Claims:We claim:
1. A computing device (106) comprising:

one or more processors (110); and

a memory (112) coupled to the one or more processors (110), the memory (112) for storing instructions which, when executed by the one or more processors (110), cause the one or more processors (110) to perform a method for determining aberrations on a road, the method comprising:

receiving a first set of data from at least one sensor of a plurality of sensors (108) of the computing device (106); and

probabilistically determining at least one aberration point on the road along a route taken by a vehicle (102), wherein the at least one aberration point being determined based on a plurality of parameters, wherein the plurality of parameters corresponds to a gravitational acceleration experienced by one or more accelerometer sensors (202) of the plurality of sensors (108) of the computing device (106), a standard deviation of an instantaneous acceleration experienced by the one or more accelerometer sensors (202) of the plurality of sensors (108) of the computing device (106) and speed of the vehicle (102).

2. The computing device (106) as claimed in claim 1, wherein the probabilistically determining the at least one aberration point comprises:

evaluating the standard deviation of the instantaneous acceleration along the route taken by the vehicle (102) on the road based on a pre-defined set of rules, wherein the instantaneous acceleration corresponds to acceleration experienced by the one or more accelerometer sensors (202) of the plurality of sensors (108) of the computing device (106) in each of three dimensions in real-time, wherein the pre-defined set of rules comprises at least one of variation in the instantaneous acceleration along the route taken by the vehicle (102) and a mean instantaneous acceleration along the route taken by the vehicle (102); and

normalizing the standard deviation of the instantaneous acceleration along the route taken by the vehicle (102) on the road based on a plurality of factors, wherein the standard deviation of the instantaneous acceleration being normalized for determining the at least one aberration point on the road for the vehicle (102), wherein the plurality of factors comprises weather conditions, the speed of the vehicle (102) and at least one of a second set of data, wherein the second set of data comprises at least one of type of the vehicle (102), ground clearance of the vehicle (102), model of the vehicle (102), type of suspension of the vehicle (102), type of transmission of the vehicle (102), wheel radius of the vehicle (102), wheel width of the vehicle (102), one or more service records of the vehicle (102), age of the vehicle (102) and tyre air pressure of the vehicle (102).

3. The computing device (106) as claimed in claim 1, wherein the at least one aberration point is determined on a point of the road where the instantaneous acceleration experienced by the one or more accelerometer sensors (202) in real time is greater than a threshold instantaneous acceleration experienced by the one or more accelerometer sensors (202), wherein the threshold instantaneous acceleration is calculated based on the plurality of parameters and a set of constant multipliers.

4. The computing device (106) as claimed in claim 3, wherein the set of constant multipliers comprises a first constant multiplier, a second constant multiplier and a third constant multiplier, wherein the first constant multiplier depends on a second set of data associated with the vehicle (102), wherein the second constant multiplier depends on value of the standard deviation of the instantaneous acceleration experienced by the one or more accelerometer sensors (202) of the computing device (106), and wherein the third constant multiplier depends on value of the speed of the vehicle (102).

5. The computing device (106) as claimed in claim 1, wherein the determining the at least one aberration point on the road comprises:

communicating with a server (126) on a communication network (124);

fetching at least one probable determined aberration point on the road along the route taken by the vehicle (102);

refining the at least one probable determined aberration point on the road along the route taken by the vehicle (102); and

rendering at least one refined aberration point on at least one of the computing device (106) and one or more other computing devices.

6. The computing device (106) as claimed in claim 1, wherein the plurality of sensors (108) comprises at least one of the one or more accelerometer sensors (202), one or more magnetometer sensors (204), one or more gyroscope sensors (206), one or more GPS sensors (208), one or more barometer sensors (210), one or more proximity sensors (212), one or more microphones (214) and one or more image sensors (216).

7. The computing device (106) as claimed in claim 1, wherein the first set of data comprises at least one of direction of the gravitational acceleration, the instantaneous acceleration, orientation of the computing device (106), acceleration experienced by the one or more accelerometer sensors (202) in each of three dimensions, position of the vehicle (102), the speed of the vehicle (102), direction of the vehicle (102) and one or more sound signals around vicinity of the computing device (106).

8. The computing device (106) as claimed in claim 1, wherein the one or more processors (110) perform segmentation of the road along the route taken by the vehicle (102) in one or more road segments, wherein the segmentation of the road along the route taken by the vehicle (102) is done based on a plurality of attributes, wherein the plurality of attributes comprises at least one of a pre-defined distance travelled by the vehicle (102), a pre-defined time travelled by the vehicle (102) and one or more turns taken by the vehicle (102).

