Temporally modulated light emission for defect detection in light detection and ranging (LIDAR) device and camera

Abstract

To provide temporally modulated light emission and defect detection in a light detection and ranging (LiDAR) device and a camera.SOLUTION: An example embodiment includes a method. The method includes detecting, by a first detector via an optical component, a background signal corresponding to a surrounding environment. The method also includes illuminating, by a first light source, a first portion of the optical component with a first light signal. Additionally, the method includes detecting the first light signal by the first detector when one or more defects are present in a body of the first portion of the optical component or on a surface of the first portion of the optical component. Further, the method includes making a determination, by a computing device, when one or more defects are present in the body of the first portion of the optical component or on the surface of the first portion of the optical component.SELECTED DRAWING: Figure 12

Description

[Background technology]

[0001] Unless otherwise stated herein, the statements in this section are not prior art to the claims of this application and should not be admitted to be prior art by inclusion in this section.

[0002] Light detection and ranging (LiDAR) devices can estimate distances to objects in a surrounding environment by emitting light pulses into the surrounding environment and determining the respective time-of-flight of each light pulse. The time-of-flight of each light pulse can be used to estimate distances to reflecting objects in the surrounding environment and / or to create a three-dimensional point cloud indicative of reflecting objects in the surrounding environment. Additionally, a camera can be used to capture images of one or more objects in the surrounding environment. Such images can be used in object detection and avoidance methods (e.g., in vehicles operating in autonomous or semi-autonomous modes). However, imperfections along one or more optical paths of the LiDAR device and / or camera can lead to erroneous detections (e.g., erroneous point clouds, or blurry and / or unclear images). Summary of the Invention

[0003] Exemplary embodiments described herein include detectors used to detect the presence of one or more defects on or in optical components of a LiDAR device or camera. For example, a LiDAR device or camera may include a lens and / or optical window used to detect the surrounding environment. The lens and / or optical window may occasionally develop one or more defects (e.g., internal cracks, external dust, or external water droplets). To detect defects, embodiments described herein may include illuminating a portion or the entire optical window using a light source. The illumination from the light source may include structured illumination or wavelength-specific illumination. Additionally, the illumination may vary over time (e.g., based on modulation frequency). Characteristics of the illumination can be used to distinguish between background signals (e.g., corresponding to the desired detection of the surrounding environment) and signals corresponding to the defects. Once a defect is identified, one or more remedial actions (e.g., cleaning the optical component, replacing the optical component, or eliminating the effects of the defect in post-processing) can be performed.

[0004] In a first aspect, a method is provided. The method includes detecting a background signal corresponding to an ambient environment using an optical component with a first detector. The method also includes illuminating a first portion of the optical component with a first optical signal modulated according to a first modulation frequency using a first light source. A sensing device is configured to detect objects in the ambient environment using the optical component. The method further includes detecting the first optical signal with the first detector if one or more defects are present within the body of the first portion of the optical component or on a surface of the first portion of the optical component. The method further includes determining, by a computing device, if one or more defects are present within the body of the first portion of the optical component or on a surface of the first portion of the optical component based on the detected background signal and the detected first optical signal. Determining if one or more defects are present within the body of the first portion of the optical component or on a surface of the first portion of the optical component based on the detected background signal and the detected first optical signal includes distinguishing between the detected background signal and the detected first optical signal based on the first modulation frequency.

[0005] In a second aspect, a system is provided. The system includes an optical component. The system also includes a sensing device configured to detect objects in a surrounding environment using the optical component. The system further includes a first light source configured to illuminate a first portion of the optical component with a first optical signal modulated according to a first modulation frequency. The system further includes a first detector. The first detector is configured to detect a background signal corresponding to the surrounding environment using the optical component. The first detector is also configured to detect the first optical signal when one or more defects are present within the body of the first portion of the optical component or on a surface of the first portion of the optical component. The system further includes a computing device configured to determine, based on the detected background signal and the detected first optical signal, if one or more defects are present within the body of the first portion of the optical component or on a surface of the first portion of the optical component. Determining, based on the detected background signal and the detected first optical signal, if one or more defects are present within the body of the first portion of the optical component or on a surface of the first portion of the optical component includes distinguishing between the detected background signal and the detected first optical signal based on the first modulation frequency.

[0006] In a third aspect, a computing device is provided. The computing device is configured to determine, based on a detected background signal and a detected first optical signal, if one or more defects exist within the body of the first portion of the optical component or on the surface of the first portion of the optical component. Determining, based on the detected background signal and the detected first optical signal, if one or more defects exist within the body of the first portion of the optical component or on the surface of the first portion of the optical component includes distinguishing between the detected background signal and the detected first optical signal based on a first modulation frequency. The detected background signal corresponds to an ambient environment and is detected by a first detector using the optical component. The sensing apparatus is configured to detect an object in the ambient environment using the optical component. The first portion of the optical component is illuminated by a first optical signal from a first light source. The first optical signal is modulated according to a first modulation frequency. The first optical signal is detected by the first detector if one or more defects exist within the body of the first portion of the optical component or on the surface of the first portion of the optical component.

[0007] These and other aspects, advantages, and alternatives will become apparent to those skilled in the art from a reading of the following detailed description, where appropriate with reference to the accompanying drawings. [Brief explanation of the drawings]

[0008] [Figure 1] FIG. 1 is a functional block diagram illustrating a vehicle, in accordance with an exemplary embodiment.

[0009] [Figure 2A] FIG. 2A is an illustrative diagram of a vehicle's physical configuration, according to an exemplary embodiment.

[0010] [Figure 2B] FIG. 2B is an illustrative diagram of a vehicle's physical configuration, according to an exemplary embodiment.

[0011] [Figure 2C] FIG. 2C is an illustrative diagram of a vehicle's physical configuration, in accordance with an exemplary embodiment.

[0012] [Figure 2D] FIG. 2D is an illustrative diagram of a vehicle's physical configuration in accordance with an exemplary embodiment.

[0013] [Figure 2E] FIG. 2E is an illustrative diagram of a vehicle's physical configuration, in accordance with an exemplary embodiment.

[0014] [Figure 2F] FIG. 2F is an illustrative diagram of a vehicle's physical configuration, according to an exemplary embodiment.

[0015] [Figure 2G] FIG. 2G is an illustrative diagram of a vehicle's physical configuration in accordance with an exemplary embodiment.

[0016] [Figure 2H] FIG. 2H is an illustrative diagram of a vehicle's physical configuration, in accordance with an exemplary embodiment.

[0017] [Figure 2I] FIG. 2I is an illustrative diagram of a vehicle's physical configuration, in accordance with an exemplary embodiment.

[0018] [Figure 2J] FIG. 2J is an illustrative diagram of the fields of view of various sensors in accordance with an illustrative embodiment.

[0019] [Figure 2K] FIG. 2K is an illustrative diagram of beam steering relative to a sensor in accordance with an example embodiment.

[0020] [Figure 3] FIG. 3 is a conceptual, illustrative diagram of wireless communication between various computing systems associated with an autonomous or semi-autonomous vehicle, in accordance with an example embodiment.

[0021] [Figure 4A] FIG. 4A is a block diagram of a system including a LiDAR device, according to an exemplary embodiment.

[0022] [Figure 4B] FIG. 4B is a block diagram of a LiDAR device, according to an exemplary embodiment.

[0023] [Figure 5A] FIG. 5A is an illustration of a system with one or more defects present in accordance with an illustrative embodiment.

[0024] [Figure 5B] FIG. 5B is an illustration of a system with one or more defects present in accordance with an illustrative embodiment.

[0025] [Figure 6A] FIG. 6A is an illustration of a LiDAR device with a defect detection component, according to an exemplary embodiment.

[0026] [Figure 6B] FIG. 6B is an illustration of a LiDAR device with a defect detection component, according to an exemplary embodiment.

[0027] [Figure 7A] FIG. 7A is an illustration of a camera with defect detection components in accordance with an exemplary embodiment.

[0028] [Figure 7B] FIG. 7B is an illustration of a camera with defect detection components in accordance with an exemplary embodiment.

[0029] [Figure 8A] FIG. 8A is an illustration of a hybrid camera / defect detection system in accordance with an example embodiment.

[0030] [Figure 8B]FIG. 8B is an illustration of a hybrid camera / defect detection system in accordance with an example embodiment.

[0031] [Figure 9A] FIG. 9A is a diagram of a hybrid camera / defect detection system in accordance with an example embodiment.

[0032] [Figure 9B] FIG. 9B is a diagram of a hybrid camera / defect detection system in accordance with an example embodiment.

[0033] [Figure 10A] FIG. 10A is an illustration of an illumination pattern, according to an example embodiment.

[0034] [Figure 10B] FIG. 10B is an illustration of an illumination pattern, according to an exemplary embodiment.

[0035] [Figure 10C] FIG. 10C is an illustration of an illumination pattern, according to an exemplary embodiment.

[0036] [Figure 10D] FIG. 10D is an illustration of an illumination pattern, according to an exemplary embodiment.

[0037] [Figure 11A] FIG. 11A illustrates a series of images captured using a camera with defect detection components when one or more defects are not present, according to an example embodiment.

[0038] [Figure 11B] FIG. 11B illustrates a series of images captured using a camera with defect detection components when one or more defects are present, according to an example embodiment.

[0039] [Figure 11C]FIG. 11C illustrates a series of images captured using a camera with defect detection components when one or more defects are not present, according to an example embodiment.

[0040] [Figure 11D] FIG. 11D illustrates a series of images captured using a camera with defect detection components when one or more defects are present, according to an example embodiment.

[0041] [Figure 12] FIG. 12 is a flowchart diagram of a method according to an exemplary embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0042] Exemplary methods and systems are contemplated herein. Any example embodiment or feature described herein should not necessarily be construed as preferred or advantageous over other embodiments or features. Moreover, the exemplary embodiments described herein are not meant to be limiting. It will be readily understood that certain aspects of the disclosed systems and methods can be arranged and combined in a wide variety of different configurations, all of which are contemplated herein. Additionally, the specific arrangements illustrated in the figures should not be considered limiting. It should be understood that other embodiments may include more or fewer of each element shown in a given figure. Additionally, some of the illustrated elements may be combined or omitted. Still further, exemplary embodiments may include elements not illustrated in the figures.

[0043] The LiDAR devices described herein may include one or more light emitters and one or more detectors used to detect light emitted by the one or more light emitters and reflected by one or more objects in the environment surrounding the LiDAR device. As an example, the surrounding environment may include an interior or exterior environment, such as the inside or outside of a building. Additionally or alternatively, the surrounding environment may include the interior of a vehicle. Still further, the surrounding environment may include the surroundings around and / or on a road. Examples of objects in the surrounding environment include, but are not limited to, other vehicles, traffic signs, pedestrians, bicyclists, road surfaces, buildings, terrain, etc. Additionally, the one or more light emitters may emit light into the local environment of the LiDAR itself. For example, light emitted from the one or more light emitters may interact with the housing of the LiDAR and / or surfaces or structures coupled to the LiDAR. In some cases, the LiDAR may be mounted on a vehicle, in which case the one or more light emitters may be configured to emit light that interacts with objects within the vehicle's vicinity. The light emitter may include a fiber optic amplifier, a laser diode, a light emitting diode (LED), among other possibilities.

[0044] LiDAR devices and / or cameras can be used to sense the surrounding environment. For example, a LiDAR device can be used to generate a point cloud associated with the environment surrounding the autonomous vehicle, which can be used by the autonomous vehicle for object detection and avoidance. Such LiDAR devices and / or cameras can include one or more optical components. For example, the LiDAR device and / or camera can include an optical window, a mirror, a lens, etc.

[0045] In some cases, defects on or within one or more optical components within a camera or LIDAR device can adversely affect the captured image / generated point cloud. For example, scratches, cracks, dirt, deformations, bubbles, impurities, deterioration, discoloration, imperfect transparency, distortions, water droplets, stains, dust, mud, leaves, rain, snow, sleet, hail, ice, insect residue, etc. can direct light from the scene to unintended / incorrect areas of the image sensor / photodetector, prevent light from the scene from even reaching the image sensor / photodetector, cause undesirable crosstalk, or alter light from the scene (e.g., change its polarization or wavelength) before it reaches the image sensor / photodetector. Such defects can result in improper object identification / distance detection or a complete absence of a reflected signal. In autonomous vehicle applications, improper object identification / distance detection can lead to traffic slowdowns or collisions.

[0046] To detect such defects (and possibly subsequently perform repair work), the devices disclosed herein can include a separate detector (e.g., a separate camera, sometimes referred to as a “defect detection camera”) used to capture images of one or more of the optical components of the LiDAR device / camera used to sense the surrounding environment. The images captured by the defect detection camera can be analyzed to determine whether one or more defects are present in a particular optical component, the location of the one or more defects on the particular optical component, the size of the one or more defects on the particular optical component, and / or the type of one or more defects on the particular optical component. Furthermore, the defect detection camera can be positioned adjacent to one or more detectors associated with the LiDAR device / camera used to collect data about the surrounding environment. Additionally or alternatively, instead of using a dedicated defect detection camera, the devices disclosed herein can utilize sensing devices (e.g., detectors) already present in the detection system to identify defects. For example, if an image sensor (e.g., of a camera) is used to identify objects in the surrounding environment, the image sensor can also be used to capture images that can be used to identify defects in the optical components of the camera. For example, images for defect detection can be captured sequentially or simultaneously with (e.g., as part of) images used for object detection, or one or more images can be used for both object detection (e.g., in a first portion of the image) and defect detection (e.g., in a second portion of the image).

[0047] In some embodiments, images captured by a defect detection camera may be susceptible to and / or adversely affected by background light (e.g., glare). For example, if an image captured by a defect detection camera includes the sun, it may be difficult to use the image to detect defects associated with optical components. This is particularly true when the dynamic range of the defect detection camera is low (e.g., low compared to the dynamic range intensity present in the surrounding scene).

