Using cleaning protocols to monitor defects associated with light detection and ranging (LIDAR) devices

Abstract

To monitor defects associated with devices.SOLUTION: Example embodiments relate to using cleaning protocols to monitor defects associated with light detection and ranging (lidar) devices. An example embodiment includes a method. The method includes applying, using a cleaning device, a cleaning protocol to one or more optical components of a light detection and ranging (lidar) device. The method also includes emitting one or more light signals from a light emitter of the lidar device. Additionally, the method includes detecting, by a light detector of the lidar device, reflection of the one or more light signals. Further, the method includes determining, by a controller of the lidar device based on the detected reflection of the one or more light signals, that one or more defects are present within the one or more optical components or within the cleaning device.SELECTED DRAWING: Figure 9

Description

[Technical field]

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

[0002] Light detection and ranging (lidar) devices can estimate distances to objects in the 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, cameras 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 an autonomous or semi-autonomous mode). However, imperfections along one or more optical paths of the lidar device and / or camera can lead to false detections (e.g., false point clouds, or blurry and / or unclear images). Summary of the Invention

[0003] Exemplary embodiments relate to techniques that include monitoring defects associated with a lidar device using a cleaning protocol. These techniques may involve using a cleaning device to apply the cleaning protocol. For example, the cleaning protocol may include spraying a cleaning solution onto one or more components associated with the lidar device (e.g., one or more optical components such as an optical window, a lens, a hydrophobic coating, or a mirror). As the cleaning protocol progresses, for example, when the cleaning solution begins to dry or is otherwise removed (e.g., by wiping the one or more optical components using a wiper of the cleaning device or by applying pressurized air onto the one or more optical components using a pressurized air source of the cleaning device), the light emitter of the lidar device may emit light signals. These light signals may be reflected (e.g., from the one or more optical components or from one or more defects on or in the one or more optical components) and then detected by a light detector of the lidar device. By analyzing these detected optical signals, a computing device (e.g., a controller of the lidar device) may determine whether one or more defects are present in one or more optical components and / or in the cleaning device, and may further determine one or more qualities of the optical components (e.g., optical quality and hydrophobicity quality) based on the identified defects.

[0004] In a first aspect, a method is provided. The method includes applying a cleaning protocol to one or more optical components of the lidar device using a cleaning device of the lidar device. The method also includes emitting one or more optical signals from an optical emitter of the lidar device. Further, the method includes detecting a reflection of the one or more optical signals by an optical detector of the lidar device. Further, the method includes determining, by a controller of the lidar device based on the detected reflection of the one or more optical signals, that one or more defects are present in the one or more optical components or in the cleaning device.

[0005] In a second aspect, a system is provided. The system includes a LIDAR device. The LIDAR device includes one or more optical components. The LIDAR device also includes an optical emitter configured to emit one or more optical signals. Additionally, the LIDAR device includes an optical detector configured to detect reflections of the one or more optical signals. The system also includes a cleaning device configured to apply a cleaning protocol to the one or more optical components. Additionally, the system includes a controller configured to determine that one or more defects are present in the one or more optical components or in the cleaning device based on the detected reflections of the one or more optical signals.

[0006] In a third aspect, a computing device is provided. The computing device is configured to receive data corresponding to reflections of one or more optical signals detected by a lidar device, the one or more optical signals emitted by an optical emitter of the lidar device in response to a cleaning device of the lidar device applying a cleaning protocol to one or more optical components of the lidar device. The computing device is also configured to determine, based on the received data, that one or more defects are present in the one or more optical components or in the cleaning device.

[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, appropriately taken from the accompanying drawings. [Brief description 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, according to 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, according to 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, according to an exemplary embodiment.

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

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

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

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

[0020] [Diagram 3] FIG. 3 is a conceptual illustration 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, in accordance with an exemplary embodiment.

[0022] [Figure 4B] FIG. 4B is a block diagram of a lidar device in accordance with an exemplary embodiment.

[0023] [Diagram 5] FIG. 5 is an illustrative diagram of a lidar device and associated hydrophobic coating, in accordance with an example embodiment.

[0024] [Figure 6A] FIG. 6A is an illustrative diagram of object detection in accordance with an example embodiment.

[0025] [Figure 6B] FIG. 6B is an illustrative diagram of crosstalk detection according to an example embodiment.

[0026] [Figure 6C]FIG. 6C is an illustrative diagram of cross-feedback detection in accordance with an example embodiment.

[0027] [Figure 6D] FIG. 6D is an illustrative diagram of occlusion detection in accordance with an example embodiment.

[0028] [Figure 7A] FIG. 7A is an illustrative diagram of a system having a cleaning device that applies a cleaning protocol, according to an example embodiment.

[0029] [Figure 7B] FIG. 7B is an illustrative diagram of a system having a cleaning device that applies a cleaning protocol, according to an example embodiment.

[0030] [Figure 8A] FIG. 8A is an illustrative diagram of a plot of intensity of a detected reflected optical signal and a fitted function associated with the plot of intensity of the detected reflected optical signal in accordance with an example embodiment.

[0031] [Figure 8B] FIG. 8B is a scatter plot of metrics determined for a fleet of systems in accordance with an illustrative embodiment.

[0032] [Figure 9] FIG. 9 is a flow chart illustration of a method according to an example embodiment. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0033] Exemplary methods and systems are contemplated herein. Any exemplary embodiment or example 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. In addition, the specific arrangements shown in the figures should not be considered limiting. It should be understood that other embodiments can include more or less of each element shown in a given figure. Additionally, some of the illustrated elements can be combined or omitted. Still further, exemplary embodiments can include elements not illustrated in the figures.

[0034] The lidar device 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 inside a building or outside 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, and the like. 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 in the vicinity of the vehicle. The light emitters may include fiber optic amplifiers, laser diodes, light emitting diodes (LEDs), among other possibilities.

[0035] The lidar devices and / or cameras can be used to sense the surrounding environment. For example, the lidar devices 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 devices and / or cameras can include optical windows, mirrors, lenses, etc.

[0036] In some cases, the captured image or generated point cloud may be adversely affected as a result of one or more defects on or in the optical components in the camera or lidar device (e.g., as a result of contamination of the aperture and / or degradation of the aperture over time). For example, scratches, cracks, dirt, deformations, bubbles, impurities, degradation, discoloration, imperfect transparency, distortions, water droplets, dirt, dust, mud, leaves, rain, snow, sleet, hail, ice, etc. may direct the light emitted by the lidar light emitter to unintended / inappropriate areas of the image sensor / photodetector, may prevent the light emitted by the lidar light emitter from even reaching the image sensor / photodetector, may result in undesirable crosstalk or internal reflections, or may modify the light emitted by the lidar light emitter (e.g., change in polarization or wavelength) before the light reaches the image sensor / photodetector. Such defects may result in improper object identification and distance detection. In autonomous vehicle applications, improper object identification / distance detection may lead to traffic slowdowns or collisions. Thus, many lidar devices / cameras (e.g., lidar devices / cameras mounted on autonomous vehicles) may also include cleaning devices (e.g., wipers, cleaning nozzles / sprayers, and air compressors configured to apply pressurized air) that can be used to clean components of the lidar device / camera. For example, the lidar device may include a cleaning sprayer used to deposit a cleaning solution on an exterior window of the lidar device, and / or a wiper used to remove the cleaning solution from the exterior window after it has been deposited.

[0037] Exemplary embodiments described herein provide techniques for identifying the presence of one or more defects. Moreover, exemplary embodiments enable the detection of debris without additional detection components (e.g., additional optics, emitters, and sensors). For example, to detect the presence of and / or monitor the condition of debris (e.g., condensation, snow, and rain) on one or more components (e.g., the degradation condition of a hydrophobic coating on a surface on an exterior window) over time, one or more cleaning components may be used to apply moisture to the one or more components. For example, a sprayer may be used to apply water or a cleaning solution to the hydrophobic coating on the surface of the exterior window. The water / cleaning solution and / or optical signals reflected from the internal components of the lidar device (e.g., cross-feedback signals, cross-talk signals, and signals interacting with objects external to the lidar device) may then be analyzed to determine the health (e.g., amount of degradation) of the hydrophobic coating. For example, one or more optical signals emitted by the light emitters that are reflected (or partially reflected) from the water / cleaning solution and then detected by the optical detector are compared to calibration measurements (e.g., made when the lidar device is on the manufacturing line or being assembled) to determine the current state of the hydrophobic coating (e.g., how droplets bead on the surface of the hydrophobic coating and / or how quickly droplets fall off the hydrophobic coating). In some embodiments, detection of the optical signals may occur while one or more additional cleaning techniques are performed (e.g., while one or more applications of pressurized air, i.e., air blows, are occurring).

[0038] Analyzing the reflected light signal may include monitoring the intensity of the reflected light signal over time. For example, when a cleaning solution is sprayed onto an optical window of the lidar device, the light signal emitted toward the optical window may be substantially reflected back toward the detector of the lidar device. This may result in a relatively high intensity detected light signal with a short travel time (e.g., based on reflection from droplets of cleaning solution on the window). Conversely, as the cleaning solution evaporates / migrates away from the optical window (e.g., due to application of pressurized air by the cleaning device), the intensity of the detected light signal within a short time window (e.g., corresponding to a short travel time) may decrease (e.g., because fewer droplets of cleaning solution are present on the optical window). Such a decrease may be observed in data corresponding to all or only a subset of the photodetectors of the lidar device, in various embodiments. However, by monitoring this decrease over time (e.g., by plotting the detected reflected intensity against time), a trend may be observed. The trend may be fitted to one or more curves / functions. For example, the controller of the lidar device may fit the trend to an exponential decay function. Such fitted exponential decay functions may have one or more figures of merit. For example, the initial / peak values ​​and time constants associated with the exponential decay may be used as figures of merit. These figures of merit may be tracked over time and / or compared to one or more calibration measurements to determine the state of the lidar device (e.g., the health of the optical window, and the health of the cleaning device itself). For example, if the initial value of the exponential decay function is too low compared to the calibration measurements, it may be an indication that the sprayer of the cleaning device is not providing enough liquid to the surface of the optical component (e.g., the cleaning solution in the cleaning device needs to be refilled). Alternatively, if the time constant is too high compared to the calibration measurements, it may be an indication that the associated hydrophobic coating has degraded (e.g., resulting in a decrease in hydrophobicity). Other types of fitted functions and / or other figures of merit are possible and are contemplated herein.

