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Smart Sensors and Infrastructures for Transportation
(July 29, 2021)

Roadway Ice/snow Detection using a Novel Infrared Thermography Technology
Presenter: Keping Zhang
Presenter’s Org: Department of Civil & Environmental Engineering, University of Utah

T3 webinars are brought to you by the Intelligent Transportation Systems (ITS) Professional Capacity Building (PCB) Program of the U.S. Department of Transportation (USDOT)’s ITS Joint Program Office (JPO). References in this webinar to any specific commercial products, processes, or services, or the use of any trade, firm, or corporation name is for the information and convenience of the public, and does not constitute endorsement, recommendation, or favoring by the USDOT.

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[The slides in this presentation contain the logo of the University of Utah.]

Slide 1: Roadway Ice/snow Detection using a Novel Infrared Thermography Technology

Presenter: Keping Zhang
Infrastructure Sensing & Experimental Mechanics (iSEM) Lab
Department of Civil & Environmental Engineering, University of Utah

In collaboration with Prof. Yang of UTRAIL at the University of Utah

ITS Professional Capacity Building Program
T3e Webinar
Smart Sensors and Infrastructures for Transportation
July 29, 2021

[This slide contains three images: (1) the logo of the Infrastructure Sensing & Experimental Mechanics (iSEM) Lab, (2) the logo for the University of Utah’s U-Trail Lab, and (3) the U.S. DOT triskelion.]

Slide 2: Outline

• Introduction
  • Background
  • Current Technologies
  • Proposed Solution
• Polarized Infrared Thermography Development
  • Strategy 1 - Filter out S-polarized reflections at certain favorable perspective angle
  • Strategy 2 - Reconstruct IR images with P- & S-polarized measurements
  • Lab Tests
• Conclusions & Ongoing Work

Slide 3: Introduction: Background

Slippery road condition during winter seasons imposes threats to traffic safety in snowy regions (70% of U.S. roads & population [1]).

Coefficient of friction for rubber tire on slippery road surface [2]

Tire on	Coefficient of friction
Snow	0.5
Compact snow	0.4
Ice	0.15

This will

• reduce tire friction
• lengthen vehicle braking distance
• induce risks on car crashing

FHWA safety data [1] reports

• average of 1,300 deaths
• average of 116,800 injuries per year due to snowy and icy roads

Takeaway: it is important to evaluate slippery road conditions & evaluate traffic safety.

[1] USDOT FHWA Road Weather Management Program website https://ops.fhwa.dot.gov/weather/weather_events/snow_ice.htm

[2] Strong, C. K., Ye, Z., Shi, X. (2010). Safety effects of winter weather: the state of knowledge and remaining challenges. Transport reviews, 30(6), 677-699.

[This slide contains a photo of black ice on a road surface.]

Slide 4: Introduction: Current Technologies

Summary of past work on roadway ice/snow detection

Item #	Phenomena / Technique	Investigators / Relevant publication
1	RWIS	Aurora 2005* [3]
2	Infrared thermometer	Vaisala DST111[4], Ye et al. [5], Jonsson et al. [6]
3	Passive infrared thermography with radiation polarization	Reed & Barbour [7]
4	Active infrared radiation backscatter	Vaisala DSC111 [4], Misener [8], Joshi [9]
5	Video camera	AerotechTelub* [10], Saito et al.* [11]
6	Laser light polarization	Schmokel [12]
7	Microwave reflection	Kubichek & Yoakum-Stover [13]
8	Car reactions on slippery surfaces: acceleration, ABS wheel speed, etc.	Robinson & Cook [14], Castillo Aguilar et al. [15]
9	Pavement temperature sensors	Albrecht* [16], SRF Consulting Group Inc.* [17]

Important attributes for each technology in roadway ice/snow detection

Y: Positive; N: Negative; N/A: Not available

Takeaway: none of current technologies satisfy all the identified attributes.