9. The computing device (106) as claimed in claim 1, wherein the one or more processors (110) perform categorization of the at least one aberration point on the road in at least one risk category, wherein the at least one risk category for each vehicle (102) depends on at least one of type of the vehicle (102), the speed of the vehicle (102) and ground clearance of the vehicle (102).

10. The computing device (106) as claimed in claim 1, wherein the one or more processors (110) perform sharing of data of the at least one aberration point with a plurality of entities, wherein the plurality of entities comprises at least one of one or more users, one or more public roads administration authorities, ?one or more toll road operators and an administrator (138).
, Description:SYSTEM FOR DETERMINING ABERRATIONS ON A ROAD
TECHNICAL FIELD
[0001] The present disclosure relates to the field of Mobile Sensing Systems and, in particular, relates to the mobile sensing systems utilizing sensor data for determining aberrations on a road.
BACKGROUND
[0002] Over the past few years, applications of sophisticated sensors in mobile phones are growing due to capability of monitoring various parameters in real-time. The various parameters include environment conditions, orientations, device acceleration, geolocation, and the like. Typically, the sensors monitor parameters that are consequential for safety of a vehicle against aberrations or anomalies on roads. In addition, these aberrations or anomalies can cause significant damage to the vehicle. Generally, passengers sitting inside the vehicle carry mobile phones while travelling along a route taken on any road. Nowadays, the mobile phones generally include accelerometer sensors, gyroscope sensors, GPS sensors, microphones, magnetometer sensors, image sensors, proximity sensors, temperature sensors, altitude sensors, pressure sensors, compass and the like. Further, the sensors of the mobile phones detect the parameters that may play significant role in determining potholes and speed bumps on the road along the route take by the vehicle. However, present systems and methods are ineffective in determining the potholes and speed bumps on the road along the route taken by the vehicle using data of the sensors of the mobile phones.
OBJECT OF THE DISCLOSURE
[0003] A primary object of the present disclosure is to determine aberrations on a road.
SUMMARY
[0004] In an aspect, the present disclosure provides a computing device. The computing device includes one or more processors, a signal generator circuitry embedded inside the computing device for generating a signal, and a memory. The memory is coupled to the one or more processors. The memory stores instructions. The instructions are executed by the one or more processors. The execution of the instructions causes the one or more processors to perform a method for determining aberrations on a road. The method includes a first step to receive a first set of data from at least one sensor of a plurality of sensors of the computing device. In addition, the method includes a second step to probabilistically determine at least one aberration point on the road along a route taken by a vehicle. Further, the at least one aberration point is determined based on a plurality of parameters. Furthermore, the plurality of parameters corresponds to a gravitational acceleration experienced by one or more accelerometer sensors of the plurality of sensors of the computing device, a standard deviation of an instantaneous acceleration experienced by the one or more accelerometer sensors of the plurality of sensors of the computing device and speed of the vehicle.
STATEMENT OF THE DISCLOSURE
[0005] The present disclosure talks about a computing device. The computing device includes one or more processors, a signal generator circuitry embedded inside the computing device for generating a signal, and a memory. The memory is coupled to the one or more processors. The memory stores instructions. The instructions are executed by the one or more processors. The execution of the instructions causes the one or more processors to perform a method for determining aberrations on a road. The method includes a first step to receive a first set of data from at least one sensor of a plurality of sensors of the computing device. In addition, the method includes a second step to probabilistically determine at least one aberration point on the road along a route taken by a vehicle. Further, the at least one aberration point is determined based on a plurality of parameters. Furthermore, the plurality of parameters corresponds to a gravitational acceleration experienced by one or more accelerometer sensors of the plurality of sensors of the computing device, a standard deviation of an instantaneous acceleration experienced by the one or more accelerometer sensors of the plurality of sensors of the computing device and speed of the vehicle.
BRIEF DESCRIPTION OF FIGURES
[0006] Having thus described the disclosure in general terms, reference will now be made to the accompanying figures, wherein:
[0007] FIG. 1 illustrates an interactive computing environment to determine at least one aberration point on a road, in accordance with various embodiments of the present disclosure;
[0008] FIG. 2 illustrates a block diagram of a plurality of sensors of FIG. 1, in accordance with various embodiments of the present disclosure;
[0009] FIG. 3 illustrates a first example of a computing device in communication with a server and placed inside a vehicle, in accordance with an embodiment of the present disclosure;
[0010] FIG. 4 illustrates a second example of the computing device in communication with the server and placed inside the vehicle, in accordance with another embodiment of the present disclosure; and
[0011] FIG. 5 illustrates a block diagram of the computing device of FIG. 1, in accordance with various embodiments of the present disclosure.