[0048] The embodiments described herein reduce the impact of background light on images captured by a detector used for defect detection (e.g., a stand-alone defect detection camera or a hybrid object detection / defect detection camera) by illuminating one or more optical components being monitored using a temporally modulated light sequence. The temporally modulated light sequence can include wavelength modulation and / or spatial modulation. The temporally modulated light sequence can be provided by one or more light sources (e.g., LEDs) positioned near the image sensor of the defect detection camera. Additionally or alternatively, the illumination may be provided by one or more light sources (e.g., LEDs) positioned adjacent to the one or more optical components being monitored. For example, the one or more light sources may be positioned to provide an optical signal that propagates within an optical component (e.g., a lens) by total internal reflection unless or until the optical signal encounters a defect, in which case the optical signal is coupled out of the optical component and directed to the defect detection camera.

[0049] Regardless of where the light source is located, the modulated light sequence can be modulated according to a particular scheme (e.g., encoding) to highlight any defects against the background. Because the defects can be highlighted against the background according to the modulation scheme, a filter can later be applied to the series of images (e.g., video) captured by the defect detection camera to separate (e.g., clarify) the defects from the background in the series of images, thereby making the defects more easily identifiable.

[0050] The modulation scheme can modulate light temporally and / or spatially. For example, a series of structured illuminations can be used to highlight optical defects against a relatively uniform background. In one embodiment, for example, in a first time step, the modulation scheme illuminates a series of strips along the optical component. Then, in a second time step, the modulation scheme can reverse the series of strips (e.g., previously illuminated areas are now unilluminated, and previously unilluminated areas are now illuminated). Alternatively, a checkerboard pattern can be used. Other structured illumination schemes are also possible. Each of the one or more light sources can correspond to a different illuminated area of ​​the optical component being monitored (e.g., different strips illuminated at a given time step). Furthermore, each of the one or more light sources can include one or more optical systems (e.g., lenses or coupling optics).

[0051] Similarly, modulation schemes can modulate light in time and / or wavelength. For example, a sequence of illuminations of different wavelengths (e.g., visible and / or infrared wavelengths) can be used to highlight optical defects against a relatively uniform background (e.g., including objects in the surrounding environment that do not change color over time or that change color slowly over time). In one embodiment, for example, a modulation scheme including green illumination for a first duration, followed by blue illumination for a second duration, followed by red illumination for a third duration may be used. Each of the one or more light sources may correspond to a different illumination wavelength. Additionally, each of the one or more light sources may include one or more optical systems (e.g., lenses or filters).

[0052] Regardless of which modulation scheme is used, the illumination can be modulated according to a modulation frequency that can later be used (e.g., by a computing device analyzing images / videos captured by the defect detection camera) to determine which portions of the captured image correspond to the background (e.g., ambient environment) and which portions, if any, correspond to defects. Additionally, in some embodiments, the type of modulation used can help determine the type of defect (e.g., whether the defect is rain or dust).

[0053] The following description and accompanying drawings highlight features of various exemplary embodiments. The embodiments provided are by way of example and are not intended to be limiting. Accordingly, dimensions of the drawings are not necessarily to scale.

[0054] Exemplary systems within the scope of the present disclosure will now be described in more detail. The exemplary system may be implemented in or take the form of an automobile. Additionally, the exemplary system may also be implemented in or take the form of a variety of vehicles, such as a car, truck (e.g., pickup truck, van, tractor, or tractor-trailer), motorcycle, bus, airplane, helicopter, drone, lawn mower, bulldozer, boat, submarine, all-terrain vehicle, snowmobile, aircraft, recreational vehicle, amusement park vehicle, farm equipment or vehicle, construction equipment or vehicle, warehouse equipment or vehicle, factory equipment or vehicle, tram, golf cart, train, trolley, walkway transport vehicle, robotic device, etc. Other vehicles are possible as well. Furthermore, in some embodiments, the exemplary system may not include a vehicle.

[0055] Referring now to the figures, FIG. 1 is a functional block diagram illustrating an example vehicle 100 that may be configured to operate fully or partially in an autonomous mode. More specifically, vehicle 100 may operate in the autonomous mode without human interaction through receiving control instructions from a computing system. As part of its operation in the autonomous mode, vehicle 100 may use sensors to detect and possibly identify objects in the surrounding environment to enable safe navigation. Additionally, example vehicle 100 may operate in a partially autonomous (i.e., semi-autonomous) mode in which some functions of vehicle 100 are controlled by a human driver of vehicle 100 and some functions of vehicle 100 are controlled by a computing system. For example, vehicle 100 may also include subsystems that enable the driver to control the operation of vehicle 100, such as steering, acceleration, and braking, while the computing system performs assistance functions, such as lane departure warning / lane keeping assist or adaptive cruise control, based on other objects (e.g., vehicles) in the surrounding environment.

[0056] As described herein, in a partially autonomous driving mode, the vehicle assists with one or more driving operations (e.g., steering, braking, and / or accelerating to perform lane centering, adaptive cruise control, advanced driver assistance systems (ADAS), or emergency braking), but the human driver is expected to maintain situational awareness of the vehicle's surroundings and supervise the assisted driving operations. Here, the vehicle may perform all driving tasks in certain situations, but the human driver is expected to remain responsible for assuming control as needed.

[0057] For simplicity and brevity, various systems and methods are described below in conjunction with autonomous vehicles; however, these or similar systems and methods may be used in various driver assistance systems that fall short of a fully autonomous driving system (i.e., a partially autonomous driving system). In the United States, the Society of Automotive Engineers (SAE) defines different levels of automated driving behavior to indicate how much or how little control the vehicle has over the driving; however, different organizations in the United States or other countries may classify the levels differently. More specifically, the disclosed systems and methods may be used in SAE Level 2 driver assistance systems that implement steering, braking, acceleration, lane centering, adaptive cruise control, and other driver support. The disclosed systems and methods may be used in SAE Level 3 driver assistance systems that are capable of autonomous driving under limited (e.g., highway) conditions. Similarly, the disclosed systems and methods may be used in vehicles using SAE Level 4 automated driving systems, which operate autonomously under most normal driving conditions and require only occasional attention from a human operator. In all such systems, accurate lane estimation is performed automatically without driver input or control (e.g., while the vehicle is moving), resulting in improved reliability of vehicle positioning and navigation, and overall safety of autonomous, semi-autonomous, and other driver assistance systems. As noted above, in addition to the way SAE classifies levels of autonomous driving performance, other organizations in the United States or other countries may classify levels of autonomous driving performance differently. Without limitation, the systems and methods disclosed herein may be used with driver assistance systems defined by these other organizations' levels of autonomous driving performance.

[0058] 1 , vehicle 100 may include various subsystems, such as propulsion system 102, sensor system 104, control system 106, one or more peripherals 108, power source 110, computer system 112 (which may also be referred to as a computing system) having data storage 114, and user interface 116. In other examples, vehicle 100 may include more or fewer subsystems, each of which may include multiple elements. The subsystems and components of vehicle 100 may be interconnected in various ways. Additionally, the functionality of vehicle 100 described herein may be divided into additional functional or physical components or combined into fewer functional or physical components within an embodiment. For example, control system 106 and computer system 112 may be combined into a single system that operates vehicle 100 according to various operations.

[0059] Propulsion system 102 may include one or more components operable to provide powered motion for vehicle 100 and may include, among other possible components, an engine / motor 118, an energy source 119, a transmission 120, and wheels / tires 121. For example, engine / motor 118 may be configured to convert energy source 119 into mechanical energy and may correspond to one or a combination of an internal combustion engine, an electric motor, a steam engine, or a Stirling engine, among other possible options. For example, in some embodiments, propulsion system 102 may include multiple types of engines and / or motors, such as gasoline engines and electric motors.

[0060] Energy source 119 represents an energy source that may fully or partially power one or more systems (e.g., engine / motor 118) of vehicle 100. For example, energy source 119 may correspond to gasoline, diesel, other petroleum-based fuels, propane, other compressed gas-based fuels, ethanol, solar panels, batteries, and / or other power sources. In some embodiments, energy source 119 may include a combination of a fuel tank, a battery, a capacitor, and / or a flywheel.

[0061] The transmission 120 may transfer mechanical power from the engine / motor 118 to the wheels / tires 121 and / or other possible systems of the vehicle 100. As such, the transmission 120 may include, among other possible components, a gearbox, a clutch, a differential, and a drive shaft. The drive shaft may include an axle that connects to one or more wheels / tires 121.

[0062] The wheels / tires 121 of the vehicle 100 may have a variety of configurations within the exemplary embodiment. For example, the vehicle 100 may exist in the form of a unicycle, a bicycle / motorcycle, a tricycle, or four wheels of a car / truck, among other possible configurations. Thus, the wheels / tires 121 may be connected to the vehicle 100 in a variety of ways and may exist in different materials, such as metal and rubber.

[0063] The sensor system 104 may include various types of sensors, such as a global positioning system (GPS) 122, an inertial measurement unit (IMU) 124, radar 126, LiDAR 128, a camera 130, a steering sensor 123, and a throttle / brake sensor 125, among other possible sensors. In some embodiments, the sensor system 104 may also include sensors configured to monitor internal systems of the vehicle 100 (e.g., an O monitor, a fuel gauge, engine oil temperature, or brake wear).

[0064] The GPS 122 may include a transceiver operable to provide information regarding the position of the vehicle 100 relative to the Earth. The IMU 124 may be configured to use one or more accelerometers and / or gyroscopes to sense changes in the position and orientation of the vehicle 100 based on inertial acceleration. For example, the IMU 124 may detect the pitch and yaw of the vehicle 100 while the vehicle 100 is stationary or moving.

[0065] Radar 126 may represent one or more systems configured to sense objects in the environment surrounding vehicle 100 using radio signals, including the object's speed and orientation. Thus, radar 126 may include an antenna configured to transmit and receive radio signals. In some embodiments, radar 126 may correspond to a mountable radar configured to obtain measurements of the environment surrounding vehicle 100.

[0066] The LiDAR 128 may include one or more laser sources, a laser scanner, and one or more detectors, among other system components, and may operate in a coherent mode (e.g., using heterodyne detection) or an incoherent detection mode (i.e., time-of-flight mode). In some embodiments, the one or more detectors of the LiDAR 128 may include one or more photodetectors, which may be particularly sensitive detectors (e.g., avalanche photodiodes). In some examples, such photodetectors may be capable of detecting single photons (e.g., single-photon avalanche diodes (SPADs)). Furthermore, such photodetectors may be arranged in an array (e.g., through serial electrical connections), such as silicon photomultiplier tubes (SiPMs). In some examples, one or more photodetectors are devices operating in Geiger mode, and the LiDAR includes subcomponents designed for such Geiger mode operation.

[0067] Camera 130 may include one or more devices (e.g., a still camera, a video camera, a thermal imaging camera, a stereo camera, or a night vision camera) configured to capture images of the environment surrounding vehicle 100.

[0068] Steering sensor 123 may sense the steering angle of vehicle 100, which may include measuring the angle of the steering wheel or measuring an electrical signal representative of the angle of the steering wheel. In some embodiments, steering sensor 123 may measure the angle of the wheels of vehicle 100, such as detecting the angle of the wheels relative to the forward axle of vehicle 100. Steering sensor 123 may also be configured to measure a combination (or subset) of the steering wheel angle, the electrical signal representative of the steering wheel angle, and the angle of the wheels of vehicle 100.

[0069] The throttle / brake sensor 125 may detect either the throttle position or the brake position of the vehicle 100. For example, the throttle / brake sensor 125 may measure the angle of both the accelerator pedal (throttle) and the brake pedal, or may measure an electrical signal representative of, for example, the accelerator pedal (throttle) angle and / or the brake pedal angle. The throttle / brake sensor 125 may also measure the angle of a throttle body of the vehicle 100, which may include part of the physical mechanism that provides modulation of the energy source 119 to the engine / motor 118 (e.g., a butterfly valve or a carburetor). Additionally, the throttle / brake sensor 125 may measure the pressure of one or more brake pads on a rotor of the vehicle 100, or a combination (or subset) of the accelerator pedal (throttle) and the brake pedal angle, an electrical signal representative of the accelerator pedal (throttle) and the brake pedal angle, the throttle body angle, and the pressure applied by at least one brake pad to a rotor of the vehicle 100. In other embodiments, the throttle / brake sensor 125 may be configured to measure pressure applied to a vehicle pedal, such as a throttle or brake pedal.

[0070] The control system 106 may include components configured to assist in navigating the vehicle 100, such as a steering unit 132, a throttle 134, a braking unit 136, a sensor fusion algorithm 138, a computer vision system 140, a navigation / pathfinding system 142, and an obstacle avoidance system 144. More specifically, the steering unit 132 may be operable to adjust the heading of the vehicle 100, and the throttle 134 may control the operating speed of the engine / motor 118 to control the acceleration of the vehicle 100. The braking unit 136 may decelerate the vehicle 100, which may involve slowing the wheels / tires 121 using friction. In some embodiments, the braking unit 136 may convert the kinetic energy of the wheels / tires 121 into electrical current for subsequent use by one or more systems of the vehicle 100.

[0071] The sensor fusion algorithm 138 may include a Kalman filter, a Bayesian network, or other algorithm capable of processing data from the sensor system 104. In some embodiments, the sensor fusion algorithm 138 may provide an assessment based on the incoming sensor data, such as an assessment of individual objects and / or features, an assessment of a particular situation, and / or an assessment of possible effects within a given situation.

[0072] Computer vision system 140 may include hardware and software (e.g., a general-purpose processor such as a central processing unit (CPU), a special-purpose processor such as a graphical processing unit (GPU) or a tensor processing unit (TPU), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), volatile memory, non-volatile memory, or one or more machine learning models) operable to process and analyze images to determine moving objects (e.g., other vehicles, pedestrians, bicyclists, or animals) and non-moving objects (e.g., traffic lights, roadway boundaries, speed bumps, or potholes). Thus, computer vision system 140 may employ object recognition, structure-from-motion (SFM), video tracking, and other algorithms used in computer vision, for example, to recognize objects, map the environment, track objects, estimate object speed, etc.

