Patent Publication Number: US-2023158842-A1

Title: Apparatus and methods for predicting locations inducing tire pressure changes

Description:
TECHNICAL FIELD 
     The present disclosure generally relates to the field of vehicle-related data aggregation and analysis, associated methods and apparatus, and in particular, concerns, for example, an apparatus configured to predict a likelihood of a tire pressure change occurring within a location based on contextual information associated with the location. 
     BACKGROUND 
     Modern vehicles are equipped with a system, such as a tire pressure monitoring system (TPMS), for detecting a tire pressure drop and informing a user indicating the tire pressure change via a user interface. Generally, the system is limited to the application of detecting and notifying a tire pressure drop and does not provide any assistance in minimizing future occurrences of tire pressure drops. 
     The listing or discussion of a prior-published document or any background in this specification should not necessarily be taken as an acknowledgement that the document or background is part of the state of the art or is common general knowledge. 
     BRIEF SUMMARY 
     According to a first aspect, an apparatus comprising at least one processor and at least one non-transitory memory including computer program code instructions is described. The computer program code instructions, when executed, cause the apparatus to receive historical data of an event in which a first vehicle is impacted by a first tire pressure change at a first road segment. The historical data indicating point-of-interest (POI) information associated with the first road segment. The computer program code instructions, when executed, further causes the apparatus to train a prediction model using the historical data, wherein the prediction model is configured to determine a likelihood in which a second road segment induces a second tire pressure change for a second vehicle based on POI information associated with the second road segment. 
     According to a second aspect, a non-transitory computer-readable storage medium having computer program code instructions stored therein is described. The computer program code instructions, when executed by at least one processor, cause the at least one processor to cause a prediction model to determine a likelihood in which a first road segment induces a first tire pressure change for a first vehicle based on point-of-interest (POI) information associated with the first road segment, wherein the prediction model is trained using historical data, the historical data indicating an event in which a second vehicle is impacted by a second tire pressure change at a second road segment and POI information associated with the second road segment. The computer program code instructions, when executed by at least one processor, further causes the at least one processor to cause a notification indicating the likelihood on a user equipment associated with the first vehicle. 
     According to a third aspect, a method of generating a route based on a road location predicted to induce a tire pressure change. The method comprising receiving a destination for a vehicle as input, selecting a subset from a plurality of road segments as the route to the destination, wherein the subset is selected based on a likelihood in which one or more of a plurality of road segments induces a tire pressure change for the vehicle, wherein the likelihood is determined by a prediction model as a function of POI information associated with the one or more of the plurality of road segments; and outputting the route or a portion thereof. 
     Also, a computer program product may be provided. For example, a computer program product comprising instructions which, when the program is executed by a computer, cause the computer to carry out the steps described herein. 
     Still other aspects, features, and advantages of the invention are readily apparent from the following detailed description, simply by illustrating a number of particular embodiments and implementations, including the best mode contemplated for carrying out the invention. The invention is also capable of other and different embodiments, and its several details can be modified in various obvious respects, all without departing from the spirit and scope of the invention. Accordingly, the drawings and description are to be regarded as illustrative in nature, and not as restrictive. 
     The steps of any method disclosed herein do not have to be performed in the exact order disclosed, unless explicitly stated or understood by the skilled person. 
     Corresponding computer programs (which may or may not be recorded on a carrier) for implementing one or more of the methods disclosed herein are also within the present disclosure and encompassed by one or more of the described example embodiments. 
     The present disclosure includes one or more corresponding aspects, example embodiments or features in isolation or in various combinations whether or not specifically stated (including claimed) in that combination or in isolation. Corresponding means for performing one or more of the discussed functions are also within the present disclosure. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The embodiments of the invention are illustrated by way of example, and not by way of limitation, in the figures of the accompanying drawings: 
         FIG.  1    illustrates a diagram of a system capable of predicting adverse road locations; 
         FIG.  2    illustrates a diagram of the database within the system of  FIG.  1   ; 
         FIG.  3    illustrates a diagram of the components of the prediction platform within the system of  FIG.  1   ; 
         FIG.  4    illustrates an example adverse road location and historical data of tire pressure changes associated thereto; 
         FIG.  5    illustrates an example visual representation indicating an adverse road location; 
         FIG.  6    illustrates a flowchart of a process for training a machine learning model to predict a likelihood in which a road location induces a tire pressure change; 
         FIG.  7    illustrates a flowchart of a process for causing a notification indicating a likelihood of a tire pressure change occurring within a road location; 
         FIG.  8    illustrates a computer system upon which an embodiment may be implemented; 
         FIG.  9    illustrates a chip set or chip upon which an embodiment may be implemented; and 
         FIG.  10    illustrates a diagram of exemplary components of a mobile terminal for communications, which is capable of operating in the system of  FIG.  1   . 
     
    
    
     DETAILED DESCRIPTION 
     Modern vehicles are equipped with a tire pressure monitoring system (TPMS) for monitoring tire pressure levels of a vehicle and alerting a user in response to one or more of the tire pressure levels falling below a threshold. One type of TPMS includes a sensor mounted in a wheel to measure air pressure in each tire. When the air pressure drops below, for example, 25 percent, the sensor transmits a flag to the vehicle&#39;s on-board computing platform, and in response, the on-board computing platform triggers a dashboard indicator light. Another type of TPMS integrates Antilock Braking System&#39;s (ABS) wheel speed sensors. If a tire pressure is at a “low” level, the wheel speed sensors determine a roll of a tire at a different wheel speed than other tires. Such information is detected by the on-board computing platform, and in response, the on-board computing platform triggers the dashboard indicator light. Generally, applications of the TPMS are limited to detecting and notifying the tire pressure loss and does not provide any assistance in prolonging the longevity of a tire. Since road networks are constantly subject to adverse conditions that render damages to tires, wheels, and components associated thereto, there is a need of a system that provides foresight of locations impacted by such conditions. There will now be described an apparatus and associated methods that may address these issues. 
       FIG.  1    is a diagram of a system  100  capable of predicting adverse road location, according to one embodiment. Herein, an adverse road location refers to a road location predicted to yield a tire pressure change for a vehicle. The system includes a user equipment (UE)  101 , a vehicle  105 , a detection entity  113 , a services platform  115 , content providers  119   a - 119   n , a communication network  121 , a prediction platform  123 , a database  125 , and a satellite  127 . Additional or a plurality of mentioned components may be provided. 
     In the illustrated embodiment, the system  100  comprises the user equipment (UE)  101  that may include or be associated with an application  103 . In one embodiment, the UE  101  has connectivity to the prediction platform  123  via the communication network  121 . The prediction platform  123  performs one or more functions associated with providing a prediction of adverse road locations. In the illustrated embodiment, the UE  101  may be any type of mobile terminal or fixed terminal such as a mobile handset, station, unit, device, multimedia computer, multimedia tablet, Internet node, communicator, desktop computer, laptop computer, notebook computer, netbook computer, tablet computer, personal communication system (PCS) device, personal digital assistants (PDAs), audio/video player, digital camera/camcorder, positioning device, fitness device, television receiver, radio broadcast receiver, electronic book device, game device, devices associated with or integrated with one or more vehicles (including the vehicle  105 ), or any combination thereof, including the accessories and peripherals of these devices. In one embodiment, the UE  101  can be an in-vehicle navigation system, a personal navigation device (PND), a portable navigation device, a cellular telephone, a mobile phone, a personal digital assistant (PDA), a watch, a camera, a computer, and/or other device that can perform navigation-related functions, such as digital routing and map display. In one embodiment, the UE  101  can be a cellular telephone. A user may use the UE  101  for navigation functions, for example, road link map updates. It should be appreciated that the UE  101  can support any type of interface to the user (such as “wearable” devices, etc.). In one embodiment, the one or more vehicles may have cellular or Wi-Fi connection either through the inbuilt communication equipment or from the UE  101  associated with the vehicles. The application  103  may assist in conveying and/or receiving information regarding at least one attribute associated a road segment via the communication network  121 . 
     In the illustrated embodiment, the application  103  may be any type of application that is executable by the UE  101 , such as a mapping application, a location-based service application, a navigation application, a content provisioning service, a camera/imaging application, a media player application, a social networking application, a calendar application, or any combination thereof. In one embodiment, one of the applications  103  at the UE  101  may act as a client for the prediction platform  123  and perform one or more functions associated with the functions of the prediction platform  123  by interacting with the prediction platform  123  over the communication network  121 . The application  103  may provide information indicating adverse road locations and/or a route that avoids the adverse road locations. 
     The vehicle  105  may be a standard gasoline powered vehicle, a hybrid vehicle, an electric vehicle, a fuel cell vehicle, and/or any other mobility implement type of vehicle. The vehicle  105  includes parts related to mobility, such as a powertrain with an engine, a transmission, a suspension, a driveshaft, and/or wheels, etc. The vehicle  105  may be a non-autonomous vehicle or an autonomous vehicle. The term autonomous vehicle may refer to a self-driving or driverless mode in which no passengers are required to be on board to operate the vehicle. An autonomous vehicle may be referred to as a robot vehicle or an automated vehicle. The autonomous vehicle may include passengers, but no driver is necessary. These autonomous vehicles may park themselves or move cargo between locations without a human operator. Autonomous vehicles may include multiple modes and transition between the modes. The autonomous vehicle may steer, brake, or accelerate the vehicle based on the position of the vehicle in order, and may respond to lane marking indicators (lane marking type, lane marking intensity, lane marking color, lane marking offset, lane marking width, or other characteristics) and driving commands or navigation commands. In one embodiment, the vehicle  105  may be assigned with an autonomous level. An autonomous level of a vehicle can be a Level 0 autonomous level that corresponds to a negligible automation for the vehicle, a Level 1 autonomous level that corresponds to a certain degree of driver assistance for the vehicle  105 , a Level 2 autonomous level that corresponds to partial automation for the vehicle, a Level 3 autonomous level that corresponds to conditional automation for the vehicle, a Level 4 autonomous level that corresponds to high automation for the vehicle, a Level 5 autonomous level that corresponds to full automation for the vehicle, and/or another sub-level associated with a degree of autonomous driving for the vehicle. 
