Method for age portraying and painting in electrochromic automobile sheet

A computer-implemented method for altering an appearance of an electrochromic coating of a vehicle is provided. The method includes obtaining vehicle information related to at least one vehicle attribute. The method also includes applying weightage factors to the at least one vehicle attribute. The method also includes determining that a vehicle is a boundary area of a testing zone based on a determined location of the vehicle. The method also includes changing a base appearance of the electrochromic coating of the vehicle to an altered appearance based on the determination that the vehicle is in the boundary area and based on the weightage factors of the vehicle components.

BACKGROUND

With the emergence of nano-technology and the progression in the material sciences, there are certain paints (or other surface coatings) that are capable of changing their color (or appearance) based on certain input. For example, electronic circuitry is attached to painted material which is used to control the color of the paint. The color of the paint may be dependent on the input current and the voltage provided by a controller. These electrochromic paints (also referring to as smart paints or electronically switchable color tuners) may be used to create partitions, or they may be used to create and display dynamic messages in the paints. In other examples, the electrochromic paints may be used to create a specific ambience, look, appearance etc. They may also be used to control the amount of heat or light that reflects from the paint based on an electronic input, giving the paints the ability to regulate temperatures or create privacy.

In general, these electrochromic materials utilize the principle of electrochromism, which allows certain materials to change color (or even opacity) when a voltage is applied. Although an amount of electricity is required for changing the appearance of the paint, no electricity is needed for maintaining a particular shade once the change has been affected. That is, electrochromic devices may switch slowly, but do not require a continuous application of an electric field to maintain an altered state. These electrochromic paints may have a variety of different applications.

Vehicle motor systems are becoming more intelligent with the integration of latest technology. The automation in self-driving vehicles is increasing and leveraging the advancements in Big Data and cloud technology for better performance. These modern automobile systems are becoming more intelligent with cognition enablement and artificial intelligence capabilities embedded therein. This may enable automatic driving vehicles where the driving decisions are made runtime by a cognitive system embedded in the vehicle. These cognition-enabled vehicles generally possess the capability to collect information from various sensors placed over the vehicle (or from various sources external to the vehicle) and process the information by applying various machine learning models on the collected information.

SUMMARY

Embodiments of the present disclosure relate to a computer-implemented method for altering an appearance of an electrochromic coating of a vehicle is provided. The method includes obtaining vehicle information related to at least one vehicle attribute. The method also includes applying weightage factors to the at least one vehicle attribute. The method also includes determining that a vehicle is a boundary area of a testing zone based on a determined location of the vehicle. The method also includes changing a base appearance of the electrochromic coating of the vehicle to an altered appearance based on the determination that the vehicle is in the boundary area and based on the weightage factors of the vehicle components.

Other embodiments relate to a computer program product utilizing the methods described above.

Other embodiments relate to a computer system utilizing the methods described above.

It should be appreciated that elements in the figures are illustrated for simplicity and clarity. Well-understood elements that may be useful or necessary in a commercially feasible embodiment may not be shown for the sake of simplicity and to aid in the understanding of the illustrated embodiments.

DETAILED DESCRIPTION

The present disclosure relates to methods and systems for proactive age portraying and painting in an electrochromic automobile sheet. In particular, the present disclosure relates to systems and methods for changing the appearance of an external surface of a vehicle when the vehicle approaches a suitable checkpoint area depending on a determination of certain characteristics of the vehicle.

Certain organizations may have an interest in being able to quickly estimate an age or a condition of a vehicle. Examples of such conditions may include the overall age of the car, required maintenance that must be performed with regard to certain components of the vehicle such as regular oil changes, fluid levels, the presence of a catalytic converter, an age of the engine of the car, greenhouse exhaust emission levels, etc. An example of an organization that may have an interest in determining the condition of a car may be an automobile rental company. As customers return a vehicle after the completion of a rental term, it may be desirable for the rental company to check on the condition of the vehicle. This could be for purposes of determining if any damage was done to the vehicle during the rental period, or simply to ensure that the vehicle is receiving proper maintenance, so the vehicle is running properly for the next customer. Such an organization may have a checkpoint (e.g., a rental car return station) or location for inspecting vehicles. Another example of an organization that may have an interest in inspecting a condition of a vehicle may be a regional transportation authority. That is, certain geographical regions (e.g., a state or a country) may have regulations or guidelines with regard to the maintenance of a vehicle, and certain of these guidelines may be aimed at helping to reduce vehicle emissions. Any of these above described organizations (or any other suitable organization) may have checkpoints at particular locations where vehicles may undergo an inspection. However, manually checking paper records of a vehicle (i.e., by an inspector) may be very time consuming and costly in terms of the checkpoints themselves as well as the checkpoint employee salaries. Moreover, checkpoint inspections may be inaccurate, and they may involve lengthy delays for the driver of the vehicle.

In certain embodiments, a system may include electrochromic paints that are used in conjunction with a smart automobile system to alter the texture or color of the vehicle based on articulated insights regarding the age and nature of the vehicle and based on positional information of the vehicle. The physical appearance of the vehicle may be observed by people outside the vehicle to provide better visual communications at desired locations, thus enabling certain advancements in automobile systems. In certain embodiments, changes to the physical appearance of the vehicle may include indications of the age or condition of the vehicle, and these include at least one of rust, dirt and dents, paint fading and paint chipping, and any other suitable visual indication of wear and tear of the vehicle. In other embodiments, the changes to the physical appearance of the vehicle may not be related to wear and tear, but may be sufficient to alert testing center personnel to the extent or degree to which the vehicle may have issues that warrant stopping the vehicle for additional inspections.