[0012] It should be noted that the accompanying figures are intended to present illustrations of exemplary embodiments of the present disclosure. These figures are not intended to limit the scope of the present disclosure. It should also be noted that accompanying figures are not necessarily drawn to scale.
DETAILED DESCRIPTION
[0013] In the following description, for purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of the present technology. It will be apparent, however, to one skilled in the art that the present technology can be practiced without these specific details. In other instances, structures and devices are shown in block diagram form only in order to avoid obscuring the present technology.
[0014] Reference in this specification to “one embodiment” or “an embodiment” means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the present technology. The appearance of the phrase “in one embodiment” in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. Moreover, various features are described which may be exhibited by some embodiments and not by others. Similarly, various requirements are described which may be requirements for some embodiments but no other embodiments.
[0015] Moreover, although the following description contains many specifics for the purposes of illustration, anyone skilled in the art will appreciate that many variations and/or alterations to said details are within the scope of the present technology. Similarly, although many of the features of the present technology are described in terms of each other, or in conjunction with each other, one skilled in the art will appreciate that many of these features can be provided independently of other features. Accordingly, this description of the present technology is set forth without any loss of generality to, and without imposing limitations upon, the present technology.
[0016] FIG. 1 illustrates an interactive computing environment 100 to determine at least one aberration point on a road, in accordance with various embodiments of the present disclosure. The interactive computing environment 100 includes a vehicle 102, a communication network 124 and a server 126 associated with an administrator 138. In addition, the vehicle 102 includes an instrument cluster module 104 and a computing device 106 associated with a user 122. Further, the computing device 106 includes a plurality of sensors 108, one or more processors 110, a memory 112, a display 118 and a user input 120. Furthermore, the server 126 includes one or more processors 128, a memory 130 and a database 136. Moreover, the memory 112, 130 includes instructions and data. The above stated elements of the interactive computing environment 100 operate coherently and synchronously to enable the determination of the at least one aberration point on the road.
[0017] The vehicle 102 is used to travel on the road from a point of origin to a destination along a route. In an example, the vehicle 102 includes one or more occupants commuting from the point of origin to destination. In another example, the vehicle 102 contains goods and items which are to be delivered from the point of origin to destination. In yet another example, the vehicle 102 is used to carry unit loads with facilitation of pulls freight container such as intermodal container, swap bodies, semi-trailer and the like for carrying unit loads. In yet another example, the vehicle 102 performs a physical process of transporting commodities, merchandise goods, cargo and the like. The vehicle 102 includes a motorcycle, a car, a truck, a bus, a motor scooter, a van, a taxi, an auto rickshaw and the like.
[0018] The vehicle 102 is used by the user 122 to travel on the road from the point of origin to destination through route. In an example, the user 122 corresponds to a driver. The driver is any person having a license to drive the vehicle 102 in a country or state. In another example, the user 122 corresponds to any of the one or more occupants. In an example, the user 122 is an owner of the vehicle 102. In another example, the user 122 is any person who is hired to drive the vehicle 102 to travel from the point of origin to destination.
[0019] The vehicle 102 includes the instrument cluster module 104. The instrument cluster module 104 is mounted in the vehicle 102 dashboard behind, or forward of steering wheel of the vehicle 102. Further, the instrument cluster module 104 includes a fuel gauge module, a speedometer gauge module and an engine temperature gauge module. Furthermore, the fuel gauge module, the speedometer gauge module and the engine temperature gauge module are illuminated by at least one of a plurality of backlighting illumination means. Moreover, the instrument cluster module 104 is operatively connected to and under the control of the one or more processors 110 of the computing device 106. Also, the plurality of backlighting illumination means represent multicolour LEDs or edge lighting means or any other type of backlighting illumination means.
[0020] The instrument cluster module 104 provides a real-time vehicular data associated with the vehicle 102 to the one or more processors 110 of the computing device 106. The instrument cluster module 104 provides the real-time vehicular data to determine the at least one aberration point on the road. In addition, the fuel gauge module, the speedometer gauge module and the engine temperature gauge module of the instrument cluster module 104 detects the real-time vehicular data associated with the vehicle 102. Further, the real-time vehicular data includes but may not be limited to speed of the vehicle 102, wheel RPM of the vehicle 102, fuel quantity in the vehicle 102, distance travelled by the vehicle 102 and temperature of engine of the vehicle 102.
[0021] The user 122 is any person or individual accessing the computing device 106. In an example, the user 122 is an owner of the computing device 106. In another example, the user 122 is not the owner of the computing device 106. In an example, the user 122 accesses the computing device 106 while driving. In another example, the user 122 accesses the computing device 106 while sitting at seat of any of the one or more occupants of the vehicle 102. In an example, the user 122 is the driver. In another example, the user 122 is an owner of the vehicle 102. In yet another example, the user 122 is an occupant in the vehicle 102. In yet another example, the user 122 is a passenger.