     In one embodiment, the UE  101  may be integrated in the vehicle  105 , which may include assisted driving vehicles such as autonomous vehicles, highly assisted driving (HAD), and advanced driving assistance systems (ADAS). Any of these assisted driving systems may be incorporated into the UE  101 . Alternatively, an assisted driving device may be included in the vehicle  105 . The assisted driving device may include memory, a processor, and systems to communicate with the UE  101 . In one embodiment, the vehicle  105  may be an HAD vehicle or an ADAS vehicle. An HAD vehicle may refer to a vehicle that does not completely replace the human operator. Instead, in a highly assisted driving mode, a vehicle may perform some driving functions and the human operator may perform some driving functions. Such vehicle may also be driven in a manual mode in which the human operator exercises a degree of control over the movement of the vehicle. The vehicle  105  may also include a completely driverless mode. The HAD vehicle may control the vehicle through steering or braking in response to the on the position of the vehicle and may respond to lane marking indicators (lane marking type, lane marking intensity, lane marking color, lane marking offset, lane marking width, or other characteristics) and driving commands or navigation commands. Similarly, ADAS vehicles include one or more partially automated systems in which the vehicle alerts the driver. The features are designed to avoid collisions automatically. Features may include adaptive cruise control, automate braking, or steering adjustments to keep the driver in the correct lane. ADAS vehicles may issue warnings for the driver based on the position of the vehicle or based on the lane marking indicators (lane marking type, lane marking intensity, lane marking color, lane marking offset, lane marking width, or other characteristics) and driving commands or navigation commands. 
     In this illustrated example, the vehicle  105  includes a plurality of sensors  107 , an on-board computing platform  109 , and an on-board communication platform  111 . The sensors  107  may include one or more sensors for monitoring tire pressure levels of one or more tires of the vehicle  105 . Such sensors and a combination of one or more processors, one or more memory devices, and/or other electronic devices may define a TPMS. By way of example, a direct TPMS may include a battery, a housing, a PCB, a pressure sensor, an analog-digital converter, a microcontroller, a system controller, an oscillator, a radio frequency transmitter, a low frequency receiver, and a voltage regulator. A TPMS may be an electronic system configured to monitor air pressure inside pneumatic tires of a vehicle. The TPMS may report real-time tire-pressure information to a user of the vehicle, either via a gauge, a pictogram display, a simple low-pressure warning light, and/or other types of user interface. TPMS can be divided into two different types— direct (dTPMS) and indirect (iTPMS). The dTPMS directly measures tire pressure using hardware sensors. For example, in the dTPMS, a battery-driven pressure sensor may be mounted inside a valve of each wheel, and said sensor transfers pressure information to the on-board computing platform  109 . In one embodiment, one or more sensors of the dTPMS may also measure and alert temperature levels of the tire. In one embodiment, one or more sensors of the dTPMS may utilize a wireless power system. The iTPMS do not use physical pressure sensors but measure air pressures using software-based systems, which by evaluating existing sensor signals like wheel speeds, accelerometers, driveline data, etc. One type of iTPMS systems is based on the principle that under-inflated tires have a slightly smaller diameter (and hence higher angular velocity) than a correctly inflated one. These differences are measurable through the wheel speed sensors of ABS/ESC systems. Another type of iTPMS can also detect simultaneous under-inflation using spectrum analysis of individual wheels, which can be realized in software using advanced signal processing techniques. The sensors  107  may also include other types of sensors, such as image sensors (e.g., electronic imaging devices of both analog and digital types, which include digital cameras, camera modules, camera phones, thermal imaging devices, radar, sonar, lidar, etc.), a global positioning sensor for gathering location data, a network detection sensor for detecting wireless signals or receivers for different short-range communications (e.g., Bluetooth, Wi-Fi, Li-Fi, near field communication (NFC), etc.), temporal information sensors, an audio recorder for gathering audio data, velocity sensors, light sensors, oriental sensors augmented with height sensor and acceleration sensor, tilt sensors to detect the degree of incline or decline of the vehicle  105  along a path of travel, etc. In a further embodiment, sensors about the perimeter of the vehicle  105  may detect the relative distance of the vehicle  105  from road objects (e.g., road markings), lanes, or roadways, the presence of other vehicles, pedestrians, traffic lights, road objects, road features (e.g., curves) and any other objects, or a combination thereof. In one embodiment, the vehicle  105  may include GPS receivers to obtain geographic coordinates from satellites  127  for determining current location and time associated with the vehicle  105 . Further, the location can be determined by a triangulation system such as A-GPS, Cell of Origin, or other location extrapolation technologies. 
     The on-board computing platform  109  performs one or more functions associated with the vehicle  105 . In one embodiment, the on-board computing platform  109  may aggregate sensor data generated by at least one of the sensors  107  and transmit the sensor data via the on-board communications platform  111 . The on-board computing platform  109  may receive control signals for performing one or more of the functions from the prediction platform  123 , the UE  101 , the services platform  115 , one or more of the content providers  119   a - 119   n , or a combination thereof via the on-board communication platform  111 . The on-board computing platform  109  includes at least one processor or controller and memory (not illustrated). The processor or controller may be any suitable processing device or set of processing devices such as, but not limited to: a microprocessor, a microcontroller-based platform, a suitable integrated circuit, one or more field programmable gate arrays (FPGAs), and/or one or more application-specific integrated circuits (ASICs). The memory may be volatile memory (e.g., RAM, which can include non-volatile RAM, magnetic RAM, ferroelectric RAM, and any other suitable forms); non-volatile memory (e.g., disk memory, FLASH memory, EPROMs, EEPROMs, non-volatile solid-state memory, etc.), unalterable memory (e.g., EPROMs), read-only memory, and/or high-capacity storage devices (e.g., hard drives, solid state drives, etc). In some examples, the memory includes multiple kinds of memory, particularly volatile memory and non-volatile memory. 
     The on-board communications platform  109  includes wired or wireless network interfaces to enable communication with external networks. The on-board communications platform  109  also includes hardware (e.g., processors, memory, storage, antenna, etc.) and software to control the wired or wireless network interfaces. In the illustrated example, the on-board communications platform  109  includes one or more communication controllers (not illustrated) for standards-based networks (e.g., Global System for Mobile Communications (GSM), Universal Mobile Telecommunications System (UMTS), Long Term Evolution (LTE) networks, 5G networks, Code Division Multiple Access (CDMA), WiMAX (IEEE 802.16m); Near Field Communication (NFC); local area wireless network (including IEEE 802.11 a/b/g/n/ac or others), dedicated short range communication (DSRC), and Wireless Gigabit (IEEE 802.11ad), etc.). In some examples, the on-board communications platform  109  includes a wired or wireless interface (e.g., an auxiliary port, a Universal Serial Bus (USB) port, a Bluetooth® wireless node, etc.) to communicatively couple with the UE  101 . 
     The detection entity  113  may be another vehicle, a drone, a road-side sensor (e.g., a sensor installed within a road pavement), or a device mounted on a stationary object within or proximate to a road segment (e.g., a traffic light post, a sign post, a post, a building, etc.). The detection entity  113  may be equipped with image sensors (e.g., electronic imaging devices of both analog and digital types, which include digital cameras, camera modules, camera phones, thermal imaging devices, radar, sonar, lidar, etc.), light sensors (e.g., photodetectors), temperature sensors, audio recorder for gathering audio data, velocity sensors, oriental sensors augmented with height sensor and acceleration sensor, tilt sensors, etc. In a further embodiment, sensors about the perimeter of the detection entity  113  may detect the relative distance thereof from road objects (e.g., road markings), lanes, or roadways, the presence of other vehicles, pedestrians, traffic lights, road objects, road features (e.g., curves) and any other objects, or a combination thereof. In one embodiment, the detection entity  113  may include GPS receivers to obtain geographic coordinates from satellites  127  for determining current location and time associated with at which the detection entity  113  acquires sensor data. The location can be determined by a triangulation system such as A-GPS, Cell of Origin, or other location extrapolation technologies. In one embodiment, if the detection entity  113  is a stationary device (e.g., a traffic camera), the detection entity  113  may store contextual information indicating a location at which the detection entity  113  is located, a direction at which a particular sensor (e.g., an image sensor) of the detection entity  113  is facing, or a combination thereof. In one embodiment, the detection entity  113  may be a mobile device (e.g., similar to the UE  101 ) and may be equipped with any of the aforementioned sensors. Such mobile device may be capable of providing images and information indicating a time, location, and orientation at which the images are acquired. 
     The communication network  121  of system  100  includes one or more networks such as a data network, a wireless network, a telephony network, or any combination thereof. The data network may be any local area network (LAN), metropolitan area network (MAN), wide area network (WAN), a public data network (e.g., the Internet), short range wireless network, or any other suitable packet-switched network, such as a commercially owned, proprietary packet-switched network, e.g., a proprietary cable or fiber-optic network, and the like, or any combination thereof. In addition, the wireless network may be, for example, a cellular network and may employ various technologies including enhanced data rates for global evolution (EDGE), general packet radio service (GPRS), global system for mobile communications (GSM), Internet protocol multimedia subsystem (IMS), universal mobile telecommunications system (UMTS), etc., as well as any other suitable wireless medium, e.g., worldwide interoperability for microwave access (WiMAX), Long Term Evolution (LTE) networks, 5G networks, code division multiple access (CDMA), wideband code division multiple access (WCDMA), wireless fidelity (Wi-Fi), wireless LAN (WLAN), Bluetooth®, Internet Protocol (IP) data casting, satellite, mobile ad-hoc network (MANET), and the like, or any combination thereof. 
     The services platform  115  may be an original equipment manufacturer (OEM) platform that provides one or more services  117   a - 117   n  (collectively referred to as services  117 ). In one embodiment the one or more service  117  may be sensor data collection services. By way of example, vehicle sensor data provided by the sensors  107  may be transferred to the UE  101 , the prediction platform  123 , the database  125 , or other entities communicatively coupled to the communication network  121  through the service platform  115 . The services  117  may also be other third-party services and include mapping services, navigation services, travel planning services, weather-based services, notification services, social networking services, content (e.g., audio, video, images, etc.) provisioning services, application services, storage services, contextual information determination services, location-based services, information-based services, etc. In one embodiment, the services platform  115  uses the output data generated by of the prediction platform  123  to provide services such as navigation, mapping, other location-based services, etc. 
