Traffic direction gesture recognition

Traffic direction gesture recognition may be implemented for a vehicle in response to traffic diversion signals in the vehicles vicinity. Sensors implemented as part of a vehicle may collect data about pedestrians and other obstacles in the vicinity of the vehicle or along the vehicle's route of travel. Sensor data may be combined and analyzed to identify a traffic diversion condition, including identifying a traffic director directing traffic using gestures or signs. Gestures of a traffic director may be interpreted and understood by the vehicle as commands to perform maneuvers related to the traffic diversion, including stopping, slowing, or turning onto a detour route. The vehicle may be equipped with a command acknowledgement device for acknowledging to a traffic director the vehicle's understanding of the traffic diversion condition or maneuver commands. Information, such as traffic diversion and detour information, may be shared with other vehicles and devices, or stored in a database.

BACKGROUND

Vehicle safety improvements and the rise of interest in automated navigation and control of vehicles have led to the inclusion of different types of remote sensing equipment installed on vehicles. These sensors can include one or more radars, ultrasonic sensors, light beam scanning devices, visible light camera devices, infrared camera devices, near-infrared camera devices, and depth camera devices which can include one or more light-scanning devices, including LIDAR devices, etc. Automated navigation and control systems may process data collected by the sensors in order to detect and characterize objects in the environment for various purposes.

One purpose for detecting and characterizing objects in the environment is to increase efficiency and safety of travel, which can also reduce travel times, decrease fuel usage, reduce environmental pollution associated with vehicular travel, and decrease overall travel costs, among other potential benefits. However, current autonomous vehicle systems typically return control to the driver in the event of an unexpected traffic diversion or any number of other atypical driving situations.

Current technologies include GPS-based navigation applications that can display real-time traffic conditions to a vehicle operator; analyze traffic conditions, a vehicle destination, and mapping data; and recommend a travel route based on various user-configured or default preferences. Other technologies allow for detection of surrounding vehicles and other traffic obstacles, primarily with the intent to help prevent collisions, as in the case of blind-spot detection and warning devices and similar systems. Some modern cruise control systems, such as a typical “adaptive cruise control” system pursue a target vehicle speed and may adjust speed for safety purposes (e.g. to maintain a safe following distance). However, none of these systems addresses unexpected traffic diversions where a pedestrian may be manually directing traffic, for example due to an accident, special event, or road hazard.

SUMMARY

Sensors implemented as part of a vehicle may collect data about an environment, surrounding a vehicle, such as the locations of pedestrians and other obstacles or the existence of an unexpected situation such as a traffic diversion. A sensor fusion module may collect and process data from various sensors. A traffic direction gesture recognition system be implemented for a vehicle in response to traffic diversion signals in the vehicles vicinity. Sensor data may be combined and analyzed to identify a traffic diversion condition, including identifying a traffic director directing traffic using gestures or signs. Gestures of a traffic director may be interpreted and understood by the vehicle as commands to perform maneuvers related to the traffic diversion, including stopping, slowing, or turning onto a detour route. The vehicle may be equipped with a command acknowledgement device for acknowledging to a traffic director the vehicle's understanding of the traffic diversion condition or maneuver commands. Information, such as traffic diversion and detour information, may be shared with other vehicles and devices, or stored in a database.

DETAILED DESCRIPTION

The systems and methods described here may implement traffic direction gesture recognition.

FIG. 1illustrates an overhead diagram of an environment for implementing traffic direction gesture recognition, according to some embodiments. Traffic direction gesture recognition may be implemented with regard to vehicle110within environment100. Environment100ofFIG. 1includes multiple roadways120A-120D intersecting at roadway intersection150.

In the example ofFIG. 1, vehicle110is approaching intersection150via roadway120A, initially intending to continue through intersection150onto roadway120B. Vehicle110includes an environmental analysis module (described in detail below with reference toFIG. 2) for monitoring the environment100in the vicinity of vehicle110. As shown, obstacles130A and130B are blocking intersection150. Obstacles130A and130B may be, for example, other vehicles on the roadway, debris, or construction equipment.