[0022] The user 122 corresponds to any number of person or individual associated with the computing device 106 communicating with the server 126 on the communication network 124. The computing device 106 performs the determination of the at least one aberration point on the road for the user 122. In addition, the computing device 106 is associated with the user 122. Further, the user 122 selects the route on the computing device 106 to travel on the road through the vehicle 102.
[0023] The computing device 106 is currently positioned inside the vehicle 102. In an example, the computing device 106 is a portable computing device. The portable computing device includes but may not be limited to a laptop, a smartphone, a tablet, a palmtop, a smart watch and any wearable computing device. In an example, the smartphone is an iOS-based smartphone, an android-based smartphone, a windows-based smartphone and the like. In another example, the computing device 106 is a fixed computing device. The fixed computing device includes but may not be limited to a desktop, a workstation, a smart TV and a mainframe computer. In addition, the computing device 106 is currently in switched-on state. The computing device 106 is any type of device having an active internet connection. In addition, the user 122 accesses the computing device 106 in real-time.
[0024] The computing device 106 performs computing operations based on a suitable operating system installed inside the computing device 106. In general, operating system is system software that manages computer hardware and software resources and provides common services for computer programs. In addition, operating system acts as an interface for software installed inside the computing device 106 to interact with hardware components of the computing device 106. In an example, the computing device 106 performs computing operations based on any suitable operating system designed for the portable computing device. In addition, operating system installed inside the computing device 106 is a mobile operating system. Further, the mobile operating system includes but may not be limited to windows operating system, android operating system, iOS operating system, Symbian operating system, BADA operating system from Samsung Electronics, BlackBerry operating system, and Sailfish operating system. However, the operating system is not limited to above mentioned operating systems. In addition, the computing device 106 operates on any version of particular operating system corresponding to above mentioned operating systems.
[0025] In another example, the computing device 106 performs computing operations based on any suitable operating system designed for the fixed computing device. In an example, operating system installed inside the computing device 106 is Windows. In another example, operating system installed inside the computing device 106 is Mac. In yet another example, operating system installed inside the computing device 106 is Linux based operating system. In yet another example, operating system installed inside the computing device 106 is Chrome OS. In yet another example, operating system installed inside the computing device 106 is one of UNIX, Kali Linux, and the like. However, operating system is not limited to above mentioned operating systems.
[0026] The computing device 106 includes the plurality of sensors 108. Referring now to FIG. 2, there is provided a more detailed block diagram of the plurality of sensors 108 of FIG. 1 that is useful to understand the present invention. As shown in FIG. 2, the plurality of sensors 108 includes at least one of one or more accelerometer sensors 202, one or more magnetometer sensors 204, one or more gyroscope sensors 206 and one or more GPS sensors 208. In addition, the plurality of sensors 108 include at least one of one or more barometer sensors 210, one or more proximity sensors 212, one or more microphones 214 and one or more image sensors 216. Further, the plurality of sensors 108 provides a first set of data. In an embodiment of the present disclosure, the computing device 106 receives the first set of data from at least one sensor of the plurality of sensors 108 of the computing device 106. In another embodiment of the present disclosure, the server 126 receives the first set of data from the at least one sensor of the plurality of sensors 108 of the computing device 106.
[0027] The first set of data includes at least one of direction of the gravitational acceleration, instantaneous acceleration and orientation of the computing device 106. In addition, the first set of data includes at least one of acceleration experienced by the one or more accelerometer sensors 202 in each of three dimensions and position of the vehicle 102. Further, the first set of data includes at least one of the speed of the vehicle 102, direction of the vehicle 102, one or more sound signals around vicinity of the computing device 106 and the like.
[0028] The computing device 106 obtains a second set of data associated with the vehicle 102. In addition, the computing device 106 is positioned at any of a plurality of areas of the vehicle 102. In an example, the computing device 106 is positioned on dashboard of the vehicle 102. In another example, the computing device 106 is positioned on rear seats of the vehicle 102. In yet another example, the computing device 106 is held in device holder of the vehicle 102. In yet another example, the computing device 106 is positioned inside pocket of the user 122 travelling on the road through the vehicle 102.
