Method and apparatus for detecting presence of vehicle using a magnetic sensor employing a magneto-resistive effect

• US 7,427,931 B1
• Filed: 03/29/2007
• Issued: 09/23/2008
• Est. Priority Date: 03/29/2007
• Status: Active Grant

• Current Assignee: Sensys Networks Inc. (Tagmaster AB)
• Original Assignee: Sensys Networks Inc. (Tagmaster AB)
• Inventors: Kavaler, Robert, Kwong, Karric
• Primary Examiner(s): Crosland; Donnie L
• Application Number:
  US11/693,396
• Publication Number:
  US 20080243366A1
• Time in Patent Office: 544 Days
• Field of Search: 340/941, 340/933
• US Class Current: 340/941
• CPC Class Codes: G08G 1/042 using inductive or magnetic detectors

Abstract

Method using raw signal from magneto-resistive sensor through the use of recent variance (RV) of raw signal (RS) for first-capture of first time RV crosses variance detect, second-capture start enable for first time when RS crosses above raw detect and RV above variance detect, third-capture ending time when RS crosses below raw undetect and RV below variance undetect. Starting and ending times are products of the process, often used for traffic flow counts. Apparatus supporting this method as a processor and/or a vehicular sensor node.

Claims

1. A method, comprising the steps:

using a raw signal received from a magnetic sensor to create a starting time and an ending time for a vehicle passing near a magnetic sensor, said magnetic sensor is a magneto-resistive sensor stimulated by said motion of said vehicle near said magnetic sensor to create said raw signal, further comprising the steps;

capturing a first time from a current time provided by a clock, when a recent variance of said raw signal crosses above a variance detect;

generating said starting time from said first time when said raw signal crosses above a raw detect and said recent variance of a raw threshold is above said variance detect; and

capturing said ending time from said current time when said recent variance of said raw signal crosses below a variance undetect while said raw signal is below a raw undetect.

2. The method of claim 1, wherein the step using said raw signal, further comprises at least one member of the group consisting of the steps:

amplifying a raw magnetic sensor signal further received from said magnetic sensor to create an amplified signal for generating said raw signal;

digitizing said raw magnetic sensor signal with a first analog-to-digital converter to create said raw signal; and

digitizing said amplified magnetic sensor signal to create said raw signal.

3. The method of claim 1, wherein said recent variance of said raw signal is a variance of said raw signal over a first time-window for capturing said first time and said starting time;

and

wherein said recent variance of said raw signal is said variance of said raw signal over a second time-window for capturing said ending time.

4. The method of claim 3, wherein said first time-window is essentially the same time duration as said second time-window.

5. The method of claim 3, wherein said raw signal, includes:

a X-axis signal in a predominant direction of flow for said vehicle'"'"'s motion;

a Z-axis signal in a direction perpendicular to a pavement said vehicle moves on; and

a Y-axis signal in said direction perpendicular to said predominant direction in the plane of said pavement.

6. The method of claim 5, wherein the step capturing said first time, further comprises the step:

capturing said first time when said variance of said first time-window of said Z-axis signal crossing above said variance detect to create said first time;

wherein the step generating said starting time, further comprises the step;

generating said starting time from said first time when said Z-axis signal crosses above a raw detect and said recent variance of said raw threshold is above said variance detect to create said starting time;

wherein the step capturing said ending time, further comprises the step;

capturing said ending time when said variance of said second time-window of said Z-axis signal crossing below said variance undetect while said Z-axis signal is below said raw undetect in said second time-window to create said ending time.

7. The method of claim 6, wherein the step capturing said ending time, further comprises the step:

capturing said ending time

when said variance of said second time-window of said Z-axis signal crosses below said variance undetect

and when said Z-axis signal crosses below said raw undetect.

8. The method of claim 6, further comprising the steps:

averaging said Z-axis signal to create a moving average signal;

using said moving average signal to create a moving minimum peak signal and a moving maximum peak signal; and

subtracting said moving minimum peak signal from said moving maximum peak signal to create a processed signal;

wherein the step capturing said first time, further comprises the step;

first determining said first time when said processed signal crosses above said variance detect while said Z-axis signal is above said raw detect in said first time-window; and

wherein the step capturing said ending time, further comprises the step;

second determining said ending time when said processed signal crossing below said variance undetect while said Z-axis signal is below said raw undetect in said second time-window.

9. The method of claim 8, wherein the step averaging said Z axis signal, further comprises at least one member of the group consisting of the steps:

averaging said Z-axis signal over a succession of time windows to create said moving average signal;

low pass filtering said Z-axis signal with a time constant less than one second to create said moving average signal; and

weighted averaging using a finite impulse response filter said Z-axis signal to create said moving average signal.

10. The method of claim 9, wherein said succession of said time windows is a succession of non-overlapping time windows.

11. The method of claim 9, wherein said succession of said time windows is a succession of overlapping time windows.

12. The method of claim 9, wherein said time windows in said succession of time windows are all of approximately the same length.

13. The method of claim 9, wherein the step of averaging, further comprises the step of:

averaging at least one sample of said Z-axis signal over at least two of said succession of said time windows.

14. A vehicular sensor node implementing the method of claim 1, comprising:

a processor receiving said raw signal through a communicative coupling to said magnetic sensor to create said start enable and said ending time for said vehicle passing near said magnetic sensor.

15. The vehicular sensor node of claim 14, wherein said processor receiving said raw signal, further comprises:

said processor capturing said first time based upon said variance of said first time-window of said raw signal and based upon said raw signal; and

said processor capturing said ending time based upon said variance of said first time-window of said raw signal and based upon said raw signal.

16. The vehicular sensor node of claim 14, wherein said processor includes at least one instance of a controller;

wherein each of said controllers receives at least one input, maintains and updates the vale of at least one state and generates output based upon at least one member of the group consisting of;

said inputs, and said value of at least one of said states;

wherein at least one of said states includes at least one member of the group, consisting of;

a non-redundant digital representation, a redundant digital representation of said non-redundant digital representation, and an analog representation;

wherein said redundant digital representation of said non-redundant digital representation includes at least one member of the group consisting of;

a numerically redundant representation, logically redundant representation, and an error controlled representation.

17. The vehicular sensor node of claim 16, wherein said controller includes at least one instance of at least one member of the group consisting of:

a finite state machine,

a computer directed by a program system and accessibly coupled to a memory,

a neural network,

an inferential engine, and

an analog component network;

wherein said computer includes at least one data processor and at least one instruction processor;

wherein each of said data processors is directed by at least one of said instruction processors; and

wherein said program system includes at least one program step residing in said memory.

18. The vehicular sensor node of claim 17, further comprising:

wherein said state represents at least the members of a minimal state group, consisting of;

a first state, a second state, a third state, a fourth state and a no-vehicle-present state.