METHOD AND APPARATUS FOR 3-D ACCELEROMTER BASED SLOPE DETERMINATION, REAL-TIME VEHICLE MASS DETERMINATION, AND VEHICLE EFFICIENCY ANALYSIS

• US 20130184964A1
• Filed: 12/21/2012
• Published: 07/18/2013
• Est. Priority Date: 12/23/2011
• Status: Active Grant

• Current Assignee: Zonar Systems, Inc. (Continental AG)
• Original Assignee: Zonar Systems, Inc. (Continental AG)
• Inventors: Hunt, Bryan, McQuade, Charles Michael
• Granted Patent: US 9,170,913 B2
• Time in Patent Office: Days
• US Class Current: 701/101
• CPC Class Codes: B60W 2510/0638 Engine speed
• CPC Class Codes: B60W 2510/0657 Engine torque
• CPC Class Codes: B60W 2520/10 Longitudinal speed
• CPC Class Codes: B60W 2552/15 Road slope, i.e. the inclination of a road segment in the longitudinal direction
• CPC Class Codes: B60W 2556/45 External transmission of data to or from the vehicle
• CPC Class Codes: B60W 2556/50 of positioning data, e.g. GPS [Global Positioning System] data
• CPC Class Codes: B60W 40/09 Driving style or behaviour
• CPC Class Codes: G05B 15/02 electric
• CPC Class Codes: G06F 11/3024 where the computing system component is a central processing unit [CPU]
• CPC Class Codes: G06F 11/3065 Monitoring arrangements determined by the means or processing involved in reporting the monitored data error or fault reporting or logging G06F11/0766
• CPC Class Codes: G06F 17/00 Digital computing or data processing equipment or methods, specially adapted for specific functions information retrieval, database structures or file system structures therefor G06F16/00
• CPC Class Codes: G06F 17/11 for solving equations , e.g. nonlinear equations, general mathematical optimization problems optimization specially adapted for a specific administrative, business or logistic context G06Q10/04
• CPC Class Codes: G06Q 10/06398 Performance of employee with respect to a job function
• CPC Class Codes: G09B 19/167 Control of land vehicles

Abstract

Three dimensional accelerometer data is used to determine a slope the vehicle is traveling over at a specific point in time. The slope data can then be combined with other metrics to provide an accurate, slope corrected vehicle mass. The vehicle mass can then be used along with other vehicle data to determine an amount of work performed by a vehicle, enabling s detailed efficiency analysis of the vehicle to be performed. To calculate slope, horizontal ground speed (V_HGS) can be calculated using the Pythagorean Theorem. One can take the Z/Up magnitude and divide it by the horizontal ground speed. Replacing Z, x and y with directional vectors enables one to calculate slope. The slope data is then used to determine the mass of the vehicle at that time. Pervious techniques to calculate mass did not factor in slope, and thus are not accurate.

Claims

1. A method of evaluating driver performance using a road surface slope metric derived from data that includes 3-D accelerometer data, comprising the steps of:

(a) while the driver is operating the vehicle, automatically collecting vehicle data, the vehicle data including;

(i) 3-D accelerometer data;

(ii) vehicle speed data;

(iii) torque data; and

(iv) engine speed data;

(b) using a first computing device to derive the road surface slope metric using vehicle position data;

(c) using the first computing device to determine a mass of the vehicle at a specific point in time, based on the road surface slope metric, vehicle speed data, torque data, and engine speed data corresponding to that point in time; and

(d) using a second computing device to evaluate driver performance using a plurality of factors, at least one of which is derived from the slope determined mass of the vehicle.

2. The method of claim 1, wherein the step of using the first computing device to derive the road surface slope metric using vehicle position data comprises the steps of:

(a) using the 3-D accelerometer data to determine horizontal ground speed; and

(b) using the horizontal ground speed and an up directional vector from the 3-D accelerometer data to determine the road surface slope metric.

3. The method of claim 1, wherein the step of using the vehicle mass derived from the road surface slope metric as one of the plurality of factors comprises the step of producing a cost per loaded mile performance metric.

4. The method of claim 1, wherein the step of using the first computing device to derive the road surface slope metric and the mass comprises the step of using a computing device disposed at the vehicle.

5. The method of claim 1, wherein the first and second computing devices are the same computing device.

6. The method of claim 5, further comprising the step of wirelessly transmitting data from the vehicle to the first computing device, the first computing device being disposed remote from the vehicle.

7. A non-transitory memory medium having machine instructions stored thereon for carrying out steps (b) and (c) of claim 1.

8. The non-transitory memory medium of claim 7, further having machine instructions stored thereon for carrying out step (d) of claim 1.

9. The non-transitory memory medium of claim 7, further having machine instructions stored thereon for carrying out the step of claim 2.

10. The non-transitory memory medium of claim 7, further having machine instructions stored thereon for carrying out the step of claim 3.

11. A method of producing a performance metric indicative of a performance of a driver using at least in part slope data derived from 3-D accelerometer data, the method comprising the steps of:

(a) while the driver is operating the vehicle, automatically collecting time indexed vehicle data, the vehicle data comprising;

(i) the 3-D accelerometer data;

(ii) vehicle speed data;

(iii) torque data; and

(iv) engine speed data;

(b) using a computing device to determine a slope the vehicle is traveling over at a specific point in time using changes in the 3-D accelerometer data;

(c) using a computing device to determine a mass of the vehicle at a specific point in time using the slope, vehicle speed data, torque data, and engine speed data for the specific point in time;

(d) using a computing device to produce the performance metric based at least in part on the mass of the vehicle, the mass having been determined using slope data derived from the 3-D accelerometer data.

12. The method of claim 11, wherein the step of using the computing device to determine the slope the vehicle is traveling over at a specific point in time comprises the steps of:

(a) determining a horizontal ground speed of the vehicle at a specific point in time based on the 3-D accelerometer data; and

;

(b) determining the slope the vehicle is traveling over at that point in time, based on the horizontal ground speed and Z axis 3-D accelerometer data at that point in time.

13. The method of claim 11, wherein the step of using the computing device to produce the performance metric comprises the step of producing a cost per loaded mile performance metric.

14. The method of claim 11, wherein the computing device used to determine slope and mass is disposed at the vehicle.

15. The method of claim 14, wherein the computing device used to determine the performance metric is remote from the vehicle, and further comprising the step of conveying the vehicle data, the slope, and vehicle mass from the vehicle to the remote computing device before the step of using the computing device to produce the performance metric.

16. The method of claim 11, wherein the computing device used to determine the slope, the mass, and the performance metric is remote from the vehicle, and further comprising the step of conveying the vehicle data from the vehicle to the remote computing device before the steps of using the computing device to determine the slope, using the computing device to determine the mass, and using the computing device to produce the performance metric.

17. A non-transitory memory medium having machine instructions stored thereon for carrying out step (b) and (c) of claim 11.

18. The non-transitory memory medium of claim 17, further having machine instructions stored thereon for carrying out step (d) of claim 11.

19. The non-transitory memory medium of claim 17, further having machine instructions stored thereon for carrying out steps (a) and (b) of claim 11.