[0073] Navigation / routing system 142 may determine a driving path for vehicle 100, which may involve dynamically adjusting navigation during operation. Thus, navigation / routing system 142 may use data from sensor fusion algorithms 138, GPS 122, and maps, among other sources, to navigate vehicle 100. Obstacle avoidance system 144 may evaluate potential obstacles based on sensor data and cause systems of vehicle 100 to avoid or otherwise navigate the potential obstacles.

[0074] 1 , vehicle 100 may also include peripherals 108, such as a wireless communication system 146, a touchscreen 148, an internal microphone 150, and / or a speaker 152. Peripherals 108 may provide controls or other elements for a user to interact with a user interface 116. For example, touchscreen 148 may provide information to a user of vehicle 100. User interface 116 may also accept input from a user via touchscreen 148. Peripherals 108 may also enable vehicle 100 to communicate with devices, such as devices in other vehicles.

[0075] The wireless communication system 146 may communicate with one or more devices directly or wirelessly via a communication network. For example, the wireless communication system 146 may use 3G cellular communications such as Code Division Multiple Access (CDMA), Evolution Data Optimized (EVDO), Global System for Mobile Communications (GSM) / General Packet Radio Service (GPRS), or 4G Worldwide Interoperability for Microwave Access (WiMAX) or Long Term Evolution (LTE), or 5G. Alternatively, the wireless communication system 146 may communicate with a wireless local area network (WLAN) using Wi-Fi or other possible connections. The wireless communication system 146 may also communicate directly with devices using, for example, an infrared link, Bluetooth, or ZigBee. Other wireless protocols, such as various vehicle communication systems, are possible within the context of this disclosure. For example, the wireless communication system 146 may include one or more dedicated short-range communication (DSRC) devices, which may include public and / or private data communications between vehicles and / or roadside gas stations.

[0076] Vehicle 100 may include a power source 110 for powering its components. Power source 110, in some embodiments, may include a rechargeable lithium-ion or lead-acid battery. For example, power source 110 may include one or more batteries configured to provide power. Vehicle 100 may also use other types of power sources. In an exemplary embodiment, power source 110 and energy source 119 may be integrated into a single energy source.

[0077] Vehicle 100 may also include a computer system 112 for performing operations such as those described therein. Accordingly, computer system 112 may include at least one processor 113 (which may include at least one microprocessor) operable to execute instructions 115 stored in a non-transitory computer-readable medium, such as data storage 114. In some embodiments, computer system 112 may represent multiple computing devices that may function to control individual components or subsystems of vehicle 100 in a distributed manner.

[0078] In some embodiments, data storage 114 may include instructions 115 (e.g., program logic) executable by processor 113 for performing various functions of vehicle 100, including those described above in connection with Figure 1. Data storage 114 may also include additional instructions, including instructions for transmitting data to, receiving data from, interacting with, and / or controlling one or more of propulsion system 102, sensor system 104, control system 106, and peripherals 108.

[0079] In addition to instructions 115, data storage 114 may store data such as road maps, route information, etc., among other information. Such information may be used by vehicle 100 and computer system 112 during operation of vehicle 100 in autonomous, semi-autonomous, and / or manual modes.

[0080] Vehicle 100 may include a user interface 116 for providing information to or receiving input from a user of vehicle 100. User interface 116 may control or allow for control of the layout of content and / or interactive images that may be displayed on touchscreen 148. Additionally, user interface 116 may include one or more input / output devices in the set of peripherals 108, such as wireless communication system 146, touchscreen 148, microphone 150, and speaker 152.

[0081] Computer system 112 may control functions of vehicle 100 based on inputs received from various subsystems (e.g., propulsion system 102, sensor system 104, or control system 106) and from user interface 116. For example, computer system 112 may utilize inputs from sensor system 104 to estimate outputs generated by propulsion system 102 and control system 106. Depending on the embodiment, computer system 112 may be operable to monitor many aspects of vehicle 100 and its subsystems. In some embodiments, computer system 112 may disable some or all functions of vehicle 100 based on signals received from sensor system 104.

[0082] Components of vehicle 100 may be configured to function in an interconnected manner with other components within or outside their respective systems. For example, in an exemplary embodiment, camera 130 may capture multiple images that may represent information about the state of the environment surrounding vehicle 100 operating in an autonomous or semi-autonomous mode. The state of the environment may include parameters of the road on which the vehicle is operating. For example, computer vision system 140 may be able to recognize slopes (gradients) or other features based on multiple images of the road. Additionally, the combination of GPS 122 and features recognized by computer vision system 140 may be used along with map data stored in data storage 114 to determine specific road parameters. Furthermore, radar 126 and / or LiDAR 128, and / or some other environmental mapping, range, and / or positioning sensor system may also provide information about the vehicle's surroundings.

[0083] In other words, a combination of various sensors (which may be referred to as input indicator sensors and output indicator sensors) and computer system 112 may interact to provide an indication of the inputs or surroundings of the vehicle that are provided to control the vehicle.

[0084] In some embodiments, computer system 112 may make decisions regarding various objects based on data provided by systems other than a wireless system. For example, vehicle 100 may have laser or other optical sensors configured to sense objects within the vehicle's field of view. Computer system 112 may use output from the various sensors to determine information about objects within the vehicle's field of view and may determine distance and direction information to the various objects. Computer system 112 may also determine whether an object is desirable or undesirable based on output from the various sensors.

[0085] 1 depicts various components of vehicle 100 (i.e., wireless communication system 146, computer system 112, data storage 114, and user interface 116) as being integrated into vehicle 100, one or more of these components may be separately mounted or associated with vehicle 100. For example, data storage 114 may exist partially or completely separate from vehicle 100. Thus, vehicle 100 may be provided in the form of device elements that may be located separately or together. The device elements that make up vehicle 100 may be communicatively coupled together in a wired and / or wireless manner.

[0086] 2A-2E show an example vehicle 200 (e.g., a fully autonomous vehicle, a semi-autonomous vehicle) that may include some or all of the functionality described in connection with vehicle 100 with reference to FIG. 1. Vehicle 200 is illustrated in FIGS. 2A-2E as a van with side mirrors for illustrative purposes, but the present disclosure is not so limited. For example, vehicle 200 may represent a truck, a passenger car, a semi-trailer truck, a motorcycle, a golf cart, an off-road vehicle, an agricultural vehicle, or any other vehicle described elsewhere herein (e.g., a bus, a boat, an airplane, a helicopter, a drone, a lawn mower, a bulldozer, a submarine, an all-terrain vehicle, a snowmobile, an aircraft, a recreational vehicle, an amusement park vehicle, farm equipment, construction machinery or vehicles, warehouse equipment or vehicles, factory equipment or vehicles, a tram, a train, a trolley, a walkway transport vehicle, or a robotic device).

[0087] Exemplary vehicle 200 may include one or more sensor systems 202, 204, 206, 208, 210, 212, 214, and 218. In some embodiments, sensor systems 202, 204, 206, 208, 210, 212, 214, and / or 218 may represent one or more optical systems (e.g., cameras), one or more LiDARs, one or more radars, one or more inertial sensors, one or more humidity sensors, one or more acoustic sensors (e.g., microphones or sonar devices), or one or more other sensors configured to sense information about the environment surrounding vehicle 200. In other words, any sensor system now known or hereafter created may be coupled to vehicle 200 and / or utilized in conjunction with various operations of vehicle 200. As an example, LiDAR may be utilized for autonomous driving or other types of navigation, planning, perception, and / or mapping operations of vehicle 200. Additionally, sensor systems 202, 204, 206, 208, 210, 212, 214, and / or 218 may represent a combination of sensors described herein (e.g., one or more LiDARs and radars, one or more LiDARs and cameras, one or more cameras and radars, one or more LiDARs, cameras, and radars).

[0088] 2A-E are intended as non-limiting examples of the locations, numbers, and types of such sensor systems for an autonomous or semi-autonomous vehicle. Alternative numbers, locations, types, and configurations of such sensors are possible (e.g., to accommodate vehicle size, shape, aerodynamics, fuel economy, aesthetics, or other considerations to reduce cost or suit particular environmental or application situations). For example, sensor systems (e.g., 202, 204) may be disposed in various other locations on the vehicle (e.g., at location 216) and may have fields of view corresponding to the interior and / or surrounding environment of vehicle 200.

[0089] Sensor system 202 may include one or more sensors mounted on top of vehicle 200 and configured to detect information about the environment surrounding vehicle 200 and output an indication of the information. For example, sensor system 202 may include any combination of cameras, radar, LiDAR, inertial sensors, humidity sensors, and acoustic sensors (e.g., microphones, sonar devices). Sensor system 202 may include one or more movable mounts that may be operable to adjust the orientation of one or more sensors in sensor system 202. In one embodiment, the movable mount may include a rotating platform that can scan the sensors to obtain information from each direction around vehicle 200. In another embodiment, the movable mount of sensor system 202 may be movable to scan within a specific range of angles and / or azimuth and / or elevation angles. Sensor system 202 may be mounted on the roof of a vehicle, although other mounting locations are also possible.

[0090] Additionally, the sensors of sensor system 202 may be distributed at various locations and need not be co-located at a single location. Further, each sensor of sensor system 202 may be configured to be moved or scanned independently of the other sensors of sensor system 202. Additionally or alternatively, multiple sensors may be mounted at one or more of sensor locations 202, 204, 206, 208, 210, 212, 214, and / or 218. For example, there may be two LiDAR devices mounted at a sensor location, and / or there may be one LiDAR device and one radar mounted at a sensor location.

[0091] One or more of sensor systems 202, 204, 206, 208, 210, 212, 214, and / or 218 may include one or more LiDAR sensors. For example, a LiDAR sensor may include multiple light emitter devices arranged over a range of angles relative to a given plane (e.g., the x-y plane). For example, one or more of sensor systems 202, 204, 206, 208, 210, 212, 214, and / or 218 may be configured to rotate or pivot about an axis perpendicular to the given plane (e.g., the z-axis) to illuminate the environment surrounding vehicle 200 with light pulses. Based on detecting various aspects of the reflected light pulses (e.g., the elapsed time of flight, polarization, and / or intensity), information about the surrounding environment may be determined.

[0092] In an exemplary embodiment, sensor systems 202, 204, 206, 208, 210, 212, 214, and / or 218 may be configured to provide respective point cloud information that may be associated with physical objects within the surrounding environment of vehicle 200. While vehicle 200 and sensor systems 202, 204, 206, 208, 210, 212, 214, and 218 are illustrated as including particular features, it will be understood that other types of sensor systems are contemplated within the scope of the present disclosure. Additionally, exemplary vehicle 200 may include any of the components described in connection with vehicle 100 of FIG. 1 .

[0093] In an exemplary configuration, one or more radars may be located on vehicle 200. Similar to RADAR 126 described above, one or more RADARs may include antennas configured to transmit and receive radio waves (e.g., electromagnetic waves having frequencies between 30 Hz and 300 GHz). Such radio waves may be used to determine the distance and / or speed of one or more objects in the vehicle's 200 environment. For example, one or more sensor systems 202, 204, 206, 208, 210, 212, 214, and / or 218 may include one or more radars. In some examples, one or more radars may be located near the rear of vehicle 200 (e.g., sensor systems 208, 210) to actively scan the environment near the rear of vehicle 200 for the presence of radio wave-reflecting objects. Similarly, one or more radars may be located near the front of vehicle 200 (e.g., sensor systems 212, 214) to actively scan the environment near the front of vehicle 200. The radar may be positioned in a location suitable for illuminating an area including the forward path of vehicle 200, for example, without being obstructed by other features of vehicle 200. For example, the radar may be embedded in and / or mounted on or near the front bumper, front headlights, cowl, and / or hood, etc. Furthermore, one or more additional radars may be positioned to actively scan the sides and / or rear of vehicle 200 for the presence of radio wave reflective objects, such as by including such devices on or near the rear bumper, side panels, rocker panels, and / or chassis, etc.

[0094] Vehicle 200 may include one or more cameras. For example, one or more sensor systems 202, 204, 206, 208, 210, 212, 214, and / or 218 may include one or more cameras. The cameras may be light-sensitive devices, such as still cameras, video cameras, thermal imaging cameras, stereo cameras, night-vision cameras, etc., configured to capture multiple images of the environment surrounding vehicle 200. To this end, the cameras may be configured to detect visible light and, additionally or alternatively, may be configured to detect light from other parts of the spectrum, such as infrared or ultraviolet light. The cameras may be two-dimensional detectors and, optionally, may have a sensitivity range in three-dimensional space. In some embodiments, the cameras may include range detectors configured to generate two-dimensional images indicating, for example, the distance from the camera to points in the surrounding environment. To this end, the cameras may use one or more range-sensing techniques. For example, the camera can provide range information by using structured light techniques, in which the vehicle 200 illuminates objects in the surrounding environment with a predetermined light pattern, such as a grid or checkerboard pattern, and uses the camera to detect reflections of the predetermined light pattern from the surrounding environment. Based on distortions in the reflected light pattern, the vehicle 200 can determine the distance to a point on the object. The predetermined light pattern can include infrared light or other wavelengths of radiation suitable for such measurements. In some examples, the camera can be mounted inside the windshield of the vehicle 200. Specifically, the camera can be positioned to capture images from a forward-looking perspective relative to the orientation of the vehicle 200. Other mounting locations and viewing angles for the camera can also be used, and can be either internal or external to the vehicle 200. The camera can also have associated optics operable to provide an adjustable field of view. Furthermore, the camera can be mounted to the vehicle 200 using a movable mount to change the pointing angle of the camera, such as via a pan / tilt mechanism.

[0095] Vehicle 200 may also include one or more acoustic sensors used to sense the vehicle's surrounding environment (e.g., one or more of sensor systems 202, 204, 206, 208, 210, 212, 214, 216, 218 may include one or more acoustic sensors). The acoustic sensors may include microphones (e.g., piezoelectric microphones, condenser microphones, ribbon microphones, and / or microelectromechanical systems (MEMS) microphones) used to sense acoustic waves (i.e., pressure differentials) in the fluid (e.g., air) of the environment surrounding vehicle 200. Such acoustic sensors may be used to identify sounds in the surrounding environment (e.g., sirens, human speech, animal sounds, and / or alarms) upon which a control strategy for vehicle 200 may be based. For example, if the acoustic sensors detect a siren (e.g., a mobile siren and / or a fire engine siren), vehicle 200 may slow down and / or navigate to the curb of the road.