Slide 5: Introduction: Current Technologies

Technology adopted by state DOTs

DST 111 Remote Surface Temperature Sensor: single point infrared temperature measurement, compensated by emissivity of road surface

DST 111 Remote Surface State Sensor: single point laser spectroscopic measurement, reporting the amounts of water and ice

Spatial resolution

• DST 111: diameter of measuring area at 10 m (33 ft) 150 cm (59.1 in)
• DSC 111: diameter of measuring area at 10 m (33 ft) 20 cm (7.87 in)

Calibration: Twice per year

Cost:

Pos	Description	Quantity	Unit Price	Total Price USD
1	DST111 Temperature Sensor Remote	1 EA	4,586.00	4,586.00
2	DST111 Road State Sensor Remote	1 EA	12,651.00	12,651.00
		Subtotal (Selling Price)		17,237.00
		Freight		10.00
Tax Due		TAX 0%		0.00
Tax Due		TAX 0%		0.00
Tax Due		TAX 0%		0.00
		Grand Total	USD	17,247.00

Takeaway: robust multi-lane measurement, less frequent calibration, less cost are desirable.

[This slide contains two images: (1) an image of a sensor and (2) an image of a sensor.]

Slide 6: Introduction: Proposed Solution

Infrared Camera for multi-lane temperature measurement

Spatial resolution:

Detector Size	Number of pixels
320 x 240	76,800
160 x 120	19,200

Target Distance (feet)	Field of View (feet)	Pixel Size 320 x 240 (inches)	Pixel Size 160 x 120 (inches)
1	0.38 x 0.29	0.014 x 0.014	0.029 x 0.029
6	2.30 x 1.73	0.086 x 0.086	0.173 x 0.173
10	3.83 x 2.88	0.144 x 0.144	0.288 x 0.288
20	7.67 x 5.76	0.288 x 0.288	0.575 x 0.575
50	19.17 x 14.41	0.719 x 0.720	1.437 x 1.441

Calibration frequency: every year (if looking for accurate temperature)

Cost: 320 x 240 detector ~$10k FLIR A325sc, ~$15k AVIO R450 Pro

Description	MSRP
FLIR A325sc w/25° Lens, 60Hz, 320x240, -20°C, w/ResearchIR Max	$9,990

Lower cost models are available.

Temperature resolution: 0.025ºC - 0.1ºC

Accuracy: ±1ºC - ±2ºC

Operating temp: -15 to 50ºC

Power consumption: 4.3 watts

Takeaway: IR camera can provide multi-lane measurement, requires less calibration, and can be less costly.

[This slide contains two images: (1) a photo of an infrared camera labeled FLIR A325sc and (2) an photo of an infrared camera labeled AVIO R450 Pro.]

Slide 7: Introduction: Proposed Solution

Develop a system for multi-lane roadway surface temperature and slippery condition evaluation exploiting tools including

• Polarized infrared thermography (eliminating ambient thermal noises)
• Dual-sensory measurement system and algorithms (accurate temperature mapping & segmentation)

Mission – improve traffic safety during winter seasons in snowy regions by enabling early warning of hazardous road conditions and facilitating snow removal performance evaluation

[This slide contains four images: (1) a diagram of polarized infrared thermography, (2) a thermal imagery photo of feet on an icy surface, (3) dual-sensory measurement system images, and (4) a flowchart of a dual-sensory measurement system.]

Slide 8: 2. Polarized infrared thermography development: Strategy 1

Strategy 1: Filter out S-polarized reflections at certain favorable perspective angle

Theoretical prediction of thermal reflections – Fresnel’s Equation

Reflected light consisted of two contributions:

• Light polarized parallel to plane of incidence Rp.
• Light polarized perpendicular to plane of incidence Rs.

Fresnel’s equations

𝑅_𝑃=((𝑛^2 𝑐𝑜𝑠θ−√(𝑛^2−〖𝑠𝑖𝑛〗^2 θ))/(𝑛^2 𝑐𝑜𝑠θ+√(𝑛^2−〖𝑠𝑖𝑛〗^2 θ)))^2

𝑅_𝑠=((𝑐𝑜𝑠θ−√(𝑛^2−〖𝑠𝑖𝑛〗^2 θ))/(𝑐𝑜𝑠θ+√(𝑛^2−〖𝑠𝑖𝑛〗^2 θ)))^2

Θ: incident angle (face angle)

𝑛: the refractivity index of the reflectivity surface

[This slide contains two images: (1) a graphic of the theoretical prediction of thermal reflections and (2) a graph of reflectivity from Cu to air according to Fresnel’s Equation.]

