APPARATUS AND METHODS FOR PROVIDING TIRE PRESSURE ANALYSIS

An apparatus, method and computer program product are provided for providing a tire pressure analysis. In one example, the apparatus receives first sensor data indicating a first tire pressure change associated with a first vehicle and causes a prediction model to generate a predicted cause of the first tire pressure change based on the first sensor data. The prediction model is trained based on historical data, the historical data defining a plurality of tire pressure signatures. Each of the plurality of tire pressure signatures correlates a cause of a second tire pressure change associated with a second vehicle to second sensor data indicating one or more tire pressure levels of the second vehicle over a period in which a wheel of the second vehicle completes a revolution. The apparatus further causes a notification of the predicted cause at a user interface associated with the first vehicle.

TECHNICAL FIELD

The present disclosure generally relates to the field of monitoring vehicle sensor data, associated methods and apparatus, and in particular, concerns, for example, an apparatus configured to provide an analysis associated with a tire pressure change, such as an analysis indicating a predicted cause of the tire pressure change.

BACKGROUND

Modern vehicles are equipped with a system for acquiring various sensor data, such as tire pressure data. Such system may include a user interface and tire pressure sensors for detecting and notifying a detection of a tire pressure change. Generally, the system is limited to the application of detecting and notifying a tire pressure loss. As such, when a vehicle is taken to an auto repair shop for repair, a mechanic is required to manually detect a cause of the tire pressure loss and the source thereof, thereby increasing the time/cost of manual labour.

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, the historical data defining a plurality of tire pressure signatures, wherein each of the plurality of tire pressure signatures correlates a cause of a first tire pressure change associated with a first vehicle to sensor data indicating one or more tire pressure levels of the first vehicle over a period in which a wheel of the first vehicle completes a revolution, and based on the historical data, train a prediction model to generate a predicted cause of a second tire pressure change associated with a second vehicle.

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 receive first sensor data indicating a first tire pressure change associated with a first vehicle, cause a prediction model to generate a predicted cause of the first tire pressure change based on the first sensor data, wherein the prediction model is trained based on historical data, the historical data defining a plurality of tire pressure signatures, wherein each of the plurality of tire pressure signatures correlates a cause of a second tire pressure change associated with a second vehicle to second sensor data indicating one or more tire pressure levels of the second vehicle over a period in which a wheel of the second vehicle completes a revolution, and cause a notification of the predicted cause at a user interface associated with the first vehicle.

According to a third aspect, a method of determining a predicted cause of a first tire pressure change is described. The method comprising receiving first sensor data indicating the first tire pressure change and first travel data associated with the first tire pressure change, identifying a past event corresponding to the first sensor data and the first travel data, wherein the past event correlates a cause of a second tire pressure change to second sensor data indicating the second tire pressure change and second travel data, and determining the predicted cause based at least in part on the past event.

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.

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.

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'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'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. As such, when a vehicle is taken to an auto repair shop, a mechanic is required to manually detect the cause of the tire pressure loss and the source thereof, thereby increasing the time/cost of manual labour. Additionally, once the TPMS detects a tire pressure loss, a user of the vehicle may be inclined to stop the vehicle since the user is not aware of a maximum amount of distance that the vehicle can safely travel. There will now be described an apparatus and associated methods that may address these issues.

FIG.1is a diagram of a system100capable of providing a tire pressure analysis, according to one embodiment. The tire pressure analysis may refer to a process of rendering: (1) a prediction for a cause of a tire pressure change; (2) a prediction of a tire pressure change associated with one or more road segments; (3) an estimation of a maximum distance a vehicle can safely travel after detecting the tire pressure change; (4) a determination of a route to a destination that yields a minimum amount of tire pressure loss after detecting the tire pressure change; or (5) a combination thereof. The system includes a user equipment (UE)101, a vehicle105, a detection entity113, a services platform115, content providers119a-119n, a communication network121, a prediction platform123, a database125, and a satellite127. Additional or a plurality of mentioned components may be provided.

In the illustrated embodiment, the system100comprises a user equipment (UE)101that may include or be associated with an application103. In one embodiment, the UE101has connectivity to the prediction platform123via the communication network121. The prediction platform123performs one or more functions associated with providing a tire pressure analysis and providing a response to the tire pressure analysis. In the illustrated embodiment, the UE101may 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 vehicle105), or any combination thereof, including the accessories and peripherals of these devices. In one embodiment, the UE101can 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 UE101can be a cellular telephone. A user may use the UE101for navigation functions, for example, road link map updates. It should be appreciated that the UE101can 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 UE101associated with the vehicles. The application103may assist in conveying information regarding at least one attribute associated a road segment via the communication network121. In one embodiment, the information may indicate a tire pressure analysis.

In the illustrated embodiment, the application103may be any type of application that is executable by the UE101, 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 applications103at the UE101may act as a client for the prediction platform123and perform one or more functions associated with the functions of the prediction platform123by interacting with the prediction platform123over the communication network121. The application103may provide the tire pressure analysis and/or be executed via the UE101to receive information needed for performing the tire pressure analysis (e.g., a desired destination subsequent to detecting a tire pressure change).