[0096] Although not shown in Figures 2A-2E, vehicle 200 may include a wireless communication system (e.g., similar to and / or in addition to wireless communication system 146 of Figure 1). The wireless communication system may include a wireless transmitter and a wireless receiver that may be configured to communicate with devices external or internal to vehicle 200. Specifically, the wireless communication system may include a transceiver configured to communicate with other vehicles and / or computing devices, for example, in a vehicle communication system or roadside gas station. Examples of such vehicle communication systems include DSRC, radio frequency identification (RFID), and other communication standards proposed for intelligent transport systems.

[0097] Vehicle 200 may include one or more other components in addition to or instead of those shown. The additional components may include electrical or mechanical functions.

[0098] A control system of vehicle 200 may be configured to control vehicle 200 according to a control strategy from among a plurality of possible control strategies. The control system may be configured to receive information from sensors (on or off vehicle 200) coupled to vehicle 200, modify the control strategy (and associated driving behavior) based on the information, and control vehicle 200 according to the modified control strategy. The control system may be further configured to monitor the information received from the sensors and continuously evaluate driving conditions, and may be configured to modify the control strategy and driving behavior based on changes in driving conditions. For example, the route taken by the vehicle from one destination to another may be modified based on driving conditions. Additionally or alternatively, speed, acceleration, turn angle, following distance (i.e., distance to the vehicle ahead of the current vehicle), lane selection, etc. may all be modified in response to changing driving conditions.

[0099] As noted above, in some embodiments, vehicle 200 may take the form of a van, although alternative forms are also possible and contemplated herein. Accordingly, FIGS. 2F-2I illustrate an embodiment in which vehicle 250 takes the form of a semi-truck. For example, FIG. 2F illustrates a front view of vehicle 250, and FIG. 2G illustrates an isometric view of vehicle 250. In embodiments in which vehicle 250 is a semi-truck, vehicle 250 may include tractor portion 260 and trailer portion 270 (illustrated in FIG. 2G). FIGS. 2H and 2I provide side and top views, respectively, of tractor portion 260. Similar to vehicle 200 illustrated above, vehicle 250 illustrated in FIGS. 2F-2I may also include various sensor systems (e.g., similar to sensor systems 202, 206, 208, 210, 212, 214 shown and described with reference to FIGS. 2A-2E). In some embodiments, vehicle 200 of FIGS. 2A-2E may include only a single copy of some sensor systems (e.g., sensor system 204), while vehicle 250 illustrated in FIGS. 2F-2I may include multiple copies of its sensor systems (e.g., sensor systems 204A and 204B, as illustrated).

[0100] While the figures and general description may refer to a given vehicle form (e.g., semi-truck vehicle 250 or van vehicle 200), it is understood that the embodiments described herein may be equally applicable in various vehicle contexts (e.g., with modifications adopted to account for the vehicle form factor). For example, sensors and / or other components described or illustrated as being part of van vehicle 200 may also be used in semi-truck vehicle 250 (e.g., for navigation and / or obstacle detection and avoidance).

[0101] FIG. 2J illustrates various sensor fields of view (e.g., associated with vehicle 250, described above). As described above, vehicle 250 may contain multiple sensors / sensor units. The various sensor locations may correspond, for example, to the sensor locations disclosed in FIGS. 2F-2I. However, in some cases, sensors may have other locations. To simplify the drawing, sensor location reference numbers are omitted from FIG. 2J. For each sensor unit of vehicle 250, FIG. 2J illustrates a representative field of view (e.g., fields of view labeled as 252A, 252B, 252C, 252D, 254A, 254B, 256, 258A, 258B, and 258C). The field of view of a sensor may include an angular region (e.g., an azimuth region and / or an elevation region) in which the sensor may detect objects.

[0102] FIG. 2K illustrates beam steering for a sensor of a vehicle (e.g., vehicle 250 shown and described with reference to FIGS. 2F-2J) according to an exemplary embodiment. In various embodiments, the sensor unit of vehicle 250 may be radar, LiDAR, sonar, or the like. Additionally, in some embodiments, during sensor operation, the sensor may be scanned within the sensor's field of view. Various different scan angles for the exemplary sensor are shown as regions 272, each indicating the angular region in which the sensor is operating. The sensor may periodically or repeatedly change the region in which it is operating. In some embodiments, multiple sensors may be used by vehicle 250 to measure region 272. Additionally, other regions may be included in other examples. For example, one or more sensors may measure the aspect of trailer 270 of vehicle 250 and / or the region ahead of vehicle 250.

[0103] At some angles, the sensor's operating area 275 may include the rear wheels 276A, 276B of the trailer 270. Thus, the sensor may measure the rear wheels 276A and / or 276B during operation. For example, the rear wheels 276A, 276B may reflect LiDAR or radar signals transmitted by the sensor. The sensor may receive signals reflected from the rear wheels 276A, 276B. Thus, the data collected by the sensor may include data from reflections from the wheels.

[0104] In some cases, such as when the sensor is a radar, reflections from the rear wheels 276A, 276B may appear as noise in the received radar signal. As a result, the radar may operate with an enhanced signal-to-noise ratio in cases where the rear wheels 276A, 276B direct the radar signal away from the sensor.

[0105] 3 is a conceptual, illustrative diagram of wireless communication between various computing systems associated with an autonomous or semi-autonomous vehicle, according to an example embodiment. In particular, wireless communication may occur between a remote computing system 302 and the vehicle 200 over a network 304. Wireless communication may also occur between a server computing system 306 and the remote computing system 302, and between the server computing system 306 and the vehicle 200.

[0106] Vehicle 200 can correspond to various types of vehicles capable of transporting passengers or objects between locations and can take the form of any one or more of the vehicles discussed above. In some cases, vehicle 200 can operate in an autonomous or semi-autonomous mode that enables a control system to safely navigate vehicle 200 between destinations using sensor measurements. When operating in an autonomous or semi-autonomous mode, vehicle 200 can navigate with or without a passenger. As a result, vehicle 200 can pick up and drop off passengers between desired destinations.

[0107] Remote computing system 302 may represent any type of device associated with remote assistance technologies, including, but not limited to, those described herein. In examples, remote computing system 302 may represent any type of device configured to (i) receive information related to vehicle 200, (ii) provide an interface through which a human operator can then perceive the information and enter a response related to the information, and (iii) transmit the response to vehicle 200 or to another device. Remote computing system 302 may take various forms, such as a workstation, a desktop computer, a laptop, a tablet, a mobile phone (e.g., a smartphone), and / or a server. In some examples, remote computing system 302 may include multiple computing devices operating together in a network configuration.

[0108] Remote computing system 302 may include one or more subsystems and components similar to or identical to those of vehicle 200. At a minimum, remote computing system 302 may include a processor configured to perform the various operations described herein. In some embodiments, remote computing system 302 may also include a user interface including input / output devices such as a touchscreen and a speaker. Other examples are possible as well.

[0109] Network 304 represents an infrastructure that enables wireless communication between remote computing system 302 and vehicle 200. Network 304 also enables wireless communication between server computing system 306 and remote computing system 302, and between server computing system 306 and vehicle 200.

[0110] The location of remote computing system 302 can vary within the examples. For example, remote computing system 302 can be at a location remote from vehicle 200 with wireless communication over network 304. In another example, remote computing system 302 can correspond to a computing device within vehicle 200 that is separate from vehicle 200 but that allows a human operator to interact with a passenger or driver of vehicle 200. In some examples, remote computing system 302 can be a computing device with a touchscreen that can be operated by a passenger of vehicle 200.

[0111] In some embodiments, the operations described herein performed by remote computing system 302 may additionally or alternatively be performed by vehicle 200 (i.e., by any system or subsystem of vehicle 200). In other words, vehicle 200 may be configured to provide remote assistance mechanisms with which a driver or passengers of the vehicle can interact.

[0112] Server computing system 306 may be configured to wirelessly communicate with remote computing system 302 and vehicle 200 over network 304 (or, in some cases, directly with remote computing system 302 and / or vehicle 200). Server computing system 306 may represent any computing device configured to receive, store, determine, and / or transmit information related to vehicle 200 and its remote assistance. As such, server computing system 306 may be configured to perform any operation or portion of such operation described herein as being performed by remote computing system 302 and / or vehicle 200. Some embodiments of wireless communication related to remote assistance may utilize server computing system 306, while other embodiments may not.

[0113] The server computing system 306 may include one or more subsystems and components similar to or identical to the subsystems and components of the remote computing system 302 and / or the vehicle 200, such as a processor configured to perform the various operations described herein, and a wireless communication interface for receiving information from and providing information to the remote computing system 302 and the vehicle 200.

[0114] The various systems described above may perform various operations, and these operations and associated features will now be described.

[0115] In keeping with the above discussion, computing systems (e.g., remote computing system 302, server computing system 306, computing systems local to vehicle 200) may operate to capture images of the autonomous or semi-autonomous vehicle's surrounding environment using cameras. Generally, at least one computing system may analyze the images and, if possible, control the autonomous or semi-autonomous vehicle.

[0116] In some embodiments, to facilitate autonomous or semi-autonomous operation, a vehicle (e.g., vehicle 200) may receive data representing objects in the environment surrounding the vehicle (also referred to herein as "environmental data") in various manners. A sensor system on the vehicle may provide the environmental data representing objects in the surrounding environment. For example, the vehicle may have various sensors including cameras, radar, LiDAR, microphones, radio units, and other sensors. Each of these sensors may communicate environmental data to a processor within the vehicle regarding the information each respective sensor receives.

[0117] In one example, the camera may be configured to capture still images and / or video. In some embodiments, a vehicle may have two or more cameras positioned at different orientations. Also, in some embodiments, the camera may be capable of moving to capture images and / or video in different directions. The camera may be configured to store captured images and video in memory for later processing by a processing system of the vehicle. The captured images and / or video may be environmental data. Additionally, the camera may include an image sensor as described herein.

[0118] In another example, a radar may be configured to transmit electromagnetic signals that are reflected by various objects near the vehicle and then capture the electromagnetic signals that reflect from the objects. The captured reflected electromagnetic signals may enable the radar (or a processing system) to make various determinations about the objects that reflected the electromagnetic signals. For example, the distance and location to the various reflecting objects may be determined. In some embodiments, a vehicle may have two or more radars at different orientations. The radar may be configured to store the captured information in a memory for later processing by the vehicle's processing system. The information captured by the radar may be environmental data.

[0119] In another example, a LiDAR may be configured to transmit an electromagnetic signal (e.g., infrared light such as from a gas or diode laser, or other possible light source) that is reflected by a target object near the vehicle. The LiDAR may be capable of acquiring the reflected electromagnetic (e.g., infrared light) signal. The captured reflected electromagnetic signal may enable a ranging system (or processing system) to determine the distance to various objects. The LiDAR may also determine the velocity or speed of the target object, which may be stored as environmental data.

[0120] Additionally, in one example, a microphone may be configured to capture audio of the environment surrounding the vehicle. Sounds captured by the microphone may include sounds of emergency vehicle sirens and other vehicles. For example, the microphone may capture the sounds of sirens from an ambulance, a fire engine, and a police vehicle. The processing system may be able to identify that the captured audio signal is indicative of an emergency vehicle. In another example, the microphone may capture the sound of an exhaust from another vehicle, such as an exhaust from a motorcycle. The processing system may be able to identify that the captured audio signal is indicative of a motorcycle. Data captured by the microphone may form part of the environmental data.

[0121] In yet another example, the radio unit may be configured to transmit an electromagnetic signal, which may take the form of a Bluetooth signal, an 802.11 signal, and / or other wireless technology signal. The first electromagnetic radiation signal may be transmitted via one or more antennas located on the radio unit. Furthermore, the first electromagnetic radiation signal may be transmitted in one of many different wireless signal modes. However, in some embodiments, it may be desirable to transmit the first electromagnetic radiation signal in a signal mode that solicits responses from devices located near the autonomous or semi-autonomous vehicle. The processing system may be able to detect nearby devices based on responses transmitted back to the radio unit and use this communicated information as part of the environmental data.

[0122] In some embodiments, the processing system may be able to combine information from various sensors to further determine the vehicle's surroundings. For example, the processing system may combine data from both radar information and captured imagery to determine whether another vehicle or pedestrian is in front of the autonomous or semi-autonomous vehicle. In other embodiments, other combinations of sensor data may be used by the processing system to make determinations about the surroundings.

[0123] While operating in autonomous mode (or semi-autonomous mode), the vehicle may control its operation with little or no human input. For example, if a human operator inputs an address into the vehicle, the vehicle may be able to drive to the specified destination without further input from the human (e.g., without the human having to steer or touch the brake / accelerator pedals). Additionally, while the vehicle is operating autonomously or semi-autonomously, the sensor system may be receiving environmental data. The vehicle's processing system may alter the control of the vehicle based on the environmental data received from the various sensors. In some embodiments, the vehicle may alter the vehicle's speed in response to the environmental data from the various sensors. The vehicle may alter its speed to avoid obstacles, obey traffic laws, etc. If the processing system in the vehicle identifies an object near the vehicle, the vehicle may be able to alter its speed or otherwise modify its behavior.

[0124] If the vehicle detects an object but is not fully confident in its detection, the vehicle can request a human operator (or a more powerful computer) to perform one or more remote assistance tasks, such as (i) verifying whether the object is actually present in the surrounding environment (e.g., is there actually a stop sign or is there actually no stop sign), (ii) verifying whether the vehicle's identification of the object is correct, (iii) correcting the identification if it is incorrect, and / or (iv) providing supplemental instructions (or modifying current instructions) to the autonomous or semi-autonomous vehicle. Remote assistance tasks also include the human operator providing instructions to control the vehicle's operation (e.g., if the human operator determines that the object is a stop sign, commanding the vehicle to stop at the stop sign), although in some scenarios the vehicle itself may control its own operation based on the human operator's feedback related to the object's identification.

