Patent Publication Number: US-2021191419-A1

Title: Detecting Unfamiliar Signs

Description:
CROSS REFERENCE FOR RELATED APPLICATIONS 
     This application is a continuation of U.S. application Ser. No. 16/220,225, filed Dec. 14, 2018, the entire disclosure of which is incorporated herein by reference 
    
    
     BACKGROUND 
     Autonomous vehicles, such as vehicles which do not require a human driver when operating in an autonomous driving mode, may be used to aid in the transport of passengers or items from one location to another. An important component of an autonomous vehicle is the perception system, which allows the vehicle to perceive and interpret its surroundings using sensors such as cameras, radar, LIDAR sensors, and other similar devices. For instance, the perception system and/or the vehicle&#39;s computing devices may process data from these sensors in order to identify objects as well as their characteristics such as location, shape, size, orientation, acceleration or deceleration, velocity, type, etc. This information is important for the vehicle&#39;s computing systems to make appropriate driving decisions for the vehicle. 
     BRIEF SUMMARY 
     Aspects of the disclosure prove a method for determining a sign type of an unfamiliar sign. The method may include: receiving, by one or more processors, an image generated by a perception system of a vehicle; identifying, by the one or more processors, image data corresponding to a traffic sign in the image; inputting, by the one or more processors, the image data corresponding to the traffic sign into a sign type model; determining, by the one or more processors, that the sign type model was unable to identify a type of the traffic sign; determining, by the one or more processors, one or more attributes of the traffic sign; comparing, by the one or more processors, the one or more attributes of the traffic sign to known attributes of other traffic signs; determining, by the one or more processors, a sign type of the traffic sign based on the comparing the one or more attributes of the traffic sign; and controlling, by the one or more processors, the vehicle in an autonomous driving mode based on the sign type of the traffic sign. 
     In some instances, the known attributes of other traffic signs may be stored in a one to many data structure. 
     In some instances, the sign type model being unable to identify a type of the traffic sign includes the model being unable to identify the sign type of the traffic sign to a minimum confidence level. 
     In some instances, the one or more attributes of the traffic sign are determined using image processing techniques. 
     In some instances, the attributes may include one or more of sign type, color, shape, reflection coefficient, placement, text, figures, or accessories. 
     In some instances, the method may further include determining content of the traffic sign by comparing the one or more attributes of the traffic sign to known attributes of other traffic signs. In some examples, the content of the traffic sign may be informative or instructive. In some examples, controlling the vehicle in an autonomous driving mode based on the sign type of the traffic sign may include taking no action based on the sign type of the traffic sign. 
     In some instances, the sign type of the traffic sign may include one or more of regulatory, warning, guide, services, recreation, construction, or school zone. 
     In some instances, controlling the vehicle in an autonomous driving mode based on the sign type of the traffic sign may include: determining content of the traffic sign is instructive of an action; and performing the action. 
     Aspects of the disclosure may provide a system for determining a sign type of an unfamiliar sign. The system may include one or more processors, and the one or more processors may be configured to: receive an image generated by a perception system of a vehicle; identify image data corresponding to a traffic sign in the image; input the image data corresponding to the traffic sign into a sign type model; determine that the sign type model was unable to identify a type of the traffic sign determining one or more attributes of the traffic sign; compare the one or more attributes of the traffic sign to known attributes of other traffic signs; determine a sign type of the traffic sign based on the comparing the one or more attributes of the traffic sign; and control the vehicle in an autonomous driving mode based on the sign type of the traffic sign 
     In some instances, the known attributes of other traffic signs may be stored in a one to many data structure. 
     In some instances, the known attributes of other traffic signs may be manually labeled by an operator and/or by using image processing techniques. 
     In some instances, the one or more attributes of the traffic sign may be determined using image processing techniques. 
     In some instances, the attributes may include one or more of sign type, color, shape, reflection coefficient, placement, text, figures, or accessories. 
     In some instances, the one or more processors may be further configured to determine content of the traffic sign by comparing the one or more attributes of the traffic sign to known attributes of other traffic signs. In some examples, the content of the traffic sign is informative or instructive. In some examples, controlling the vehicle in an autonomous driving mode based on the sign type of the traffic sign may include taking no action based on the sign type. In some examples, wherein controlling the vehicle in an autonomous driving mode based on the sign type of the traffic sign may include: determining content of the traffic sign is instructive of an action; and performing the action. 
     In some instances, the system may include the vehicle. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a functional diagram of an example vehicle in accordance with an exemplary embodiment. 
         FIG. 2  is an example of map information in accordance with aspects of the disclosure. 
         FIG. 3  is an example external view of a vehicle in accordance with aspects of the disclosure. 
         FIG. 4  is a pictorial diagram of an example system in accordance with aspects of the disclosure. 
         FIG. 5  is a functional diagram of the system of  FIG. 4  in accordance with aspects of the disclosure. 
