Patent Publication Number: US-2022223024-A1

Title: Operator proficiency-based infrastructure articles

Description:
TECHNICAL FIELD 
     The present application relates generally to electrically powered scooters and roadway infrastructure. 
     BACKGROUND 
     Electric scooters are often used to transport people over relatively short distances. A user of an electric scooter typically rides the scooter on a roadway, street, pathway or a sidewalk, and frequently may use the scooter in urban or campus settings as a convenient mode of transportation. In many situations, the roadway/street used by the scooter (or adjacent to the path or lane used by the scooter) may by occupied by vehicles travelling at relatively high speeds compared to the scooter. Moreover, sidewalks are often occupied by pedestrians travelling at relatively low speeds compared to the scooter. Navigating roadways, streets, paths and/or sidewalks may pose a risk to the safety of the user of the electric scooter, occupants of a vehicle, pedestrians, or any other person, pet, or property in proximity to the scooter. 
    
    
     
       BRIEF DESCRIPTION OF DRAWINGS 
         FIG. 1  is a conceptual diagram illustrating an example physical environment having transportation system that includes one or more electrically powered scooters, in accordance with techniques of this disclosure. 
         FIG. 2  is a block diagram illustrating an example system for improving safety associated with an electrically powered scooter based at least in part on operator proficiency, in accordance with techniques of this disclosure. 
         FIG. 3  is a block diagram illustrating an example computing device, in accordance with one or more aspects of the present disclosure. 
         FIG. 4  is a conceptual diagram of an electrically powered scooter, in accordance with techniques of this disclosure. 
         FIG. 5  is a flow diagram illustrating example operations of a computing device that use data for operator proficiency, in accordance with one or more techniques of this disclosure. 
     
    
    
     DETAILED DESCRIPTION 
       FIG. 1  is a conceptual diagram illustrating an example physical environment having a transportation system that includes one or more electrically powered scooters, in accordance with techniques of this disclosure. In the example of  FIG. 1 , the transportation system  100  includes a variety of different infrastructure elements (generally referred to as “infrastructure”). As shown in the example of  FIG. 1 , the infrastructure may include dedicated transportation pathways  106 A- 106 D (collectively, transportation pathways  106 ) as well as infrastructure articles  107 A- 107 E (collectively, infrastructure articles  107 ) positioned and oriented within the environment. 
     As shown in  FIG. 1 , system  100  includes one or more micro-mobility devices. Examples of micro-mobility devices include electrically-powered food delivery devices, electrically powered hoverboards or skateboards, electrically powered scooters  110 A- 110 C (collectively, electrically powered scooters  110 ), or other small-profile devices that may use or travel upon a roadway or sidewalk. Electrically powered scooters  110  (also referred to herein simply as scooters  110 ) may operate on transportation pathways  106 . As described in more detail with reference to  FIG. 3 , in this example, electrically powered scooters  110  includes a chassis, a front wheel, a back wheel, an electric motor, a steering assembly, and a battery  119 . In this example, the chassis includes a rear-wheel mount at one end of the chassis, a front-wheel mount at another end of the chassis that is opposite the rear-wheel mount, and a chassis support extending horizontally between the rear-wheel mount and the front-wheel mount. The front and rear wheels are mounted to the front and rear wheel mounts of the chassis, respectively. The front wheel mount is coupled to a steering assembly. In some examples, the steering assembly includes handlebars such that turning the handle bars causes the front wheel to turn. In some examples, the electric motor is physically coupled to the scooter chassis and is configured by a motor controller to drive at least one of the chassis-supported front wheel or chassis-supported rear-wheel for powered movement over a ground surface. 
     Examples of transportation pathways  106  include a vehicle pathway (e.g., pathway  106 A,  106 D), a bicycle pathway (e.g., pathway  106 B), or a pedestrian pathway (e.g., pathway  106 C), among others. In other examples, transportation pathways  106  may be sidewalks, public spaces, or other surfaces not specifically dedicated to certain types of vehicles or traffic. Vehicle pathways (e.g.,  106 A) may be used by vehicles  104 A- 104 C (collectively, vehicles  104 ) to transport people or goods. Examples of vehicles  104  include automobiles (e.g.,  104 B,  104 C) such as cars, trucks, passenger vans; buses; motorcycles; recreational vehicles (RVs); or lorries (e.g.,  104 A), etc. Examples of vehicle pathways can also include alleys, streets, and highways (or a vehicle specific portion thereof, such as a vehicle driving lane), among others. Bicycle pathways (e.g.,  106 B) may be used by bicycles or vehicles and bicycles. Examples of bicycle pathways include a street or a portion of a street designated for bicycles, a bicycle trail, among others. In some instances, a pedestrian pathway (e.g.,  106 C) is primarily used by pedestrians  108 . Examples of pedestrian pathways include a pedestrian sidewalk or a jogging path. In some examples, one of transportation pathways  106  may include two or more different types of pathways. For instance, transportation pathway  106 A may include a vehicle driving lane of a vehicle pathway and a bicycle pathway adjacent to the driving lane. Transportation pathways  106  may include portions not limited to the respective pathways themselves. In the example of transportation pathway  106 A (e.g., a vehicle pathway), transportation pathway  106  may include the road shoulder, physical structures near the pathway such as toll booths, railroad crossing equipment, traffic lights, guardrails, and generally encompassing any other properties or characteristics of the pathway or objects/structures in proximity to the pathway. 
     Examples of infrastructure articles include a pavement marking (e.g., infrastructure article  107 A), a roadway sign (e.g., infrastructure article  107 B), a license plate (e.g., infrastructure article  107 C), a conspicuity tape (e.g., infrastructure article  107 D), and a hazard marker (e.g., infrastructure article  107 E, such as a construction barrel, a traffic cone, a traffic barricade, a safety barrier, among others). Pavement markings may include liquid markings, tape, or raised pavement markings to name only a few examples. In some examples, pavement markings may include sensors, materials, or structures that permit the detection of the marking and/or communication of information between the pavement marking and a receiving device. Additional examples of infrastructure articles  107  include traffic lights, guardrails, billboards, electronic traffic sign (also referred to as a variable-message sign), among others. Infrastructure articles  107  may include information that may be detected by one or more sensors of computing device  116 . 
     In some examples, an infrastructure article, such as infrastructure article  107 B, may include an article message  126  on the physical surface of the infrastructure article. Article message  126  may include characters, images, and/or any other information that may be printed, formed, or otherwise embodied on infrastructure article  107 B. For example, each infrastructure article  107  may have a physical surface having an article message  126  embodied thereon. Article message  126  may include human-perceptible information and machine-perceptible information. 
     Human-perceptible information may include information that indicates one or more first characteristics of a pathway, such as information typically intended to be interpreted by human drivers. In other words, the human-perceptible information may provide a human-perceptible representation that is descriptive of at least a portion of the transportation pathway. As described herein, human-perceptible information may generally refer to information that indicates a general characteristic of a transportation pathway and that is intended to be interpreted by a human driver. For example, the human-perceptible information may include words (e.g., “STOP” or the like), symbols, graphics (e.g., an arrow indicating the road ahead includes a sharp turn) or shapes (e.g., signs or lane markings). Human-perceptible information may include the color of the article, the article message or other features of the infrastructure article, such as the border or background color. For example, some background colors may indicate information only, such as “scenic overlook” while other colors may indicate a potential hazard (e.g., the red octagon of a stop sign, or the double yellow line of a no passing zone). 