[0125] To facilitate this, the vehicle may analyze environmental data representative of objects in the surrounding environment to determine at least one object having a detection confidence below a threshold. A processor in the vehicle may be configured to detect various objects in the surrounding environment based on the environmental data from various sensors. For example, in one embodiment, the processor may be configured to detect objects that may be important for the vehicle to recognize. Such objects may include pedestrians, bicyclists, street signs, other vehicles, indicator signals of other vehicles, and various other objects detected in the captured environmental data.

[0126] The detection confidence may indicate the likelihood that a determined object is correctly identified or present in the surrounding environment. For example, the processor may perform object detection of an object in image data in the received environmental data and determine that an object has a detection confidence below a threshold based on not being able to identify at least one object with a detection confidence above a threshold. If the object detection or object recognition results for an object are inconclusive, the detection confidence may be low or below a set threshold.

[0127] A vehicle may detect objects in the surrounding environment in a variety of ways, depending on the source of the environmental data. In some embodiments, the environmental data may be image or video data coming from a camera. In other embodiments, the environmental data may come from a LiDAR. The vehicle may analyze the captured image or video data to identify objects in the image or video data. Methods and apparatus may be configured to monitor the image and / or video data for objects in the surrounding environment. In other embodiments, the environmental data may be radar, audio, or other data. The vehicle may be configured to identify objects in the surrounding environment based on the radar, audio, or other data.

[0128] In some embodiments, the technique used by the vehicle to detect objects may be based on a set of known data. For example, data related to environmental objects may be stored in a memory located in the vehicle. The vehicle may compare the received data with the stored data to determine the object. In other embodiments, the vehicle may be configured to determine the object based on the context of the data. For example, construction-related street signs may generally have an orange color. Thus, the vehicle may be configured to detect an orange object located near the side of the road as a construction-related street sign. Additionally, as the vehicle's processing system detects objects in the captured data, it may also calculate a confidence score for each object.

[0129] Additionally, the vehicle may also have a confidence threshold. The confidence threshold may vary depending on the type of object detected. For example, the confidence threshold may be lower for an object that may require a quick response action from the vehicle, such as the brake lights of another vehicle. However, in other embodiments, the confidence threshold may be the same for all detected objects. If the confidence associated with a detected object is higher than the confidence threshold, the vehicle may assume that the object was correctly recognized and responsively adjust the vehicle's controls based on that assumption.

[0130] If the confidence associated with the detected object is lower than a confidence threshold, the action taken by the vehicle may vary. In some embodiments, the vehicle may react as if the detected object is present despite the low confidence level. In other embodiments, the vehicle may react as if the detected object is not present.

[0131] Upon detecting an object in the surrounding environment, the vehicle may also calculate a confidence level associated with the particular detected object. The confidence level may be calculated in various ways depending on the embodiment. In one example, upon detecting an object in the surrounding environment, the vehicle may compare environmental data to predetermined data associated with known objects. The closer the match between the environmental data and the predetermined data, the higher the confidence level. In other embodiments, the vehicle may use a mathematical analysis of the environmental data to determine the confidence level associated with the object.

[0132] In response to determining that the object has a detection confidence below a threshold, the vehicle may transmit a request for remote assistance along with an identification of the object to a remote computing system. As discussed above, the remote computing system may take a variety of forms. For example, the remote computing system may be a computing device separate from the vehicle, but within the vehicle, such as a touchscreen interface for displaying remote assistance information, through which a human operator can interact with a passenger or driver of the vehicle. Additionally or alternatively, as another example, the remote computing system may be a remote computer terminal or other device located at a location not near the vehicle.

[0133] The request for remote assistance may include environmental data, including objects, such as image data, audio data, etc. The vehicle may transmit the environmental data over a network (e.g., network 304) to a remote computing system, in some embodiments, via a server (e.g., server computing system 306). A human operator of the remote computing system may then use the environmental data as a basis for responding to the request.

[0134] In some embodiments, if an object is detected as having a confidence below a confidence threshold, the object may be given a preliminary identification and the vehicle may be configured to adjust the vehicle's operation in response to the preliminary identification. Such adjustments in operation may take the form of stopping the vehicle, switching the vehicle to a human-controlled mode, changing the vehicle's operation (e.g., speed and / or direction), among other possible adjustments.

[0135] In other embodiments, if the vehicle detects an object with a confidence level that meets or exceeds a threshold, the vehicle may still act on the detected object (e.g., stop if the object is identified with high confidence as a stop sign), but may be configured to request remote assistance at the same time (or after) the vehicle acts on the detected object.

[0136] 4A is a block diagram of a system according to an example embodiment. In particular, FIG. 4A shows a system 400 including a system controller 402, a LiDAR device 410, a number of sensors 412, and a number of controllable components 414. The system controller 402 includes a processor 404, a memory 406, and instructions 408 stored on the memory 406 and executable by the processor 404 to implement functions.

[0137] The processor 404 may include one or more processors, such as one or more general-purpose microprocessors (e.g., having a single core or multiple cores) and / or one or more special-purpose microprocessors. The one or more processors may include, for example, one or more central processing units (CPUs), one or more microcontrollers, one or more graphical processing units (GPUs), one or more tensor processing units (TPUs), one or more ASICs, and / or one or more field-programmable gate arrays (FPGAs). Other types of processors, computers, or devices configured to execute software instructions are also contemplated herein.

[0138] The memory 406 may include a computer-readable medium such as a non-transitory computer-readable medium, which may include, without limitation, read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), non-volatile random access memory (e.g., flash memory), solid-state drive (SSD), hard disk drive (HDD), compact disc (CD), digital video disc (DVD), digital tape, read / write (R / W) CD, R / W DVD, etc.

[0139] The LiDAR device 410, described further below, includes a plurality of light emitters configured to emit light (e.g., in light pulses) and one or more light detectors configured to detect the light (e.g., reflected portions of the light pulses). The LiDAR device 410 may generate three-dimensional (3D) point cloud data from the outputs of the light detectors and provide the 3D point cloud data to the system controller 402. The system controller 402 may then perform operations on the 3D point cloud data to determine characteristics of the surrounding environment (e.g., relative positions of objects within the surrounding environment, edge detection, object detection, and / or proximity sensing).

[0140] Similarly, the system controller 402 may use output from multiple sensors 412 to determine characteristics of the system 400 and / or the surrounding environment. For example, the sensors 412 may include one or more of a GPS, an IMU, an image capture device (e.g., a camera), a light sensor, a heat sensor, and other sensors that indicate parameters related to the system 400 and / or the surrounding environment. The LiDAR device 410 is depicted as being separate from the sensors 412, for illustrative purposes, and in some examples may be considered part of or as the sensors 412.

[0141] Based on characteristics of the surrounding environment determined by the system controller 402 based on output from the system 400 and / or the LiDAR device 410 and sensors 412, the system controller 402 may control the controllable components 414 to perform one or more actions. For example, the system 400 may correspond to a vehicle, in which case the controllable components 414 may include the vehicle's braking system, turning system, and / or acceleration system, and the system controller 402 may alter aspects of these controllable components based on characteristics determined from the LiDAR device 410 and / or sensors 412 (e.g., when the system controller 402 controls the vehicle in an autonomous or semi-autonomous mode). In an example, the LiDAR device 410 and sensors 412 are also controllable by the system controller 402.

[0142] 4B is a block diagram of a LiDAR device according to an example embodiment. In particular, FIG. 4B shows a LiDAR device 410 having a controller 416 configured to control a plurality of light emitters 424 and one or more light detectors, such as a plurality of light detectors 426. The LiDAR device 410 may further include a firing circuit 428 configured to select and provide power to each light emitter of the plurality of light emitters 424 and may include a selector circuit 430 configured to select each light detector of the plurality of light detectors 426. The controller 416 includes a processor 418, a memory 420, and instructions 422 stored on the memory 420.

[0143] Similar to processor 404, processor 418 may include one or more processors, such as one or more general-purpose microprocessors and / or one or more special-purpose microprocessors. The one or more processors may include, for example, one or more CPUs, one or more microcontrollers, one or more GPUs, one or more TPUs, one or more ASICs, and / or one or more FPGAs. Other types of processors, computers, or devices configured to execute software instructions are also contemplated herein.

[0144] Similar to memory 406, memory 420 may include computer-readable media such as, but not limited to, ROM, PROM, EPROM, EEPROM, non-volatile random access memory (e.g., flash memory), SSD, HDD, CD, DVD, digital tape, R / W CD, R / W DVD, and other non-transitory computer-readable media.

[0145] Instructions 422 are stored on memory 420 and executable by processor 418 to perform functions related to controlling firing circuitry 428 and selector circuitry 430 to generate 3D point cloud data and to process the 3D point cloud data (or perhaps to facilitate processing of the 3D point cloud data by another computing device, such as system controller 402).

[0146] The controller 416 may determine the 3D point cloud data by using the light emitters 424 to emit pulses of light. An emission time is established for each light emitter, and the relative location of the emission time is also tracked. Aspects of the LiDAR device 410's surrounding environment, such as various objects, reflect the pulses of light. For example, if the LiDAR device 410 is in an environment that includes a road, such objects may include vehicles, signs, pedestrians, road surfaces, construction cones, etc. Some objects may be more reflective than others, such that the intensity of the reflected light may indicate the type of object reflecting the light pulse. Furthermore, object surfaces may be at different locations relative to the LiDAR device 410 and therefore may take more or less time to reflect a portion of the light pulse back to the LiDAR device 410. Thus, the controller 416 may track the detection time at which the reflected light pulse is detected by the photodetector and the relative location of the photodetector at the detection time. By measuring the time difference between the emission time and the detection time, the controller 416 can determine how far the light pulse travels before being received and, therefore, the relative distance of the corresponding object. By tracking the relative positions at the emission and detection times, the controller 416 can determine the orientation of the light pulse and reflected light pulse relative to the LiDAR device 410 and, therefore, the relative orientation of the object. By tracking the intensity of the received light pulse, the controller 416 can determine how reflective the object is. 3D point cloud data determined based on this information can therefore indicate the relative positions of the detected reflected light pulses (e.g., in a coordinate system such as a Cartesian coordinate system) and the intensity of each reflected light pulse.

[0147] A firing circuit 428 is used to select the light emitter for emitting a light pulse. Similarly, a selector circuit 430 is used to sample the output from the photodetector.

[0148] As noted above, various types of defects can adversely affect measurements made by a sensing device (e.g., a LiDAR device or a camera). For example, some sensing devices may include one or more optical components (e.g., lenses, mirrors, waveguides, and / or windows), and one or more defects in or on portions of such optical components can cause improper detection of the surrounding environment. For example, an optical window (e.g., the optical element through which a camera or LiDAR captures images of the surrounding environment) may be adversely affected by one or more scratches, cracks, dirt, deformations, bubbles, impurities (e.g., chemical impurities in the glass or plastic of the optical window), degradation (e.g., degradation of optical properties over time), discoloration, imperfect transparency, or distortions within one or more portions of the optical window (e.g., within the body). Additionally or alternatively, such optical windows may be adversely affected by water droplets, dirt, dust, mud, leaves, rain, snow, sleet, hail, ice, or insect residue (i.e., remnants of dead insects) on one or more portions of the optical window (e.g., on one or more portions of the optical window that interface with the ambient environment, i.e., on one or more exterior portions of the optical window). It is understood that other types of defects are possible and are contemplated herein. Furthermore, such defects may attenuate, obscure, block, scatter, redirect, or otherwise interfere with optical signals received from the ambient environment.

[0149] FIG. 5A illustrates an exemplary system (e.g., the system 400 shown and described with reference to FIG. 4A) adversely affected by a defect 502 on an optical component 504 (e.g., an optical window) of the system 400. While the defect 502 is shown as a single lobe on the exterior of the optical component 504, this is shown for illustrative purposes only, and it is understood that any number and / or type of defect (e.g., on or within the optical component 504) is possible. As illustrated, the defect 502 can interfere with an optical signal emitted by an optical emitter (e.g., the optical emitter 424 of the LiDAR device 410 shown and illustrated in FIG. 4B) and / or an optical signal being detected by an optical detector (e.g., the optical detector 426 of the LiDAR device 410 shown and illustrated in FIG. 4B). Thus, as illustrated in FIG. 5A, the defect 502 can affect measurements of the surrounding environment using the optical emitter 424 / optical detector 426.

[0150] Similar to FIG. 5A , FIG. 5B illustrates an exemplary system (e.g., the system 400 shown and described with reference to FIG. 4A ) adversely affected by a defect 502 on an optical component 504 (e.g., an optical window) of the system 400. While the defect 502 is shown as a single lobe on the exterior of the optical component 504, this is shown for illustrative purposes only, and it is understood that any number and / or type of defect (e.g., on or within the optical component 504) is possible. As illustrated, the defect 502 may interfere with the optical path 510 of an image sensor 506 (e.g., the image sensor of a camera, such as the camera 130 shown and described with reference to FIG. 1 ) attempting to capture an image of the surrounding environment. In some embodiments, the image sensor 506 may include a charge-coupled device (CCD) or other type of image sensor. As shown in FIG. 5B , the defect 502 may affect measurements of the surrounding environment using the image sensor 506.

[0151] It may be desirable to mitigate the adverse effects of a defect (e.g., defect 502 shown and described with reference to FIGS. 5A and 5B ). This can be done by detecting the presence of one or more defects and then performing one or more remedial actions (e.g., cleaning the problematic optical component, replacing the problematic optical component, removing all or part of the problematic optical component from use, flagging data acquired by a sensor using the optical component as including the effects of the defect, and performing post-processing on the data acquired by the sensor using the optical component to compensate for the effects of the defect). To detect the presence of one or more defects, techniques described herein can be implemented. Furthermore, such techniques can be implemented using one or more devices or systems described herein.