In the present embodiments, neural networks and other deep learning systems may be utilized to aid in automated determination of an age (or condition) of a vehicle. An Artificial Neural Network (ANN) (also referred to more generally as a neural network) is a computing system made up of a number of simple, highly interconnected processing elements (nodes), which process information by their dynamic state response to external inputs. ANNs are processing devices (algorithms and/or hardware) that are loosely modeled after the neuronal structure of the mammalian cerebral cortex, but on much smaller scales. Such systems progressively and autonomously learn tasks by means of examples, and they have successfully been applied to, for example, speech recognition, text processing and computer vision. A large ANN might have hundreds or thousands of processor units, whereas a mammalian brain has billions of neurons with a corresponding increase in magnitude of their overall interaction and emergent behavior.

Many types of neural networks are known, starting with feedforward neural networks, such as multilayer perceptrons, deep learning neural networks (DNNs) and convolutional neural networks. A feedforward neural network is an artificial neural network (ANN) where connections between the units do not form a cycle. A deep learning neural network is an artificial neural network with multiple hidden layers of units between the input and output layers. Similar to shallow ANNs, DNNs can model complex non-linear relationships. DNN architectures, e.g., for object detection and parsing, generate compositional models where the object is expressed as a layered composition of image primitives. The extra layers enable composition of features from lower layers, giving the potential of modeling complex data with fewer units than a similarly performing shallow network. DNNs are typically designed as feedforward networks.

In certain embodiments described herein, systems, methods and computer program products are provided that use Big Data and Artificial Intelligence (AI) to facilitate anomaly detection with regard to different real-time sources of Big Data (e.g., parking lot occupancy data gathered over time via a plurality of sensors). Machine learning, which is a subset of AI, utilizes algorithms to learn from data (e.g., Big Data) and create foresights based on this data. AI refers to the intelligence when machines, based on information, are able to make decisions, which maximize the chance of success in a given topic. More specifically, AI is able to learn from a data set to solve problems and provide relevant recommendations. AI is a subset of cognitive computing, which refers to systems that learn at scale, reason with purpose, and naturally interact with humans. Cognitive computing is a mixture of computer science and cognitive science. Cognitive computing utilizes self-teaching algorithms that use data, visual recognition, and natural language processing to solve problems and optimize processes.

As used herein, “Big Data” refers to data that is characterized, in part, by large volumes of data (e.g., terabytes, petabytes, etc. in size), a large variety of data (e.g., including structured data, unstructured data, etc.), and different sources of data, etc. An example of structured data is transactional data in a relational database. Examples of unstructured data include images, email data, sensor data, resource monitoring data, etc. Some examples for sources of Big Data include banking information, travel information, medical records, geographical information, transportation system data, passenger data, parking lot occupancy data, resource monitoring data from various layers of a cloud deployment, etc.

As used herein, a “Smart City” generally refers to a metropolitan area that utilizes different types of Big Data, and is collected from a variety of citizens, electronic Internet of Things (IoT) sensors, and other devices. The information is processed and analyzed to monitor and manage different aspects of metropolitan infrastructure such as traffic and transportation systems, power plants, water supply networks, waste management, police and fire departments, information systems, schools, libraries, hospitals, community services, etc. The data may be used to optimize the efficiency of city operations and services, such as efficiently utilizing parking facilities, as discussed herein.

Referring now to the figures and initially toFIG.1, a diagram is shown of an example of the age portraying of a vehicle including an electrochromic automobile sheet when the automobile passes through a checkpoint zone, according to embodiments. As shown inFIG.1, the vehicle100includes an electrochromic sheet102. As the vehicle100moves in a direction of travel114, the vehicle100begins in a zone before the boundary area106, then passes through a boundary area108(that may be consistent with, for example, a rental car return zone or transportation authority checkpoint region, etc.), and then after passing through the boundary area108the vehicle continues into a zone after the boundary area110. As shown in the example ofFIG.1, in the zone before the boundary area106, the electrochromic sheet102of the vehicle100is in an unaltered state (i.e., a state that does not indicated wear or age or some other indication of the vehicle condition). When the vehicle100enters the boundary area108, the electrochromic sheet102of the vehicle100is altered to provide one or more visual indications of wear104. These indications of wear104may be any suitable visual features (e.g., rust, dents, dirt, smears on the windows etc.) that may allow for an observer outside the vehicle100to readily and easily determine a condition of the vehicle100as the vehicle100is passing through the boundary area108. In practical terms, an outside observer (such as a checkpoint worker) would be able to easily identify if the vehicle needs to be flagged (or stopped) in order to further inspect the condition of the vehicle100because the electrochromic sheet102shows indications of wear. However, when not in the boundary area108, the electrochromic sheet102of the vehicle100switches back to an unaltered state (i.e., a normal appearance of the car).

In certain embodiments, the driver (or owner) of the vehicle has an option to opt-out (or opt-in) of the checkpoint testing. In certain examples, an organization may provide one or more incentives (e.g., reduced rental fees, rebates, etc.) to encourage someone to allow testing of the vehicle.

Referring now toFIG.2, this figure shows a diagram of a system200architecture for controlling the appearance of a vehicle250, according to certain embodiments. As shown inFIG.2, several system components are communicatively coupled via a platform messaging queue (PLMQ) or system bus.

