Patent Publication Number: US-11647296-B2

Title: Auto exposure using multiple cameras and map prior information

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     This application is a continuation of U.S. application Ser. No. 16/730,640, filed on Dec. 30, 2019, entitled, AUTO EXPOSURE USING MULTIPLE CAMERAS AND MAP PRIOR INFORMATION, which is hereby expressly incorporated by reference in its entirety. 
    
    
     BACKGROUND 
     1. Technical Field 
     The subject technology provides solutions for adjusting an exposure setting and in particular, for adjusting an exposure setting based on a region of interest. 
     2. Introduction 
     Overexposure and underexposure is an ongoing issue for image processing systems. As a point of focus of a camera changes or the amount of light suddenly increases or decreases, the corresponding image can be negatively affected by the change in light exposure. Typically, exposure algorithms attempt to minimize the contrast between the brightest and darkest points to obtain the best image. The entire viewing screen is affected by the change in light exposure, thereby rendering a point of focus not viewable because of the over/underexposure of the viewable area. In conventional workflows, users are typically responsible for identifying the point of focus and for determining which area of the screen to compensate for exposure across the entire image without consideration of the present environment. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       Certain features of the subject technology are set forth in the appended claims. However, the accompanying drawings, which are included to provide further understanding, illustrate disclosed aspects and together with the description serve to explain the principles of the subject technology. In the drawings: 
         FIG.  1    illustrates an example environment that includes an autonomous vehicle in communication with a remote computing system, according to some aspects of the disclosed technology. 
         FIG.  2    illustrates an example process for adjusting an exposure setting, according to some aspects of the disclosed technology. 
         FIG.  3    illustrates an example process for adjusting an exposure setting based on multiple cameras, according to some aspects of the disclosed technology. 
         FIG.  4    illustrates an example processor-based system with which some aspects of the subject technology can be implemented. 
     
    
    
     DETAILED DESCRIPTION 
     Various examples of the present technology are discussed in detail below. While specific implementations are discussed, it should be understood that this is done for illustration purposes only. A person skilled in the relevant art will recognize that other components and configurations may be used without parting from the spirit and scope of the present technology. In some instances, well-known structures and devices are shown in block diagram form in order to facilitate describing one or more aspects. Further, it is to be understood that functionality that is described as being carried out by certain system components may be performed by more or fewer components than shown. 
     The disclosed technologies address a need in the art for improvements in the adjustment of exposure settings. In some examples, a computer-implemented method for adjusting an exposure setting can include determining a localization parameter of an autonomous vehicle, the localization parameter including a geographic position of the autonomous vehicle. The method also can include determining a region of interest based on the localization parameter of the autonomous vehicle. The method further can include receiving a first image including the region of interest based on the localization parameter of the autonomous vehicle. The method also can include determining an exposure setting for the region of interest in the first image. The method further can include adjusting an exposure setting of the first image to the exposure setting for the region of interest in the first image. 
       FIG.  1    illustrates an example autonomous vehicle environment  100 . The example autonomous vehicle environment  100  includes an autonomous vehicle  102 , a remote computing system  150 , and a ridesharing application  170 . The autonomous vehicle  102 , remote computing system  150 , and ridesharing application  170  can communicate with each other over one or more networks, such as a public network (e.g., a public cloud, the Internet, etc.), a private network (e.g., a local area network, a private cloud, a virtual private network, etc.), and/or a hybrid network (e.g., a multi-cloud or hybrid cloud network, etc.). 
     The autonomous vehicle  102  can navigate about roadways without a human driver based on sensor signals generated by sensors  104 - 108  on the autonomous vehicle  102 . The sensors  104 - 108  on the autonomous vehicle  102  can include one or more types of sensors and can be arranged about the autonomous vehicle  102 . For example, the sensors  104 - 108  can include, without limitation, one or more inertial measuring units (IMUs), one or more image sensors (e.g., visible light image sensors, infrared image sensors, video camera sensors, surround view camera sensors, etc.), one or more light emitting sensors, one or more global positioning system (GPS) devices, one or more radars, one or more light detection and ranging sensors (LIDARs), one or more sonars, one or more accelerometers, one or more gyroscopes, one or more magnetometers, one or more altimeters, one or more tilt sensors, one or more motion detection sensors, one or more light sensors, one or more audio sensors, etc. In some implementations, sensor  104  can be a radar, sensor  106  can be a first image sensor (e.g., a visible light camera), and sensor  108  can be a second image sensor (e.g., a thermal camera). Other implementations can include any other number and type of sensors. 
