Cloud-based driving score report generation system and method thereof

Abstract

To provide a useful driving score generation system.SOLUTION: A system includes a driving environment, one or more sensing devices within the driving environment, and a processor including an artificial neural network. The processor can be operable to generate and execute a plurality of stored instructions. The processor can be further operable to receive sensing information from the one or more sensing devices and generate a driving score report in response to the sensing information received from the one or more sensing devices. The driving score report includes a driving style score evaluation and an environmental-friendliness score evaluation. A cloud-based data sharing system and a method for generating the driving score report are also disclosed.SELECTED DRAWING: Figure 5

Description

[Technical Field]

[0001] The present disclosure relates to the field of communications technology, and more particularly to a cloud-based driving score report generation system for motor vehicle operators that uses artificial intelligence and machine learning processing to assign scores to motor vehicle operators that can be shared with motor vehicle operators or service providers. [Background technology]

[0002] Traditional driver scoring systems use limited sensing information from onboard sensors within a motor vehicle. Current data analysis focuses on vehicle network data related to vehicle trajectory, such as driving speed and acceleration, to determine how the motor vehicle is being driven. There is little to no processing of inertial measurement unit (IMU) sensors, and no fusion of data from driver behavior inputs from cameras, wearables, and personal devices. This lack of data is detrimental to the accuracy of predicting driver behavior or driving style.

[0003] Furthermore, existing scoring calculations are based on conventional algorithms that cannot easily scale to the increasing amount of data inputs / sensors in the vehicle. Therefore, existing systems and algorithms are not scalable, and it is not possible to simply include more sources of sensory information within existing systems to produce accurate results. Despite the above, such analysis of driver behavior and driving style is also beneficial to sharing economy businesses.

[0004] The background art discussion provided herein is intended to generally present the context for the present disclosure. The inventors' work, to the extent that it is described in this background art section, and aspects of the description that may not be admitted as prior art at the time of filing, are not admitted expressly or implicitly as prior art to the present disclosure. Summary of the Invention

[0005] The aim of the present disclosure is to remedy the problems as discussed in the background section by providing the subject matter of the independent claims.

[0006] Further objects of the present disclosure are set out in the attached dependent claims.

[0007] In one aspect of the present disclosure, a driving score report generation system is provided. The driving score report generation system may include a driving environment, one or more sensing devices in the driving environment, and a processor including an artificial neural network. The processor may be operable to generate and execute a plurality of stored instructions. The processor may be further operable to receive sensing information from the one or more sensing devices. The processor may be further operable to generate a driving score report in response to the sensing information received from the one or more sensing devices. The driving score report may include a driving style score rating and an environmental-friendliness score rating.

[0008] Advantageously, the driving score report generation system executes algorithms to process and generate motor vehicle operator driving score reports associated with sensed information collected from within the driving environment, and uses artificial intelligence and machine learning to generate reports indicating the operator's driving style and environmental friendliness score assessment to increase the speed of calculations and improve the accuracy of the score assessment.

[0009] In some embodiments, the processor may be operable to transmit information data including the driving score report to a driving score report server via a wireless communication network. The driving score report server may be operable to store information data including the driving score report received via the wireless communication network. The driving score report server may be operable to retrieve information data including the driving score report in response to one or more inputs by a requester.

[0010] Advantageously, the score generation system is capable of wireless communication with a wireless network so that the generated reports can be shared with service providers.

[0011] In some embodiments, the sensed information acquired by the processor may include an operational dataset including vehicle operational data related to the driving environment. In some embodiments, the sensed information acquired by the processor may include an operator health dataset including health data related to an operator operating in the driving environment.

[0012] Advantageously, the system processes and analyzes multiple sensory information from different sources, which may be from on-board sensing devices or wearables worn by the operator.

[0013] In some embodiments, the processor may be further operable to execute a plurality of instructions to cause the artificial neural network to perform a machine learning process to obtain a set of segmented parameters for processing the driving scoring driving style score evaluation and the environmental friendliness score evaluation.