     In some instances, the human-perceptible information may correspond to words or graphics included in a specification. For example, in the United States (U.S.), the human-perceptible information may correspond to words or symbols included in the Manual on Uniform Traffic Control Devices (MUTCD), which is published by the U.S. Department of Transportation (DOT) and includes specifications for many conventional signs for roadways. Other countries have similar specifications for traffic control symbols and devices. 
     Machine-perceptible information may generally refer to information configured to be interpreted by an electrically powered scooter. For example, article message  126  may be encoded via a 2-dimensional bar code, such as a QR code. In some examples, machine-perceptible information may be interpreted by a human driver. In other words, machine-perceptible information may include a feature of the graphical symbol that is a computer-interpretable visual property of the graphical symbol. In some examples, the machine-perceptible information may relate to the human-perceptible information, e.g., provide additional context for the human-perceptible information. In an example of an arrow indicating a sharp turn, the human-perceptible information may be a general representation of an arrow, while the machine-perceptible information may provide an indication of the shape of the turn including the turn radius, any incline of the roadway, a distance from the sign to the turn, or the like. The additional information may be visible to a human operator; however, the additional information may not be readily interpretable by the human operator, particularly at speed. In other examples, the additional information may not be visible to a human operator but may still be machine readable and visible to a vision system of an electrically powered scooter. In some examples, an enhanced infrastructure article may be an optically active article in that the infrastructure article is readily detectible by vision systems, which may include an infrared camera or other camera configured for detecting electromagnetic radiation in one or more bands of the electromagnetic spectrum, which may include the visible band, the infrared band, the ultraviolet band, and so forth. For example, the infrastructure articles may be reflective, such as retroreflective, within one or more bands of the electromagnetic spectrum that are readily detectible by visions systems of the computing device  116 . 
     Article message  126  may indicate a variety of types of information. In some examples, article message  126  may, for instance, provide computing device  116  with static information related to a region of a pathway  106 . Static information may include any information that is related to navigation of the pathway associated with article message  126 , and not subject to change. For example, certain features of pathways  106  may be standardized and/or commonly used, such that article message  126  may correspond to a pre-defined classification or operating characteristic of the respective pathway. As some examples, article message  126  may indicate a navigational characteristic or feature of the pathway, an operating rule or set of operating rules of the pathway, or the like. 
     Infrastructure articles  107  may include a variety of indicators and/or markers. For example, infrastructure article  107  may include one or more of an optical tag, a radio-frequency identification tag, a radio-frequency tag, an acoustic surface pattern, or a material configured to provide a signature to a signature-sending system. In some examples, electrically powered scooters  110  may receive data from infrastructure articles  107  via near-field communication (NFC) protocols and signals, laser, or infrared-based readers, or other communication type. 
     Electrically powered scooters  110  may each include one or more sensors that perceive characteristics of the environment, infrastructure, and other objects around electrically powered scooter  110 A. Examples of sensors include an image sensor, sonar, LiDAR, among others. The sensors may generate sensor data indicative of sensed characteristics. For example, the sensor data may include infrastructure data indicative of the infrastructure proximate to a respective scooter of electrically powered scooters  110 . An object may be proximate to a particular electrically powered scooter  110  when the object is detectable by one or more sensors of particular electrically powered scooter  110 . As one example, the infrastructure data may be indicative of one or more infrastructure articles  107  proximate to a respective scooter of electrically powered scooters  110 . 
     In accordance with techniques of this disclosure, infrastructure articles, such as pavement marking  128 A- 128 H may be configured to perform operations based at least in part on an operator proficiency of an operator of an electrically powered scooter. In environments that include micromobility devices, such as electrically powered scooters, operators of such devices may have different degrees or levels of operator proficiency. For example, an operator of an electrically powered scooter with a high degree of operator proficiency may have engaged in more rides, longer distances driven, or have greater familiarity with certain portions of a pathway, to name only a few examples. Conversely, an operator of an electrically powered scooter with a lower degree of operator proficiency may have engaged in fewer rides, fewer distances driven, or have less familiarity with certain portions of a pathway, to name only a few examples. In some instances, the probability of a safety risk may be higher to an operator with a lower operator proficiency. Accordingly, techniques and systems that can operate based on operator proficiency may reduce risks. While conventional infrastructure articles on a pathway may be agnostic to operatory proficiency, techniques and systems of this disclosure may operate based at least in part on operatory proficiency. As such, infrastructure articles and associated techniques and systems of this disclosure may improve safety by changing operation based on operatory proficiency. 
     In the example of  FIG. 1 , pavement marking  128 E may be an infrastructure article configured in accordance with techniques and systems of this disclosure. Example details of one or more constructions of a pavement marker, such as pavement marking  128 E, are described in U.S. Pat. No. 6,551,014, the entire contents of which are hereby incorporated by reference herein in its entirety. Although described with respect to pavement marking  128 E, any infrastructure article may be configured in accordance with techniques and systems of this disclosure. For instance, stationary hazard maker  130  or any other infrastructure article may implement techniques of this disclosure. In the example of  FIG. 1 , pavement marking  128 E may include a signal receiver device configured to receive a wireless signal from a signal emitter device configured at an electrically powered scooter. For instance, the signal receiver device may be a light capture device, image capture device, DSRC sensor, WiFi sensor, Bluetooth sensor, LIDAR sensor, or any other receiver device that is capable of receiving a signal. In some examples, the signal receiver device may also be configured to send one or more wireless signals, which may be of any of the types describe with respect to the signal receiver device. In such examples, the combination of receiving and sending functionality in a device may refer to a transceiver. 
     Pavement marking  128 E may include a controller configured to determine information, coded in the wireless signal, that is based on an operator proficiency of an operator of the electrically powered scooter. As further described in this disclosure, the controller may be a general-purpose processor, a digital signal processor (DSP), application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination thereof designed to perform the functions described herein. A general-purpose processor can be a microprocessor, but in the alternative, the processor can be any processor, microcontroller, or state machine. A controller can also be implemented as a combination of computing devices, for example, a combination of a DSP and a microprocessor, multiple microprocessors, one or more microprocessors in conjunction with a DSP core, or any other such configuration. 
     In accordance with techniques of this disclosure, a computing device associated with electrically powered scooter  110 A (e.g., computing device  116 A or a computing device associated with an operator of electrically powered scooter  110 A) may code information into a wireless signal that is based on an operator proficiency of an operator of the electrically powered scooter. As described above the wireless signal may be light, audible, DSRC, WiFi, Bluetooth, LIDAR, or any other suitable signal. The information encoded into the wireless signal may include information that indicates or is based at least in part on operator proficiency. In some examples, the information encoded in the wireless signal may be information that is different from or not based at least in part on operator proficiency. In some examples, the signal receiver device may be a light signal receiver device configured to receive a set of light pulses as the wireless signal. The light pulses may be emitted at a frequency which may not be visible to the human eye. For instance, the light pulses may be emitted at a frequency greater than 80 Hz. By emitting light pulses of different lengths over periods of time, a signal emitted by a signal emitter device of an electrically powered scooter may code information in the wireless signal where different information corresponds to different lengths of light pulses. Other techniques for coding information within light pulses within the skill of one with ordinary skill in the art may also be used. 