     The autonomous vehicle  102  can include several mechanical systems that are used to effectuate motion of the autonomous vehicle  102 . For instance, the mechanical systems can include, but are not limited to, a vehicle propulsion system  130 , a braking system  132 , and a steering system  134 . The vehicle propulsion system  130  can include an electric motor, an internal combustion engine, or both. The braking system  132  can include an engine brake, brake pads, actuators, and/or any other suitable componentry configured to assist in decelerating the autonomous vehicle  102 . The steering system  134  includes suitable componentry configured to control the direction of movement of the autonomous vehicle  102  during navigation. 
     The autonomous vehicle  102  can include a safety system  136 . The safety system  136  can include lights and signal indicators, a parking brake, airbags, etc. The autonomous vehicle  102  can also include a cabin system  138 , which can include cabin temperature control systems, in-cabin entertainment systems, etc. 
     The autonomous vehicle  102  can include an internal computing system  110  in communication with the sensors  104 - 108  and the systems  130 ,  132 ,  134 ,  136 , and  138 . The internal computing system  110  includes one or more processors and at least one memory for storing instructions executable by the one or more processors. The computer-executable instructions can make up one or more services for controlling the autonomous vehicle  102 , communicating with remote computing system  150 , receiving inputs from passengers or human co-pilots, logging metrics regarding data collected by sensors  104 - 108  and human co-pilots, etc. 
     The internal computing system  110  can include a control service  112  configured to control operation of the vehicle propulsion system  130 , the braking system  132 , the steering system  134 , the safety system  136 , and the cabin system  138 . The control service  112  can receive sensor signals from the sensors  104 - 108  can communicate with other services of the internal computing system  110  to effectuate operation of the autonomous vehicle  102 . In some examples, control service  112  may carry out operations in concert with one or more other systems of autonomous vehicle  102 . 
     The internal computing system  110  can also include a constraint service  114  to facilitate safe propulsion of the autonomous vehicle  102 . The constraint service  116  includes instructions for activating a constraint based on a rule-based restriction upon operation of the autonomous vehicle  102 . For example, the constraint may be a restriction on navigation that is activated in accordance with protocols configured to avoid occupying the same space as other objects, abide by traffic laws, circumvent avoidance areas, etc. In some examples, the constraint service  114  can be part of the control service  112 . 
     The internal computing system  110  can also include a communication service  116 . The communication service  116  can include software and/or hardware elements for transmitting and receiving signals to and from the remote computing system  150 . The communication service  116  can be configured to transmit information wirelessly over a network, for example, through an antenna array or interface that provides cellular (long-term evolution (LTE), 3rd Generation (3G), 5th Generation (5G), etc.) communication. 
     In some examples, one or more services of the internal computing system  110  are configured to send and receive communications to remote computing system  150  for reporting data for training and evaluating machine learning algorithms, requesting assistance from remote computing system  150  or a human operator via remote computing system  150 , software service updates, ridesharing pickup and drop off instructions, etc. 
     The internal computing system  110  can also include a latency service  118 . The latency service  118  can utilize timestamps on communications to and from the remote computing system  150  to determine if a communication has been received from the remote computing system  150  in time to be useful. For example, when a service of the internal computing system  110  requests feedback from remote computing system  150  on a time-sensitive process, the latency service  118  can determine if a response was timely received from remote computing system  150 , as information can quickly become too stale to be actionable. When the latency service  118  determines that a response has not been received within a threshold period of time, the latency service  118  can enable other systems of autonomous vehicle  102  or a passenger to make decisions or provide needed feedback. 
