Vehicle to Pavement Passive Sensing for AV Lateral Position Detection - - PowerPoint PPT Presentation

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Vehicle to Pavement Passive Sensing for AV Lateral Position Detection - - PowerPoint PPT Presentation

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Vehicle to Pavement Passive Sensing for AV Lateral Position Detection Jeff Roesler, PhD, PE Sachindra Dahal, PhD Candidate Department of Civil and Environmental Engineering University of Illinois at Urbana Champaign September 29, 2020

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Vehicle to Pavement Passive Sensing for AV Lateral Position Detection

Jeff Roesler, PhD, PE Sachindra Dahal, PhD Candidate Department of Civil and Environmental Engineering University of Illinois at Urbana Champaign

September 29, 2020

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SLIDE 2

Acknowledgement

  • Funding for research provided by Center for Connected and

Automated Transportation under Grant No. 69A3551747105 of the U.S. Department of Transportation, Office of the Assistant Secretary for Research and Technology (OST‐R), University Transportation Centers Program (2017‐2021).

  • PhD Student working on the project

‐ Sachindra Dahal

  • Papers
  • Dahal, S., Roesler, J., (2020). Cracking Patterns and Properties in CRCP with Internal Curing and Active Cracking.

(Submitted: Transportation Research Record)

  • Dahal, S., Roesler, J., (2019). Passive Sensing of Electromagnetic Concrete for Lateral Vehicle Positioning.

(Accepted: 12th International Conference on Concrete Pavements)

  • Dahal, S., Hernandez, J., & Roesler, J. (2018). Infrastructure enhancements for CAV navigation (Report No. ICT‐20‐

008). Illinois Center for Transportation. https://doi.org/10.36501/0197‐ 9191/20‐008

  • Dahal, S., Roesler, J., Gupta, P., Zhang, Y. (2018). Performance Monitoring of Re‐engineered CRCP Test Sections:

Volume 2. (Pre‐publication after review from Illinois Tollway).

  • Dahal, S., Roesler, J., Gillen, S., Gancarz, D. (2018), “Re‐Engineering CRCP Design,” 13th International Symposium
  • n Concrete Roads, 19‐22, June 2018, Berlin, Germany, 15 pp.
  • Award
  • Intelligent Transportation Society (ITS) Michigan Scholar Award. – 26 February, 2019
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SLIDE 3

Driver/passenger safety Roadway capacity Improved mobility

Elderly, disabled, and youth

Traffic congestion Fuel consumption

Autonomous Vehicles (AVs) have arrived!

3

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Roadway Centerline Miles in USA

  • Total Rural road (miles) = 2,933,528 (~70%)
  • Total Urban road (miles) = 1,181,068 (~30%)
  • Urban Roadway Distribution:

(FHWA, 2020)

Roadway Functional Classes Percentage (%) Interstate 1.5 Other Freeways & Expressways 1.0 Other Principal Arterial 5.4 Minor Arterial 8.8 Major Collector 10.2 Minor Collector 1.3 Local Roads 71.8

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SLIDE 5

How do Connected & Autonomous Vehicles work?

Sense Plan Act

`

DSRC / Cellular-based

`

V2X  V2V / V2I / V2P

Automated Connected

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SLIDE 6

How do AVs work?

Different types of sensors.

  • Camera
  • RADAR
  • LIDAR
  • GPS
  • Ultrasonic sensors

6

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SLIDE 7

Plan vehicle movement.

  • Computer Vision
  • Machine Learning
  • Path planning algorithm

How do AVs work?

7

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SLIDE 8

Execute the command.

  • Engine
  • Steering
  • Breaking

How do AVs work?

8

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SLIDE 9

How do AVs stay in lane?

GPS

  • Coordinates from satellite

Camera

  • Computer vision to find lane

Sensor fusion  GPS + Camera (+ Lidar + Existing maps) Use redundant information to predict better

9

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What is the problem with existing system?

  • Improper lane marking
  • Bad weather (snowfall, fog, and rainfall)
  • Lane markings not visible  Camera
  • Signal worsen  GPS
  • 33 states w/ 10+ inches of snow annually.

(Rutgers University, 2020)

  • 60% of US covered in snow in Feb-2019.

(NOAA.gov, 2019)

10

W eather independence needed for large scale AV deploym ent.

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SLIDE 11

How is the weather problem dealt w/ today?

