The following specification describes the invention and the manner in which it is to be performed.
PRIORITY INFORMATION AND DEFINITIVE STATEMENT
[001] This patent application is a patent of addition and is an improvement over a patent application titled “VEHICLE FUEL EFFICIENCY ANALYTICS”, having application number 4173/DEL/2015 and filed on 18th December, 2015.
TECHNICAL FIELD
[002] The present subject matter described herein, in general, relates to analytics and in particularly a system and method to generate driving performance corresponding to a driver of a vehicle.
BACKGROUND
[003] Typically, Internet of Things (IoT) means an inter connectivity between physical devices, machines, tools and others. IoT creates a network for exchanging data between the physical devices, machines, tools and others. One of the example of IoT is a connected vehicle. The connected vehicle is generally embedded with electronics, software, sensors, actuators, and network connectivity enabling the exchange of data with the other IoT enabled physical devices, machines, tools and others. The connected vehicle continuously generates huge amount of data. However, it has always been a challenge to deduce the accurate meaningful information out of the huge amount of data.
[004] Now a day, due to of ever increasing number of vehicle thefts and road accidents, vehicle safety and security has become one of the foremost research areas in the field of connected vehicles. The criticality of securing connected vehicles is because huge amount of data related to the driver and the vehicle is transmitting from the vehicle. Further, theft of such connected vehicle not only leads to the loss of a vehicle but also to the loss of the personal data of the driver of the vehicle. Thus, conventional systems and methods fail to provide insights about the driver of the connected vehicle while simultaneously keeping the vehicle safe and secure.
SUMMARY
[005] Before the present systems and methods for generating a driving performance corresponding to a driver, are described, it is to be understood that this application is not limited to the particular systems, and methodologies described, as there can be multiple possible embodiments which are not expressly illustrated in the present disclosure. It is also to be understood that the terminology used in the description is for the purpose of describing the particular versions or embodiments only, and is not intended to limit the scope of the present application. This summary is provided to introduce concepts related to systems and methods for generating a driving performance corresponding to a driver. This summary is not intended to identify essential features of the claimed subject matter nor is it intended for use in determining or limiting the scope of the claimed subject matter.
[006] In one implementation, a method for generating a driving performance corresponding to a driver is disclosed. In order to generate driving performance, initially, biometric data corresponding to a driver may be received. In one aspect, the biometric data may comprise one or more of voice data, facial data, finger print data, and iris data. Once the biometric data is received, the driver may be authenticated to drive a vehicle using a two-step authentication process. In one aspect, the two-step authentication process may comprise a first level authentication, by comparing the biometric data of the driver with a previously stored biometric data, and a second level authentication using a Fast Identification Online (FIDO) authentication methodology. Once the driver is authenticated, driving data associated with the driver and vehicle data associated with the vehicle may be obtained. After obtaining the driving data and the vehicle data, a driving performance of the driver is generated based on one or more of a predefined analytics model, the vehicle data and the driving data from the vehicle. In one aspect, the aforementioned method for generating a driving performance corresponding to a driver may be performed by a processor using programmed instructions stored in a memory.
[007] In another implementation, a system for generating a driving performance corresponding to a driver is disclosed. The system may comprise a processor and a memory coupled to the processor. The processor may execute a plurality of modules present in the memory. The plurality of modules may comprise a data receiving module, an authentication module, a data obtaining module, and a generation module. The data receiving module may receive biometric data corresponding to a driver. In one aspect, the biometric data may comprise one or more of voice data, facial data, finger print data, and iris data. The authentication module may authenticate the driver using a two-step authentication process to drive a vehicle. In one aspect, the two step authentication process comprises a first level authentication, by comparing the biometric data of the driver with a previously stored biometric data and a second level authentication using a Fast Identification Online (FIDO) authentication methodology. The data obtaining module may obtain driving data associated with the driver and vehicle data associated with the vehicle. The generation module may generate a driving performance of the driver based on one or more of a predefined analytics model, the vehicle data and the driving data from the vehicle.
[008] In yet another implementation, non-transitory computer readable medium embodying a program executable in a computing device for generating a driving performance corresponding to a driver is disclosed. The program may comprise a program code for receiving biometric data corresponding to a driver. In one aspect, the biometric data may comprise one or more of voice data, facial data, finger print data, and iris data. The program may further comprise a program code authenticating the driver using a two-step authentication process to drive a vehicle. In one aspect, the two step authentication process comprises a first level authentication, by comparing the biometric data of the driver with a previously stored biometric data and a second level authentication using a Fast Identification Online (FIDO) authentication methodology. The program may further comprise a program code for obtaining driving data associated with the driver and vehicle data associated with the vehicle. The program may further comprise a program code for generating a driving performance of the driver based on one or more of a predefined analytics model, the vehicle data and the driving data from the vehicle.