[0152] 6A illustrates a LiDAR device 600 including a defect detection component. The LiDAR device 600 can be used to detect the presence (e.g., and location) of one or more defects on one or more optical components of the LiDAR device 600. As illustrated, the LiDAR device 600 can include an array of light emitters (e.g., light emitters 424 of the LiDAR device 410 illustrated and described with reference to FIG. 4B), an array of light detectors (e.g., light detectors 426 of the LiDAR device 410 illustrated and described with reference to FIG. 4B), optical components (e.g., optical components 504 illustrated and described with reference to FIGS. 5A and 5B), one or more light sources 602 (e.g., light sources arranged in an array), and one or more detectors 604.

[0153] The light emitter 424 / light detector 426 (e.g., similar to the light emitter 424 / light detector 426 shown and described with reference to FIG. 4B ) can be used by the LiDAR device 600 to detect the surrounding environment. As shown by the dashed line in FIG. 6A , the light emitter 424 can emit one or more light signals that are transmitted through the optical component 504 (e.g., if the optical component 504 is an optical window, lens, and / or waveguide) or reflected from the optical component 504 (e.g., if the optical component 504 is a mirror) and transmitted into the surrounding environment. The light signals may be reflected by objects in the surrounding environment and directed (e.g., back through the optical component 504) to the light detector 426 to provide information about the surrounding environment (e.g., distance to objects in the surrounding environment based on time of flight, etc.). However, as illustrated in Figure 6B, if a defect 502 (e.g., the defect 502 shown and described with reference to Figure 5A) is present, the defect 502 may interfere with (e.g., redirect and / or scatter) the optical signal for the surrounding environment, which may cause a detection event to be missed or erroneously altered (e.g., resulting in inaccurate data about the surrounding environment being obtained).

[0154] The light emitter 424 / light detector 426 can be positioned, sized, and / or oriented in a particular manner to accommodate the light source 602 and / or detector 604. For example, the light emitter 424 and light detector 426 can be positioned along a focal plane (e.g., based on the focal length of one or more lenses in the LiDAR device 600) with enough space adjacent to the light emitter 424 / light detector 426 in the LiDAR device 600 to accommodate the light source 602 and / or detector 604 (e.g., the light source 602 and / or detector 604 can also be positioned along the focal plane of the LiDAR device 600). In various embodiments, the light emitter 424 / light detector 426 can be parallel to each other and / or to the light source 602 / detector 604, as shown in FIG. 6A . In another embodiment, the light emitters 424 / light detectors 426 may be at an angle (eg, perpendicular) to each other and / or to the light source 602 / detector 604.

[0155] The optical component 504 may be part of the transmit path and / or receive path of the LiDAR device 600. For example, the optical component 504 may comprise an optical window through which optical signals are transmitted to or received from the surrounding environment. Additionally or alternatively, the optical component 504 may comprise a lens, a mirror, etc. The optical component 504 may be used to direct and / or modify the optical signals used to sense the surrounding environment.

[0156] The defect detection components of the LiDAR device 600 (e.g., the light source 602 and / or the detector 604) can be used to identify one or more defects present in or on (e.g., on the exterior of) the optical component 504. For example, as illustrated by the solid line in FIG. 6A , the light source 602 can emit one or more optical signals. If one or more defects are present, such optical signals can be redirected by (e.g., reflected from) the defects toward and detected by the detector 604. For example, as illustrated in FIG. 6A , if no defects are present, the defect detection signal from the light source 602 propagates outside the LiDAR device 600 and / or is directed toward a portion of the LiDAR device 600 other than the detector 604. However, as illustrated in FIG. 6B , if a defect 502 (e.g., a defect as shown and described with reference to FIG. 5A ) is present, the defect 502 may reflect or redirect the defect detection signal from the light source 602 toward the detector 604. Thus, a detection event corresponding to detector 604 can be used to determine whether one or more defects are present in or on optical component 504 .

[0157] The light source 602 may include one or more LEDs, one or more laser diodes, one or more fluorescent lamps, etc. Furthermore, as illustrated, the light sources 602 may be arranged in an array. However, it is understood that in other embodiments, only the signal light source 602 may be present in the LiDAR device 600. In embodiments having multiple light sources 602 (e.g., embodiments similar to those of FIGS. 6A and 6B ), the light sources 602 may each be the same or different light sources. For example, the two or more light sources 602 may have different illumination power / intensity, different illumination wavelengths, different illumination polarizations, different focal lengths, different emission angles, etc. Furthermore, in some embodiments, one or more of the light sources 602 may be adjustable. For example, one or more light sources 602 may be coupled using motors and / or mechanical stages to change the position and / or orientation (e.g., resulting emission direction) of each light source 602. For example, one or more light sources 602 may be coupled to scan different portions of the optical component 504 over time to detect defects. Additionally or alternatively, one or more light sources 602 may include one or more filters that can be interchanged (e.g., mechanically using an actuator controlled by a technician or by a controller) to provide different emission characteristics. For example, one or more light sources 602 may include white LEDs having a baseline emission intensity and different polarizations mixed in. Color filters can be interchanged to change the output wavelength of the light source 602, neutral density filters can be interchanged to change the emission intensity of the light source 602, or polarizing filters can be interchanged to change the polarization emission of the light source 602. Furthermore, in some embodiments, multiple filters can be used simultaneously (e.g., cascaded) to modify the defect detection signal output by the light source 602 in various ways.

[0158] In some embodiments, the light source 602 can be positioned at a focal plane of the light emitter 424 / light detector 426 detection system. Furthermore, a focal length associated with the light source 602 (e.g., defined by one or more lenses associated with the light source 602) can correspond to a distance between the light source 602 and the optical component 504 to enhance detection of the defect 502. Similarly, a focal length associated with the detector 604 (e.g., defined by one or more lenses associated with the detector 604) can correspond to a distance between the detector 604 and the optical component 504 to enhance detection of the defect 502. Furthermore, the light source 602 can be positioned at a focal plane of the sensing device (e.g., a focal plane corresponding to the light emitter 424 / light detector 426 detection system, as shown in FIGS. 6A and 6B ), although it will be understood that this is not true in all embodiments.

[0159] As described above, to determine whether one or more defects 502 are present, detector 604 can be used to detect defect detection signals emitted from light source 602 as these signals are redirected / reflected from one or more defects 502. In some embodiments, detector 604 can include a CCD or other single image sensor. Alternatively, in some embodiments, detector 604 can include one or more photodetector elements (e.g., APDs, SPADs, SiPMs, photodiodes, and / or photoresistors). Furthermore, in some embodiments, detector 604 can be arranged in an array. For example, detector 604 can include an array of photodetector elements, where the number of photodetector elements in the array corresponds to the number of light sources 602 in the corresponding array of light sources 602 (i.e., there can be a one-to-one correspondence between detectors 604 in the detector array and light sources 602 in the light source array). Furthermore, the position / orientation of the detector 604 within the detector array is based on (e.g., designed to correspond to) the radiation vector corresponding to the light source 602 of the light source array, such that the defect detection signal reflected from one or more defects 502 can be detected by the detector 604.

[0160] FIG. 6B illustrates the LiDAR device 600 of FIG. 6A. However, unlike the illustration of FIG. 6A, in FIG. 6B a defect (e.g., similar to the defect 502 shown and described with reference to FIG. 5A) may be present. As illustrated, the defect 502 may reflect an optical signal emitted by the optical emitter 424, which is intended to probe the surrounding environment, and redirect the optical signal toward the optical detector 426. Also illustrated, the redirected optical signal may be diffused or dispersed after interacting with the defect 502. As shown in FIG. 6B, the defect-detection optical signal emitted by the optical source 602 may interact with (e.g., be reflected by) the defect 502 and be directed toward the detector 604.

[0161] 7A and 7B illustrate a camera 700 according to an example embodiment. Similar to the LiDAR device 600 illustrated in FIGS. 6A and 6B, the camera 700 may include a sensing device configured to detect objects in the surrounding environment using optical components. However, unlike the LiDAR device 600 of FIGS. 6A and 6B, the sensing device of the camera 700 may be an image sensor (e.g., the image sensor 506 shown and described with reference to FIG. 5B). As illustrated, the image sensor 506 may be configured to capture images of the surrounding environment through an optical path (e.g., the optical path shown and described with reference to FIG. 5B) that passes through the optical components (e.g., the optical component 504 shown and described with reference to FIG. 5). Similar to the LiDAR device 600 of FIGS. 6A and 6B, the camera 700 may include one or more light sources 602 and one or more detectors 604 used to detect the presence (e.g., and location) of one or more defects 502. For example, as illustrated in FIG. 7B (e.g., similar to the arrangement of FIG. 6B), the light source 602 can emit a defect detection signal that is reflected / redirected from the defect 502 and detected by the detector 604 to determine whether any defect 502 is present in or on the optical component 504.

[0162] As described above with reference to FIGS. 6A-7B , in some embodiments, a system (e.g., a LiDAR device 600 or a camera 700) may include a dedicated defect detection system (e.g., including one or more light sources 602 and one or more detectors 604). However, it is understood that other embodiments are possible. For example, as illustrated in FIGS. 8A and 8B , some embodiments may include a hybrid system 800. The hybrid system 800 shown in FIGS. 8A and 8B includes a light source 602 configured to emit a defect detection signal that can be redirected / reflected from a defect 502 in or on an optical component 504 (e.g., as shown in FIG. 8B ). However, unlike FIGS. 6A-7B , the reflected / redirected defect detection signal may not be detected by a dedicated defect detector. Instead, the hybrid system 800 may include a hybrid image sensor 802 used to detect the reflected / redirected defect detection signal.

[0163] As illustrated in FIG. 8B , hybrid image sensor 802 can be configured to both detect reflected defect detection signals and capture images of the surrounding environment through an optical path through optical components 504 (e.g., optical path 510 shown and described with reference to FIG. 5B ). Hybrid image sensor 802 may be the same type of image sensor as illustrated in FIGS. 7A and 7B . For example, hybrid image sensor 802 may include a CCD. In some embodiments, hybrid image sensor 802 can include one or more integrated optical components (e.g., lenses and / or mirrors) configured to direct, focus, or redirect light. For example, hybrid image sensor 802 may include integrated optical components configured to direct light from the surrounding environment to an area of ​​hybrid image sensor 802 different from the reflected / redirected defect detection signals. In some embodiments, images used for defect detection can be captured intermittently by hybrid image sensor 802 through optical path 510 along with images captured to detect objects in the surrounding environment. For example, a series of images can be captured by the hybrid image sensor 802, including images in which one or more light sources 602 are illuminating the optical component 504 using the defect detection signal followed by images in which the one or more light sources 602 are not illuminating the optical component 504.

[0164] Additionally, as noted above, one or more light sources of the defect detection system (e.g., light source 602 illustrated in FIGS. 6A-8B ) can be positioned in the focal plane of the sensing device (e.g., light emitter 424 / light detector 426 combination in FIGS. 6A and 6B , image sensor 506 in FIGS. 7A and 7B , or hybrid image sensor 802 in FIGS. 8A and 8B ). However, it is understood that this need not be the case. For example, in various embodiments, one or more light sources 602 may be positioned closer to or farther away from the optical component 504 than the sensing device. However, in still other embodiments, the one or more light sources can illuminate the optical component 504 in a manner entirely different from that illustrated in FIGS. 6A-8B . For example, FIGS. 9A and 9B illustrate an embodiment of a camera 900 having a light source 602 configured to illuminate the optical component using total internal reflection (i.e., by propagating the defect detection signal within the optical component 504 at an angle such that the defect detection signal is internally reflected from the interior surface of the optical component 504). Further, as illustrated in Figures 9A and 9B, one or more light sources 902 can be positioned adjacent to the edge of the optical component 504 (e.g., in a line and / or array) so that the defect detection signal is edge coupled into the optical component 504.

[0165] As illustrated in FIG. 9A , if a defect 502 is not present, the defect detection signal can propagate from one end of the optical component 504 to the opposite end (e.g., exit the optical component 504) by total internal reflection. In some embodiments, another detector (e.g., another defect detector) can be positioned at the end of the optical component 504 opposite the light source 902. If the another defect detector detects the defect detection signal emitted from the light source 902, it can be determined that no defect is present in or on the optical window 504. Additionally or alternatively, the another defect detector can also determine whether a defect is present by analyzing the relative signal strength reported by the another defect detector. For example, if the another defect detector detects a signal having an intensity that is not zero but is below an expected intensity (e.g., based on the intensity of the defect detection signal emitted by one or more light sources 902 and / or based on the detected intensity during a previous calibration measurement by the another defect detector, e.g., where no defect is present), it can be determined that a defect is present in or on the optical window 504.

[0166] However, if a defect is present in or on the optical window 504 (e.g., as illustrated in FIG. 9B ), one or more of the defect detection signals from the one or more light sources 902 can be redirected / reflected from the defect 502. The redirected / reflected defect detection signals can then be transmitted from the optical component 504 toward the hybrid image sensor 802. For example, as illustrated by the solid lines in FIG. 9B , one or more defect detection signals can be reflected from the defect 502 (e.g., along optical path 510) and detected by the hybrid image sensor 802. As described above with respect to FIGS. 8A and 8B , the hybrid image sensor 802 can detect a single image that includes both the defect detection signals and light from the surrounding environment. Additionally or alternatively, in some embodiments, multiple images can be captured (e.g., some images with the light source 902 illuminating the optical component 504 using one or more defect detection signals and some images with the light source 902 not illuminating the optical component 504 using the defect detection signals). 9A and 9B illustrate a camera 900 with a hybrid image sensor 802, it is understood that other embodiments using a defect detection signal propagated using total internal reflection are possible and contemplated herein. For example, the LiDAR device 600 of FIGS. 6A and 6B can have the light source 602 replaced with the light source 902 illustrated in FIGS. 9A and 9B. Similarly, the camera 700 of FIGS. 7A and 7B can have the light source 602 replaced with the light source 902 illustrated in FIGS. 9A and 9B.

[0167] As discussed above, it can be difficult to disambiguate a background signal (e.g., a signal corresponding to the surrounding scene) from a defect detection signal. For example, when an image is captured using the hybrid image sensor 802 of the hybrid system 800 of FIGS. 8A and 8B , it can be difficult to determine which portion of the image (if any) is affected by the presence of one or more defects. Therefore, the techniques described herein can time-modulate one or more light sources 602 to help disambiguate a background signal (e.g., used for object detection and avoidance) from a defect detection signal (e.g., used to determine whether one or more defects are present in or on the optical component 504).