[0029] The second set of data includes at least one of type of the vehicle 102, ground clearance of the vehicle 102, model of the vehicle 102, type of suspension of the vehicle 102 and type of transmission of the vehicle 102. The second set of data includes at least one of wheel radius of the vehicle 102, wheel width of the vehicle 102, one or more service records of the vehicle 102, age of the vehicle 102 and tyre air pressure of the vehicle 102.
[0030] In an embodiment of the present disclosure, the computing device 106 obtains the second set of data from the user input 120. In another embodiment of the present disclosure, the computing device 106 obtains the second set of data from the instrument cluster module 104 of the vehicle 102. In yet another embodiment of the present disclosure, the computing device 106 obtains the second set of data from one or more vehicular sensors of the vehicle 102. In yet another embodiment of the present disclosure, the server 126 obtains the second set of data from the user input 120. In yet another embodiment of the present disclosure, the server 126 obtains the second set of data from the instrument cluster module 104 of the vehicle 102. In yet another embodiment of the present disclosure, the server 126 obtains the second set of data from the one or more vehicular sensors of the vehicle 102.
[0031] The computing device 106 communicates with the server 126 on the communication network 124. The communication network 124 provides a medium for the user 122 accessing the computing device 106 to communicate with the server 126. In an example, the communication network 124 is an internet connection. In another example, the communication network 124 is a wireless mobile network. In yet another example, the communication network 124 is a wired network with a finite bandwidth. In yet another example, the communication network 124 is a combination of the wireless and the wired network for optimum throughput of data transmission. In yet another example, the communication network 124 is an optical fibre high bandwidth network that enables a high data rate with negligible connection drops. The communication network 124 includes a set of channels. Each channel of the set of channels supports a finite bandwidth. Moreover, the finite bandwidth of each channel of the set of channels is based on capacity of the communication network 124. The communication network 124 connects the computing device 106 to the server 126 using a plurality of methods. The plurality of methods used to provide network connectivity to the computing device 106 includes 2G, 3G, 4G, 5G, Wifi and the like.
[0032] In an embodiment of the present disclosure, the computing device 106 evaluates a standard deviation of the instantaneous acceleration along the route taken by the vehicle 102 on the road based on a pre-defined set of rules. In another embodiment of the present disclosure, the server 126 evaluates the standard deviation of the instantaneous acceleration along the route taken by the vehicle 102 on the road based on the pre-defined set of rules. In addition, the instantaneous acceleration corresponds to the acceleration experienced by the one or more accelerometer sensors 202 of the plurality of sensors 108 of the computing device 106 in each of three dimensions in real-time. Further, the pre-defined set of rules include at least one of variation in the instantaneous acceleration along the route taken by the vehicle 102 and a mean instantaneous acceleration along the route taken by the vehicle 102.
[0033] In an embodiment of the present disclosure, the computing device 106 normalizes the standard deviation of the instantaneous acceleration along the route taken by the vehicle 102 on the road based on a plurality of factors. In another embodiment of the present disclosure, the server 126 normalizes the standard deviation of the instantaneous acceleration along the route taken by the vehicle 102 on the road based on the plurality of factors. In addition, the standard deviation of the instantaneous acceleration is normalized to determine the at least one aberration point on the road for the vehicle 102. Further, the plurality of factors includes weather conditions, the speed of the vehicle 102, at least one of the second set of data and the like.
[0034] In an embodiment of the present disclosure, the computing device 106 probabilistically determines the at least one aberration point on the road along the route taken by the vehicle 102. In another embodiment of the present disclosure, the server 126 probabilistically determines the at least one aberration point on the road along the route taken by the vehicle 102. In addition, the at least one aberration point is determined based on a plurality of parameters. Further, the plurality of parameters corresponds to the gravitational acceleration experienced by the one or more accelerometer sensors 202 of the plurality of sensors 108 of the computing device 106, the standard deviation of the instantaneous acceleration experienced by the one or more accelerometer sensors 202 of the plurality of sensors 108 of the computing device 106 and the speed of the vehicle 102.
[0035] In an example, a vehicle V1 (Let’s say a luxury sedan) is associated with a user U1 (Let’s say owner of the luxury sedan). The user U1 drives the vehicle V1 from a point of origin (Let’s say home) to a destination (Let’s say Amusement park) in heavy fog conditions at an average speed of 50 Kmph. In addition, the user U1 has an iOS-based smartphone consisting processors, memory, accelerometer sensors, GPS sensor, proximity sensor and microphone. The accelerometer sensors of the iOS-based smartphone senses the instantaneous acceleration experienced by the iOS-based smartphone in each of three dimensions throughout the journey. Further, the processors evaluate standard deviation of the instantaneous acceleration along the route. Furthermore, the processors normalize the standard deviation of the instantaneous acceleration based on the heavy fog conditions, type of the vehicle VI and the average speed of 50 Kmph. Moreover, the iOS-based smartphone determines the at least one aberration point on the road along the route taken by the vehicle V1. Also, the iOS-based smartphone associates the at least one aberration point with geolocation based on the GPS sensor.