BRIEF DESCRIPTION OF THE DRAWINGS
[009] The foregoing detailed description of embodiments is better understood when read in conjunction with the appended drawings. For the purpose of illustrating the disclosure, example constructions of the disclosure are shown in the present document; however, the disclosure is not limited to the specific methods and apparatus disclosed in the document and the drawings.
[010] The detailed description is given with reference to the accompanying figures. In the figures, the left-most digit(s) of a reference number identifies the figure in which the reference number first appears. The same numbers are used throughout the drawings to refer like features and components.
[011] Figure 1 illustrates a network implementation of a system for generating a driving performance corresponding to a driver, in accordance with an embodiment of the present subject matter.
[012] Figure 2 illustrates the system, in accordance with an embodiment of the present subject matter.
[013] Figure 3 illustrates a method for generating a driving performance corresponding to a driver, in accordance with an embodiment of the present subject matter.

DETAILED DESCRIPTION
[014] Some embodiments of the present disclosure, illustrating all its features, will now be discussed in detail. The words "receiving", "authenticating", "obtaining", "generating", "transmitting", "mapping", "encrypting", "associating" and "publishing" and other forms thereof, are intended to be equivalent in meaning and be open ended in that an item or items following any one of these words is not meant to be an exhaustive listing of such item or items, or meant to be limited to only the listed item or items. It must also be noted that as used herein and in the appended claims, the singular forms "a," "an," and "the" include plural references unless the context clearly dictates otherwise. Although any systems and methods similar or equivalent to those described herein can be used in the practice or testing of embodiments of the present disclosure, the exemplary, systems and methods for generating a driving performance corresponding to a driver are now described. The disclosed embodiments of the system and method for generating a driving performance corresponding to a driver are merely exemplary of the disclosure, which may be embodied in various forms.
[015] Various modifications to the embodiment will be readily apparent to those skilled in the art and the generic principles herein may be applied to other embodiments. However, one of ordinary skill in the art will readily recognize that the present disclosure for generating a driving performance corresponding to a driver is not intended to be limited to the embodiments illustrated, but is to be accorded the widest scope consistent with the principles and features described herein.
[016] In an implementation, a system and method for generating a driving performance corresponding to a driver, is described. In the implementation biometric data corresponding to a driver may be received. Examples of the biometric data comprise one or more of voice data, facial data, finger print data, and iris data. Further to receiving the biometric data, the driver may be registered by using a combination of one or more of the biometric data. During registration, a private key for encryption and a private key for decryption may be generated. In one aspect, the public key and the private key is associated with the driver. After generating the private key and the public key, the public key may be transmitted to the Fast Identification Online (FIDO) server. Subsequent to the transmission of the public key, a unique identification number may be received from the FIDO server. In one aspect, the unique identification number may be a registration number of the driver. In one other aspect, the unique identification number may be an acknowledgement of registration of the driver in the FIDO server. Upon receiving the unique identification number, the unique identification number may be mapped with the private key and the biometric data of the driver.
[017] Further to receiving the biometric data, the driver may be authenticated to drive a vehicle using a two-step authentication process. The two-step authentication process may comprise a first level authentication and a second level authentication. The first level authentication compares the biometric data of the driver with the previously stored biometric data. The second level authentication uses the FIDO authentication methodology. In the FIDO authentication methodology, the biometric data of the driver may be encrypted using the private key associated with the driver. Upon encrypting the biometric data, the unique identification number of the driver may be associated with the encrypted biometric data. Further to associating the unique identification number of the driver with the encrypted biometric data, the unique identification number and the encrypted biometric data may be transmitted to the FIDO server. Upon transmitting the unique identification number and the encrypted biometric data, the unique identification number and a decrypted biometric data may be received from the FIDO server. In one aspect, the decryption of the biometric data is performed by using the unique identification number and the public key of the driver. In another aspect, the public key is associated with private key.
[018] The aforementioned two-step authentication process is enabled by FIDO authentication methodology after communicating to the FIDO server. The FIDO authentication methodology safeguards the biometric data against data thefts such as data tampering, data leakage and others. Also implementing two-step authentication process to authenticate the driver significantly enhances the vehicle security.
[019] Further to authentication of the driver to drive the vehicle, driving data associated with the driver and vehicle data associated with the vehicle may be obtained. The driving data may comprise a unique identification number of the driver, frequency of braking, force of braking, time of driving, duration of driving, number of turns, number of sudden turns, traffic data, and the like. The vehicle data may comprise vehicle registration number, speed data, braking frequency data, gear data, engine data, distance travelled, make and model of vehicle, manufacture of the vehicle, vehicle age, transmission gear, engine rpm accelerator pedal position, GPS location, emission data, route data, and the like.