         FIG. 6  is an example camera image in accordance with aspects of the disclosure. 
         FIG. 7  is example images and corresponding labels in accordance with aspects of the disclosure. 
         FIG. 8  is an example images and corresponding labels and attributes in accordance with aspects of the disclosure. 
         FIG. 9  is an example image in accordance with aspects of the disclosure. 
         FIG. 10  is an example image in accordance with aspects of the disclosure. 
         FIG. 11  is an example flow diagram in accordance with aspects of the disclosure. 
         FIG. 12  is an example flow diagram in accordance with aspects of the disclosure. 
     
    
    
     DETAILED DESCRIPTION 
     Overview 
     This technology relates to using characteristics of a traffic sign to determine whether an autonomous vehicle should perform an action. Human drivers regularly encounter and react to traffic signs during a trip. In this regard, a human driver, upon observing a traffic sign, may take a particular action or no actions at all in response to the traffic sign. For instance, a human driver may observe a stop sign and stop the vehicle they are driving. In another example, a human driver may observe a traffic sign for a rest area and take no action at all in response to observing the traffic sign. 
     Autonomous vehicles, which do not have the same ability to reason about traffic signs as humans, must also determine when and when not to take action in response to traffic signs. In this regard, map information used by an autonomous vehicle may show where particular traffic signs are located. For instance, the location of all stop signs may be found within the map information and autonomous vehicle may stop at those locations. However, traffic signs may be moved, removed, or replaced with different signs, thereby making the map information inaccurate. As such, vehicles may send a request for remote assistance to a human operator in order to receive instructions when no traffic sign is detected or a new sign is detected, thereby leading to travel delays. Moreover, a lot of resources are required to map every traffic sign and assure that such map information is up to date. 
     To address these issues, an autonomous vehicle may detect traffic signs in real time and determine an appropriate action to take in response to detecting the traffic sign and its content. For instance, one or more sensors on an autonomous vehicle may detect a traffic sign in the vehicle&#39;s vicinity. The sensor data corresponding to the traffic sign may be analyzed by one or more computing devices of the autonomous vehicle and characteristics of the traffic sign, such as its type and other attributes, such as color, shape, reflection coefficient, placement, text, figures, accessories, etc., may be determined. In some instances, a machine learning model may be used to assist in determining a sign type. Depending on the traffic sign type, and potentially some of the other attributes of the traffic sign, a determination of the sign&#39;s content (e.g., instructive content, informative content, etc.,) may be made. Based on the sign&#39;s type and content, a determination as to whether an action should be performed by the vehicle may be made. 
     The features described herein may allow an autonomous vehicle to detect and respond to traffic signs in real time without requiring those traffic signs to be previously identified, or rather, already stored in the vehicle&#39;s map information. This can be especially helpful in unmapped areas or in areas where the map is incorrect or not up to date. Moreover, the features described herein may allow an autonomous vehicle to identify traffic signs which are not identifiable by machine learning models or found within the map information, such as temporary or handmade signs. As such, the autonomous vehicle may be able to determine whether an action should be taken even for unfamiliar signs. In addition, the detection and identification of a sign&#39;s content may assist in the addition of unfamiliar signs to mapping data, particularly if the newly identified sign is of a type typically added to mapping data. 
     In addition, the features described herein may allow requests for assistance to be prioritized to a human operator when a vehicle detects (i.e., successfully determines,) a sign type but is unable to determine its content. In this regard, when a vehicle encounters an unfamiliar sign which may affect the safe operation of the vehicle, such as regulatory signs, the vehicle&#39;s request for assistance may be prioritized to human operators over requests for assistance with unfamiliar signs which do not affect the safe operation of the vehicle, such as recreation signs. In some instances, when a vehicle detects an unfamiliar signs having a certain sign type, no requests for assistance may be made and/or the human operator may disregard such a request. 
     Example Systems 
     As shown in  FIG. 1 , a vehicle  100  in accordance with one aspect of the disclosure includes various components. While certain aspects of the disclosure are particularly useful in connection with specific types of vehicles, the vehicle may be any type of vehicle including, but not limited to, cars, trucks, motorcycles, buses, recreational vehicles, etc. The vehicle may have one or more computing devices, such as computing device  110  containing one or more processors  120 , memory  130  and other components typically present in general purpose computing devices. 
     The memory  130  stores information accessible by the one or more processors  120 , including instructions  134  and data  132  that may be executed or otherwise used by the processor  120 . The memory  130  may be of any type capable of storing information accessible by the processor, including a computing device-readable medium, or other medium that stores data that may be read with the aid of an electronic device, such as a hard-drive, memory card, ROM, RAM, DVD or other optical disks, as well as other write-capable and read-only memories. Systems and methods may include different combinations of the foregoing, whereby different portions of the instructions and data are stored on different types of media. 
