Road Condition Monitoring System

Abstract

A vehicle has a traffic light preemption system with a GPS receiver and an Inertial Measurement Unit (IMU). A processor is configured to log GPS data in correlation with IMU data, and to detect and map road surface defects. The processor may be configured to identify and report unmapped roads, and to correlate the road surface defects with traffic load, road construction type, and/or environmental factors. The processor may also be configured to detect and monitor changes in the IMU data associated with a given road surface defect, and/or road surface changes precursor to the development of road surface defects. The processor may be further configured to correlate the effectiveness of repairs to road surface defects with traffic load, road construction type, repair type, repairing entity, and/or environmental factors.

Claims

1. A vehicle having a Road Condition Monitoring System, comprising: a traffic light preemption system having a GPS receiver and an Inertial Measurement Unit (IMU); at least one processor configured to log GPS data in correlation with IMU data, and to detect and map road surface defects; and the at least one processor being further configured to detect and monitor changes in the IMU data associated with a given road surface defect.

2. The vehicle of claim 1, wherein: the at least one processor being further configured to identify and report unmapped roads.

3. The vehicle of claim 1, wherein: the at least one processor being further configured to correlate changes in the road surface defects with at least one of traffic load, road construction type, and an environmental factor.

4. The vehicle of claim 1, wherein: the at least one processor being further configured to detect and monitor road surface changes and to predict the development of road surface defects using at least one of industry data concerning road construction, industry data concerning road deterioration, and specific local data concerning road construction and/or deterioration.

5. The vehicle of claim 4, wherein: the at least one processor being further configured to at least one of map predicted road surface defects and predict at least one characteristic of the predicted road surface defect.

6. The vehicle of claim 1, wherein: the at least one processor being further configured to monitor repairs to road surface defects.

7. The vehicle of claim 6, wherein: the at least one processor being further configured to correlate the effectiveness of repairs to road surface defects with at least one of traffic load, road construction type, repair type, repairing entity, and an environmental factor.

8. The vehicle of claim 6, wherein: the at least one processor being further configured to track the settling of an overfill type of road surface defect repair.

9. The vehicle of claim 1, wherein: the at least one processor being further configured to determine which roads have the highest frequency and/or severity of road surface defects; the at least one processor being further configured to accept at least one input including at least one of: a total repair budget, a cost per length to repave a road or a lane of a road, a cost per pothole for manual repair, traffic estimates for a road; and the at least one processor being further configured to calculate at least one cost and to recommend at least one possible repair strategy.

10. A Road Condition Monitoring System for use with a vehicle having a traffic light preemption system having a GPS receiver and an IMU, comprising: at least one processor configured to log GPS data in correlation with IMU data, and to detect and map road surface defects, the at least one processor being further configured to detect and monitor changes in the IMU data associated with a given road surface defect.

11. The Road Condition Monitoring System of claim 10, wherein: the at least one processor being further configured to identify and report unmapped roads.

12. The Road Condition Monitoring System of claim 10, wherein: the at least one processor being further configured to correlate changes in the road surface defects with at least one of traffic load, road construction type, and an environmental factor.

13. The Road Condition Monitoring System of claim 10, wherein: the at least one processor being further configured to detect and monitor road surface changes and to predict the development of road surface defects using at least one of industry data concerning road construction, industry data concerning road deterioration, and specific local data concerning road construction and/or deterioration.

14. The Road Condition Monitoring System of claim 10, wherein: the at least one processor being further configured to at least one of map predicted road surface defects and predict at least one characteristic of the predicted road surface defect.

15. The Road Condition Monitoring System of claim 10, wherein: the at least one processor being further configured to monitor repairs to road surface defects.

16. The Road Condition Monitoring System of claim 15, wherein: the at least one processor being further configured to correlate the effectiveness of repairs to road surface defects with at least one of traffic load, road construction type, repair type, repairing entity, and an environmental factor.

17. The Road Condition Monitoring System of claim 15, wherein: the at least one processor being further configured to track the settling of an overfill type of road surface defect repair.

