated intensity values may contain information indicative of the material off of which the light pulse reflected. Making use of these values, machine learning algorithms may provide robust and comprehensive material recognition capabilities.

The process described above of emitting a pulse of light, the light diffusely reflecting off an object, the reflected light being measured by the detectors, and the location of the object relative to the source being determined may be repeated on the order of one to millions of times per second. A point is generated each time, and these points are mapped onto the same coordinate system to create a point cloud.

Once the point cloud is generated, one or more machine learning algorithms may be used to cluster points into objects, and ultimately characterize the respective material(s) of each object (e.g., where a plurality of objects is detected). Points may be clustered based on, for example, one or more values of the measured intensities, also, in some examples, based on proximity of similar points.

Once a cluster is determined using intensity values, a machine learning algorithm may be used to correlate the measured value to a database of known materials to classify the material of that cluster of points. This process may be repeated for all clusters in the system. An understanding of surrounding materials enables the system (e.g., as implemented in an automobile, robot, drone, etc.) to make faster, more educated decisions about what the objects themselves might be. From there, factors such as risk involved can be assessed and decisions can be subsequently made (e.g., in the case of the system detecting black ice ahead of a vehicle). As the process continues over time, more information can be extracted from changes perceived and an even better understanding of the surroundings developed.

The MST classification technology is also applicable to the detection of an object whose surface is modified to enhance detection, for example, that is painted or textured with a macroscale, microscale, nanoscale, or molecular pattern to produce the reflected beams with the specific optical response adapted to the fast MST classification by LIDARs. Examples of such surface treatment include paints containing additives that produce reflected, scattered or otherwise emitted light with specific linear, circular, or elliptical polarization. In one instance, metal nano/micro/macro wires or axial carbon nanomaterials are added to the base paint. An alignment pattern can be random, linear, spiral, herring-bone or any other pattern that produces the specific polarization signature enabling fast identification of a specific object. By way of non-limiting example, this may be used for creating markers on roads, road signs, barriers, pylons, guard rails, vehicles, bicycles, clothing, and other objects.

Another implementation of surface treatment facilitating MST classification may include the addition of chiral inorganic nanoparticles to base paint used to coat such objects described above, such as road markers, vehicles, bicycles, clothing, etc. The chiral nanoparticles may display a specific and very strong circular polarization response to the beams used by the LIDARs. They can be mixed in the paint with a specific ratio to create polarization signatures (e.g., “bar-codes”) for specific objects.

Another example of polarization tagging of an object may include using surface texturing that creates a particular polarization response. One example of such texturing may include creating nanoscale patterns of metal, semiconductor, insulating or ceramic nanoparticles with specific geometrical characteristics resulting in a defined polarization response to the laser in LIDARs. Two examples of such patterns include (a) linear nanoscale or microscale surface features that result in a linear polarization of the reflected, scattered, or emitted light from an object and (b) out-of-plane protruding chiral patterns on the metal surfaces resulting in a specific chirality and therefore circular polarization of the reflected, scattered, or emitted light from an object.

According to some examples, the foregoing system and method may be employed to accurately recognize materials for use in autonomous vehicles, machine learning, medical applications, and advanced robotics.

Existing, conventional LIDAR systems primarily work via measurement of the distance between an object and the laser source, typically using either time-of-flight data or phase shifts. In such cases, the objects are classified based on geometries and patterns in the arrangement of points in the cloud. Some more advanced LIDAR point cloud classification methods make use of an additional parameter: overall intensity.

Based on how strong the signal of the returning light pulse is, a system can effectively detect differences in color. This additional piece of data makes it easier to recognize object boundaries within point clouds, decreasing the amount of processing required to classify all points. However, applications like autonomous vehicles may require a higher degree of certainty, which overall intensity cannot achieve. Furthermore, the detection of objects at long distances may be achieved with a single point detection taking advantage of MST classification, rather than multiple point detection and processing of the type employed in conventional LIDAR systems.

Accordingly, the method described herein changes the approach machine vision currently takes towards object recognition. Instead of solely relying on geometry, movement, and color to determine the identity of an object, the system described herein takes into account yet another parameter: polarization. Upon reflecting off of a material interface, light experiences some change in polarization. This polarization change is quantified by measuring the intensities of light after having passed through both co-polarized and cross-polarized filters. This additional data may be paired with machine learning approaches to significantly improve clustering, and by extension, object recognition capabilities. Traditional object recognition methods are quite computationally expensive. The approach described herein may significantly reduce the processing power required by LIDAR by using a material-based approach rather than the current, geometry-based approach.

In addition to traditional distance measurements, the polarization data collected by the instant system allows machine learning algorithms to determine the material(s) from which an object is composed. Current LIDAR systems lack awareness or information about the materials in a surrounding environment. When achieved, such information provides context for greater situational understanding and smarter decisions. In the case of an autonomous vehicle, this enhanced understanding of the environment may lead to improved safety of the passengers because accurate, timely detection of potential hazards produce improved decision-making capabilities.