Vehicle110may additionally include one or more sensors115via which vehicle110can monitor the environment100and send data to the environmental analysis module or other control modules of the vehicle. In some embodiments, the sensors115may include one or more radars, ultrasonic sensors, light beam scanning devices, visible light camera devices, infrared camera devices, near-infrared camera devices, and depth camera devices which can include one or more light-scanning devices, including LIDAR devices, etc. These sensors may be physically located within vehicle110. In some embodiments, some or all of the sensors may be physically located separately from the vehicle, for example on surrounding vehicles or other obstacles, mounted on stationary objects such as light poles or street signs, or embedded within a road surface.

In some embodiments, one or more sensors coupled to vehicle110detect one or more of obstacles130A and130B, which may be surrounding vehicles or other obstacles such as road debris or road surface imperfections located in roadways120A-120D, or other locations now shown inFIG. 1. In some embodiments, sensors coupled to vehicle110may detect and measure characteristics of travel lanes in the vicinity of vehicle110. For example, sensors coupled to vehicle110may detect one or more of a lateral position, width, curvature, lane marking type and position, or other characteristics of a travel lane. In some embodiments, the sensors may be configured to detect one or all of a position, speed, and size of one or more of obstacles120a-120k. In some embodiments, some data associated with travel lanes or obstacles may be provided to vehicle110from one or more other sources, such as sensors located external of vehicle110, a navigation system or database, a GPS system, a handheld device, or another vehicle's sensor or traffic gesture recognition system.

The environmental analysis module of vehicle110may detect at140an abnormal traffic condition or traffic diversion. For example, the environmental analysis module may use any combination of local and remote navigational data, road signs, past observations at the same intersection, data and time information, or other suitable information available to vehicle110in order to determine that the present situation of intersection150includes an abnormal condition.

Vehicle110may detect160a pedestrian170near the abnormal traffic condition. A gesture recognition module (described in detail below with reference toFIG. 2) of vehicle110may detect if a gesture is a recognized traffic direction command. If a gesture is recognized, the vehicle may prepare to take the action or perform a maneuver associated with the gesture.

When vehicle110has recognized a traffic direction command, according to some embodiments, the vehicle may communicate an acknowledgement to the pedestrian acting as a traffic director. For example, if the traffic direction command indicates that vehicle110should turn, vehicle110may use its turn signal indicator. Alternatively or additionally, vehicle110may use a display to communicate with the traffic director—for example, an LED or other light display, a video screen, or an audible notification.

In some embodiments and situations—vehicle110may not wait for confirmation of its correct understanding of a traffic direction gesture. For example, vehicle110may be configured to always stop immediately upon detecting a stop signal. Safety equipment on a typical autonomous vehicle may include software to slow or stop the car when obstacles or safety hazards are detected, regardless of traffic gesture detection logic, systems, or methods.

Alternatively or additionally, vehicle110may detect a pedestrian acting as a traffic director independently of any other indication or detection that an abnormal traffic condition exists. For example, if a detected pedestrian performs a recognized command gesture, the vehicle110may recognize that pedestrian as a traffic director without having first detected an abnormal traffic condition. In some embodiments, additional functionality may be implemented to verify whether a detected command is legitimate when vehicle110has not otherwise detected an abnormal traffic condition.

FIG. 2illustrates a logical block diagram of a vehicle that implements traffic direction gesture recognition, according to some embodiments. In some embodiments, vehicle110may include one or more sensors230as described herein. In some embodiments, the sensors may include one or more radars, ultrasonic sensors, light beam scanning devices, visible light camera devices, infrared camera devices, near-infrared camera devices, and depth camera devices which can include one or more light-scanning devices, including LIDAR devices, etc. Vehicle110may include interfaces250for user control or various communication of data as described herein or required for operation of an autonomous vehicle.

In some embodiments, sensor230collects data and sends the data to a memory220of vehicle110, where some or all of the raw sensor data may be stored as sensor data222within memory220. Memory220may also exchange other data with sensors230in some embodiments, for example to set or modify operating parameters of one or more sensors230or monitor error conditions of sensors230.

Memory220according to some embodiments may also store known gestures224, which may be compared at gesture recognition module214to gestures detected environmental analysis module212. In some embodiments, some or all of known gesture data224may be stored by a manufacturer or operator of a vehicle, or supplied from an outside database such as a law or traffic enforcement database. Examples of possible traffic gestures are shown and discussed below with reference toFIG. 7.