The vehicle105may 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 vehicle105includes parts related to mobility, such as a powertrain with an engine, a transmission, a suspension, a driveshaft, and/or wheels, etc. The vehicle105may 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 vehicle105may 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 vehicle105, 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 UE101may be integrated in the vehicle105, 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 UE101. Alternatively, an assisted driving device may be included in the vehicle105. The assisted driving device may include memory, a processor, and systems to communicate with the UE101. In one embodiment, the vehicle105may 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 vehicle105may 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 vehicle105includes a plurality of sensors107, an on-board computing platform109, and an on-board communication platform111. The sensors107may include one or more sensors for monitoring tire pressure levels of one or more tires of the vehicle105. 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 platform109. 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 sensors107may 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 vehicle105along a path of travel, etc. In a further embodiment, sensors about the perimeter of the vehicle105may detect the relative distance of the vehicle105from 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 vehicle105may include GPS receivers to obtain geographic coordinates from satellites127for determining current location and time associated with the vehicle105. 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 platform109performs one or more functions associated with the vehicle105. In one embodiment, the on-board computing platform109may aggregate sensor data generated by at least one of the sensors107and transmit the sensor data via the on-board communications platform111. The on-board computing platform109may receive control signals for performing one or more of the functions from the prediction platform123, the UE101, the services platform115, one or more of the content providers119a-119n, or a combination thereof via the on-board communication platform111. The on-board computing platform109includes 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 platform109includes wired or wireless network interfaces to enable communication with external networks. The on-board communications platform109also 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 platform109includes 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 platform109includes 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 UE101.

The detection entity113may 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 entity113may 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 entity113may 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 entity113may include GPS receivers to obtain geographic coordinates from satellites127for determining current location and time associated with at which the detection entity113acquires 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 entity113is a stationary device (e.g., a traffic camera), the detection entity113may store contextual information indicating a location at which the detection entity113is located, a direction at which a particular sensor (e.g., an image sensor) of the detection entity113is facing, or a combination thereof. In one embodiment, the detection entity113may be a mobile device (e.g., similar to the UE101) 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 service platform115may be an original equipment manufacturer (OEM) platform that provides one or more services117a-117n(collectively referred to as services117). In one embodiment the one or more service117may be sensor data collection services. By way of example, vehicle sensor data provided by the sensors107may be transferred to the UE101, the prediction platform123, the database125, or other entities communicatively coupled to the communication network121through the service platform115. The services117may 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 platform115uses the output data generated by of the prediction platform123to provide services such as navigation, mapping, other location-based services, etc.

In one embodiment, the content providers119a-119n(collectively referred to as content providers119) may provide content or data (e.g., including geographic data, parametric representations of mapped features, etc.) to the UE101, the vehicle105, services platform115, the vehicle105, the database125, the prediction platform123, 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 providers119may provide content that may aid in providing a tire pressure analysis, and/or other related characteristics. In one embodiment, the content providers119may also store content associated with the UE101, the vehicle105, services platform115, the vehicle105, the database125, the prediction platform123, or the combination thereof. In another embodiment, the content providers119may manage access to a central repository of data, and offer a consistent, standard interface to data, such as a repository of the database125.

In the illustrated embodiment, the prediction platform123may be a platform with multiple interconnected components. The prediction platform123may include multiple servers, intelligent networking devices, computing devices, components and corresponding software for providing a tire pressure analysis. It should be appreciated that that the prediction platform123may be a separate entity of the system100, included within the UE101(e.g., as part of the applications103), included within the vehicle105(e.g., as part of an application stored in memory of the on-board computing platform109), included within the services platform115(e.g., as part of an application stored in server memory for the services platform115), or a combination thereof.

The prediction platform123is capable of providing a tire pressure analysis and providing information and/or a response associate with the tire pressure analysis. The tire pressure analysis may refer to a process of rendering: (1) a prediction for a cause of a tire pressure change; (2) a prediction of a tire pressure change associated with one or more road segments; (3) an estimation of a maximum distance a vehicle can safely travel after detecting the tire pressure change; (4) a determination of a route to a destination that yields a minimum amount of tire pressure loss after detecting the tire pressure change; or (5) a combination thereof.

The prediction platform123may embody a machine learning model for rendering a prediction for a cause of a tire pressure change. The machine learning model may be trained based on historical data including a plurality of tire pressure signatures. Each of the plurality of tire pressure signatures may include: (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 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; (4) one or more lesser periods within the period in which the vehicle was impacted by the tire pressure change; or (5) a period in which a wheel of the vehicle completes a revolution and 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; (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, such as a speed of the vehicle, may be acquired via one or more detection entities113that is proximate to the vehicle at a given time. 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 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 entities113(such as system that can identify vehicles and classify the vehicles to acquire attributes associated with the vehicles), the services platform115, one or more content providers119, the database125, or a combination thereof.