[0036] In another example, a vehicle V2 (Let’s say a hatchback) is associated with a user U2 (Let’s say driver of the hatchback). The user U2 drives the vehicle V2 from a point of origin (Let’s say college) to a destination (Let’s say shopping mall) in heavy rain conditions at an average speed of 100 Kmph. In addition, the user U2 has a windows-based smartphone consisting processors, memory, accelerometer sensors, GPS sensor, proximity sensor and a microphone. The accelerometer sensors of the the windows-based smartphone senses the instantaneous acceleration experienced by the windows-based smartphone in each of three dimensions throughout the journey. Further, the processors evaluate standard deviation of the instantaneous acceleration along the route. Furthermore, the processors normalize the standard deviation of the instantaneous acceleration based on heavy rain conditions, type of the vehicle V2, the average speed of 100 Kmph, and rattling sound sensed by the microphone. Moreover, the windows-based smartphone determines the at least one aberration point on the road along the route taken by the vehicle V2. Also, the windows-based smartphone associates the at least one aberration point with geolocation based on the GPS sensor.
[0037] The at least one aberration point is determined on a point of the road where the instantaneous acceleration experienced by the one or more accelerometer sensors 202 is greater than a threshold instantaneous acceleration experienced by the one or more accelerometer sensors 202. In addition, the threshold instantaneous acceleration is calculated based on the plurality of parameters and a set of constant multipliers. Further, the set of constant multipliers include a first constant multiplier, a second constant multiplier and a third constant multiplier. Furthermore, the first constant multiplier depends on the second set of data associated with the vehicle 102. Moreover, the second constant multiplier depends on value of the standard deviation of the instantaneous acceleration experienced by the one or more accelerometer sensors 202 of the computing device 106. Also, the third constant multiplier depends on value of the speed of the vehicle 102.
[0038] The determination of the at least one aberration point on the road includes fetching at least one probable determined aberration point on the road along the route taken by the vehicle 102. In an embodiment of the present disclosure, the server 126 fetches the at least one probable determined aberration point on the road along the route taken by the vehicle 102. In another embodiment of the present disclosure, the computing device 106 fetches the at least one probable determined aberration point on the road along the route taken by the vehicle 102.
[0039] The determination of the at least one aberration point on the road includes refining the at least one probable determined aberration point on the road along the route taken by the vehicle 102. In an embodiment of the present disclosure, the server 126 refines the at least one probable determined aberration point on the road along the route taken by the vehicle 102. In another embodiment of the present disclosure, the computing device 106 refines the at least one probable determined aberration point on the road along the route taken by the vehicle 102. Further, the determination of the at least one aberration point on the road includes rendering at least one refined aberration point on at least one of the computing device 106 and one or more other computing devices.
[0040] The computing device 106 includes the one or more processors 110. The one or more processors 110 perform segmentation of the road along the route taken by the vehicle 102 in one or more road segments. In addition, the segmentation of the road along the route taken by the vehicle 102 is done based on a plurality of attributes. Further, the plurality of attributes include at least one of a pre-defined distance travelled by the vehicle 102, a pre-defined time travelled by the vehicle 102, one or more turns taken by the vehicle 102 and the like.
[0041] The one or more processors 110 perform categorization of the at least one aberration point on the road in at least one risk category. In addition, the at least one risk category for each vehicle 102 depends on at least one of the type of the vehicle 102, the speed of the vehicle 102 and the ground clearance of the vehicle 102. Further, the one or more processors 110 perform sharing of data of the at least one aberration point with a plurality of entities. Furthermore, the plurality of entities includes at least one of one or more users, one or more public roads administration authorities, ?one or more toll road operators, the administrator 138 and the like.
[0042] In an embodiment of the present disclosure, the computing device 106 determines potential traffic on the road for the route taken by the vehicle 102 based on determination of a plurality of braking events. In another embodiment of the present disclosure, the server 126 determines the potential traffic on the road for the route taken by the vehicle 102 based on the determination of the plurality of braking events. In addition, the plurality of braking events is determined based on a combination of the variation of the acceleration in the direction of the route and the one or more sound signals of a honk of the vehicle 102.
[0043] In an embodiment of the present disclosure, the computing device 106 identifies one or more features associated with the at least one aberration point on the road for the route taken by the vehicle 102. The computing device 106 identifies the one or more features using the one or more sound signals from the one or more microphones 214 of the computing device 106. In another embodiment of the present disclosure, the server 126 identifies the one or more features associated with the at least one aberration point on the road using the one or more sound signals from the one or more microphones 214. In addition, the one or more features include rattling sound, knocking sound, ticking sound and vibration sound associated with one or more components of the vehicle 102. Further, the one or more components include but may not be limited to suspensions, dashboard, the steering wheel, bonnet, doors, bumper and windows.