Memory220according to some embodiments may additionally store traffic director characteristics226, which may be used by environmental analysis module212to recognize a pedestrian acting as a traffic director. In some embodiments, some or all of traffic director characteristics226may be stored by a manufacturer or operator of a vehicle, or supplied from an outside database such as a law or traffic enforcement database.

Vehicle110may also include a processor210, according to some embodiments. In some embodiments, processor210may be a general computer processor of vehicle110with many other functions. In other embodiments, processor210may include a processor dedicated to functions related to implementing traffic direction gesture recognition.

Processor210may implement environmental analysis module212for analyzing stored sensor data222and/or raw data from sensors230. In some embodiments, environmental analysis module212may receive information about pedestrians and other obstacles in the vicinity of vehicle110. For example, environmental analysis module212may implement lidar-based obstacle detection and scene segmentation, for example using a particle-based occupancy grid.

In some example embodiments, environmental analysis module212may project lidar obstacle points into a calibrated image plane of a camera of vehicle110. Environmental analysis module212may further implement various pedestrian detection methods, for example, machine-learning-based pedestrian detection methods based on aggregated channel features, adaptive boosting, or neural networks. In some example embodiments, each piece of data available to an environmental analysis module212(e.g. colors, signs, gestures, etc.) may be combined or fused and analyzed using a random forest classifier.

In some example embodiments, environmental analysis module212may additionally attempt to characterize detected pedestrians, for example to identify a pedestrian acting as a traffic director. In some situations, the appearance of a pedestrian acting as a traffic director may include distinguishing features. For example, various authorities may wear particular colors (e.g. bright yellow or orange, or dark blue) when directing traffic. This information may, for example, be stored in a database, learned by the environmental analysis module, or a combination thereof.

In some embodiments, a pedestrian may hold a road sign, for example, one or more of a “SLOW,” “STOP,” “CROSSING,” or “DETOUR” sign. In other examples, temporary or permanent road signs may be taken into account by the environmental analysis module212, in combination or independently of a detected traffic director, and/or independently of whether the sign is held by a pedestrian.

In some situations, the appearance of a pedestrian legitimately acting as a traffic director may not include any distinguishing characteristics. For example, consider the situation of a traffic accident or road hazard when no law enforcement or emergency personnel or equipment is present at the scene. A passerby or motorist who has exited his or her vehicle may legitimately direct other vehicles away from the danger or road hazard. In such a situation, this traffic director may be detected based solely on gestures, for example gestures stored in a database of known traffic direction gestures. In other embodiments, a traffic director may be identified using a combination of a detected abnormal traffic condition and gestures of the traffic director that indicate or suggest in some way that the traffic director is not a typical pedestrian.

Environmental analysis module212according to some embodiments may detect gestures of pedestrians. For example, a pedestrian who makes a gesture known to environmental analysis module212or gesture recognition module214to be a traffic direction gesture may be identified as acting as a traffic director. In some embodiments, the identification as a traffic director may further require concurrent detection of an abnormal traffic condition, particularly if the environmental analysis module can detect no other distinguishing characteristic of the pedestrian that suggests the pedestrian is validly acting as a traffic director.

According to some embodiments, a pedestrian exhibiting behavior consistent with a traffic director, even in the absence of a recognized gesture, may be characterized as a traffic director or possible traffic director. For example, a pedestrian who stands near a roadway and exhibits behavior not recognized by environmental analysis module212as being typical of a pedestrian may be recognized as a possible traffic director, particularly when an abnormal traffic condition is detected. According to some embodiments, vehicle110may then seek additional resources to attempt to identify the gestures, such as contacting other nearby vehicles or law enforcement, or accessing remote databases via a network.

When environmental analysis module212has detected a gesture of an identified traffic director, possible traffic director, or other pedestrian, gesture recognition module214may attempt to identify the gesture. For example, gesture recognition module may search a local (known gestures224) or remote database for known gestures that match a recognized gesture.

According to some embodiments, environmental analysis module212or gesture recognition module214may employ a hidden Markov model, Recurrent Neural Network, or other sequential classifier—applied, for example, as a spatio-temporal classification of a pedestrian's activity. A hidden Markov model, Recurrent Neural Network, or other sequential classifier may be developed, for example, for a “directing traffic” state, which if detected would indicate that a pedestrian is a likely traffic director.