[0039] In some embodiments, the analysis of the detected signals (e.g., comparison to calibration measurements) may take into account additional factors in determining the condition of the hydrophobic coating. For example, when a sprayer applies water / cleaning solution to the hydrophobic coating, but there is additional rain / snow in the surrounding environment (e.g., as determined by an auxiliary sensor such as a radar device), the series of detected optical signals may be artificially modified (e.g., an offset may be applied to the intensity of the series of detected optical signals) to simulate a series of signals in the absence of rain / snow, and then the simulated series of signals may be compared to the calibration measurements. Alternatively, there may be a bank of calibration measurements made in different environmental conditions to which the run-time measurements can be compared (e.g., based on the environmental conditions when the run-time measurements were made) to determine the condition of the hydrophobic coating.

[0040] To monitor the condition / health of the lidar device's components, such cleaning techniques may be performed (and corresponding optical signals analyzed) at regular intervals. For example, the lidar device may perform a cleaning protocol and learn characteristics of the reflected optical signal once a month, once a week, once a day, once an hour, once every 15 minutes, once a minute, etc. Additionally or alternatively, the cleaning techniques may be performed (and corresponding optical signals analyzed) in response to a triggering event. For example, a user (e.g., a lidar in an autonomous vehicle) may send one or more signals (e.g., via a user interface on the vehicle or using a mobile application on a mobile device) to the lidar device to request cleaning or to indicate that a component reliability analysis should be performed. Upon receiving such a signal, the lidar device may initiate a cleaning protocol. Other triggering events are possible and contemplated herein (e.g., a weather event, a communication from an off-board computing device such as a fleet management device, a determination by the controller of the lidar device that one or more objects detected in the surrounding environment have been improperly identified, a determination made by the lidar controller that one or more detected optical signals, such as detected cross-feedback signals or detected crosstalk signals, exceed a given intensity threshold).

[0041] Furthermore, upon determining using the techniques described herein that one or more defects are present in one or more components associated with the lidar device (e.g., degradation of one or more optical components, one or more defects present in one or more optical components, and debris on one or more optical components), appropriate corrective action may be initiated. For example, additional cleaning protocols may be implemented (e.g., longer cleaning protocols or increased cleaning protocols such as cleaning protocols using more concentrated solutions or enhanced pressurized airflow may be implemented). Alternatively, one or more optical components associated with the one or more defects may be repaired or replaced. In still other embodiments, data captured via one or more optical components associated with the one or more defects may be flagged for potential review and / or may be flagged as potentially unreliable. In still other embodiments, post-processing may be performed on data captured via one or more optical components to account for the effects of defects present in the one or more optical components. For example, if the defect results in a decrease in apparent range to objects in the surrounding environment, an offset to the range may be applied to correct the data. Similarly, if the defect results in a decrease in apparent intensity of the optical signal reflected by objects in the surrounding environment, an offset to the intensity may be applied to correct the data.

[0042] The following description and the accompanying drawings clarify features of various exemplary embodiments. The embodiments provided are by way of example and are not intended to be limiting. Thus, dimensions of the drawings are not necessarily drawn to scale.

[0043] An exemplary system 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 cars, trucks (e.g., pickup trucks, vans, tractors, and tractor trailers, etc.), motorcycles, buses, airplanes, helicopters, drones, lawnmowers, bulldozers, boats, submarines, all-terrain vehicles, snowmobiles, aircraft, recreational vehicles, amusement park vehicles, farm equipment or vehicles, construction equipment or vehicles, warehouse equipment or vehicles, factory equipment or vehicles, trams, golf carts, trains, trolleys, walkway transport vehicles, robotic devices, and the like. Other vehicles are possible as well. Furthermore, in some embodiments, the exemplary system may not include a vehicle.

[0044] 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, the vehicle 100 may operate in the autonomous mode without human interaction through receiving control instructions from a computing system. As part of the operation in the autonomous mode, the vehicle 100 may use sensors to detect and possibly identify objects in the surrounding environment to enable safe navigation. Additionally, the example vehicle 100 may operate in a partially autonomous (i.e., semi-autonomous) mode in which some functions of the vehicle 100 are controlled by a human driver of the vehicle 100 and some functions of the vehicle 100 are controlled by a computing system. For example, the vehicle 100 may also include subsystems that enable the driver to control the operation of the 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.

[0045] 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), and emergency braking), but the human driver is expected to provide situational awareness of the vehicle's surroundings and supervise the assisted driving operations. Here, the vehicle may perform all driving tasks in a particular situation, but the human driver is expected to be responsible for assuming control as necessary.

[0046] For simplicity and brevity, the various systems and methods are described below in conjunction with autonomous vehicles, although these or similar systems and methods may be used in various driver assistance systems that do not reach the level 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, but different organizations in the United States or other countries may categorize 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, etc., 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 that use 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 manner in which the SAE classifies levels of autonomous driving operation, other organizations in the United States or other countries may classify levels of autonomous driving operation differently. Without being limited thereto, the systems and methods disclosed herein may be used for driver assistance systems defined by the levels of autonomous driving operation of these other organizations.

[0047] As shown in FIG. 1, the vehicle 100 may include various subsystems, such as a propulsion system 102, a sensor system 104, a control system 106, one or more peripheral devices 108, a power source 110, a computer system 112 (which may also be referred to as a computing system) having data storage 114, and a user interface 116. In other examples, the vehicle 100 may include more or fewer subsystems, each of which may include multiple elements. The subsystems and components of the vehicle 100 may be interconnected in various ways. In addition, the functions of the 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, the control system 106 and the computer system 112 may be combined into a single system that operates the vehicle 100 according to various operations.

[0048] Propulsion system 102 may include one or more components operable to provide powered motion for vehicle 100 and may include an engine / motor 118, an energy source 119, a transmission 120, and wheels / tires 121, among other possible components. 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.

[0049] 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.

[0050] 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 a gearbox, a clutch, a differential, and a drive shaft, among other possible components. The drive shaft may include an axle that connects to one or more wheels / tires 121.

[0051] 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.

[0052] The sensor system 104 may include various types of sensors, such as a global positioning system (GPS) 122, an inertial measurement unit (IMU) 124, a radar 126, a 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 includes sensors configured to monitor internal systems of the vehicle 100 (e.g., O 2 monitors, fuel gauge, engine oil temperature, and brake wear).

[0053] 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 have a configuration using one or more accelerometers and / or gyroscopes and may 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 in motion.

[0054] Radar 126 may represent one or more systems configured to sense objects in the environment surrounding vehicle 100 using radio signals, including the speed and orientation of the objects. 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.

[0055] 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, etc.) or in 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., as in silicon photomultipliers (SiPMs)) (e.g., through serial electrical connections). In some examples, the one or more photodetectors are devices that operate in Geiger mode, and the LIDAR includes subcomponents designed for such Geiger mode operation.

[0056] Camera 130 may include one or more devices (eg, a still camera, a video camera, a thermal imaging camera, a stereo camera, and a night vision camera) configured to capture images of the environment surrounding vehicle 100.

[0057] The steering sensor 123 may sense the steering angle of the 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, the steering sensor 123 may measure the angle of the wheels of the vehicle 100, such as detecting the angle of the wheels relative to the forward axle of the vehicle 100. The steering sensor 123 may also be configured to measure a combination (or a subset) of the steering wheel angle, the electrical signal representative of the steering wheel angle, and the angles of the wheels of the vehicle 100.

[0058] The throttle / brake sensor 125 may detect the position of 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., butterfly valves and carburetors). 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 brake pedal angles, an electrical signal representative of the accelerator pedal (throttle) and brake pedal angles, the throttle body angle, and the pressure that at least one brake pad exerts on 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.

[0059] 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.

[0060] The sensor fusion algorithm 138 may include a Kalman filter, a Bayesian network, or other algorithms 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 influences within a given situation.

[0061] The 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 graphics 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 signals, roadway boundaries, speed bumps, or potholes). Thus, the computer vision system 140 may use 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 the speed of objects, etc.

[0062] The navigation / routing system 142 may determine a driving path for the vehicle 100, which may involve dynamically adjusting navigation during operation. Thus, the navigation / routing system 142 may use data from sensor fusion algorithms 138, GPS 122, and maps, among other sources, to navigate the vehicle 100. The obstacle avoidance system 144 may evaluate potential obstacles based on the sensor data and cause systems of the vehicle 100 to avoid or otherwise navigate the potential obstacles.

[0063] 1, the 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. The peripherals 108 may provide controls or other elements for a user to interact with a user interface 116. For example, the touchscreen 148 may provide information to a user of the vehicle 100. The user interface 116 may also accept input from a user via the touchscreen 148. The peripherals 108 may also enable the vehicle 100 to communicate with devices, such as other vehicle devices.

[0064] The wireless communication system 146 may communicate with one or more devices directly or wirelessly through 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 cellular communications such as 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 WIFI 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 the present disclosure. For example, the wireless communication system 146 may include one or more Dedicated Short Range Communications (DSRC) devices, which may include public and / or private data communications between vehicles and / or roadside gas stations.

[0065] Vehicle 100 may include a power source 110 for powering the components. Power source 110 may include a rechargeable lithium ion or lead acid battery in some embodiments. 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.

[0066] Vehicle 100 may also include a computer system 112 for performing operations such as those described therein. Thus, 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.

[0067] In some embodiments, data storage 114 may include instructions 115 (e.g., program logic) executable by processor 113 to perform various functions of vehicle 100, including those described above in connection with Figure 1. Data storage 114 may also include additional instructions, including instructions to transmit data to, receive data from, interact with, and / or control one or more of propulsion system 102, sensor system 104, control system 106, and peripherals 108.

[0068] In addition to instructions 115, data storage 114 may store data such as road maps, route information, 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.

[0069] 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 content and / or the layout of interactive images that may be displayed on touch screen 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, touch screen 148, microphone 150, and speaker 152.

[0070] The computer system 112 may control functions of the vehicle 100 based on inputs received from various subsystems (e.g., the propulsion system 102, the sensor system 104, or the control system 106) and from the user interface 116. For example, the computer system 112 may utilize inputs from the sensor system 104 to estimate outputs generated by the propulsion system 102 and the control system 106. Depending on the embodiment, the computer system 112 may be operable to monitor many aspects of the vehicle 100 and its subsystems. In some embodiments, the computer system 112 may disable some or all functions of the vehicle 100 based on signals received from the sensor system 104.

[0071] The components of vehicle 100 may be configured to function in an interconnected manner with other components within their respective systems or externally. For example, in an exemplary embodiment, camera 130 may capture multiple images that may represent information about the state of the surrounding environment of vehicle 100 operating in an autonomous or semi-autonomous mode. The state of the surrounding environment may include parameters of the road on which the vehicle is operating. For example, computer vision system 140 may be capable of recognizing the slope (gradient) or other features based on multiple images of the road. Additionally, a combination of GPS 122 and the 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 systems may also provide information about the vehicle's surroundings.