Each of the plurality of tire pressure signatures may further include correlation data that correlate the sensor data, the travel data, the vehicle attribute data, or the combination thereof to ground truth data indicating a cause of the tire pressure change associated with one or more tires of the vehicle. The ground truth data may be recorded based on an actual observation of a cause of the tire pressure change. For example, a mechanic may review the state of a tire and record the ground truth data. The cause of the tire pressure change may indicate a direct source causing the tire pressure change (e.g., an issue within a tire, a rim, or a valve) and a location of a source with respect to a wheel that is causing the tire pressure change. For each of the plurality of tire pressure signatures, one or more pressure levels may be generated over the predetermined period to uniquely define said tire pressure signature and a cause associated therewith. As such, a cause indicated by a tire pressure signature may indicate: (1) a nail or metal penetrating a tire; (2) a damaged rim; (3) a defective valve; (4) an incorrect seating of a valve within a rim; (5) an over-tightened electronic sensor cap (e.g., ignoring a proper torque thereof at approximately 4 Nm); (6) an unsealed inner layer protecting a tire (e.g., a micro-crack); (7) a damaged tire bead during installation or removal of a product on a rim; (8) dirt within a contact point of a tire bead and a flange of the rim; (9) a temperature drop in a cold weather; or (10) small punctures or cuts within the tire.

Once the machine learning model is trained, the prediction platform123may render a prediction of a cause for a tire pressure change in response to receiving a request from the vehicle105. The vehicle105may transmit the request to the prediction platform123when a rate at which a tire pressure changes satisfies a threshold rate. In one embodiment, the vehicle105may transmit the request when a tire pressure drops for more than 5 percent of the maximum tire pressure of a wheel. To render the prediction, the prediction platform123may require at least sensor data indicating all tire pressure levels recorded over a period in which a wheel of the vehicle105completes a revolution (will be referred as a revolution period, herein). The revolution period may be calculated by the prediction platform123as a function of a speed of the vehicle105and the vehicle attribute data indicating a circumference of a wheel. In one embodiment, the prediction platform123may analyze one or more pressure levels within a revolution period following a duration in which the tire pressure change is detected. In one embodiment, if the duration in which the tire pressure change is detected is greater than a revolution period, the prediction platform123may analyze one or more pressure levels of a period within said duration. In one embodiment, the prediction platform123may acquire a plurality of pressure levels over a plurality of revolution periods and determine an average for a single revolution period. The prediction platform123may analyze one or more pressure levels over the revolution period to derive a pattern and/or a rate of change over the revolution period. Subsequently, the prediction platform123may identify a tire pressure signature and a cause thereof that correspond to the derived pattern and/or rate of change. In one embodiment, the prediction platform123may normalize sensor data acquired during the revolution period to identify a tire pressure signature corresponding to the derived pattern and/or rate of change. For example, tire pressure levels acquired during the revolution period may be normalized based on ambient temperature levels, road pavement temperature levels, atmospheric temperature levels, or a combination thereof. If the tire pressure signature is identified, the prediction platform123may further identify a location of a source within a wheel of the vehicle105that is causing the tire pressure change. In one embodiment, the prediction platform123may provide a confidence at which the derived pattern and/or rate of change indicate a predicted cause of an identified tire pressure signature and a predicted location of a source of the predicted cause. In one embodiment, the prediction platform123may further acquire travel data and/or vehicle attribute data associated with the vehicle105and use the travel data and/or the vehicle attribute data to facilitate a search for at least one tire pressure signature that corresponds to the derived pattern and/or rate of change.

In one embodiment, the machine learning model may be further trained to predict a tire pressure change associated with one or more road segments based on sensor data associated with a vehicle's tire pressure change, travel data associated with the vehicle, vehicle attribute data thereof, or a combination thereof. By way of example, the machine learning model is trained to learn one or more tire pressure levels of a type of vehicle that has moved at a certain speed with a type of tire of a certain age at a road segment having certain attributes (e.g., composition of the surface thereof) and impacted by a certain weather condition. Once the machine learning model is trained and the prediction platform123detects that the vehicle105is experiencing a tire pressure change, the prediction platform123uses the machine learning model to predict an amount of tire pressure loss associated one or more road segments that the vehicle105is to traverse. In such embodiment, the machine learning model refers to past instances in which a similar vehicle with a similar tire type has traversed the one or more road segments (or similar road segments) and lost tire pressure. Based on such instances, the machine learning model may predict an amount of tire pressure lost for traversing the one or more road segments. In one embodiment, the prediction platform123may estimate an amount of tire pressure change based on a current rate at which the tire pressure of the vehicle105is changing. In one embodiment, the prediction platform123may estimate a maximum distance a vehicle can safely travel after detecting the tire pressure change. For example, the prediction platform123may determine that it is unsafe for a vehicle to travel once the tire pressure thereof falls below 25 percent of the maximum tire pressure capacity. In one embodiment, after detecting a tire pressure change, the prediction platform123may identify a destination (e.g., the nearest auto repair shop, home, dealership, etc.) and use the machine learning model to estimate a route to the destination that yields a minimum amount of tire pressure loss.

In one embodiment, after the prediction platform123renders a prediction for a cause of a tire pressure change, a wheel of the vehicle105may be inspected by a service personnel (e.g., a mechanic) to validate the actual cause of the tire pressure change. If the inspection validates the prediction, the service personnel may provide an input via the UE101to indicate that the prediction is true. Conversely, the service personnel may provide an input via the UE101to indicate that the prediction is false and provide the actual cause of the tire pressure change. In such embodiment, the prediction platform123may receive the input and use the input to further train the machine learning model.