According to some embodiments, if a gesture is recognized, for example, by matching a known maneuver command, vehicle110may perform the maneuver immediately. In other embodiments, vehicle110may acknowledge the recognized command or seek confirmation from the traffic director.

For example, command acknowledgement module216may operate command acknowledgement device240to communicate the vehicle's understanding of the detected command. According to some embodiments, command acknowledgement device240may include any or all of a turn signal, LED or other visual display, a video screen, or an audible notification. A traffic director according to some embodiments may acknowledge, positively or negatively, for example using another simple gesture (e.g. thumbs up or down) whether vehicle110has understood the traffic command correctly. In some embodiments, a command acknowledgement or traffic director acknowledgement may be made electronically, for example as a signal to an autonomous control or other computing system.

One of ordinary skill will recognize that separation of responsibilities between elements212-216are abstractions, and that any of elements212-216may perform one or several of the functions described herein with reference to any other of those modules.

In various embodiments, any or all of the data described herein as being generated or processed at vehicle110may be shared with other devices and systems, for example with other nearby vehicles, law or traffic enforcement officials or systems, a navigation system, or a remote database or other storage system.

FIG. 3illustrates a logical block diagram including interactions of a vehicle that implements traffic gesture recognition, according to some embodiments. Vehicle305of example system300includes example sensors310,320, and330. Camera310according to some embodiments may include a visible light camera used for detecting lane information as described herein.

Lidar320of vehicle305, according to some embodiments, may be used to detect pedestrians, other vehicles, and other obstacles in the vicinity of vehicle305, as described herein. Lidar320according to some embodiments may detect a three-dimensional position, three-dimensional size, and three-dimensional velocity of one or more obstacles located in or near one or more travel lanes in the vicinity of vehicle305.

Radar330of vehicle305, according to some embodiments, may be used, at least in part, to detect or verify velocities of other vehicles or obstacles traveling or stationary in the vicinity of vehicle305. One of ordinary skill in the art will understand that sensors of vehicle305, including example camera310, lidar320, and radar330, may overlap in function or may be redundant with specific types of data. Such redundancy according to some embodiments may serve to verify or increase the overall accuracy of sensor data of vehicle305.

Vehicle305may communicate, according to some embodiments, via one or more communications interfaces340. In some embodiments, one or more satellite devices350, such as a device implementing a global positioning system (“GPS”), may communicate to vehicle305via communications interface340. For example, vehicle305may receive information about its position via data including vehicle location data342.

Vehicle305may also communicate with network360, for example to send data344including for example abnormal traffic conditions, detour information, traffic director information, traffic metrics, vehicle operator data, lane recommendations, and other data associated with implementing traffic direction gesture recognition. Vehicle location data342may also be shared with network360via satellite350or vehicle305.

A variety of other devices and systems may communicate with network360regarding information related to traffic direction gesture recognition. For example, other devices380and vehicles390may send and receive data364, which may include traffic metrics, lane and road information, or any other data associated with pedestrians, traffic directors, or gesture recognition.

In some embodiments, a navigation system370may exchange, with network360, data362related to traffic and road conditions, vehicle destination(s), or other data related to navigation. In some embodiments, data362may supplement or replace information stored in a database372of navigation system370. In various embodiments, navigation system370may be implemented within vehicle305or another vehicle in communication with network360. In some embodiments, vehicle305may communicate directly with any of navigation system370, other devices380, or other vehicles390.

FIGS. 1-3provide examples of a vehicle that may implement traffic direction gesture recognition. However, numerous other types or configurations of vehicles or other systems may implement the methods and systems described herein.FIGS. 4-6 and 8are high-level flowcharts illustrating various methods and techniques to implement traffic direction gesture recognition, according to some embodiments. The various components described above may implement these techniques as well as various other systems.

FIG. 4is a high-level flowchart illustrating various methods and techniques of recognizing a traffic diversion, according to some embodiments. In some embodiments one or more elements of method400may be implemented within a processor, for example processor210of vehicle110as described with reference toFIG. 2. According to various example embodiments, one or more of the steps of method400may be implemented remotely from the vehicle for which traffic direction gesture recognition is implemented.

At step410of method400, data is received from one or more sensors coupled to the vehicle for which traffic direction gesture recognition is implemented. As described herein, sensor data may include information about an environment surrounding a vehicle, including data about pedestrians and other obstacles, abnormal traffic conditions, and other information about the vicinity in which traffic direction gesture recognition is implemented, for example the gestures, three-dimensional size, three-dimensional velocity, and three-dimensional location of such vehicles and obstacles.