     The internal computing system  110  can also include a user interface service  120  that can communicate with cabin system  138  to provide information or receive information to a human co-pilot or passenger. In some examples, a human co-pilot or passenger can be asked or requested to evaluate and override a constraint from constraint service  114 . In other examples, the human co-pilot or passenger may wish to provide an instruction to the autonomous vehicle  102  regarding destinations, requested routes, or other requested operations. 
     As described above, the remote computing system  150  can be configured to send and receive signals to and from the autonomous vehicle  102 . The signals can include, for example and without limitation, data reported for training and evaluating services such as machine learning services, data for requesting assistance from remote computing system  150  or a human operator, software service updates, rideshare pickup and drop off instructions, etc. 
     The remote computing system  150  can include an analysis service  152  configured to receive data from autonomous vehicle  102  and analyze the data to train or evaluate machine learning algorithms for operating the autonomous vehicle  102 . The analysis service  152  can also perform analysis pertaining to data associated with one or more errors or constraints reported by autonomous vehicle  102 . 
     The remote computing system  150  can also include a user interface service  154  configured to present metrics, video, images, sounds reported from the autonomous vehicle  102  to an operator of remote computing system  150 , maps, routes, navigation data, notifications, user data, vehicle data, software data, and/or any other content. User interface service  154  can receive, from an operator, input instructions for the autonomous vehicle  102 . 
     The remote computing system  150  can also include an instruction service  156  for sending instructions regarding the operation of the autonomous vehicle  102 . For example, in response to an output of the analysis service  152  or user interface service  154 , instructions service  156  can prepare instructions to one or more services of the autonomous vehicle  102  or a co-pilot or passenger of the autonomous vehicle  102 . 
     The remote computing system  150  can also include a rideshare service  158  configured to interact with ridesharing applications  170  operating on computing devices, such as tablet computers, laptop computers, smartphones, head-mounted displays (HMDs), gaming systems, servers, smart devices, smart wearables, and/or any other computing devices. In some cases, such computing devices can be passenger computing devices. The rideshare service  158  can receive from passenger ridesharing app  170  requests, such as user requests to be picked up or dropped off, and can dispatch autonomous vehicle  102  for a requested trip. 
     The rideshare service  158  can also act as an intermediary between the ridesharing app  170  and the autonomous vehicle  102 . For example, rideshare service  158  can receive from a passenger instructions for the autonomous vehicle  102 , such as instructions to go around an obstacle, change routes, honk the horn, etc. The rideshare service  158  can provide such instructions to the autonomous vehicle  102  as requested. 
     The remote computing system  150  can also include a package service  162  configured to interact with the ridesharing application  170  and/or a delivery service  172  of the ridesharing application  170 . A user operating ridesharing application  170  can interact with the delivery service  172  to specify information regarding a package to be delivered using the autonomous vehicle  102 . The specified information can include, for example and without limitation, package dimensions, a package weight, a destination address, delivery instructions (e.g., a delivery time, a delivery note, a delivery constraint, etc.), and so forth. 
     The package service  162  can interact with the delivery service  172  to provide a package identifier to the user for package labeling and tracking. Package delivery service  172  can also inform a user of where to bring their labeled package for drop off. In some examples, a user can request the autonomous vehicle  102  come to a specific location, such as the user&#39;s location, to pick up the package. While delivery service  172  has been shown as part of the ridesharing application  170 , it will be appreciated by those of ordinary skill in the art that delivery service  172  can be its own separate application. 
     One beneficial aspect of utilizing autonomous vehicle  102  for both ridesharing and package delivery is increased utilization of the autonomous vehicle  102 . Instruction service  156  can continuously keep the autonomous vehicle  102  engaged in a productive itinerary between rideshare trips by filling what otherwise would have been idle time with productive package delivery trips. 
     Having disclosed some example system components and concepts, the disclosure now turns to  FIGS.  2  and  3   , which illustrate example method  200  for adjusting an exposure setting based on a region of interest. The steps outlined herein are exemplary and can be implemented in any combination thereof, including combinations that exclude, add, or modify certain steps. 
