SYSTEM AND A METHOD FOR IMPROVED CAR PROGNOSIS

• US 20150051785A1
• Filed: 03/05/2013
• Published: 02/19/2015
• Est. Priority Date: 03/22/2012
• Status: Active Grant

• Current Assignee: TATA Consultancy Services Limited (Tata Sons Pvt Ltd.)
• Original Assignee: TATA Consultancy Services Limited (Tata Sons Pvt Ltd.)
• Inventors: Pal, Arpan, Bhaumik, Chirabrata, Sinha, Aniruddha, Chattopadhyay, Tanushyam, Ghosh, Kallol, Biswas, Provat, Sinharay, Arijit, Syed, Mohd Bilal Arif
• Granted Patent: US 9,767,622 B2
• Time in Patent Office: Days
• US Class Current: 701/29.1
• CPC Class Codes: B60W 2420/905 the sensor being an xyz axis sensor
• CPC Class Codes: B60W 2520/14 Yaw
• CPC Class Codes: B60W 2520/16 Pitch
• CPC Class Codes: B60W 2520/18 Roll
• CPC Class Codes: B60W 2552/00 Input parameters relating to infrastructure
• CPC Class Codes: B60W 2556/45 External transmission of data to or from the vehicle
• CPC Class Codes: B60W 40/06 Road conditions
• CPC Class Codes: B60W 50/14 Means for informing the driver, warning the driver or prompting a driver intervention
• CPC Class Codes: G01P 15/18 in two or more dimensions
• CPC Class Codes: G07C 5/008 communicating information to a remotely located station transmission systems for measured values G08C
• CPC Class Codes: G07C 5/0808 Diagnosing performance data testing of vehicles G01M17/00; testing of electrical installation on vehicles G01R31/005

Abstract

A system and method for a real-time prognosis of a vehicle comprising a personal communication device comprising an arbitrarily oriented three-axis accelerometer configured to capture a pitch motion and/or roll motion of the vehicle and an onboard diagnostics system communicably connected with the personal communication device enabling bi-directional communication. The personal communication device comprising a processor configured for geometric mapping of a three dimensional Cartesian coordinate of the three-axis accelerometer with the vehicle. The processor virtually orients the coordinates of three-axis accelerometer to coincide with the coordinates of the vehicle. The arbitrarily oriented three-axis accelerometer is configured to capture a road condition and a driver behaviour using a sampling rate between 4 Hertz (Hz) to 10 Hertz (Hz). The system for the real-time prognosis of the vehicle, wherein the real-time prognosis utilizes at least one predictive analysis model to determine real-time prognosis for the said vehicle.

Claims

1. A method for real-time prognosis of a vehicle using a three axis accelerometer embedded in a personal communication device, the method comprising:

orienting dynamically the three axis accelerometer from an arbitrary position in a Cartesian coordinate for each variation in a position of the personal communication device to coincide with a reference coordinate assigned to the vehicle;

capturing a first set of information relating to a road condition and a second set of information relating to a driver behaviour in a sample window and counting a number of sample recorded in the sample window using the dynamically oriented three axis accelerometer, wherein an instantaneous velocity of the vehicle determines the sample window;

receiving a plurality of sensory input from an onboard vehicle diagnostic system that is communicably coupled to the personal communication device;

compiling a plurality of prognosis report based on at least one predictive analysis model utilizing the first set of information and the second set of information captured in conjugation with the plurality of sensory inputs received; and

rendering the complied plurality of prognosis report determined by the attributes of a user.

2. The method for real-time prognosis of the vehicle of claim 1, wherein the orientation of the three axis accelerometer further comprising steps of:

assigning the reference coordinate (X, Y, Z) for the vehicle and storing said coordinates on a flash memory of the personal communication device;

assigning an initial coordinate to the three axis accelerometer (Xd, Yd, Zd) and storing said coordinates on the flash memory of the personal communication device;

computing a first intermediate coordinates (Xo, Yo, Zo) using at least three first scalar value, at least three second scalar value and at least three third scalar value;

computing a first degree derivative for the each first intermediate coordinates (Xo, Yo, Zo);

computing a second intermediate coordinates (Xog, Yog, Zog);

computing a roll (θ

) and pitch (Ψ

) between the reference coordinates (X, Y, Z) and the initial coordinates (Xd, Yd, Zd) respectively, of the three axis accelerometer;

computing an angle (α

) between the initial coordinate (Zd) and the reference coordinate Z( );

computing a corrected coordinate (Xcg, Ycg, Zcg) for the three axis accelerometer based on the first degree derivative for the each first intermediate coordinates (Xo, Yo, Zo); and

assigning the computed corrected coordinate (Xcg, Ycg, Zcg) at each next dynamic instance of occurrence of the event case with the personal communication device.