At step420, traffic and environmental conditions are analyzed, at least in part using the received sensor data. Step420may include performing some or all of the functions described herein with reference to environmental analysis module212ofFIG. 2. For example, step420may include any of detecting an abnormal traffic condition, detecting pedestrians, detecting pedestrian characteristics, detecting possible traffic directors, or detecting gestures.

As discussed herein, in some embodiments, various traffic metrics describing characteristics of nearby vehicles and obstacles may be calculated. Traffic hazards may be noted, especially where the hazard is serious enough that it might necessitate overriding any portion of a typical process for identifying traffic direction gestures.

At steps430-450, the vehicle or environmental analysis module according to some embodiments attempts to determine whether an abnormal traffic condition exists. For example, at step430the vehicle looks for unexpected obstacles or heavier-than-expected traffic. At step440, the vehicle looks for emergency signage. At step450, the system determines whether an unexpected pedestrian is present. If none of those conditions exists, control may pass back to step410according to some embodiments.

At each of steps430-450according to some embodiments, if the described hazard is detected, the vehicle or environmental analysis module may proceed at step460to a traffic director detection mode. Example traffic director detection methods are described below with reference toFIG. 5.

FIG. 5is a high-level flowchart illustrating various methods and techniques of detecting a traffic director, according to some embodiments. In some embodiments, one or more elements of method500may be implemented within a processor, for example processor210of vehicle110as described with reference toFIG. 2. According to various example embodiments, one or more of the steps of method500may be implemented remotely from the vehicle for which traffic direction gesture recognition is implemented.

At step510, according to some embodiments, a vehicle or environmental analysis module may detect pedestrians. For example, an environmental analysis module may project lidar obstacle points into a calibrated image plane of a camera of a vehicle. The environmental analysis module may further implement various methods such as machine-learning-based pedestrian detection based on aggregated channel features, adaptive boosting, or neural networks. In some example embodiments, each piece of data available to an environmental analysis module (e.g. colors, signs, gestures, etc.) may be combined or fused and analyzed using a random forest classifier.

At step520, according to some embodiments, a vehicle or environmental analysis module may detect characteristics of pedestrians, which it may characterize at step530. In some situations, the appearance of a pedestrian acting as a traffic director may include distinguishing features. For example, various authorities may wear particular colors (e.g. bright yellow or orange, or dark blue) when directing traffic. This information may, for example, be stored in a database, learned by the environmental analysis module, or a combination thereof.

In some embodiments, a pedestrian may hold a road sign, for example, one or more of a “SLOW,” “STOP,” “CROSSING,” or “DETOUR” sign. In other examples, temporary or permanent road signs may be taken into account by the environmental analysis module212, in combination or independently of a detected traffic director, and/or independently of whether the sign is held by a pedestrian

At step540, according to some embodiments, a vehicle or environmental analysis module may attempt to characterize detected pedestrians by comparing pedestrian characteristics to traffic director characteristics in order to decide at step550whether any pedestrian has characteristics of a traffic director. For example, if a pedestrian wears a known uniform of law enforcement, the pedestrian may be characterized as a traffic director. In other examples, a pedestrian may be recognized as a traffic director simply by using a recognized and detected valid traffic direction gesture.

At step550, if no pedestrian is detected to exhibit characteristics of a traffic director, control may pass back to step510. If one or more pedestrians has been detected as a traffic director or possible traffic director, control may pass at step560to a gesture detection mode. Example gesture detection modes are described below with reference toFIG. 6.

FIG. 6is a high-level flowchart illustrating various methods and techniques of command detection, according to some embodiments. In some embodiments, one or more elements of method600may be implemented within a processor, for example processor210of vehicle110as described with reference toFIG. 2. According to various example embodiments, one or more of the steps of method600may be implemented remotely from the vehicle for which traffic direction gesture recognition is implemented.

Command detection may begin at step610with detection of pedestrians, for example by any means described in detail herein with reference toFIG. 2,FIG. 5, orFIG. 1. Alternatively, command detection may begin at step620with one or more traffic directors or possible traffic directors already identified, for example by the processes described herein with reference toFIG. 2orFIG. 5.