Turning now toFIGS. 2a-2b, scanning electron microscope (SEM) images of nano-kirigami sheets are shown that may be used to form the nano-kirigami nanocomposite optic components incorporated into an M-LIDAR system. The optically active kirigami sheets, such as those shown inFIGS. 2a-2b, may be manufactured from ultra-strong nanoscale composites with cut patterns of 0.5-5 μm in length, according to some examples of the present disclosure. In certain aspects, composite materials (including highly conductive composite materials) can be modified by using a concept from the ancient Japanese art of paper cutting known as “kirigami.” The present disclosure thus provides a kirigami approach to engineer elasticity by using a plurality of cuts or notches that create a network on a planar polymeric material, such as a composite or nanocomposite material. Such cuts (extending from one side to the other of the material, for example, in a polymeric or composite material) can be made by top-down patterning techniques, such as photolithography, to uniformly distribute stresses and suppress uncontrolled high-stress singularities within the polymeric or nanocomposite material. This approach can prevent unpredictable local failure and increases the ultimate strain of rigid sheets from 4% to 370%, by way of non-limiting example.

By using microscale kirigami patterning, a stiff nanocomposite sheet can acquire high extensibility. Moreover, kirigami cut-patterned composite sheets maintain their electrical conductance over the entire strain regime, in marked contrast to most stretchable conductive materials. The kirigami structure may comprise a composite, such as a nanocomposite. In certain aspects, the kirigami structure may be a multilayered structure having at least two layers, where at least one layer is a polymeric material. The polymeric material may be a composite or nanocomposite material. The composite material comprises a matrix material, such as a polymer, a polyelectrolyte, or other matrix (e.g., cellulose paper), and at least one reinforcement material distributed therein. In certain aspects, nanocomposite materials are particularly suitable for use in a kirigami structure, which is a composite material comprising a reinforcement nanomaterial, such as nanoparticles. The composite may be in the form of a sheet or film in certain variations.

A “nanoparticle” is a solid or semi-solid material that can have a variety of shapes or morphologies, however, which are generally understood by those of skill in the art to mean that the particle has at least one spatial dimension that is less than or equal to about 10 μm (10,000 nm). In certain aspects, a nanoparticle has a relatively low aspect ratio (AR) (defined as a length of the longest axis divided by diameter of the component) of less than or equal to about 100, optionally less than or equal to about 50, optionally less than or equal to about 25, optionally less than or equal to about 20, optionally less than or equal to about 15, optionally less than or equal to about 10, optionally less than or equal to about 5, and in certain variations, equal to about 1. In other aspects, a nanoparticle that has a tube or fiber shape has a relatively high aspect ratio (AR) of greater than or equal to about 100, optionally greater than or equal to about 1,000, and in certain variations, optionally greater than or equal to about 10,000.

In certain variations, a nanoparticle's longest dimension is less than or equal to about 100 nm. In certain embodiments, the nanoparticles selected for inclusion in the nanocomposite are electrically conductive nanoparticles that create an electrically conductive nanocomposite material. The nanoparticles may be substantially round-shaped nanoparticles, that have low aspect ratios as defined above, and that have a morphology or shape including spherical, spheroidal, hemispherical, disk, globular, annular, toroidal, cylindrical, discoid, domical, egg-shaped, elliptical, orbed, oval, and the like. In certain preferred variations, the morphology of the nanoparticle has a spherical shape. Alternatively, the nanoparticle may have an alternative shape, such as a filament, fiber, rod, a nanotube, a nanostar, or a nanoshell. The nanocomposite may also include combinations of any such nanoparticles.

Furthermore, in certain aspects, a particularly suitable nanoparticle for use in accordance with the present teachings has a particle size (an average diameter for the plurality of nanoparticles present) of greater than or equal to about 10 nm to less than or equal to about 100 nm. The conductive nanoparticles may be formed of a variety of conductive materials including metallic, semiconducting, ceramic, and/or polymeric nanoscale particles having plurality of shapes. The nanoparticles may have magnetic or paramagnetic properties. The nanoparticles may comprise conductive materials, such as carbon, graphene/graphite, graphene oxide, gold, silver, copper, aluminum, nickel, iron, platinum, silicon, cadmium, mercury, lead, molybdenum, iron, and alloys or compounds thereof. Thus, suitable nanoparticles can be exemplified by, but are not limited to, nanoparticles of graphene oxide, graphene, gold, silver, copper, nickel, iron, carbon, platinum, silicon, seedling metals, CdTe, CdSe, CdS, HgTe, HgSe, HgS, PbTe, PbSe, PbS, MoS2, FeS2, FeS, FeSe, WO3-x, and other similar materials known to those of skill in the art. Graphene oxide is a particularly suitable conductive material for use as reinforcement in the composite. In certain variations, the nanoparticles can comprise carbon nanotubes, such as single walled nanotubes (SWNTs) or multi-walled nanotubes (MWNTs), for example. SWNTs are formed from a single sheet of graphite or graphene, while MWNTs include multiple cylinders arranged in a concentric fashion. The typical diameters of SWNT can range from about 0.8 nm to about 2 nm, while MWNT can have diameters in excess of 100 nm.

