Abstract

Three different configurations utilizing polarized short-wave infrared light to classify winter road conditions have been investigated. In the first configuration, polarized broadband light was detected in the specular and backward directions, and the quotient between the detected intensities was used as the classification parameter. Best results were obtained for the SS-configuration. This sensor was shown to be able to distinguish between the smooth road conditions of water and ice from the diffuse road conditions of snow and dry asphalt with a probability of wrong classification as low as 7%. The second sensor configuration was a pure backward architecture utilizing polarized light with two distinct wavelengths. This configuration was shown to be effective for the important problem of distinguishing water from ice with a probability of wrong classification of only 1.5%. The third configuration was a combination of the two previous ones. This combined sensor utilizing bispectral illumination and bidirectional detection resulted in a probability of wrong classification as low as 2% among all four surfaces.

© 2012 Optical Society of America

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References

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  1. C.-G. Wallman and H. Åström, Friction Measurement Methods and the Correlation between Road Friction and Traffic Safety (Swedish National Road and Transport Research Institute, 2001).
  2. J. Casselgren, M. Sjödahl, and J. LeBlanc, “Angular spectral response from covered asphalt,” Appl. Opt. 46, 4277–4288 (2007).
    [CrossRef]
  3. J. Casselgren, M. Sjödahl, M. Sanfridsson, and S. Woxneryd, “Classification of road conditions—to improve safety,” in Advanced Microsystems for Automotive Applications 2007 (Springer, 2007).
  4. C. Ciamberlini, G. Innocenti, and G. Longobardi, “An optoelectronic prototype for the detection of road surface conditions,” Rev. Sci. Instrum. 66, 2684–2689 (1995).
    [CrossRef]
  5. W. M. Irvine and J. B. Pollack, “Infrared optical properties of water and ice spheres,” Icarus 8, 324–360 (1968).
  6. A. W. Nolan and J. Dozier, “A hyperspectral method for remotely sensing the grain size of snow,” Remote Sens. Environ. 74, 207–216 (2000).
    [CrossRef]
  7. M. M. Matsumoto, “Molecular dynamics simulation of the ice nucleation and growth process leading to water freezing,” Nature 416, 409–413 (2002).
    [CrossRef]
  8. B. A. Barbour, “Ice monitoring and detection system,” U.S. patent 5,557,261 (17September1994).
  9. D. Gregoris, S. Yu, and F. Teti, “Multispectral imaging of ice,” in Canadian Conference on Electrical and Computer Engineering (IEEE, 2004), Vol. 4, pp. 2051–2056.
  10. C. I. R. Blackwood, “Apparatus and method for ice detection,” U.S. patent 5,243,185 (7September1993).
  11. J. Reed and B. Barbour, “Remote passive road ice sensor system,” NRC report, http://ntl.bts.gov/lib/20000/20400/20453/PB98128572.pdf .
  12. M. Born and E. Wolf, Principles of Optics: Electromagnetic Theory of Propagation, Interference and Diffraction of Light (Cambridge University, 1999).

2007 (1)

2002 (1)

M. M. Matsumoto, “Molecular dynamics simulation of the ice nucleation and growth process leading to water freezing,” Nature 416, 409–413 (2002).
[CrossRef]

2000 (1)

A. W. Nolan and J. Dozier, “A hyperspectral method for remotely sensing the grain size of snow,” Remote Sens. Environ. 74, 207–216 (2000).
[CrossRef]

1995 (1)

C. Ciamberlini, G. Innocenti, and G. Longobardi, “An optoelectronic prototype for the detection of road surface conditions,” Rev. Sci. Instrum. 66, 2684–2689 (1995).
[CrossRef]

1968 (1)

W. M. Irvine and J. B. Pollack, “Infrared optical properties of water and ice spheres,” Icarus 8, 324–360 (1968).

Åström, H.

C.-G. Wallman and H. Åström, Friction Measurement Methods and the Correlation between Road Friction and Traffic Safety (Swedish National Road and Transport Research Institute, 2001).

Born, M.

M. Born and E. Wolf, Principles of Optics: Electromagnetic Theory of Propagation, Interference and Diffraction of Light (Cambridge University, 1999).

Casselgren, J.

J. Casselgren, M. Sjödahl, and J. LeBlanc, “Angular spectral response from covered asphalt,” Appl. Opt. 46, 4277–4288 (2007).
[CrossRef]

J. Casselgren, M. Sjödahl, M. Sanfridsson, and S. Woxneryd, “Classification of road conditions—to improve safety,” in Advanced Microsystems for Automotive Applications 2007 (Springer, 2007).

Ciamberlini, C.

C. Ciamberlini, G. Innocenti, and G. Longobardi, “An optoelectronic prototype for the detection of road surface conditions,” Rev. Sci. Instrum. 66, 2684–2689 (1995).
[CrossRef]

Dozier, J.

