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DIGITAL HIGHWAY MEASUREMENTS. TURNER-FAIRBANK HIGHWAY RESEARCH CENTER David Gibson Milton (Pete) Mills Morton Oskard ADVANCED RESEARCH PROJECT. 1. Roadside features. Vegetation. Vehicle Traffic. Contour/Terrain. Edge of Roadway. Lane Markings. Right of Way. Road Geometry.

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Digital highway measurements

DIGITAL HIGHWAY MEASUREMENTS

TURNER-FAIRBANK HIGHWAY RESEARCH CENTER

David Gibson

Milton (Pete) Mills

Morton Oskard

ADVANCED RESEARCH PROJECT

1


Long term measurement needs

Roadside features

Vegetation

Vehicle Traffic

Contour/Terrain

Edge of Roadway

Lane Markings

Right of Way

Road Geometry

Pavement Condition

Long-Term Measurement Needs

2


Vision
Vision

  • Build a foundation to capture geometrics at required levels of accuracy not currently provided by the state of the practice

    • With State of the art sensors

    • With data fusion

    • With advanced analyses .

  • Introduce a set of highway metrics for entire right of way, (beyond simply highway geometrics) to capture health condition accurately and objectively

    • With State of the art sensors

    • With data fusion

    • With advanced analyses .

3



Potential sensors
Potential Sensors

  • Sound Intensity Pressure Device (SIPD)

  • Ground Penetrating Radar (GPR)

  • LIDAR

  • Infrared Sign Retro-Reflectivity (IR)

  • Downward facing

    Camera for

    Pavements

IR

GPR

SIPD

5

Camera

LIDAR


6


Horizontal alignment
Horizontal Alignment

SEGMENTED

APPROXIMATE PCs & PTs

7

PC = Point of Curvature, PT = Point of Tangency


Vertical Alignment

DETAIL

11 MILES

8


Super elevation
Super-Elevation

Rod and Level Data (Blue)

High Accuracy INU data (Orange)

Comparison with rod and level data over 2 miles

9


Penndot route 851 fhwa r d driving simulator
PennDOT Route 851: FHWA R&D Driving Simulator

  • Highway Geometrics for driving simulator collected in April 2004

PA Route 851

3.2 Miles

10


Vdot application safety improvement
VDOT Application Safety Improvement

West Virginia

Border

  • Highway Geometrics for IHSDM in April 2004

  • VA Route 9 From Leesburg to West Virginia Border -- 12 Miles

11

LEESBURG


Data types
Data Types

  • Vertical & Horizontal alignments including: PC, PT, Curve information

  • Super Elevation

  • Pavement Surface Condition

  • Lane Definition ( Markings and Edge )

  • Roadside hardware

  • Linear and XYZ Referencing of data

12


Preliminary results of va rt 9 safety improvement study
Preliminary Results of Va. Rt.9 Safety Improvement Study

  • Algorithms modified to handle stop and go conditions

  • Geometry of site extracted

  • Segmentation of alignments in progress

  • Data found very repeatable

  • Coverage of DGPS found intermittent

13


Elevation View

HEAVY FOLIAGE

PROJECT ELEVATION IN FEET

VALLEYS

14

PROJECT LONGITUDE IN FEET

Differential GPS superimposed in blue - Arrows indicate blocked Reception


Cross sectional scans
CROSS-SECTIONAL SCANS

GUARD RAIL

ELEVATION IN INCHES

15

OFFSET FROM CENTERLINE OF VEHICLE IN INCHES

POSITION OF GUARD RAIL


Cross sectional scans1
Cross-Sectional Scans

EDGE OF CUT

ELEVATION IN INCHES

16

OFFSET FROM CENTERLINE OF VEHICLE IN INCHES

CLEARANCES


Lane attributes
Lane Attributes

  • LANE MARKINGS

  • LANE WIDTH

17



Comparing dhm to ndgps and sop
COMPARING DHM TO NDGPS AND SOP

SOP (State-Of-Practice)

NDGPS (National Differential GPS)

Color-Coded by no. of satellites received:

3 4&5 6&789

DHM

SEQUENCE OF SLIDES SHOWING CONTINUOUS HORIZONTAL ALIGNMENT DATA (1 of 9 )

19


Comparing dhm to ndgps and sop1
COMPARING DHM TO NDGPS AND SOP

SEQUENCE OF SLIDES SHOWING CONTINUOUS HORIZONTAL ALIGNMENT DATA ( 7 of 9 )

20


Gps reception
GPS Reception

  • No. of Satellites:

    • 3

    • 4 & 5

    • 6 & 7

    • 8

    • 9

21


SOP

22


Measuring vehicle wander in lane using dhm laser
Measuring vehicle wander in lane using DHM laser

Position of vehicle in lane

Pavement

Markings

Edge of Pavement

23


Scanning laser inu
Scanning Laser + INU

Pavement Markings and Edge of Pavement features fused with Trajectory

24


Multiple lanes
Multiple lanes

25

Six-Points Cross-Sections of two-lane Rural Road -- resolution = 2 feet.


Cross sections
Cross-Sections

26

SEQUENCE OF SLIDES SHOWING CROSS-SECTIONS


Cross sections1
Cross-Sections

27

SEQUENCE OF SLIDES SHOWING CROSS-SECTIONS


Cross sections2
Cross-Sections

28

SEQUENCE OF SLIDES SHOWING CROSS-SECTIONS


Visualization
Visualization

29

3-D Rendering of Roadway in AUTOCAD


What is next
What is next ?

  • Optimize data reduction process

  • Reduce Data

  • Study Ground Truth -

    • Manual survey using static scanning laser

    • Satellite imaging using VGIN

  • Error & Statistical Analysis

  • Validation & Accuracy Report

30


Preparations for gpr field trial
Preparations for GPR Field Trial

Mounting step frequency GPR

hardware prior to the field trial

Pavement core location - coring was carried out at selected locations in advance


Utility Detection Data –

Collected Previously

Manhole

Horizontal slice at 18 cm depth


Utility Detection Data

Power cable

Horizontal slice at 110 cm depth


Utility Detection Data Collection Site

Excavation

Tram rails

Backfill

Clay

Old gas pipe


Conclusion
Conclusion

  • It is possible to capture geometrics and roadway surface and structure data at high levels of accuracy using State of the art sensors, data fusion and advanced analysis procedures

  • These results are significantly more accurate then the state of the practice

  • These results would benefit from being fused with aerial surveillance data

  • Pooled fund study to provide one or more prototype DHM vans for use by participating states (Contact [email protected])

  • Coordinate with Florida DOT on pooled fund studies on data.


The End

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Questions at Breaks


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