     In one embodiment, the content providers  119   a - 119   n  (collectively referred to as content providers  119 ) may provide content or data (e.g., including geographic data, parametric representations of mapped features, etc.) to the UE  101 , the vehicle  105 , services platform  115 , the vehicle  105 , the database  125 , the prediction platform  123 , or the combination thereof. The content provided may be any type of content, such as map content, textual content, audio content, video content, image content, etc. In one embodiment, the content providers  119  may provide content that may aid in providing a prediction of adverse road locations, and/or other related characteristics. In one embodiment, the content providers  119  may also store content associated with the UE  101 , the vehicle  105 , services platform  115 , the vehicle  105 , the database  125 , the prediction platform  123 , or the combination thereof. In another embodiment, the content providers  119  may manage access to a central repository of data, and offer a consistent, standard interface to data, such as a repository of the database  125 . 
     In the illustrated embodiment, the prediction platform  123  may be a platform with multiple interconnected components. The prediction platform  123  may include multiple servers, intelligent networking devices, computing devices, components and corresponding software for rendering a prediction of adverse road locations. It should be appreciated that that the prediction platform  123  may be a separate entity of the system  100 , included within the UE  101  (e.g., as part of the applications  103 ), included within the vehicle  105  (e.g., as part of an application stored in memory of the on-board computing platform  109 ), included within the services platform  115  (e.g., as part of an application stored in server memory for the services platform  115 ), or a combination thereof. 
     The prediction platform  123  is capable of training a machine learning model based on historical data of a plurality of events in which vehicles are impacted by a tire pressure change and using the trained machine learning model to predict an adverse road location. In one embodiment, the prediction platform  123  defines a tire pressure change as a predetermined rate of tire pressure loss. Each of the events indicates an occurrence in which one or more tires of a vehicle is impacted by a tire pressure change. Each event is associated with: (1) sensor data acquired from one or more sensors of the vehicle; (2) travel data of the vehicle; (3) vehicle attribute data indicating one or more attributes of the vehicle; (4) or a combination thereof. 
     The sensor data may indicate one or more tire pressure levels of one or more tires of the vehicle over a predetermined period. The predetermined period may define: (1) a total amount of time elapsed for the vehicle to travel a route in which the vehicle was impacted by the tire pressure change; (2) an amount of time less than the total amount but including a period in which the vehicle was impacted by the tire pressure change; (3) the period in which the vehicle was impacted by the tire pressure change; or (4) one or more lesser periods within the period in which the vehicle was impacted by the tire pressure change. The sensor data may represent the one or more tire pressure levels as a function of time (e.g., tire pressure loss per minute, per second, per milli-second, etc.). The sensor data may also indicate: (1) one or more speed levels of the vehicle within the predetermined period; (2) one or more ambient temperature levels of the vehicle within the predetermined period (or ambient temperature levels with respect to a tire of the vehicle); (3) one or more internal temperature levels of one or more tires of the vehicle within the predetermined period; (4) an amount of time elapsed starting from a time point of the latest instalment of a tire to the start of the predetermined period; (5) a total amount of distance travelled by the vehicle; (6) a total amount of distance travelled by the vehicle since the latest instalment of a tire; or (7) a combination thereof. Generally, the sensor data may be acquired from the vehicle; however, certain sensor data may be acquired via one or more detection entities  113  that is proximate to the vehicle during the predetermined period. Such sensor data may be: (1) a speed of the vehicle; (2) a temperature level of an environment in which the vehicle is located; (3) a temperature level of a road pavement on which the vehicle is positioned; (3) an atmospheric pressure level of the environment; or (4) a combination thereof. 
     The travel data may indicate: (1) one or more road segments in which the vehicle has travelled within the predetermined period; (2) one or more road attributes of the one or more road segments; (3) one or more weather conditions that has impacted the one or more road segment; (4) whether one or more road events (e.g., road works, road accident, etc.) was impacting the one or more road segments; (5) a degree of traffic impacting the one or more road segments; (6) a season in which the predetermined period occurs; (7) a date in which the predetermined period occurs; or (8) a combination thereof. The one or more road attributes may indicate: (1) a type of road; (2) a classification of a road segment; (3) a curvature of the road segment; (4) coefficient of friction associated with the road segment; (5) a composition of the road segment; (6) a slope of the road segment; (7) whether the road segment includes one or more objects, such as a bump, a crack, a pothole, a rail, etc.; or (8) a combination thereof. For each road segment defined by the travel data, the prediction platform  123  identifies point-of-interest (POI) information associated with said road segment. Specifically, the POI information may indicate that a POI is associated with a road segment if the road segment is: (1) proximate to the POI (e.g., within 20 meters of the road segment); (2) is directly connected to a private road segment of the POI; or (3) a combination thereof. The POI information may indicate: (1) a type of POI; (2) a classification of POI; (3) a type of personnel associated with the POI (e.g., occupational data associated with an employee of the POI); (4) a type of vehicle associated with the POI (e.g., a service vehicle associated with the POI); (5) one or more time points in which one or more vehicles associated with the POI enters and/or exists the POI (e.g., scheduled deliveries); (6) hours of operation associated with the POI; (7) other types of data that indicate a function of the POI (e.g., information indicating a type of service provided by the POI); (8) types goods or items manufactured in the POI and/or transported in and out of the POI; or (9) a combination thereof. The POI information may provide contextual information regarding a type of debris that may be present within a road segment that is associated with a POI. For example, a road segment associated with a nail factory may include nails, or a road segment associated with a glass factory may include glass pieces, etc. 
     The vehicle attribute data may indicate: (1) a size of the vehicle; (2) a weight of the vehicle; (3) a make and/or model of the vehicle; (4) a classification of the vehicle; (5) a type of tire equipped by the vehicle; (6) one or more specifications associated with the vehicle or one or more functions thereof; or (7) a combination thereof. The travel data and the vehicle attribute data may be acquired from the vehicle, one or more detection entities  113  (such as system that can identify vehicles and classify the vehicles to acquire attributes associated with the vehicles), the services platform  115 , one or more content providers  119 , the database  125 , or a combination thereof. 
     The prediction platform  123  may use the historical data to identify one or more locations in which a plurality of vehicles is impacted by a tire pressure change. Specifically, for each of the plurality of vehicles, the prediction platform  123  identifies a period (i.e., the predetermined period) in which said vehicle is impacted by a tire pressure change. Such period may indicate an event in which said vehicle has traversed a portion of a road segment. The prediction platform  123  may further determine whether one or more additional vehicles has been impacted by a tire pressure change at the portion of the road segment. If a plurality of vehicles has been impacted by a tire pressure change at the portion, the prediction platform  123  may identify the portion as an adverse road location. For each adverse road location, the prediction platform  123  acquires relevant sensor data, travel data, vehicle attribute data associated with one or more vehicles that has traversed the adverse road location. Using the data, the prediction platform  123  may train the machine learning model to predict one or more adverse road locations within a road network. In one embodiment, the prediction platform  123  may use the data to identify one or more map related features (e.g., map attributes, POIs, etc.) that contributes the most to the tire pressure changes that have occurred at an adverse road location. In such embodiment, the prediction platform  123  may further identify contextual features, such as weather conditions, sensor data associated with vehicles impacted by tire pressure changes and/or adverse road locations, traffic conditions, road works, accidents, etc., and identify relationships between such features and the map related features to identify one or more combinations that attributes the most to the tire pressure changes. In one embodiment, the prediction platform  123  may define the most contributing factors for the tire pressure changes based on a number and/or a frequency at which said factors were observed at one or more adverse road locations in which the tire pressure changes have occurred. 
     In one embodiment, to record an accurate event of a tire pressure change for the historical data, the prediction platform  123  may normalize sensor data acquired by the vehicle impacted by the tire pressure change. Specifically, since road pavement temperature levels, ambient temperature levels, and atmospheric pressure levels impact tire pressure levels, the prediction platform  123  may acquire sensor data associated with the vehicle (such as tire pressure levels) over a period defining the tire pressure change and sensor data associated with one or more road segments (such as road pavement temperature levels, atmospheric temperature levels, atmospheric pressure levels, etc.) over the period and normalize the sensor data associated with the vehicle in view of the sensor data associated with the one or more road segments, thereby causing the historical data to only indicate events of tire pressure changes due to attributes of a road and/or physical objects within the road (e.g., sharp objects within a road, pot holes, etc.). 
     Once the machine learning is trained, the prediction platform  123  may cause the machine learning model to predict an adverse road location in response to receiving a request. In one embodiment, the request may be transmitted from the vehicle  105 . In such embodiment, the request may include information indicating: (1) a route of the vehicle  105 ; (2) a current road segment in which the vehicle  105  is located; (3) a heading of the vehicle  105 ; (4) sensor data associated with the vehicle  105 ; or (5) a combination thereof. Using the information, the prediction platform  123  may identify one or more road segments in which the vehicle  105  is predicted to traverse. For each road segment, the prediction platform  123  acquires map related features and contextual features associated with said road segment from one or more detection entities  113  located within said road segment, the services platform  115 , one or more content providers  119 , the database  125 , or a combination thereof. The prediction platform  123  may input such features of each road segment to the machine learning model, and in response, the machine learning model outputs, for each road segment, a likelihood of which said road segment will induce a tire pressure change for the vehicle  105 . If the likelihood for a road segment exceed a threshold, the prediction platform  123  may: (1) cause a notification to the UE  101  and/or a user interface associated with the vehicle  105  indicating the road segment; (2) cause a notification to the UE  101  and/or a user interface associated with the vehicle  105  indicating that the route includes the road segment; (3) generate an alternative route that avoids the road segment and provide the alternative route to the UE  101  and/or a user interface associated with the vehicle  105 ; (4) cause a notification to an establishment; or (5) a combination thereof. In one embodiment, the prediction platform  123  may input any road segment and features associated therewith to the machine learning model, and in response, the machine learning model may output the likelihood of which said road segment will induce a tire pressure change. 
