Patent Description:
Aspects of the disclosure provide a method for determining a sign type of an unfamiliar sign. The method includes: 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, wherein 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; responsive to determining 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; and 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 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 provide a system for determining a sign type of an unfamiliar sign. The system includes one or more processors, and the one or more processors are 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, wherein 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; responsive to determining that the sign type model was unable to identify a type of the traffic sign: determining one or more attributes of the traffic sign; and 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 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 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.

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'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's content (e.g., instructive content, informative content, etc.,) may be made. Based on the sign'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'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'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'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.

As shown in <FIG>, a vehicle <NUM> 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 <NUM> containing one or more processors <NUM>, memory <NUM> and other components typically present in general purpose computing devices.

The one or more processor <NUM> 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> functionally illustrates the processor, memory, and other elements of computing device <NUM> 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 <NUM>. 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 <NUM> 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 <NUM> (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 <NUM> as well as one or more speakers <NUM> to provide information or audio visual experiences. In this regard, internal electronic display <NUM> may be located within a cabin of vehicle <NUM> and may be used by computing device <NUM> to provide information to passengers within the vehicle <NUM>.

Computing device <NUM> may also include one or more wireless network connections <NUM> 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 <NUM> may be an autonomous driving computing system incorporated into vehicle <NUM>. 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>, computing device <NUM> may be in communication with various systems of vehicle <NUM>, such as deceleration system <NUM>, acceleration system <NUM>, steering system <NUM>, signaling system <NUM>, planner system <NUM>, positioning system <NUM>, and perception system <NUM> in order to control the movement, speed, etc. of vehicle <NUM> in accordance with the instructions <NUM> of memory <NUM> in the autonomous driving mode. Again, although these systems are shown as external to computing device <NUM>, in actuality, these systems may also be incorporated into computing device <NUM>, again as an autonomous driving computing system for controlling vehicle <NUM>.

As an example, computing device <NUM> may interact with deceleration system <NUM> and acceleration system <NUM> in order to control the speed of the vehicle. Similarly, steering system <NUM> may be used by computing devices <NUM> in order to control the direction of vehicle <NUM>. For example, if vehicle <NUM> 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 <NUM> may be used by computing device <NUM> in order to signal the vehicle's intent to other drivers or vehicles, for example, by lighting turn signals or brake lights when needed.

Planning system <NUM> may be used by computing device <NUM> in order to determine and follow a route to a location. In this regard, the planning system <NUM> and/or data <NUM> 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> is an example of map information <NUM> for a section of roadway including intersections <NUM> and <NUM>. The map information <NUM> may be a local version of the map information stored in the memory <NUM> of the computing devices <NUM>. Other versions of the map information may also be stored in the storage system <NUM> discussed further below. In this example, the map information <NUM> includes information identifying the shape, location, and other characteristics of lane lines <NUM>, <NUM>, <NUM>, traffic lights <NUM>, <NUM>, stop line <NUM>, crosswalks <NUM>, <NUM> sidewalks <NUM>, and traffic signs <NUM>, <NUM>. 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 <NUM> may be used by computing device <NUM> in order to determine the vehicle's relative or absolute position on a map or on the earth. For example, the position system <NUM> may include a GPS receiver to determine the device'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 <NUM> may also include other devices in communication with computing device <NUM>, 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's provision of location and orientation data as set forth herein may be provided automatically to the computing device <NUM>, other computing devices and combinations of the foregoing.

The perception system <NUM> 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 <NUM> may include lasers, sonar, radar, cameras and/or any other detection devices that record data which may be processed by computing device <NUM>. 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> is an example external view of vehicle <NUM>. In this example, roof-top housing <NUM> and dome housing <NUM> may include a LIDAR sensor as well as various cameras and radar units. In addition, housing <NUM> located at the front end of vehicle <NUM> and housings <NUM>, <NUM> on the driver's and passenger's sides of the vehicle may each store a LIDAR sensor. For example, housing <NUM> is located in front of driver door <NUM>. Vehicle <NUM> also includes housings <NUM>, <NUM> for radar units and/or cameras also located on the roof of vehicle <NUM>. Additional radar units and cameras (not shown) may be located at the front and rear ends of vehicle <NUM> and/or on other positions along the roof or roof-top housing <NUM>.

In one example, computing devices <NUM> may be control computing devices of an autonomous driving computing system or incorporated into vehicle <NUM>. The autonomous driving computing system may be capable of communicating with various components of the vehicle in order to control the movement of vehicle <NUM> according to primary vehicle control code of memory <NUM>. For example, returning to <FIG>, computing devices <NUM> may be in communication with various systems of vehicle <NUM>, such as deceleration system <NUM>, acceleration system <NUM>, steering system <NUM>, signaling system <NUM>, planning system <NUM>, positioning system <NUM>, perception system <NUM>, and power system <NUM> (i.e. the vehicle's engine or motor) in order to control the movement, speed, etc. of vehicle <NUM> in accordance with the instructions <NUM> of memory <NUM>. Again, although these systems are shown as external to computing devices <NUM>, in actuality, these systems may also be incorporated into computing devices <NUM>, again as an autonomous driving computing system for controlling vehicle <NUM>.