In certain variations, the nanocomposite may comprise a total amount of a plurality of nanoparticles of greater than or equal to about 1% by weight to less than or equal to about 97% by weight, optionally greater than or equal to about 3% by weight to less than or equal to about 95% by weight, optionally greater than or equal to about 5% by weight to less than or equal to about 75% by weight, optionally greater than or equal to about 7% by weight to less than or equal to about 60% by weight, optionally greater than or equal to about 10% by weight to less than or equal to about 50% by weight of a total amount of nanoparticles in the nanocomposite. Of course, appropriate amounts of nanoparticles in a composite material depend upon material properties, percolation thresholds, and other parameters for a particular type of nanoparticle in a specific matrix material.

In certain variations, the nanocomposite may comprise a total amount of a polymeric matrix material of greater than or equal to about 1% by weight to less than or equal to about 97% by weight, optionally greater than or equal to about 10% by weight to less than or equal to about 95% by weight, optionally greater than or equal to about 15% by weight to less than or equal to about 90% by weight, optionally greater than or equal to about 25% by weight to less than or equal to about 85% by weight, optionally greater than or equal to about 35% by weight to less than or equal to about 75% by weight, optionally greater than or equal to about 40% by weight to less than or equal to about 70% by weight of a total amount of matrix material in the nanocomposite.

In certain variations, the nanocomposite material comprises a plurality of electrically conductive nanoparticles and has an electrical conductivity of greater than or equal to about 1.5×103S/cm. In certain other aspects, the nanocomposite material may comprise a plurality of electrically conductive nanoparticles as a reinforcement nanomaterial and thus may have an electrical resistivity of less than or equal to about 1×10−4Ohm·m. In certain other variations, an impedance (Z) of the electrically conductive nanocomposite comprising a plurality of nanoparticles may be less than or equal to about 1×104Ohms (e.g., measured using an AC sinusoidal signal of 25 mV in amplitude with impedance values measured at a frequency of 1 kHz).

The polymeric or nanocomposite material may be in a planar form, such as a sheet, in an initial state (prior to being cut), but may be folded or shaped into a three-dimensional structure and thus used as a structural component after the cutting process. By way of example, a structure220including a portion of an exemplary nanocomposite material sheet230having a surface with tessellated cut pattern is shown inFIG. 10. Sheet230includes a first row232of first discontinuous cuts242(that extend through the sheet230to create an opening) in a pattern that defines a first uncut region252between the discontinuous cuts242. A discontinuous cut is a partial or discrete cut formed in the sheet that leaves the entire sheet intact in its original dimensions, rather than being divided into separate smaller sheets or portions. If multiple discontinuous cuts242are present, at least some of them are noncontiguous and unconnected with one another so that at least one uncut region remains on the sheet as a bridge between the discontinuous sheets. While many cut patterns are possible, a simple kirigami pattern of straight lines in a centered rectangular arrangement as shown inFIG. 10is used herein as an exemplary pattern. The first uncut region252has a length “x.” Each discontinuous cut242has a length “L.”

In certain aspects, the length of each discontinuous cut (e.g., discontinuous cut242) may be on the micro- meso-, nano- and/or macroscales. Macroscale is typically considered to have a dimension of greater than or equal to about 500 μm (0.5 mm), while mesoscale is greater than or equal to about 1 μm (1,000 nm) to less than or equal to about 500 μm (0.5 mm). Microscale is typically considered to be less than or equal to about 100 μm (0.5 mm), while nanoscale is typically less than or equal to about 1 μm (1,000 nm). Thus, conventional mesoscale, microscale, and nanoscale dimensions may be considered to overlap. In certain aspects, the length of each discontinuous cut may be on a microscale, for example, a length that is less than about 100 μm (i.e., 100,000 nm), optionally less than about 50 μm (i.e., 50,000 nm), optionally less than about 10 μm (i.e., 10,000 nm), optionally less than or equal to about 5 μm (i.e., 5,000 nm), and in certain aspects less than or equal to about 1 μm (i.e., 1,000 nm). In certain aspects, the discontinuous cuts42may have a length that is less than about 50 μm (i.e., 50,000 nm), optionally less than about 10 μm (i.e., 10,000 nm), and optionally less than about 1 μm (i.e., less than about 1,000 nm).

In certain other variations, these dimensions can be reduced by at least 100 times to a nanoscale, for example a cut having a length of less than or equal to about 1 μm (1,000 nm), optionally less than or equal to about 500 nm, and in certain variations, optionally less than or equal to about 100 nm.

It should be noted that “x” and “L” may vary within rows depending on the pattern formed, although in preferred aspects, these dimensions remain constant.