Slide 9: Polarized infrared thermography development: Strategy 1

Strategy 1: Filter out S-polarized reflections at certain favorable perspective angle

Suppression of thermal reflections using polarizer – Example

Unpolarized radiation become polarized by passing a polarizer

[This slide contains three images: (1) a chart of unpolarized radiation becoming polarized by passing a polarizer, (2) a line graph of Theory: Glass, (3) a compilation of photos of S-Polarizer and P-Polarizers with Reflection and Reflection Suppression.]

Slide 10: Polarized infrared thermography development: Strategy 1

Strategy 1: Filter out S-polarized reflections at certain favorable perspective angle

Suppression of thermal reflections using polarizer – Lens-polarizer assembly design

[This slide contains two images: (1) a diagram of the lens from front view and side views and (2) a photo of lens-polarizer assembly.]

Slide 11: Polarized infrared thermography development: Strategy 2

Strategy 2: Reconstruct IR images with P- & S-polarized measurements based on Kirchhoff’s Law

Re-imaging IR measurement

Separate emitted energy and reflected energy steps:

1. Take IR image at polarizer angle 0°: E_p
2. Take IR image at polarizer angle 90°: E_s
3. Quantitative separation of the energy using: E_e = 2(𝑅_𝑃E_S - 𝑅_SE_P) / τ(𝑅_𝑃ε_S - 𝑅_Sε_P)
   E_r = 2(ε_𝑃E_S - ε_SE_P) / τ(𝑅_Sε_P - 𝑅_Pε_S)
4. Re-imaging E_e without background reflection E_r

[This slide contains a graph of re-imaging IR measurement.]

Slide 12: Polarized infrared thermography development: Lab tests

[This slide contains 2 images: (1) a photo of lab test sample preparation (dry, wet, and ice-covered), (2) a photo of data collection on dry, wet, and ice-covered samples in the laboratory using a polarized IR camera.]

Slide 13: Polarized infrared thermography development: Lab tests

Data Analysis - dry concrete

[This slide contains 5 images: (1) a photo of data analysis of dry concrete in the laboratory. (2) a line graph of theory: dry concrete. (3) a couple of polarized images of an original sample infrared thermography development at 60 degrees and 75 degrees. (4) a couple of polarized images of the strategy 1 sample infrared thermography development at 60 degrees and 75 degrees. (5) a couple of polarized images of the strategy 2 sample infrared thermography development at 60 degrees and 75 degrees.]

Slide 14: Polarized infrared thermography development: Lab tests

Data Analysis - wet concrete

[This slide contains 5 images: (1) a photo of data analysis of wet concrete in the laboratory. (2) a line graph of theory: water. (3) a couple of polarized images of an original sample infrared thermography development at 60 degrees and 75 degrees. (4) a couple of polarized images of the strategy 1 sample infrared thermography development at 60 degrees and 75 degrees. (5) a couple of polarized images of the strategy 2 sample infrared thermography development at 60 degrees and 75 degrees.]

Slide 15: Polarized infrared thermography development: Lab tests

Data Analysis - ice-covered concrete

[This slide contains 5 images: (1) a photo of data analysis of ice-covered concrete in the laboratory. (2) a line graph of theory: ice. (3) a couple of polarized images of an original sample infrared thermography development at 60 degrees and 75 degrees. (4) a couple of polarized images of the strategy 1 sample infrared thermography development at 60 degrees and 75 degrees. (5) a couple of polarized images of the strategy 2 sample infrared thermography development at 60 degrees and 75 degrees.]

Slide 16: Conclusions & Ongoing work

• Theoretically predict the reflectivity of concrete with dry, wet, and ice-covered condition;
• Designed and fabricated the lens-polarizer assembly;
• Performed laboratory tests to measure the temperature field of dry, wet, ice-covered concrete surface;
• For dry concrete surface, IR reflections from ambient environment are negligible;
• For wet and ice-covered concrete surface, IR reflections are significant and can be effectively suppressed by the proposed strategies;
• Strategy 1 is preferred for field tests considering its effectiveness & easiness in implementation.