[0072] 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 an input or an indication of the vehicle's surroundings that is provided to control the vehicle.

[0073] 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 field of view of the vehicle. Computer system 112 may use output from the various sensors to determine information regarding objects within the field of view of the vehicle 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.

[0074] 1 illustrates 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.

[0075] 2A-2E illustrate an example vehicle 200 (e.g., a fully autonomous vehicle or 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 disclosure is not so limited. For example, vehicle 200 may represent a truck, a 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 equipment or vehicles, warehouse equipment or vehicles, factory equipment or vehicles, a tram, a train, a trolley, a walkway transport vehicle, and a robotic device).

[0076] 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, and 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 the sensors described herein (e.g., one or more lidar and radar, one or more lidar and cameras, one or more cameras and radar, one or more lidar, cameras, and radar).

[0077] 2A-E are intended as non-limiting examples of the locations, numbers, and types of such sensor systems on 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 conditions to reduce cost or to suit a particular environmental or application situation). For example, the sensor systems (e.g., 202 and 204) may be disposed in various other locations on the vehicle (e.g., at location 216) and may have a field of view that corresponds to the interior and / or surrounding environment of vehicle 200.

[0078] The sensor system 202 may include one or more sensors mounted on top of the vehicle 200 and configured to detect information about the environment surrounding the vehicle 200 and output an indication of the information. For example, the sensor system 202 may include any combination of cameras, radar, lidar, inertial sensors, humidity sensors, and acoustic sensors (e.g., microphones, and sonar devices). The sensor system 202 may include one or more movable mounts that may be operable to adjust the orientation of one or more sensors in the 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 the vehicle 200. In another embodiment, the movable mount of the sensor system 202 may be movable in a scanning manner within a certain range of angles and / or azimuth and / or elevation angles. The sensor system 202 may be mounted on the roof of the vehicle, although other mounting locations are also contemplated.

[0079] 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.

[0080] One or more of the 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., an xy plane). For example, one or more of the 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., a z-axis) to illuminate the environment surrounding the vehicle 200 with light pulses. Based on detection of various aspects of the reflected light pulses (e.g., elapsed time of flight, polarization, and intensity), information about the surrounding environment may be determined.

[0081] 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 certain 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.

[0082] In an exemplary configuration, one or more radars may be located on the vehicle 200. Similar to the radar 126 described above, the one or more radars may include an antenna configured to transmit and receive radio waves (e.g., electromagnetic waves having frequencies between 30 Hz and 300 GHz, etc.). Such radio waves may be used to determine the distance and / or speed of one or more objects in the surrounding environment of the vehicle 200. 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 the vehicle 200 (e.g., sensor systems 208 and 210) to actively scan the environment near the rear of the vehicle 200 for the presence of radio wave reflective objects. Similarly, one or more radars may be located near the front of the vehicle 200 (e.g., sensor systems 212, 214) to actively scan the environment near the front of the 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. Additionally, 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 undercarriage, etc.

[0083] Vehicle 200 may include one or more cameras. For example, one or more of sensor systems 202, 204, 206, 208, 210, 212, 214, and / or 218 may include one or more cameras. The cameras may be light-sensitive instruments, such as still cameras, video cameras, thermal imaging cameras, stereo cameras, night vision cameras, etc., configured to capture multiple images of the surrounding environment of vehicle 200. To this end, the cameras may be configured to detect visible light and may additionally or alternatively 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 may optionally have a sensitivity range in three-dimensional space. In some embodiments, the cameras may include range detectors configured to generate two-dimensional images, for example, indicative of distances from the camera to several points in the surrounding environment. To this end, the cameras may use one or more range detection 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 on the inside of 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 of the camera, whether interior or exterior of the vehicle 200, can be used. The camera can also have associated optics operable to provide an adjustable field of view. Furthermore, the camera can be mounted on the vehicle 200 with a moveable mount to change the pointing angle of the camera, such as via a pan / tilt mechanism.

[0084] The vehicle 200 may also include one or more acoustic sensors used to sense the vehicle's 200 surrounding environment (e.g., one or more of the 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 micro-electromechanical system (MEMS) microphones) used to sense acoustic waves (i.e., pressure differentials) in the fluid (e.g., air) of the environment surrounding the vehicle 200. Such acoustic sensors may be used to identify sounds (e.g., sirens, human speech, animal sounds, and alarms) in the surrounding environment upon which a control strategy of the vehicle 200 may be based. For example, if the acoustic sensor detects a siren (e.g., a mobile siren, and / or a fire engine siren), the vehicle 200 may slow down and / or navigate to the edge of the road.

[0085] Although not shown in FIGS. 2A-2E, vehicle 200 may include a wireless communication system (e.g., similar to and / or in addition to wireless communication system 146 of FIG. 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.

[0086] The 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.

[0087] A control system of the vehicle 200 may be configured to control the 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 the vehicle 200) coupled to the vehicle 200, modify the control strategy (and associated driving behavior) based on the information, and control the 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 the driving conditions, and may be configured to modify the control strategy and driving behavior based on changes in the driving conditions. For example, the path taken by the vehicle from one destination to another may be modified based on the 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 changes in driving conditions.

[0088] As noted above, in some embodiments, the vehicle 200 may take the form of a van, although alternative forms are also possible and contemplated herein. Accordingly, Figures 2F-2I illustrate an embodiment in which the vehicle 250 takes the form of a semi-truck. For example, Figure 2F illustrates a front view of the vehicle 250, and Figure 2G illustrates an isometric view of the vehicle 250. In an embodiment in which the vehicle 250 is a semi-truck, the vehicle 250 may include a tractor portion 260 and a trailer portion 270 (illustrated in Figure 2G). Figures 2H and 2I provide side and top views, respectively, of the tractor portion 260. Similar to the vehicle 200 illustrated above, the vehicle 250 illustrated in Figures 2F-2I may also include various sensor systems (e.g., similar to the sensor systems 202, 206, 208, 210, 212, 214 shown and described with reference to Figures 2A-2E). In some embodiments, the vehicle 200 of FIGS. 2A-2E may include only a single copy of some sensor systems (e.g., sensor system 204), while the vehicle 250 illustrated in FIGS. 2F-2I may include multiple copies of its sensor systems (e.g., as illustrated, sensor systems 204A and 204B).

[0089] 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 applied in various vehicle contexts (e.g., with modifications employed 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).

[0090] 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 to the sensor locations disclosed in FIGS. 2F-2I, for example. However, in some cases, the sensors may have other locations. For simplicity of the drawings, 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 the sensor may include an angular region (e.g., azimuth region and / or elevation region) in which the sensor may detect objects.

[0091] 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, etc. Additionally, in some embodiments, during operation of the sensor, the sensor may be scanned within the field of view of the sensor. Various different scan angles for the exemplary sensor are shown as regions 272, each indicating an 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 aspects of trailer 270 of vehicle 250 and / or the region ahead of vehicle 250.

[0092] 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 the rear wheels 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.

[0093] 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.

[0094] 3 is a conceptual illustration 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.

[0095] Vehicle 200 may correspond to various types of vehicles capable of transporting passengers or objects between locations and may take the form of any one or more of the vehicles discussed above. In some cases, vehicle 200 may 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 may navigate with or without a passenger. As a result, vehicle 200 may pick up and drop off passengers between desired destinations.

[0096] 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 observe 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 a variety of 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.

[0097] The remote computing system 302 may include one or more subsystems and components similar to or identical to those of the vehicle 200. At a minimum, the remote computing system 302 may include a processor configured to perform various operations described herein. In some embodiments, the remote computing system 302 may also include a user interface including input / output devices such as a touch screen and a speaker. Other examples are possible as well.

[0098] The network 304 represents an infrastructure that enables wireless communication between the remote computing system 302 and the vehicle 200. The network 304 also enables wireless communication between the server computing system 306 and the remote computing system 302, and between the server computing system 306 and the vehicle 200.

[0099] The location of the remote computing system 302 can vary within the examples. For example, the remote computing system 302 can be at a location remote from the vehicle 200 having wireless communication over the network 304. In another example, the remote computing system 302 can correspond to a computing device within the vehicle 200 that is separate from the vehicle 200 but that allows a human operator to interact with a passenger or driver of the vehicle 200. In some examples, the remote computing system 302 can be a computing device equipped with a touch screen that can be operated by a passenger of the vehicle 200.

[0100] In some embodiments, 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.

[0101] The server computing system 306 may be configured to wirelessly communicate with the remote computing system 302 and the vehicle 200 over the network 304 (or, in some cases, directly with the remote computing system 302 and / or the vehicle 200). The server computing system 306 may represent any computing device configured to receive, store, determine, and / or transmit information regarding the vehicle 200 and its remote assistance. As such, the server computing system 306 may be configured to perform any operation or portion of such operation described herein as being performed by the remote computing system 302 and / or the vehicle 200. Some embodiments of wireless communication related to remote assistance may utilize the server computing system 306, while other embodiments may not.

[0102] 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 various operations described herein, as well as a wireless communication interface for receiving information from and providing information to the remote computing system 302 and the vehicle 200.

[0103] The various systems described above may perform various operations, and these operations and associated features will now be described.

[0104] In keeping with the above discussion, computing systems (e.g., remote computing system 302, server computing system 306, and computing systems local to vehicle 200) may operate to capture images of the surrounding environment of the autonomous or semi-autonomous vehicle using cameras. Typically, at least one of the computing systems may analyze the images and possibly control the autonomous or semi-autonomous vehicle.

[0105] In some embodiments, to facilitate autonomous or semi-autonomous operation, a vehicle (e.g., vehicle 200) may receive data representative of objects in the environment surrounding the vehicle (also referred to herein as "environmental data") in various manners. The vehicle's sensor system may provide the environmental data representative of objects in the surrounding environment. For example, the vehicle may have a variety of 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.

[0106] In one example, the camera may be configured to capture still images and / or video. In some embodiments, the 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 the captured images and video in a 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.

[0107] 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, distances and locations to 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.

[0108] 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 distances to various objects. The LIDAR may also determine the velocity or speed of the target object, which may be stored as environmental data.

[0109] Additionally, in one example, a microphone may be configured to capture audio of the vehicle's surrounding environment. Sounds captured by the microphone may include emergency vehicle sirens and other vehicle sounds. For example, the microphone may capture the sounds of ambulance, fire engine, and police sirens. The processing system may be capable of identifying that the captured audio signal is indicative of an emergency vehicle. In another example, the microphone may capture the sounds of an exhaust from another vehicle, such as an exhaust from a motorcycle. The processing system may be capable of identifying that the captured audio signal is indicative of a motorcycle. Data captured by the microphone may form a portion of the environmental data.