     The instructions  134  may be any set of instructions to be executed directly (such as machine code) or indirectly (such as scripts) by the processor. For example, the instructions may be stored as computing device code on the computing device-readable medium. In that regard, the terms “instructions” and “programs” may be used interchangeably herein. The instructions may be stored in object code format for direct processing by the processor, or in any other computing device language including scripts or collections of independent source code modules that are interpreted on demand or compiled in advance. Functions, methods and routines of the instructions are explained in more detail below. 
     The data  132  may be retrieved, stored or modified by processor  120  in accordance with the instructions  134 . For instance, although the claimed subject matter is not limited by any particular data structure, the data may be stored in computing device registers, in a relational database as a table having a plurality of different fields and records, XML documents or flat files. The data may also be formatted in any computing device-readable format. 
     The one or more processor  120  may be any conventional processors, such as commercially available CPUs or GPUs. Alternatively, the one or more processors may be a dedicated device such as an ASIC or other hardware-based processor. Although  FIG. 1  functionally illustrates the processor, memory, and other elements of computing device  110  as being within the same block, it will be understood by those of ordinary skill in the art that the processor, computing device, or memory may actually include multiple processors, computing devices, or memories that may or may not be stored within the same physical housing. For example, memory may be a hard drive or other storage media located in a housing different from that of computing device  110 . Accordingly, references to a processor or computing device will be understood to include references to a collection of processors or computing devices or memories that may or may not operate in parallel. 
     Computing device  110  may include all of the components normally used in connection with a computing device such as the processor and memory described above as well as a user input  150  (e.g., a mouse, keyboard, touch screen and/or microphone) and various electronic displays (e.g., a monitor having a screen or any other electrical device that is operable to display information). In this example, the vehicle includes an internal electronic display  152  as well as one or more speakers  154  to provide information or audio visual experiences. In this regard, internal electronic display  152  may be located within a cabin of vehicle  100  and may be used by computing device  110  to provide information to passengers within the vehicle  100 . 
     Computing device  110  may also include one or more wireless network connections  156  to facilitate communication with other computing devices, such as the client computing devices and server computing devices described in detail below. The wireless network connections may include short range communication protocols such as Bluetooth, Bluetooth low energy (LE), cellular connections, as well as various configurations and protocols including the Internet, World Wide Web, intranets, virtual private networks, wide area networks, local networks, private networks using communication protocols proprietary to one or more companies, Ethernet, WiFi and HTTP, and various combinations of the foregoing. 
     In one example, computing device  110  may be an autonomous driving computing system incorporated into vehicle  100 . The autonomous driving computing system may be capable of communicating with various components of the vehicle in order to control the vehicle in an autonomous driving mode. For example, returning to  FIG. 1 , computing device  110  may be in communication with various systems of vehicle  100 , such as deceleration system  160 , acceleration system  162 , steering system  164 , signaling system  166 , planner system  168 , positioning system  170 , and perception system  172  in order to control the movement, speed, etc. of vehicle  100  in accordance with the instructions  134  of memory  130  in the autonomous driving mode. Again, although these systems are shown as external to computing device  110 , in actuality, these systems may also be incorporated into computing device  110 , again as an autonomous driving computing system for controlling vehicle  100 . 
     As an example, computing device  110  may interact with deceleration system  160  and acceleration system  162  in order to control the speed of the vehicle. Similarly, steering system  164  may be used by computing devices  110  in order to control the direction of vehicle  100 . For example, if vehicle  100  is configured for use on a road, such as a car or truck, the steering system may include components to control the angle of wheels to turn the vehicle. Signaling system  166  may be used by computing device  110  in order to signal the vehicle&#39;s intent to other drivers or vehicles, for example, by lighting turn signals or brake lights when needed. 
     Planning system  168  may be used by computing device  110  in order to determine and follow a route to a location. In this regard, the planning system  168  and/or data  132  may store detailed map information, e.g., highly detailed maps identifying the shape and elevation of roadways, lane lines, intersections, crosswalks, speed limits, traffic signals, buildings, signs, real time traffic information, pull over spots, vegetation, or other such objects and information. 
       FIG. 2  is an example of map information  200  for a section of roadway including intersections  202  and  204 . The map information  200  may be a local version of the map information stored in the memory  130  of the computing devices  110 . Other versions of the map information may also be stored in the storage system  450  discussed further below. In this example, the map information  200  includes information identifying the shape, location, and other characteristics of lane lines  210 ,  212 ,  214 , traffic lights  220 ,  222 , stop line  224 , crosswalks  230 ,  232  sidewalks  240 , and traffic signs  250 ,  252 . The map information is depicted herein as an image-based map, the map information need not be entirely image based. For example, the map information may include one or more roadgraphs or graph networks of information such as roads, lanes, intersections, and the connections between these features which may be represented by road segments. Each feature may be stored as graph data and may be associated with information such as a geographic location and whether or not it is linked to other related features, for example, a stop sign may be linked to a road and an intersection, etc. In some examples, the associated data may include grid-based indices of a roadgraph to allow for efficient lookup of certain roadgraph features. 