Slide 17: Conclusions & Ongoing work

Field Data Collection and Dual-sensory Algorithm Development

Pattern recognition development with dual-sensory system

[This slide contains eight images of snow-covered road using pattern recognition development with dual-sensory system.]

Slide 18: Acknowledgements

• FHWA Aurora Program
• Aurora Board
• Jeff Williams and Cody Oppermann, Utah DOT
• Narwhal group
• University of Utah
• Tina Greenfield, Iowa DOT
• Zachary N. Hans and Neal R. Hawkins, Aurora

[This slide contains three images: (1) the logo of FHWA Aurora program, (2) the logo of Utah DOT, and (3) the logo of the University of Utah.]

Slide 19: Reference

1. USDOT FHWA Road Weather Management Program website https://ops.fhwa.dot.gov/weather/weather_events/snow_ice.htm
2. Strong, C. K., Ye, Z., Shi, X. (2010). Safety effects of winter weather: the state of knowledge and remaining challenges. Transport reviews, 30(6), 677-699.
3. Aurora Project (2005). New Road Surface Condition Sensors. Aurora Project 2005-06.
4. Bridge, P. (2008). Non-invasive road weather sensors. 4th National Conference on Surface Transportation Weather, Indianapolis, Indiana.
5. Ye, Z., Shi, X., Strong, C.K., Larson, R.E. (2011). Vehicle-based sensor technologies for winter highway operations. IET Intelligent Transport Systems.
6. Josson, P., Riehm, M. (2012). Infrared Thermometry in winter road maintenance. Journal of Atmospheric and Oceanic Technology, Vol. 29.
7. Reed, J., Barbour, B. (1997). Remote passive road ice sensor system (RPRISS). IDEA Intelligent transportation systems Project final report, TRB IDEA ITS-34
8. Misener, J. A. (1998). Investigation of an optical method to determine the presence of ice on road surfaces. California PATH working paper, UCB-ITS-PWP-98-17.
9. Joshi, P. (2002). A Mobile Road Condition Sensor as Winter Maintenance Aid. Final Report for ITS-IDEA Project 85.
10. Vinnova, AerotechTelub, Hogskolan, Vagverket (2002). Final Report on Signal and Image Processing for Road Condition Classification. Aurora report 2002-02-06.
11. Saito, M., Yamagata, S. (2014). Effect of video camera-based remote roadway condition monitoring on snow removal-related maintenance operations. Aurora Project 2012-03.
12. Schmokel, P. (2004). Development of a wide area optical surface contamination detection system for public transportation application. Final report for highway IDEA project 83.
13. Kubichek, R. F., Yoakum-Stover, S. (1998). Cost-Effective Microwave Sensing of Highway Road Conditions. IDEA project Final report, contract NCHRP-31.
14. Robinson, R., Cook, S. J. (2012). Slippery road detection and evaluation. Michigan DOT report.
15. Castillo Aguilar, J., Cabrera Carrillo, J. A., Guerra Fernandez, A. J., Acosta, E. C. (2015). Robust Road Condition Detection System Using In-Vehicle Standard Sensors. Sensors, 15(12).
16. Albrecht, C. (2005). Pavement temperature sensors. Aurora project 2001-04.
17. SRF Consulting Group, Inc. (2005). Laboratory and field studies of pavement temperature sensors. Aurora final report, SRF No.0024544.
18. Suzuki, S., Ogasawara, N. (2018). Quantitative evaluation of polarized emissivity and polarized reflectivity using infrared thermographic instrument. Advanced Experimental Mechanics, Vol. 3.
19. Williams, J., Oppermann, C., (2018). UDOT Snow and Ice Performance Measure. Western States Rural Transportation Technology Implementers Forum, Yreka, CA, June 20, 2018.

Slide 20: Thanks and Questions?

[This slide contains a graphic of a question mark with a man on it.]

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