[0014] In some embodiments, the set of segmented parameters can be selected from an operational dataset that includes a historical trajectory of the driving environment. In some embodiments, the set of segmented parameters can be selected from an operational dataset that includes a historical fuel consumption rate of the driving environment.

[0015] In some embodiments, the set of segmented parameters can be selected from an operational dataset including historical engine load values. In some embodiments, the set of segmented parameters can be selected from an operational dataset including historical gyroscope rates. In some embodiments, the set of segmented parameters can be selected from an operational dataset including historical accelerations. In some embodiments, the set of segmented parameters can be selected from an operational dataset including vehicle bus data. In some embodiments, the set of segmented parameters can be selected from an operational dataset including inertial measurement unit data.

[0016] The set of segmented parameters may be selected from an operator health dataset. In some embodiments, the operator health dataset may include a heart rate of the vehicle operator. In some embodiments, the operator health dataset may include a respiratory rate of the vehicle operator. In some embodiments, the operator health dataset may include a body temperature of the vehicle operator. In some embodiments, the operator health dataset may include a historical health record of the vehicle operator.

[0017] In some embodiments, the processor may be further operable to retrieve and receive the generated driving scoring report from the processor or to retrieve and receive the generated driving scoring report from a server. In some embodiments, the processor may be further operable to operate the artificial neural network in a training mode.

[0018] In one aspect of the present disclosure, a method for generating a driving score report is provided. The method can include acquiring a set of sensory datasets acquired within a driving environment by one or more sensing devices and generating, by a processor having an artificial neural network, a driving score report. The method can further include acquiring the set of sensory datasets acquired within the driving environment to generate a set of segmented parameters and generating the driving score report in response to the set of segmented parameters. The driving score report can include a driving style score rating and an environmental-friendliness score rating.

[0019] In some embodiments, the method may further include labeling, by the processor, the set of segmented parameters with a time factor. In some embodiments, the set of segmented parameters may include a vehicle operation dataset. In some embodiments, the set of segmented parameters may include a vehicle operator health dataset.

[0020] In some embodiments, in response to the set of segmented parameters, the method may further include executing a training mode for generating, by the artificial neural network, a score rating categorized according to driving style and driving characteristics related to environmental friendliness.

[0021] In one aspect of the present disclosure, a cloud-based data sharing system is provided. The cloud-based data sharing system may include a driving environment, a driving score report server, a client device, and a wireless communication network that wirelessly communicates with the driving environment, the driving score report server, and the client device. The client device may include a client module operable to receive one or more inputs from a requester. The one or more inputs from the requester define a data sharing service for the requester. In response to the one or more inputs received from the requester, a signal including the one or more inputs may be wirelessly transmitted to the wireless communication network, the signal carrying information data including a data sharing service request including a user identifier entered by the requester. The driving score report server may include a requester system, which may be operable to receive the data sharing service request from the wireless communication network and generate the data sharing service request from the client device. In response to the data sharing service request received by the requester system, the driving score report server may be operable to retrieve a stored driving score report from a user database associated with the user identifier entered by the request and wirelessly transmit the retrieved driving score report to the client device over the wireless communication network.

[0022] Advantageously, the foregoing aspects of the present disclosure enable driving score reports generated by the driving score reporting system to be shared with third party service providers via a wireless communication network.

[0023] In some embodiments, the requester may be a human user. In some embodiments, the requester may be a service provider. In some embodiments, the service provider may be an insurance policy provider. In some embodiments, the service provider may be a fleet management service provider. In some embodiments, the service provider may be a car rental service provider.

[0024] In one aspect of the present disclosure, a non-transitory computer-readable medium may be provided that may include stored computer-executable instructions that, when executed by a driving score report generation system, perform a method for generating a driving score report.

[0025] Other objects, features and characteristics, as well as the method of operation and function of the associated elements of construction, combination of parts and economy of manufacture, will become more apparent from a consideration of the following detailed description and the appended claims, taken in conjunction with the accompanying drawings, all of which form a part hereof. It should be understood that the detailed description and specific examples, while indicating non-limiting embodiments of the present disclosure, are for purposes of illustration only and are not intended to limit the scope of the present disclosure.