[020] Further to obtaining the driving data and the vehicle data, a driving performance of the driver based on one or more of a predefined analytics model, the vehicle data and the driving data from the vehicle may be generated. Once the driving performance of the driver is generated, the driving performance of the driver may be mapped with one or more of the vehicles. Upon mapping the deriving performance of the driver, a report of the driving performance of the driver may be published on one or more of a web portal, a mobile application or an infotainment system installed in the vehicle. In one aspect, the report may comprise one or more of the unique identification number of the driver, and the vehicle registration number.
[021] As mentioned above, the driving performance of the driver is advantageous for analyzing the driver and classifying the driver for example as a good driver, a bad driver, or an average driver. The driving performance of the driver indicates vehicle safety. The driver with the good driving performance indicates higher vehicle safety, on the other hand the driver with the bad driving performance indicates high risk for the vehicle safety. These advantages are not the only advantages and any person skilled in the art may identify one or more advantages for the aforementioned description.
[022] Referring to Figure 1, a network implementation of a system 102 for generating a driving performance corresponding to a driver, in accordance with an embodiment of the present subject matter may be described. In one embodiment, the present subject matter is explained considering that the system 102 may be implemented as a standalone system connected to a network. It may be understood that the system 102 may also be implemented in a variety of computing systems, such as a laptop computer, a desktop computer, a notebook, a workstation, a mainframe computer, a server, a network server, a distributed computing architecture, a virtual machine environment, a cloud-based computing environment, a vehicle 112 and the like. It may be understood that the system 102 may be configured to receive, share and store data from a system database configured inside the system 102. In one implementation, the system 102 may access data from databases installed at different geographical locations, or in parts with the system database. In one implementation, the system 102 may comprise the cloud-based computing environment in which a user may operate individual computing systems configured to execute remotely located applications.
[023] In one implementation of the system 102 for generating a driving performance corresponding to a driver, a vehicle 112 may be connected to a network 106 via Wi-Fi or 2G/3G/4G/5G connection. The vehicle 112 may be a car, a truck, a bus, a tractor and the alike. It will be understood that the system 102 may be accessed by multiple users of one or more vehicles 112-1….112-N. It may be understood that a set of sensors and a set of data aggregator may be installed in the vehicle 112. Examples of the sensor can be GPS sensor, gyro meter, piezoelectric sensor, automobile oxygen sensor, parking sensor, speedometer, and the like. In one example, the sensors installed in the vehicle 112 may be continuously transmitting data to the system 102 via network 106. In another embodiment, the vehicle 112 may comprise a finger print scanner 104, an infotainment system 106, a microphone 108, a camera 110 and the like. In one embodiment, the finger print scanner 104 may be a wireless scanner or a wired scanner. In one example the finger print scanner 104 may be mounted on the infotainment system 106, a key of the vehicle 112, a steering of the vehicle 112, door of the vehicle 112 and the like. In another embodiment, the microphone 108 may be a wired or a wireless microphone. In another embodiment, the camera 110 may be a wired or a wireless camera. In yet another embodiment, the infotainment system 106 may be a wired or wireless infotainment system. In one example, the infotainment system 106 may be connected to a mobile phone of the driver of the vehicle 112.
[024] In one implementation, the multiple users may access the system by interacting with one or more of the finger print scanner 104-1, 104-2, ……104-N (hereinafter referred as 104, in singular or plural as 104), the infotainment system 106-1, 106-2, ……106-N (hereinafter referred as 106, in singular or plural as 106), the microphone 108-1, 108-2……108-N (hereinafter referred as 108, in singular or plural as 108), and the camera 110-1, 110-2….110-N (hereinafter referred as 110, in singular or plural as 110), installed in the one or more vehicles 112-1….112-N (hereinafter referred as 112, in singular or plural as 112). The finger print scanner 104, the infotainment system 106, the microphone 108 and the camera 110 are communicatively coupled to each other and the vehicle 112 by means of Bluetooth, Wi-Fi, and the like. In one embodiment, the finger print scanner 104, the infotainment system 106, the microphone 108, and the camera 110 are configured to receive data from the vehicles 112 and transmit the data to system 102 for analysis. Further, the vehicles 112 may be communicatively coupled to the system 102 via the network 106. In one implementation, the system 102 may be communicatively coupled to the vehicle 112 and a Fast Identification Online (FIDO) server 114 via network 106. It may be noted that the FIDO server 114 may authenticate data received from the system 102 by a FIDO authentication methodology.