[0044] The computing device 106 recommends at least one position for the computing device 106 to determine the at least one aberration point on the road for the route taken by the vehicle 102. In addition, the recommendation of the at least one position for the computing device 106 is based on proximity information associated with the computing device 106 using the one or more proximity sensors 212 and the one or more image sensors 216.
[0045] The computing device 106 is associated with the server 126. The server 126 is utilized to receive the data from the computing device 106, store the received data, process the received data, and/or communicate information associated with the received or processed data. In addition, the server 126 includes the one or more processors 128 adapted and configured to execute various software applications and components of the computing device 106.
[0046] The server 126 includes the database 136. The database 136 stores data related to operation of the computing device 106, the vehicle 102 and its autonomous operation features. In an example, data includes the first set of data, the second set of data, the real-time vehicular data, the data of the at least one aberration point or other data relating to use of the vehicle 102 and the autonomous operation features. In an example, data is uploaded to the server 126 via the communication network 124.
[0047] The server 126 access data stored in the database 136 when executing various functions and tasks associated with the determination of the at least one aberration point relating to the vehicle 102. Although the interactive computing environment 100 is shown to include one vehicle 102, one computing device 106 and one server 126, it should be understood that more numbers of vehicles 102, computing devices 106, and/or servers 126 may be utilized. In an example, the interactive computing environment 100 includes a plurality of servers 126 and hundreds or thousands of computing devices 106, all of which may be interconnected via the communication network 124. Furthermore, the database storage or processing performed by the one or more servers 126 may be distributed among a plurality of servers 126 in an arrangement known as “cloud computing.” This configuration may provide various advantages, such as enabling near real-time uploads and downloads of information as well as periodic uploads and downloads of information.
[0048] The server 126 is associated with the administrator 138. In addition, the administrator 138 manages different components of the computing device 106 communicating with the server 126. The administrator 138 coordinates activities of components involved in the computing device 106 and the server 126. The administrator 138 is any person or individual who monitors the working of the computing device 106 and the server 126 in real-time. The administrator 138 monitors the working of the computing device 106 and the server 126 through a communication device. The communication device includes a laptop, a desktop computer, a tablet, a personal digital assistant and the like.
[0049] FIG. 3 illustrates a first example 300 of the computing device 106 in communication with the server 126 and placed inside the vehicle 102, in accordance with an embodiment of the present disclosure. In the first example 300, the vehicle 102 is a hatchback car. In addition, the computing device 106 is positioned at front passenger seat of the hatchback car. In the first example 300, the computing device 106 is a smartphone. Further, the smartphone is an android-based smartphone. In the first example 300, the communication network 124 is internet connection. Furthermore, the plurality of sensors 108 of the android-based smartphone detects the first set of data for the hatchback car in real-time. Moreover, the first set of data for the hatchback car is computed at the android-based smartphone to determine the at least one aberration point on the road along the route taken by the hatchback car. Also, the android-based smartphone sends the first set of data and the data related to the at least one aberration point to the server 126 through the internet connection.
[0050] FIG. 4 illustrates a second example 400 of the computing device 106 in communication with the server 126 and placed inside the vehicle 102, in accordance with another embodiment of the present disclosure. In the second example 400, the vehicle 102 is a coupe passenger car. In addition, the computing device 106 is positioned at rear passenger seats of the coupe passenger car. In the second example 400, the computing device 106 is a laptop. Further, the laptop is Mac-based laptop. In the second example 400, the communication network 124 is internet connection. Furthermore, the plurality of sensors 108 of the Mac-based laptop detects the first set of data for the coupe passenger car in real-time. Moreover, the first set of data for the coupe passenger car is computed at the server 126 to determine the at least one aberration point on the road along the route taken by the coupe passenger car.