In the illustrated embodiment, the database125stores 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 database125may include any multiple types of information that can provide means for aiding in providing a tire pressure analysis. It should be appreciated that the information stored in the database125may be acquired from any of the elements within the system100, other vehicles, sensors, database, or a combination thereof.

FIG.2is a diagram of a database125(e.g., a map database), according to one embodiment. In one embodiment, the database125includes geographic data1250used 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 database125.

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 database125follows 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 database125includes node data records1251, road segment or link data records1253, point of interest (POI) data records1255, tire pressure records1257, other records1259, and indexes1261, 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 indexes1261may improve the speed of data retrieval operations in the database125. In one embodiment, the indexes1261may be used to quickly locate data without having to search every row in the database125every time it is accessed.

In exemplary embodiments, the road segment data records1253are 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 records1251are end points (such as intersections) corresponding to the respective links or segments of the road segment data records1253. The road link data records1253and the node data records1251represent a road network, such as used by vehicles, cars, and/or other entities. Alternatively, the database125can 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, buildings, stores, parks, etc. The database125can include data about the POIs and their respective locations in the POI data records1255. The data about the POIs may include attribute data associated with the POIs. The attribute data may indicate a type of POI, a shape of POI, a dimension(s) of POI, a number of stories included in each of the POIs, etc. The database125can 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 records1255or can be associated with POIs or POI data records1255(such as a data point used for displaying or representing a position of a city).

The tire pressure records1257includes, for each of a plurality of road segments, historical data including sensor data of a vehicle impacted by a tire pressure change, travel data associated with the vehicle, and vehicle attribute data associated with the vehicle. The tire pressure records1257also includes tire pressure signatures, where each tire pressure signatures include sensor data, travel data, vehicle attribute data, a cause of tire pressure change, and correlation data that correlate the cause to the sensor data, the travel data, the vehicle attribute data, or a combination thereof.

Other records1259may include computer code instructions and/or algorithms for executing a machine learning model that is capable of providing a tire pressure analysis. The other records1259may further include verification data indicating: (1) whether a verification of a prediction for a cause of a tire pressure was conducted; (2) whether the verification validates the prediction; (3) an actual cause of the tire pressure change; or (4) a combination thereof.

In one embodiment, the database125can be maintained by one or more of the content providers119in association with a map developer. The map developer can collect geographic data to generate and enhance the database125. 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 database125can 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.

FIG.3is a diagram of the components of the prediction platform123, according to one embodiment. By way of example, the prediction platform123includes one or more components for providing a tire pressure analysis. 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 platform123includes a detection module301, a calculation module303, a notification module305, and a presentation module307.

The detection module301is capable of acquiring data from the UE101, the vehicle105(or one or more other vehicles similar to the vehicle105), one or more detection entities113, services platform115, content providers119, the database125, or a combination thereof to provide a tire pressure analysis. In one embodiment, the detection module301acquires data indicating a tire pressure change associated with one or more vehicles for training a machine learning model to render a prediction for a cause of a tire pressure change. 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 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; (4) one or more lesser periods within the period in which the vehicle was impacted by the tire pressure change; or (5) a period in which a wheel of the vehicle completes a revolution and 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; (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. In one embodiment, the detection module301may acquire certain sensor data, such as a speed of the vehicle, from one or more detection entities113that is proximate to the vehicle at the predetermined period. 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 segments; (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) composition of a surface of the road; (3) age of the road (e.g., an amount time and days elapsed since the latest road works); (4) a number of road objects such as potholes, bumps, pavement markers, etc. within the road and locations thereof; (5) or 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.

The detection module301may further receive ground truth data indicating an actual observation of a cause of the tire pressure change. For example, a mechanic may review the state of a tire and provide the ground truth data through a user interface (e.g., UE101), and the detection module301may receive the ground truth data. The cause of the tire pressure change may indicate a direct source causing the tire pressure change (e.g., an issue within a tire, a rim, or a valve) and a location of a source with respect to a wheel that is causing the tire pressure change. By way of example, a cause of a tire pressure change may indicate: (1) a nail or metal penetrating a tire; (2) a damaged rim; (3) a defective valve; (4) an incorrect seating of a valve within a rim; (5) an over-tightened electronic sensor cap (e.g., ignoring a proper torque thereof at approximately 4 Nm); (6) an unsealed inner layer protecting a tire (e.g., a micro-crack); (7) a damaged tire bead during installation or removal of a product on a rim; (8) dirt at a contact point of a tire bead with a flange of the rim; (9) a temperature drop in a cold weather; or (10) small punctures or cuts within the tire.