[025] In one implementation of the system 102 for generating a driving performance corresponding to a driver, the network 106 may be a wireless network, a wired network or a combination thereof. The network 106 can be implemented as one of the different types of networks, such as intranet, local area network (LAN), wide area network (WAN), the internet, and the like. The network 106 may either be a dedicated network or a shared network. The shared network represents an association of the different types of networks that use a variety of protocols, for example, Hypertext Transfer Protocol (HTTP), Transmission Control Protocol/Internet Protocol (TCP/IP), Wireless Application Protocol (WAP), and the like, to communicate with one another. Further the network 106 may include a variety of network devices, including routers, bridges, servers, computing devices, storage devices, and the like.
[026] Referring again to Figure 1, a network implementation 100 of a system 102 generating the driving performance corresponding to the driver is disclosed. In order to generate the driving performance corresponding to the driver, initially, the system 102 receives biometric data corresponding to a driver. In one aspect, the biometric data may comprise voice data, facial data, finger print data, iris data, and the like. In one embodiment, the finger print scanner 104 may receive the finger print data, the infotainment system 106 may receive facial data, and iris data, the microphone 108 may receive the voice data, and the camera 110 may receive iris data and the facial data. Once the biometric data is received, the system 102 authenticates the driver using a two-step authentication process to drive a vehicle. In one aspect, the two-step authentication process may comprise a first level authentication, by comparing the biometric data of the driver with the previously stored biometric data, and a second level authentication using a Fast Identification Online (FIDO) authentication methodology. In one embodiment, the system 102 may communicate to the FIDO server 114 via network 106 for FIDO authentication methodology. After authenticating the driver to drive the vehicle, the system 102 obtains driving data associated with the driver and vehicle data associated with the vehicle. Upon obtaining the driving data and the vehicle data, the system 102 generates the driving performance of the driver based on a predefined analytics model, the vehicle data, the driving data from the vehicle and the like.
[027] Referring now to Figure 2, the system 102 is illustrated in accordance with an embodiment for generating a driving performance corresponding to a driver. In one embodiment, the system 102 may include at least one processor 202, an input/output (I/O) interface 204, and a memory 206. The at least one processor 202 may be implemented as one or more microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, state machines, logic circuitries, and/or any devices that manipulate signals based on operational instructions. Among other capabilities, the at least one processor 202 is configured to fetch and execute computer-readable instructions stored in the memory 206.
[028] The I/O interface 204 may include a variety of software and hardware interfaces, for example, a web interface, a graphical user interface, and the like. The I/O interface 204 may allow the system 102 to interact with the user directly or through the client devices 104. Further, the I/O interface 204 may enable the system 102 to communicate with other computing devices, such as web servers and external data servers (not shown). The I/O interface 204 can facilitate multiple communications within a wide variety of networks and protocol types, including wired networks, for example, LAN, cable, etc., and wireless networks, such as WLAN, cellular, or satellite. The I/O interface 204 may include one or more ports for connecting a number of devices to one another or to another server.
[029] The memory 206 may include any computer-readable medium or computer program product known in the art including, for example, volatile memory, such as static random access memory (SRAM) and dynamic random access memory (DRAM), and/or non-volatile memory, such as read only memory (ROM), erasable programmable ROM, flash memories, hard disks, optical disks, cloud storages and magnetic tapes. The memory 206 may include modules 208 and data 210.
[030] The modules 208 include routines, programs, objects, components, data structures, etc., which perform particular tasks or implement particular abstract data types. In one implementation, the modules 208 may include a data receiving module 212, an authentication module 214, a data obtaining module 216, and a generation module 218, and other modules 220. The other modules 220 may include programs or coded instructions that supplement applications and functions of the system 102. The modules 208 described herein may be implemented as software modules that may be executed in the cloud-based computing environment of the system 102.
[031] The data 210, amongst other things, serves as a repository for storing data processed, received, and generated by one or more of the modules 208 of the system 102 for generating a driving performance corresponding to a driver. The data 210 may also include a central data 222, and other data 224. The other data 224 may include data generated as a result of the execution of one or more modules in the other modules 220.
[032] In one embodiment, in order to generate the driving performance, at first, the user may use the client device 104 to access the system 102 via the I/O interface 204. The user may register using the I/O interface 204 in order to use the system 102. In one aspect, the user may access the I/O interface 204 of the system 102. The system 102 may employ the data receiving module 212, the authentication module 214, the data obtaining module 216, and the generation module 218. The detail functioning of the modules is described below with the help of figure 2.