     The information encoded into the wireless signal may include supplemental information in addition to information that indicates or is based at least in part on operator proficiency. Examples of supplemental information may include information that corresponds to: the electrically powered scooter (e.g., type, age, operating capabilities, or other descriptive information of the scooter); the operator of the electrically powered scooter (e.g., operator history, operator demographics, information from an operator profile stored by an operator of a service for the electrically powered scooter or other descriptive information of the operator of the electrically powered scooter); the environment of the electrically powered scooter (e.g., pathway surface quality, pathway surface type, complexity of pathway; weather for pathway or other descriptive information of the pathway); other operators or vehicles of operators near the operator of the electrically powered scooter (e.g., operator proficiency of other operators, vehicle type, vehicle operating capabilities, or other descriptive information of operators or vehicles of operators near the operator of the electrically powered scooter); and/or other infrastructure articles in proximity to the electrically powered scooter (e.g., type, age, applicability to electrically powered scooters). 
     In accordance with techniques of this disclosure, computing device  116 A and/or one or more other computing devices (e.g., remote computing system  150  in  FIG. 2 ) may generate, store, process, and/or send/receive information that indicates an operator proficiency of an operator for an electrically powered scooter. In some examples, operatory proficiency may refer to a competence, experience, skill or comfort of the operator when using an electrically powered scooter. For instance, operatory proficiency may be based at least in part on: a quantity of rides driven by the operator, a cumulative distance driven by the operator, or whether the operator is operating within a designated region for the operator, to name only a few examples. In some examples, operator proficiency of the operator is based at least in part on whether the operator experienced a risk event during prior operation of one or more electrically powered scooters. A risk event may be an accident or a near-miss of an accident, or an unexpected event during operation of an electrically powered scooter. 
     Information indicating operator proficiency may be generated by one or more of: a computing device associated with the operator, a computing device associated with the electrically powered scooter, a computing device associated with other vehicles in proximity to the operator, infrastructure articles in proximity to the operator, or one or more other computing devices (e.g., remote computing device  150  in  FIG. 2 ) that process data used to indicate operator proficiency. One or more sensors may be used to generate information indicating operator proficiency, such as but not limited to: accelerometers, image capture devices, wireless sensors (e.g., Wifi, Bluetooth, RFID, DSCR and the like), impact sensors, speedometers, gyroscopes, magnetometers, and the like. By generating and collecting this data, a computing device may determine, for example, a quantity of rides driven by the operator, a cumulative distance driven by the operator, whether the operator experienced a risk event, or any other examples of operator proficiency. 
     Information that indicates operator proficiency may be coded into a wireless signal that is sent from a computing device associated with the electrically powered scooter or operator to the infrastructure article such as pavement marking  128 E. In some examples, the information that indicates operator proficiency is generated at the computing device associated with the electrically powered scooter or operator to the infrastructure article and send to pavement marking  128 E. In some examples, the information that indicates operator proficiency is received from a remote computing device at the computing device associated with the electrically powered scooter or operator to the infrastructure article and sent to pavement marking  128 E. In some examples, the information that indicates operator proficiency is received by pavement marking  128 E from a remote computing device without being sent via the computing device associated with the electrically powered scooter or operator to the infrastructure article. 
     An infrastructure article, such as pavement marking  128 E may receive the wireless signal which includes the information that indicates operator proficiency and perform, based at least in part on the information that indicates the operator proficiency of the operator of the electrically powered scooter, at least one operation. In some examples, the information included in the wireless signal may be a metric, such as a discrete or non-discrete value in a set or range of values that indicates operator proficiency. In some examples, the information included in the wireless signal may be based on operator proficiency but the information itself may not indicate operator proficiency. For instance, the information included in the wireless signal may indicate a command to be performed by pavement marking  128 E based at least in part on operator proficiency. In any case, pavement marking  128 E may receive the wireless signal and perform one or more operations based on operator proficiency. 
     Pavement marking  128 E may generate an output that is based at least in part on the information that indicates the operator proficiency of the operator of the electrically powered scooter. For instance, the output may be at least one of a visible output, an audible output, a motion-based output, or communication output (e.g., sending a wireless signal). The output may be based at least in part on the information that indicates the operator proficiency of the operator of the electrically powered scooter. For instance, the type of output may be based at least in part on the information that indicates the operator proficiency of the operator of the electrically powered scooter. Examples of types of output for a visual output may be color, light intensity, emitting frequency (e.g., outputs per second) may be based on operator proficiency of the operator of the electrically powered scooter. Types of output for audible output may be amplitude, frequency, or any audio property, any of which may be based on operator proficiency of the operator of the electrically powered scooter. Types of output for haptic output may be intensity, frequency, or any haptic property, any of which may be based on operator proficiency of the operator of the electrically powered scooter. 
     In some instances, pavement marking  128 E is configured in a set of infrastructure articles  128 A- 128 F, and whether to generate output  128 E is based at least in part on whether one or more other infrastructure articles of the set of infrastructure articles generate a respective output. For instance, if an operator proficiency is high within a range or set of operator proficiencies, pavement marking  128 E may not generate an output if one or more other infrastructure articles in proximity to pavement marking  128 E generate an output. Such techniques may save power when it may not be necessary to generate an output at each of infrastructure articles  128 A- 128 F because the operator has a higher level of operator proficiency. 
     An infrastructure article, such as pavement marking  128 E, that is configured in accordance with techniques of this disclosure may be configured to send a message to one or more computing devices associated with one or more electrically powered scooters. For instance, pavement marking  128 E may send one or more messages to electrically powered scooters  110 A and/or  110 B. In some examples, computing device  116 A may send one or more messages to one or more vehicles, infrastructure articles, or remote computing devices (e.g., remote computing system  150 ). The one or more messages may be based at least in part on an operator proficiency of an operator of electrically powered scooter  116 A. In some examples, the message is based at least in part on one or more properties of an environment that includes the infrastructure article. 
     In some examples, an infrastructure article, such as pavement marking  128 E may be configured to perform an operation based at least in part on a plurality of wireless signals received at the infrastructure article. For instance, pavement marking  128 E, in response to receiving wireless signals from electrically powered scooters  110 A and  110 B may perform an operation based at least in part on determination that multiple wireless signals were received at the infrastructure article. As an example, the type of output may change based on the determination that multiple wireless signals were received at the infrastructure article, although any operations are possible. In some examples, pavement marking  128 E, in response to a plurality of wireless signals received at the infrastructure article, may send wireless signals to one or more scooters, vehicles, pedestrians, infrastructure articles or other receivers, that indicate the operator proficiency of one or a set of the operators. 
     In some examples, an infrastructure article, such as pavement marking  128 E may perform at least one operation based at least in part on a determination by the controller of pavement marking  128 E that electrically powered scooter  110 A is approaching the infrastructure article. For instance, pavement marking  128 E may determine that electrically powered scooter  110 A is approaching the infrastructure article based on a speed, direction, or receipt of a wireless signal from electrically powered scooter  110 A. In this way, pavement marking  128 E may perform one or more operations prior to electrically powered scooter  110 A arriving at or near the location of pavement marking  128 E. In some examples, an infrastructure article, such as pavement marking  128 E may perform at least one operation based at least in part on a determination by the controller of pavement marking  128 E that electrically powered scooter  110 A is departing away from the infrastructure article. For instance, pavement marking  128 E may determine that electrically powered scooter  110 A is departing away from the infrastructure article based on a speed, direction, or receipt of a wireless signal from electrically powered scooter  110 A. In this way, pavement marking  128 E may perform one or more operations after electrically powered scooter  110 A has departed at or near the location of pavement marking  128 E. In some examples, pavement marking  128 E may perform the at least one operation based at least in part on an angularity of the wireless signal received at the infrastructure article. For example, pavement marking  128 E may determine an angularity of a wireless signal, such as an angle relative to an axis that originates at pavement marking  128 E, a sensor, and/or an electrically powered scooter. In some examples, pavement marking  128 E may perform different operations based on different angles at which the wireless signal is received. In some examples, pavement marking  128 E may include one or more retroreflectors. 