18. The Road Condition Monitoring System of claim 10, wherein: the at least one processor being further configured to determine which roads have the highest frequency and/or severity of road surface defects; the at least one processor being further configured to accept at least one input including at least one of: a total repair budget, a cost per length to repave a road or a lane of a road, a cost per pothole for manual repair, traffic estimates for a road; and the at least one processor being further configured to calculate at least one cost and to recommend at least one possible repair strategy.

19. A method of monitoring the condition of roads using a vehicle having a traffic light preemption system having a GPS receiver and an IMU, comprising the steps of: configuring at least one processor to log GPS data in correlation with IMU data, and to detect and map road surface defects; and configuring the at least one processor to detect and monitor changes in the IMU data associated with a given road surface defect.

20. The method of claim 19, further comprising the step of: configuring the at least one processor to identify and report unmapped roads.

21. The method of claim 19, further comprising the step of: configuring the at least one processor to correlate changes in the road surface defects with at least one of traffic load, road construction type, and an environmental factor.

22. The method of claim 19, further comprising the step of: configuring the at least one processor to detect and monitor road surface changes and to predict the development of road surface defects using at least one of industry data concerning road construction, industry data concerning road deterioration, and specific local data concerning road construction and/or deterioration.

23. The method of claim 19, further comprising the step of: configuring the at least one processor to at least one of map predicted road surface defects and predict at least one characteristic of the predicted road surface defect.

24. The method of claim 19, further comprising the step of: configuring the at least one processor to monitor repairs to road surface defects.

25. The method of claim 24, further comprising the step of: configuring the at least one processor to correlate the effectiveness of repairs to road surface defects with at least one of traffic load, road construction type, repair type, repairing entity, and an environmental factor.

26. The method of claim 24, further comprising the step of: configuring the at least one processor to track the settling of an overfill type of road surface defect repair.

27. The method of claim 19, further comprising the step of: configuring the at least one processor to determine which roads have the highest frequency and/or severity of road surface defects; configuring the at least one processor to accept at least one input including at least one of: a total repair budget, a cost per length to repave a road or a lane of a road, a cost per pothole for manual repair, traffic estimates for a road; and configuring the at least one processor to calculate at least one cost and to recommend at least one possible repair strategy.

Description

DESCRIPTION OF THE DRAWINGS

[0020] The above-mentioned and other features of embodiments of the Road Condition Monitoring System, and the manner of their working, will become more apparent and will be better understood by reference to the following description of embodiments of the Road Condition Monitoring System taken in conjunction with the accompanying drawings, wherein:

[0021] FIG. 1 is a table showing IMU data recorded by an embodiment of the Road Condition Monitoring System of the present invention, as described herein;

[0022] FIG. 2 is a screenshot of an embodiment of a mapping application as used in conjunction with the Road Condition Monitoring System of the present invention, as described herein;

[0023] FIG. 3 is a screenshot of a road surface having an event that may be logged by an embodiment of the Road Condition Monitoring System of the present invention, as described herein;

[0024] FIG. 4 is a screenshot of an embodiment the Road Condition Monitoring System of the present invention, as described herein; and

[0025] FIG. 5 is a screenshot of an embodiment the Road Condition Monitoring System of the present invention, as described herein.

[0026] Corresponding reference numbers indicate corresponding parts throughout the several views. The exemplifications set out herein illustrate embodiments of the Road Condition Monitoring System, and such exemplifications are not to be construed as limiting the scope of the claims in any manner.

DETAILED DESCRIPTION

[0027] The following detailed description and appended drawing describe and illustrate various exemplary embodiments of the invention. The description and drawings serve to enable one skilled in the art to make and use the invention, and are not intended to limit the scope of the invention in any manner. In respect of the methods disclosed and illustrated, the steps presented are exemplary in nature, and thus, the order of the steps is not necessary or critical.