DATA RECEIVING MODULE 212
[033] In one embodiment, the data receiving module 212 receives biometric data corresponding to a driver. In one example, biometric data comprises voice data, facial data, finger print data, iris data and the like. Upon receiving the biometric data of the driver, the data receiving module 212 may compare the biometric data corresponding to the driver with a previously stored biometric data. In one example, if the biometric data corresponding to the driver is not registered with the system, the data receiving module 212 may register the driver by using a combination of one or more of the biometric data. In order to register the driver, initially, the data receiving module 212 may generate a private key for encryption and a private key for decryption. In one example, the public key and the private key is associated with the driver. Subsequent to the generation of the public key and the private key, the data receiving module 212 may transmit the public key to a Fast Identification Online (FIDO) server. Subsequent to the transmission of the public key, the data receiving module 212 may receive a unique identification number from a FIDO server. In one example, the FIDO server may validate the biometric data corresponding to the driver by using the FIDO authentication methodologies. It may be understood that the FIDO authentication methodologies may only work if the vehicle is connected to the FIDO sever via internet. In another example, the unique identification number is a registration number of the driver. In one other aspect, the unique identification number may be an acknowledgement of registration of the driver in the FIDO server. Upon receiving the unique identification number, the data receiving module 212 may map the unique identification number with the private key and the biometric data of the driver.
AUTHENTICATION MODULE 214
[034] In one embodiment, if the biometric data corresponding to the driver is registered in the system, the authentication module 214 authenticates the driver using a two-step authentication process to drive a vehicle. In one aspect, the vehicle is a connected vehicle and examples of the vehicle includes, but not limited to, a car, a bus, a truck, a bike, and alike. In one example, the two-step authentication process may comprise a first level authentication, by comparing the biometric data of the driver with the previously stored biometric data, and a second level authentication using a FIDO authentication methodology. During the FIDO authentication methodology, the authentication module 214 may encrypt the biometric data of the driver using the private key associated with the driver. Upon encrypting the biometric data, the authentication module 214 may associate the unique identification number of the driver with the encrypted biometric data. Further to associating the unique identification number of the driver with the encrypted biometric data, the authentication module 214 may transmit the unique identification number and the encrypted biometric data to the FIDO server. Upon transmitting the unique identification number and the encrypted biometric data, the authentication module 214 may receive the unique identification number and a decrypted biometric data from the FIDO server. In one example, the decryption of the biometric data is performed by using the unique identification number and the public key of the driver. In another example, the public key is associated with private key.
DATA OBTAINING MODULE 216
[035] In one embodiment, the data obtaining module 216 obtains driving data associated with the driver and vehicle data associated with the vehicle. In one example, the driving data may comprise a unique identification number of the driver, frequency of braking, force of braking, time of driving, duration of driving, number of turns, number of sudden turns, traffic data, and the like. In another example, the vehicle data may comprise vehicle registration number, speed data, braking frequency data, gear data, engine data, distance travelled, make and model of vehicle, manufacture of the vehicle, vehicle age, transmission gear, engine rpm accelerator pedal position, GPS location, emission data, route data and the like.
GENERATION MODULE 218
[036] In one embodiment, the generation module 218 generates a driving performance of the driver based on a predefined analytics model, the vehicle data, the driving data from the vehicle and the like. In one aspect, the generation module 218 may map the driving performance of the driver with one or more of the vehicles. Upon mapping the driving performance of the driver, the generation module 218 may publish a report of the driving performance of the driver on a web portal, a mobile application, an infotainment system and the like, installed in the vehicle. In one example, the report may comprise the unique identification number of the driver, the vehicle registration number and the like
[037] In one embodiment, the generation module 218 generates the report of the driving performance of the driver. In one example, the report may comprise parameters including, but not limited to, “unique identification number”, “total vehicle driven”, “aggressive rating”, “rating on safe driving”, “remarks”, “driving performance”, “fuel efficiency rating”, “longest drive”, “total number of trips during daytime”, “total number of trips during nighttime”, and the like. The generation module 218 may receive the “unique identification number” from the FIDO server for each of the drivers. In one example, the “unique identification number” indicates registration number of the driver. In another example, the “unique identification number” may be represented as a combination of alphabets, special characters, symbols, numbers and the like.
[038] In one embodiment, the generation module 218 generates the “total vehicle driven” where the “total vehicle driven” indicates number of the vehicle driven by the driver.
[039] In one embodiment, the generation module 218 genetrates the “aggressive rating” where the “aggressive rating” indicates a score of the driver based on a hard braking, a hard acceleration, an over speeding incidence, sharp turns, history of the driver and the like. In one example, the “aggressive rating” may be indicative of a good driving, a poor driving, and an average driving. The “aggressive rating” may be a rating from 1 to 5 where 1 indicates good driving and 5 indicates poor driving.