Once the machine learning model is trained, the detection module301may monitor the vehicle105to determine whether the vehicle105generates data indicating a tire pressure change. Such data may be generated when a rate at which the vehicle105loses tire pressure satisfies a threshold rate or when the vehicle105loses more than 5 percent of the maximum pressure of a wheel. In one embodiment, the detection module301may periodically receive from the vehicle105sensor data indicating a current tire pressure level of a wheel of the vehicle105, and the detection module301may determine that the vehicle105is impacted by the tire pressure change based on the received sensor data. When the detection module301receives the data indicate the tire pressure change, detection module301may acquire sensor data associated with the vehicle105, vehicle attribute data associated with the vehicle105, and travel data associated with the vehicle105from the UE101, the vehicle105(or one or more other vehicles similar to the vehicle105), one or more detection entities113, services platform115, content providers119, the database125, or a combination thereof.

The calculation module303trains the machine learning model based on data acquired by the detection module301. To train the machine learning model, the calculation module303may receive ground truth data indicating a true cause of a tire pressure change for a vehicle and generate correlation data that correlate sensor data associated with the vehicle, travel data associated with the vehicle, vehicle attribute data associated with the vehicle, or the combination thereof to the ground truth data. The sensor data, the travel data, the vehicle attribute data, the correlation data, and the ground truth data may be grouped and labelled as a tire pressure signature. Once a plurality of tire pressure signatures is acquired, the calculation module303provides the plurality of tire pressure signatures as an input for training the machine learning model. In one embodiment, the machine learning model may be a random forest, a logistic, a decision trees, neural networks, or a combination thereof.

When the machine learning model is trained, the calculation module303performs a prediction of a cause for tire pressure change associated with the vehicle105by receiving, via the detection module301, sensor data of the vehicle105indicating all tire pressure levels recorded over a revolution period. The revolution period may be calculated by the calculation module303as a function of a speed of the vehicle105and the vehicle attribute data associated thereto that indicate a circumference of a wheel of the vehicle105. In one embodiment, the calculation module303may analyze one or more pressure levels within a revolution period following a duration in which the tire pressure change is detected. In one embodiment, if the duration in which the tire pressure change is detected is greater than a revolution period, the calculation module303may analyze one or more pressure levels of a period within the duration. In one embodiment, the calculation module303may acquire a plurality of pressure levels over a plurality of revolution periods and determine an average for a single revolution period. The calculation module303may analyze one or more pressure levels over the revolution period to derive a pattern and/or a rate of change over the revolution period. Subsequently, the calculation module303may identify a tire pressure signature and a cause thereof that correspond to the derived pattern and/or rate of change. In one embodiment, the calculation module303may normalize sensor data acquired during the revolution period to identify a tire pressure signature corresponding to the derived pattern and/or rate of change. For example, tire pressure levels acquired during the revolution period may be normalized based on ambient temperature levels, road pavement temperature levels, atmospheric temperature levels, or a combination thereof. If the tire pressure signature is identified, the calculation module303may further identify a location of a source within a wheel of the vehicle105that is causing the tire pressure change. In one embodiment, the calculation module303may provide a confidence at which the derived pattern and/or rate of change indicate a predicted cause of an identified tire pressure signature and a predicted location of a source of the predicted cause. In one embodiment, the calculation module303may further acquire travel data and/or vehicle attribute data associated with the vehicle105and use the travel data and/or the vehicle attribute data to facilitate a search for at least one tire pressure signature that corresponds to the derived pattern and/or rate of change.

In one embodiment, the machine learning model may be further trained to predict a tire pressure change associated with one or more road segments based on sensor data associated with a vehicle's tire pressure change, travel data associated with the vehicle, vehicle attribute data thereof, or a combination thereof. By way of example, the machine learning model is trained to learn one or more tire pressure levels of a type of vehicle that has moved at a certain speed with a type of tire of a certain age at a road segment having certain attributes (e.g., composition of the surface thereof) and impacted by a certain weather condition. Once the machine learning model is trained and the calculation module303detects that the vehicle105is experiencing a tire pressure change, the calculation module303uses the machine learning model to predict an amount of tire pressure loss associated one or more road segments that the vehicle105is predicted to traverse. In such embodiment, the machine learning model refers to past instances in which a similar vehicle with a similar tire type has traversed the one or more road segments (or similar road segments) and lost tire pressure. Based on such instances, the machine learning model may predict an amount of tire pressure lost for traversing the one or more road segments. In one embodiment, the calculation module303may estimate an amount of tire pressure change based on a current rate at which the tire pressure of the vehicle105is changing. In one embodiment, the calculation module303may estimate of a maximum distance a vehicle can safely travel after detecting the tire pressure change. For example, the calculation module303may determine it is unsafe for a vehicle to travel once the tire pressure thereof is below 25 percent of the maximum tire pressure capacity. In one embodiment, after detecting a tire pressure change, the calculation module303may identify a destination (e.g., the nearest auto repair shop, home, dealership, etc.) and use the machine learning model to estimate a route to the destination that yields a minimum amount of tire pressure loss.

In one embodiment, after the calculation module303renders a prediction for a cause of a tire pressure change, a wheel of the vehicle105may be inspected by a service personnel (e.g., a mechanic) to validate the actual cause of the tire pressure change. If the inspection validates the prediction, the service personnel may provide an input via the UE101to indicate that the prediction is true. Conversely, the service personnel may provide an input via the UE101to indicate that the prediction is false and provide the actual cause of the tire pressure change. In such embodiment, the calculation module303may receive the input and use the input to further train the calculation module303.