A second row234of second discontinuous cuts244is also patterned on the sheet230. The second discontinuous cuts244define a second uncut region254therebetween. A third row236of third discontinuous cuts246is also patterned on the sheet230. The third discontinuous cuts246define a third uncut region256therebetween. It should be noted that the first row232, second row234, and third row236are used for exemplary and nominative purposes, but as can be seen, the tessellated pattern on the surface of sheet230has in excess of three distinct rows. The first row232is spaced apart from the second row234, as shown by the designation “y.” The second row234is likewise spaced apart from the third row236. It should be noted that “y” may vary between rows, although in certain aspects, it remains constant between rows. Such spacing between rows may likewise be on a micro- meso-, nano- and/or macroscale, as described above.

Notably, the first discontinuous cuts242in the first row232are offset in a lateral direction (along the dimension/axis shown as “x”) from the second discontinuous cuts244in the second row234, thus forming a tessellated pattern. Likewise, the second discontinuous cuts244in the second row234are offset in a lateral direction from the third discontinuous cuts246in the third row236. Thus, the first uncut region252, second uncut region254, and third uncut region256in each respective row cooperates to form a structural bridge260that extends from the first row232, across second row234, and to third row236.

In this regard, the sheet230having the patterned tessellated surface with the plurality of discontinuous cuts (e.g.,242,244, and246) can be stretched in at least one direction (e.g., along the dimension/axis shown as “y” or “x”). The sheet230formed of a nanocomposite thus exhibits certain advantageous properties, including enhanced strain.

In various aspects, an optic device incorporating a stretchable multilayered polymeric or composite material formed by a kirigami process is contemplated. By “stretchable” it is meant that materials, structures, components, and devices are capable of withstanding strain, without fracturing or other mechanical failure. Stretchable materials are extensible and thus are capable of stretching and/or compression, at least to some degree, without damage, mechanical failure or significant degradation in performance.

“Young's modulus” is a mechanical property referring to a ratio of stress to strain for a given material. Young's modulus may be provided by the expression:

E=(stress)(strain)=σϵ=LoΔ⁢L×FA
where engineering stress is σ, tensile strain is ϵ, E is the Young's modulus, L0is an equilibrium length, ΔL is a length change under the applied stress, F is the force applied and A is the area over which the force is applied.

In certain aspects, stretchable composite materials, structures, components, and devices may undergo a maximum tensile strain of at least about 50% without fracturing; optionally greater than or equal to about 75% without fracturing, optionally greater than or equal to about 100% without fracturing, optionally greater than or equal to about 150% without fracturing, optionally greater than or equal to about 200% without fracturing, optionally greater than or equal to about 250% without fracturing, optionally greater than or equal to about 300% without fracturing, optionally greater than or equal to about 350% without fracturing, and in certain embodiments, greater than or equal to about 370% without fracturing.

Stretchable materials may also be flexible, in addition to being stretchable, and thus are capable of significant elongation, flexing, bending or other deformation along one or more axes. The term “flexible” can refer to the ability of a material, structure, or component to be deformed (for example, into a curved shape) without undergoing a permanent transformation that introduces significant strain, such as strain indicating a failure point of a material, structure, or component.

Thus, the present disclosure provides in certain aspects, a stretchable polymeric material. In further aspects, the present disclosure provides a stretchable composite material that comprises a polymer and a plurality of nanoparticles or other reinforcement materials. The polymer may be an elastomeric or thermoplastic polymer. One suitable polymer includes polyvinyl alcohol (PVA), by way of non-limiting example.

For example, for certain materials, creating the surface having patterned kirigami cuts in accordance with certain aspects of the present disclosure can increase ultimate strain of initially rigid sheets to greater than or equal to about 100% from an initial ultimate strain prior to any cutting, optionally greater than or equal to about 500%, optionally greater than or equal to about 1,000%, and in certain variations, optionally greater than or equal to about 9,000%.

Notably, a wide range of maximum attainable strains or expansion levels can be achieved based on the geometry of the cut pattern used. The ultimate strain is thus determined by the geometry. The ultimate strain (% strain) is a ratio between a final achievable length, while being stretched to a point before the structure breaks, over the original or initial length (Li):

%⁢⁢strain⁢=Δ⁢LLi=Lc-x-2⁢y2⁢y
where Lcis a length of the cut, x is spacing between discontinuous cuts, and y is distance between discrete rows of discontinuous cuts. Thus, in certain variations, the polymeric materials, such as nanocomposites, having a surface with patterned cuts in accordance with certain aspects of the present disclosure can increase ultimate strain to greater than or equal to about 100%, optionally greater than or equal to about 150%, optionally greater than or equal to about 200%, optionally greater than or equal to about 250%, optionally greater than or equal to about 300%, optionally greater than or equal to about 350%, and in certain variations, optionally greater than or equal to about 370%. Additional discussion on kirigami composite materials and methods of making them are described in U.S. Publication No. 2016/0299270 filed as U.S. application Ser. No. 15/092,885 filed on Apr. 7, 2016 to Kotov et al. entitled “Kirigami Patterned Polymeric Materials and Tunable Optic Devices Made Therefrom,” the relevant portions of which are incorporated herein by reference.