At step630, gestures of identified pedestrians may be detected. For example, a movement of a pedestrian's arms, head, legs, etc. may be detected as potential gestures. In some embodiments, these movements must be separated from typical behavior of pedestrians, such as movements inherent to walking.

According to some embodiments, a pedestrian who makes a gesture known to an environmental analysis module or gesture recognition module to be a traffic direction gesture may be identified as acting as a traffic director. In some embodiments, the identification as a traffic director may further require concurrent detection of an abnormal traffic condition, particularly if the environmental analysis module can detect no other distinguishing characteristic of the pedestrian that suggests the pedestrian is validly acting as a traffic director.

At step640, detected pedestrian gestures may be compared to known command gestures to determine if a traffic director or possible traffic director is attempting to issue a command to the vehicle. For example, gesture recognition module may search a local or remote database for known gestures that match a recognized gesture.

According to some embodiments, an environmental analysis module or gesture recognition module214may employ a hidden Markov model, Recurrent Neural Network, or other sequential classifier—applied, for example, as a spatio-temporal classification of a pedestrian's activity. A hidden Markov model, Recurrent Neural Network, or other sequential classifier may be developed, for example, for a “directing traffic” state, which if detected would indicate that a pedestrian is a likely traffic director.

If no pedestrian gesture is recognized as a command gesture, then at step650, control may pass back to step610or620. Where a pedestrian gesture has been recognized as a command, for example a command to maneuver the vehicle, control may pass at step660to a command acknowledgement logic. An example command acknowledgement method is described below with reference toFIG. 8.

FIG. 7shows an example traffic direction gesture database including several examples of traffic direction gestures, according to some embodiments.

Gestures710a-710findicate several examples of a gesture indicating a “STOP” command. To perform gestures710aand710b, a traffic director holds up one of his or her hands. To perform gestures710cand710d, a traffic director holds up both of his or her hands. Gestures710eand710finclude a stop sign held in one hand of the traffic director. Similarly, gestures740aand740binclude a slow sign held in one hand of the traffic director to indicate to a vehicle that the vehicle should slow its speed.

In performing example gesture720, a traffic director holds out his or her left arm while facing a vehicle to indicate that the vehicle should turn right in the direction the arm is pointing. In performing example gesture730, a traffic director holds out his or her right arm while facing a vehicle to indicate that the vehicle should turn left in the direction the arm is pointing.

One having ordinary skill in the art will understand that the example gestures ofFIG. 7are merely illustrative of a very few possible gestures, and that the specific gestures may vary, for example by region, country, and law or traffic enforcement jurisdiction.

FIG. 8is a high-level flowchart illustrating various methods and techniques of acknowledging a command and performing a commanded maneuver, according to some embodiments. In some embodiments, one or more elements of method800may be implemented within a processor, for example processor210of vehicle110as described with reference toFIG. 2. According to various example embodiments, one or more of the steps of method800may be implemented remotely from the vehicle for which traffic direction gesture recognition is implemented.

At step810, according to some embodiments, a command acknowledgement module may operate a command acknowledgement device to communicate the vehicle's understanding of the detected command. According to some embodiments, the command acknowledgement device may include any or all of a turn signal, LED or other visual display, a video screen, or an audible notification.

At step820, a traffic director according to some embodiments may acknowledge, positively or negatively, for example using another simple gesture (e.g. thumbs up or down) whether a vehicle has understood the traffic command correctly. A vehicle or environmental analysis module may detect the traffic director's acknowledgement using the same or similar techniques as described herein for detecting various other traffic direction gestures. In some embodiments, a command acknowledgement or traffic director acknowledgement may be made electronically, for example as a signal to an autonomous control or other computing system.

If the vehicle has correctly understood the traffic direction signal at830, the vehicle may proceed directly to performing the commanded maneuver or maneuvers at840. Various control systems typical of autonomous vehicles may be called on to complete the necessary maneuvers.

If the vehicle has misunderstood a command or, for example, the traffic director has not issued a command, or a vehicle has mistaken a normal pedestrian for a traffic director, control may revert to a gesture detection logic at step850.

According to some embodiments, if a gesture is recognized, for example, by matching a known maneuver command, a vehicle may perform the maneuver immediately. In other embodiments, vehicle may acknowledge the recognized command or seek confirmation from the traffic director as described herein.