The notification module305may generate a notification associated with a tire pressure analysis. The notification module305may cause the notification to the UE101and/or one or more other UEs associated with the vehicle105. In such embodiment, the notification may indicate: (1) an occurrence of a tire pressure change; (2) a predicted cause of the tire pressure change; (3) a location of a source of the predicted cause; (4) an estimated maximum amount of distance the vehicle105can safely travel (e.g., no less than 25 percent of the maximum tire pressure) after a detection of the tire pressure change; (5) a route to a destination (e.g., the nearest auto repair shop, home, dealership, etc.) that yields a minimum amount of tire pressure loss after the detection of the tire pressure change; (6) a confidence at which the derived pattern and/or rate of change indicate a predicted cause of an identified tire pressure signature and a predicted location of a source of the predicted cause; or (7) a combination thereof. The notification may include sound notification, display notification, vibration, or a combination thereof. In one embodiment, the notification module305may provide the notification to a local municipality/establishment.

The presentation module307obtains a set of information, data, and/or calculated results from other modules, and continues with providing a presentation of a visual representation to the UE101and/or any other user interface associated with the vehicle105. The visual representation may indicate any of the information presented by the notification module305.FIG.4illustrates a first example visual representation400of a wheel among a plurality of wheels that is impacted by a tire pressure change and a location of a source that is causing the tire pressure change. The first representation400may be rendered by the presentation module307. In the illustrated embodiment, the first visual representation400include a first model410of a plurality of tires of the vehicle105and a second model420of one of the plurality of tires of the vehicle105. The first model410represents four wheels411,413,415, and417. In such embodiment, it is assumed that the front right tire of the vehicle105is impacted by a tire pressure change. As such, the wheel411is highlighted to indicate a detection of the tire pressure change. The second model420focuses on the wheel411. The calculation module303predicts the cause of the tire pressure change, and the presentation module307generates a display421that indicates the cause. In the illustrated example, the display421states “NAIL OR METAL PENETRATING THE TIRE AT THIS LOCATION.” In one embodiment, the first visual representation400may further indicate coordinates of an exact location of the source. For example, the coordinates may define a degree of a wheel in which the source is located, a depth in which the source is located (e.g., a point between the outermost circumference of a wheel and the central axis of the wheel), and a width of the wheel in which the source is located (e.g., a point between the inner surface of the wheel and the outer surface of the wheel). In one embodiment, the second model420may be interactable with a user input, thereby enabling the second model420to rotate in a plurality of directions to illustrate a plurality of perspectives views. In one embodiment, the second model420may be presented such that one or more components thereof is transparent and/or semi-transparent to display a source of a tire pressure change that is within a tire. An exploded view and/or a cross-sectional view of the second model420is also contemplated. The visual representation400may further present other relevant information, such as a rate at which the wheel411is losing tire pressure, a maximum distance at which the vehicle105can safely travel, etc.FIG.5illustrates a second example visual representation500of a map including a route. The second representation500may be rendered by the presentation module307. In the illustrated embodiment, the second visual representation500is a map including a representation501of the vehicle105, a route503, a destination505, and a message507. In such embodiment, it is assumed that the vehicle105is impacted a tire pressure change. As such, the calculation module303has rendered the route503and the message507, which states “TIRE PRESSURE LOSS DETECTED. MOVE TO THE NEAREST AUTO REPAIR SHOP USING THE ROUTE?” In such embodiment, the route503is a route that is predicted to yield a minimum amount of tire pressure loss after the detection of the tire pressure change, and the destination505may be the nearest auto repair shop, home, dealership, etc. In one embodiment, the second visual representation500may be presented as a combination of map layers including a map layer representing a predict amount of tire pressure loss for one or more road segments and other map layers indicating other information such road link, segment, node information, POI information, a type of weather affecting one or more areas, etc.

The above presented modules and components of the prediction platform123can be implemented in hardware, firmware, software, or a combination thereof. Though depicted as a separate entity inFIG.3, it is contemplated that the prediction platform123may be implemented for direct operation by the UE101, the vehicle105, the services platform115, one or more of the content providers119, or a combination thereof. As such, the prediction platform123may generate direct signal inputs by way of the operating system of the UE101, the vehicle105, the services platform115, the one or more of the content providers119, of the combination thereof for interacting with the applications103. The various executions presented herein contemplate any and all arrangements and models.

FIG.6is a flowchart of a process600for training a machine learning model to predict a cause of a tire pressure change, according to one embodiment. In one embodiment, the prediction platform123performs the process600and is implemented in, for instance, a chip set including a processor and a memory as shown inFIG.9.

In step601, the prediction platform123receives historical data associated with a plurality of tire pressure changes. The historical data may be defined by a plurality of tire pressure signatures, where each of the plurality of tire pressure signatures includes: (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) ground truth data indicating a true cause of the tire pressure change; and (5) correlation data correlating the cause to the sensor data, travel data, and the vehicle attribute data. The sensor data may indicate one or more tire pressure levels over a period in which a wheel of the vehicle completes a revolution.