In certain aspects, the kirigami nanocomposites can form tunable optical grating structures that can maintain stable periodicity over macroscopic length scale even under 100% stretching. The lateral spacing in diffraction patterns shows negative correlation with the amount of stretch, which is consistent with the reciprocal relationship between the dimensions in diffraction pattern and the spacing of the corresponding grating. The longitudinal spacing in the diffraction patterns exhibits less dependency on the amount of stretch, owing to the relatively small changes in longitudinal periodicity with lateral stretch. The diffraction patterns also show significant dependence on the wavelength of the incoming laser. The polymeric stretchable tunable optic grating structures present elastic behavior with the stretch and spontaneously recovers to the relaxed (i.e., un-stretched) geometry as the stretch is removed under cyclic mechanical actuation. The diffracted beams form clear patterns that change consistently with the deformation of the polymeric stretchable tunable optic grating structures. This behavior indicates excellent capability for dynamic, wavelength-dependent beam steering.

Three-dimensional (3D) kirigami nanocomposites thus provide a new dimension to traditional reflective and refractive optics due to the out-of-plane surface features, as illustrated inFIGS. 2a-2b. For example, the reconfigurable fins and slits formed by the cuts illustrated in the nano-kirigami sheets shown inFIGS. 2a-2ballow for efficient modulation of light by reversible expansion (or strain levels) of the kirigami cut sheets. Consequently, nano-kirigami sheets such as those shown inFIGS. 2a-2bmay be incorporated into one or more optic components of the M-LIDAR system described here. More specifically, these light, thin and inexpensive optical components may be used, for example, for the red and infrared portions of the light spectrum to achieve beam steering and/or polarization modulation. According to some implementations, kirigami nanocomposites of the type shown inFIGS. 2a-2bmay be utilized to form the beam steer106, s-polarization linear polarizer114, and/or p-polarization linear polarizer116of the system illustrated inFIG. 1.

In certain variations, kirigami nanocomposites can form kirigami optical modules manufactured from ultrastrong layer-by-layer (LbL) assembled nanocomposites. These nanocomposite materials having high strength, for example, about 650 MPa and an elastic modulus (E) of about 350 GPa, for example, providing exceptional mechanical properties, environmental robustness, along with a wide temperature range of operations (e.g., from −40° to +40° C.), and proven scalability. High elasticity of LbL composites makes them reconfigurable and their high temperature resilience enables integration with different types of actuators and CMOS compatibility. In certain aspects, the nanocomposite material may be coated with plasmonic films such as titanium nitride, gold, and the like to enhance interaction with target wavelengths of photons, for example, 1550 nm photons where the laser source has a wavelength of 1550 nm.

In certain other variations, the kirigami nanocomposite sheets can include magnetic materials distributed therein or coated thereon. For example, a layer of nickel may be deposited on an ultra-strong composite. The layer of nickel can serve as a magnetic and reflective layer, thus providing a magentoactive kirigami element. The kirigami units thus can be directly integrated with LIDAR components and serve as beam steerers (for example, using first and second order diffraction beams) or as polarizers (for example, using the first order diffraction beams).

Referring now toFIGS. 3a-3c, images depicting laser diffraction patterns from nano-kirigami-based graphene composites are shown. For reference, the scale bars shown in the upper right hand corners ofFIGS. 3a-3crepresent 25 mm.FIG. 3adepicts the laser diffraction pattern for nano-kirigami-based graphene composites for 0% strain (relaxed state).FIG. 3bdepicts the laser diffraction pattern for from nano-kirigami-based graphene composites for 50% strain. Finally,FIG. 3cdepicts the laser diffraction pattern for from nano-kirigami-based graphene composites for 100% strain.

Polarization modulation of LIDAR beams and polarization analysis of returned photons will enable the acquisition of information about the object material that is currently lacking in, for example, car safety and robot vision devices. Machine Learning (ML) algorithms may be trained to recognize different materials and MST classification based on their unique polarization signatures may be achieved. The MST classification of objects by material may, among other advantages, accelerate object recognition and improve the accuracy of machine perception of surroundings.

Before turning to the specifics ofFIGS. 4a-4d, it bears noting that system100ofFIG. 1and corresponding methods of material detection and object classification may be carried out, according to some examples of the present disclosure, utilizing non-nano-kirigami optical elements. Indeed, such non-nano-kirigami optical element-based M-LIDAR systems may be preferred for certain applications (e.g., where size and weight are not major concerns). Accordingly, implementations of the presently disclosed M-LIDAR system in which nano-kirigami optical elements are not utilized, other traditional optical components such as (i) IR polarizers; (ii) beam splitters; (iii) lenses made from CdS, ZnS, silicon; and/or (iv) similarly suitable optical elements may be equally employed without deviating from the teachings herein. However, nano-kirigami optical elements are generally favored for M-LIDAR systems that benefit from being light and small.