     It is contemplated that the accuracy at which the machine learning model renders an accurate prediction is defined, at least in part by, a degree at which attributes of the vehicle  105 , the map related features, and the contextual features correspond to the historical data. By way of example, if the vehicle  105  corresponds to a vehicle that was impacted by a tire pressure change, as indicated by the historical data (e.g., similar vehicle types, tire types, etc.), the machine learning model may yield a higher chance of providing an accurate prediction. By way of another example, if a road segment in which the vehicle  105  is predicted to traverse and the condition thereof (e.g., atmospheric pressure and temperature, road pavement temperature, etc.) correspond to an adverse road location and the condition thereof, as indicated by the historical data, the machine learning model may yield a higher chance of providing an accurate prediction. It is further contemplated that the historical data may not be sufficient enough to detail all road segments within a road network. As such, in one embodiment, the prediction platform  123  may adjust sensor data (e.g., atmospheric pressure and temperature of a location, road pavement temperature, etc.) associated with a road segment of which the vehicle  105  is predicted to traverse to increase a likelihood of a match between said sensor data and an event of a tire pressure change, as indicated by the historical data. Specifically, since temperature is proportional to pressure, the prediction platform  123  may adjust the sensor data to yield a match. Similarly, other types of data (e.g., vehicle weight, vehicle speed, tire characteristics, etc.) may be adjusted for the purposes of yielding a match to a corresponding event of a tire pressure change, as indicated by the historical data. 
     Subsequent to rendering a prediction of an adverse road location, the prediction platform  123  may further train the machine learning model based on the outcome of the prediction. For example, subsequent to rendering the prediction for the vehicle  105 , the prediction platform  123  may receive sensor data, such as tire pressure levels associated with the vehicle  105 , ambient temperature levels associated with the adverse road location, road pavement temperature associated with the adverse road location, atmospheric pressure level associated with the road location, etc., as the vehicle  105  traverses the adverse road location. If the sensor data indicates a positive observation (i.e., the vehicle  105  is impacted by a tire pressure change), the prediction platform  123  may increase a confidence value associated with the prediction. Conversely, if the sensor data indicates a negative observation (i.e., the vehicle  105  is not impacted by a tire pressure change), the prediction platform  123  may lower the confidence value and update the machine learning model based on the sensor data. 
     In one embodiment, the prediction platform  123  may store user preference information indicating a configurable threshold at which the prediction platform  123  is triggered to render a prediction of a tire pressure change associated with one or more road segments. For example, a user of the vehicle  105  may input, via the UE  101 , a threshold of 5 PSI for a tire pressure drop, and the prediction platform  123  causes the machine learning model to render a prediction of a tire pressure change associated with one or more road segments in response to a tire pressure of the vehicle  105  dropping below the 5 PSI threshold during a course of travel for the vehicle  105 . 
     In one embodiment, the prediction platform  123  may predict one or more road attributes associated with one or more road segments based on historical data of a plurality of events in which tire pressure levels associated with a plurality of vehicles fluctuate over one or more periods. In such embodiment, the historical data is not limited to indicating events in which tire pressure drops have occurred and may further indicate events in which tire pressure levels of one or more vehicles have increased. The prediction platform  123  may use the historical data to train a machine learning model, and once the machine learning model is trained, the machine learning model may output a prediction of one or more road attributes of a road segment as a function of sensor data acquired from a vehicle that is currently traversing or has traversed the road segment. The one or more road attributes may indicate: (1) a type of road; (2) a classification of a road segment; (3) a curvature of the road segment; (4) coefficient of friction associated with the road segment; (5) a composition of the road segment; (6) a slope of the road segment; (7) whether the road segment includes one or more objects, such as a bump, a crack, a pothole, a rail, etc.; or (8) a combination thereof. For example, if the vehicle  105  is traversing a road segment, the prediction platform  123  may acquire sensor data indicating tire pressure levels associated with the vehicle  105  and input the sensor data to the machine learning model. In response, the machine learning model may output a prediction that the vehicle  105  is traversing a paved road segment. The output of the machine learning model may be validated through sensors, such as cameras, and/or user input, and the machine learning model may be further trained based on the validation. 
     In the illustrated embodiment, the database  125  stores information on road links (e.g., road length, road breadth, slope information, curvature information, etc.) and probe data for one or more road links (e.g., traffic density information). In one embodiment, the database  125  may include any multiple types of information that can provide means for aiding in providing a prediction of adverse road locations. It should be appreciated that the information stored in the database  125  may be acquired from any of the elements within the system  100 , other vehicles, sensors, database, or a combination thereof. 
     In one embodiment, the UE  101 , the vehicle  105 , the detection entity  113 , the services platform  115 , the content providers  119 , the prediction platform  123  communicate with each other and other components of the communication network  121  using well known, new or still developing protocols. In this context, a protocol includes a set of rules defining how the network nodes within the communication network  121  interact with each other based on information sent over the communication links. The protocols are effective at different layers of operation within each node, from generating and receiving physical signals of various types, to selecting a link for transferring those signals, to the format of information indicated by those signals, to identifying which software application executing on a computer system sends or receives the information. The conceptually different layers of protocols for exchanging information over a network are described in the Open Systems Interconnection (OSI) Reference Model. 
     Communications between the network nodes are typically affected by exchanging discrete packets of data. Each packet typically comprises (1) header information associated with a particular protocol, and (2) payload information that follows the header information and contains information that may be processed independently of that particular protocol. In some protocols, the packet includes (3) trailer information following the payload and indicating the end of the payload information. The header includes information such as the source of the packet, its destination, the length of the payload, and other properties used by the protocol. Often, the data in the payload for the particular protocol includes a header and payload for a different protocol associated with a different, higher layer of the OSI Reference Model. The header for a particular protocol typically indicates a type for the next protocol contained in its payload. The higher layer protocol is said to be encapsulated in the lower layer protocol. The headers included in a packet traversing multiple heterogeneous networks, such as the Internet, typically include a physical (layer 1) header, a data-link (layer 2) header, an internetwork (layer 3) header and a transport (layer 4) header, and various application (layer 5, layer 6 and layer 7) headers as defined by the OSI Reference Model. 
       FIG.  2    is a diagram of a database  125  (e.g., a map database), according to one embodiment. In one embodiment, the database  125  includes geographic data  1250  used for (or configured to be compiled to be used for) mapping and/or navigation-related services. In one embodiment, the following terminology applies to the representation of geographic features in the database  125 . 
     a. “Node”— A point that terminates a link.
 
b. “road/line segment”— A straight line connecting two points.
 
c. “Link” (or “edge”)— A contiguous, non-branching string of one or more road segments terminating in a node at each end.
 
     In one embodiment, the database  125  follows certain conventions. For example, links do not cross themselves and do not cross each other except at a node. Also, there are no duplicated shape points, nodes, or links. Two links that connect each other have a common node. 
     As shown, the database  125  includes node data records  1251 , road segment or link data records  1253 , POI data records  1255 , tire pressure records  1257 , other records  1259 , and indexes  1261 , for example. More, fewer or different data records can be provided. In one embodiment, additional data records (not shown) can include cartographic (“carto”) data records, routing data, and maneuver data. In one embodiment, the indexes  1261  may improve the speed of data retrieval operations in the database  125 . In one embodiment, the indexes  1261  may be used to quickly locate data without having to search every row in the database  125  every time it is accessed. 
     In exemplary embodiments, the road segment data records  1253  are links or segments representing roads, streets, or paths, as can be used in the calculated route or recorded route information for determination of one or more personalized routes. The node data records  1251  are end points (such as intersections) corresponding to the respective links or segments of the road segment data records  1253 . The road link data records  1253  and the node data records  1251  represent a road network, such as used by vehicles, cars, and/or other entities. Alternatively, the database  125  can contain path segment and node data records or other data that represent pedestrian paths or areas in addition to or instead of the vehicle road record data, for example. 
     Links, segments, and nodes can be associated with attributes, such as geographic coordinates, a number of road objects (e.g., road markings, road signs, traffic light posts, etc.), types of road objects, traffic directions for one or more portions of the links, segments, and nodes, traffic history associated with the links, segments, and nodes, street names, address ranges, speed limits, turn restrictions at intersections, presence of roadworks, and other navigation related attributes, as well as POIs, such as gasoline stations, hotels, restaurants, museums, stadiums, offices, automobile dealerships, auto repair shops, factories, buildings, stores, parks, etc. The database  125  can include data about the POIs and their respective locations in the POI data records  1255 . The data about the POIs may include attribute data associated with the POIs, such as: (1) a type of POI; (2) a classification of POI; (3) a type of personnel associated with the POI (e.g., occupational data associated with an employee of the POI); (4) a type of vehicle associated with the POI (e.g., a service vehicle associated with the POI); (5) one or more time points in which one or more vehicles associated with the POI enters and/or exists the POI (e.g., scheduled deliveries); (6) hours of operation associated with the POI; (7) directories associated with the POI; (8) types goods or items manufactured in the POI and/or transported in and out of the POI; (9) other types of data that indicate a function of the POI (e.g., information indicating a type of service provided by the POI); or (10) a combination thereof. The database  125  can also include data about places, such as cities, towns, or other communities, and other geographic features, such as bodies of water, mountain ranges, etc. Such place or feature data can be part of the POI data records  1255  or can be associated with POIs or POI data records  1255  (such as a data point used for displaying or representing a position of a city). 