Past Attempts:

  • 3D maps + camera + GPS in

snow/fog. (Belaroussi et. al, 2011)

  • Remove rain/snow by different

filtering methods. (Köylüoglu and

Hennicks, 2019)

  • Lidar data pattern in snow and wet
  • condition. (Alidibaja, 2016)
  • Transponder embedded in road.

(Houdali et. al, 2014)

Problem:

  • Computationally expensive.

Not real time.

  • Does not eliminate snow.

Not real time

  • Extremely complex lidar pattern in snow.

Not real time.

  • Electronics component in pavement.

Require power and maintenance.

11

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DSRC and Cellular‐based approach

  • Use wireless protocol or cellular

network.

  • Provide crucial information to driver.
  • Warning of potential crash.
  • Vehicle information (speed,

acceleration, heading, brake status, path history, path prediction, etc.)

  • 360 degree of awareness and create

network of vehicles

Source: (Qualcomm, 2020)

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Curr Curren ent V2I V2I sy system

  • V2I research focuses on:
  • Roadside units (RSUs)
  • Traffic controller unit
  • Road is the biggest infrastructure

and could be used to expand communication with vehicles.

  • Passive or active

Source: (Kakkasageri and Manvi, 2014)

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SLIDE 14

14

  • Discrete magnetic marker @4 ft. (Chan, 2002)
  • Magnetic tape with alternating North/South pattern. (Bajikar et al.,

1997)

  • Need:
  • Known magnetic model.
  • Traffic broke magnet  changing pattern from known model
  • Sensor “missed” reading magnets as a result
  • Background noise prior to marker installation.

Past attempts for vehicle-pavement communication

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SLIDE 15

Are the current roads smart enough?

  • NO! Pavement currently not designed to communicate with AVs.
  • Create a unique and repeatable signature that AV can identify accurately

PASSIVE SENSING

  • Strategic modification of the roadway electromagnetic properties

 changing lane markings or pavement material properties

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Proposed passive sensing solution:

  • Create an electromagnetic signature of the pavement that is

standardized for AVs.

How?

  • Strategic positioning of electromagnetic construction materials, e.g.,

steel fibers or steel slag aggregate in pavements.

  • During new construction or retrofitting.
  • Establish AV to pavement interaction.
  • Weather independent.
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Passive Sensing: working principle

  • Material that changes electromagnetic property at desired location.
  • How?  Addition of metallic particles in the concrete or asphalt.
  • Metals in general increase electric conductivity,

i.e., allow current to flow more easily

Low conductivity High conductivity

17

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Induction‐based eddy current method

  • Eddy current  Induced in conductor under changing magnetic field.

Coil Metal

Changing magnetic field Eddy Current

18

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Magnetic field strength at target

  • Magnetic field (H) decreases with

distance of target from coil.

  • H at target = 2nIr2/(r2+z2)3/2
  • I  current
  • n number of turns
  • r  radius of coil
  • z  target distance from coil
  • Lower H induces lower eddy current.

r z

Coil height and radius matters.

Target Material

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SLIDE 20

Eddy Current sensor in lab

  • Alternating magnetic field using

10‐inch diameter search antenna.

  • Frequency: ~ 200 Hz.

Power Supply Search Antenna Output Signal

21

Steel Fiber Reinforced Concrete Beam

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Laboratory test frame setup

  • Aluminum frame with sensor
  • Motorized setup
  • Drives sensors/coil at constant

speed above slab.

23

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Sample preparation

  • Notched concrete slab
  • 1 inch x 1 inch
  • 2 inch x 2 inch
  • 3 inch x 3 inch
  • Prism
  • Notch dimension
  • Notched slab  Normal concrete
  • Prism  concrete w/ EM material at various dosages

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Scan from 5” - 7” (12-18 cm) above the slab to detect signature.

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SLIDE 23

Surface condition of slab

  • Normal  notched slab surface ‐ dry (nothing).
  • Adverse  surface material in plastic container above notched slab.
  • 0.5‐inch, 1 inch, 2 inch  three severity levels
  • Water
  • Ice and snow
  • Sand

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Eddy Current Results – steel fiber volume

  • Passing the coil transversely over the

slab

  • Signal strength
  • High signal above the EM signature.
  • Depends on lateral position
  • f sensor coil.

Steel fiber dosage

3” Prism 5” Height

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Source: (Dahal and Roesler, 2019)

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Eddy Current Results – Coil Height

  • Signal strength depends on

height/distance of coil above the signature.

  • As the coil height increases

above the surface, signal decreases.