[0028] Turning now to FIG. 1, a table having IMU data 12 recorded by an embodiment of the Road Condition Monitoring System 10 of the present invention is shown. Compass heading 14 is given in Column I and speed 16 is given in Column J. X axis IMU data (header X) 18 is given in Column K, Y axis IMU data (header Y) 22 is given in Column M, and Z axis IMU data (header Z) 26 is given in Column O. In the present embodiment, each of these raw IMU data are divided by 1024 according to the specifications of the IMU to give X axis real value (header XX) 20 in Column L, Y axis real value (header YY) 24 in Column N, and Z axis real value (header ZZ) 28 in Column P. In another embodiment, each of the X axis real value 20, the Y axis real value 24, and the Z axis real value 28 may be squared by the at least one processor. These squared values may then be summed, and the square root of the sum taken, in order to find an absolute value of the magnitude of the acceleration. Further data may include latitude and longitude (not shown). In the highlighted row it is noted that the Z axis real value 28 jumps to greater than 1.65, indicating a bump in the road. It is further noted that the bump indicated in the highlighted row was not preceded or followed by a substantial decrease. For the purpose of the embodiment of the Road Condition Monitoring System 10 shown in FIG. 1, generally any Z axis real value 28 below 0.8 or above 1.2 may be considered a significant event. Any Z axis real value 28 in the 1.6 range or higher may be considered a serious event, and any Z axis real value 28 below 0.4 may be considered a serious event.

[0029] FIG. 2 shows a screenshot of an embodiment of a mapping application as used in conjunction with the Road Condition Monitoring System 10 of the present invention. A map of the reading area 50 is displayed, including the roads 52 to be traversed by a vehicle having an embodiment of the Road Condition Monitoring System 10. Similarly, FIG. 3 displays a road 60 having a median 62, as well as a bump in road 64, and a change in elevation 66, which may be logged by an embodiment of the Road Condition Monitoring System 10.

[0030] FIGS. 4 and 5 show screenshots of embodiments of the Road Condition Monitoring System 10. In FIG. 4, a data map 80 is displayed showing roads 82 over which an emergency vehicle runs a route 84. As the emergency vehicle runs the route 84, the Road Condition Monitoring System 10 logs events 86 corresponding to potholes, bumps, cracks, and other anomalies. Similarly, in FIG. 5, a data map 100 is displayed showing roads 102 over which an emergency vehicle runs a route 104. As the emergency vehicle runs the route 104, the Road Condition Monitoring System 10 logs events 106 corresponding to potholes, bumps, cracks, and other anomalies. In the embodiment of the Road Condition Monitoring System 10 shown in FIG. 5, the events are shown sorted and classified at 108. The sorted and classified events 108 are shown along with their severity, location longitude and latitude, and the vehicle speed when encountering the event. A View on Map button may be provided that allows a user to locate the events 106 on the data map 100 that correspond to the sorted and classified events 108. A color coding may be provided along with the events 106 shown on the data map 100. In the embodiment of the Road Condition Monitoring System 10 shown in FIG. 5, red markers indicating events 106 represent Z axis real values of between 1.8 and 2.9, orange markers represent Z axis real values of 1.7, yellow markers represent Z axis real values of 1.6, blue markers represent Z axis real values of 1.5, and green markers represent Z axis real values of between 1.2 and 1.5.

[0031] While the Road Condition Monitoring System has been described with respect to at least one embodiment, the Road Condition Monitoring System can be further modified within the spirit and scope of this disclosure, as demonstrated previously. This application is therefore intended to cover any variations, uses, or adaptations of the Road Condition Monitoring System using its general principles. Further, this application is intended to cover such departures from the present disclosure as come within known or customary practice in the art to which the disclosure pertains and which fall within the limits of the appended claims.

REFERENCE NUMBER LISTING

[0032] 10 Road condition monitoring system [0033] 12 IMU data [0034] 14 Compass heading [0035] 16 Speed [0036] 18 X axis IMU data (col X) [0037] 20 X axis real value (col XX) [0038] 22 Y axis IMU data (col Y) [0039] 24 Y axis real value (col YY) [0040] 26 Z axis IMU data (col Z) [0041] 28 Z axis real value (col ZZ) [0042] 50 Map of reading area (FIG. 2) [0043] 52 Roads [0044] 60 Road (FIG. 3) [0045] 62 Median [0046] 64 Bump in road [0047] 66 Change in elevation [0048] 80 Data map (FIG. 4) [0049] 82 Roads [0050] 84 Vehicle route [0051] 86 Events [0052] 100 Data map (FIG. 5) [0053] 102 Roads [0054] 104 Vehicle route [0055] 106 Events [0056] 108 Events, sorted and classified