     At step  202 , the method  200  can include determining a localization parameter of an autonomous vehicle  102 . The localization parameter can assist in determining when and where light (e.g., sunlight or artificial light) is directed at the autonomous vehicle  102 . For example, the localization parameter can include information of a locality of the autonomous vehicle  102  (e.g., a map of the general vicinity), a geographic position of the autonomous vehicle  102 , a direction of light rays, geographic positions of structures (e.g., buildings), or any other locality information suitable for the intended purpose and understood by a person of ordinary skill in the art. 
     In some implementations, the localization parameter of the autonomous vehicle  102  can utilize high definition maps, LiDAR sensor output, vision, and other sensors to determine current parameters (e.g., x, y, z, r, p, and yw) of the autonomous vehicle  102 . The method  200  further can include utilizing localization, a pre-existing map, a current map, tracked objects (e.g., preloaded object or currently tracked objects), and regions of interest (ROI) based on sensors (such as LiDAR, other cameras, ultrasonics, etc.). 
     In some cases, as the autonomous vehicle  102  moves from a first position to a second position, the amount of sunlight directed at the autonomous vehicle  102  can change abruptly (e.g., the autonomous vehicle  102  exits a shadow of a building). In some instances, the autonomous vehicle  102  can be preloaded with map information such that the autonomous vehicle  102  is made aware of the direction and amount of sunlight that are directed towards mounted cameras of the autonomous vehicle  102 . In anticipation of a change in sunlight exposure, the cameras of the autonomous vehicle  102  can prepare for adjusting an exposure setting of the cameras accordingly to minimize “white out,” where the image is too bright or too dark to distinguish objects in the image. 
     Previously, if the autonomous vehicle  102  exited the shadow of the building into direct sunlight, the camera would adjust the exposure setting for the increase in light, thereby whiting out the entire image. In this case, the autonomous vehicle  102  would be unable to detect and distinguish traffic lights because the entire image has been whited out. 
     Present embodiments solve this problem as the autonomous vehicle  102  can be made aware of its geographic position (e.g., with a global positioning system device) with localization information. With its geographic position, the autonomous vehicle  102  can relatively know the amount of sunlight and the direction of the sunlight rays to then determine whether to adjust the exposure setting on its cameras. 
     At step  204 , the method  200  can include determining a region of interest based on the localization parameter of the autonomous vehicle  102 . In some embodiments, the autonomous vehicle  102  can focus on specific regions of the environment to determine how to adjust the exposure setting on its cameras. In some instances, the region of interest can include a street surface (e.g., pavement, road, street, tarmac, etc.), a sidewalk (e.g., crosswalk, side pavement/concrete area, etc.), a traffic sign (e.g., traffic light, stop sign, pedestrian signs, etc.), a pedestrian (e.g., a pedestrian or cyclist crossing the street), or any other region of interest suitable for the intended purpose and understood by a person of ordinary skill in the art. The regions of interests also can be detectable regions of the above-mentioned examples such as clothes of a pedestrian, a reflector jacket of a cyclist, color-type of a sidewalk, or any other detectable region suitable for the intended purpose and understood by a person of ordinary skill in the art. 
     In some implementations, the regions of interest also can have a priority to determine which region of interest has precedence. For example, a pedestrian can have a higher priority than a sidewalk. As such, the autonomous vehicle  102  can adjust the exposure setting of its cameras to focus on the pedestrian rather than the sidewalk (i.e., the exposure setting can be corrected for the pedestrian and not the sidewalk). 
     At step  206 , the method  200  can include receiving a first image including the region of interest based on the localization parameter of the autonomous vehicle  102 . In some instances, the image can be of an intersection and the region of interest can be a traffic light near the geographic position of the autonomous vehicle  102 . The image can be captured by a camera mounted on the autonomous vehicle  102 . 
     At step  208 , the method  200  can include determining an exposure setting for the region of interest in the first image. For example, if a sidewalk is the region of interest, the internal computing system  110  of the autonomous vehicle  102  can determine the appropriate exposure setting (e.g., between the lightest and darkest setting) for the sidewalk such that the sidewalk is appropriately viewable by the camera. 