With that as a backdrop,FIGS. 4a-4dillustrate a step-by-step lithographic-type process for manufacturing a nano-kirigami-based optical element, such as a beam splitter or linear polarizer. According to one example, the process for manufacturing the nano-kirigami-based optical element set forth inFIGS. 4a-4dmay include using a vacuum assisted filtration (VAF), whereby nanocomposite material may be deposited as a layer on a stiff (e.g., plastic) substrate suitable for lithographic patterning. As noted above, U.S. Publication No. 2016/0299270 describes methods of making such nanocomposite materials, including by vacuum assisted filtration (VAF) and layer-by-layer (LBL) deposition process techniques. Nanocomposites manufactured according to this process are known to display high toughness and strong light absorption.

FIG. 4a. is a simplified illustration of the first step of the process400whereby a nanocomposite layer404ais deposited on a substrate402via VAF, layer-by-layer deposition (LBL), or any other suitable deposition method known in the art.FIG. 4billustrates the second step in the process400after the nanocomposite404aofFIG. 4has been patterned to produce a patterned kirigami nanocomposite404bthrough select regions of the nanocomposite layer404a, for example, via a photolithographic cutting process, atop the substrate402.FIG. 4cillustrates the third step in the process400whereby the cut or patterned kirigami nanocomposite404bis released (e.g., lifted) from the substrate402. Finally,FIG. 4dillustrates the final step of the process400whereby at least a portion of the patterned kirigami nanocomposite408has been incorporated into a subassembly configured for, among other things, beam steering and/or modulation.

The subassembly shown inFIG. 4dincludes the patterned kirigami nanocomposite portion408, a microfabricated silicon layer406housing, and one or more bent beam actuators410. Dual sided arrows412illustrate the potential directions of the actuator410motions. As discussed in additional detail below, the bent beam actuators410may be configured to exert reversible strain on the kirigami nanocomposite portion408so as to, for example, adjust the size and/or orientation of various slits and/or fins making up the pattern of the kirigami nanocomposite portion408. Thus, the kirigami nanocomposite portion408may thus be reversibly stretched at strain levels ranging from 0% to 100%.

A more detailed discussion of the patterning aspect of the process400shown inFIGS. 4a-4dfollows. The manufacturing of the kirigami transmissive optical modules may follow the step-by-step diagram shown inFIGS. 4a-4d. The modulation of LIDAR laser beams in visible and IR ranges may require feature sizes at, for example, 3 μm, created over 0.1-1 cm widths. The feasibility of such patterns has already been demonstrated. The 2D geometry of the patterns may be selected based on computer simulations of their 2D to 3D reconfiguration when stretched or strained. The 3D geometry may be modeled for optical properties, for instance, polarization modulation in the desirable wavelength range. Photolithography may be a primary patterning tool, enabled by the chemistry of VAF composites described above. The patterning protocol may be substantially similar to that currently being used for large scale microfabrication. For example, VAF composites on glass substrates may be coated by standard SU8 photoresist following photo patterning using commercial conventional mask aligner. Examples of kirigami patterns prepared are shown inFIGS. 5a-5c, discussed in greater detail below.

Kirigami optical elements may be manufactured by integrating kirigami nanocomposite sheets with commercial microelectromechanical actuators as show, for example, inFIG. 4d. The microelectromechanical systems (MEMS) kirigami units may be directly integrated with LIDAR components and serve as beam steerers (using, for example, first and second order diffraction beams) and/or polarizers (using, for example, the first order diffraction beams). Considering the nearly endless number of kirigami patterns and wide variety of 2D to 3D reconfigurations, kirigami optical elements with both beam steering and polarization capabilities, as well as other optical functions, are contemplated within the teachings herein.

With brief reference toFIGS. 5a-5c, various images of example kirigami optical elements are shown. For example,FIG. 5ais an image of a kirigami optical element, such as the kirigami optical elements described herein, fabricated on a wafer in line with the process400described above with regard toFIGS. 4a-4d.FIG. 5bis a SEM image of the kirigami optical element ofFIG. 5aunder 0% strain. Finally,FIG. 5cis a SEM image of the kirigami optical element ofFIG. 5aunder 100% strain. The scale bars depicted in the upper right hand corners ofFIGS. 5b-5care 50 μm.

Photolithographic techniques can be used to manufacture kirigami transmissive or reflective optical modules/elements. By way of example, modulation of LIDAR laser beams having a wavelength of about 1550 nm may have feature sizes from greater than or equal to about 1 μm to less than or equal to about 2 μm, created over widths ranging from greater than or equal to about 0.1 cm to less than or equal to about 1 cm exemplified by the current patterns inFIGS. 5a-5c. The two dimensional (2D) geometry of the patterns can be selected based on computer simulations of their 2D to three dimensional (3D) reconfiguration when stretched. The 3D geometry can be modeled for optical properties, for instance, polarization modulation in the desirable wavelength range.