[0026] The present disclosure will become more fully understood from the detailed description and the accompanying drawings, wherein: [Brief explanation of the drawings]

[0027] [Figure 1] FIG. 1 illustrates a driving scoring system according to the present disclosure. [Figure 2] 1 is a flowchart illustrating a method for generating a driving score report according to the present disclosure. [Figure 3] FIG. 10 illustrates a table of detection information for the driving score evaluation system according to the present disclosure. [Figure 4] FIG. 1 illustrates an exemplary artificial neural network according to the present disclosure. [Figure 5]FIG. 1 illustrates an operating environment for a cloud-based data sharing system according to the present disclosure. DETAILED DESCRIPTION OF THE INVENTION

[0028] It should be understood that like numerals identify corresponding or similar elements throughout the several views. Although particular component configurations are disclosed and illustrated in these exemplary embodiments, it should be understood that other configurations can benefit from the teachings of the present disclosure.

[0029] Hereinafter, the terms "wearable" and / or "wearable" may refer to wearable technology, such as a smart watch, wearable medical monitor, skin patch, or smart tattoo, that includes flexible electronic components operable to obtain health information relevant to the wearer, which may include, but is not limited to, body temperature, fever rate. System 100

[0030] FIG. 1 of the accompanying drawings illustrates a driving scoring system 100 according to the present disclosure. The driving scoring system 100 may include a driving environment 102. In some embodiments, the driving environment 102 may be a motor vehicle. In some embodiments, the driving environment 102 may be a driving simulator. The driving environment 102 may include one or more sensing devices 108, 108′ operable to acquire a set of sensed information. Suitable types of sensing devices 108, 108′ may include sensors operable to acquire information or vehicle data related to vehicle operation and health data sets related to an operator of the driving environment 102. Suitable types of sensing devices 108, 108′ may include, but are not limited to, inertial measurement unit (IMU) sensors 110, engine load sensors 112, accelerometers 116, automated driving systems 118, in-vehicle monitoring systems 120, and wearables 124. Other types of sensing devices suitable for acquiring information related to vehicle operation, vehicle data, and / or health data may also be applicable.

[0031] The driving environment 102 may further include a processor 104 having an artificial neural network (ANN) 106. An example of such a processor 104 may be an artificial intelligence (AI) processing unit, also known as an AI system-on-chip, also known as an AI PU or AI SoC. The processor 104 may be operable to receive sensing information from one or more sensing devices 108, 108′ and generate a driving score report 126 in response to the sensing information received from the one or more sensing devices. The driving score report 126 may include a driving style score rating and an environmental friendliness score rating.

[0032] In some embodiments, an ANN may be trained to classify the sensed information obtained from the sensing devices 108, 108′. A suitable type of ANN for categorizing the obtained information may be a convolutional neural network (CNN) architecture 200 as shown in FIG. 2. Those skilled in the art will appreciate that other types of neural network architectures operable to classify or categorize information may be applicable. method 200

[0033] 2 is a flow chart illustrating a method 200 for generating a driving score report according to the present disclosure. At step 202, a collection of sensory data sets may be acquired within a driving environment by one or more sensing devices.

[0034] In step 204, a processor having an artificial neural network may be used to generate a driving score report. Method 200 may further include classifying the collection of sensory datasets acquired in the driving environment to generate a set of segmented parameters by the processor in step 206. The driving score report may be generated in response to the set of segmented parameters. A set of instructions may be stored in the memory of the processor (not shown) to execute an information processing algorithm to generate the set of segmented parameters. Optionally, pre-processing of the acquired dataset may be required, for example, removing duplicate or null data; the pre-processing step may be known as data cleaning to those skilled in the art of data processing or data analysis.

[0035] In step 206, the processor may perform classifying the collection of sensory data sets acquired within the driving environment to generate a set of segmented parameters. In response to the set of segmented parameters, a driving score report is generated, including a driving style score rating and an environmental friendliness score rating, as described in step 204.