[0110] In yet another example, the radio unit may be configured to transmit an electromagnetic signal that 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 requests a response from devices located near the autonomous or semi-autonomous vehicle. The processing system may be capable of detecting nearby devices based on responses transmitted back to the radio unit and using this communicated information as part of the environmental data.

[0111] 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 images 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 decisions about the surrounding environment.

[0112] While operating in an 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 receive environmental data. The vehicle's processing system may alter control of the vehicle based on the environmental data received from the various sensors. In some embodiments, the vehicle may alter the speed of the vehicle in response to the environmental data from the various sensors. The vehicle may alter its speed to avoid obstacles, obey traffic laws, etc. When the processing system at the vehicle identifies an object near the vehicle, the vehicle may be able to alter its speed or otherwise change its movement.

[0113] If the vehicle detects an object but is not fully confident in the detection of the object, 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 can also include the human operator providing instructions to control the vehicle's behavior (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 behavior based on the human operator's feedback related to the object's identification.

[0114] 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 of the vehicle may be configured to detect various objects in the surrounding environment based on the environmental data from the 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.

[0115] 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 at least one object not being able to be identified 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.

[0116] The 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. The 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 radar, audio, or other data.

[0117] In some embodiments, the technique used by the vehicle to detect the object 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 to 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 level for each object.

[0118] 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 objects that may require a quick responsive 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 control based on that assumption.

[0119] If the confidence associated with the detected object is below 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.

[0120] 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 a variety of ways depending on the embodiment. In one example, upon detecting an object in the surrounding environment, the vehicle may compare the environmental data to predefined data associated with known objects. The closer the match between the environmental data and the predefined 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.

[0121] In response to determining that the object has a detection confidence below the 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 an in-vehicle computing device separate from the vehicle, but with which a human operator may interact with a passenger or driver of the vehicle, a touch screen interface for displaying remote assistance information, and the like. 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.

[0122] A request for remote assistance may include environmental data, including objects, 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.

[0123] 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 operation of the vehicle 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 velocity (e.g., speed and / or direction), among other possible adjustments.

[0124] 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., stopping 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.

[0125] 4A is a block diagram of a system according to an example embodiment. In particular, FIG. 4A shows a system 400 that includes 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.

[0126] 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 graphic 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.

[0127] The memory 406 may include computer readable media such as non-transitory computer readable media, 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, and the like.

[0128] The LIDAR device 410, described further below, includes a number 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 output 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).

[0129] Similarly, system controller 402 may use output from multiple sensors 412 to determine characteristics of system 400 and / or the surrounding environment. For example, sensors 412 may include one or more of a GPS, an IMU, an image capture device (e.g., a camera), a light sensor, a thermal sensor, and other sensors indicative of parameters related to system 400 and / or the surrounding environment. Lidar device 410 is depicted as separate from sensors 412, for illustrative purposes, and in some examples may be considered as part of or as sensors 412.

[0130] Based on characteristics of the system 400 and / or the surrounding environment determined by the system controller 402 based on output from the lidar device 410 and the 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 the 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 the sensors 412 are also controllable by the system controller 402.

[0131] FIG. 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 photodetectors, such as a plurality of photodetectors 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 photodetector of the plurality of photodetectors 426. The controller 416 includes a processor 418, a memory 420, and instructions 422 stored on the memory 420.

[0132] 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.

[0133] 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.

[0134] Instructions 422 are stored on the memory 420 and executable by the processor 418 to perform functions related to controlling the firing circuitry 428 and the 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 the system controller 402).

[0135] 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 surrounding environment of the lidar device 410, 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, the surfaces of the objects may be in different positions 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 light detector, and the relative location of the light detector at the detection time. By measuring the time difference between the emission time and the detection time, the controller 416 may determine how far the light pulse travels before it is received, and therefore the relative distance of the corresponding object. By tracking the relative positions at emission and detection times, the controller 416 can determine the orientation of the light pulses and reflected light pulses relative to the LIDAR device 410, and thus the relative orientation of the object. By tracking the intensity of the received light pulses, the controller 416 can determine how reflective the object is. 3D point cloud data determined based on this information can thus 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.

[0136] The launch circuit 428 is used to select the light emitter for emitting a light pulse. Similarly, the selector circuit 430 is used to sample the output from the photodetector.

[0137] As noted above, various types of defects can adversely affect measurements made by a detection device (either a lidar device or a camera). For example, some detection devices may include one or more optical components (e.g., lenses, mirrors, waveguides, optical coatings, 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 an image 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 in one or more portions of the optical window (e.g., inside 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 are in contact with the ambient environment, i.e., on one or more exterior portions of the optical window). As additional examples of defects, one or more optical components may include optical coatings (e.g., anti-reflective coatings, hydrophobic coatings, polarizing filters, including color filters, and neutral density filters), and such optical coatings may degrade over time (e.g., suffer from a decrease in their anti-reflective, hydrophobic, or filtering properties) as a result of exposure of the optical coating to the ambient environment (e.g., due to wind, condensation, insects, precipitation, ambient temperature fluctuations, ambient pressure fluctuations, exposure to ultraviolet light, or dust / dirt accumulation). Additionally, in some embodiments, the defects may include one or more protective layers (e.g., protective films) remaining on the optical components after the optical components should be removed (e.g., before operation). Such protective films may have undesirable or poorly characterized optical properties that may adversely affect detection. It is understood that other types of defects are possible and are contemplated herein.Further, such defects may attenuate the optical signal, obscure the optical signal, block the optical signal, scatter the optical signal, redirect the optical signal, or otherwise interfere with the optical signal received from the surrounding environment. In view of the above, the term defect is understood to broadly encompass both debris present on the surface of one or more components (e.g., from the external environment or internal to the lidar device), as well as defects within one or more components (e.g., manufacturing defects, and degradation).

[0138] FIG. 5 illustrates an exemplary system 500 (e.g., similar to system 400 shown and described with reference to FIG. 4A ). System 500 may include a lidar device 410 (e.g., including a light emitter 424 and a light detector 426). Additionally, as illustrated, system 500 may include an optical component 502 (e.g., an optical coating, such as a hydrophobic coating) on ​​or within the lidar device 410 (e.g., an optical coating on an optical window of the lidar device 410). As discussed above, in some embodiments, the optical component 502 may suffer from defects (e.g., degradation and debris) that degrade the performance of the lidar device 410 (e.g., resulting in internal reflection of an optical signal from the light emitter 424 that is detected by the light detector 426, as shown in FIG. 5 ). Such defects may be visible to the naked eye (e.g., leaves) or invisible to the naked eye (e.g., small debris or small cracks, deformations, and changes in refractive index). Further, it is understood that although there may be a single defect external to the optical component 502, any number and / or types of defects are possible (eg, on or within the optical component 502).

[0139] As illustrated in Figure 5 by internal reflection, defects can interfere with the optical signal emitted by the light emitter (e.g., light emitter 424 of the LIDAR device 410 as shown and illustrated in Figure 4B) and / or the optical signal being detected by the light detector (e.g., light detector 426 of the LIDAR device 410 as shown and illustrated in Figure 4B). Thus, as illustrated in Figure 5, defects can affect measurements of the surrounding environment using the light emitter 424 / light detector 426.

[0140] It may be desirable to mitigate the adverse effects of the defects. This can be done by detecting the presence of one or more defects and then taking one or more corrective 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 the sensor with the optical component as including the effects of the defects, and performing post-processing on the data acquired by the sensor with the optical component to compensate for the effects of the defects). To detect the presence of one or more defects, techniques described herein can be performed. Moreover, such techniques can be performed using one or more devices or systems described herein.

[0141] In some embodiments, detecting the presence of one or more defects in an optical component (e.g., optical component 502 of FIG. 5) may include sensing one or more types of defect signals. FIGS. 6A-6D illustrate various types of signals that may be detected by a lidar device. By disambiguating the different types of signals that may be detected by a lidar device and analyzing those disambiguated signals, a determination may be made as to what types of defects are present, how many are present, and what types of defects are present.

[0142] FIG. 6A illustrates a system for detecting an object 602 in a surrounding environment (e.g., in the absence of defects). The system may include a lidar device (e.g., the lidar device 410 shown and described with reference to FIGS. 4A-5) and an optical component (e.g., the optical component 502 shown and described with reference to FIG. 5) that may be positioned on one or more components of the lidar device (e.g., an optical window 604 defined in a housing of the lidar device). As illustrated, light emitters 424A, 424B and corresponding optical detectors 426A, 426B may be used by the lidar device to detect an object 602 in the surrounding environment (e.g., similar to the light emitter 424 and the optical detector 426 described and illustrated with reference to FIG. 4B). For example, as illustrated by the arrows in FIG. 6A, the first light emitter 424A and the second light emitter 424B may each emit one or more optical signals that are transmitted to the surrounding environment through the optical window 604 and the optical component 502 (e.g., where the optical component 502 is an optical coating). The optical signal may be reflected by an object 602 in the surrounding environment and directed (e.g., again through the optical window 604 / optical component 502) to a corresponding first and second optical detectors 426A, 426B to provide information about the surrounding environment (e.g., distance to the object 602 in the surrounding environment based on time of flight). Although FIG. 6A shows two light emitters 424A, 424B, two optical detectors 426A, 426B, and optical component 502, it is understood that other numbers and / or arrangements of light emitters, optical detectors, and optical components are possible and contemplated herein. For example, in some embodiments, the system may include an array of light emitters (e.g., more than two light emitters) and a corresponding array of optical detectors (e.g., more than two optical detectors). The arrays may be arranged such that light emitters and optical detectors in the same channel are adjacent to one another. Additionally or alternatively, some embodiments may include multiple optical components and / or different types / positions of optical components.

[0143] Under certain circumstances (e.g., different from those illustrated in FIG. 6A), the optical signals detected by the optical detectors 426A, 426B may be emitted by the light emitters 424A, 424B and then reflected by something other than the target object 602 in the surrounding environment. In still other circumstances (e.g., different from those illustrated in FIG. 6A), the optical signals detected by the optical detectors 426A, 426B may be reflected such that they are directed toward the optical detectors 426A, 426B for which they are not intended. FIGs. 6B and 6C show examples of the above mentioned circumstances. For example, FIG. 6B shows an example of crosstalk detection, and FIG. 6C shows an example of cross-feedback detection.