In step603, the prediction platform123trains a prediction model to generate a predicted cause of a tire pressure change associated with a vehicle based on the historical data. The predicted cause may indicate a source of the cause and the location thereof. The predicted cause may indicate: (1) a nail or metal penetrating a tire; (2) a damaged rim; (3) a defective valve; (4) an incorrect seating of a valve in a rim; (5) an over-tightened electronic sensor cap (e.g., ignoring a proper torque thereof at approximately 4 Nm); (6) an unsealed inner layer protecting a tire (i.e., a micro-crack); (7) a damaged tire bead during installation or removal of a product on a rim; (8) dirt at a contact point of a tire bead with flange of the rim; (9) a temperature drop in a cold weather; or (10) small punctures or cuts within the tire.

FIG.7is a flowchart of a process700for causing a notification of a predicted cause of a tire pressure change. In one embodiment, the prediction platform123performs the process700and is implemented in, for instance, a chip set including a processor and a memory as shown inFIG.9.

In step701, the prediction platform123receives sensor data indicating a tire pressure change associated with a vehicle. The sensor data may indicate one or more tire pressure levels over a period in which a wheel of the vehicle completes a revolution (i.e., the revolution period). The revolution period may be subsequent to a period in which the tire pressure change is detected. Alternatively, the revolution period may be within the period in which the tire pressure change is detected. In one embodiment, the prediction platform123further acquires travel data associated with the vehicle and/or vehicle attribute data indicating one or more attributes of the vehicle impacted by the tire pressure change.

In step703, the prediction platform123causes a prediction model to generate a predicted cause of the tire pressure change based on the sensor data. The prediction model may be trained based on historical data defining a plurality of tire pressure signatures, where each of the plurality of tire pressure signatures includes: (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) ground truth data indicating a true cause of the tire pressure change; and (5) correlation data correlating the cause to the sensor data, travel data, and the vehicle attribute data. The prediction platform123may analyze the sensor data to derive a pattern and/or a rate of change over the revolution period. Subsequently, the prediction platform123may identify a tire pressure signature and a cause that correspond to the derived pattern and/or rate of change. In one embodiment, the prediction platform123may use the travel data and/or the vehicle attribute data to facilitate a search for a tire pressure signature and a cause that corresponds to the derived pattern and/or rate of change.

In step705, the prediction platform123causes a notification of the predicted cause at a user interface associated with the vehicle. For example, the user interface may be the UE101or any other UE associated with the vehicle. In one embodiment, the notification may further include a source of a cause of the tire pressure change and a location thereof.

The system, apparatus, and methods described herein enable a map-based server/platform to predict a cause of a tire pressure change, thereby enabling a vehicle operator and/or an auto repair personnel to quickly identify the cause and remedy the source of the tire pressure change. Further, since the map-based server/platform is capable of informing a user regarding a maximum amount of distance that a vehicle can safely travel subsequent to detecting a tire pressure change associated with the vehicle, the user may have the confidence for reaching the desired destination thereof.

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.

A bus810includes one or more parallel conductors of information so that information is transferred quickly among devices coupled to the bus810. One or more processors802for processing information are coupled with the bus810.

Computer system800also includes a memory804coupled to bus810. The memory804, such as a random access memory (RAM) or any other dynamic storage device, stores information including processor instructions for providing a tire pressure analysis. Dynamic memory allows information stored therein to be changed by the computer system800. 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 memory804is also used by the processor802to store temporary values during execution of processor instructions. The computer system800also includes a read only memory (ROM)806or any other static storage device coupled to the bus810for storing static information, including instructions, that is not changed by the computer system800. Some memory is composed of volatile storage that loses the information stored thereon when power is lost. Also coupled to bus810is a non-volatile (persistent) storage device808, such as a magnetic disk, optical disk or flash card, for storing information, including instructions, that persists even when the computer system800is turned off or otherwise loses power.

Information, including instructions for providing a tire pressure analysis, is provided to the bus810for use by the processor from an external input device812, 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 system800. Other external devices coupled to bus810, used primarily for interacting with humans, include a display device814, 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 device816, 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 display814and issuing commands associated with graphical elements presented on the display814, and one or more camera sensors894for 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 system800performs all functions automatically without human input, one or more of external input device812, display device814and pointing device816may be omitted.

In the illustrated embodiment, special purpose hardware, such as an application specific integrated circuit (ASIC)820, is coupled to bus810. The special purpose hardware is configured to perform operations not performed by processor802quickly enough for special purposes. Examples of ASICs include graphics accelerator cards for generating images for display814, 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.

Network link878typically provides information communication using transmission media through one or more networks to other devices that use or process the information. For example, network link878may provide a connection through local network890to a host computer892or to equipment884operated by an Internet Service Provider (ISP). ISP equipment884in turn provides data communication services through the public, world-wide packet-switching communication network of networks now commonly referred to as the Internet8100.

A computer called a server host892connected to the Internet hosts a process that provides a service in response to information received over the Internet. For example, server host892hosts a process that provides information representing video data for presentation at display814. It is contemplated that the components of system800can be deployed in various configurations within other computer systems, e.g., host892and server8102.