[0036] The set of segmented parameters may be labeled by the processor with time factors in the next step 208 to achieve information processing. In some embodiments, the step 208 for labeling time factors may include, for example, labeling the data set by 500 msec / 1 sec for each row. In some embodiments, the step 208 for labeling time factors may further include labeling a 2 second time window. In some embodiments, the step 208 for labeling time factors may perform labeling of both time factors to provide accuracy in the information processing results.

[0037] In some embodiments, the set of segmented parameters may include a vehicle performance dataset obtained from a sensing device operable to receive vehicle performance sensing information. In some embodiments, the set of segmented parameters may include a vehicle performance dataset obtained from a sensing device operable to receive a health dataset associated with an operator operating in a driving environment.

[0038] The set of segmented parameters may be stored in the memory of the processor and retrievable by the processor when needed to generate the driving score report.

[0039] In some embodiments, the information processing algorithm may be implemented by an artificial neural network through a machine learning implementation. The artificial neural network may perform a training mode using a set of segmented parameters to generate a score categorized according to driving style and driving characteristics related to environmental friendliness. The training mode may be based on supervised learning algorithms and models to generate an accurate score of the operator's driving style based on a ranking of 1 to 10, with 1 being the lowest rank and 10 being the highest rank. A similar ranking mode may be applied to the environmental friendliness assessment score, which is also based on a ranking of 1 to 10, with 1 being the lowest rank and 10 being the highest rank.

[0040] The AI ​​training model used to execute the training mode may process data sets acquired from vehicle operation while the operator is operating in a driving environment, such as driving speed, revolutions per minute (rpm), engine load, fuel consumption, mass air flow (MAF) sensors, gyroscope sensors, and accelerometers, to determine vehicle operation, such as how fast the operator is driving in the driving environment, how frequently the brake system is operating, and fuel consumption rate correlated with driving speed. In some embodiments, the personal wearable may be applicable to acquire health data related to the operator, such as the operator's heart rate, body temperature, etc., to determine the operator's mood. As an example, when the operator is operating in a driving environment at a high speed, the acquired heart rate information may have a higher frequency.

[0041] In some embodiments, the collection of sensory data sets may further include vehicle subsystems such as navigation systems and automated driving systems for determining driving trajectories to ascertain how quickly an operator is maneuvering the driving environment.

[0042] A driving score rating can then be assigned, for example a low ranking score of 1 can indicate that the operator's driving style is reckless and that the environmental friendliness rating is also a low ranking score, for example between 1 and 3.

[0043] Similarly, if the fuel consumption rates of the dataset are consistent or normalized, the driving style may be considered economical and therefore the driving style rating may be average, for example a score rating of 5, and the environmental friendliness rating has a score of 5. In some embodiments, the environmental friendliness rating score may be correlated with the driving style rating, for example using fuel consumption rates and predictable carbon dioxide emissions depending on the fuel consumption rates of the driving environment based on the operator's driving style.

[0044] An AI dataset for running the training mode may consist of several columns, including vehicle speed, acceleration rate and frequency, deceleration rate and frequency, vehicle yaw, etc., all of which are sensory information necessary to determine the behavior of the driving environment, such as sudden changes in direction, steering actions, braking and acceleration of the driving environment, etc., and stimulate the operator's driving style. In some embodiments, each row may be represented by a time factor, such as a second-by-second snapshot of driving data collected by one or more sensing devices or sensory information acquisition systems. For each row, a score based on traditional algorithms and experts is updated (e.g., driving, braking, acceleration, etc.). This is further divided into segments, and a score is calculated for each segment.

[0045] 3 is a table of sensory information for a driving scoring system according to the present disclosure. The operational dataset 304 may include vehicle operational data related to the driving environment, such as vehicle network data, IMU sensor data, engine load data, fuel consumption data, accelerometer data, autonomous driving function data, on-board monitoring data, navigation system data, etc. The operator health dataset 306 may include sensory information from wearables, more specifically heart rate and temperature, as described above.

[0046] 1, the one or more pieces of sensory information 302 acquired by the processor 104 may include an operational dataset 304 and an operator health dataset 306. As described above, the acquired sensory information may be processed to generate a set of segmented parameters.