3. The method for real-time prognosis of the vehicle of claim 2, wherein the computing for intermediate coordinate (Xo, Yo, Zo) is performed by summing a product of the at least three first scalar value with at least three distinct components for Xo, the at least three second scalar value with the at least three distinct components for Yo and the at least three third scalar value with the at least three distinct components for Zo, wherein the first scalar value, the second scalar value and the third scalar value are a function of the roll (θ

), the pitch (Ψ

) and a yaw (Φ

) respectively.

4. The method for real-time prognosis of the vehicle of claim 3, wherein the at least three distinct components consist of a gravitational acceleration, change of rate of velocity and road condition.

5. The method for real-time detection for prognosis of the vehicle of claim 2, wherein the first degree of derivative for the each coordinates (Xo, Yo, Zo) is computed with respect to time.

6. The method for real-time detection for prognosis of the vehicle of claim 2, wherein the second intermediate coordinates (Xog, Yog, Zog) are estimated as a difference between the values of the first intermediate coordinates (Xo, Yo, Zo) and integrated value of the first degree of derivative of coordinate (Xo, Yo, Zo) respectively.

7. The method for real-time detection for prognosis of the vehicle of claim 2, wherein the roll (θ

) is computed as a sine inverse of summation of values of Yog divided by the product of gravitational acceleration and the number of sample recorded.

8. The method for real-time detection for prognosis of the vehicle of claim 2, wherein the pitch (Ψ

) is computed as a sine inverse of summation of values of Xog divided by the product of gravitational acceleration and the number of sample collected.

9. The method for real-time detection for prognosis of the vehicle of claim 2, wherein the angle (α

) is computed as a cosine inverse of summation of values of Zog divided by the product of gravitational acceleration and the number of sample recorded.

10. The method for real-time detection for prognosis of the vehicle of claim 8, wherein the corrected coordinate (Xcg, Ycg) for the three axis accelerometer is computed as a difference of respective values of the second intermediate coordinates (Xog, Yog) and product of the gravitational acceleration with sine function of the roll (θ

) and the pitch (Ψ

).

11. The method for real-time detection for prognosis of the vehicle of claim 9, wherein the corrected coordinate (Zcg) for the three axis accelerometer with the respective coordinate of the vehicle is computed as a summation of values of intermediates coordinates and product of gravitational acceleration with difference of 1 minus cosine α

.

12. The method for real-time detection for prognosis of the vehicle of claim 2, wherein the initial coordinate (Xd, Yd, Zd) of the three axis accelerometer are substituted with the corrected coordinates (Xcg, Ycg, Zcg) in the flash memory.

13. The method for real-time prognosis of the vehicle of claim 1, wherein the capturing of the first set of information relating to road condition further comprises of:

detecting a road anomaly; and

distinguishing between the different detected road anomalies.

14. The method for real-time prognosis of the vehicle of claim 13, wherein the road anomaly is detected by computing a rate change of acceleration on Z-axis followed by energy calculation in a time domain.

15. The method for real-time prognosis of the vehicle of claim 13, wherein the road anomaly detected is distinguished for a bump and a pothole by using a standard deviation of the ratio of summation of accelerations along x-axis and y-axis with acceleration along z-axis.

16. A system for a real-time prognosis of a vehicle comprising:

a personal communication device comprising an arbitrarily oriented three axis accelerometer configured to capture a pitch motion and/or roll motion of the vehicle, wherein the personal communication comprises;

a processor configured for geometric mapping of a three dimensional Cartesian co-ordinate of the three-axis accelerometer with the vehicle, wherein the processor is further configured to virtually orient the coordinates of three-axis accelerometer to coincide with the coordinates of the vehicle; and

an onboard diagnostics (OBD) system communicably connected with the personal communication device enabling bi-directional communication, wherein the onboard diagnostic system captures vehicular data.

17. The system for the real-time prognosis of the vehicle of claim 16, wherein the arbitrarily oriented three-axis accelerometer is configured to capture a road condition and a driver behaviour using a sampling rate between 4 Hertz (Hz) to 10 Hertz (Hz).

18. The system for the real-time prognosis of the vehicle of claim 17, wherein the road condition is captured by the three-axis accelerometer is converted into an understandable format utilizing an energy of jerk on a settling time vibration spectrum of the vehicle.

19. The system for the real-time prognosis of the vehicle of claim 16, wherein the real-time prognosis utilizes at least one predictive analysis model to determine real-time prognosis for the said vehicle.