     Positioning system  170  may be used by computing device  110  in order to determine the vehicle&#39;s relative or absolute position on a map or on the earth. For example, the position system  170  may include a GPS receiver to determine the device&#39;s latitude, longitude and/or altitude position. Other location systems such as laser-based localization systems, inertial-aided GPS, or camera-based localization may also be used to identify the location of the vehicle. The location of the vehicle may include an absolute geographical location, such as latitude, longitude, and altitude as well as relative location information, such as location relative to other cars immediately around it which can often be determined with less noise that absolute geographical location. 
     The positioning system  170  may also include other devices in communication with computing device  110 , such as an accelerometer, gyroscope or another direction/speed detection device to determine the direction and speed of the vehicle or changes thereto. By way of example only, an acceleration device may determine its pitch, yaw or roll (or changes thereto) relative to the direction of gravity or a plane perpendicular thereto. The device may also track increases or decreases in speed and the direction of such changes. The device&#39;s provision of location and orientation data as set forth herein may be provided automatically to the computing device  110 , other computing devices and combinations of the foregoing. 
     The perception system  172  also includes one or more components for detecting objects external to the vehicle such as other vehicles, obstacles in the roadway, traffic signals, signs, trees, etc. For example, the perception system  172  may include lasers, sonar, radar, cameras and/or any other detection devices that record data which may be processed by computing device  110 . In the case where the vehicle is a passenger vehicle such as a minivan, the minivan may include a laser or other sensors mounted on the roof or other convenient location. For instance,  FIG. 3  is an example external view of vehicle  100 . In this example, roof-top housing  310  and dome housing  312  may include a LIDAR sensor as well as various cameras and radar units. In addition, housing  320  located at the front end of vehicle  100  and housings  330 ,  332  on the driver&#39;s and passenger&#39;s sides of the vehicle may each store a LIDAR sensor. For example, housing  330  is located in front of driver door  360 . Vehicle  100  also includes housings  340 ,  342  for radar units and/or cameras also located on the roof of vehicle  100 . Additional radar units and cameras (not shown) may be located at the front and rear ends of vehicle  100  and/or on other positions along the roof or roof-top housing  310 . 
     In one example, computing devices  110  may be control computing devices of an autonomous driving computing system or incorporated into vehicle  100 . The autonomous driving computing system may be capable of communicating with various components of the vehicle in order to control the movement of vehicle  100  according to primary vehicle control code of memory  130 . For example, returning to  FIG. 1 , computing devices  110  may be in communication with various systems of vehicle  100 , such as deceleration system  160 , acceleration system  162 , steering system  164 , signaling system  166 , planning system  168 , positioning system  170 , perception system  172 , and power system  174  (i.e. the vehicle&#39;s engine or motor) in order to control the movement, speed, etc. of vehicle  100  in accordance with the instructions  134  of memory  130 . Again, although these systems are shown as external to computing devices  110 , in actuality, these systems may also be incorporated into computing devices  110 , again as an autonomous driving computing system for controlling vehicle  100 . 
     The various systems of the vehicle may function using autonomous vehicle control software in order to determine how to control the vehicle and to control the vehicle. As an example, a perception system software module of the perception system  172  may use sensor data generated by one or more sensors of an autonomous vehicle, such as cameras, LIDAR sensors, radar units, sonar units, etc., to detect and identify objects and their characteristics. These characteristics may include location, type, heading, orientation, velocity, acceleration, change in acceleration, size, shape, etc. In some instances, characteristics may be input into a behavior prediction system software module which uses various behavior models based on object type to output a predicted future behavior for a detected object. In other instances, the characteristics may be put into one or more detection system software modules, such as a traffic light detection system software module configured to detect the states of known traffic signals, construction zone detection system software module configured to detect construction zones from sensor data generated by the one or more sensors of the vehicle as well as an emergency vehicle detection system configured to detect emergency vehicles from sensor data generated by sensors of the vehicle. Each of these detection system software modules may use various models to output a likelihood of a construction zone or an object being an emergency vehicle. Detected objects, predicted future behaviors, various likelihoods from detection system software modules, the map information identifying the vehicle&#39;s environment, position information from the positioning system  170  identifying the location and orientation of the vehicle, a destination for the vehicle as well as feedback from various other systems of the vehicle may be input into a planner system software module of the planning system  168 . The planning system and/or computing devices  110  may use this input to generate a route and trajectories for the vehicle to follow for some brief period of time into the future. A control system software module of the computing devices  110  may be configured to control movement of the vehicle, for instance by controlling braking, acceleration and steering of the vehicle, in order to follow a trajectory. 