[0168] One technique for temporal modulation involves illuminating the optical component using one or more light sources 602 according to one or more structured illumination patterns. In some embodiments, this can include illuminating half of the optical component 504 using one or more light sources 602 for a first duration during a first period, and then illuminating the other half of the optical component 504 using one or more light sources 602 for a second duration during a second period. For example, there can be two light sources 602, one illuminating the left half of the optical component 504 during a first period and the other illuminating the right half of the optical component 504 during a second period. By comparing detected events (e.g., images captured using the hybrid image sensor 802, or, in the case of the LiDAR system illustrated in FIGS. 6A-7B , light detected by the detector 604) during the first and second periods with detections of the ambient environment when no light source is illuminating the ambient environment, background signals (e.g., indicative of an object in the ambient environment) and defect signals (e.g., indicative of the presence, and possibly location, of one or more defects in or on the optical component 504) can be readily distinguished. For example, in an embodiment in which only the left half of the optical component 504 is illuminated during a first time period and only the right half of the optical component 504 is illuminated during a second time period, when determining whether a defect is present in the left half of the optical component 504, the computing device may compare images captured by the hybrid image sensor 802 during the first time period with images captured by the hybrid image sensor 802 during a time period in which no portion of the optical component 504 was illuminated. Additionally or alternatively, the images captured during the first and second time periods (or, in the case of the LiDAR system illustrated in FIGS. 6A-7B , light detected by the detector 604) may be compared to one another to determine the presence and / or location of one or more defects.

[0169] In various embodiments, the structured illumination pattern generated on the optical component 504 by the one or more light sources 602 can vary. In some embodiments, for example, the light source 602 may simply alternate between two structured illumination patterns on the optical component 504. For example, as illustrated in FIGS. 10A and 10B , the structured illumination pattern (e.g., when the optical component 504 corresponds to a curved lens or a curved external optical window) may be an alternating checkerboard pattern (e.g., white areas represent illuminated areas and black areas represent non-illuminated areas). In some cases, there may be a single light source within the light source 602 corresponding to each square of the checkerboard pattern, and thus each can be turned on or off to illuminate or not illuminate each square on the optical component 504. Alternatively, an adjustable mask can be placed over the single light source or array of light sources and used to selectively illuminate various portions of the optical component 504. Also, instead of a checkerboard pattern, two alternating stripes (e.g., as illustrated in FIGS. 10C and 10D ) can instead be used for the structured illumination pattern. Such a stripe pattern can be used when optical component 504 is a curved lens or an external optical window, and / or light source 602 can be, for example, an edge-coupled light source that propagates the defect signal through optical component 504 by total internal reflection (e.g., similar to Figures 9A and 9B).

[0170] 11A and 11B illustrate a series of images (i.e., an image stream) captured using the hybrid image sensor 802 when the optical component 504 is continuously illuminated, for example, according to the alternating stripe patterns of FIGS. 10C and 10D . The dark bands in the images of FIGS. 11A and 11B represent portions of the optical component 504 (e.g., the optical window through which the hybrid image sensor 802 captures images of the surrounding environment) that are not currently illuminated by one or more light sources 602, while the other bands in the images of FIGS. 11A and 11B represent portions of the optical component 504 that are currently illuminated by one or more light sources 602. It is understood that FIGS. 11A and 11B are provided for illustrative purposes only, and that images captured using the techniques described herein may not actually include physical light and dark regions. Rather, the light and dark regions in FIGS. 11A and 11B are simply used to help explain the illumination pattern used. In FIG. 11A , no defects are present in or on the optical component 504. Thus, various portions of the image appear the same regardless of which illumination pattern is currently being used. Thus, by comparing different regions of the scene at different times, a computing device can determine that no defect is present. However, in FIG. 11B , a defect is present in or on the optical component 504. This defect may result in a defect artifact 1102 in the captured image. The defect artifact 1102 may be, for example, blur. As illustrated in FIG. 11B , the defect artifact 1102 may be present in the area currently illuminated by one or more light sources 602 (e.g., in a strip illuminated in a predetermined illumination pattern). Thus, by comparing the currently illuminated strip between different images (or by comparing an image of the currently illuminated strip with a previously captured image in which no illumination was applied to the optical component 504), the presence and / or location of the defect can be identified.Such techniques are particularly useful for identifying defects when the background signal (e.g., the surrounding environment) changes over time (e.g., as a result of the relative motion of a vehicle operating in an automated or semi-automated mode in which the detection system is deployed). For example, a series of captured images (or, in the case of the LiDAR system illustrated in Figures 6A and 7B, the light detected by detector 604) can be particularly useful for detecting defects because the structured illumination pattern remains consistent while the background signal changes, and the presence and / or location of the defect does not change over time (or changes over a much longer timescale than the background signal).

[0171] It will be appreciated that various embodiments may use different numbers and / or types of patterns. For example, in some embodiments, there may be an illumination sequence of three or more illumination patterns (e.g., 3, 4, 5, 6, 7, 8, 9, and / or 10). For example, multiple patterns consisting of different subsets of the checkerboard areas illustrated in FIGS. 10A and 10B may be sequentially illuminated. Such additional patterns may be used to assist in determining the location of one or more defects on the optical component 504. Furthermore, in some embodiments, there may be checkerboard squares of various numbers or orientations illustrated in FIGS. 10A and 10B, stripes of various numbers or orientations illustrated in FIGS. 10C and 10D, entirely different shapes (e.g., triangular patterns, pentagonal patterns, and / or hexagonal electronic patterns), etc.

[0172] In addition to, or instead of, using a structured illumination pattern to help distinguish between background and defect signals, temporal modulation of illumination wavelengths can be used. For example, one or more light sources 602 can illuminate a portion (e.g., all) of the optical component 504 using a defect detection signal at a first wavelength (e.g., within a first wavelength band / range, e.g., a 5 nm wavelength range) for a first period of time. The first wavelength can be a wavelength that is unlikely to be present (e.g., not present at all) in the surrounding environment. This can further help distinguish between background and defect detection signals. After illuminating a portion (e.g., all) of the optical component 504 using a first wavelength, the one or more light sources 602 can (i) illuminate the same portion (e.g., all) of the optical component 504 using a defect detection signal in a second wavelength (e.g., within a second wavelength band / range, e.g., within a 5 nm wavelength range) (that does not overlap or at least partially overlaps with the first wavelength band), (ii) illuminate the same portion (e.g., all) of the optical component 504 using white light (e.g., light over a wide range of wavelengths), or (iii) not illuminate a portion (e.g., all) of the optical component 504 at all.

[0173] 11C and 11D illustrate a series of images (i.e., an image stream) captured (e.g., by the hybrid image sensor 802 of FIGS. 8A and 8B ) while temporal modulation of the illumination wavelength is used (e.g., by one or more light sources 602). For example, one or more light sources 602 may be powered off during the first and third captured images in each series, and may be powered on and illuminating the optical component 504 using a first wavelength during the second captured image in each series. As with FIGS. 11A and 11B , FIGS. 11C and 11D are provided for illustrative purposes only, and it is understood that images captured using the techniques described herein may not actually appear dark or light. Rather, the light and dark in FIGS. 11C and 11C are used solely to facilitate illustrating the presence and absence of illumination on the optical component 504. In the series of images in FIG. 11C , no defects were present in or on the optical component 504. However, in Figure 11D, a defect was present in or on optical component 504 that caused defect artifact 1104 to be present in the series of images and most noticeable in the second image. As with the images in Figure 11B, by comparing the images in Figure 11D to each other, the presence and / or location of a defect in or on optical component 504 can be readily identified.

[0174] In addition to, or instead of, selecting illumination wavelengths for one or more light sources 602 based on wavelengths that are unlikely to be present (e.g., not present at all) in the surrounding environment, illumination wavelengths may be selected to detect specific types of defects. For example, illumination wavelengths in the green portion of the visible spectrum may be selected to facilitate detection of green leaves. Other wavelengths are possible and contemplated herein for other types of defects. Furthermore, in some embodiments, a series of illuminations at different wavelengths may be used to identify various types of defects. For example, red, then blue, and then green wavelengths may be used to illuminate the optical component 504. The coloration and / or reflectance of the defect may then be determined based on the relative intensities in the captured images corresponding to the defect. For example, if the defect artifact in the captured image corresponding to blue illumination is strong and the defect artifact in the other two captured images is relatively weak, the defect may be determined to be blue. Similarly, if the defect artifact in the captured images corresponding to green and red illumination is moderately strong, but the defect artifact in the captured image corresponding to blue illumination is relatively weak, the defect may be determined to be yellow. Other combinations are possible and contemplated herein. The identified color shade can be used, for example, to determine the type of defect.

[0175] As described herein, both spectral (e.g., wavelength) and spatial (e.g., structured illumination) temporal modulation can be performed to facilitate identifying defects relative to the background signal. Regardless of which type of modulation is used (or whether both are used), a modulation frequency can be used. For example, one or more light sources 602 used to illuminate the optical component 504 can intermittently illuminate the optical component 504 according to a modulation frequency (e.g., 1 Hz, 2 Hz, 3 Hz, 4 Hz, 5 Hz, 10 Hz, 20 Hz, 30 Hz, 40 Hz, and / or 50 Hz). Such a modulation frequency can be achieved by powering the light sources 602 on and off at a predetermined rate (e.g., using a controller). Furthermore, the modulation frequency used can be static or variable (e.g., controlled by a controller). For example, the modulation frequency can be adjusted (e.g., by a controller) based on the time scale over which the background signal changes (e.g., based on the speed of the vehicle on which the detection system is installed). Modulating one or more light sources 602 according to a modulation frequency provides an additional or alternative method of separating the defect detection signal from the background signal. For example, if one or more light sources 602 are modulated at a relatively low frequency when illuminating the optical component 504, a filter (e.g., a low-pass filter) can be applied to the image stream (e.g., video) captured by the hybrid image sensor 802 to extract only the defect detection signal from the image stream.

[0176] Furthermore, in some embodiments, multiple modulation frequencies may be used. For example, when using structured illumination (e.g., as shown in FIGS. 10A-10D), different regions of the optical component 504 may be intermittently illuminated according to different modulation frequencies. For example, a first stripe of a stripe pattern may be periodically illuminated according to a first modulation frequency, and a second stripe of the stripe pattern may be periodically illuminated according to a second modulation frequency. Similarly, when using time-varying spectral illumination, different illumination wavelengths may intermittently illuminate the optical component 504 according to different modulation frequencies. For example, a first wavelength (e.g., from a first light source) may periodically illuminate a portion (e.g., all) of the optical component 504 according to a first modulation frequency, while a second wavelength (e.g., from a second light source) may periodically illuminate a portion (e.g., all) of the optical component 504 according to a second modulation frequency. Furthermore, in some embodiments, both time-varying structured illumination and time-varying spectral illumination may be used together. In such embodiments, different modulation frequencies may be used for these different illumination schemes. Regardless of how they are used, different modulation frequencies can allow for further separation of defect detection signals (which can be used, for example, to determine defect location, defect type, etc.).

[0177] In disentangling the defect detection signal from the background signal, or in the process of disentangling the defect detection signal from the background signal, one or more machine learning models can be used to determine whether a defect is present, the location of the defect, and / or the type of defect. For example, a classifier trained based on labeled training data corresponding to a particular type of defect, a particular location of the defect on the optical component 504, and / or the presence or absence of a defect can be used. Additionally or alternatively, once it is determined that one or more defects are present in or on the optical component 504, one or more remedial actions can be taken (e.g., cleaning the optical component in question, replacing the optical component in question, removing all or a portion of the optical component in question, flagging data acquired by a sensor using the optical component as including the effects of the defect, and performing post-processing on the data acquired by the sensor using the optical component to compensate for the effects of the defect). In some embodiments, the type of remedial action to be taken can be determined based on the determined location of the one or more defects and / or based on the determined defect type of the one or more defects.

[0178] 12 is a flowchart diagram of a method 1200 according to an example embodiment. In some embodiments, the method 1200 can be performed to detect defects in a LiDAR device or a camera. In some embodiments, the method 1200 can be performed by a system (e.g., the LiDAR device 600 illustrated in FIGS. 6A and 6B, the camera 700 illustrated in FIGS. 7A and 7B, the hybrid system 800 illustrated in FIGS. 8A and 8B, and / or the camera 900 illustrated in FIGS. 9A and 9B).

[0179] At block 1202, the method 1200 may include detecting a background signal corresponding to the surrounding environment by a first detector using an optical component.

[0180] At block 1204, the method 1200 may include illuminating, by a first light source, a first portion of the optical component with a first optical signal modulated according to a first modulation frequency. The sensing device may be configured to detect objects in a surrounding environment using the optical component.

[0181] At block 1206, the method 1200 may include detecting a first optical signal by a first detector if one or more defects are present in the body of the first portion of the optical component or on a surface of the first portion of the optical component.

[0182] At block 1208, method 1200 may include detecting, by the computing device, if one or more defects are present in the body of the first portion of the optical component or on the surface of the first portion of the optical component based on the detected background signal and the detected first optical signal. Determining if one or more defects are present in the body of the first portion of the optical component or on the surface of the first portion of the optical component based on the detected background signal and the detected first optical signal may include disambiguating the detected background signal from the detected first optical signal based on the first modulation frequency.

[0183] In some embodiments of method 1200, the first optical signal may be in a first wavelength range. Determining if one or more defects are present may further include disambiguating the detected first optical signal from the detected background signal based on the first wavelength range.

[0184] In some embodiments, method 1200 can include illuminating the second portion of the optical component with a second optical signal modulated according to a second modulation frequency by a second light source. The second modulation frequency can be different from the first modulation frequency. The second optical signal can be within a second wavelength range. The second wavelength range may not overlap with the first wavelength range. Method 1200 can also include detecting the second optical signal by a second optical detector if one or more defects are present within the body of the second portion of the optical component or on the surface of the second portion of the optical component. Additionally, method 1200 can include determining, by a computing device, if one or more defects are present within the body of the second portion of the optical component or on the surface of the second portion of the optical component based on the detected background signal and the detected second optical signal. Determining if one or more defects are present within the body of the second portion of the optical component or on the surface of the second portion of the optical component based on the detected background signal and the detected second optical signal can include distinguishing between the detected background signal and the detected second optical signal based on the second modulation frequency and the second wavelength range.