First, as shown inFIG.2, a boundary area manager204module or component is communicatively coupled to the other modules through the PLMQ202. As discussed with regard toFIG.1, the system200determines a boundary area108for a vehicle250, which may indicate the presence of a vehicle check point post. This boundary area108generally represents a distance or area where the vehicle250should (if necessary) alter the appearance of the electrochromic sheet exterior so that checkpoint personnel can view the car and make informed decisions as to whether to stop the vehicle250or not. Referring again toFIG.2, the boundary area manager204module may include one or more components. In the example shown inFIG.2, the boundary area manager204includes a time-speed-distance map module206, a speed data collector208and a GPS-based interconnect210. The time-speed-distance map module206may include a map that displays (or includes information related to) the amount of time that is estimated before the vehicle250reaches one or more different checkpoints. The estimate of time may be based on a current velocity or average velocity of the vehicle250over a period of time, and a distance to the next checkpoint. It should be appreciated that other information in addition to distance and velocity (e.g., traffic conditions, vehicle accidents, road conditions, weather, etc.) may be used to determine an estimated time to the next checkpoint. The speed-data collector208module may include one or more sensors on the vehicle250to detect the velocity of the vehicle250, one or more memory devices to store a record of the vehicle250velocity, and any other suitable components for tracking and storing a record of the vehicle250velocity over time. The boundary area manager204may also include a GPS-based interconnect210device, which may be able to track the location of the vehicle250using the global positioning system of satellites. The GPS device may be any suitable type of device known to a person of skill in the art.

Thus, in certain embodiments, the boundary area manager204includes any suitable number of components/modules capable of predicting an estimated time of arrival of a vehicle250at a particular checkpoint. Moreover, the boundary area manager204may adjust the size (i.e., the distance) of the boundary area108depending on how fast the vehicle250is travelling. For example, if there is a target inspection time of five seconds (i.e., the time needed for a checkpoint inspector to visually assess the condition of the vehicle250based on the electrochromic sheet appearance) a fast moving vehicle250would need to have a larger boundary area108. That is, in order to achieve the target inspection time, the fast moving vehicle250would need to have a longer distance inspection zone. Similarly, if the time-speed distance map206and speed data collector208of the boundary area manager204determines that a vehicle250is moving relatively slowly, then the distance of the boundary area108would not need to be as long in order the meet the target inspection time. In other embodiments, the boundary area108may have a fixed size, and not be dependent on the vehicle250speed.

Next, as shown inFIG.2, a metadata mapper212module or component is communicatively coupled to the other modules through the PLMQ202. The metadata mapper212is a module that is configured to receive the information that is being generated by the system200. In certain examples, the metadata mapper212may store the locations of the various checkpoints, and this stored information may be used to trigger changes in the appearance of the vehicle250when it is determined that the vehicle250is passing through a particular checkpoint. In certain embodiments, the metadata mapper212may also store a history of movement of the vehicle250. Thus, in certain embodiments, if it is determined that a vehicle250has recently passed an inspection at a particular checkpoint station and the vehicle250is current at the same checkpoint station, the metadata mapper212may aid in determining that another inspection of the same vehicle and same inspection station is not necessary.

Next, as shown inFIG.2, in certain embodiments, a device location services214module is commutatively coupled to the rest of the system200though the PLMQ202. The device location services214module is something that is enabled on the vehicle250, and may be able to determine whether or not the vehicle250in a geographic region that requires (or does not require) checkpoint examinations. In other words, certain geographical regions (e.g., vehicle rental company return locations, countries, towns, cities, or other suitable locations) may simply not require checks of the vehicle250condition. If the device location services214module determines that a vehicle250is location in a region that does not require checks, then the system200would deactivate any altering of the vehicle250appearance in these regions. It should be appreciated that the device location services214module may deactivate one more additional modules (i.e., to save on memory or processing power) if it determined that the vehicle250is in a geographic location where testing is not required.

Next, as shown inFIG.2, in certain embodiments, a device integrator216is operatively coupled to the rest of the system200through the PLMQ202. The device integrator216may be able to communicate with one or more electronic devices of the current driver of the vehicle250. For example, as discussed above, the vehicle250itself may have a GPS-based interconnect210module that is able to track the location of the vehicle250. However, the device integrator216may be able to communicate with, for example, the GPS system of a cellular phone of the driver of the vehicle250as well. Then, a comparison of the GPS data of the driver of the vehicle250may be compared with the GPS data of the vehicle itself to determine who the driver of the vehicle250is. In other words, the device integrator216may be able to utilize data from one or more of the electronic devices of the driver of the vehicle250in order to determine an identity of the current driver of the vehicle250. In certain embodiments, the device integrator216, after identifying an identity of the driver of the vehicle250, may make a comparison between the current driver and the registered owner of the vehicle250. In other examples, in the case of a rented vehicle, the comparison may be between the driver of the vehicle, and the person who signed the rental lease agreement.

Next, as shown inFIG.2, in certain embodiments, a context awareness218module is operatively coupled to the rest of the system200through the PLMQ202. The context awareness218module may allow for determining a context of a particular environment, and then cause an appropriate message to be displayed to the particular audience through the electrochromic sheet of the vehicle250. For example, the context awareness218module may determine that a particular vehicle checkpoint station is only interested in viewing details related to the exhaust emissions characteristics of a vehicle and is not interested in viewing details about the age of the vehicle250engine. Thus, depending on the context (e.g., the requirements of a particular checkpoint station), different messages (or different visual characteristics) of the same vehicle250may be generated in the smart paint. Therefore, the context awareness218module may bring context awareness in the electrochromic sheet on the vehicle body to convey a context appropriate message that will deliver only relevant information to the selected audience, and therefore time savings may be achieved.