At least some embodiments of the invention are related to the use of computer system800for implementing some or all of the techniques described herein. According to one embodiment of the invention, those techniques are performed by computer system800in response to processor802executing one or more sequences of one or more processor instructions contained in memory804. Such instructions, also called computer instructions, software and program code, may be read into memory804from another computer-readable medium such as storage device808or network link878. Execution of the sequences of instructions contained in memory804causes processor802to perform one or more of the method steps described herein. In alternative embodiments, hardware, such as ASIC820, 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 link878and other networks through communications interface870, carry information to and from computer system800. Computer system800can send and receive information, including program code, through the networks890,8100among others, through network link878and communications interface870. In an example using the Internet8100, a server host892transmits program code for a particular application, requested by a message sent from computer800, through Internet8100, ISP equipment884, local network890and communications interface870. The received code may be executed by processor802as it is received or may be stored in memory804or in storage device808or any other non-volatile storage for later execution, or both. In this manner, computer system800may 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 processor802for execution. For example, instructions and data may initially be carried on a magnetic disk of a remote computer such as host892. 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 system800receives 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 link878. An infrared detector serving as communications interface870receives the instructions and data carried in the infrared signal and places information representing the instructions and data onto bus810. Bus810carries the information to memory804from which processor802retrieves and executes the instructions using some of the data sent with the instructions. The instructions and data received in memory804may optionally be stored on storage device808, either before or after execution by the processor802.

In one embodiment, the chip set or chip900includes 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 processor903and accompanying components have connectivity to the memory905via the bus901. The memory905includes 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 tire pressure analysis. The memory905also stores the data associated with or generated by the execution of the inventive steps.

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 unit1007provides a display to the user in support of various applications and mobile terminal functions that perform or support the steps of providing a tire pressure analysis. The display1007includes display circuitry configured to display at least a portion of a user interface of the mobile terminal (e.g., mobile telephone). Additionally, the display1007and display circuitry are configured to facilitate user control of at least some functions of the mobile terminal. An audio function circuitry1009includes a microphone1011and microphone amplifier that amplifies the speech signal output from the microphone1011. The amplified speech signal output from the microphone1011is fed to a coder/decoder (CODEC)1013.

A radio section1015amplifies power and converts frequency in order to communicate with a base station, which is included in a mobile communication system, via antenna1017. The power amplifier (PA)1019and the transmitter/modulation circuitry are operationally responsive to the MCU1003, with an output from the PA1019coupled to the duplexer1021or circulator or antenna switch, as known in the art. The PA1019also couples to a battery interface and power control unit1020.

The encoded signals are then routed to an equalizer1025for 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 modulator1027combines the signal with a RF signal generated in the RF interface1029. The modulator1027generates a sine wave by way of frequency or phase modulation. In order to prepare the signal for transmission, an up-converter1031combines the sine wave output from the modulator1027with another sine wave generated by a synthesizer1033to achieve the desired frequency of transmission. The signal is then sent through a PA1019to increase the signal to an appropriate power level. In practical systems, the PA1019acts as a variable gain amplifier whose gain is controlled by the DSP1005from information received from a network base station. The signal is then filtered within the duplexer1021and optionally sent to an antenna coupler1035to match impedances to provide maximum power transfer. Finally, the signal is transmitted via antenna1017to 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 terminal1001are received via antenna1017and immediately amplified by a low noise amplifier (LNA)1037. A down-converter1039lowers the carrier frequency while the demodulator1041strips away the RF leaving only a digital bit stream. The signal then goes through the equalizer1025and is processed by the DSP1005. A Digital to Analog Converter (DAC)1043converts the signal and the resulting output is transmitted to the user through the speaker1045, all under control of a Main Control Unit (MCU)1003which can be implemented as a Central Processing Unit (CPU).

The MCU1003receives various signals including input signals from the keyboard1047. The keyboard1047and/or the MCU1003in combination with other user input components (e.g., the microphone1011) comprise a user interface circuitry for managing user input. The MCU1003runs a user interface software to facilitate user control of at least some functions of the mobile terminal1001to provide a tire pressure analysis. The MCU1003also delivers a display command and a switch command to the display1007and to the speech output switching controller, respectively. Further, the MCU1003exchanges information with the DSP1005and can access an optionally incorporated SIM card1049and a memory1051. In addition, the MCU1003executes various control functions required of the terminal. The DSP1005may, depending upon the implementation, perform any of a variety of conventional digital processing functions on the voice signals. Additionally, DSP1005determines the background noise level of the local environment from the signals detected by microphone1011and sets the gain of microphone1011to a level selected to compensate for the natural tendency of the user of the mobile terminal1001.

An optionally incorporated SIM card1049carries, for instance, important information, such as the cellular phone number, the carrier supplying service, subscription details, and security information. The SIM card1049serves primarily to identify the mobile terminal1001on a radio network. The card1049also contains a memory for storing a personal telephone number registry, text messages, and user specific mobile terminal settings.

Further, one or more camera sensors1053may be incorporated onto the mobile station1001wherein 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.