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 <NUM> 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's environment, position information from the positioning system <NUM> 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 <NUM>. The planning system and/or computing devices <NUM> 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 <NUM> 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 <NUM> may control the vehicle by controlling various components. For instance, by way of example, computing device <NUM> may navigate the vehicle to a destination location completely autonomously using data from the detailed map information and planning system <NUM>. Computing device <NUM> may use the positioning system <NUM> to determine the vehicle's location and perception system <NUM> to detect and respond to objects when needed to reach the location safely. Again, in order to do so, computing device <NUM> 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 <NUM> by acceleration system <NUM>), decelerate (e.g., by decreasing the fuel supplied to the engine or power system <NUM>, changing gears, and/or by applying brakes by deceleration system <NUM>), change direction (e.g., by turning the front or rear wheels of vehicle <NUM> by steering system <NUM>), and signal such changes (e.g., by lighting turn signals of signaling system <NUM>). Thus, the acceleration system <NUM> and deceleration system <NUM> 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 <NUM> may also control the drivetrain of the vehicle in order to maneuver the vehicle autonomously.

Computing device <NUM> of vehicle <NUM> 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. <FIG> and <FIG> are pictorial and functional diagrams, respectively, of an example system <NUM> that includes a plurality of computing devices <NUM>, <NUM>, <NUM>, <NUM> and a storage system <NUM> connected via a network <NUM>. System <NUM> also includes vehicle <NUM>, and vehicles 100A, 100B which may be configured the same as or similarly to vehicle <NUM>. Although only a few vehicles and computing devices are depicted for simplicity, a typical system may include significantly more.

In one example, one or more computing devices <NUM> 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 <NUM> may include one or more server computing devices that are capable of communicating with computing device <NUM> of vehicle <NUM> or a similar computing device of vehicle 100A, 100B as well as computing devices <NUM>, <NUM>, <NUM> via the network <NUM>. For example, vehicles <NUM>, 100A, 100B may be a part of a fleet of vehicles that can send and receive information from the server computing devices <NUM>. In addition, the server computing devices <NUM> may use network <NUM> to transmit and present information to a user, such as one or more of users <NUM>, <NUM>, <NUM> on a display, such as one or more of displays <NUM>, <NUM>, <NUM> of computing devices <NUM>, <NUM>, <NUM>. In this regard, computing devices <NUM>, <NUM>, <NUM> may be considered client computing devices.

As shown in <FIG>, each client computing device <NUM>, <NUM>, <NUM> may be a personal computing device intended for use by one or more of users <NUM>, <NUM>, <NUM>, 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 <NUM>, <NUM>, <NUM> (e.g., a monitor having a screen, a touchscreen, a projector, a television, or other device that is operable to display information), and user input devices <NUM>, <NUM>, <NUM> (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 <NUM>, <NUM>, and <NUM> 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 <NUM> 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 <NUM> may be a wearable computing system, shown as a wristwatch as shown in <FIG>. 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 <NUM>, storage system <NUM> can be of any type of computerized storage capable of storing information accessible by the server computing devices <NUM>, such as a hard-drive, memory card, ROM, RAM, DVD, CD-ROM, write-capable, and read-only memories. In addition, storage system <NUM> 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 <NUM> may be connected to the computing devices via the network <NUM> as shown in <FIG> and <FIG>, and/or may be directly connected to or incorporated into any of the computing devices <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, etc..

Storage system <NUM> 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 <NUM>, in order to perform some or all of the features described herein.

For instance, the storage system <NUM> may store sensor data captured by a vehicle's perception system, such as perception system <NUM> of vehicle <NUM>. This sensor data may include a plurality of images <NUM>. 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 <NUM> or 100A and uploaded via network <NUM> or otherwise sent to the storage system <NUM> 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 <NUM> 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 <NUM> 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 <NUM> as well as data <NUM> of vehicle <NUM> may store one or more models <NUM> as well as model parameter values <NUM> for each such model. For instance, the storage system may store one or more models for determining traffic sign types and context. A model <NUM> may include a classifier such as an artificial neural network, a deep neural network, decision tree, boosted tree, etc. In addition, the storage system <NUM> may include a training subsystem <NUM> that can be used to train a model as discussed further below. In some instances the storage system may store a hierarchical data structure <NUM> as described herein.

As with memory <NUM>, storage system <NUM> can be of any type of computer storage capable of storing information accessible by the server computing devices <NUM>, such as a hard-drive, memory card, ROM, RAM, DVD, CD-ROM, write-capable, and read-only memories. In addition, storage system <NUM> 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 <NUM> may be connected to the computing devices via the network <NUM> as shown in <FIG> and/or may be directly connected to or incorporated into any of the computing devices <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, etc..

As noted above, a model <NUM> 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 <NUM> 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 <NUM> via network <NUM> and wireless network connections <NUM>. For instance, one or more of server computing devices <NUM> may generate the model parameter values <NUM> by first retrieving training data from the storage system <NUM>.