     The tire pressure records  1257  includes historical data of a plurality of events in which vehicles are impacted by a tire pressure change. Each of the events indicates an occurrence in which one or more tires of a vehicle is impacted by a tire pressure change. Each event is associated with: (1) sensor data acquired from one or more sensors of the vehicle; (2) travel data of the vehicle; (3) vehicle attribute data indicating one or more attributes of the vehicle; (4) or a combination thereof. In one embodiment, the historical data may indicate instances in which vehicles traverse a plurality of types of road segments, sensor data associated with the vehicles and the road segment types, travel data associated with the vehicles, and vehicle attribute data of the vehicles. The types of road segments may be defined by: (1) a curvature of the road segment; (2) a number of turns within the road segment; (3) roughness of the road segment; (4) a slope of the road segment; (5) a composition of the road segment; (6) coefficient of friction associated with the road segment; (7) whether the road segment includes one or more objects, such as a bump, a crack, a pothole, a rail, etc.; or (8) a combination thereof. 
     Other records  1259  may include computer code instructions and/or algorithms for executing a machine learning model that is capable of providing a prediction of adverse road locations ysis. The other records  1259  may further include verification data indicating: (1) whether a verification of a prediction for an adverse road location was conducted; (2) whether the verification validates the prediction; or (3) a combination thereof. 
     In one embodiment, the database  125  can be maintained by one or more of the content providers  119  in association with a map developer. The map developer can collect geographic data to generate and enhance the database  125 . There can be different ways used by the map developer to collect data. These ways can include obtaining data from other sources, such as municipalities or respective geographic authorities. In addition, the map developer can employ field personnel to travel by vehicle along roads throughout the geographic region to observe attributes associated with one or more road segments and/or record information about them, for example. Also, remote sensing, such as aerial or satellite photography, can be used. 
     The database  125  can be a master database stored in a format that facilitates updating, maintenance, and development. For example, the master database or data in the master database can be in an Oracle spatial format or other spatial format (e.g., accommodating different map layers), such as for development or production purposes. The Oracle spatial format or development/production database can be compiled into a delivery format, such as a geographic data files (GDF) format. The data in the production and/or delivery formats can be compiled or further compiled to form database products or databases, which can be used in end user navigation devices or systems. 
     For example, geographic data is compiled (such as into a platform specification format (PSF) format) to organize and/or configure the data for performing navigation-related functions and/or services, such as route calculation, route guidance, map display, speed calculation, distance and travel time functions, and other functions, by a navigation device, such as by the vehicle  105 , for example. The navigation-related functions can correspond to vehicle navigation, pedestrian navigation, or other types of navigation. The compilation to produce the end user databases can be performed by a party or entity separate from the map developer. For example, a customer of the map developer, such as a navigation device developer or other end user device developer, can perform compilation on a received database in a delivery format to produce one or more compiled navigation databases. 
     The processes described herein for providing a prediction of adverse road locations may be advantageously implemented via software, hardware (e.g., general processor, Digital Signal Processing (DSP) chip, an Application Specific Integrated Circuit (ASIC), Field Programmable Gate Arrays (FPGAs), etc.), firmware or a combination thereof. Such exemplary hardware for performing the described functions is detailed below. 
       FIG.  3    is a diagram of the components of the prediction platform  123 , according to one embodiment. By way of example, the prediction platform  123  includes one or more components for providing a prediction of adverse road locations. It is contemplated that the functions of these components may be combined in one or more components or performed by other components of equivalent functionality. In this embodiment, the prediction platform  123  includes a detection module  301 , a calculation module  303 , a notification module  305 , and a presentation module  307 . 
     The detection module  301  is capable of acquiring data from the UE  101 , the vehicle  105  (or one or more other vehicles similar to the vehicle  105 ), one or more detection entities  113 , services platform  115 , content providers  119 , the database  125 , or a combination thereof to provide a prediction of adverse road locations. The data may be: (1) sensor data associated with a tire pressure change; (2) travel data of a vehicle impacted by the tire pressure change; (3) vehicle attribute data indicating one or more attributes of the vehicle impacted by the tire pressure change; (4) or a combination thereof. The detection module  301  may also receive a request for rendering a prediction of an adverse road location. Such request may be received via the UE  101  or a user equipment associated with the vehicle  105 . The request may include information indicating: (1) a route of the vehicle  105 ; (2) a current road segment in which the vehicle  105  is located; (3) a heading of the vehicle  105 ; (4) sensor data associated with the vehicle  105 ; or (5) a combination thereof. The detection module  301  may also acquire map related features (e.g., map attributes, POIs, etc.) and contextual features (e.g., such as weather conditions, sensor data associated with vehicles impacted by tire pressure changes and/or adverse road locations, traffic conditions, road works, accidents, etc.) associated with one or more road segments. Contextual features that indicate sensor data may indicate tire temperature levels and tire pressure levels of the vehicle  105 , environmental temperature levels of the one or more road segments, road pavement temperature levels of the one or more road segments, atmospheric pressure levels of the one or more road segments, or a combination thereof. 
     The calculation module  303  uses the data acquired by the detection module  301  to render a prediction of one or more adverse road location. In one embodiment, the calculation module  303  uses the historical data to identify one or more locations in which a plurality of vehicles is impacted by a tire pressure change. Specifically, for each of the plurality of vehicles, the calculation module  303  identifies a period in which said vehicle is impacted by a tire pressure change. Such period may indicate an event in which said vehicle has traversed a portion of a road. The calculation module  303  may further determine whether one or more additional vehicles has been impacted by a tire pressure change at the portion of the road. If a plurality of vehicles has been impacted by a tire pressure change at the portion, the calculation module  303  may identify the portion as an adverse road location. For each adverse road location, the calculation module  303  acquires relevant sensor data, travel data, vehicle attribute data associated with one or more vehicles that has traversed the adverse road location. For example,  FIG.  4   , illustrates an example adverse road location and historical data of tire pressure changes associated thereto. In the illustrated embodiment, a road segment  401  within a geographic location  400  is associated with historical data  403  of a plurality of events indicating tire pressure changes. Each of the plurality of events indicates a road segment in which a vehicle is impacted by a tire pressure change, temporal information associated with the tire pressure change, vehicle sensor data associated with the tire pressure change, attributes of the road segment, and vehicle attribute data associated with the vehicle. Using the sensor data, the travel data, and the vehicle attribute data, the calculation module  303  may train the machine learning model to predict one or more adverse road locations within a road network. In one embodiment, the machine learning model may be a random forest, a logistic, a decision trees, neural networks, or a combination thereof. In one embodiment, the calculation module  303  may use the data to identify one or more map related features (e.g., map attributes, POIs, etc.) that contributes the most to the tire pressure changes that have occurred at an adverse road location. In such embodiment, the calculation module  303  may further identify contextual features, such as weather conditions, sensor data associated with vehicles impacted by tire pressure changes and/or adverse road locations, traffic conditions, road works, accidents, etc., and identify relationships between such features and the map related features to identify one or more combinations that attributes the most to the tire pressure changes. In one embodiment, the calculation module  303  may define the most contributing factors for the tire pressure changes based on a number and/or a frequency at which said factors were observed at one or more adverse road locations in which the tire pressure changes have occurred. 
     In one embodiment, the calculation module  303  may normalize sensor data acquired by the vehicle impacted by the tire pressure change. Specifically, since road pavement temperature levels, ambient temperature levels, and atmospheric pressure levels impact tire pressure levels, the calculation module  303  may acquires sensor data associated with the vehicle (such as tire pressure levels) over a period defining the tire pressure change and sensor data associated with one or more road segments (such as road pavement temperature levels, atmospheric temperature levels, atmospheric pressure levels, etc.) over the period and normalizes the sensor data associated with the vehicle in view of the sensor data associated with the one or more road segments, thereby causing the historical data to only indicate events of tire pressure changes due to attributes of a road and/or physical objects within the road (e.g., sharp objects within a road, pot holes, etc.). 
     Once the machine learning is trained, the calculation module  303  may cause the machine learning model to predict an adverse road location in response to receiving a request via the detection module  301 . In one embodiment, the request may be transmitted from the vehicle  105 . In such embodiment, the request may include information indicating: (1) a route of the vehicle  105 ; (2) a current road segment in which the vehicle  105  is located; (3) a heading of the vehicle  105 ; (4) sensor data associated with the vehicle  105 ; or (5) a combination thereof. Using the information, the calculation module  303  may identify one or more road segments in which the vehicle  105  is predicted to traverse. For each road segment, the calculation module  303  acquires map related features and contextual features associated with said road segment via the detection module  301 . The calculation module  303  may input such features of each road segment to the machine learning model, and in response, the machine learning model outputs, for each road segment, a likelihood of which said road segment will induce a tire pressure change for the vehicle  105 . In one embodiment, the calculation module  303  may input any road segment and features associated therewith to the machine learning model, and in response, the machine learning model may output the likelihood of which said road segment will induce a tire pressure change. 
     In one embodiment, the calculation module  303  may adjust sensor data (e.g., atmospheric pressure and temperature of a location, road pavement temperature, etc.) associated with a road segment of which the vehicle  105  is predicted to traverse to increase a likelihood of a match between said sensor data and an event of a tire pressure change, as indicated by the historical data. Specifically, since temperature is proportional to pressure, the calculation module  303  may adjust the sensor data to yield a match. Similarly, other types of data (e.g., vehicle weight, vehicle speed, tire characteristics, etc.) may be adjusted for the purposes of yielding a match of a corresponding event of a tire pressure change, as indicated by the historical data. 
     Subsequent to rendering a prediction of an adverse road location, the calculation module  303  may further train the machine learning model based on the outcome of the prediction. For example, subsequent to rendering the prediction for the vehicle  105 , the calculation module  303  may receive sensor data, such as tire pressure levels associated with the vehicle  105 , ambient temperature levels associated with the adverse road location, road pavement temperature associated with the adverse road location, atmospheric pressure level associated with the road location, etc., as the vehicle  105  traverses the adverse road location. If the sensor data indicates a positive observation (i.e., the vehicle  105  is impacted by a tire pressure change), the calculation module  303  may increase a confidence value associated with the prediction. Conversely, if the sensor data indicates a negative observation (i.e., the vehicle  105  is not impacted by a tire pressure change), the calculation module  303  may lower the confidence value and update the machine learning model based on the sensor data. 