     While computing device  116 A is described as dynamically controlling scooter  110 A, techniques of this disclosure may enable a computing device to control any other type of micro-mobility device, such as a powered food-delivery device, hoverboard, or skateboard. In still other examples, techniques of this disclosure may enable a computing device to control any other type of vehicle, such as an automobile. 
       FIG. 2  is a block diagram illustrating an example system for improving safety associated with an electrically powered scooter based at least in part on operator proficiency, in accordance with techniques of this disclosure. System  140  illustrates additional details of system  100  of  FIG. 1 . In the examples of  FIG. 2 , system  140  includes electrically powered scooter  110 A, vehicle  104 B, and a remote computing system  150 . In some examples, the devices shown in  FIG. 2  are communicatively coupled to one another via network  114 . In some examples, the devices shown in  FIG. 2  are communicatively coupled to one another directly, for example, via a DSRC transceiver. 
     Electrically powered scooter  110 A includes computing device  116 A and vehicle  104 B includes computing device  116 B. Computing devices  116 A,  116 B (collectively, computing devices  116 ) may each include one or more communication unit  214 A,  214 B, and sensors  117 A,  117 B, respectively. Although computing device  116 A is shown as attached to electrically powered scooter  110 A, in other examples, functionality of computing device  116 A may be included in a computing device (e.g., smartphone, smartwatch, wearable, or other portable computing device) that is associated with the operator of an electrically powered scooter  100 . In such examples, computing device  116 A and the computing device that is associated with the operator of electrically powered scooter  100  may communicate with one another and/or one or more other computing devices. 
     Communication units  214 A,  214 B (collectively, communication units  214 ) of computing devices  116  may communicate with external devices by transmitting and/or receiving data. For example, computing device  116  may use communication units  214  to transmit and/or receive radio signals on a radio network such as a cellular radio network or other networks, such as networks  114 . In some examples communication units  214  may transmit and receive messages and information to other vehicles, such as information interpreted from infrastructure article  107 . In some examples, communication units  214  may transmit and/or receive satellite signals on a satellite network such as a Global Positioning System (GPS) network. In some examples, communications units  214  may transmit and/or receive data through network  114  to remote computing system  150  via communication unit  154 . 
     Sensors  117 A,  117 B (collectively, sensors  117 ) may be image sensors  102 A,  102 B (collectively, image sensors  102 ), temperature sensors, LiDAR, or a combination thereof, to name only a few examples of sensors. Examples of image sensors  102  may include semiconductor charge-coupled devices (CCD) or active pixel sensors in complementary metal-oxide-semiconductor (CMOS) or N-type metal-oxide-semiconductor (NMOS, Live MOS) technologies. Digital sensors include flat panel detectors. In one example, electrically powered scooter  110 A or vehicle  104 B includes at least two different sensors for detecting light in two different wavelength spectra. Image sensors  102  may have a fixed field of view or may have an adjustable field of view. An image sensor  102  with an adjustable field of view may be configured to pan left and right, up and down relative to electrically powered scooter  110  or vehicle  104 B as well as be able to widen or narrow focus. In some examples, image sensors  102  may include a first lens and a second lens. Electrically powered scooter  110  and/or vehicle  104 B may have more or fewer image sensors  102  in various examples. 
     In the example of  FIG. 2 , computing device  116 A includes a user component  118 , a user interface (UI) component  124 , and a control component  144 . Components  118 A,  124 , and  144  may perform operations described herein using software, hardware, firmware, or a mixture of both hardware, software, and firmware residing in and executing on computing device  116  and/or at one or more other remote computing devices. In some examples, components  118 A,  124 , and  144  may be implemented as hardware, software, and/or a combination of hardware and software. 
     Computing device  116 A may execute components  118 A,  124 , and  144  with one or more processors. Computing device  116 A may execute any of components  118 A,  124 ,  144  as or within a virtual machine executing on underlying hardware. Components  118 A,  124 ,  144  may be implemented in various ways. For example, any of components  118 A,  124 ,  144  may be implemented as a downloadable or pre-installed application or “app.” In another example, any of components  118 A,  124 ,  144  may be implemented as part of an operating system of computing device  116 . 
     UI component  124  may include any hardware or software for communicating with a user of electrically powered scooter  110 . In some examples, UI component  124  includes outputs to a user such as displays, such as a display screen, indicator or other lights, audio devices to generate notifications or other audible functions, and/or haptic feedback devices. UI component  124  may also include inputs such as knobs, switches, keyboards, touch screens or similar types of input devices. 
     In general, sensors  117  may be used to gather information about infrastructure proximate to electrically powered scooter  110 A and vehicle  104 B, such as information about transportation pathways  106 . Sensors  117  may generate infrastructure data indicative of the infrastructure proximate to electrically powered scooter  110 A or vehicle  104 B. For example, image sensors  102  may capture images of infrastructure articles, such as lane markings, centerline markings, edge of roadway or shoulder markings, as well as the general shape of the transportation pathway. The general shape of a transportation pathway may include turns, curves, incline, decline, widening, narrowing or other characteristics. 
     Computing device  116 A may include a user component  118 A configured to perform techniques of this disclosure. For example, user component  118 A may receive data that indicates operator proficiency from one or more other devices, such as but not limited to remote computing system  150 , computing device  116 B, and/or infrastructure article  128 E. In some examples, user component  118 A may receive data from sensors  117 A and/or communication unit  214 A that indicate operator proficiency or may be used to generator data indicating operator proficiency. User component  118 A may cause communication unit  214 A to send and receive data indicating operator proficiency with another device such as remote computing system  150 . As further described herein, remote computing system  150  may store usage data for micromobility devices and/or user data associated with operators of micromobility devices. 
     In the example of  FIG. 2 , user component  118 A may cause communication unit  214 A to code information in a wireless signal that indicates operator proficiency. Communication unit  214 A may send the wireless signal to infrastructure article  128 E, which may change its operation based at least in part on the operator proficiency. Operations that may be performed by infrastructure article  128 E are described throughout this disclosure, such as but not limited to, generating an output or sending data to another device. 
     Control component  144  may be configured to perform an operation by adjusting operation of electrically powered scooter  110 A. Control component  144  may include, for example, any circuitry or other hardware, or software that may adjust one or more functions of the vehicle. Some examples include adjustments to change a speed of electrically powered scooter  110 A, shut off an electric motor that drives one or more wheels, or both. In some examples, control component  144  may be configured based at least in part on operator proficiency such that operation of electrically powered scooter  110 A is based on operator proficiency. Control component  144  may perform operations based at least in part on data received from infrastructure article  128 E. In some examples, control component  144  adjusts operation of the electric motor of electrically powered scooter  110 A. For example, control component  144  may cause the electric motor to slow down or stop, which may slow or stop the electrically powered scooter  110 A. In another example, control component  144  causes a brake apparatus to slow or stop electrically powered scooter  110 A. As yet another example, control component  144  may adjust a maximum allowable speed of electrically powered scooter  110 A. 
     UI component  124  may perform the at least one operation by generating an output. For example, the output may include an audio output, a visual output, a haptic output, or a combination thereof. As one example, computing device  116 A may output a visual alert via one or more LED lights or output a haptic alert (e.g., causing the steering assembly to vibrate) indicating that electrically powered scooter  110 A is not permitted in its current location. 