Photolithography is a primary patterning technique that can be used in combination with LbL composites to form kirigami optical elements. In one example, the patterning protocol can include providing an LbL composite on a glass substrate that is coated by a standard SU-8 photoresist following photo patterning using a commercial mask aligner (UM Lurie Nanofabrication Facility, LNF). Such a process can form kirigami elements like those shown inFIGS. 5a-5c.

Another representative simplified compact M-LIDAR system300for use in a vehicle, such as an autonomous vehicle, is provided inFIG. 11. To the extent that the components in the M-LIDAR system300are similar to those in the M-LIDAR system100ofFIG. 1, for brevity, their function will not be repeated herein. The M-LIDAR system300may include a laser310, a beam steerer312, one or more polarizers (not shown, but similar to the first polarizer114and second polarizer116described in the context ofFIG. 1), and a processor (not shown, but similar to processor126that shown inFIG. 1). In the M-LIDAR system300, a pulse generator312is connected to laser310and generates a polarized or unpolarized first light pulse314and a polarized or unpolarized second light pulse316. The pulse generator312is connected to an oscilloscope324. The first light pulse314and second light pulse316generated by laser310are directed towards a beam steerer318, which may in certain aspects, be a kirigami-based beam steerer like those discussed previously above. The beam steerer318is connected to and controlled by a servo-motor/Arduino352. The servo-motor/Arduino352is connected to a controller350that may be a MATLAB™ control, by way of non-limiting example. As noted previously above, the beam steerer318may polarize, modify, split, and/or modulate one or both of the first light pulse314and second light pulse316by way of non-limiting example, as discussed previously above. The first light pulse314and second light pulse316are then directed towards an object340to be detected.

The first light pulse314and second light pulse316may be diffusively reflected off the object110. One or more pulses of light collectively form a first reflected beam342and a second reflected beam344that constitutes a reflected version of the first light pulse314and second light pulse316. According to some examples, the first reflected beam342and the second reflected beam344may have a different polarization than the first light pulse314and second light pulse316(i.e., prior to reflection off of the object340). After reflecting from the object340, the first reflected beam342and second reflected beam344may be directed towards an off-axis parabolic reflector/mirror330that redirects the first reflected beam342and second reflected beam344towards a beam splitter360.

The first reflected beam342is thus split and directed to both a first detector362and a second detector364. The first detector362and the second detector364may be connected to the oscilloscope324. The first detector362may be an s-polarization detector configured to detect an intensity of one or more reflected s-polarization light pulses forming the first reflected light beam342. Likewise, the second detector364may be a p-polarization detector configured to detect an intensity of one or more reflected p-polarization light pulses forming the first reflected light beam342. After passing through beam splitter360, the second reflected light beam344is directed to both the first detector362and the second detector364, where an intensity of the s-polarization light pulses and/or p-polarization light pulses can be detected from the second reflected light beam344. The first detector362and the second detector364may be connected to a processor (not shown), which further analyzes information received therefrom as described previously above. The M-LIDAR system300is compact and may have dimensions of about 7 inches by 12 inches, by way of non-limiting example, making it particularly suitable to mount in a vehicle.

MST classification, as introduced above, may be realized according to examples of the present disclosure through the use of light source-based MST classification with light polarization classifiers added to point clouds. In one example, for each 3D range measurement of a point cloud, linear/circular polarization of returned photons may be acquired. In addition, local curvature and local scattering conditions may be made directly based on the polarization state of the returned photons, although the relationship between surface properties and polarization state may, in some instances, be noisy due to surface roughness.

Referring now toFIG. 6, MST polarization analysis of reflected laser light was performed using AI data processing with a neural network algorithm to produce the confusion matrix ofFIG. 6. More specifically, the confusion matrix was produced based on analysis of s and p-polarized light beams (such as the s and p-polarized light beams118,120shown inFIG. 1). Along the x-axis, predicted types of materials for a test object subjected to the M-LIDAR system and processing methods described herein are identified. Along the y axis, the true types of materials for the test object are identified. The accuracy of various predictions of the AI algorithm for the various material types are reflected at the intersections of the predicted material types and true material types. As shown, the materials detection functionality of the M-LIDAR system may be accomplished with a high degree of accuracy (including at or above 99% in some instances) using these polarized light beams.

Referring now toFIG. 7, a confusion matrix for the detection of simulated black ice compared to other materials is shown. Again, the materials detection functionality of the M-LIDAR system may be accomplished with a high degree of accuracy (including at 100% in some instances).

FIG. 8illustrates one example of an M-LIDAR device800for use in, for example, black ice detection (e.g., when installed in a vehicle or the like). While the present example focuses on a black-ice detection application, those having ordinary skill will recognize that the device800is not limited to black ice detection, and may be suitably employed for a wide range of material detection and object classification applications, including on autonomous vehicles. The device800includes a housing802, an emitter804(i.e., an emitter for emitting light pulses making up a laser light beam), a first detection806a, and a second detector806b. According to one example, one or more of the detectors806a,806binclude orthogonal polarization analyzers. Furthermore, according to one example, one or more of the emitter804, detector806a, and/or detector806bmay be made with kirigami optical elements. Although the primary example of the device is use within an automobile, the device could also be used, for example, within an aircraft, such as a drone or the like.