     At step  210 , the method  200  can include adjusting an exposure setting of the first image to the exposure setting for the region of interest in the first image. The adjusting of the exposure setting of the first image can occur automatically after the determining of the exposure setting for the region of interest in the first image. In contrast to other methods that require a user to select a region to focus the camera, the method  200  can be utilized by the autonomous vehicle  102  that may require near instantaneous feedback and adjustments to provide a safe driving experience. The internal computing system  110  of the autonomous vehicle  102  can be configured to adjust the exposure settings of images captured by cameras or sensors of the autonomous vehicle  102 . 
     In some instances, the adjustment of the exposure setting of the first image may disregard potential exposure settings for portions of the image that are not the region of interest. This may be the case because portions of the image that are not the region of interest may be of less importance, such as the sky. If the camera adjusted the exposure setting to focus on the sky, the entire image would become too dark to view objects in the image. In some cases, the region of interest may take precedence over other regions because the region of interest may be critical to providing a safe driving experience. 
     In some implementations, the adjusting of the exposure setting of the first image can adjust a portion of the first image to the exposure setting for the region of interest, rather than the entire image. In such cases, the image may include regions with different exposure settings to provide the most viewable image for the autonomous vehicle  102 . 
     At step  212 , the method  200  can include receiving a second image at a second camera that includes the region of interest of the first image. In some instances, a portion of the region of interest can be detectable in the first image and the second image at a first time period (i.e., at the same time). 
     In some implementations, the first camera may be mounted on a side region of the autonomous vehicle  102 , while the second camera may be mounted on a front region of the autonomous vehicle  102 . In the instance where a cyclist (i.e., region of interest) proceeds past the autonomous vehicle  102 , the cyclist may first appear in the range of the side camera, but as the cyclist continues to pass the autonomous vehicle  102 , the cyclist may eventually also appear in the range of the front camera. In this instance, the image of the front camera includes the same region of interest as the side camera of the autonomous vehicle  102 . 
     At step  214 , the method  200  can include receiving the exposure setting for the region of interest from the first camera. In some instances, the same exposure setting for the region of interest for both cameras may be preferred for the autonomous vehicle  102 . For example, as the cyclist passes both cameras, if the front camera has a different exposure setting than the side camera, the front camera may be overexposed or underexposed thereby potentially being unable to “see” the cyclist pedaling by the autonomous vehicle  102 . However, if the exposure setting for the cyclist is substantially the same for both cameras, then the transition of the cyclist from the side camera to the front camera can be even. 
     At step  216 , the method  200  can include adjusting an exposure setting of the second image of the second camera to the exposure setting for the region of interest received from the first camera. By adjusting the exposure setting of the second image of the second camera, there may be continuity between the first and second camera such that the transition of the region of interest between the two cameras is even and readily perceptible by the autonomous vehicle  102 . 
       FIG.  4    illustrates an example computing system  400  which can be, for example, any computing device making up internal computing system  110 , remote computing system  150 , a passenger device executing rideshare application  170 , or any other computing device. In  FIG.  4   , the components of the computing system  400  are in communication with each other using connection  405 . Connection  405  can be a physical connection via a bus, or a direct connection into processor  410 , such as in a chipset architecture. Connection  405  can also be a virtual connection, networked connection, or logical connection. 
     In some embodiments, computing system  400  is a distributed system in which the functions described in this disclosure can be distributed within a datacenter, multiple data centers, a peer network, etc. In some embodiments, one or more of the described system components represents many such components each performing some or all of the function for which the component is described. In some embodiments, the components can be physical or virtual devices. 
     Example system  400  includes at least one processing unit (CPU or processor)  410  and connection  405  that couples various system components including system memory  415 , such as read-only memory (ROM)  420  and random access memory (RAM)  425  to processor  410 . Computing system  400  can include a cache of high-speed memory  412  connected directly with, in close proximity to, or integrated as part of processor  410 . 
     Processor  410  can include any general purpose processor and a hardware service or software service, such as services  432 ,  434 , and  436  stored in storage device  430 , configured to control processor  410  as well as a special-purpose processor where software instructions are incorporated into the actual processor design. Processor  410  may essentially be a completely self-contained computing system, containing multiple cores or processors, a bus, memory controller, cache, etc. A multi-core processor may be symmetric or asymmetric. 