     Remote computing system  150  may include a distributed computing platform (e.g., a cloud computing platform executing on various servers, virtual machines and/or containers within an execution environment provided by one or more data centers), physical servers, desktop computing devices, or any other type of computing system. In some examples, communication unit  214  of computing device  116  of electrically powered scooter  110 A outputs messages or other data to a remote computing device, such as remote computing system  150 . In some examples, the message indicates usage data for electrically powered scooter  110 A. The usage data may include a current location of electrically powered scooter  110 A, whether the current location of electrically powered scooter  110 A is permitted, a type of the current location (e.g., a transportation pathway  106 , a park, a scooter parking zone, etc.), an amount of time that electrically powered scooter  110 A has been in its current location, information indicating the occurrence of a scooter-specific event, among other information. In some examples, the user data may include data about operator proficiency as described in this disclosure. That is, the data about operator proficiency may be data from sensors  117 A, communication unit  214 A or other components illustrated or not illustrated in  FIG. 2 . Data about operator proficiency may also be generated by computing device  116 A based at least in part on data from sensors  117 A, communication unit  214 A or other components illustrated or not illustrated in  FIG. 2 . 
     Remote computing system  150  may receive the message from computing device  116 A. The message may include usage data associated with electrically powered scooter  110 A. Analysis module  152  of remote computing system  150  may store the usage data within usage data  156 . For example, analysis module  152  may store a user account for each user of electrically powered scooters  110  within user data  158 . The user account may include information indicating whether the user complied with various scooter operating rules, such as complying with speed limits, operating electrically powered scooter  110 A in prohibited locations, or parking electrically powered scooter  110 A within a pre-defined set of delineated parking regions. The user account may include information indicating an operator proficiency of the user. In some examples, analysis module  152  may send operator proficiency data to one or more other devices, such as computing device  116 A, infrastructure article  128 E, computing device  116 B or any other devices. 
     While user component  118 A of computing device  116 A is described as performing various functionality of computing device  116 A, in some examples, user component  118 B of computing device  116 B may perform similar functionality. For example, user component  116 B may determine an operator proficiency of a vehicle operator based on data from one or more of sensors  117 B and/or communication unit  214 B, either of which may interact with infrastructure article  128 E. In some examples, user component  118 B may receive data indicating an operator proficiency of electrically powered scooter  110 A from one or more devices, such as infrastructure article  128 E, computing device  116 A, and/or remote computing system  150 . 
       FIG. 3  is a block diagram illustrating an example computing device, in accordance with one or more aspects of the present disclosure.  FIG. 3  illustrates only one example of a computing device. Many other examples of computing device  116 A may be used in other instances and may include a subset of the components included in example computing device  116 A or may include additional components not shown in example computing device  116 A in  FIG. 3 . 
     As shown in the example of  FIG. 3 , computing device  116 A may be logically divided into user space  202 , kernel space  204 , and hardware  206 . Hardware  206  may include one or more hardware components that provide an operating environment for components executing in user space  202  and kernel space  204 . User space  202  and kernel space  204  may represent different sections or segmentations of memory, where kernel space  204  provides higher privileges to processes and threads the user space  202 . For instance, kernel space  204  may include operating system  220 , which operates with higher privileges than components executing in user space  202 . 
     As shown in  FIG. 3 , hardware  206  includes one or more processors  208 , input components  210 , storage devices  212 , communication units  214 , output components  216 , and sensors  117 . Processors  208 , input components  210 , storage devices  212 , communication units  214 , output components  216 , and sensors  117  may each be interconnected by one or more communication channels  218 . Communication channels  218  may interconnect each of the components  208 ,  210 ,  212 ,  214 ,  216 , and  117  and other components for inter-component communications (physically, communicatively, and/or operatively). In some examples, communication channels  218  may include a hardware bus, a network connection, one or more inter-process communication data structures, or any other components for communicating data between hardware and/or software. 
     One or more processors  208  may implement functionality and/or execute instructions within computing device  116 A. For example, processors  208  on computing device  116 A may receive and execute instructions stored by storage devices  212  that provide the functionality of components included in kernel space  204  and user space  202 . These instructions executed by processors  208  may cause computing device  116 A to store and/or modify information, within storage devices  212  during program execution. Processors  208  may execute instructions of components in kernel space  204  and user space  202  to perform one or more operations in accordance with techniques of this disclosure. That is, components included in user space  202  and kernel space  204  may be operable by processors  208  to perform various functions described herein. 
     One or more input components  210  of computing device  116 A may receive input. Examples of input are tactile, audio, kinetic, and optical input, to name only a few examples. Input components  210  of computing device  116 A, in one example, include a voice responsive system, video camera, buttons, control pad, microphone or any other type of device for detecting input from a human or machine. In some examples, input component  210  may be a presence-sensitive input component, which may include a presence-sensitive screen, touch-sensitive screen, etc. 
     One or more communication units  214  of computing device  116 A may communicate with external devices by transmitting and/or receiving data. For example, computing device  116 A may use communication units  214  to transmit and/or receive radio signals on a radio network such as a cellular radio network. In some examples, communication units  214  may transmit and/or receive satellite signals on a satellite network such as a Global Positioning System (GPS) network. Examples of communication units  214  include a DSRC transceiver, an optical transceiver, a radio frequency transceiver, a GPS receiver, or any other type of device that can send and/or receive information. Other examples of communication units  214  may include Bluetooth®, GPS, 3G, 4G, and Wi-Fi® radios found in mobile devices as well as Universal Serial Bus (USB) controllers and the like. 
     One or more output components  216  of computing device  116 A may generate output. Examples of output are tactile, audio, and video output. Output components  216  of computing device  116 A, in some examples, include a presence-sensitive screen, sound card, video graphics adapter card, speaker, cathode ray tube (CRT) monitor, liquid crystal display (LCD), or any other type of device for generating output to a human or machine. Output components may include display components such as a liquid crystal display (LCD), a Light-Emitting Diode (LED) or any other type of device for generating tactile, audio, and/or visual output. Output components  216  may be integrated with computing device  116 A in some examples. 
     In other examples, output components  216  may be physically external to and separate from computing device  116 A but may be operably coupled to computing device  116 A via wired or wireless communication. An output component may be a built-in component of computing device  116 A located within and physically connected to the external packaging of computing device  116 A (e.g., a screen on a mobile phone). In another example, a presence-sensitive display may be an external component of computing device  116 A located outside and physically separated from the packaging of computing device  116 A (e.g., a monitor, a projector, etc. that shares a wired and/or wireless data path with a tablet computer). 
     Output components  216  may also include control component  144 , in examples where computing device  116 A is onboard an electrically powered scooter. Control component  144  has the same functions as control component  144  described in relation to  FIG. 1 . 
     One or more storage devices  212  within computing device  116 A may store information for processing during operation of computing device  116 A. In some examples, storage device  212  is a temporary memory, meaning that a primary purpose of storage device  212  is not long-term storage. Storage devices  212  on computing device  116 A may configured for short-term storage of information as volatile memory and therefore not retain stored contents if deactivated. Examples of volatile memories include random access memories (RAM), dynamic random-access memories (DRAM), static random-access memories (SRAM), and other forms of volatile memories known in the art. 
     Storage devices  212 , in some examples, also include one or more computer-readable storage media. Storage devices  212  may be configured to store larger amounts of information than volatile memory. Storage devices  212  may further be configured for long-term storage of information as non-volatile memory space and retain information after activate/off cycles. Examples of non-volatile memories include magnetic hard discs, optical discs, floppy discs, flash memories, or forms of electrically programmable memories (EPROM) or electrically erasable and programmable (EEPROM) memories. Storage devices  212  may store program instructions and/or data associated with components included in user space  202  and/or kernel space  204 . 