     The computing device  110  may control the vehicle by controlling various components. For instance, by way of example, computing device  110  may navigate the vehicle to a destination location completely autonomously using data from the detailed map information and planning system  168 . Computing device  110  may use the positioning system  170  to determine the vehicle&#39;s location and perception system  172  to detect and respond to objects when needed to reach the location safely. Again, in order to do so, computing device  110  may generate trajectories and cause the vehicle to follow these trajectories, for instance, by causing the vehicle to accelerate (e.g., by supplying fuel or other energy to the engine or power system  174  by acceleration system  162 ), decelerate (e.g., by decreasing the fuel supplied to the engine or power system  174 , changing gears, and/or by applying brakes by deceleration system  160 ), change direction (e.g., by turning the front or rear wheels of vehicle  100  by steering system  164 ), and signal such changes (e.g., by lighting turn signals of signaling system  166 ). Thus, the acceleration system  162  and deceleration system  160  may be a part of a drivetrain that includes various components between an engine of the vehicle and the wheels of the vehicle. Again, by controlling these systems, computing device  110  may also control the drivetrain of the vehicle in order to maneuver the vehicle autonomously. 
     Computing device  110  of vehicle  100  may also receive or transfer information to and from other computing devices, such as those computing devices that are a part of the transportation service as well as other computing devices.  FIGS. 4 and 5  are pictorial and functional diagrams, respectively, of an example system  400  that includes a plurality of computing devices  410 ,  420 ,  430 ,  440  and a storage system  450  connected via a network  460 . System  400  also includes vehicle  100 , and vehicles  100 A,  100 B which may be configured the same as or similarly to vehicle  100 . Although only a few vehicles and computing devices are depicted for simplicity, a typical system may include significantly more. 
     As shown in  FIG. 4 , each of computing devices  410 ,  420 ,  430 ,  440  may include one or more processors, memory, data and instructions. Such processors, memories, data and instructions may be configured similarly to one or more processors  120 , memory  130 , data  132 , and instructions  134  of computing device  110 . 
     The network  460 , and intervening nodes, may include various configurations and protocols including short range communication protocols such as Bluetooth, Bluetooth LE, the Internet, World Wide Web, intranets, virtual private networks, wide area networks, local networks, private networks using communication protocols proprietary to one or more companies, Ethernet, WiFi and HTTP, and various combinations of the foregoing. Such communication may be facilitated by any device capable of transmitting data to and from other computing devices, such as modems and wireless interfaces. 
     In one example, one or more computing devices  110  may include one or more server computing devices having a plurality of computing devices, e.g., a load balanced server farm, that exchange information with different nodes of a network for the purpose of receiving, processing and transmitting the data to and from other computing devices. For instance, one or more computing devices  410  may include one or more server computing devices that are capable of communicating with computing device  110  of vehicle  100  or a similar computing device of vehicle  100 A,  100 B as well as computing devices  420 ,  430 ,  440  via the network  460 . For example, vehicles  100 ,  100 A,  100 B may be a part of a fleet of vehicles that can send and receive information from the server computing devices  410 . In addition, the server computing devices  410  may use network  460  to transmit and present information to a user, such as one or more of users  422 ,  432 ,  442  on a display, such as one or more of displays  424 ,  434 ,  444  of computing devices  420 ,  430 ,  440 . In this regard, computing devices  420 ,  430 ,  440  may be considered client computing devices. 
     As shown in  FIG. 4 , each client computing device  420 ,  430 ,  440  may be a personal computing device intended for use by one or more of users  422 ,  432 ,  442 , and have all of the components normally used in connection with a personal computing device including one or more processors (e.g., a central processing unit (CPU)), memory (e.g., RAM and internal hard drives) storing data and instructions, a display such as displays  424 ,  434 ,  444  (e.g., a monitor having a screen, a touch-screen, a projector, a television, or other device that is operable to display information), and user input devices  426 ,  436 ,  446  (e.g., a mouse, keyboard, touchscreen or microphone). The client computing devices may also include a camera for recording video streams, speakers, microphones, a network interface device, and all of the components used for connecting these elements to one another. 
     Although the client computing devices  420 ,  430 , and  440  may each comprise a full-sized personal computing device, they may alternatively comprise mobile computing devices capable of wirelessly exchanging data with a server over a network such as the Internet. By way of example only, client computing device  420  may be a mobile phone or a device such as a wireless-enabled PDA, a tablet PC, a wearable computing device or system, or a netbook that is capable of obtaining information via the Internet or other networks. In another example, client computing device  430  may be a wearable computing system, shown as a wristwatch as shown in  FIG. 4 . As an example the user may input information using a small keyboard, a keypad, microphone, using visual signals with a camera, or a touch screen. 
     As with memory  130 , storage system  450  can be of any type of computerized storage capable of storing information accessible by the server computing devices  410 , such as a hard-drive, memory card, ROM, RAM, DVD, CD-ROM, write-capable, and read-only memories. In addition, storage system  450  may include a distributed storage system where data is stored on a plurality of different storage devices which may be physically located at the same or different geographic locations. Storage system  450  may be connected to the computing devices via the network  460  as shown in  FIGS. 4 and 5 , and/or may be directly connected to or incorporated into any of the computing devices  110 ,  410 ,  420 ,  430 ,  440 , etc. 