Referring now toFIG. 9, a flowchart illustrating a method900of performing object classification using an M-LIDAR system is provided. The method900begins at902where an unpolarized light pulse is generated. At904, the unpolarized light pulse is linearly polarized to produce a linearly polarized light pulse. The linearly polarized light pulse may be emitted towards an object and reflect back off of the object to produce a reflected linearly polarized light pulse. At906, the reflected linearly polarized light pulse may be linearly polarized for s-polarization to produce a reflected s-polarization light pulse.

At908, the reflected linearly polarized light pulse may be linearly polarized for p-polarization to produce a reflected p-polarization light pulse. At910, an intensity of the reflected s-polarization light pulse may be detected. At912, an intensity of the reflected p-polarization light pulse may be detected. At914, at least one material of the object may be detected based on the intensity of the reflected s-polarization light pulse and the intensity of the reflected p-polarization light pulse. Finally, at916, the object may be classified based on the detected at least one material. Following916, the method900concludes.

Finally, according to some examples, kirigami patterns may be used as MST tags for the polarization-based detection of objects. Mass-produced kirigami components can also be added to paints to impart a specific polarization response in road signs, clothing, markers, vehicles, household items, or any other suitable objects.

In certain variations, LIDAR systems of the present disclosure can provide modulation of transmitted and reflected beams. Kirigami-based optical elements can be added to an emitter side of the LIDAR to serve as beam steerers, which can thus replace conventional bulky rotational or liquid crystal phase array beam steerers. Magnetic actuation modules can be integrated with a 1550 nm laser source. To reduce the bulk of the beam steerer, fiber optics can be coupled directly with the module.

In certain variations, LIDAR systems provided by the present disclosure may provide enhanced detection in precipitation and/or humid atmospheric conditions. For example, the LIDAR systems contemplated by the present disclosure may be particularly suitable for use in low visibility conditions by employing a laser with a wavelength of about 1550 nm, by way of non-limiting example, which provides enhanced detection and performance during poor weather conditions, including low visibility conditions that accompany fog, rain, and snow. Such LIDAR systems can enable long-range warnings, for example, up to 200 meters, which is especially useful for highway driving conditions. Conventional LIDARs use laser with wavelengths of about 900 nm, which is convenient for silicon-based detectors. However, these conventional laser beams experience relatively strong scattering in humid atmospheric conditions. LIDARs operating with 1550 nm can utilize high transparency of humid air, which is advantageous for all different levels of autonomy from proximity warnings to assisted driving and full autonomous ride modality. However, such LIDARs can be bulky and expensive due to high weight and cost of near-infra-red optics. In accordance with certain aspects of the present disclosure, kirigami-based optic elements can resolve this issue by taking advantage of the space-charge and subwavelength effects possible for the patterned kirigami sheets, see for example,FIGS. 2a-2b. As shown inFIGS. 3a-3b, such kirigami sheets can effectively modulate and beam steer near-infrared light lasers using the reconfigurable out-of-plane patterns of the kirigami sheets. A 1550 nm beam steering device incorporating such kirigami-based optical elements can be used as thin, light, and inexpensive solid-state LIDAR. Furthermore, the versatility of kirigami technology allows one to potentially adapt patterns for specific applications, for example, customizing the LIDAR system to specific vehicles and/or to adapt it to surfaces of different curvature of automotive parts.

In certain aspects, the present disclosure can provide LIDAR systems with relatively fast detection, by using two-stage object proposal and detection methods without sacrificing accuracy for latency. For example, improved model accuracy and generalizability for classification models can include enhancing static object classifiers by adding a material dimension to data. Objects with material fingerprints that contain plastic, wood and brick are very unlikely to move, while those with metal or fabric fingerprints are more likely be pedestrians and vehicles. Moreover, as the material dimension is more robust to scenario variations, these models generalize better to rare and complicated cases, such as construction sites and streets with complex festival decorations. Thus, material fingerprints greatly enhance model accuracy for point cloud association models with impact on tracking and autonomous vehicle maps. For example, the material dimension of the point clouds can make the detection and classification much more reliable, as pedestrians walking with bicycles can be picked up as point clouds, with metal materials on the lower side with some fabric or skin features from the pedestrian. It is then much easier for the self-driving systems to discern it from a pure pedestrian. Also, the material fingerprints of the object make it easier for the system to associate the point clouds with the correct object classification, helping to maintain the correct and consistent composite-object classification.

The present disclosure thus provides inexpensive and compact LIDAR systems with enhanced object recognition, including an ability to distinguish material types, provide earlier detection and warning systems, including an ability to identify an object within milliseconds, and high efficacy in low visibility conditions, among other benefits.