     As shown in  FIG. 3 , application  228  executes in user space  202  of computing device  116 A. Application  228  may be logically divided into presentation layer  222 , application layer  224 , and data layer  226 . Presentation layer  222  may include user interface (UI) component  124 , which generates and renders user interfaces of application  228 . Application  228  may include, but is not limited to: UI component  124 , user component  118 A, security component  120 , and one or more service components  122 . For instance, application layer  224  may include user component  118 A, service component  122 , and security component  120 . Presentation layer  222  may include UI component  124 . 
     Data layer  226  may include one or more datastores. A datastore may store data in structure or unstructured form. Example datastores may be any one or more of a relational database management system, online analytical processing database, table, or any other suitable structure for storing data. 
     Service data  233  may include any data to provide and/or resulting from providing a service of service component  122 . For instance, service data  233  may include information about infrastructure articles  107 , user information, operating rule sets, or any other information transmitted between one or more components of computing device  116 A. Operating data  236  may include instructions for scooter operating rule sets for operating electrically powered scooter  110 A. 
     Sensor data  232  may include infrastructure data, such as image data, signature data, or any other data indicative of infrastructure proximate to electrically powered scooter  110 A. For example, communication units  214  may receive, from an image sensor  102 , image data indicative of infrastructure proximate to electrically powered scooter  110 A and may store the image data in sensor data  232 . Image data may include one or more images that are received from one or more image sensors, such as image sensors  102 . In some examples, the images are bitmaps, Joint Photographic Experts Group images (JPEGs), Portable Network Graphics images (PNGs), or any other suitable graphics file formats. In some examples, the image data includes images of one or more infrastructure articles  107  of  FIG. 1 . In one example, the image data includes images of one or more article message  126  associated with one or more infrastructure articles  107 . 
     In some examples, user component  118 A causes control component  144  to adjust control of electrically powered scooter  110 A based on data received from one or more devices such as a remote computing system or infrastructure article. For example, user component  118 A may cause control component  144  to adjust operation of the electric motor and/or adjust operation of the braking assembly (e.g., to adjust a speed of electrically powered scooter  110 A). In some examples, user component  118 A causes control component  144  to adjust control of electrically powered scooter  110 A based on data generated by one or more components or modules in computing device  116 A. User component  118 A may determine, generate or receive data that indicates an operator proficiency for operating an electrically powered scooter. 
     In some examples, user component  118 A may select information that is based on an operator proficiency of an operator of the electrically powered scooter. User component  118 A may code, into a wireless signal, the information that is based on an operator proficiency of an operator of the electrically powered scooter. In some examples, user component  118 A may code the information by causing one or more of hardware  206  to convert a digital representation of the information into the wireless signal. For example, one or more of output components  216  and/or communication unit  214  may convert and/or send the wireless signal to one or more other devices. In some examples, the wireless signal may be sent to an infrastructure article configured to receive the wireless signal. 
       FIG. 4  is a conceptual diagram of an electrically powered scooter  110 A, in accordance with techniques of this disclosure. 
     Electrically powered scooter  110 A include a chassis  402 , a rear wheel  404 , a front wheel  406 , and a steering assembly  408 . Chassis  402  includes chassis support member  412  extending substantially horizontally between a rear-wheel mount  414  at one end of chassis  402  and a front-wheel mount  416  at another end of chassis  402  that is opposite the rear-wheel mount  414 . 
     In the example of  FIG. 4 , rear wheel  404  is mounted to rear wheel mount  414  and front wheel  406  is mounted to front wheel mount  416 . Front wheel  406  is mounted to front wheel mount  416  for turning steering movement with respect to the front wheel mount  406  and rear wheel  404 . Front wheel mount  416  may be coupled to steering assembly  408 . Steering assembly  408  may extend generally vertically relative to chassis support member  412 . Steering assembly may be angled relative to chassis support member  412 . In one example, an angle between chassis support member  412  and steering assembly  408  is between approximately 60 degrees to approximately 90 degrees. Steering assembly  408  may include handlebars  410 . Steering assembly  408  may be coupled to front wheel mount  416  such that turning handlebars  410  may cause front wheel  406  to turn. 
     Electrically powered scooter  110 A includes at least one electric motor  420 , at least one motor controller  422 , and at least one battery  424 . Motor controller  422  may be operatively coupled to electric motor  420  to drive rear wheel  404  and/or front wheel  406 . In the example of  FIG. 4 , electric motor  420  is configured to drive rear wheel  404 , in some examples, electric motor  420  may be configured to drive front wheel  406 . In one example, electrically powered scooter  110 A includes a plurality of motors that are each configured to drive a respective wheel. 
     Electrically powered scooter  110 A may include a braking apparatus  430 . In the example of  FIG. 4 , braking apparatus  430  is operatively coupled to rear wheel  404  to selectively slow and/or stop rear wheel  404 . In some examples, electrically powered scooter  110 A includes a braking apparatus coupled to front wheel  406 . 
       FIG. 4  illustrates a block diagram of components of infrastructure article  128 E, in accordance with one or more aspects of the present disclosure.  FIG. 4  illustrates only one example of a computing device. Many other examples of infrastructure article  128 E may be used in other instances and may include a subset of the components included in example infrastructure article  128 E or may include additional components not shown in example infrastructure article  128 E in  FIG. 4 . 
     As shown in  FIG. 4 , infrastructure article  128 E includes one or more processors  458 , input components  460 , storage devices  462 , communication units  464 , output components  466 , and sensors  467 . Processors  458 , input components  460 , storage devices  462 , communication units  464 , output components  466 , and sensors  467  may each be interconnected by one or more communication channels  468 . Communication channels  468  may interconnect each of the components  458 ,  460 ,  462 ,  464 ,  466 , and  467  and other components for inter-component communications (physically, communicatively, and/or operatively). In some examples, communication channels  468  may include a hardware bus, a network connection, one or more inter-process communication data structures, or any other components for communicating data between hardware and/or software. 
     One or more processors  458  may implement functionality and/or execute instructions within infrastructure article  128 E. For example, processors  458  on infrastructure article  128 E may receive and execute instructions stored by storage devices  462  that provide the functionality of components included in storage devices  462 . These instructions executed by processors  458  may cause infrastructure article  128 E to store and/or modify information, within storage devices  462  during program execution. Processors  458  may execute instructions of components in storage devices  462  to perform one or more operations in accordance with techniques of this disclosure. That is, components included in storage devices  462  may be operable by processors  458  to perform various functions described herein. In some examples, processor  458  and storage devices  462  interoperating as a logical component may be referred to as a controller of infrastructure article  128 E. 
     One or more input components  460  of infrastructure article  128 E may receive input. Examples of input are tactile, audio, kinetic, electromagnetic (e.g., wireless signals) and optical input, to name only a few examples. Input components  460  of infrastructure article  128 E, in one example, include a, audio responsive system, video camera, buttons, control pad, microphone, pressure input device, light capture device, or any other type of device for detecting input from a human or machine or environment. In some examples, input component  460  may be a presence-sensitive input component, which may include a presence-sensitive screen, touch-sensitive screen, etc. 
