Computing system with driver behavior detection mechanism and method of operation thereof

1. A method of operating a computing system comprising:

generating a filtered data set based on a data set;

grouping the filtered data set based on a previous origin indicator to obtain a device group data;

generating a label based on the device group data;

generating a training moving average based on the label and the device group data;

generating an operating pattern based on the training moving average;

identifying a change in a driving behavior based on comparing the operating pattern and a detection moving average;

generating a notification based on the change; and

sending the notification for display on a display interface.

2. The method as claimed in claim 1 wherein generating the operating pattern includes grouping the training moving average based on K-Means Clustering.

3. The method as claimed in claim 1 wherein generating the label includes:

generating a deviation speed profile for the device group data based on a previous average speed and a known speed limit for one or more segments of one or more paths traversed during consecutive trips;

calculating a proximity score based on the deviation speed profile;

determining whether the proximity score is greater than a proximity threshold value; and

generating the label based on the proximity score being greater than the proximity threshold value.

4. The method as claimed in claim 1 wherein generating the label includes:

generating an acceleration speed profile for the device group data based on a previous sum of acceleration force values during consecutive trips;

calculating a proximity score based on the acceleration speed profile;

determining whether the proximity score is greater than a proximity threshold value; and

generating the label based on the proximity score being greater than the proximity threshold value.

5. The method as claimed in claim 1 further comprising normalizing the device group data based on a previous trip length to compare the device group data based on the previous trip length.

6. The method as claimed in claim 1 further comprising rescaling the training moving average based on a scale from [0, 1] to compare the training moving average based on the scale.

7. The method as claimed in claim 1 wherein sending the notification for display on the display interface includes sending the notification based on a decision window.

8. A computing system comprising:

a control unit configured to:

generate a filtered data set based on a data set;

group the filtered data set based on a previous origin indicator to obtain a device group data;

generate a label based on the device group data;

generate a training moving average based on the label and the device group data;

generate an operating pattern based on the training moving average;

identify a change in a driving behavior based on comparing the operating pattern and a detection moving average;

generate a notification based on the change; and

a communication unit, coupled to the control unit, configured to:

send the notification for display on a display interface.

9. The computing system as claimed in claim 8 wherein the control unit is further configured to group the training moving average based on K-Means Clustering.

10. The computing system as claimed in claim 8 wherein the control unit is further configured to:

generate a deviation speed profile for the device group data based on a previous average speed and a known speed limit for one or more segments of one or more paths traversed during consecutive trips;

calculate a proximity score based on the deviation speed profile;

determine whether the proximity score is greater than a proximity threshold value; and

generate the label based on the proximity score being greater than the proximity threshold value.

11. The computing system as claimed in claim 8 wherein the control unit is further configured to:

generate an acceleration speed profile for the device group data based on a previous sum of acceleration force values during consecutive trips;

calculate a proximity score based on the acceleration speed profile;

determine whether the proximity score is greater than a proximity threshold value; and

generate the label based on the proximity score being greater than the proximity threshold value.

12. The computing system as claimed in claim 8 wherein the control unit is further configured to normalize the device group data based on a previous trip length to compare the device group data based on the previous trip length.

13. The computing system as claimed in claim 8 wherein the control unit is further configured to rescale the training moving average based on a scale from [0, 1] to compare the training moving average based on the scale.

14. A non-transitory computer readable medium including instructions for operating a computing system comprising:

generating a filtered data set based on a data set;

grouping the filtered data set based on a previous origin indicator to obtain a device group data;

generating a label based on the device group data;

generating a training moving average based on the label and the device group data;

generating an operating pattern based on the training moving average;

identifying a change in a driving behavior based on comparing the operating pattern and a detection moving average;

generating a notification based on the change; and

sending the notification for display on a display interface.

15. The non-transitory computer readable medium as claimed in claim 14 with instructions wherein generating the operating pattern includes grouping the training moving average based on K-Means Clustering.

16. The non-transitory computer readable medium as claimed in claim 14 with instructions wherein generating the label includes:

generating a deviation speed profile for the device group data based on a previous average speed and a known speed limit for one or more segments of one or more paths traversed during consecutive trips;

calculating a proximity score based on the deviation speed profile;

determining whether the proximity score is greater than a proximity threshold value; and

generating the label based on the proximity score being greater than the proximity threshold value.

17. The non-transitory computer readable medium as claimed in claim 14 with instructions wherein generating the label includes:

generating an acceleration speed profile for the device group data based on a previous sum of acceleration force values during consecutive trips;

calculating a proximity score based on the acceleration speed profile;

determining whether the proximity score is greater than a proximity threshold value; and

generating the label based on the proximity score being greater than the proximity threshold value.

18. The non-transitory computer readable medium as claimed in claim 14 with instructions further comprising normalizing the device group data based on a previous trip length to compare the device group data based on the previous trip length.

19. The non-transitory computer readable medium as claimed in claim 14 with instructions further rescaling the training moving average based on a scale from [0, 1] to compare the training moving average based on the scale.

20. The non-transitory computer readable medium as claimed in claim 14 with instructions wherein sending the notification for display on the display interface includes sending the notification based on a decision window.