     In one embodiment, the calculation module  303  may receive user preference information indicating a configurable threshold at which the calculation module  303  is triggered to render a prediction of a tire pressure change associated with one or more road segments. For example, a user of the vehicle  105  may input, via the UE  101 , a threshold of 5 PSI for a tire pressure drop, and the calculation module  303  causes the machine learning model to render a prediction of a tire pressure change associated with one or more road segments in response to a tire pressure of the vehicle  105  dropping below the 5 PSI threshold during a course of travel for the vehicle  105 . 
     In one embodiment, the calculation module  303  may predict one or more road attributes associated with one or more road segments based on historical data of a plurality of events in which tire pressure levels associated with a plurality of vehicles fluctuate over one or more periods. In such embodiment, the historical data is not limited to indicating events in which tire pressure drops have occurred and may further indicate events in which tire pressure levels of one or more vehicles have increased. The calculation module  303  may use the historical data to train a machine learning model, and once the machine learning model is trained, the machine learning model may output a prediction of one or more road attributes of a road segment as a function of sensor data acquired from a vehicle that is currently traversing or has traversed the road segment. The one or more road attributes may indicate: (1) a type of road; (2) a classification of a road segment; (3) a curvature of the road segment; (4) coefficient of friction associated with the road segment; (5) a composition of the road segment; (6) a slope of the road segment; (7) whether the road segment includes one or more objects, such as a bump, a crack, a pothole, a rail, etc.; or (8) a combination thereof. For example, if the vehicle  105  is traversing a road segment, the calculation module  303  may acquire sensor data indicating tire pressure levels associated with the vehicle  105  and input the sensor data to the machine learning model. In response, the machine learning model may output a prediction that the vehicle  105  is traversing a paved road segment. The output of the machine learning model may be validated through sensors, such as cameras, and/or user input, and the machine learning model may be further trained based on the validation. 
     The notification module  305  may generate a notification associated with providing a prediction of adverse road locations. The notification module  305  may cause the notification to the UE  101  and/or one or more other UEs associated with the vehicle  105 . In such embodiment, the notification may indicate: (1) a likelihood of which a road location induces a tire pressure change; (2) an alternative route that avoids one or more adverse road locations; (3) a confidence associated with a prediction of adverse road locations; (4) historical data associated with tire pressure changes at one or more road locations; or (5) a combination thereof. The notification may include sound notification, display notification, vibration, or a combination thereof. In one embodiment, the notification module  305  may provide the notification to a local municipality/establishment. 
     The presentation module  307  obtains a set of information, data, and/or calculated results from other modules, and continues with providing a presentation of a visual representation to the UE  101  and/or any other user interface associated with the vehicle  105 . The visual representation may indicate any of the information presented by the notification module  305 .  FIG.  5    illustrates an example visual representation  500  indicating an adverse road location. The first visual representation  500  depicts a scenario in which a vehicle  501  is traversing a route  503  to a destination  505 , and the calculation module  303  has rendered a prediction that a road segment  507  of the route  503  is likely to induce a tire pressure drop for the vehicle  501  (i.e., the adverse road location). In response, the presentation module  307  displays a message  509  stating “TIRE PRESSURE DROP IS LIKELY TO OCCUR IN THIS AREA. FIND ALTERNATIVE ROUTE?” In one embodiment, a visual representation may further include an alternative route that avoids an adverse road location. 
     The above presented modules and components of the prediction platform  123  can be implemented in hardware, firmware, software, or a combination thereof. Though depicted as a separate entity in  FIG.  3   , it is contemplated that the prediction platform  123  may be implemented for direct operation by the UE  101 , the vehicle  105 , the services platform  115 , one or more of the content providers  119 , or a combination thereof. As such, the prediction platform  123  may generate direct signal inputs by way of the operating system of the UE  101 , the vehicle  105 , the services platform  115 , the one or more of the content providers  119 , of the combination thereof for interacting with the applications  103 . The various executions presented herein contemplate any and all arrangements and models. 
       FIG.  6    is a flowchart of a process  600  for training a machine learning model to predict a likelihood in which a road location induces a tire pressure change, according to one embodiment. In one embodiment, the prediction platform  123  performs the process  600  and is implemented in, for instance, a chip set including a processor and a memory as shown in  FIG.  9   . 
     In step  601 , the prediction platform  123  receives historical data of a plurality of events in which vehicles are impacted by a tire pressure change and uses the trained machine learning model to predict an adverse road location. Each of the events indicates an occurrence in which one or more tires of a vehicle is impacted by a tire pressure change. Each event is associated with: (1) sensor data acquired from one or more sensors of the vehicle; (2) travel data of the vehicle; (3) vehicle attribute data indicating one or more attributes of the vehicle; (4) or a combination thereof. The sensor data may indicate one or more tire pressure levels of one or more tires of a vehicle, temperature levels associated with one or more road segments on which the vehicle traversed, and/or atmospheric pressure levels associated with the one or more road segments on which the vehicle traversed. The travel data may indicate: (1) one or more road segments in which the vehicle has travelled within the predetermined period; (2) one or more road attributes of the one or more road segments; (3) one or more weather conditions that has impacted the one or more road segment; (4) whether one or more road events (e.g., road works, road accident, etc.) was impacting the one or more road segments; (5) a degree of traffic impacting the one or more road segments; (6) a season in which the predetermined period occurs; (7) a date in which the predetermined period occurs; or (8) a combination thereof. Each road segment defined by the travel data may be associated with POI information. The POI information may indicate that a POI is associated with a road segment if the road segment is: (1) proximate to the POI (e.g., within 20 meters of the road segment); (2) is directly connected to a private road segment of the POI; or (3) a combination thereof. The POI information may indicate: (1) a type of POI; (2) a classification of POI; (3) a type of personnel associated with the POI (e.g., occupational data associated with an employee of the POI); (4) a type of vehicle associated with the POI (e.g., a service vehicle associated with the POI); (5) one or more time points in which one or more vehicles associated with the POI enters and/or exists the POI (e.g., scheduled deliveries); (6) hours of operation associated with the POI; (7) other types of data that indicate a function of the POI (e.g., information indicating a type of service provided by the POI); (8) types goods or items manufactured in the POI and/or transported in and out of the POI; or (9) a combination thereof. The vehicle attribute data may indicate: (1) a size of the vehicle; (2) a weight of the vehicle; (3) a make and/or model of the vehicle; (4) a classification of the vehicle; (5) a type of tire equipped by the vehicle; (6) one or more specifications associated with the vehicle or one or more functions thereof; or (7) a combination thereof. 
     In step  603 , the prediction platform  123  trains a machine learning model to predict adverse road locations by using the historical data. Specifically, the prediction platform  123  identifies one or more road locations in which a plurality of vehicles is impacted by a tire pressure change. For each of the plurality of vehicles, the prediction platform  123  identifies a period in which said vehicle is impacted by a tire pressure change. Such period may indicate an event in which said vehicle has traversed a portion of a road segment. The prediction platform  123  may further determine whether one or more additional vehicles has been impacted by a tire pressure change at the portion of the road segment. If a plurality of vehicles has been impacted by a tire pressure change at the portion, the prediction platform  123  may identify the portion as an adverse road location. For each adverse road location, the prediction platform  123  acquires relevant sensor data, travel data, vehicle attribute data associated with one or more vehicles that has traversed the adverse road location. Using the data, the prediction platform  123  may train the machine learning model to predict one or more adverse road locations within a road network. 
       FIG.  7    is a flowchart of a process  700  for causing a notification indicating a likelihood of a tire pressure change occurring within a road location. In one embodiment, the prediction platform  123  performs the process  700  and is implemented in, for instance, a chip set including a processor and a memory as shown in  FIG.  9   . 
     In step  701 , the prediction platform  123  causes a machine learning model to determine a likelihood in which a road segment induces a tire pressure change for a vehicle. In one embodiment, the prediction platform  123  may cause the machine learning model to determine the likelihood in response receiving a request. The request may include information indicating: (1) a route of the vehicle; (2) a current road segment in which the vehicle is located; (3) a heading of the vehicle; (4) sensor data associated with the vehicle; or (5) a combination thereof. Using the information, the prediction platform  123  may identify one or more road segments in which the vehicle is predicted to traverse. For each road segment, the prediction platform  123  acquires map related features and contextual features associated with said road segment. The prediction platform  123  may input such features of each road segment to the machine learning model, and in response, the machine learning model outputs, for each road segment, a likelihood of which said road segment will induce a tire pressure change for the vehicle. 
     In step  703 , the prediction platform  123  causes a notification indicating the likelihood on a user equipment associated with the vehicle. In one embodiment, the prediction platform  123  may cause such notification if the likelihood exceeds a threshold. In one embodiment, the notification may indicate an alternate route to a destination that avoids the road segment that is associated with the likelihood. 
     The system, apparatus, and methods described herein enable a map-based server/platform to predict road locations that are likely to induce tire pressure drops for vehicle, thereby ensuring longevity of tires, wheels, and components associated thereto. Further, since the system, apparatus, and methods described herein enables a machine learning model to predict a likelihood of a tire pressure drop occurring in a road location based on features of past events of tire pressure drops that have occurred in other road locations, the application of the machine learning model can be used in road locations that do not have any past records tire pressure drop that have occurred therein. 
     The processes described herein may be advantageously implemented via software, hardware, firmware or a combination of software and/or firmware and/or hardware. For example, the processes described herein, may be advantageously implemented via processor(s), Digital Signal Processing (DSP) chip, an Application Specific Integrated Circuit (ASIC), Field Programmable Gate Arrays (FPGAs), etc. Such exemplary hardware for performing the described functions is detailed below. 
       FIG.  8    illustrates a computer system  800  upon which an embodiment may be implemented. Although computer system  800  is depicted with respect to a particular device or equipment, it is contemplated that other devices or equipment (e.g., network elements, servers, etc.) within  FIG.  8    can deploy the illustrated hardware and components of system  800 . Computer system  800  is programmed (e.g., via computer program code or instructions) to providing a prediction of adverse road locations, as described herein and includes a communication mechanism such as a bus  810  for passing information between other internal and external components of the computer system  800 . Information (also called data) is represented as a physical expression of a measurable phenomenon, typically electric voltages, but including, in other embodiments, such phenomena as magnetic, electromagnetic, pressure, chemical, biological, molecular, atomic, sub-atomic and quantum interactions. For example, north and south magnetic fields, or a zero and non-zero electric voltage, represent two states (0, 1) of a binary digit (bit). Other phenomena can represent digits of a higher base. A superposition of multiple simultaneous quantum states before measurement represents a quantum bit (qubit). A sequence of one or more digits constitutes digital data that is used to represent a number or code for a character. In some embodiments, information called analog data is represented by a near continuum of measurable values within a particular range. Computer system  800 , or a portion thereof, constitutes a means for providing a prediction of adverse road locations. 