Next, as shown inFIG.2, in certain embodiments, an image to situation manager220is operatively coupled to the rest of the system200through the PLMQ202. The image to situation manager220is something that may validate whether or not a checkpoint is near to the vehicle250or not. In certain embodiments, information may be gathered by acquiring images with an image capture device located on the vehicle250, and then the image to situation manager220performs image processing on the images to determine whether or not a vehicle250is close to a particular checkpoint. For example, a checkpoint may have visual characteristics that could be identified using image processing. In another example, image processing on the checkpoint building may be performed to determine whether an examiner is actually physically present in the checkpoint facilities. In other embodiments, the image to situation manager220controls the type of coloring that is applied to the electrochromic paint depending on the context of the vehicle checkpoint. In certain examples, different images may display on the exterior of the vehicle250depending on the checkpoint facility.

Next, as shown inFIG.2, in certain embodiments, a color and attribute manager222is operatively coupled to the rest of the system200through the PLMQ202. The color and attribute manager222may include a database of information that relates certain color characteristics to be displayed to certain attributes of the vehicle. For example, if the age of the engine is greater than a certain amount, one particular color (or other visual attribute) may be displayed by the system200. In another example, if the tires are very old, a different color (or other visual attribute) may be displayed by the system200. The color and attribute manager222may also account for different combinations of attributes of the vehicle250. For example, if the engine is old, and the tires are old, and the oil has not been changed recently, and the emissions tests have failed, this particular combination of detrimental attributes may result in a more severe (or eye catching) color to be displayed on the smart paint. This would alert the example of a checkpoint station to the fact that a very many things may be wrong with this particular vehicle250.

Next, as shown inFIG.2, in certain embodiments, a time based texture manager224is operatively coupled to the rest of the system200through the PLMQ202. In certain embodiments, the time based texture manager224set the colors (or other visual attributes) of the vehicle250depending on the time of day. For example, in the daytime hours certain colors may be used. However, in the dark hours of the night other colors may be used. In other words, the time based texture manager224selects colors depending on the time of day (or the time of year, the amount of daylight, weather conditions, etc.) to make is easier for the examiner of the checkpoint station to see.

Next, as shown inFIG.2, in certain embodiments, an object library226is operatively coupled to the rest of the system200through the PLMQ202. The object library226may include records of which components of the vehicle250need to be tracked for inspection purposes. For example, the object library226may include the tires, displays, engine, lights, etc. In certain embodiments, object library226keeps track of and monitors the status of the different physical components of the vehicle250. This information of the object library226may be stored in, for example, a vehicle insights database228(e.g., a relational database). In one example, if a vehicle250owner changes a battery of the car, the object library226will update the records in the vehicle insights database228accordingly. If the owner of the vehicle250has been maintaining the car properly and in a timely manner, these records may be a factor in determining how to display the age of the car to the examiner of the checkpoint station. A well maintained car may be displayed as relatively new, even the overall age of the vehicle may be very old. In contrast, a newer car than has not been maintained well may actually display as an older car due to the lack of timely maintenance by the owner of the vehicle250.

In certain embodiments, static computing platforms230may be coupled to the system200, and they may be configuration based mechanisms. That is, the static computing platforms230may map, for example, a maximum allowed age of an engine of the vehicle250to the actual age of the vehicle250engine. Thus, in this example, the component of the vehicle250would be the engine, and the data regarding the age of the engine would be mapped to the age category. In certain examples, this mapping is a static mapping.

Next, as shown inFIG.2, in certain embodiments, the system200is coupled through the PLMQ202to a plurality of internet situated resources232. The internet situated resources232may provide data obtained from a plurality of different devices connected through a network (i.e., the Internet of Things (IoT)). In general, the IoT is a is a system of interrelated computing devices, mechanical and digital machines provided with unique identifiers (UIDs) and the ability to transfer data over a network without requiring human-to-human or human-to-computer interaction. In one example of IoT data, if the make and model of a vehicle is known, data may be obtained through one or more internet situated resources232regarding vehicle information, such as the engine type, engine size, fuel efficiency etc. Other information may be obtained such as vehicle warranty information, vehicle component lifecycle information. It should be appreciated that any other suitable information regarding the vehicle may be obtained through the internet situated resources232that would aide in determining a condition of the vehicle. For example, the actual vehicle records may indicate that the rear differential fluid was changed 40,000 miles ago, and information obtained through the internet situated resources232may indicate that the rear differential fluid should be changed every 30,000 miles. Such a comparison may indicate that this particular vehicle maintenance activity is overdue, and this determination may factor into the final determination of the vehicle condition (and therefore the decision on what extent to alter the physical appearance of the electrochromic sheet of the vehicle). In other words, if comparisons of the service records of the vehicle and the internet situated resources232data show several instances where the vehicle is not being maintained properly, the appearance of the vehicle may be altered to show a very old, or dirty, or rusty vehicle.

Next, as shown inFIG.2, in certain embodiments, a weightage mapper270is operatively coupled to the rest of the system200through the PLMQ202. A weightage is a factor that is associated with a particular component of the vehicle250. For example, in an electric vehicle, more weightage may be associated with the batteries because they are more critical to the operation of the car relative to a traditional gas engine. The weightage mapper270may associate weight values to particular components of the car. Vehicle250components with a high importance factor associated with the weightage of the object may factor more into the final appearance of the electrochromic sheet of the vehicle.

Next, as shown inFIG.2, in certain embodiments, an object to importance mapper272is operatively coupled to the rest of the system200through the PLMQ202. In certain embodiments, the importance of the object (i.e., a component of the vehicle250) may be related to the weighting values discussed above with respect to the weightage mapper270. The importance of an object may be related to the timeline (or age) of a particular object relative to its expiration date. As such, the closer an object gets to its expiration date (or the amount of time it is beyond its expiration date) may factor into how importance the object is. Thus, the object to importance mapper272maps and keeps track of the importance of each object.