[040] In one embodiment, the generation module 218 generates the “rating on safe driving” where the “rating on safe driving” indicates a score of the driver based on total number of accidents, the total incidences of violating traffic rules and the like. In one example, the “rating on safe driving” may be indicative of a good driving, a poor driving, and an average driving. The “rating on safe driving” may be a rating from 1 to 5 where 1 indicates poor driving and 5 indicates good driving. It may be understood that the system may compute the “aggressive rating” and the “rating on safe driving” using the predefined analytics model. Examples of the predefined analytics model may be descriptive analytics model, predictive analytics model, prescriptive analytics model and the like.
[041] In one embodiment, the generation module 218 generates “the driving performance” where the “the driving performance” is an indicative of a good driver, a bad driver and an average driver. The driving performance may be generated as a rating from 1 to 5.
[042] In another embodiment, the generation module 218 generates the “fuel efficiency rating” where the “fuel efficiency rating” indicates the “driving performance” of the driver. In one example, the “fuel efficiency rating” corresponding to the driver may be a rating from 1 to 5 where 1 indicates high fuel efficiency rating of the driver and 5 indicates low fuel efficiency rating of the driver.
[043] In another embodiment, the generation module 218 generates the “longest drive” indicating maximum distance driven by the driver in one stretch.
[044] In other embodiment, the generation module 218 generates the “total number of trips during daytime” indicating total number of trips driven by the driver during the day.
[045] In other embodiment, the generation module 218 generates the “total number of trips during nighttime” indicating total number of trips driven by the driver during the night.
[046] In another embodiment, the generation module 218 generates the “remarks” where the “remarks” indicates the “driving performance” of the driver. In one example, the “remarks” for the driving performance of rating 1 may be indicated as the bad driver and for the driving performance of rating 5 may be indicated as the good driver.
[047] In one embodiment, the unique identification number of the driver may be mapped to the driving performance of the driver and number of trips driven by the driver with one or more vehicles. Further, the driving performance of the driver registered in one or more of the vehicles may be generated by computing an average of the driving performance of the driver for each trip of each of the vehicles.
[048] In one embodiment to explain the functioning of the aforementioned modules, consider an example where “John” intends to drive a POLOTM of “Joshua”. The POLOTM comprise an In-Vehicle Infotainment (IVI) system, a finger print scanner, a microphone, an infotainment system, a camera, sensors, and alike. The POLOTM is a connected vehicle and is connected to the FIDO server via Wi-Fi or 3G/4G/5G network. Initially, the data receiving module 212 receives biometric data corresponding to John. The biometric data comprise voice data, facial data, finger print data, and iris data and the like. If John is registered with the system installed in the Joshua’s POLOTM, John may be granted access to drive the vehicle. In one aspect, if John is not registered with the system installed in the Joshua’s POLOTM, the system may not permit John to derive the vehicle. In this scenario, the system may send alert to Joshua. Upon seeking Joshua’s permission, the data receiving module 212 may register John based on the biometric data corresponding to John. Upon registration, the data receiving module 212 receives a unique identification number corresponding to John.
[049] Further to receiving the biometric data, the authentication module 214authenticates John using a two-step authentication process. At the first level authentication, the system compares the biometric data with the data stored in the central data 222. At the second level authentication, the authentication module 214autheticates John using the FIDO authentication methodology. Further to authentication of John to drive the vehicle, the data obtaining module 216 receives driving data associated with John and vehicle data associated with the POLOTM in real-time.
[050] Further to obtaining the driving data and the vehicle data, the generation module 218 generates the driving performance of John based on the predefined analytics model, the vehicle data of the POLOTM, the driving data of John and the like. The generation module 218 may generate a report corresponding to the driving performance of John. Further, the system may notify John about the driving performance via an email, a text message, a multimedia message, a social media posts and the like.
[051] In one other example, a report for driving performance of drivers “John”, “Joshua”, and “Jenny” may be generated as below:

Table 1: Comparison of the driving performance of the drivers
Name Unique Identification Number Total Vehicle Driven Aggressive Rating Rating on Safe Driving Driving Performance Remarks
John Jk12345 10 1 5 5 Good
Joshua Jo12345 3 3 5 3 Average
Jenny Ji12345 1 5 2 1 Poor

[052] In reference to the Table 1, it is evident that the driver “John” has a good driving performance as compared to “Joshua” and “Jenny”.
[053] In one example, the driving performance of the driver may be referred by the government authorities, and insurance companies. In one aspect, the government authorities may warn the driver about the bad driving performance. In other aspect, the insurance companies may categorize the driver with the bad driving performance into a “high risk entity” where the driver with the good driving performance may be categorized to into a “low risk entity”.
[054] In one example, the aforementioned system may provide real time alerts about the driving performance of the driver for each vehicle to an owner of a cab service company.