     A bus  810  includes one or more parallel conductors of information so that information is transferred quickly among devices coupled to the bus  810 . One or more processors  802  for processing information are coupled with the bus  810 . 
     A processor (or multiple processors)  802  performs a set of operations on information as specified by computer program code related to providing a prediction of adverse road locations. The computer program code is a set of instructions or statements providing instructions for the operation of the processor and/or the computer system to perform specified functions. The code, for example, may be written in a computer programming language that is compiled into a native instruction set of the processor. The code may also be written directly using the native instruction set (e.g., machine language). The set of operations include bringing information in from the bus  810  and placing information on the bus  810 . The set of operations also typically include comparing two or more units of information, shifting positions of units of information, and combining two or more units of information, such as by addition or multiplication or logical operations like OR, exclusive OR (XOR), and AND. Each operation of the set of operations that can be performed by the processor is represented to the processor by information called instructions, such as an operation code of one or more digits. A sequence of operations to be executed by the processor  802 , such as a sequence of operation codes, constitute processor instructions, also called computer system instructions or, simply, computer instructions. Processors may be implemented as mechanical, electrical, magnetic, optical, chemical, or quantum components, among others, alone or in combination. 
     Computer system  800  also includes a memory  804  coupled to bus  810 . The memory  804 , such as a random access memory (RAM) or any other dynamic storage device, stores information including processor instructions for providing a prediction of adverse road locations. Dynamic memory allows information stored therein to be changed by the computer system  800 . RAM allows a unit of information stored at a location called a memory address to be stored and retrieved independently of information at neighboring addresses. The memory  804  is also used by the processor  802  to store temporary values during execution of processor instructions. The computer system  800  also includes a read only memory (ROM)  806  or any other static storage device coupled to the bus  810  for storing static information, including instructions, that is not changed by the computer system  800 . Some memory is composed of volatile storage that loses the information stored thereon when power is lost. Also coupled to bus  810  is a non-volatile (persistent) storage device  808 , such as a magnetic disk, optical disk or flash card, for storing information, including instructions, that persists even when the computer system  800  is turned off or otherwise loses power. 
     Information, including instructions for providing a prediction of adverse road locations, is provided to the bus  810  for use by the processor from an external input device  812 , such as a keyboard containing alphanumeric keys operated by a human user, a microphone, an Infrared (IR) remote control, a joystick, a game pad, a stylus pen, a touch screen, or a sensor. A sensor detects conditions in its vicinity and transforms those detections into physical expression compatible with the measurable phenomenon used to represent information in computer system  800 . Other external devices coupled to bus  810 , used primarily for interacting with humans, include a display device  814 , such as a cathode ray tube (CRT), a liquid crystal display (LCD), a light emitting diode (LED) display, an organic LED (OLED) display, a plasma screen, or a printer for presenting text or images, and a pointing device  816 , such as a mouse, a trackball, cursor direction keys, or a motion sensor, for controlling a position of a small cursor image presented on the display  814  and issuing commands associated with graphical elements presented on the display  814 , and one or more camera sensors  894  for capturing, recording and causing to store one or more still and/or moving images (e.g., videos, movies, etc.) which also may comprise audio recordings. In some embodiments, for example, in embodiments in which the computer system  800  performs all functions automatically without human input, one or more of external input device  812 , display device  814  and pointing device  816  may be omitted. 
     In the illustrated embodiment, special purpose hardware, such as an application specific integrated circuit (ASIC)  820 , is coupled to bus  810 . The special purpose hardware is configured to perform operations not performed by processor  802  quickly enough for special purposes. Examples of ASICs include graphics accelerator cards for generating images for display  814 , cryptographic boards for encrypting and decrypting messages sent over a network, speech recognition, and interfaces to special external devices, such as robotic arms and medical scanning equipment that repeatedly perform some complex sequence of operations that are more efficiently implemented in hardware. 
     Computer system  800  also includes one or more instances of a communications interface  870  coupled to bus  810 . Communication interface  870  provides a one-way or two-way communication coupling to a variety of external devices that operate with their own processors, such as printers, scanners and external disks. In general, the coupling is with a network link  878  that is connected to a local network  880  to which a variety of external devices with their own processors are connected. For example, communication interface  870  may be a parallel port or a serial port or a universal serial bus (USB) port on a personal computer. In some embodiments, communications interface  870  is an integrated services digital network (ISDN) card or a digital subscriber line (DSL) card or a telephone modem that provides an information communication connection to a corresponding type of telephone line. In some embodiments, a communication interface  870  is a cable modem that converts signals on bus  810  into signals for a communication connection over a coaxial cable or into optical signals for a communication connection over a fiber optic cable. As another example, communications interface  870  may be a local area network (LAN) card to provide a data communication connection to a compatible LAN, such as Ethernet. Wireless links may also be implemented. For wireless links, the communications interface  870  sends or receives or both sends and receives electrical, acoustic or electromagnetic signals, including infrared and optical signals, that carry information streams, such as digital data. For example, in wireless handheld devices, such as mobile telephones like cell phones, the communications interface  870  includes a radio band electromagnetic transmitter and receiver called a radio transceiver. 
     The term “computer-readable medium” as used herein refers to any medium that participates in providing information to processor  802 , including instructions for execution. Such a medium may take many forms, including, but not limited to computer-readable storage medium (e.g., non-volatile media, volatile media), and transmission media. Non-transitory media, such as non-volatile media, include, for example, optical or magnetic disks, such as storage device  808 . Volatile media include, for example, dynamic memory  804 . Transmission media include, for example, twisted pair cables, coaxial cables, copper wire, fiber optic cables, and carrier waves that travel through space without wires or cables, such as acoustic waves and electromagnetic waves, including radio, optical and infrared waves. Signals include man-made transient variations in amplitude, frequency, phase, polarization or other physical properties transmitted through the transmission media. Common forms of computer-readable media include, for example, a floppy disk, a flexible disk, hard disk, magnetic tape, any other magnetic medium, a CD-ROM, CDRW, DVD, any other optical medium, punch cards, paper tape, optical mark sheets, any other physical medium with patterns of holes or other optically recognizable indicia, a RAM, a PROM, an EPROM, a FLASH-EPROM, an EEPROM, a flash memory, any other memory chip or cartridge, a carrier wave, or any other medium from which a computer can read. The term computer-readable storage medium is used herein to refer to any computer-readable medium except transmission media. 
     Logic encoded in one or more tangible media includes one or both of processor instructions on a computer-readable storage media and special purpose hardware, such as ASIC  820 . 
     Network link  878  typically provides information communication using transmission media through one or more networks to other devices that use or process the information. For example, network link  878  may provide a connection through local network  880  to a host computer  882  or to equipment  884  operated by an Internet Service Provider (ISP). ISP equipment  884  in turn provides data communication services through the public, world-wide packet-switching communication network of networks now commonly referred to as the Internet  890 . 
     A computer called a server host  882  connected to the Internet hosts a process that provides a service in response to information received over the Internet. For example, server host  882  hosts a process that provides information representing video data for presentation at display  814 . It is contemplated that the components of system  800  can be deployed in various configurations within other computer systems, e.g., host  882  and server  892 . 
     At least some embodiments of the invention are related to the use of computer system  800  for implementing some or all of the techniques described herein. According to one embodiment of the invention, those techniques are performed by computer system  800  in response to processor  802  executing one or more sequences of one or more processor instructions contained in memory  804 . Such instructions, also called computer instructions, software and program code, may be read into memory  804  from another computer-readable medium such as storage device  808  or network link  878 . Execution of the sequences of instructions contained in memory  804  causes processor  802  to perform one or more of the method steps described herein. In alternative embodiments, hardware, such as ASIC  820 , may be used in place of or in combination with software to implement the invention. Thus, embodiments of the invention are not limited to any specific combination of hardware and software, unless otherwise explicitly stated herein. 
     The signals transmitted over network link  878  and other networks through communications interface  870 , carry information to and from computer system  800 . Computer system  800  can send and receive information, including program code, through the networks  880  among others, through network link  878  and communications interface  870 . In an example using the Internet  890 , a server host  882  transmits program code for a particular application, requested by a message sent from computer  800 , through Internet  890 , ISP equipment  884 , local network  880  and communications interface  870 . The received code may be executed by processor  802  as it is received or may be stored in memory  804  or in storage device  808  or any other non-volatile storage for later execution, or both. In this manner, computer system  800  may obtain application program code in the form of signals on a carrier wave. 
     Various forms of computer readable media may be involved in carrying one or more sequence of instructions or data or both to processor  802  for execution. For example, instructions and data may initially be carried on a magnetic disk of a remote computer such as host  882 . The remote computer loads the instructions and data into its dynamic memory and sends the instructions and data over a telephone line using a modem. A modem local to the computer system  800  receives the instructions and data on a telephone line and uses an infra-red transmitter to convert the instructions and data to a signal on an infra-red carrier wave serving as the network link  878 . An infrared detector serving as communications interface  870  receives the instructions and data carried in the infrared signal and places information representing the instructions and data onto bus  810 . Bus  810  carries the information to memory  804  from which processor  802  retrieves and executes the instructions using some of the data sent with the instructions. The instructions and data received in memory  804  may optionally be stored on storage device  808 , either before or after execution by the processor  802 . 