Next, as shown inFIG.2, in certain embodiments, a situation to importance mapper274is operatively coupled to the rest of the system200through the PLMQ202. The situation to importance mapper274may determine how relatively strict (or how relatively lax) the examination rules are at a particular examination checkpoint. For example, at checkpoint A, the rules regarding the age of the engine may be very strict. In this case, the situation to importance mapper274may map a relatively high importance of the engine to the particular checkpoint A. However, at checkpoint B, the rules regarding the engine may be very lax (or nonexistent). Thus, the situation to importance mapper274may map a relatively low importance of the engine to the particular checkpoint A. This the situation to importance mapper274is similar in concept to the object to importance mapper272discussed above, except that it relates more specifically to the rules of the different testing facilities rather than vehicle250components.

Next, as shown inFIG.2, in certain embodiments, a vehicle control unit264is operatively coupled to the rest of the system200through the PLMQ202. The vehicle control unit264may include a resource data collector266and a serial or parallel data classifier268. The resource data collector266collects information from various resources. One example of data that the resource data collector266collects is from the internet situated resources232discussed above (e.g., Smart Data from IoT devices). Another example of data that the resource data collector266collects is from the metadata mapper212also discussed above, which keeps data related to the history and movements of the vehicle.

Once the data has been collected, it needs to be classified. In certain embodiments, the data is classified with the serial or parallel data classifier268. For example, if a model type of the vehicle250is known, then from web situated APIs, information can be selectively retrieved that is particularly relevant to the particular model of the vehicle250of interest. All of the data that is collected by the resource data collector266, and all of the data that is classified by the serial or parallel data classifier268is controlled by the vehicle control unit264.

Next, as shown inFIG.2, in certain embodiments, a time based age mapper258is operatively coupled to the rest of the system200through the PLMQ202. The time based age mapper258is something that gives a mapping of time to age for different components of the vehicle250. That is, a static mapping is performed between the overall age of the component and the components itself.

Next, as shown inFIG.2, in certain embodiments, a calendar age injector260is operatively coupled to the rest of the system200through the PLMQ202. Each of the vehicle components ages day by day. So, even though the system200has previously performed a static mapping of the age of the components (i.e., this information does not change), every day the components are getting older. In other words, the time based age mapper may have determined two years ago that the engine was three years old, it may be necessary to update the current age of the engine based on the present date. Accordingly, the calendar age injector260basically determined the current age of the all the components of the vehicle250by injecting (or adding) an amount of time that is a difference between the date that the component was originally statically mapped with the time based age mapper258and the current date.

Next, as shown inFIG.2, in certain embodiments, timeline manifestation module262is operatively coupled to the rest of the system200through the PLMQ202. The timeline manifestation module262uses the information from the time based age mapper258and the calendar age injector260to determine a current age for all of the components of the vehicle250. Thus, when the vehicle250travels past a particular checkpoint station on a given day, the information generated by the timeline manifestation module262will always be accurate.

Next, as shown inFIG.2, in certain embodiments, an information parsing logic and bridge module252is operatively coupled to the rest of the system200through the PLMQ202. In certain examples, the car has a present color, which can be a static color (i.e., a color during normal driving conditions when the car is not at a checkpoint) or it can be an already changed color when the car is in a boundary area108. That information will be collected by the information parsing logic and bridge module252. Based on the current information, we need to see if the color needs to be changed. Essentially, the information parsing logic and bridge module252has the current color state (or color map) of the vehicle250.

Based on the current information, we need to see if the color needs to be changed. Next, as shown inFIG.2, in certain embodiments, a validity manager254is operatively coupled to the system200through the PLMQ202. The validity manager254analyzes the current information gathered by the information parsing logic and bridge module252to determine whether or not the color needs to be changed based on the current location or situation of the vehicle250.

Next, as shown inFIG.2, in certain embodiments, a color altering communication demon256is operatively coupled to the rest of the system200through the PLMQ202. Based on the current color state of the vehicle250determined by the information parsing logic and bridge module252, and based on a determination of the validity manager254that at least some of the color needs to be changed, the color altering communication demon256communicates a command through the PLMQ202to the device OS connector interface234.

Next, as shown inFIG.2, in certain embodiments, the device OS connector interface234is operatively coupled to the rest of the system200through the PLMQ202. In certain examples, the information regarding which portions (e.g., pixels) of the electrochromic sheet102are transmitted to the device OS connector interface234.

Next, as shown inFIG.2, in certain embodiments, a device operating system236is present on the vehicle250and is operatively coupled to the rest of the system200through the device OS connector interface234and then through the PLMQ202. The device operating system236includes a number of actuators238that control (or trigger) the changes to the electrochromic sheet102of the vehicle250. In particular, the actuators238may include a number of different operating system modules, including an electrochromic state collector240, a differential map calculator242, an electrochromic controller244, and one or more device communication APIs246. The electrochromic state collector240may identify the state of each of the pixels (or more generally portions) of the electrochromic sheet102of the vehicle250. In other words, the electrochromic state collector240determines the exact color state of the vehicle250at the current moment. The differential map calculator242uses the information collected by the electrochromic state collector240, as well as the information transmitted through the device OS connector interface234about what colors need to be changed, and determines a differential color map (i.e., what needs to be changed). Based on this generated differential color map information, the electrochromic controller244causes the actual color changes to take place on the vehicle250through one or more of the device communication APIs246. For example, voltages can be applied to certain pixels of the electrochromic sheet102to cause the color changes to occur. It should be appreciated that the information provided through the device OS connector interface may be based on all, or just some of the modules discussed above with respect to the system200shown inFIG.2.