3” Prism 1% SFRC

Coil Height Source: (Dahal and Roesler, 2019) RJR12

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SLIDE 26

Eddy Current Results – Notch dimension

  • Signal strength depends on the

size of the notch dimension.

  • Larger notch size has higher

signal compared to smaller.

1% SFRC 5” Height

Notch Size Source: (Dahal and Roesler, 2019)

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SLIDE 27

Eddy Current Results – Water and Ice

  • Clear signals observed even when the slabs

are imposed with adverse conditions.

  • 2 inch (5.1 cm) of standing water and ice on

top of the slab.

  • Ice did not attenuate signal.
  • Low dielectric constant.

29

Water Ice

3” Prism 5” Height 1% SFRC

Source: (Dahal and Roesler, 2019)

3” Prism 5” Height 1% SFRC

RJR13

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SLIDE 28

Eddy Current: Summary

Low Medium High Coil Height 5 inch 6 inch 7 inch EM prism size 3.5 inch 2.5 inch 1.5 inch Steel Fiber % 1% 0.75% 0.50% Surface Water 0 inch 1 inch 2 inch Surface Ice 0 inch 1 inch 2 inch

  • Signal attenuated most by:

Prism size, Sensor height, Fiber content.

  • Factors can be controlled in design

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Signal Attenuation Level

Source: (Dahal and Roesler, 2019)

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Eddy Current: Summary (2)

Low Medium High Coil Height 5 inch 6 inch 7 inch EM prism size 3.5 inch 2.5 inch 1.5 inch Steel Fiber % 1% 0.75% 0.50% Surface Water 0 inch 1 inch 2 inch Surface Ice 0 inch 1 inch 2 inch

  • 2” water attenuated signal by 23%.
  • Still easily detectable.
  • Ice did not attenuate signal.
  • Low dielectric constant.

31

Signal Attenuation Level

Source: (Dahal and Roesler, 2019)

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SLIDE 30

Details about eddy current sensor tradeoffs

  • Large diameter coil
  • More winds on coil
  • Reduced height of coil over the slab
  • Higher conductive material
  • Any metal can be detected but geometry of target material matters
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SLIDE 31

Field test of similar concept – Three Paths

Centerline Offset Meandering

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Camera vs Electromagnetic

  • Even when lane marking is not visible, EM signature is detectable.

Estimated location Estimated location

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Conclusion

  • AV interaction w/ pavement through passive sensors can assist in AV

lateral position detection and maneuvering

  • Complements current AV sensors
  • Strategic modification of electromagnetic material in pavement
  • Creates an EM signature that AVs can detect for lateral maneuvering
  • Functions during adverse weather conditions for lateral position
  • Pavement infrastructure can be exploited to increase reliability of AV

lateral position in adverse weather conditions.

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SLIDE 34

Reference

  • Aldibaja, M., Suganuma, N., & Yoneda, K. (2016, December). Improving localization accuracy for autonomous driving in snow‐rain
  • environments. In 2016 IEEE/SICE International Symposium on System Integration (SII) (pp. 212‐217). IEEE.
  • Bajikar, S., Morellas, V., & Donath, M. (1997). Magnetic lateral indication system evaluation.
  • Chan, C. Y. (2002). Magnetic sensing as a position reference system for ground vehicle control. IEEE Transactions on

instrumentation and measurement, 51(1), 43‐52.

  • Dahal, S., Roesler, J., (2019). Passive Sensing of Electromagnetic Concrete for Lateral Vehicle Positioning. (Accepted: 12th

International Conference on Concrete Pavements)

  • N. Houdali, T. Ditchi, E. Gérome, J. Lucas, and S. Holé, “RF infrastructure cooperative systems for in lane vehicle localization,”

Electronics, vol. 3, no. 4, pp. 598–608, 2014.

  • Kakkasageri, M. S., & Manvi, S. S. (2014). Information management in vehicular ad hoc networks: A review. Journal of network and

computer applications, 39, 334‐350.

  • Köylüoglu, T., & Hennicks, L. (2019). Evaluating rain removal image processing solutions for fast and accurate object detection.
  • R. Belaroussi, J. Tarel and N. Hautière, "Vehicle attitude estimation in adverse weather conditions using a camera, a GPS and a 3D

road map," 2011 IEEE Intelligent Vehicles Symposium (IV), Baden‐Baden, 2011, pp. 782‐787, doi: 10.1109/IVS.2011.5940485.

  • Qualcomm (2020), retrieved from: https://www.qualcomm.com/media/documents/files/nr‐c‐v2x‐webinar‐march‐2020‐

presentation.pdf