[0047] In some embodiments, the set of segmented parameters may include a history of trajectory of the driving environment. In some embodiments, the set of segmented parameters may include a history of specific fuel consumption of the driving environment. In some embodiments, the set of segmented parameters may include a history of engine load values. In some embodiments, the set of segmented parameters may include a history of gyroscope rates. In some embodiments, the set of segmented parameters may include a history of accelerations. In some embodiments, the set of segmented parameters may include vehicle bus data. In some embodiments, the set of segmented parameters may include inertial measurement unit data.

[0048] In some embodiments, the set of segmented parameters may include a heart rate of the vehicle operator. In some embodiments, the set of segmented parameters may include a respiratory rate of the vehicle operator. In some embodiments, the set of segmented parameters may include a body temperature of the vehicle operator. In some embodiments, the set of segmented parameters may include a historical health record of the vehicle operator. ANN Architecture 400

[0049] As mentioned above, a possible type of ANN can be a convolutional neural network (CNN) or any other type of ANN that allows for classification of information for machine learning purposes. As can be seen from FIG. 4 of the accompanying drawings, which illustrates an exemplary artificial neural network according to the present disclosure, an ANN architecture 400 can include a feature learning layer that can include an input layer 402 containing neurons for introducing initial data or data sets into the system for further processing by subsequent layers. In the next feature learning layer, the ANN architecture 400 can include a convolution and rectification or rectified linear unit (ReLu) layer 404, using a mathematical model to pass the results to successive layers. Beneficially, ReLu is an activation function that introduces nonlinear characteristics into machine learning or deep learning models to address the missing gradient problem.

[0050] The pooling layer 406 is operable to reduce the number of parameters for learning, and the amount of computation required in the neural network is reduced by summarizing the features generated by the previous layer, i.e., the convolutional and rectification layer 404.

[0051] Below the classification layer, a flattening layer 408 can collapse the spatial dimensions of the input from the previous layer, the pooling layer 406. Following the collapsed spatial dimensions of the data, the processed information from the pooling layer 406 passes through a fully connected layer 410, in which all inputs from the layer are connected to all activation units in the next layer to compile the data extracted from the previous layer and form the final output. The output layer 412 is the final layer of neurons that generates a given output for the program, i.e., to generate an output result. In some embodiments, the output result may be segmented parameters that can be used for further training of the ANN. In some embodiments, the output result may be a driving score report 126 including a driving style score rating and an environmental friendliness score rating as disclosed herein.

[0052] 5 illustrates an operating environment 500 of a cloud-based data sharing system according to the present disclosure. The operating environment 500 includes a wireless communication network 502, a driver score report server 504, and a driving environment 102. In some embodiments, the driving environment 102 is a motor vehicle. In some embodiments, the driving environment 102 is a driving simulator.

[0053] The operating environment 500 may include a client device 506 including a client module 508 operable to receive one or more inputs from a requestor 514. The requestor 514 may be a human, for example, a service provider such as a vehicle operator or a customer service representative. The one or more inputs from the requestor 514 define a data sharing service request for the requestor 514. In response to the one or more inputs received by the requestor 514, a signal carrying information data is wirelessly transmitted to the wireless communication network 502 including the data sharing service request. The data sharing service request may further include a user identifier entered by the requestor 514. The user identifier input may include information for identifying a user or operator of the driving environment 102 so that the generated driving score report can be retrieved.

[0054] The driving score report server 504 may include a requester system 522a, which may be operable to receive a data sharing service request from the wireless communication network 502 and generate a data sharing service request from the client device 506. In response to the data sharing service request received by the requester system 522a, the driving score report server 504 may be operable to retrieve a stored driving score report from the user database 512 associated with a user identifier entered by the requester 514 and wirelessly transmit the retrieved driving score report to the client device 506 over the wireless communication network 502.

[0055] In some embodiments, the service provider may be an insurance policy provider. In some embodiments, the service provider may be a fleet management service provider. In some embodiments, the service provider may be a car rental service provider. In embodiments including a service provider, the operating environment 500 may include a third-party data sharing server 530 having a memory 516 for storing and retrieving request history and a requester system 522b. The third-party data sharing server 530 may be operable to wirelessly transmit signals carrying information data over the wireless communication network 502 to retrieve the driving score evaluation reports 126a stored in the driving score report server 504.