Referring now toFIG.3, a diagram is shown that explains the determination of different boundary areas for different vehicles traveling at different velocities and at different distances from a vehicle checkpoint. In particular, a first vehicle300is travelling at a first velocity V1, and it is located at a position on the right side of the first trajectory308for the first vehicle300. The first vehicle300has a relatively high first velocity V1, and therefore the first boundary area312for the first vehicle300must be relatively large (i.e., because the examiner at the checkpoint station306must have a sufficiently long time to be able to view the colors of the vehicle and make a determination as to whether or not to stop the first vehicle300for further inspection). Accordingly, the system200discussed above with respect toFIG.2must cause any color changes to occur when the first vehicle300enters the first boundary area312(i.e., at the right side of the first boundary area312and the left side of the first trajectory308). As also shown inFIG.3, a second vehicle302is travelling at a second velocity V2, and it is located at a position on the right side of the second trajectory310for the second vehicle302. The second vehicle302has a relatively low second velocity V2, and therefore the second boundary area314for the second vehicle302does not need to be as large as the first boundary area312(i.e., because with the slower moving second vehicle302the examiner at the checkpoint station306will have a sufficiently long time to be able to view the colors of the vehicle and make a determination as to whether or not to stop the second vehicle302which is moving slower). Accordingly, the system200discussed above with respect toFIG.2must cause any color changes to occur when the second vehicle302enters the second boundary area314(i.e., at the right side of the second boundary area314and the left side of the second trajectory310). As can be seen from the diagram inFIG.3, different vehicles may have different sizes for the boundary areas, and the start of the boundary areas may occur at different distances from the checkpoint station306. It should be appreciated that additional factors other than the velocity of the vehicle and the distance of the vehicle from the checkpoint station306may be used in determining the dimensions of the boundary areas. For example, a more experiences checkpoint station306operator may not need as much time to make a determination of the color state of the vehicles, and the sizes of the boundary areas may be adjusted based on these (or other) factors.

Referring now toFIG.4, this diagram shows a cognitive system404that processes an alteration of the appearance of a vehicle, according to embodiments. It should be appreciated that the cognitive system404may include some of the modules described above with respect toFIG.2, all the modules shown inFIG.2, or additional modules as well. As shown inFIG.4, a vehicle400A approaching a checkpoint station402is processed by a cognitive system404, and when the vehicle is in the boundary area of the checkpoint station402the vehicle400B has an altered appearance. The cognitive system404may have any suitable number of processing modules. In this example embodiment, the cognitive system404includes a location and age finder406, a cognitive engine (CE) for region computation408, a self-exploration system410and one or more learning systems412. The location and age finder406module is configured to determine a location of the vehicle and an age of the vehicle itself or the age of the various components of the vehicle. The cognitive engine for region computation408is related to location based services such as GPS systems. In certain embodiments, the cognitive engine (CE) for region computation408may include one or more of the time-speed-distance maps206module fromFIG.2, the speed data collector208module fromFIG.2, and the GPS-based interconnect210module fromFIG.2. Thus, the cognitive engine (CE) for region computation408detects the case that the vehicle is in the boundary region, then instructs the electrochromic controller to change the appearance of the vehicle. The self-exploration system410is a system that, once it is determined that the vehicle is in (or is approaching) a checkpoint station, determines what (if any) changes to the appearance of the vehicle are needed. In one example, the self-exploration system410generates a new texture map for the electrochromic sheet, and it causes the cognitive system404to impose this texture map on the vehicle400B. One or more deep learning systems412may also be used to aid in determining a texture map for the car. After the cognitive system404has determined a texture map (i.e., the colors and textures for the various pixels in the electrochromic sheet), these colors and textures are pushed from the cognitive system404to the vehicle400B. Then, the actuators on the vehicle400B detect the current colors of the vehicle400B and other parameters, and then they cause an alteration of the relevant fields (i.e., the color and texture is changed to portray the age of the car).

Referring now toFIG.5, a flowchart is presented that explains a method500of altering the appearance of the vehicle in a checkpoint area, according to embodiments. As shown inFIG.5, at operation502, the system collects, processes and parses the vehicle information. As discussed above in detail with respect toFIG.2, the method includes the collection of information from various API-based information sources, and from various internal resources on the smart vehicle system. Then the method includes classifying the stream of information using a serial or parallel stream-based classifier to form articulated information related to the vehicle. In particular, in an example, the information is collected from one or more internet situated resources (IoT devices) using an internet based native APIs about the type of vehicle and other characters including engine capacity, engine aging information, valid age for each component (e.g., battery, engine, brake-pads, etc.). Then, metadata objects are created based on the information collected from the internet situated resources. The collected parsed information is stored into metadata mapper objects of vehicle general class objects of the vehicle control unit (VCU).

At operation504, the method determines the vehicle location, velocity, and trajectory. A dynamic computing platform may trigger GPS interconnect APIs which gives the current location of the vehicle.

At operation506, dynamic computing platforms are triggered to capture the local regulation information of the vehicle checkpoint stations. This information may be collected using existing information datastores of smart vehicle monitoring units using inbound API instructions and saved to the metadata mappers. Upon determination of the current checkpoint station locations, the internet positioned resources are triggered to capture the area based vehicle inspection criteria (i.e., what elements of the vehicle should be checked, and any standards or rules associated with such checks). The collected rules are classified using a serial or parallel classifier to get the information insights and articulated insights will be used to generate the color and texture in later stages of the process flow.

At operation508, once the information about the vehicle is collected, parsed, classified, and saved to the object mappers, the weightage factors may be assigned to each of the elements (e.g., vehicle components or features) of the vehicle. In certain examples, this may include importance based weightage assignments for each of the component in a list of components. The importance to the texture will be identified by the cognitive system which will be used at the time of changing the overall sheet texture.