Various embodiments of automated capture of image data for points of interest may be executed in one or more computer systems900, which may interact with various other devices. Note that any component, action, or functionality described above with respect toFIGS. 1 through 8may be implemented on one or more computers configured as computer system900ofFIG. 9, according to various embodiments. In the illustrated embodiment, computer system900includes one or more processors910coupled to a system memory920via an input/output (I/O) interface930. Computer system900further includes a network interface940coupled to I/O interface930, and one or more input/output devices, which can include one or more user interface (also referred to as “input interface”) devices. In some cases, it is contemplated that embodiments may be implemented using a single instance of computer system900, while in other embodiments multiple such systems, or multiple nodes making up computer system900, may be configured to host different portions or instances of embodiments. For example, in one embodiment some elements may be implemented via one or more nodes of computer system900that are distinct from those nodes implementing other elements.

In various embodiments, computer system900may be a uniprocessor system including one processor910, or a multiprocessor system including several processors910(e.g., two, four, eight, or another suitable number). Processors910may be any suitable processor capable of executing instructions. For example, in various embodiments processors910may be general-purpose or embedded processors implementing any of a variety of instruction set architectures (ISAs), such as the x86, PowerPC, SPARC, or MIPS ISAs, or any other suitable ISA. In multiprocessor systems, each of processors910may commonly, but not necessarily, implement the same ISA.

System memory920may be configured to store program instructions, data, etc. accessible by processor910. For example, memory920of computer system900may include executable instructions925for performing various tasks. In various embodiments, system memory920may be implemented using any suitable memory technology, such as static random access memory (SRAM), synchronous dynamic RAM (SDRAM), nonvolatile/Flash-type memory, or any other type of memory. In the illustrated embodiment, program instructions included in memory920may be configured to implement some or all of a traffic direction gesture recognition system, incorporating any of the functionality described above. Additionally, existing control data of memory920may include any of the information or data structures described above. In some embodiments, program instructions and/or data may be received, sent or stored upon different types of computer-accessible media or on similar media separate from system memory920or computer system900. While computer system900is described as implementing the functionality of functional blocks of previous Figures, any of the functionality described herein may be implemented via such a computer system.

Input/output devices may, in some embodiments, include one or more display terminals, keyboards, keypads, touchpads, scanning devices, voice or optical recognition devices, or any other devices suitable for entering or accessing data by one or more computer systems900. Multiple input/output devices may be present in computer system900or may be distributed on various nodes of computer system900. In some embodiments, similar input/output devices may be separate from computer system900and may interact with one or more nodes of computer system900through a wired or wireless connection, such as over network interface940.

Memory920may include program instructions, which may be processor-executable to implement any element or action described above. In one embodiment, the program instructions may implement the methods described above. In other embodiments, different elements and data may be included. Note that data may include any data or information described above.

Although specific embodiments have been described above, these embodiments are not intended to limit the scope of the present disclosure, even where only a single embodiment is described with respect to a particular feature. Examples of features provided in the disclosure are intended to be illustrative rather than restrictive unless stated otherwise. The scope of the present disclosure includes any feature or combination of features disclosed herein (either explicitly or implicitly), or any generalization thereof, whether or not it mitigates any or all of the problems addressed herein. Accordingly, new claims may be formulated during prosecution of this application (or an application claiming priority thereto) to any such combination of features. In particular, with reference to the appended claims, features from dependent claims may be combined with those of the independent claims and features from respective independent claims may be combined in any appropriate manner and not merely in the specific combinations enumerated in the appended claims.

Various ones of the methods described herein may be implemented in software, hardware, or a combination thereof, in different embodiments. In addition, the order of the blocks of the methods may be changed, and various elements may be added, reordered, combined, omitted, modified, etc. Various modifications and changes may be made as would be obvious to a person skilled in the art having the benefit of this disclosure. The various embodiments described herein are meant to be illustrative and not limiting. Many variations, modifications, additions, and improvements are possible. Boundaries between various components and operations are somewhat arbitrary, and particular operations are illustrated in the context of specific illustrative configurations. Other allocations of functionality are envisioned and may fall within the scope of claims that follow. Finally, structures and functionality presented as discrete components in the exemplary configurations may be implemented as a combined structure or component. These and other variations, modifications, additions, and improvements may fall within the scope of embodiments as defined in the claims that follow.