[055] In one example, the system may recommend the driver, in real time, to improve the driving performance. The recommendation may be generated by analyzing the driving data associated with the driver and the vehicle data associated with the vehicle. The recommendation may be sent via a dashboard of the vehicle, a text message, a multimedia message, email, and the like.
[056] In one example, the driving performance of the driver may also be referred for hiring the driver in a nearby geography.
[057] Referring now to Figure 3, a method 300 for generating a driving performance corresponding to a driver is shown, in accordance with an embodiment of the present subject matter. The method 300 may be described in the general context of computer executable instructions. Generally, computer executable instructions can include routines, programs, objects, components, data structures, procedures, modules, functions, etc., that perform particular functions or implement particular abstract data types. The method 300 may also be practiced in a distributed computing environment where functions are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, computer executable instructions may be located in both local and remote computer storage media, including memory storage devices.
[058] The order in which the method 300 is described is not intended to be construed as a limitation, and any number of the described method blocks can be combined in any order to implement the method 300 or alternate methods. Additionally, individual blocks may be deleted from the method 300 without departing from the spirit and scope of the subject matter described herein. Furthermore, the method can be implemented in any suitable hardware, software, firmware, or combination thereof. However, for ease of explanation, in the embodiments described below, the method 300 may be considered to be implemented as described in the system 102.
[059] At block 302, biometric data corresponding to a driver is received. In one example, biometric data comprises one or more of voice data, facial data, finger print data, and iris data. In one implementation, the data receiving module 212 receives biometric data corresponding to a driver and store the biometric data in the central data 222.
[060] At block 304, the driver is authenticated using a two-step authentication process. In one example, the two-step authentication process may comprise a first level authentication, by comparing the biometric data of the driver with previously stored biometric data, and a second level authentication using a Fast Identification Online (FIDO) authentication methodology. In one implementation, the authentication module 214 authenticates the driver using a two-step authentication process to drive a vehicle and stores the unique identification number associated with the driver in the central data 222.
[061] At block 306, driving data associated with the driver and vehicle data associated with the vehicle is obtained. In one implementation, the data obtaining module 216 obtains driving data associated with the driver and vehicle data associated with the vehicle and stores the driving data and the vehicle data in the central data 222.
[062] At block 308, a driving performance of the driver is generated. In one implementation, the generation module 218 generates a driving performance of the driver based on one or more of a predefined analytics model, the vehicle data and the driving data from the vehicle and the driving performance is stored in the central data 222.
[063] Exemplary embodiments discussed above may provide certain advantages. Though not required to practice aspects of the disclosure, these advantages may include those provided by the following features.
[064] Some embodiments enable a system and a method to ensure the safety of the vehicle by permitting authenticated drivers to drive the vehicle.
[065] Some embodiments enable a system and a method to prevent theft of the vehicle.
[066] Some embodiments enable a system and a method to provide ratings to the driver based on the driving performance.
[067] Some embodiments enable a system and a method to compare drivers based on the driving performance.
[068] Although implementations for methods and systems for generating a driving performance corresponding to a driver have been described in language specific to structural features and/or methods, it is to be understood that the appended claims are not necessarily limited to the specific features or methods described. Rather, the specific features and methods are disclosed as examples of implementations for generating a driving performance corresponding to a driver.

Claims:1.A method for generating a driving performance corresponding to a driver, the method comprising:
receiving, by a processor (202), biometric data corresponding to a driver, wherein the biometric data comprises one or more of voice data, facial data, finger print data, and iris data;
authenticating, by the processor (202), the driver using a two-step authentication process to drive a vehicle, wherein the two step authentication process comprises
a first level authentication, by comparing the biometric data of the driver with previously stored biometric data, and
a second level authentication using a Fast Identification Online (FIDO) authentication methodology;
obtaining, by the processor (202), driving data associated with the driver and vehicle data associated with the vehicle; and
generating, by the processor (202), a driving performance of the driver based on one or more of a predefined analytics model, the vehicle data and the driving data from the vehicle.
2.The method of claim 1 further comprises registering, by the processor (202), the driver, wherein the registration comprises:
generating a private key for encryption and a public key for decryption, wherein the public key and the private key is associated with the driver;
transmitting the public key to a FIDO server;
receiving a unique identification number from the FIDO server, wherein the unique identification number is an indicative registration of the driver; and
mapping the unique identification number with the private key and the biometric data of the driver.
3.The method of claim 1, wherein the FIDO authentication methodology comprises
encrypting, by the processor (202), the biometric data of the driver using the private key associated with the driver;
associating, by the processor (202), the unique identification number of the driver with the encrypted biometric data;
transmitting, by the processor (202), the unique identification number and the encrypted biometric data to the FIDO server; and
receiving, by the processor (202), the unique identification number and a decrypted biometric data from the FIDO server, wherein the decryption of the biometric data is performed by using the unique identification number and the public key of the driver, and wherein the public key is associated with private key.