     Storage system  450  may store various types of information as described in more detail below. This information may be retrieved or otherwise accessed by a server computing device, such as one or more server computing devices  410 , in order to perform some or all of the features described herein. 
     For instance, the storage system  450  may store sensor data captured by a vehicle&#39;s perception system, such as perception system  172  of vehicle  100 . This sensor data may include a plurality of images  472 . This plurality of images may include images captured by a perception system of an autonomous vehicle in order to provide the type of sign that may appear in those images. For instance, the plurality of images may be images or frames captured by still and/or video cameras or other sensors mounted on one or more vehicles such as vehicles  100  or  100 A and uploaded via network  460  or otherwise sent to the storage system  450  for storage. Thus, the images may accurately reflect perception of the road and various objects from the perspective of the cameras or perception system of the vehicle. At least some of these images may be associated with labels and other information as discussed further below. Storage system  450  may also store traffic sign types and their respective attributes, as discussed herein. 
     Each image may be associated with location information identifying the location and orientation from which the image was captured and/or more details, such as geographic information for various surfaces within the image as determined from comparing with other images and/or from LIDAR sensor data captured by a LIDAR sensor of the perception system  172  contemporaneously with the image. For instance, the LIDAR sensor data may include data points corresponding to the locations and intensity (or reflectivity) of surfaces off of which light generated by the LIDAR sensor is reflected back to the LIDAR sensor. This information may be used to determine the correspondences of those surfaces in the camera images. 
     The storage system  450  as well as data  132  of vehicle  100  may store one or more models  470  as well as model parameter values  474  for each such model. For instance, the storage system may store one or more models for determining traffic sign types and context. A model  470  may include a classifier such as an artificial neural network, a deep neural network, decision tree, boosted tree, etc. In addition, the storage system  450  may include a training subsystem  476  that can be used to train a model as discussed further below. In some instances the storage system may store a hierarchical data structure  478  as described herein. 
     As with memory  130 , storage system  450  can be of any type of computer storage capable of storing information accessible by the server computing devices  410 , such as a hard-drive, memory card, ROM, RAM, DVD, CD-ROM, write-capable, and read-only memories. In addition, storage system  450  may include a distributed storage system where data is stored on a plurality of different storage devices which may be physically located at the same or different geographic locations. Storage system  450  may be connected to the computing devices via the network  460  as shown in  FIG. 4  and/or may be directly connected to or incorporated into any of the computing devices  110 ,  410 ,  420 ,  430 ,  440 , etc. 
     Example Methods 
     In addition to the operations described above and illustrated in the figures, various operations will now be described. It should be understood that the following operations do not have to be performed in the precise order described below. Rather, various steps can be handled in a different order or simultaneously, and steps may also be added or omitted. 
     As noted above, a model  470  may take the characteristics of a traffic sign and outputs a traffic sign type. In this regard, traffic sign types may include regulatory, warning, guide, services, recreation, construction, school zone, etc. In some instances, certain signs such as stop signs or railroad crossing signs may be considered sign types. 
     In order to be able to use the model  470  to identify traffic sign types, the model may first be trained “offline” that is, ahead of time and/or at a remote computing device and thereafter sent to the vehicle  100  via network  460  and wireless network connections  156 . For instance, one or more of server computing devices  410  may generate the model parameter values  474  by first retrieving training data from the storage system  450 . 
     For instance, the one or more server computing devices  410  may retrieve a set of images. As noted above, these images may include the plurality of images  472  of storage system  450  corresponding to locations where traffic signs are likely to be visible, such as images that are a predetermined distance from and oriented towards known traffic signs. For instance, images captured by cameras or other sensors mounted on vehicles, such as vehicle  100 , where the cameras are within a certain distance of a traffic sign and are oriented towards the traffic sign may be retrieved and/or included in the set.  FIG. 6  is an example camera image  600  captured by a camera of perception system  172  of vehicle  100  as the vehicle approaches intersection  204  of the map information. In this example, traffic sign  253  and part of lane  216  are captured in camera image  600 . This camera image may be processed and used to generate initial training data for the model. As noted above, the images of the storage system may be associated with information identifying the location and orientation at which the image was captured. 
     The initial training data for the model  470  may be generated from the set of images in various ways. For instance, human operators may label images of traffic signs as well as the type of traffic sign by reviewing the images, drawing bounding boxes around traffic signs, and identifying the types of traffic signs. In addition or alternatively, existing models or image processing techniques may be used to label images of traffic signs as well as the type of traffic sign. 