     One or more communication units  464  of infrastructure article  128 E may communicate with external devices by transmitting and/or receiving data. For example, infrastructure article  128 E may use communication units  464  to transmit and/or receive radio signals on a radio network such as a cellular radio network. In some examples, communication units  464  may transmit and/or receive satellite signals on a satellite network such as a Global Positioning System (GPS) network. Examples of communication units  464  include a DSRC transceiver, an optical transceiver, a radio frequency transceiver, a GPS receiver, or any other type of device that can send and/or receive information. Other examples of communication units  464  may include Bluetooth®, GPS, 3G, 4G, and Wi-Fi® radios found in mobile devices as well as Universal Serial Bus (USB) controllers and the like. 
     One or more output components  466  of infrastructure article  128 E may generate output. Examples of output are haptic, audio, electromagnetic (e.g., wireless signals), and visual output to name only a few examples. Output components  466  of infrastructure article  128 E, in some examples, include a presence-sensitive screen, sound card, video graphics adapter card, speaker, cathode ray tube (CRT) monitor, liquid crystal display (LCD), or any other type of device for generating output to a human or machine or environment. Output components may include display components such as a liquid crystal display (LCD), a Light-Emitting Diode (LED) or any other type of device for generating tactile, audio, and/or visual output. Output components  466  may be integrated with infrastructure article  128 E in some examples. 
     In other examples, output components  466  may be physically external to and separate from infrastructure article  128 E but may be operably coupled to infrastructure article  128 E via wired or wireless communication. An output component may be a built-in component of infrastructure article  128 E located within and physically connected to the external packaging of infrastructure article  128 E. In another example, output components  466  may be external components of infrastructure article  128 E located outside and physically separated from the packaging of infrastructure article  128 E. Output components  466  may also include control component  463 , which may perform techniques of this disclosure described with respect to infrastructure article  128 E. 
     One or more storage devices  462  within infrastructure article  128 E may store information for processing during operation of infrastructure article  128 E. In some examples, storage device  462  is a temporary memory, meaning that a primary purpose of storage device  462  is not long-term storage. Storage devices  462  on infrastructure article  128 E may configured for short-term storage of information as volatile memory and therefore not retain stored contents if deactivated. Examples of volatile memories include random access memories (RAM), dynamic random-access memories (DRAM), static random-access memories (SRAM), and other forms of volatile memories known in the art. 
     Storage devices  462 , in some examples, also include one or more computer-readable storage media. Storage devices  462  may be configured to store larger amounts of information than volatile memory. Storage devices  462  may further be configured for long-term storage of information as non-volatile memory space and retain information after activate/off cycles. Examples of non-volatile memories include magnetic hard discs, optical discs, floppy discs, flash memories, or forms of electrically programmable memories (EPROM) or electrically erasable and programmable (EEPROM) memories. Storage devices  462  may store program instructions and/or data associated with components included in storage devices  462 . 
     In some examples, an infrastructure article may receive a signal from a micromobility device, determine the source of the micromobility device based on the micromobility signal relative to the infrastructure article, determine at least one state of the micromobility device, and perform an operation based on the location of the source relative to the micromobility or relative to the infrastructure article location and the micromobility state. Techniques and systems of this disclosure may enable improved tracking and compliance of micromobility devices related to enabling city regulations directed towards safe operation and storage of micromobility devices in urban environments. Additionally, such signals could be used to trigger an infrastructure response or a micromobility device response in high risk areas such as intersections. These techniques and systems may enable more accurate and more trustworthy localization of micromobility devices. 
     In some examples, system includes a pulsed light source on a micromobility device where the light travels to an infrastructure article that receives the light and directs it back towards the sources where the light is received by a camera. The distance from the micromobility device to the infrastructure article may be calculated by time of flight. In some examples, the light may be altered by the infrastructure article to indicates state information about the infrastructure article, which may include the location or region or the infrastructure article. In some examples, the micromobility device may send a light or radio signal that is received by an element of the infrastructure that changes in response to the signal, for example, blinking lights across an intersection. 
     In some instances, micromobility usage may be tracked with GPS or cellular signals which may not be accurate or precise depending on natural challenges and incentivized challenges. Tracking micromobility rider location provides information on the ending and potential starting points of a micromobility device as well as a potential compliance metrics with city ordinances. If the systems are not accurate or precise, the metrics and decisions made from those metrics become less meaningful. This could result in dissatisfied customers, or cause rise of potential safety issues. Micromobility devices may have battery/electric systems capable of emitting a pulse of energy that can be used to increase the visibility of the micromobility device to other roadway users. 
     GPS may have challenges with providing high resolution location information and potentially any information in urban canyons, which may be an operating environment for micromobility devices. Another approach used to track micromobility devices is with the use of cellular signals. Cell signals may locate a micromobility device using triangulation when there are three towers within line of sight of the mobile and can locate the distance of a mobile from a tower when there is only one tower within line of sight. Location may be computed via a measure of the signal strength from any cellular towers within range. Several factors including, bounces in the signal path and traveling through absorbing or scattering media (such as walls or building materials) may influence the result and often cause some unknown amount of error in the location determination. A combination of these modalities may be used to increase the overall accuracy and can act as a redundant measure in challenging environments, however, techniques of this disclosure may augment and improve location finding in the midst of these challenges. 
     Even when in specified lanes, micromobility devices may not be visible to vehicle drivers, especially when the vehicles are turning across the special use lanes at intersections. By triggering a response in an upcoming intersection, either via the infrastructure or via the micromobility device, the micromobility device can create additional awareness to larger roadway users. 
     Micromobility devices that are charged a fee based on miles driven may be incentivized to use a GPS spoofer or jammer, resulting in the user to ride it for free or for a decreased fee. Without GPS for location finding, other approaches may attempt to use an accelerometer for positional tracking, however the calculation may introduce errors due to the integration of an unknown constant term when attempting to dead reckon by calculating position from acceleration measurements. 
     Municipalities may enact regulations to meet the city demand for micromobility devices while also maintaining public safety and the character or appeal of the city. Public safety depends often on how and where the micromobility devices are operated. In some cases, where they are parked may also impact safety, as is true in the example where a micromobility device is parked in the middle of a street. The character or appeal of the city may be more impactful with dockless micromobility device where they may be parked and amassed in any location to the point where it impacts the appearance of the city and potentially pedestrian and vehicle congestion. 
     In some examples, micromobility service providers that manage fleets of micromobility devices may benefit from a fiducial marking system in localizing and determining the location of the micromobility device. Techniques of this disclosure may use fiducials and systems for enabling more meaningful enforcement of regulations. Micromobility service providers may be incentivized to ensure the safety of micromobility users and other roadway users as their systems become more prominent in urban environments. Additional safety capabilities could increase the acceptance of micromobility devices in the busy, mixed-user environment in a city. 
     Regulations may drive improvement of infrastructure materials to enable accurate and reliable usage and tracking of micromobility devices. With a technology that is less sensitive to natural and incentivized challenges as more reliable availability and operations may be important to the success and adoption of micromobility devices in urban environments. 
     Localizing a micromobility device in an environment where standard localization techniques may not be adequate (e.g. cellular triangulation, GPS, dead reckoning, pure SLAM) may improve safety. Possible deficiencies in standard methods (GPS, Cellular Triangulation) were described in the background summary. In addition to those challenges, a micromobility device is faced with and additional challenge of being a relatively low power device (when compared to a gasoline or even electric vehicle) and this provides real constraints on power consumption and compute capability. Another way of performing localization may include using SLAM with naturally occurring fiducials. This is challenging in automotive let alone in the micromobility device space. The state-of-the-art techniques for performing SLAM include LiDAR or a heavy lifting camera system. Both solutions are expensive (in all senses of the word: money, power, and computational) and they both require some amount of stability assuming that you are not going to continuously run them because of battery capacity. 