[0144] Sometimes, when interacting with certain objects in the surrounding environment, the light signals emitted by the light emitters 424A, 424B may result in crosstalk. For example, as shown in FIG. 6B, the first light emitter 424A may emit a light signal into the surrounding environment. In general, when the emitted light signal is reflected by a surface with low or moderate reflectivity, a reflected light signal of low or moderate intensity may be returned to the first detector 426A. However, as shown in FIG. 6B, when the light signal emitted from the first light emitter 424A into the surrounding environment is reflected by an object with high or very high reflectivity (e.g., a reflectivity above a threshold, which may be part of a road sign, such as a retroreflector 612), the intensity of the reflected signal may be correspondingly high and / or the reflected signal may occupy a correspondingly larger detectable area when incident on the light detectors 426A, 426B (sometimes referred to as a blooming effect). As shown in FIG. 6B, the high intensity reflected light signal may illuminate multiple photodetectors 426A, 426B rather than just the intended photodetector corresponding to the channel of the emitted signal (e.g., the first photodetector 426A). For example, the high intensity reflected light signal may illuminate the second photodetector 426B in addition to the first photodetector 426A. Thus, the second photodetector 426B may detect crosstalk (i.e., detect crosstalk signals resulting from the emitted signal of the first light emitter 424A), which means that the second photodetector 426B may be undesirably affected by the light from the first channel (e.g., may cause noise or inappropriate detection events based on the detection of the second photodetector 426B).

[0145] Which photodetectors in a lidar device (e.g., which of the photodetectors 426 shown and described with reference to FIG. 4B ) can detect a given reflected signal (e.g., which photodetectors 426 are susceptible to crosstalk) may depend on the strength of the reflected signal (e.g., based on the reflectivity of surfaces in the surrounding environment), the sensitivity of the photodetectors 426, the location of the photodetectors 426 in the lidar device, the orientation of the photodetectors 426 in the lidar device (e.g., the azimuth / yaw and / or elevation / pitch orientations of the photodetectors 426), and the distance to the reflecting object in the surrounding environment. For example, the reflected optical signal may be attenuated / diverged as it propagates through the surrounding environment (e.g., due to dust, smoke, etc. in the surrounding environment), so the greater the distance between the lidar device and the reflecting object, the greater the decrease in the strength of the optical signal. As a result, the number of photodetectors 426 in a lidar device that detect crosstalk as a result of optical signals reflected from a highly reflective object may be greater the closer the lidar device is to the highly reflective object.

[0146] Under certain circumstances, the optical signals detected by the optical detectors 426A, 426B may be emitted by the light emitters 424A, 424B and then reflected by objects other than the target object in the surrounding environment. For example, as shown in FIG. 6C, a defect 622 may be present (e.g., debris such as mud or condensation, deterioration, and bends / warping present in or on the optical component 502). As illustrated, the defect 622 may reflect the optical signal emitted by the light emitters 424A, 424B, which is intended to survey the surrounding environment, and redirect the optical signal to the optical detectors 426A, 426B. Furthermore, the first light emitter 424A may emit an optical signal intended for the first optical detector 426A when reflected from an object in the surrounding environment (e.g., as illustrated in FIG. 6A), and the second light emitter 424B may emit an optical signal intended for the second optical detector 426B when reflected from an object in the surrounding environment (e.g., as illustrated in FIG. 6A). However, when such emitted optical signals are reflected from the defect 622, they may be directed to an unintended optical detector. For example, an optical signal emitted by the first light emitter 424A may be directed to and detected by the second light detector 426B, and an optical signal emitted by the second light emitter 426B may be directed to and detected by the first light detector 426A (e.g., as illustrated in FIG. 6C). In some embodiments, such redirected optical signals may be partially diffused or otherwise dispersed after interacting with the defect 622. Because such optical signals are redirected by the defect 622 to an optical detector for which they were not originally intended, such redirected optical signals may be referred to herein as "cross-feedback signals." In other words, a cross-feedback signal occurs when one or more optical signals emitted from a light emitter of one channel are internally reflected (e.g., from a defect on or in one or more optical components of the system) and redirected, resulting in an optical detector of a different channel (e.g., an adjacent channel) detecting the optical signal.Although FIG. 6C shows a "cross-feedback signal" being redirected by a defect 622, it is understood that a "cross-feedback signal" having a non-zero intensity may be detected even in the absence of a defect. For example, internal reflections from one or more optical components (e.g., lenses and mirrors) of the lidar device may be detected as a cross-feedback signal. It is understood that a "cross-feedback signal" may be detected even in the absence of a defect, but in some embodiments, the intensity of the detected "cross-feedback signal" may be higher in the presence of a defect than in the absence of a defect.

[0147] Various techniques may be used to disambiguate the "cross-feedback signal" from other signals. For example, the "cross-feedback signal" may arrive at the detector earlier during a detection cycle than other signals. Thus, based on a timing window associated with a detection event, the detected signal may be labeled as either cross-feedback or non-cross-feedback. In some embodiments, the energization of the photodetector (e.g., the energization of the SiPM used in the photodetector) may be set to be able to detect cross-feedback but not non-cross-feedback. For example, the photodetector may be de-energized after a certain time has elapsed that corresponds to a detection distance beyond which cross-feedback occurs.

[0148] 6C illustrates an example in which a defect 622 external to the optical component 502 and the lidar device reflects / redirects the optical signals emitted by the light emitters 424A, 424B, although it should be appreciated that other embodiments are possible and contemplated herein. For example, the cross-feedback signal may be detected based on reflections from internal optics (mirrors and lenses) and / or mechanical components (e.g., mounts, actuators, motors) within the system (e.g., within the lidar device associated with the light emitters 424A, 424B and light detectors 426A, 426B).

[0149] The crosstalk and cross-feedback signals (e.g., as shown and described with reference to FIGS. 6B and 6C ) may be useful in identifying a type of defect associated with the system (e.g., a defect associated with the optical component 502). For example, signals detected by the photodetector 426 of the lidar device may be analyzed (e.g., by a computing device such as the system controller 402 or the lidar controller 416 illustrated in FIGS. 4A and 4B ) to determine the presence, absence, location, and / or strength (e.g., intensity waveform) of the crosstalk and / or cross-feedback signals. A determination of the presence, absence, type, and / or location of one or more defects can then be made by comparing the determined presence, absence, location, and / or strength (e.g., intensity waveform) of the crosstalk and / or cross-feedback signals to calibration measurements (e.g., obtained when no defects are known to be present, when a particular type of defect is present, and / or when a defect is present in a known location). In addition to, or instead of, making such determinations using the crosstalk and cross-feedback signals (e.g., detected by the photodetector 426 of the LIDAR device), images captured by an image sensor (e.g., a camera) can be used to identify defects.

[0150] As shown in FIG. 6D , the image sensor 632 (e.g., a charge-coupled device (CCD)) may be positioned to capture an image of the optical component 502 (e.g., the entire optical component 502 or a portion of the optical component 502 through which an optical signal detectable by the photodetectors 426, 426B travels). Additionally or alternatively, the image sensor 632 may be configured to capture an image of the optical window 604 and / or an interior portion of the lidar device. As shown in FIG. 6D , the image sensor 632 may be positioned at a focal plane associated with the photodetectors 426A, 426B. Although not shown in FIG. 6D , it will be appreciated that additional components may be used to assist the image sensor 632 in capturing an image of the optical component 502 or other portions of the system. For example, one or more lenses, photographic flashes, mirrors, apertures, etc. may be used in association with the image sensor 632 to capture an image.

[0151] Upon capturing an image of the optical component 502 or other portion of the system, the image may be analyzed (e.g., by a computing device) to determine what percentage of the optical component 502 (or other portion of the system), if present, is occluded (e.g., by mud, condensation, cracks, and deformations) by one or more defects 622. Such image analysis may be performed using a machine learning model (e.g., a classifier trained on one or more labeled training images having known occlusion percentages). The determined occlusion percentage may represent an additional metric (in addition to the strength, location, timing, etc. of detected crosstalk and / or cross-feedback signals) used to characterize the presence, absence, location, and / or type of defect 622 associated with the system at the time the image was captured. Similar to the crosstalk and cross-feedback signals described above, the percentage of occlusion and / or the location of the occlusion captured in the image may be compared (e.g., by a computing device) to one or more calibration images or calibration metrics (e.g., a calibrated percentage of occlusion) to determine the presence, absence, location, and / or type of defect present on or within the optical component 502.

[0152] As discussed above, the crosstalk signal (e.g., detected by the photodetector 426 of the LIDAR device 410), the cross-feedback signal (e.g., detected by the photodetector 426 of the LIDAR device 410), and / or the occlusion rate (e.g., determined by a computing device based on images captured by the image sensor 632) may be used to analyze defects associated with a system (e.g., the LIDAR device 410 and / or associated optical components 502). With this in mind, changes in the crosstalk signal, the cross-feedback signal, and / or the rate of occlusion over time may be used to characterize changes in defects over time. Additionally, however, some types of defects will occur over time based on the behavior of the system. For example, if the optical component 502 is a hydrophobic coating and one or more water droplets are applied to the optical component 502 (e.g., as a result of rain and / or condensation), these water droplets should aggregate and fall off the optical component 502 over time. By monitoring the evolution of the cross-feedback signal, the cross-talk signal, and / or the percentage of occlusion over time, a determination can be made regarding the hydrophobic quality of the hydrophobic coating (e.g., how quickly / completely water aggregates / sheds from the hydrophobic coating). In other words, defects (e.g., resulting in reduced hydrophobicity), such as degradation of the hydrophobic coating (e.g., chemical or physical degradation as a result of exposure to the ambient environment) can be monitored over time. Similarly, if one or more defects (e.g., air bubbles, cracks, imperfect transparency, and bends) are present in the optical component 502, the occurrence of such defects can be monitored by monitoring the cross-talk signal, the cross-feedback signal, and / or the percentage of occlusion over time. For example, by analyzing the cross-talk signal, the cross-feedback signal, and / or the percentage of occlusion over time, it can be determined whether a crack in the optical component 502 is expanding over time. Other types of defect monitoring over time (e.g., various types of optical components) are also possible and are contemplated herein using the signals and metrics described throughout.