     Given an image of a traffic sign, which may be considered a training input, and a label indicating the type of traffic sign, which may be considered a training output, the model may be trained to output the type of traffic sign found in a captured image. In other words, the training input and training output are used to train the model on what input it will be getting and what output it is to generate. As an example, the model may receive images  753 ,  754 , and  253 , as shown in  FIG. 7 . The model may also receive labels  763 ,  764 , and  764  indicating the type of sign each image shows including “regulatory sign”, “warning sign”, and “recreation sign”. In some instances, the type of sign may be specific, such as “yield sign” and “railroad crossing ahead”, as further shown in labels  763  and  764  of  FIG. 7 . Based on this training data, the model may learn to identify similar traffic signs. In this regard, the training may increase the precision of the model such that the more training data (input and output) used to train the model, the greater the precision of the model at identifying sign types. 
     In some instances, the model may be configured to provide additional labels indicative of the content of the sign. In this regard, during the training of the machine learning model, the training data may include labels corresponding to the attributes of the traffic signs. For instance, labels  863  indicative of the attributes of a service sign including “rectangular shape,” “blue color,” and “text” stating “rest area next right”, as illustrated in  FIG. 8 , may be input into the machine learning model along with a label indicating the sign type as a service sign. As such, when the training model is run on an image of the service sign  853  and the label  863 , the model may learn that that the sign  853  is a service sign indicating a rest area ahead. Based on this determination, the model may learn that other signs which include attributes such as a “rectangular shape,” “blue color,” and “text” stating “rest area next right” may also be service signs. 
     Once the model  470  is trained, it may be sent or otherwise loaded into the memory of a computing system, such as memory  150  of vehicle  100  for use. For example, as a vehicle, such as vehicle  100  drives around, the vehicle&#39;s perception system  172  may capture sensor data of its surroundings. This sensor data, including any images of traffic signs, may be periodically, or continuously, input into the model  470  by the computing device  110 . The model  470  may then provide a corresponding sign type for each traffic sign in the images. For example, a vehicle, such as vehicle  100  may capture an image, such as image  900  containing sign  953 , as shown in  FIG. 9 . The model may output a label indicating the sign type is a “warning sign.” In some instances, the model may also provide the specific type of sign. For example, the model may output “warning sign” and “railroad crossing ahead” sign types for sign  953 . The provided sign type and attributes may then be used to determine how to control the vehicle in order to respond appropriately to the detected signs as described herein. 
     For each label indicating a specific sign type output by the model, the model may determine and assign a confidence level indicative of the probability that an output label is correct. In some instances, the model may be unable to determine a label indicating a traffic sign type for a traffic sign image input into the model. In this case, the model may categorize the traffic sign image as “unidentifiable” or “unknown”. Labels indicating a specific sign type having a confidence level which fails to satisfy a particular threshold level may also be categorized as unidentifiable or unknown. In some circumstances, an autonomous vehicle, such as vehicle  100 , may encounter a traffic sign which is not identifiable or not identifiable with a particular confidence (e.g., the model is unable to determine the traffic sign type to satisfy a particular confidence threshold), by the model  470  or within the map information. However, traffic signs are generally categorized by type according to regulations by government agencies such as the Federal Highway Administration, although the regulations may vary from region to region/country to country, etc. As such, most traffic signs can be categorized by whether certain attributes, such as color and shape, satisfy the regulations corresponding to certain sign types. The attributes assigned to each type of traffic sign may be color or shape based. For instance, red signs may be regulatory (e.g., stop and yield signs), yellow signs may be warning signs (e.g., railroad or yield up ahead signs), guide signs may be green (e.g., roadway exit signs), temporary traffic control signs may be orange (e.g., road work ahead signs,) recreation signs may be brown (e.g., picnic area signs), service signs may be blue (e.g., rest area signs), etc. 
     However, many traffic signs are unique or relatively obscure (collectively, “unfamiliar signs”), and, while these unfamiliar signs may follow regulations according to a sign type, their content may not be identified by the machine learning model, particularly when the machine learning model has not been provided with training data corresponding to the type and content of the unfamiliar sign. Moreover, some traffic signs may not follow regulations or otherwise fit into typical traffic sign categories. For instance, LED boards or handmade signs may not fit into typical traffic sign categories or follow regulations. For example, an LED board including a message stating “construction up ahead” or a handmade temporary sign instructing cars to turn may not fit the regulations of “temporary traffic control” signs and may not be identifiable or not identifiable with a high enough confidence, by a model, such as model  470 . 
     To determine the type and content of an unfamiliar sign, the attributes of the unfamiliar sign may be compared to those of known signs. In this regard, attributes of known traffic signs may be labeled by human operators and/or by using image processing techniques as described herein. The labeled attributes may then be arranged in a data structure such as data structure  478 , which may be a relational database or another associative structure that supports one to many mapping. For instance, attributes of known signs, such as their type, color, shape, reflection coefficient, placement, text, figures, accessories, etc., may be labeled and these labels may be stored into the data structure. For instance, the labels  863  of attributes of traffic sign  853  may be stored in the data structure in relation to the traffic sign  853 , such as in storage  450 . The computing device, such as computing device  110  in vehicle  100 , may compare the attributes of the unfamiliar sign with those in the data structure to determine the type and content of the unfamiliar sign. Although the examples herein describe comparing the attributes of unfamiliar signs with those in the data structure to determine the type and content of the unfamiliar sign after implementing a machine learning model, such as model  470 , the comparison of attributes may occur without, or before, implementing a machine learning model. 