In certain embodiments, the method further comprises the cognition enablement of age-to-color identification and texture mapping based on the time, situation and nature of the event. In certain embodiments, the method polls for the regulator check posts as the event triggering location using a demon process that monitors the trajectory of the vehicle and the check post locations (or any other trigger pointers). This demon keeps the GPS based triggering center along with the boundary area region to activate the color and texture change.

At operation510, the method determines whether or not the vehicle is in the boundary area of a testing zone. As discussed above with respect toFIG.2, current vehicle speed statistics may be captured with the collaboration of a vehicle control unit (VCU). The boundary area may be defined based on the velocity of the vehicle and the time required to reach the target point. For example, if the car is moving with high speed, then the color change needs to be triggered earlier. Thus, the determination of the boundary zone cannot be simply distance based, and it should include distance and speed to get a better user experience. With this, it is identified whether the vehicle is in the boundary area or not, and accordingly the decisions are made by cognitive engine regarding the color or texture changes. At operation510, if it is determined that the vehicle is in a boundary area of a testing zone (510: YES), then the method proceeds to operation512to determine whether or not the appearance attributes of the vehicle need to be changes. For example, in a brand new car, where all the components have to aging factors, it may not be necessary to change the appearance of the vehicle at all. In this case, the inspector at the vehicle checkpoint station would know that there is nothing wrong with the car. It should be appreciated that even if a car is brand new, color alterations (e.g., changing the entire electrochromic sheet of the car to green to signify an acceptable state of the vehicle) still may be made in order to alert the inspector that nothing is wrong with the vehicle.

At operation514, in the case that the vehicle is detected in the boundary region and it is determined that color and texture change are needed (512: YES), then the pre-classified and decided color and texture is pushed to the electrochromic controller of the car sheet. In response to these instructions, the current color and texture data is gathered locally at the electrochromic controller and differential maps are generated. In certain embodiments, a differential map comprises the pixels on the sheet that need to be changed. The region based electrochromic controllers are triggered to change the color and texture of the car sheet. In certain examples, this will portray a texture like rust or dust on the car sheet based on its determined weightage factors. In certain embodiments, the mechanism comprising the color, opacity, texture, font and other interrelated artifacts are based on external luminance factors (e.g., the time of day) and the geographical locations. For example, in the evening time, the colors can be different than morning colors for same situation. These details are also discussed in detail above with respect toFIG.2. In certain embodiments, the system may articulate insights based on embedding symbols identification and texture and color overriding based on the nature of the event. In certain embodiments, the system further comprises an interactive dialog for input data collection and accordingly instructs the attribute manager of electrochromic controller. In certain embodiments, the system may have the ability to provide the APIs for the electrochromic controller to change the color, intensity and luminance factors from a cognitive engine (CE). In certain embodiments, information in metadata mappers for color and texture change are moved to a training data set which may be used by the CE for history and deep learning purposes. After the change of the vehicle color and/or texture at operation514, the process returns to operation504.

At operation512, in the case that the vehicle is detected in the boundary region and it is determined that a color and texture change are not needed (512: NO), the process continues back to operation504to continue determining the vehicle location, velocity and trajectory. If the vehicle travels far enough to encounter another checkpoint testing facility, the process described above repeats.

At operation516, in certain embodiments, the system polls for the validity of the event and dissolves the attributes to normal based on change in the internal conditions. In other words, if it is determined that the vehicle has traveled out of the boundary zone of the testing center, the method includes reverting to a standard color and texture of the vehicle (i.e., normal operation color and texture). After the vehicle has been reverted to the normal texture, the process continues back to operation504to continue determining the vehicle location, velocity and trajectory. If the vehicle travels far enough to encounter another checkpoint testing facility, the process described above repeats.

At operation510, in the case that the vehicle is determined not to be in a boundary area of a testing zone (510: NO), it is then determined whether or not the vehicle has just recently passed through a boundary area. In other words, once the vehicle leaves the boundary, it may be desirable to revert the appearance of the vehicle back to the original state. Thus, if it is determined at operation516that the vehicle did recently leave a boundary area (512: YES), then the method continues to operation518, where the system causes the electrochromic sheet of the vehicle to revert to a standard color and texture (e.g., a condition under normal driving conditions when the vehicle is not at a testing station). Then, after the colors have been reverted, the process goes back to operation504to continue to check the location, velocity and trajectory of the vehicle. If it is determined at operation516that the vehicle did not recently leave a boundary area (e.g., the vehicle never went through a checkpoint station in the first place), then the process continues back to operation504.

In certain embodiments, a system includes an electrochromic paint controller on a smart vehicle, and a method of controlling the appearance of the vehicle includes collecting vehicle information from various elements of the vehicle to articulate an appropriate color, texture and/or the embedding of symbol attributes on the vehicle body. In certain embodiments, the method includes data collector demons that collectively gather an information stream from a vehicle control unit (VCU). The VCU may include an audit event log, and a VCU audit snippet may include associated timestamped values. In certain embodiments, once an element (or component) of the vehicle is swapped or changed, then this information will be automatically provided to the system demon using a proactive interrupt driven alert.

In certain embodiments, a mechanism in the vehicle control unit and an age-based color changing apparatus collects static computing resources of the car by in-band or out-of-bound APIs. In certain embodiments, the vehicle identification number (VIN), static color, age, registration details, type of vehicle, and other data may be fetched and saved to one or more metadata mappers. In certain embodiments, metadata mappers are maintained to get vehicle information insights based on a classification of collected data. Once the information is parsed, this information is supplied to a serial or parallel classifier using a machine learning model to identify the collective age and weightage factors of the vehicle.