     To enable user interaction, computing system  400  includes an input device  445 , which can represent any number of input mechanisms, such as a microphone for speech, a touch-sensitive screen for gesture or graphical input, keyboard, mouse, motion input, speech, etc. Computing system  400  can also include output device  435 , which can be one or more of a number of output mechanisms known to those of skill in the art. In some instances, multimodal systems can enable a user to provide multiple types of input/output to communicate with computing system  400 . Computing system  400  can include communications interface  440 , which can generally govern and manage the user input and system output. There is no restriction on operating on any particular hardware arrangement, and therefore the basic features here may easily be substituted for improved hardware or firmware arrangements as they are developed. 
     Storage device  430  can be a non-volatile memory device and can be a hard disk or other types of computer readable media which can store data that are accessible by a computer, such as magnetic cassettes, flash memory cards, solid state memory devices, digital versatile disks, cartridges, random access memories (RAMs), read-only memory (ROM), and/or some combination of these devices. 
     The storage device  430  can include software services, servers, services, etc., that when the code that defines such software is executed by the processor  410 , it causes the system to perform a function. In some embodiments, a hardware service that performs a particular function can include the software component stored in a computer-readable medium in connection with the necessary hardware components, such as processor  410 , connection  405 , output device  435 , etc., to carry out the function. 
     For clarity of explanation, in some instances, the present technology may be presented as including individual functional blocks including functional blocks comprising devices, device components, steps or routines in a method embodied in software, or combinations of hardware and software. 
     Any of the steps, operations, functions, or processes described herein may be performed or implemented by a combination of hardware and software services or services, alone or in combination with other devices. In some embodiments, a service can be software that resides in memory of a client device and/or one or more servers of a content management system and perform one or more functions when a processor executes the software associated with the service. In some embodiments, a service is a program or a collection of programs that carry out a specific function. In some embodiments, a service can be considered a server. The memory can be a non-transitory computer-readable medium. 
     In some embodiments, the computer-readable storage devices, mediums, and memories can include a cable or wireless signal containing a bit stream and the like. However, when mentioned, non-transitory computer-readable storage media expressly exclude media such as energy, carrier signals, electromagnetic waves, and signals per se. 
     Methods according to the above-described examples can be implemented using computer-executable instructions that are stored or otherwise available from computer-readable media. Such instructions can comprise, for example, instructions and data which cause or otherwise configure a general purpose computer, special purpose computer, or special purpose processing device to perform a certain function or group of functions. Portions of computer resources used can be accessible over a network. The executable computer instructions may be, for example, binaries, intermediate format instructions such as assembly language, firmware, or source code. Examples of computer-readable media that may be used to store instructions, information used, and/or information created during methods according to described examples include magnetic or optical disks, solid-state memory devices, flash memory, USB devices provided with non-volatile memory, networked storage devices, and so on. 
     Devices implementing methods according to these disclosures can comprise hardware, firmware and/or software, and can take any of a variety of form factors. Typical examples of such form factors include servers, laptops, smartphones, small form factor personal computers, personal digital assistants, and so on. The functionality described herein also can be embodied in peripherals or add-in cards. Such functionality can also be implemented on a circuit board among different chips or different processes executing in a single device, by way of further example. 
     The instructions, media for conveying such instructions, computing resources for executing them, and other structures for supporting such computing resources are means for providing the functions described in these disclosures. 
     Although a variety of examples and other information was used to explain aspects within the scope of the appended claims, no limitation of the claims should be implied based on particular features or arrangements in such examples, as one of ordinary skill would be able to use these examples to derive a wide variety of implementations. Further and although some subject matter may have been described in language specific to examples of structural features and/or method steps, it is to be understood that the subject matter defined in the appended claims is not necessarily limited to these described features or acts. For example, such functionality can be distributed differently or performed in components other than those identified herein. Rather, the described features and steps are disclosed as examples of components of systems and methods within the scope of the appended claims. 
     Claim language reciting “at least one of” a set indicates that one member of the set or multiple members of the set satisfy the claim. For example, claim language reciting “at least one of A and B” means A, B, or A and B.