       FIG.  9    illustrates a chip set or chip  900  upon which an embodiment may be implemented. Chip set  900  is programmed to provide a prediction of adverse road locations, as described herein, and includes, for instance, the processor and memory components described with respect to  FIG.  9    incorporated in one or more physical packages (e.g., chips). By way of example, a physical package includes an arrangement of one or more materials, components, and/or wires on a structural assembly (e.g., a baseboard) to provide one or more characteristics such as physical strength, conservation of size, and/or limitation of electrical interaction. It is contemplated that in certain embodiments the chip set  900  can be implemented in a single chip. It is further contemplated that in certain embodiments the chip set or chip  900  can be implemented as a single “system on a chip.” It is further contemplated that in certain embodiments a separate ASIC would not be used, for example, and that all relevant functions as disclosed herein would be performed by a processor or processors. Chip set or chip  900 , or a portion thereof, constitutes a means for performing one or more steps of providing user interface navigation information associated with the availability of functions. Chip set or chip  900 , or a portion thereof, constitutes a means for providing a prediction of adverse road locations. 
     In one embodiment, the chip set or chip  900  includes a communication mechanism such as a bus  901  for passing information among the components of the chip set  900 . A processor  903  has connectivity to the bus  901  to execute instructions and process information stored in, for example, a memory  905 . The processor  903  may include one or more processing cores with each core configured to perform independently. A multi-core processor enables multiprocessing within a single physical package. Examples of a multi-core processor include two, four, eight, or greater numbers of processing cores. Alternatively or in addition, the processor  903  may include one or more microprocessors configured in tandem via the bus  901  to enable independent execution of instructions, pipelining, and multithreading. The processor  903  may also be accompanied with one or more specialized components to perform certain processing functions and tasks such as one or more digital signal processors (DSP)  907 , or one or more application-specific integrated circuits (ASIC)  909 . A DSP  907  typically is configured to process real-world signals (e.g., sound) in real time independently of the processor  903 . Similarly, an ASIC  909  can be configured to performed specialized functions not easily performed by a more general purpose processor. Other specialized components to aid in performing the inventive functions described herein may include one or more field programmable gate arrays (FPGA), one or more controllers, or one or more other special-purpose computer chips. 
     In one embodiment, the chip set or chip  900  includes merely one or more processors and some software and/or firmware supporting and/or relating to and/or for the one or more processors. The processor  903  and accompanying components have connectivity to the memory  905  via the bus  901 . The memory  905  includes both dynamic memory (e.g., RAM, magnetic disk, writable optical disk, etc.) and static memory (e.g., ROM, CD-ROM, etc.) for storing executable instructions that when executed perform the inventive steps described herein to provide a prediction of adverse road locations. The memory  905  also stores the data associated with or generated by the execution of the inventive steps. 
       FIG.  10    is a diagram of exemplary components of a mobile terminal  1001  (e.g., a mobile device or vehicle or part thereof) for communications, which is capable of operating in the system of  FIG.  1   , according to one embodiment. In some embodiments, mobile terminal  1001 , or a portion thereof, constitutes a means for providing a prediction of adverse road locations. Generally, a radio receiver is often defined in terms of front-end and back-end characteristics. The front-end of the receiver encompasses all of the Radio Frequency (RF) circuitry whereas the back-end encompasses all of the base-band processing circuitry. As used in this application, the term “circuitry” refers to both: (1) hardware-only implementations (such as implementations in only analog and/or digital circuitry), and (2) to combinations of circuitry and software (and/or firmware) (such as, if applicable to the particular context, to a combination of processor(s), including digital signal processor(s), software, and memory(ies) that work together to cause an apparatus, such as a mobile phone or server, to perform various functions). This definition of “circuitry” applies to all uses of this term in this application, including in any claims. As a further example, as used in this application and if applicable to the particular context, the term “circuitry” would also cover an implementation of merely a processor (or multiple processors) and its (or their) accompanying software/or firmware. The term “circuitry” would also cover, if applicable, to the particular context, for example, a baseband integrated circuit or applications processor integrated circuit in a mobile phone or a similar integrated circuit in a cellular network device or other network devices. 
     Pertinent internal components of the telephone include a Main Control Unit (MCU)  1003 , a Digital Signal Processor (DSP)  1005 , and a receiver/transmitter unit including a microphone gain control unit and a speaker gain control unit. A main display unit  1007  provides a display to the user in support of various applications and mobile terminal functions that perform or support the steps of providing a prediction of adverse road locations. The display  1007  includes display circuitry configured to display at least a portion of a user interface of the mobile terminal (e.g., mobile telephone). Additionally, the display  1007  and display circuitry are configured to facilitate user control of at least some functions of the mobile terminal. An audio function circuitry  1009  includes a microphone  1011  and microphone amplifier that amplifies the speech signal output from the microphone  1011 . The amplified speech signal output from the microphone  1011  is fed to a coder/decoder (CODEC)  1013 . 
     A radio section  1015  amplifies power and converts frequency in order to communicate with a base station, which is included in a mobile communication system, via antenna  1017 . The power amplifier (PA)  1019  and the transmitter/modulation circuitry are operationally responsive to the MCU  1003 , with an output from the PA  1019  coupled to the duplexer  1021  or circulator or antenna switch, as known in the art. The PA  1019  also couples to a battery interface and power control unit  1020 . 
     In use, a user of mobile terminal  1001  speaks into the microphone  1011  and his or her voice along with any detected background noise is converted into an analog voltage. The analog voltage is then converted into a digital signal through the Analog to Digital Converter (ADC)  1023 . The control unit  1003  routes the digital signal into the DSP  1005  for processing therein, such as speech encoding, channel encoding, encrypting, and interleaving. In one embodiment, the processed voice signals are encoded, by units not separately shown, using a cellular transmission protocol such as enhanced data rates for global evolution (EDGE), general packet radio service (GPRS), global system for mobile communications (GSM), Internet protocol multimedia subsystem (IMS), universal mobile telecommunications system (UMTS), etc., as well as any other suitable wireless medium, e.g., microwave access (WiMAX), Long Term Evolution (LTE) networks, 5G networks, code division multiple access (CDMA), wideband code division multiple access (WCDMA), wireless fidelity (WiFi), satellite, and the like, or any combination thereof. 
     The encoded signals are then routed to an equalizer  1025  for compensation of any frequency-dependent impairments that occur during transmission though the air such as phase and amplitude distortion. After equalizing the bit stream, the modulator  1027  combines the signal with a RF signal generated in the RF interface  1029 . The modulator  1027  generates a sine wave by way of frequency or phase modulation. In order to prepare the signal for transmission, an up-converter  1031  combines the sine wave output from the modulator  1027  with another sine wave generated by a synthesizer  1033  to achieve the desired frequency of transmission. The signal is then sent through a PA  1019  to increase the signal to an appropriate power level. In practical systems, the PA  1019  acts as a variable gain amplifier whose gain is controlled by the DSP  1005  from information received from a network base station. The signal is then filtered within the duplexer  1021  and optionally sent to an antenna coupler  1035  to match impedances to provide maximum power transfer. Finally, the signal is transmitted via antenna  1017  to a local base station. An automatic gain control (AGC) can be supplied to control the gain of the final stages of the receiver. The signals may be forwarded from there to a remote telephone which may be another cellular telephone, any other mobile phone or a land-line connected to a Public Switched Telephone Network (PSTN), or other telephony networks. 
     Voice signals transmitted to the mobile terminal  1001  are received via antenna  1017  and immediately amplified by a low noise amplifier (LNA)  1037 . A down-converter  1039  lowers the carrier frequency while the demodulator  1041  strips away the RF leaving only a digital bit stream. The signal then goes through the equalizer  1025  and is processed by the DSP  1005 . A Digital to Analog Converter (DAC)  1043  converts the signal and the resulting output is transmitted to the user through the speaker  1045 , all under control of a Main Control Unit (MCU)  1003  which can be implemented as a Central Processing Unit (CPU). 
     The MCU  1003  receives various signals including input signals from the keyboard  1047 . The keyboard  1047  and/or the MCU  1003  in combination with other user input components (e.g., the microphone  1011 ) comprise a user interface circuitry for managing user input. The MCU  1003  runs a user interface software to facilitate user control of at least some functions of the mobile terminal  1001  to provide a prediction of adverse road locations. The MCU  1003  also delivers a display command and a switch command to the display  1007  and to the speech output switching controller, respectively. Further, the MCU  1003  exchanges information with the DSP  1005  and can access an optionally incorporated SIM card  1049  and a memory  1061 . In addition, the MCU  1003  executes various control functions required of the terminal. The DSP  1005  may, depending upon the implementation, perform any of a variety of conventional digital processing functions on the voice signals. Additionally, DSP  1005  determines the background noise level of the local environment from the signals detected by microphone  1011  and sets the gain of microphone  1011  to a level selected to compensate for the natural tendency of the user of the mobile terminal  1001 . 
     The CODEC  1013  includes the ADC  1023  and DAC  1043 . The memory  1061  stores various data including call incoming tone data and is capable of storing other data including music data received via, e.g., the global Internet. The software module could reside in RAM memory, flash memory, registers, or any other form of writable storage medium known in the art. The memory device  1061  may be, but not limited to, a single memory, CD, DVD, ROM, RAM, EEPROM, optical storage, magnetic disk storage, flash memory storage, or any other non-volatile storage medium capable of storing digital data. 
     An optionally incorporated SIM card  1049  carries, for instance, important information, such as the cellular phone number, the carrier supplying service, subscription details, and security information. The SIM card  1049  serves primarily to identify the mobile terminal  1001  on a radio network. The card  1049  also contains a memory for storing a personal telephone number registry, text messages, and user specific mobile terminal settings. 
     Further, one or more camera sensors  1063  may be incorporated onto the mobile station  1001  wherein the one or more camera sensors may be placed at one or more locations on the mobile station. Generally, the camera sensors may be utilized to capture, record, and cause to store one or more still and/or moving images (e.g., videos, movies, etc.) which also may comprise audio recordings. 
     While the invention has been described in connection with a number of embodiments and implementations, the invention is not so limited but covers various obvious modifications and equivalent arrangements, which fall within the purview of the appended claims. Although features of the invention are expressed in certain combinations among the claims, it is contemplated that these features can be arranged in any combination and order.