For instance, the one or more server computing devices <NUM> may retrieve a set of images. As noted above, these images may include the plurality of images <NUM> of storage system <NUM> 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 <NUM>, 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> is an example camera image <NUM> captured by a camera of perception system <NUM> of vehicle <NUM> as the vehicle approaches intersection <NUM> of the map information. In this example, traffic sign <NUM> and part of lane <NUM> are captured in camera image <NUM>. 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 <NUM> 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 <NUM>, <NUM>, and <NUM>, as shown in <FIG>. The model may also receive labels <NUM>, <NUM>, and <NUM> 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 <NUM> and <NUM> of <FIG>. 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 <NUM> indicative of the attributes of a service sign including "rectangular shape," "blue color," and "text" stating "rest area next right", as illustrated in <FIG>, 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 <NUM> and the label <NUM>, the model may learn that that the sign <NUM> 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 <NUM> is trained, it may be sent or otherwise loaded into the memory of a computing system, such as memory <NUM> of vehicle <NUM> for use. For example, as a vehicle, such as vehicle <NUM> drives around, the vehicle's perception system <NUM> 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 <NUM> by the computing device <NUM>. The model <NUM> may then provide a corresponding sign type for each traffic sign in the images. For example, a vehicle, such as vehicle <NUM> may capture an image, such as image <NUM> containing sign <NUM>, as shown in <FIG>. 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 <NUM>. 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 is 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 are categorized as unidentifiable or unknown. In some circumstances, an autonomous vehicle, such as vehicle <NUM>, 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 <NUM> 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 <NUM>.

To determine the type and content of an unfamiliar sign, the attributes of the unfamiliar sign are 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 <NUM>, 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 <NUM> of attributes of traffic sign <NUM> may be stored in the data structure in relation to the traffic sign <NUM>, such as in storage <NUM>. The computing device, such as computing device <NUM> in vehicle <NUM>, 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 <NUM>, the comparison of attributes may occur without, or before, implementing a machine learning model.

In one example, an autonomous vehicle's sensor, such as a sensor in the perception system <NUM>, may capture image <NUM> containing an unfamiliar traffic sign <NUM> above road <NUM> as it travels, as shown in <FIG>. The computing device <NUM> may attempt to determine the type and content of the unfamiliar traffic sign <NUM> by comparing the traffic sign's attributes to those in the data structure. For instance, the computing device <NUM> 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 <NUM> 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 <NUM> 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 <NUM> of <FIG> may be determined to be a "recreation sign" having text <NUM>. The vehicle's computing device, such as computing device <NUM> may analyze the text <NUM>, such as by using optical character recognition (OCR), to determine the text <NUM> states "Yosemite National Park Next Right. " Based on this text, the computing device <NUM> may determine the content of the sign <NUM> is informative and provides instructions to Yosemite National Park. In this regard, the vehicle'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's computing device may determine the content of the sign. For instance, the computing device <NUM> may determine the keywords "Yosemite National Park" and "Next," and "Right" from text <NUM> and based on these keywords determine the content of the sign <NUM> 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's planning system <NUM> may instruct the vehicle <NUM> 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's planning system <NUM> unless the vehicle <NUM> is attempting to travel to those areas.

<FIG> is an example flow diagram <NUM> 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 <NUM> of computing devices <NUM>, in order to train a machine learning model to determine sign types. At block <NUM> 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 <NUM>.

<FIG> is an example flow diagram <NUM> in accordance with aspects of the disclosure which is performed by one or more processors of one or more computing devices, such as processors <NUM> of computing devices <NUM>, in order to determine a sign type and control the vehicle based on the determined sign type. At block <NUM>, an image generated by a perception system of a vehicle is received by one or more processors. One or more processors identify image data corresponding to a traffic sign in the image at block <NUM> and the image data corresponding to the traffic sign is input into a model to generate a sign type of the traffic sign at block <NUM>. The one or more processors determine that the sign type model was unable to identify a type of the traffic sign at block <NUM> and determine one or more attributes of the traffic sign at block <NUM>. The one or more processors compare the traffic sign to known attributes of other traffic signs, as shown at block <NUM>. Based on the comparing the one or more attributes of the traffic sign a sign type of the traffic sign is determined, as shown in block <NUM>. The vehicle, in autonomous driving mode, is controlled by the one or more processors based on the sign type of the traffic sign as shown in block <NUM>.

Claim 1:
A method of determining a sign type of an unfamiliar traffic sign, the method comprising
receiving (<NUM>), by one or more processors, an image generated by a perception system of a vehicle;
identifying (<NUM>), by the one or more processors, image data corresponding to a traffic sign in the image;
inputting (<NUM>), by the one or more processors, the image data corresponding to the traffic sign into a sign type model;
determining (<NUM>), by the one or more processors, that the sign type model was unable to identify a type of the traffic sign, wherein 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;
responsive to determining that the sign type model was unable to identify a type of the traffic sign:
determining (<NUM>), by the one or more processors, one or more attributes of the traffic sign; and
comparing (<NUM>), by the one or more processors, the one or more attributes of the traffic sign to known attributes of other traffic signs;
determining (<NUM>), 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 (<NUM>), by the one or more processors, the vehicle in an autonomous driving mode based on the sign type of the traffic sign.