     Techniques and systems of this disclosure may improve power consumption and power requirements, while still being able to aid in localization at a manageable interval. Such techniques and systems may utilize passive or active uniquely identified fiducial markers to help locate micromobility devices and to share information about the location of the infrastructure article (e.g., in an urban environment). 
     Various examples of a distributed fiducial and interface device (reader, camera etc) are included in this disclosure. For example, a system using a pulsed light and camera with reduced duty cycle may use a passive marker in an infrastructure article and a pulsed light signal with a camera with a low refresh rate. In some examples, a frame rate could be 1 frame a second and still not miss a code at full speed. In this embodiment the micromobility device may relay information about the surrounding environment to a database. The fiducials could be positioned on an infrastructure article. The fiducials could be positioned on pavement markings or signage, or other environmental objects. This system may utilize any of the human visible or human invisible technologies from other disclosures (IR, Polarized, Wavelength signature, visible, etc.) 
     A pulsed light camera system may also be used to send a signal to an infrastructure receiver (e.g. an infrastructure article) that triggers a response in the environment. For example, the light energy signal (visible or IR) could be received at the edge of an intersection triggering a lighted “path” across the intersection that warns turning vehicles about an oncoming micromobility device. The receiver could also be on a curb, a structure or a vehicle. 
     In some examples, an encoded blinking infrastructure article may provide a non-perceptible blink rate (e.g., at 500 hz or a rate greater than 450 hz) which could be detected by a photodiode at a signal receiver device and decoded to reveal a unique ID. The blinking could be modulated by many different schemes: amplitude, frequency, or more complex schema if necessary. Location can be determined by a computing device if multiple devices are in the field at the same time, particularly if a pattern is known then distance estimation can be calculated. In some examples, extra information can be encoded into these blink patterns. The camera may or may not require a light source on the micromobility device. 
     In some examples, an infrastructure article, such as a radar readable raised pavement marking could convey information utilizable for localization. In some examples, RFID systems, such as UHF or HF systems, may be designed to enable remote sensing of a unique ID and some small data elements. This implementation may be suited for a micromobility device as line of sight is not required, and the bottom of the scooter is close to the ground and could have an antenna incorporated into the base of the scooter. Any edge line or other pavement marking may have an RFID tag embedded into it (or directly on the road surface) where the micromobility device could read the RFID tags and gain location information as well as other stored info. In such examples, such as system may be more effective if the scooter would drive over the pavement marking (and would potentially work with HF RFID. The reader may be mounted on the belly (e.g., underside) of the scooter. In some examples, Bluetooth low energy devices could be used. In some examples, the low energy Bluetooth device could also be used to trigger a response in the infrastructure when directed. 
     In some examples, speed of the micromobility device may change behavior its behavior relative to infrastructure articles. In some examples, if a system determines that a rule is being violated consistently or above some threshold frequency, the computing device could recommend an infrastructure change. 
       FIG. 5  is a flow diagram illustrating example operations of a computing device that use data for operator proficiency, in accordance with one or more techniques of this disclosure. The techniques are described in terms of computing device  116 A. However, the techniques may be performed by other computing devices. 
     In the example of  FIG. 5 , computing device  116 A may select information that is based on an operator proficiency of an operator of the electrically powered scooter ( 502 ). As described in this disclosure, the information based on operator proficiency may be generated at a computing device associated with an electrically powered scooter and/or received from a remote computing device not associated with the electrically powered scooter. Computing device  116 A may code, into a wireless signal, the information that is based on an operator proficiency of an operator of the electrically powered scooter ( 504 ). As described in this disclosure, the wireless signal may be in any suitable form such as RFID, DSRC, Bluetooth, light pulses, or any technique. Computing device  116 A may send the wireless signal to an infrastructure article configured to receive the wireless signal from a signal emitter device configured at the electrically powered scooter ( 506 ). For example, computing device  116 A may cause a signal emitting device to emit the wireless signal in a way that the information may be determined at a signal receiver device from the wireless signal. 
     In one or more examples, the functions described may be implemented in hardware, software, firmware, or any combination thereof. If implemented in software, the functions may be stored on or transmitted over, as one or more instructions or code, a computer-readable medium and executed by a hardware-based processing unit. Computer-readable media may include computer-readable storage media, which corresponds to a tangible medium such as data storage media, or communication media including any medium that facilitates transfer of a computer program from one place to another, e.g., according to a communication protocol. In this manner, computer-readable media generally may correspond to (1) tangible computer-readable storage media, which is non-transitory or (2) a communication medium such as a signal or carrier wave. Data storage media may be any available media that can be accessed by one or more computers or one or more processors to retrieve instructions, code and/or data structures for implementation of the techniques described in this disclosure. A computer program product may include a computer-readable medium. 
     By way of example, and not limitation, such computer-readable storage media can comprise RAM, ROM, eEPROM, CD-ROM or other optical disk storage, magnetic disk storage, or other magnetic storage devices, flash memory, or any other medium that can be used to store desired program code in the form of instructions or data structures and that can be accessed by a computer. Also, any connection is properly termed a computer-readable medium. For example, if instructions are transmitted from a website, server, or other remote source using a coaxial cable, fiber optic cable, twisted pair, digital subscriber line (DSL), or wireless technologies such as infrared, radio, and microwave, then the coaxial cable, fiber optic cable, twisted pair, DSL, or wireless technologies such as infrared, radio, and microwave are included in the definition of medium. It should be understood, however, that computer-readable storage media and data storage media do not include connections, carrier waves, signals, or other transient media, but are instead directed to non-transient, tangible storage media. Disk and disc, as used, includes compact disc (CD), laser disc, optical disc, digital versatile disc (DVD), floppy disk and Blu-ray disc, where disks usually reproduce data magnetically, while discs reproduce data optically with lasers. Combinations of the above should also be included within the scope of computer-readable media. 
     Instructions may be executed by one or more processors, such as one or more digital signal processors (DSPs), general purpose microprocessors, application specific integrated circuits (ASICs), field programmable logic arrays (FPGAs), or other equivalent integrated or discrete logic circuitry. Accordingly, the term “processor”, as used may refer to any of the foregoing structure or any other structure suitable for implementation of the techniques described. In addition, in some aspects, the functionality described may be provided within dedicated hardware and/or software modules. Also, the techniques could be fully implemented in one or more circuits or logic elements. 
     The techniques of this disclosure may be implemented in a wide variety of devices or apparatuses, including a wireless handset, an integrated circuit (IC) or a set of ICs (e.g., a chip set). Various components, modules, or units are described in this disclosure to emphasize functional aspects of devices configured to perform the disclosed techniques, but do not necessarily require realization by different hardware units. Rather, as described above, various units may be combined in a hardware unit or provided by a collection of interoperative hardware units, including one or more processors as described above, in conjunction with suitable software and/or firmware. 
     It is to be recognized that depending on the example, certain acts or events of any of the methods described herein can be performed in a different sequence, may be added, merged, or left out altogether (e.g., not all described acts or events are necessary for the practice of the method). Moreover, in certain examples, acts or events may be performed concurrently, e.g., through multi-threaded processing, interrupt processing, or multiple processors, rather than sequentially. 
     In some examples, a computer-readable storage medium includes a non-transitory medium. The term “non-transitory” indicates, in some examples, that the storage medium is not embodied in a carrier wave or a propagated signal. In certain examples, a non-transitory storage medium stores data that can, over time, change (e.g., in RAM or cache). 
     Various examples have been described. These and other examples are within the scope of the following claims.