[0051] FIG. 5 illustrates a block diagram of the computing device 106 of FIG. 1, in accordance with various embodiments of the present disclosure. The computing device 106 includes a bus 502 (as shown in FIG. 5) that directly or indirectly couples the following devices: the memory 112, the one or more processors 110, one or more presentation components 504, one or more input/output (I/O) ports 506, one or more input/output components 508, and a power supply 510. The bus 502 represents what may be one or more busses (such as an address bus, data bus, or combination thereof). Although the various blocks of FIG. 5 are shown with lines for the sake of clarity, in reality, delineating various components is not so clear, and metaphorically, the lines would more accurately be grey and fuzzy. For example, one may consider a presentation component such as a display device to be an I/O component. Also, the one or more processors 110 have memory. The inventors recognize that such is the nature of the art, and reiterate that the diagram of FIG. 5 is merely illustrative of the computing device 106 that can be used in connection with one or more embodiments of the present invention. Distinction is not made between such categories as “workstation,” “server,” “laptop,” “hand-held device,” etc., as all are contemplated within the scope of FIG. 5 and reference to “computing device.”
[0052] The computing device 106 typically includes a variety of computer-readable media. The computer-readable media can be any available media that can be accessed by the computing device 106 and includes both volatile and non-volatile media, removable and non-removable media. By way of example, and not limitation, the computer-readable media may comprise computer storage media and communication media. The computer storage media includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storage of information such as computer-readable instructions, data structures, program modules or other data.
[0053] The computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can be accessed by the computing device 106. The communication media typically embodies computer-readable instructions, data structures, program modules or other data in a modulated data signal such as a carrier wave or other transport mechanism and includes any information delivery media. The term “modulated data signal” means a signal that has one or more of its characteristics set or changed in such a manner as to encode information in the signal. By way of example, and not limitation, communication media includes wired media such as a wired network or direct-wired connection, and wireless media such as acoustic, RF, infrared and other wireless media. Combinations of any of the above should also be included within the scope of computer-readable media.
[0054] The memory 112 includes computer-storage media in the form of volatile and/or non-volatile memory. The memory 112 may be removable, non-removable, or a combination thereof. Hardware devices include solid-state memory, hard drives, optical-disc drives, etc. The computing device 106 includes the one or more processors 110 that read data from various entities such as the memory 112 or I/O components 508. The one or more presentation components 504 present data indications to a user or other device. Presentation components include a display device, speaker, printing component, vibrating component, etc. The one or more I/O ports 506 allow the computing device 106 to be logically coupled to other devices including the one or more I/O components 508, some of which may be built in. Illustrative components include a microphone, joystick, game pad, satellite dish, scanner, printer, wireless device, etc.
[0055] The memory 112 may include the data that may be retrieved, manipulated or stored by the one or more processors 110. The memory 112 may be of any non-transitory type capable of storing information accessible by the one or more processors 110, such as a hard-drive, memory card, ROM, RAM, DVD, CD-ROM, write-capable, and read-only memories.
[0056] The memory 112 includes the instructions that may be any set of instructions to be executed directly, such as machine code, or indirectly, such as scripts, by the one or more processors 110. In that regard, the terms “instructions,” “application,” “steps” and “programs” may be used interchangeably. The instructions may be stored in object code format for direct processing by the one or more processors 110, or in any other computing device language including scripts or collections of independent source code modules that are interpreted on demand or compiled in advance.
[0057] The data may be retrieved, stored or modified by the one or more processors 110 in accordance with the instructions. For instance, although the subject matter described herein is not limited by any particular data structure, the data may be stored in computer registers, in a relational database as a table having many different fields and records, or XML documents. The data may also be formatted in any computing device-readable format such as, but not limited to, binary values, ASCII or Unicode. Moreover, the data may comprise any information sufficient to identify the relevant information, such as numbers, descriptive text, proprietary codes, pointers, references to data stored in other memories such as at other network locations, or information that is used by a function to calculate the relevant data.
[0058] The one or more processors 110 may be any conventional processors, such as a commercially available CPU. Alternatively, the one or more processors 110 may be dedicated components such as an application specific integrated circuit (“ASIC”) or other hardware-based processor. Although not necessary, the computing device 106 may include specialized hardware components to perform specific computing processes, such as evaluating the standard deviation, normalizing the standard deviation and probabilistically determining the at least one aberration point.
[0059] Although FIG. 1 functionally illustrates the one or more processors 110, the memory 112, and other elements of the computing device 106 as being within the same block, the processor, computing device, or memory may actually comprise multiple processors, computing devices, or memories that may or may not be stored within the same physical housing. For example, the memory may be a hard drive or other storage media located in housings different from that of the computing devices 106. Accordingly, references to a processor, computing device, or memory will be understood to include references to a collection of processors, computers, computing devices, or memories that may or may not operate in parallel.