[0185] In some embodiments of the method 1200, the first wavelength range can correspond to a first type of defect and the second wavelength range can correspond to a second type of defect.

[0186] In some embodiments of the method 1200, the first portion of the optical component and the second portion of the optical component can at least partially overlap.

[0187] In some embodiments of the method 1200, the background signal corresponding to the ambient environment may not include light within the first wavelength range.

[0188] In some embodiments of the method 1200, the first portion of the optical component may not occupy the entire optical component.

[0189] In some embodiments, method 1200 can include illuminating, by a second light source, a second optical signal modulated according to a second modulation frequency. The second modulation frequency can be different from the first modulation frequency. The second portion of the optical component can be non-overlapping with the first portion of the optical component. Method 1200 can also include detecting, by a second optical detector, the second optical signal if one or more defects are present within the body of the second portion of the optical component or on the surface of the second portion of the optical component. Additionally, method 1200 can include determining, by a computing device, based on the detected background signal and the detected second optical signal if one or more defects are present within the body of the second portion of the optical component or on the surface of the second portion of the optical component. Determining based on the detected background signal and the detected second optical signal if one or more defects are present within the body of the second portion of the optical component or on the surface of the second portion of the optical component can include disambiguating the detected background signal from the detected second optical signal based on the second modulation frequency.

[0190] In some embodiments of the method 1200, the first portion of the optical component and the second portion of the optical component can form at least a portion of a striped or checkerboard pattern across the optical component.

[0191] In some embodiments of method 1200, the first portion of the optical component may be illuminated with a first light source according to a first illumination sequence. Further, method 1200 may include illuminating the second portion of the optical component with a second light signal modulated according to a first modulation frequency by a second light source. The second portion of the optical component may not overlap with the first portion of the optical component. The second portion of the optical component may be illuminated with a second light source according to a second illumination sequence. The second illumination sequence may be different from the first illumination sequence. Method 1200 may also include detecting a second light signal by a second light detector if one or more defects are present within the body of the second portion of the optical component or on the surface of the second portion of the optical component. Further, method 1200 may include determining, by a computing device, based on the detected background signal and the detected second light signal if one or more defects are present within the body of the second portion of the optical component or on the surface of the second portion of the optical component. If one or more defects are present in the body of the second portion of the optical component or on the surface of the second portion of the optical component, determining based on the detected background signal and the detected second optical signal can include disambiguating the detected background signal from the detected second optical signal based on the first modulation frequency.

[0192] In some embodiments of the method 1200, the first detector may not be part of the sensing device.

[0193] In some embodiments of method 1200, illuminating the first portion of the optical component with the first optical signal can include coupling the first optical signal into a body of the first portion of the optical component. Illuminating the first portion of the optical component with the first optical signal can also include propagating the first optical signal through the body of the first portion of the optical component using total internal reflection.

[0194] In some embodiments of the method 1200, the first light source may be located in a focal plane of the sensing device, or the first detector is located at the focal plane of the sensing device.

[0195] In some embodiments of the method 1200, the sensing device may comprise an image sensor, and the first detector is at least a part of the image sensor.

[0196] In some embodiments of method 1200, the detected background signal can correspond to one or more background images captured using the image sensor. The detected first optical signal can correspond to one or more defect images captured using the image sensor. Disentangling the detected background signal and the detected first optical signal based on the first modulation frequency can include performing background subtraction from the one or more defect images using the one or more background images.

[0197] In some embodiments of method 1200, the detected background signal can correspond to one or more background images captured using the image sensor. The detected first optical signal can correspond to one or more defect images captured using the image sensor. The image stream can include one or more background images and one or more defect images. Distinguishing the detected background signal from the detected first optical signal based on the first modulation frequency can include applying a low-pass filter to the image stream.

[0198] In some embodiments of the method 1200, the first detector may not be part of the sensing device.

[0199] In some embodiments, the method 1200 may include determining a defect type of at least one of the one or more defects by applying a machine learning model to the disambiguated and detected first optical signal.

[0200] In some embodiments of method 1200, the defect types may include scratches, cracks, dirt, deformations, bubbles, impurities, deterioration, discoloration, imperfect transparency, or distortions in the optical component. The defect types additionally or alternatively include water droplets, dirt, dust, mud, fallen leaves, rain, snow, sleet, hail, ice, or insect residue on the optical component.

[0201] In some embodiments, the method 1200 can include performing one or more repair operations in response to determining if one or more defects exist within the body of the first portion of the optical component or on a surface of the first portion of the optical component.

[0202] The present disclosure is not limited with respect to the specific embodiments described in this application, which are intended as illustrations of various aspects. Many modifications and variations can be made without departing from the spirit and scope of the present disclosure, as will be apparent to those skilled in the art. In addition to the methods and apparatus recited herein, functionally equivalent methods and apparatus within the scope of the present disclosure will be apparent to those skilled in the art from the foregoing description. Such modifications and variations are intended to fall within the scope of the appended claims.

[0203] The above detailed description, with reference to the accompanying drawings, describes various features and functions of the disclosed systems, devices, and methods. In the figures, like symbols typically refer to like components identically, unless context dictates otherwise. The example embodiments described herein and in the figures are not meant to be limiting. Other embodiments may be utilized, and other changes may be made, without departing from the scope of the subject matter presented herein. It will be readily understood that the aspects of the present disclosure, as generally described herein and illustrated in the figures, can be arranged, substituted, combined, separated, and designed in a wide variety of different configurations, all of which are expressly contemplated.

[0204] With respect to any or all of the message flow diagrams, scenarios, and flowcharts in the figures and discussed herein, each step, block, operation, and / or communication may represent the processing of information and / or the transmission of information according to example embodiments. Alternative embodiments are included within the scope of these example embodiments. In these alternative embodiments, for example, operations described as steps, blocks, transmissions, communications, requests, responses, and / or messages may be executed in an order different from that shown or discussed, such as substantially simultaneously or in reverse order, depending on the functionality involved. Furthermore, more or fewer blocks and / or operations may be used in any of the message flow diagrams, scenarios, and flowcharts discussed herein, and these message flow diagrams, scenarios, and flowcharts may be combined with each other, either in part or in whole.

[0205] A step, block, or operation corresponding to the processing of information may correspond to circuitry that can be configured to perform specific logical functions of the methods or techniques described herein. Alternatively or additionally, a step or block corresponding to the processing of information may correspond to a module, segment, or portion of program code (including associated data). The program code may include one or more instructions executable by a processor to perform specific logical operations or actions in the method or technique. The program code and / or associated data may be stored in any type of computer-readable medium, such as a storage device including RAM, a disk drive, a solid-state drive, or another storage medium.

[0206] Additionally, steps, blocks, or acts representing one or more information transmissions may correspond to information transmissions between software and / or hardware modules in the same physical device, whereas other information transmissions may be information transmissions between software and / or hardware modules in different physical devices.

[0207] The particular arrangement shown in the figures should not be considered limiting. It should be understood that other embodiments may include more or fewer of each element shown in a given figure. Additionally, some of the illustrated elements may be combined or omitted. Still further, example embodiments may include elements not shown in the figures.

[0208] While various aspects and embodiments are disclosed herein, other aspects and embodiments will be apparent to those skilled in the art. The various aspects and embodiments disclosed herein are for purposes of illustration and are not intended to be limiting, the true scope of which is indicated by the following claims.

Claims

1. detecting a background signal corresponding to an ambient environment with a first detector using optical components; illuminating a first portion of the optical component with a first optical signal modulated according to a first modulation frequency by a first light source, wherein a sensing device is configured to detect objects in the surrounding environment using the optical component; detecting the first optical signal by the first detector when one or more defects are present in a body of the first portion of the optical component or on a surface of the first portion of the optical component; determining, by a computing device, if one or more defects are present in the body of the first portion of the optical component or on the surface of the first portion of the optical component based on the detected background signal and the detected first optical signal, wherein determining, if one or more defects are present in the body of the first portion of the optical component or on the surface of the first portion of the optical component based on the detected background signal and the detected first optical signal, comprises disambiguating the detected background signal from the detected first optical signal based on the first modulation frequency.

2. 10. The method of claim 1, wherein the first optical signal is within a first wavelength range, and determining if one or more defects are present further comprises disambiguating the detected background signal from the detected first optical signal based on the first wavelength range.

3. illuminating, by a second light source, a second optical signal modulated according to a second modulation frequency, the second modulation frequency being different from the first modulation frequency, the second optical signal being within a second wavelength range, the second wavelength range not overlapping with the first wavelength range; detecting the second optical signal with a second optical detector when one or more defects are present in the body of the second portion of the optical component or on a surface of the second portion of the optical component; 3. The method of claim 2, comprising: determining, by a computing device, if one or more defects are present in the body of the second portion of the optical component or on the surface of the second portion of the optical component based on the detected background signal and the detected second optical signal, wherein determining, if one or more defects are present in the body of the second portion of the optical component or on the surface of the second portion of the optical component based on the detected background signal and the detected second optical signal comprises disambiguating the detected background signal and the detected second optical signal based on the second modulation frequency and the second wavelength range.

4. The method of claim 3 , wherein the first wavelength range corresponds to a first type of defect and the second wavelength range corresponds to a second type of defect.

5. The method of claim 3 , wherein the first portion of the optical component and the second portion of the optical component at least partially overlap.

6. The method of claim 2 , wherein the background signal corresponding to the ambient environment does not include light within the first wavelength range.

7. The method of claim 1 , wherein the first portion of the optical component does not occupy the entire optical component.

8. illuminating a second portion of the optical component with a second optical signal modulated according to a second modulation frequency by a second light source, the second modulation frequency being different from the first modulation frequency, and the second portion of the optical component not overlapping the first portion of the optical component; detecting the second optical signal with a second optical detector when one or more defects are present in the body of the second portion of the optical component or on a surface of the second portion of the optical component; 8. The method of claim 7, further comprising: determining, by the computing device, if one or more defects are present in the body of the second portion of the optical component or on the surface of the second portion of the optical component based on the detected background signal and the detected second optical signal, wherein determining, if one or more defects are present in the body of the second portion of the optical component or on the surface of the second portion of the optical component based on the detected background signal and the detected second optical signal, comprises disambiguating the detected background signal and the detected second optical signal based on the second modulation frequency.

9. The method of claim 8 , wherein the first portion of the optical component and the second portion of the optical component form at least a portion of a striped or checkerboard pattern across the optical component.

10. illuminating the first portion of the optical component with the first optical signal comprises: coupling the first optical signal into the body of the first portion of the optical component; and propagating the first optical signal through the body of the first portion of the optical component using total internal reflection.

11. The method of claim 1 , wherein the first light source may be located in a focal plane of the sensing device, or the first detector is located at the focal plane of the sensing device.

12. The method of claim 1 , wherein the sensing device comprises an image sensor, and the first detector is at least a part of the image sensor.

13. 13. The method of claim 12, wherein the detected background signal corresponds to one or more background images captured using the image sensor and the detected first optical signal corresponds to one or more defect images captured using the image sensor, and wherein disambiguating the detected background signal and the detected first optical signal based on the first modulation frequency comprises performing background subtraction from the one or more defect images using the one or more background images.

14. 13. The method of claim 12, wherein the detected background signal corresponds to one or more background images captured using the image sensor, and the detected first optical signal corresponds to one or more defect images captured using the image sensor, an image stream including the one or more background images and the one or more defect images, and wherein disambiguating the detected background signal and the detected first optical signal based on the first modulation frequency comprises applying a low-pass filter to the image stream.

15. The method of claim 1 , wherein the first detector is not part of the sensing device.

16. 10. The method of claim 1, further comprising determining a defect type of at least one of the one or more defects by applying a machine learning model to the characterized detected first optical signal.

17. The type of defect is: Scratches, cracks, stains, deformations, bubbles, impurities, deterioration, discoloration, imperfect transparency, or distortions in the optical components; or The method of claim 16 , including water droplets, dirt, dust, mud, leaves, rain, snow, sleet, hail, ice, or insect residue on the optical components.

18. 10. The method of claim 1, further comprising performing one or more repair operations in response to determining if one or more defects exist within the body of the first portion of the optical component or on the surface of the first portion of the optical component.

19. an optical component; a sensing device configured to detect objects in a surrounding environment using the optical component; a first light source configured to illuminate a first portion of the optical component with a first optical signal modulated according to a first modulation frequency; a first detector, Detecting a background signal corresponding to the surrounding environment using the optical component; and a first detector configured to detect the first optical signal when one or more defects are present in a body of the first portion of the optical component or on a surface of the first portion of the optical component; and a computing device configured to determine, based on the detected background signal and the detected first optical signal, if one or more defects are present in the body of the first portion of the optical component or on the surface of the first portion of the optical component, wherein determining, based on the detected background signal and the detected first optical signal, if one or more defects are present in the body of the first portion of the optical component or on the surface of the first portion of the optical component, includes disambiguating the detected background signal from the detected first optical signal based on the first modulation frequency.

20. 1. A computing device configured to determine, based on a detected background signal and a detected first optical signal, if one or more defects are present in a body of a first portion of an optical component or on a surface of the first portion of the optical component, wherein determining, based on the detected background signal and the detected first optical signal, if one or more defects are present in the body of the first portion of the optical component or on the surface of the first portion of the optical component, comprises disambiguating the detected background signal from the detected first optical signal based on a first modulation frequency; the detected background signal corresponds to the ambient environment and is detected by a first detector using the optical component; a sensing device configured to detect objects in the surrounding environment using the optical component; the first portion of the optical component is illuminated by the first light signal from a first light source; the first optical signal is modulated according to the first modulation frequency; The first optical signal is detected by the first detector if one or more defects are present in the body of the first portion of the optical component or on the surface of the first portion of the optical component.

Images