In certain embodiments, element data may be classified using a serial and or parallel classifier as a dynamic computing platform, while static computing platforms of the cognitive systems collects the data from internet situated repositories and other static metadata learning mappers of deep learning.

In certain embodiments, a mechanism is included to articulate the age-based intuition of the car and the element (or vehicle component) age color factors. In some examples, the insights database (DB) is supplied to a color and texture determination module of the system which cognitively selects the color of the vehicle body, a texture of the vehicle body, and may also embed supplementary objects (like rust) with suitable color-combinations. In certain examples, the color and texture identification are dependent on the age-based need of the message.

In certain embodiments, collaborative age determination is generated with an internal component replace interrupt. In certain embodiments, a calendar age weightage may be injected each day as a cronjob to trigger changing the communitive age factors.

In certain embodiments, a geographical location identifier and GPS maps of the system collects the information about regularity checkpoints. In certain embodiments, once it is detected that the check-posts are in a defined boundary area, then the predetermined color and texture characteristics are supplied to electrochromic controller to change the color of car sheet. In certain embodiments, the color, texture, fonts, message, design and other interrelated designs are made, the electrochromic controller is instructed with all the codes, and accordingly the attributes of the electrochromic coating are changed.

In certain embodiments, the system further comprises the selection of the area for texture change based on the importance-based weightage of the internal components and decides the color and texture points of the vehicle body. For example, the engine of the vehicle may be considered to be a key element of the vehicle, so the communitive age is more heavily weighted. In this example, more of the area of vehicle's electrochromic sheet may display rusted characteristics. It should be appreciated that other visual characteristics other than rust may be emphasized as well.

In certain embodiments, the electrochromic controller receives a signal, identifies the existing color and texture of the vehicle, and a differential map is generated. The color and texture changes are imposed to the pixels of differential map with new values. In certain examples, the contents are pushed to the electrochromic paint manager to dissolve (or revert) the attributes to a normal state when the GPS has detected that the vehicle in no longer in the boundary area definition.

Referring now toFIG.6, an exemplary processing system600to which the present embodiments may be applied is shown in accordance with one embodiment. The processing system600includes at least one processor (CPU)604operatively coupled to other components via a system bus602. A cache606, a Read Only Memory (ROM)608, a Random-Access Memory (RAM)610, an input/output (I/O) adapter620, a sound adapter630, a network adapter640, a user interface adapter650, a display adapter660, and a vehicle color change component670are operatively coupled to the system bus602.

A first storage device622and a second storage device624are operatively coupled to system bus602by the I/O adapter620. The storage devices622and624may be any of a disk storage device (e.g., a magnetic or optical disk storage device), a solid-state magnetic device, and so forth. The storage devices622and624may be the same type of storage device or different types of storage devices.

A speaker632is operatively coupled to system bus602by the sound adapter630. A transceiver642is operatively coupled to system bus602by network adapter640. A display device662is operatively coupled to system bus602by display adapter660.

A first user input device652, a second user input device654, and a third user input device656are operatively coupled to system bus602by user interface adapter650. The user input devices652,654, and656may be any of a keyboard, a mouse, a keypad, an image capture device, a motion sensing device, a microphone, a device incorporating the functionality of at least two of the preceding devices, or any other suitable types of input devices. The user input devices652,654, and656may be the same type of user input device or different types of user input devices. The user input devices652,654, and656are used to input and output information to and from system600. In certain embodiments, component690with a context and anomaly detection mode is operatively coupled to system bus602.

Characteristics are as follows:

Service Models are as follows:

Deployment Models are as follows:

Referring now toFIG.7, illustrative cloud computing environment750is depicted. As shown, cloud computing environment750includes one or more cloud computing nodes710with which local computing devices used by cloud consumers, such as, for example, personal digital assistant (PDA) or cellular telephone754A, desktop computer754B, laptop computer754C, and/or automobile computer system754N may communicate. Nodes710may communicate with one another. They may be grouped (not shown) physically or virtually, in one or more networks, such as Private, Community, Public, or Hybrid clouds as described hereinabove, or a combination thereof. This allows cloud computing environment750to offer infrastructure, platforms and/or software as services for which a cloud consumer does not need to maintain resources on a local computing device. It is understood that the types of computing devices754A-N shown inFIG.6are intended to be illustrative only and that computing nodes710and cloud computing environment750can communicate with any type of computerized device over any type of network and/or network addressable connection (e.g., using a web browser).

Referring now toFIG.8, a set of functional abstraction layers provided by cloud computing environment750(FIG.7) is shown. It should be understood in advance that the components, layers, and functions shown inFIG.8are intended to be illustrative only and embodiments of the invention are not limited thereto. As depicted, the following layers and corresponding functions are provided:

Hardware and software layer760includes hardware and software components. Examples of hardware components include: mainframes761; RISC (Reduced Instruction Set Computer) architecture-based servers762; servers763; blade servers764; storage devices765; and networks and networking components766. In some embodiments, software components include network application server software767and database software768.

Virtualization layer770provides an abstraction layer from which the following examples of virtual entities may be provided: virtual servers771; virtual storage772; virtual networks773, including virtual private networks; virtual applications and operating systems774; and virtual clients775.

Workloads layer790provides examples of functionality for which the cloud computing environment may be utilized. Examples of workloads and functions which may be provided from this layer include: mapping and navigation791; software development and lifecycle management792; virtual classroom education delivery793; data analytics processing794; transaction processing795; and neural network anomaly detection training processing796.