[0153] In view of the above, techniques described herein may include intentionally introducing one or more defects into a system (e.g., in or on optical component 502) and then monitoring various metrics (e.g., metrics based on crosstalk signals, cross-feedback signals, and / or captured images) over time to determine the state of the system (e.g., the ability of the system to adequately handle, remove, address, account for, etc., defects present in the system). FIGS. 7A and 7B illustrate one technique for intentionally introducing defects and monitoring the handling of those defects over time. In particular, FIGS. 7A and 7B illustrate a system 700 including a lidar device (e.g., lidar device 410 shown and described with reference to FIGS. 4A and 4B), an optical component (e.g., optical component 502 shown and described with reference to FIGS. 5-6D), and a cleaning device. As shown, the cleaning device may include a sprayer 710 and a pressurized air source 720. The cleaning device illustrated in FIGS. 7A and 7B is provided by way of example only, and other cleaning devices are possible and contemplated herein (e.g., a cleaning device including one or more wipers, such as a windshield wiper, a heater, such as a defroster, a cooling unit, a fan, a vacuum, and an actuator, such as a motor, a centrifugal unit, a mechanical stage, or a vibrator). Still further, in some embodiments, the cleaning device may serve multiple purposes. For example, the cleaning device may include a motor that is used to rotate one or more components of the lidar device 410 (e.g., to rotate the light emitters 424 and light detectors 426 of the lidar device 410 relative to the surrounding environment). As the motor turns the lidar device 410, this may enable the light emitters 424 and light detectors 426 to analyze a larger portion of the surrounding environment (e.g., by azimuthally scanning the surrounding environment). Additionally, as the motor turns the lidar device 410, the motor may function to clean the lidar device 410 (e.g., by removing a previously applied cleaning solution or by removing condensation, rain, snow, and debris from the lidar device 410).The system 700 of Figures 7A and 7B can be configured to perform a multi-stage cleaning protocol, as illustrated in Figures 7A and 7B.

[0154] FIG. 7A illustrates steps of a cleaning protocol. As illustrated, the cleaning protocol may include a sprayer 710 that applies a cleaning solution 712 to one or more components of the system 700 (e.g., to the exterior surface of the optical component 502). The cleaning solution 712 may be applied to a portion of the exterior surface of the optical component 502 or to the entire exterior surface of the optical component 502. In some embodiments, the cleaning solution 712 may be applied, for example, through a nozzle of the sprayer 710. The sprayer 710 may also include one or more actuators (e.g., motors and stages) configured to direct the sprayer 710 to specific areas of the lidar device 410 and / or the optical component 502. Additionally, the cleaning solution 712 may include various compounds in various concentrations (e.g., deionized water and isopropyl alcohol). Further, the cleaning protocol may include a predetermined or selectable number of sprays of cleaning solution 712 applied (e.g., 1 spray, 2 sprays, 3 sprays, 4 sprays, 5 sprays, 6 sprays, 7 sprays, 8 sprays, 9 sprays, and 10 sprays). If the number of sprays is greater than one, the number of sprays may be distributed (e.g., evenly) over a predefined or selectable period (e.g., 5 seconds, 10 seconds, 20 seconds, 30 seconds, and 1 minute) according to an application frequency (e.g., 0.25 Hz, 0.5 Hz, 0.75 Hz, 1 Hz, 1.5 Hz, 2 Hz, and 2.5 Hz). Still further, the cleaning protocol may be initiated according to a periodic schedule (e.g., once every 30 minutes, once every hour, once every 4 hours, once every 12 hours, once a day, once every 2 days, once every 4 days, once a week, once every 2 weeks, and once a month). Additionally or alternatively, the cleaning protocol may be initiated in response to a request (e.g., from a user providing instructions via a mobile application or browser-based application, from the rider controller 416, and from the system controller 402).Still further, the cleaning protocol may be initiated in response to one or more thresholds being met (e.g., a determination by the lidar controller 416 that the strength or number of detected cross-feedback signals exceeds a predetermined threshold).

[0155] As illustrated by the arrows, the light emitters 424 of the lidar device 410 may emit one or more optical signals while the cleaning solution 712 is being applied to one or more components of the system 700. These optical signals may be reflected by the cleaning solution 712 being applied to the surface of the optical component 502 and then detected by the optical detectors 426 of the lidar device 410. Such detected signals may thus represent cross-feedback signals (as shown and described above with respect to FIG. 6C ) when they are directed to a photodetector 426 on a different channel than the corresponding light emitter 424 that emitted the optical signal.

[0156] FIG. 7B illustrates another step of the cleaning protocol. As shown, the cleaning protocol may include a pressurized air source 720 that applies pressurized air 722 to one or more components of the system 700 (e.g., the exterior surface of the optical component 502). The pressurized air 722 may be applied after application of the cleaning solution 712 (e.g., as shown and described with reference to FIG. 7A). The pressurized air 722 may be applied to a portion of the exterior surface of the optical component 502 (e.g., the portion to which the cleaning solution 712 was applied, if the cleaning solution 712 was applied to only a portion of the optical component 502), or to the entire exterior surface of the optical component 502. The pressurized air source 720 may also include one or more actuators (e.g., motors and stages) configured to direct the pressurized air source 720 to specific regions of the lidar device 410 and / or the optical component 502. Further, the cleaning protocol may include a predetermined or selectable number of applications of pressurized air 722 (e.g., one blow, two blows, three blows, four blows, five blows, six blows, seven blows, eight blows, nine blows, and ten blows). If the number of blows is greater than one, the number of blows may be distributed (e.g., evenly) over a predefined or selectable period of time (e.g., 5 seconds, 10 seconds, 20 seconds, 30 seconds, and 1 minute) according to an application frequency (e.g., 0.25 Hz, 0.5 Hz, 0.75 Hz, 1 Hz, 1.5 Hz, 2 Hz, and 2.5 Hz). Still further, the steps of the cleaning protocol illustrated in FIG. 7B may be initiated in response to completion of the steps of the cleaning protocol illustrated in FIG. 7A.

[0157] As illustrated by the arrows, while pressurized air 722 is applied to one or more components of the system 700, the light emitters 424 of the lidar device 410 may emit one or more optical signals. These optical signals may be reflected by the cleaning solution 712 on the surface of the optical components 502 and then detected by the optical detectors 426 of the lidar device 410. Such detected signals may thus represent cross-feedback signals (e.g., as shown and described above with respect to FIG. 6C ) when they are directed to a different channel optical detector 426 than the corresponding light emitter 424 that emitted the optical signal.

[0158] As illustrated by the above description, optical signals may be emitted by the light emitter 424 and detected by the optical detector 426 during the performance of a cleaning protocol by the cleaning device. By analyzing the detected optical signals, a determination may be made regarding the presence of a defect, the absence of a defect, the location of a defect, the type of defect, and / or the time evolution of a defect in the system 700 (e.g., on the exterior surface of the optical component 502). Such a determination may be made, for example, by the system controller 402 and / or the lidar controller 416. Furthermore, such a determination may be used to estimate the health of one or more components of the system 700. For example, based on the time evolution of the cleaning solution 712 being removed from the exterior surface of the optical component 502 (e.g., by application of the pressurized air 722 during the cleaning protocol), a determination may be made (e.g., by the system controller 402 and / or the lidar controller 416) about the hydrophobic quality of the optical component 502 (e.g., when the optical component 502 is or includes a hydrophobic coating). These determined hydrophobic qualities may indicate the level of degradation of the hydrophobic coating over time (e.g., due to exposure to the ambient environment). A lower level of degradation may correspond, for example, to larger drips of cleaning solution 712 and / or faster shedding of cleaning solution 712 by the applied pressurized air 722. Other qualities can be analyzed using similar techniques and are also contemplated herein (e.g., optical qualities such as polarization, reflectance, absorptance, transmittance, and refractive index).

[0159] Exemplary techniques for determining the quality of one or more components of the system 700 based on the detected optical signals are described in further detail below with reference to Figures 8A and 8B. In some embodiments, such determinations regarding the quality of one or more components may be performed every time a cleaning protocol is performed (e.g., the cleaning protocol illustrated in Figures 7A and 7B). Alternatively, such determinations regarding the quality of one or more components may be performed only for a subset of cleanings (e.g., every second time a cleaning protocol is performed, every third time a cleaning protocol is performed, and every fourth time a cleaning protocol is performed), and / or only for certain types of cleanings (e.g., only when the optical component 502 is cleaned and only when the sprayer 710 / pressurized air source 720 are involved in cleaning).

[0160] A cleaning protocol may be initiated for the purpose of cleaning the system 700 (e.g., cleaning debris or other blockages from the exterior surfaces of the optical components 502). However, in some embodiments, a cleaning protocol may be initiated with the sole intent of performing diagnostics on one or more components of the system 700 (e.g., to determine the current state of the optical components 502). Because the cleaning apparatus may include one or more components that can apply defects to the system 700 (e.g., applying a cleaning solution 712 to the optical components 502 using a sprayer 710), initiating a cleaning protocol may be a simple, repeatable, and consistent process by which defects can be introduced into the system 700 and monitored. Furthermore, because such a process is repeatable and consistent (e.g., as opposed to defects that arise as a result of precipitation from the surrounding environment), results can be compared from one diagnostic test to another (e.g., enabling a process to accurately monitor the health of the components of the system 700 over time). In various embodiments, a cleaning protocol initiated to perform diagnostics of one or more components of system 700 can be a standardized cleaning protocol (e.g., the same cleaning protocol under the same conditions as would be performed to simply clean system 700). Alternatively, a cleaning protocol initiated to perform diagnostics of one or more components of system 700 can be a cleaning protocol that is modified to enhance its diagnostic capabilities (e.g., a cleaning protocol of different intensity or duration than a standard cleaning protocol and a cleaning protocol that uses a different cleaning device).

[0161] Further, as shown and described with reference to FIGS. 7A and 7B, the light emitter 424 may emit optical signals that are reflected from one or more defects and detected by one or more optical detectors 426. However, it is understood that some portions of the optical signals emitted by the light emitter 424 may pass through to the surrounding environment unaffected by the defects present (e.g., cleaning solution 712). Such optical signals may then be reflected from objects in the surrounding environment and redirected to and detected by corresponding optical detectors 426 of the lidar device 410 (e.g., similar to the process illustrated in FIG. 6A). With this in mind, while some of the optical signals detected by the optical detectors 426 may represent cross-feedback signals (e.g., and thus may be used to perform diagnostics of the optical component 502), other optical signals may still be used to provide information about the surrounding environment (e.g., distance to objects in the surrounding environment based on the time of flight of the respective optical signals). In some embodiments, the cross-feedback signal may be disambiguated from the object return signal based on time of flight (e.g., a signal corresponding to a time of flight representing a distance close to a threshold distance, such as 5 cm from the optical component 502, may be determined to represent a cross-feedback signal). Additionally or alternatively, the cross-feedback signal may be disambiguated from the object return signal based on empirical data (e.g., a determination may be made during calibration that certain light emitter / photodetector channels detect cross-feedback signals when a cleaning protocol is performed, and thus signals from those channels are considered cross-feedback signals at run-time). Data from those detected signals corresponding to object detection signals may be arranged (e.g., by the system controller 402 and / or the lidar controller 416) into a three-dimensional point cloud (albeit a point cloud having missing points corresponding to those areas containing defects). In this way, partial detection of the surrounding environment may occur (i) during the same emission / detection cycle as diagnostic testing of the components of the system, and / or (ii) during the performance of a cleaning protocol using the cleaning device:

[0162] As described above, the detected optical signals (e.g., cross-feedback signals) during application of one or more cleaning protocols may be used to determine the quality of one or more components of the system 700 (e.g., by tracking the status of one or more defects over time). One method of making such determinations includes tracking the intensity of such cross-feedback signals over time. For example, each time an optical signal is emitted and a corresponding cross-feedback signal is detected, the intensity of the detected cross-feedback signal (e.g., maximum / peak intensity of the intensity waveform corresponding to the optical signal) and a timestamp associated with the detected cross-feedback signal may be recorded (e.g., stored in a memory associated with the system controller 402 or the lidar controller 416). The optical detector 426 from which the detected cross-feedback signal is captured over time may be selected based on empirical studies in which the optical detector 426 in the array of optical detectors 426 detects the maximum intensity of cross-feedback and / or the optical detector 426 in the array of optical detectors 426 detects the cross-feedback that has the highest correlation with the presence of a defect. By recording a series of timestamps and associated intensities over time (e.g., as additional puffs of pressurized air 722 are applied to the optical component 502), a plot of intensity versus time for the detected cross-feedback signal can be made. Figure 8A illustrates such a plot.