     In one example, an autonomous vehicle&#39;s sensor, such as a sensor in the perception system  172 , may capture image  600  containing an unfamiliar traffic sign  253  above road  216  as it travels, as shown in  FIG. 6 . The computing device  110  may attempt to determine the type and content of the unfamiliar traffic sign  253  by comparing the traffic sign&#39;s attributes to those in the data structure. For instance, the computing device  110  may determine the unfamiliar traffic sign has attributes of a rectangular shape, a brown color, and written text. These attributes may be compared against the attributes of signs in the data structure and, based on the comparison the unfamiliar traffic sign may be determined to be consistent with a recreation sign. In another example, a captured traffic sign may be determined to have attributes of an octagonal shape, red color, and text. The computing device  110  may compare these attributes against those of signs in the data structure and determine the captured traffic sign matches a regulatory sign with instructional content to “stop”. 
     Additional attributes of the sign may be used by the computing device  110  to determine the content of the sign. In this regard, the text or figures of a sign may be analyzed to determine the content of a signs. For instance, the unfamiliar sign  1053  of  FIG. 10  may be determined to be a “recreation sign” having text  1054 . The vehicle&#39;s computing device, such as computing device  110  may analyze the text  1054 , such as by using optical character recognition (OCR), to determine the text  1054  states “Yosemite National Park Next Right.” Based on this text, the computing device  110  may determine the content of the sign  1053  is informative and provides instructions to Yosemite National Park. In this regard, the vehicle&#39;s computing device may identify one or more keywords within the text, such as names of nearby landmarks/locations, directional terms, and/or numbers. Based on these keywords, the vehicle&#39;s computing device may determine the content of the sign. For instance, the computing device  110  may determine the keywords “Yosemite National Park” and “Next,” and “Right” from text  1054  and based on these keywords determine the content of the sign  1053  is informative provides directions to Yosemite National Park. Similar determinations may be made based on figures. For instance, an arrow pointing to the right may result in the computing device determining the content of the sign is instructive of a detour to the right. 
     Based on the sign type and/or the content, the various systems of the autonomous vehicle may determine whether or not to take an action. In this regard, some signs may automatically trigger an action by the vehicle, such as regulatory, warning, guide signs, construction signs, etc. For instance, the vehicle&#39;s planning system  168  may instruct the vehicle  100  to stop, change course, or change speeds upon encountering a stop sign, detour sign, or speed limit sign, respectively. Other signs, such as recreation or rest area signs, may be ignored by the vehicle&#39;s planning system  168  unless the vehicle  100  is attempting to travel to those areas. 
       FIG. 11  is an example flow diagram  1100  in accordance with aspects of the disclosure which may be performed by one or more processors of one or more computing devices, such as processors  120  of computing devices  110 , in order to train a machine learning model to determine sign types. At block  1110  image data including an image and associated label(s) corresponding to at least one traffic sign within the image is received. The model may be trained using the image data such that the model is configured to, in response to receiving an image of a traffic sign, output a sign type and/or content for the traffic sign, as shown in block  1120 . 
       FIG. 12  is an example flow diagram  1200  in accordance with aspects of the disclosure which may be performed by one or more processors of one or more computing devices, such as processors  120  of computing devices  110 , in order to determine a sign type and control the vehicle based on the determined sign type. At block  1210 , an image generated by a perception system of a vehicle may be received by one or more processors. One or more processors may identify image data corresponding to a traffic sign in the image at block  1220  and the image data corresponding to the traffic sign may be input into a model to generate a sign type of the traffic sign at block  1230 . The one or more processors may determine that the sign type model was unable to identify a type of the traffic sign at block  1240  and determine one or more attributes of the traffic sign at block  1250 . The one or more processors may compare the traffic sign to known attributes of other traffic signs, as shown at block  1260 . Based on the comparing the one or more attributes of the traffic sign a sign type of the traffic sign may be determined, as shown in block  1270 . The vehicle, in autonomous driving mode, may be controlled by the one or more processors based on the sign type of the traffic sign as shown in block  1280 . 
     Unless otherwise stated, the foregoing alternative examples are not mutually exclusive, but may be implemented in various combinations to achieve unique advantages. As these and other variations and combinations of the features discussed above can be utilized without departing from the subject matter defined by the claims, the foregoing description of the embodiments should be taken by way of illustration rather than by way of limitation of the subject matter defined by the claims. In addition, the provision of the examples described herein, as well as clauses phrased as “such as,” “including” and the like, should not be interpreted as limiting the subject matter of the claims to the specific examples; rather, the examples are intended to illustrate only one of many possible embodiments. Further, the same reference numbers in different drawings can identify the same or similar elements.