4.The method of claim 1 further comprises
mapping, by the processor (202), the driving performance of the driver with one or more of the vehicles; and
publishing, by the processor (202), a report of the driving performance of the driver on one or more of a web portal, a mobile application or an infotainment system installed in the vehicle, wherein the report comprises at least one of the unique identification number of the driver, and the vehicle registration number.
5.The method of claim 1, wherein the vehicle data comprise one or more of vehicle registration number, speed data, braking frequency data, gear data, engine data, distance travelled, make and model of vehicle, manufacture of the vehicle, vehicle age, transmission gear, engine rpm accelerator pedal position, GPS location, emission data and route data.
6.The method of claim 1, wherein the driving data comprise one or more of a unique identification number of the driver, frequency of braking, force of braking, time of driving, duration of driving, number of turns, number of sudden turns, and traffic data.
7.The method of claim 1, wherein the driving performance is an indicative of one of a good driver, a bad driver, and an average driver.

8.A system for generating a driving performance corresponding to a driver, the system comprising:
a memory (206); and
a processor (202) coupled to the memory (206), wherein the processor (202) is capable of executing a plurality of modules stored in the memory (206), and wherein the plurality of modules comprising:
a data receiving module (212) for receiving biometric data corresponding to a driver, wherein the biometric data comprises one or more of voice data, facial data, finger print data, and iris data;
an authentication module (214) for authenticating the driver using a two-step authentication process to drive a vehicle, wherein the two step authentication process comprises
a first level authentication, by comparing the biometric data of the driver with previously stored biometric data, and
a second level authentication using a Fast Identification Online (FIDO) authentication methodology;
a data obtaining module (216) for obtaining driving data associated with the driver and vehicle data associated with the vehicle; and
a generation module (218) for generating a driving performance of the driver based on one or more of a predefined analytics model, the vehicle data and the driving data from the vehicle.
9.The system of claim 8 further comprises registering the driver, wherein the registration comprises:
generating a private key for encryption and a public key for decryption, wherein the public key and the private key is associated with the driver;
transmitting the public key to a FIDO server; and
receiving a unique identification number from the FIDO server, wherein the unique identification number is an indicative registration of the driver; and
mapping the unique identification number with the private key and the biometric data of the driver.
10.The system of claim 8, wherein the FIDO authentication methodology comprises
encrypting, by the processor, the biometric data of the driver using the private key associated with the driver;
associating, by the processor, the unique identification number of the driver with the encrypted biometric data;
transmitting, by the processor, the unique identification number and the encrypted biometric data to the FIDO server; and
receiving, by the processor, the unique identification number and a decrypted biometric data from the FIDO server, wherein the decryption of the biometric data is performed by using the unique identification number and the public key of the driver, and wherein the public key is associated with private key.
11.The system of claim 8 further comprises
mapping, by the processor, the driving performance of the driver with one or more of the vehicles; and
publishing, by the processor, a report of the driving performance of the driver on one or more of a web portal, a mobile application or an infotainment system installed in the vehicle, wherein the report comprises at least one of the unique identification number of the driver, and the vehicle registration number.
12.The system of claim 8, wherein the vehicle data comprise one or more of vehicle registration number, speed data, braking frequency data, gear data, engine data, distance travelled, make and model of vehicle, manufacture of the vehicle, vehicle age, transmission gear, engine rpm accelerator pedal position, GPS location, emission data and route data.
13.The system of claim 8, wherein the driving data comprise one or more of a unique identification number of the driver, frequency of braking, force of braking, time of driving, duration of driving, number of turns, number of sudden turns, and traffic data.
14. The system of claim 8, wherein the driving performance is an indicative of one of a good driver, a bad driver, and an average driver.
15.A non-transitory computer readable medium embodying a program executable in a computing device for generating a driving performance corresponding to a driver, the program comprising a program code:
a program code for receiving biometric data corresponding to a driver, wherein the biometric data comprises one or more of voice data, facial data, finger print data, and iris data;
a program code for authenticating the driver using a two-step authentication process to drive a vehicle, wherein the two step authentication process comprises
a first level authentication, by comparing the biometric data of the driver with previously stored biometric data, and
a second level authentication using a Fast Identification Online (FIDO) authentication methodology;
a program code for obtaining driving data associated with the driver and vehicle data associated with the vehicle; and
a program code for generating a driving performance of the driver based on one or more of a predefined analytics model, the vehicle data and the driving data from the vehicle.