[0056] In some embodiments, client device 506 may be a display panel 518 in driving environment 102. In some embodiments, client device 506 may be a mobile communication device such as a smartphone, tablet, personal digital assistant, laptop, desktop computer, etc. connected to wireless communication network 502.

[0057] In some embodiments, the driving environment 102 is a motor vehicle, the generated driving score report 126b may be wirelessly transmitted to the driving score report server 504. In some embodiments, the driving environment 102 is a motor vehicle, the generated driving score report 126b may be displayed on a display panel 518 for presentation to the operator. In some embodiments, the driving environment 102 is a motor vehicle, the generated driving score report 126b may be wirelessly transmitted to the driving score report server 504 over the wireless communication network 502 via a communication module 510 mounted on the motor vehicle. A suitable communication module 510 may be a telematics unit or an antenna. In some embodiments, the driving environment 102 is a motor vehicle, the sensing information obtained from one or more sensing devices 108, 108′ may be transmitted to the processor 104 over the vehicle bus network 520.

[0058] A major advantage of the present disclosure is the use of the driving score report for data sharing with service providers, for example, car rental service providers or insurance providers, to offer competitive pricing to members or consumers using the driving style rating and environmental friendliness rating as determining factors.

[0059] The foregoing description should be construed as illustrative, and not limiting. Those skilled in the art will recognize that certain modifications may fall within the scope of the present disclosure. Although different non-limiting embodiments are shown as having particular components or steps, embodiments of the present disclosure are not limited to combinations thereof. Some of the components or features from any of the non-limiting embodiments may be used in combination with features or components from any of the other non-limiting embodiments. For these reasons, the following claims should be studied to determine the true scope and content of the present disclosure. [Explanation of symbols]

[0060] 100 Driving Score Report Generation System 102 Driving Environment 104 processors 106 Artificial Neural Networks (ANN) 108 One or more sensing devices 110 Inertial Measurement Unit (IMU) Sensor 112 Engine load sensor 114 Fuel economy sensor 116 Accelerometer 118 Autonomous driving function 120 In-vehicle monitoring system 122 Navigation System 124 Wearables 126, 126a, 126b Driving Score Report 200 Flowchart 202 obtaining a set of sensing datasets within the driving environment; 204. Generating a driving score report using a processor with an ANN 206. Classifying a set of sensory data sets acquired within the driving environment to generate a set of segmented parameters. 208 Labeling the Time Factor 300 tables 302 one or more types of detection information 304 Motion Dataset 306 Operator Health Dataset 400 ANN Architecture 402 Input 404 CNN+ReLu layer 406 Pooling 408 Planarization layer 410 Fully Connected Layer 412 Output 500 Operating environment 502 Wireless Communication Network 504 Driving Score Server 506 Client Device 508 Client Module 510 Communication Module 512 Human User Profile Database 514 Requester 516 Third Party Requests 518 Display Panel 520 Vehicle Bus Network 522a, 522b Requester System

Claims

1. A driving score report generation system (100), comprising: A driving environment (102); one or more sensing devices (108, 108') within the operating environment (102); a processor (104) including an artificial neural network (106) and operable to generate and execute a plurality of stored instructions; The processor (104) receiving sensing information (302) from said one or more sensing devices (108, 108'); further operable to generate a driving score report (126, 126a, 126b) in response to the sensing information (302) received from the one or more sensing devices (108, 108'); The driving score report (126, 126a, 126b) Driving style score evaluation and Eco-friendliness score rating and A driving score report generation system (100) comprising:

2. the processor (104) is operable to transmit information data including the driving score report (126) to a driving score report server (504) via a wireless communication network (502); The driving score report server (504) storing the information data including the driving score reports (126, 126a, 126b) received via the wireless communication network (502); Operable to retrieve said information data including said driving score report (126, 126a, 126b) in response to one or more inputs by a requester (514); The system (100) of claim 1, characterized in that:

3. The sensed information obtained by the processor (104) a motion dataset (304) including vehicle motion data associated with the driving environment (102); an operator health dataset (306) including health data associated with an operator operating in the driving environment (102); 10. The system (514) of claim 1, comprising:

4. the processor (104) is further operable to execute the plurality of instructions to cause the artificial neural network (106) to perform a machine learning process to obtain a set of segmented parameters for processing the driving scoring driving style score evaluation and the environmental-friendliness score evaluation. A system (100) according to claim 1 or 2, characterized in that

5. The segmented parameter set is History of driving environment trajectory, Driving environment fuel economy history, engine load value history, Gyroscope rate history, Acceleration rate history, Vehicle bus data, Inertial Measurement Unit data, The system (100) of any one of claims 1 to 3, characterized in that the operational dataset (304) comprises:

6. The segmented parameter set is the vehicle operator's heart rate; the vehicle operator's respiratory rate; the body temperature of the vehicle operator; the vehicle operator's historical health records; or a combination thereof. A system (100) according to any one of claims 1 to 3, characterized in that

7. The processor (104) Retrieving and receiving the generated driving scoring report (126, 126a, 126b) from the processor (104) or retrieving and receiving the generated driving scoring report (126, 126a, 126b) from the driving score report server (504); 7. The system (100) of any one of claims 1 to 6, further operable to operate said artificial neural network (106) in a training mode.

8. A method (200) for generating a driving score report (126, 126a, 126b), the method (200) comprising: acquiring (202) a set of sensory data sets (302) acquired within a driving environment (102) by one or more sensing devices (108, 108'); generating (204) a driving score report (126, 126a, 126b) by a processor (104) having an artificial neural network (106); The method (200) classifying (206) the set of sensory data sets acquired within the driving environment (102) by the processor (104) to generate a set of segmented parameters; generating the driving score report (126, 126a, 126b) in response to the set of segmented parameters, the driving score report (126, 126a, 126b) comprising: Driving style score evaluation and and generating an environmental friendliness score rating.

9. The method comprises:

9. The method (200) of claim 8, further comprising labeling, by the processor, the segmented parameter set with a time factor.

10. The segmented parameter set is a vehicle motion dataset (304); Vehicle Operator Health Data Set (304); 10. The method according to claim 8 or 9, characterized in that it comprises:

11. in response to the set of segmented parameters; The artificial neural network (106) Driving style and Eco-friendliness score rating and 11. The method according to claim 8, further comprising: performing a training mode to generate a score rating categorized by driving characteristics associated with the driving characteristics.

12. A cloud-based data sharing system (500), comprising: A driving environment (102); A driving score report server (504); a client device (506); a wireless communication network (502) in wireless communication with the driving environment (102), the driving score report server (504), and the client device (506); The client device (506) receiving one or more inputs from a requestor (514), the one or more inputs from the requestor (514) defining a data sharing service for the request; a client module (508) operable to transmit wirelessly to the wireless communication network (502) in response to the one or more inputs received from the requestor (514); the data sharing service request includes a user identifier input; The driving score report server (504) includes a requester system (522a, 522b), the requester system (522a, 522b) Operable to receive the data sharing service request from the wireless communication network (502) and generate the data sharing service request from the client device (506); In response to the data sharing service request received by the requester system (522a, 522b), the driving score report server (504) retrieves a stored driving score report (126a) from a user database (512) associated with the user identifier; A data sharing system (500) operable to wirelessly transmit the retrieved driving score report (126, 126a, 126b) to the client device (506) over the wireless communication network (502).

13. The requester (514): Human users, Service providers, 13. The data sharing system (500) of claim 12, wherein the data sharing system (500) is a data sharing system that ....

14. The service provider: insurance policy providers; fleet management service providers, car rental service providers, 14. The data sharing system (500) of claim 13, wherein the data sharing system (500) is selected from the group consisting of: and combinations thereof.

15. A non-transitory computer-readable medium (104) having stored thereon computer-executable instructions that are executed by a driving score report (126, 126a, 126b) generating system (100) to perform a method (200) for generating a driving score report (126, 126a, 126b).

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