[0163] Plot 802 represents the measured intensity (e.g., arbitrary units) of the cross-feedback signal over time (e.g., in seconds). In other embodiments, rather than simply plotting the intensity of one cross-feedback signal corresponding to one photodetector as it is detected at various times, an average of the cross-feedback intensity can be recorded over a series of time points. For example, any cross-feedback signal detected within a set period (e.g., having lengths of 1 μs, 5 μs, 10 μs, 20 μs, and 50 μs) can be grouped with other cross-feedback signals within that period, and the intensity of the cross-feedback signal over that period can be averaged and stored with an associated time stamp. A plot of these average intensities versus time can then be generated. Such a plot may look similar to FIG. 8A (e.g., including plot 802 representing the average intensity over time, in seconds).

[0164] Once a plot 802 of intensity versus time (or average intensity versus time) is generated, the plot 802 may be fitted to a curve (e.g., exponential, quadratic, logarithmic, and linear). For example, as shown in FIG. 8A, the plot 802 may be fitted to a curve 804. Various values ​​from the equation of the fitted curve may be used to evaluate various characteristics of the underlying system. For example, as shown in FIG. 8A, the fitted curve 804 may have an equation of the following form: f(t)=A 0 e -t / τ +A final where f(t) is the intensity value at time t and A 0 +A final is the initial value (i.e., the peak value) of the curve 804 at time t=0, and A final is the final value of curve 804 as t→∞, and τ is the time constant associated with the exponential decay.

[0165] Curve 804 correlates the time evolution of one or more defects over time, so that the value A 0、 A finalEach of A, and τ can provide information that can be used to evaluate the quality of the component in question (e.g., the hydrophobicity of a hydrophobic coating) and / or cleaning device. For example, for a hydrophobic coating under test, A 0 A may relate to the ability of the hydrophobic coating to cause the water / cleaning solution on the hydrophobic coating to refuse to form large droplets on and / or adhere to the hydrophobic coating, while τ may relate to the ability of the hydrophobic coating to shed from the hydrophobic coating. Additionally or alternatively, if a particular feature is present in the fitted curve 804 (e.g., A 0 +A final is very high), one or more decisions can be made about the cleaning device (e.g., the cleaning device has a clog and is not able to apply an adequate amount of cleaning solution 712 via the sprayer 710). The metrics extracted from the curve 804 can be compared, for example, to the same metrics determined during a calibration measurement. For example, a calibration curve can be generated by fitting a curve to similar measurements of the cross-feedback signal on an optical component (e.g., a hydrophobic coating) whose quality (e.g., hydrophobicity) is well characterized (e.g., a brand new hydrophobic coating whose hydrophobic properties are specified within manufacturing tolerances, or a fully resolved hydrophobic coating whose hydrophobic properties are at their most realistic values). Then, A of the calibration curve can be compared to the same metrics determined during a calibration measurement. 0 +A final The values ​​of , and τ may be stored in memory associated with the system controller 402 or the rider controller 416 and / or may be stored in a server memory accessible by the system controller 402 / rider controller 416 (e.g., via the Internet or other data network).

[0166] When performing diagnostic testing, A 0、 A final The determined values ​​of τ and τ are used to determine the quality of the optical component 502. 0、 A final, and τ. For example, A 0、 A final The determined values ​​of , and τ are compared with the A recorded for an ideal optical component (e.g., a brand new optical component). 0 , A final , and can be compared with a calibrated value of τ. 0、 A final , and one or more of the determined values ​​of τ are greater than or equal to 1% of the ideal optical component (e.g., greater than 1% difference, greater than 5% difference, greater than 10% difference, greater than 15% difference, greater than 20% difference, and greater than 25% difference). 0、 A final , and τ are greater than a threshold difference from a calibrated value, then it may be determined that one or more qualities of the optical component are deficient (e.g., the hydrophobicity of a hydrophobic coating has deteriorated over time).

[0167] A 0、 A final In addition to, or instead of, simply comparing the determined values ​​of τ, and τ to a threshold, alternative metrics can be generated. For example, a single logarithmic metric can be used to determine whether an optical component under test “passes” the test or “fails” the diagnostic test:

number

[0168] The exemplary fit curve 804 shown in FIG. 8A corresponds to an exponential decay function, although it is understood that other types of functions are possible and contemplated herein. For example, linear or quadratic functions may also be used. If a different type of function is used rather than an exponential function, the figure of merit may also be different. For example, if a linear function is used, the slope and y-intercept may be used instead as figures of merit.

[0169] In some embodiments, adverse weather conditions may affect measurements taken during application of a cleaning protocol (e.g., detected cross-feedback signal). For example, if it is raining, in addition to the cleaning solution 712 that is applied to the optical component 502 (e.g., as illustrated in FIG. 7A), rain may be deposited on the optical component 502. Thus, there may be additional defects (e.g., additional water droplets) on the optical component 502 when performing the cross-feedback measurements. In this manner, any figure of merit (e.g., A) associated with the fitted curve determined during the rainfall may be considered to be a measure of the accuracy of the measurement. 0、 A final , τ, or pass / fail metrics) may be different than they would be otherwise in the absence of rain. In light of this, any decisions made about the underlying optical components 502 based on these figures of merit may not be reliable. The embodiments herein provide several techniques to compensate for this. For example, rather than comparing the strengths of the cross-feedback signals to calibration measurements made in clear weather conditions, they may instead be compared to calibration measurements made in the rain (e.g., a brand new hydrophobic coating may have been subjected to a calibration test in the rain). Additionally or alternatively, an offset may be applied to either the detected strengths of the cross-feedback signals and / or the metrics determined based on the detected strengths. For example, each of the cross-feedback strengths detected during rain conditions may have its detected strength adjusted by a fixed amount (e.g., 5%, 10%, 15%, etc., or 1 arb. unit, 2 arb. unit, and 3 arb. unit). Similarly, A final Or the value of τ can be adjusted after it is determined to account for rain. For example, the determined τ can be decreased by 5%, 10%, 15%, etc., or by 1 second, 2 seconds, 3 seconds, etc.

[0170] Although the examples provided herein are for rain, it is understood that other adverse weather conditions (e.g., snow, wind, cloudy, splashing, and heil) may be addressed in a similar manner. Additionally, the type of adverse weather condition present and / or the intensity of such weather conditions (e.g., rainfall / minute and wind speed) may be detected by other sensors associated with the system 700 (e.g., the radar 126 of the vehicle 100 shown and described with reference to FIG. 1 ) or transmitted to the lidar device 410 by another device (e.g., from a management server on the Internet). The intensity of such weather conditions may be used to determine the degree to which each of the intensities of the cross-feedback signals and / or determined metrics is adjusted (e.g., rainfall / minute at a first level corresponds to a 5% reduction, and rainfall / minute at a second level corresponds to a 10% reduction). The degree to which the adjustment of the intensity of the cross-feedback signals and / or determined metrics depends on the weather condition (e.g., rainfall amount / minute) may be determined based on previous calibration measurements taken during various weather conditions.

[0171] Techniques are described above for fitting the intensity of the cross-feedback signal to a curve 804 and then making a decision about the quality of the underlying optical components based on a figure of merit (e.g., time constant) associated with the fitted curve 804. In some embodiments, the thresholds for one or more figures of merit may be determined using a machine learning model. For example, a classifier may be trained using labeled training data corresponding to cross-feedback intensity values ​​captured during a calibration experiment in which the quality of the underlying optical components was known. Alternatively, rather than plotting the intensity values ​​and then fitting a curve to the plotted values, a machine learning model (e.g., a classifier) ​​may be trained using labeled training data corresponding to the cross-feedback signal itself (e.g., full intensity waveform corresponding to the cross-feedback signal intensity variation over time) when the quality of the underlying optical components is known. Then, when performing a diagnostic test, a series of detected cross-feedback signals (e.g., full intensity waveforms) may be fed to the machine learning model to determine the quality of the underlying optical components.

[0172] While FIG. 8A illustrates an analysis of cross-feedback signals detected during a cleaning protocol to assess the health of the optical component 502, it is understood that this is provided merely as an example and that other signals may additionally or alternatively be detected and analyzed. For example, crosstalk signals (as discussed and described above with reference to FIG. 6B) may be detected and / or images (as discussed and described above with reference to FIG. 6D) may be captured. These crosstalk signals and / or images may also be analyzed to determine a figure of merit regarding the condition of the optical component 502. Additionally, still other signals may be detected / analyzed. For example, internal reflections of optical signals emitted by the light emitter 424 may be detected and analyzed by the photodetector 426 (e.g., even if those optical signals do not represent cross-feedback signals, i.e., if the optical signals are emitted / detected by the light emitter 424 / photodetector 426, respectively, in the same channel of the lidar device 410).

[0173] Cleaning protocols (e.g., A 0、 A final A figure of merit determined by analyzing the cross-feedback signal during the flight (i.e., τ, or pass / fail metric) may also be used to characterize an entire fleet. For example, FIG. 8B is a scatter plot of several optical components 812 across a fleet. For example, each of the optical components 812 may represent a hydrophobic coating on the lidar device 410 of a different vehicle in a fleet of vehicles. As shown in FIG. 8B, each optical component 812 is analyzed using the process described above to determine a 0 Based on these values, each of the optical components 812 may be positioned on the scatter plot. 0 A curve 814 showing the location of the pass / fail metric is placed on the scatter plot of FIG. 8B as it depends on τ and τ. Any optical components 812 on the curve or above and to the left of the curve 814 may correspond to optical components 812 that "pass" under the pass / fail metric, for example, while any optical components 812 below and to the right of the curve 814 may correspond to optical components 812 that "fail" under the pass / fail metric. Such scatter plots may be stored in memory (e.g., a fleet management server) for later access / review.

[0174] By reviewing the scatter plot of FIG. 8B, the overall health of the optical components 812 across the fleet may be evaluated. For example, if a large number of optical components 812 fall into the "fail" category under a pass / fail metric, it may be determined that a different type of optical component 812 (e.g., a different type of hydrophobic coating) should replace those currently used in the fleet. Additionally or alternatively, if different types of optical components 812 (e.g., different types of hydrophobic coating) are used across the fleet, the relative performance of the different types of optical components 812 may be compared to determine if one type is superior to another type. The scatter plot of FIG. 8B may also be used to set thresholds. For example, if it is determined that 80% of the fleet of optical components 812 should correspond to "pass" under a pass / fail metric, a review of the scatter plot of FIG. 8B may help determine where to place the curve 814 that separates "pass" from "fail."

[0175] Although the illustration of FIG. 8B is provided for a fleet of optical components 812, it will be appreciated that similar plots may be generated for a single optical component at various times. For example, a single optical component may be analyzed at various times to provide a corresponding A at each time point. 0 The A and τ values ​​for various time points can then be determined. 0 Each combination of τ and τ can be plotted on a scatter plot. By reviewing such scatter plots, an analysis of the figure of merit of the optical components over time can be performed. Furthermore, while only two figures of merit are plotted in FIG. 8B, it is understood that in other scatter plots (e.g., three-dimensional and n-dimensional), the scatter plot can also be used to track more than one figure of merit. Additionally or alternatively, multiple curves (or planes in the case of three-dimensional or n-dimensional scatter plots) that divide "pass" and "fail" can be used (e.g., when multiple thresholds are considered).

[0176] 9 is a flowchart diagram of a method 900, according to an example embodiment. In some embodiments, method 900 may be performed by a system (e.g., system 700 shown in FIGS. 7A and 7B). Method 900 may be performed in response to a request from a user (e.g., transmitted via a mobile application), in response to determining that one or more optical components may have a defect (e.g., based on data previously detected by one or more optical components), at startup (e.g., when the lidar device 410 begins use for object detection and avoidance or when a vehicle including the lidar device 410 leaves a depot), at regular intervals (e.g., hourly, six hourly, twelve hourly, daily, and weekly), etc.

[0177] At block 902, the method 900 may include applying a cleaning protocol to one or more optical components of the lidar device using a cleaning device.

[0178] At block 904, the method 900 may include emitting one or more optical signals from an optical emitter of the lidar device.

[0179] At block 906, the method 900 may include detecting reflections of the one or more optical signals by a photodetector of the lidar device.

[0180] At block 908, the method 900 may include determining, by the controller, based on the detected reflection of the one or more optical signals, that one or more defects are present within the one or more components or within the cleaning apparatus.

[0181] In some embodiments of method 900, block 902 may include spraying a cleaning solution from a sprayer of the cleaning device onto at least one of the one or more optical components. Block 902 may also include applying pressurized air onto the at least one optical component by a pressurized air supply of the cleaning device.

[0182] In some embodiments of the method 900, the one or more defects may include a degradation of the hydrophobic coating. The degradation may correspond to a decrease in the hydrophobicity of the hydrophobic coating.

[0183] In some embodiments of the method 900, block 904 may include emitting a series of optical signals. Further, block 906 may include detecting a series of reflections of the one or more optical signals.

[0184] In some embodiments of method 900, block 908 may include generating a plot of a series of intensity of reflections of one or more optical signals over time and fitting the generated plots to a function.

[0185] In some embodiments of method 900, block 908 may include comparing the function to which the generated plot is fitted to a calibration function generated during calibration of the lidar device.

[0186] In some embodiments of the method 900, the function to which the generated plot is fitted may include an exponential function. Further, the peak value and time constant of the exponential function may represent a figure of merit.

[0187] In some embodiments of method 900, block 908 may include accounting for weather conditions in an environment surrounding the LIDAR device. Further, in some embodiments, accounting for weather conditions in an environment surrounding the LIDAR device may include applying one or more offsets to the intensity plot based on the weather conditions.

[0188] In some embodiments, the method 900 may also include implementing corrective action based on the one or more defects determined to exist.

[0189] In some embodiments of method 900, block 902 may include spraying a cleaning solution from a sprayer of the cleaning device onto at least one of the one or more optical components. Block 902 may also include wiping the at least one optical component to remove the cleaning solution with a wiper of the cleaning device.

[0190] The present disclosure is not limited with respect to the specific embodiments described in this application, which are intended as illustrations of various aspects. As will be apparent to those skilled in the art, many modifications and variations can be made without departing from the spirit and scope of the present disclosure. In addition to the methods and devices recited herein, functionally equivalent methods and devices 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.

[0191] The above detailed description describes various features and functions of the disclosed systems, devices, and methods with reference to the accompanying drawings. In the figures, similar symbols typically refer to similar components identically unless the context dictates otherwise. The exemplary embodiments described herein and in the figures are not intended 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.

[0192] 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 processing of information and / or transmission of information according to the exemplary embodiments. Alternative embodiments are included within the scope of these exemplary embodiments. In these alternative embodiments, for example, operations described as steps, blocks, transmissions, communications, requests, responses, and / or messages may be performed in a different order than 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 in part or in whole.

[0193] A step, block, or operation corresponding to the processing of information may correspond to circuitry that can be configured to perform a particular logical function of a method or technique 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). Program code may include one or more instructions executable by a processor to perform a particular logical operation or operation in a 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 a RAM, a disk drive, a solid-state drive, or another storage medium.

[0194] Additionally, steps, blocks, or operations corresponding to 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.

[0195] The particular arrangement shown in the figures should not be considered as limiting. It should be understood that other embodiments may include more or less of each element shown in a given figure. Further, some of the illustrated elements may be combined or omitted. Additionally, example embodiments may include elements not illustrated in the figures.

[0196] Various aspects and embodiments are disclosed herein, while other aspects and embodiments will be apparent to those of ordinary skill in the art. The various aspects and embodiments disclosed herein are for purposes of illustration and are not intended to be limiting, with the true scope being indicated by the following claims.

Claims

1. applying a cleaning protocol to one or more optical components of a Light Detection and Ranging (LIDAR) device using a cleaning device; emitting one or more optical signals from a light emitter of the lidar device; detecting a reflection of the one or more optical signals by a photodetector of the LIDAR device; and determining, by a controller, that one or more defects are present within the one or more optical components or within the cleaning device based on the detected reflections of the one or more optical signals.

2. Applying said cleaning protocol spraying a cleaning solution from a sprayer of the cleaning device onto at least one of the one or more optical components; 2. The method of claim 1, further comprising: applying pressurized air onto the at least one optical component with a pressurized air source of the cleaning device.

3. The method of claim 1 , wherein the one or more defects comprise a degradation of a hydrophobic coating, the degradation corresponding to a reduction in hydrophobicity of the hydrophobic coating.

4. 10. The method of claim 1 , wherein emitting the one or more optical signals comprises emitting a series of optical signals, and wherein detecting the reflections of the one or more optical signals comprises detecting a series of reflections of the one or more optical signals.

5. determining that one or more defects are present in the one or more optical components or in the cleaning device; generating a plot of the intensity of the series of reflections of the one or more optical signals over time; and fitting the generated plot to a function.

6. 6. The method of claim 5, wherein determining that one or more defects are present in the one or more optical components or in the cleaning apparatus further comprises comparing the function to which the generated plot is fitted to a calibration function generated during calibration of the LIDAR device.

7. The method of claim 5 , wherein the function to which the generated plot is fitted comprises an exponential function, the peak value and time constant of the exponential function representing a figure of merit.

8. 6. The method of claim 5, wherein determining that one or more defects are present in the one or more optical components or in the cleaning device includes accounting for weather conditions in a surrounding environment of the lidar device.

9. 10. The method of claim 8, wherein accounting for the weather conditions in the surrounding environment of the LIDAR device includes applying one or more offsets to the intensity plot based on the weather conditions.

10. The method of claim 1 , further comprising: performing a corrective action based on the determined existence of one or more defects.

11. Applying said cleaning protocol spraying a cleaning solution from a sprayer of the cleaning device onto at least one of the one or more optical components; 2. The method of claim 1, further comprising wiping the at least one optical component with a wiper of the cleaning device to remove the cleaning solution.

12. one or more optical components; an optical emitter configured to emit one or more optical signals; a light detection and ranging (LIDAR) device comprising: a light detector configured to detect reflections of the one or more optical signals; a cleaning device configured to apply a cleaning protocol to the one or more optical components; and a controller configured to determine, based on the detected reflections of the one or more optical signals, that one or more defects are present within the one or more optical components or within the cleaning apparatus.

13. the cleaning device includes a sprayer and a source of pressurized air; Applying said cleaning protocol spraying a cleaning solution from a sprayer of the cleaning device onto at least one of the one or more optical components; and applying pressurized air onto the at least one optical component with a pressurized air source of the cleaning device.

14. The system of claim 12 , wherein the one or more defects include a degradation of a hydrophobic coating, the degradation corresponding to a decrease in hydrophobicity of the hydrophobic coating.

15. 13. The system of claim 12, wherein emitting the one or more optical signals comprises emitting a series of optical signals, and detecting the reflections of the one or more optical signals comprises detecting a series of reflections of the one or more optical signals.

16. determining that one or more defects are present in the one or more optical components or in the cleaning device; generating a plot of the intensity of the series of reflections of the one or more optical signals over time; and fitting the generated plot to a function.

17. 17. The system of claim 16, wherein determining that one or more defects are present in the one or more optical components or in the cleaning apparatus further comprises comparing the function to which the generated plot is fitted to a calibration function generated during calibration of the LIDAR device.

18. 17. The system of claim 16, wherein the function to which the generated plot is fitted comprises an exponential function, the peak value and time constant of the exponential function representing a figure of merit.

19. 20. The system of claim 16, wherein determining that one or more defects are present in the one or more optical components or in the cleaning device includes accounting for weather conditions in a surrounding environment of the lidar device.

20. receiving data corresponding to reflections of one or more optical signals detected by a photodetector of a Light Detection and Ranging (LIDAR) device, the one or more optical signals being emitted by a light emitter of the LIDAR device in response to a cleaning device applying a cleaning protocol to one or more optical components of the LIDAR device; A computing device configured to determine, based on the received data, that one or more defects are present within the one or more optical components or within the cleaning device.

Images