A. W. Nolan and J. Dozier, “A hyperspectral method for remotely sensing the grain size of snow,” Remote Sens. Environ. 74, 207–216 (2000).
[CrossRef]

Innocenti, G.

C. Ciamberlini, G. Innocenti, and G. Longobardi, “An optoelectronic prototype for the detection of road surface conditions,” Rev. Sci. Instrum. 66, 2684–2689 (1995).
[CrossRef]

Irvine, W. M.

W. M. Irvine and J. B. Pollack, “Infrared optical properties of water and ice spheres,” Icarus 8, 324–360 (1968).

LeBlanc, J.

Longobardi, G.

C. Ciamberlini, G. Innocenti, and G. Longobardi, “An optoelectronic prototype for the detection of road surface conditions,” Rev. Sci. Instrum. 66, 2684–2689 (1995).
[CrossRef]

Matsumoto, M. M.

M. M. Matsumoto, “Molecular dynamics simulation of the ice nucleation and growth process leading to water freezing,” Nature 416, 409–413 (2002).
[CrossRef]

Nolan, A. W.

A. W. Nolan and J. Dozier, “A hyperspectral method for remotely sensing the grain size of snow,” Remote Sens. Environ. 74, 207–216 (2000).
[CrossRef]

Pollack, J. B.

W. M. Irvine and J. B. Pollack, “Infrared optical properties of water and ice spheres,” Icarus 8, 324–360 (1968).

Sanfridsson, M.

J. Casselgren, M. Sjödahl, M. Sanfridsson, and S. Woxneryd, “Classification of road conditions—to improve safety,” in Advanced Microsystems for Automotive Applications 2007 (Springer, 2007).

Sjödahl, M.

J. Casselgren, M. Sjödahl, and J. LeBlanc, “Angular spectral response from covered asphalt,” Appl. Opt. 46, 4277–4288 (2007).
[CrossRef]

J. Casselgren, M. Sjödahl, M. Sanfridsson, and S. Woxneryd, “Classification of road conditions—to improve safety,” in Advanced Microsystems for Automotive Applications 2007 (Springer, 2007).

Wallman, C.-G.

C.-G. Wallman and H. Åström, Friction Measurement Methods and the Correlation between Road Friction and Traffic Safety (Swedish National Road and Transport Research Institute, 2001).

Wolf, E.

M. Born and E. Wolf, Principles of Optics: Electromagnetic Theory of Propagation, Interference and Diffraction of Light (Cambridge University, 1999).

Woxneryd, S.

J. Casselgren, M. Sjödahl, M. Sanfridsson, and S. Woxneryd, “Classification of road conditions—to improve safety,” in Advanced Microsystems for Automotive Applications 2007 (Springer, 2007).

Appl. Opt. (1)

Icarus (1)

W. M. Irvine and J. B. Pollack, “Infrared optical properties of water and ice spheres,” Icarus 8, 324–360 (1968).

Nature (1)

M. M. Matsumoto, “Molecular dynamics simulation of the ice nucleation and growth process leading to water freezing,” Nature 416, 409–413 (2002).
[CrossRef]

Remote Sens. Environ. (1)

A. W. Nolan and J. Dozier, “A hyperspectral method for remotely sensing the grain size of snow,” Remote Sens. Environ. 74, 207–216 (2000).
[CrossRef]

Rev. Sci. Instrum. (1)

C. Ciamberlini, G. Innocenti, and G. Longobardi, “An optoelectronic prototype for the detection of road surface conditions,” Rev. Sci. Instrum. 66, 2684–2689 (1995).
[CrossRef]

Other (7)

J. Casselgren, M. Sjödahl, M. Sanfridsson, and S. Woxneryd, “Classification of road conditions—to improve safety,” in Advanced Microsystems for Automotive Applications 2007 (Springer, 2007).

B. A. Barbour, “Ice monitoring and detection system,” U.S. patent 5,557,261 (17September1994).

D. Gregoris, S. Yu, and F. Teti, “Multispectral imaging of ice,” in Canadian Conference on Electrical and Computer Engineering (IEEE, 2004), Vol. 4, pp. 2051–2056.

C. I. R. Blackwood, “Apparatus and method for ice detection,” U.S. patent 5,243,185 (7September1993).

J. Reed and B. Barbour, “Remote passive road ice sensor system,” NRC report, http://ntl.bts.gov/lib/20000/20400/20453/PB98128572.pdf .

M. Born and E. Wolf, Principles of Optics: Electromagnetic Theory of Propagation, Interference and Diffraction of Light (Cambridge University, 1999).

C.-G. Wallman and H. Åström, Friction Measurement Methods and the Correlation between Road Friction and Traffic Safety (Swedish National Road and Transport Research Institute, 2001).

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Figures (10)

Fig. 1.
Fig. 1.

At the left top, a sketch over the measuring procedure. At the left bottom (a), an illumination casing. (b) The piece of asphalt used. (c) The polarizers. (d) The spectrometer with the extension (1). At the right, the experimental setup.

Fig. 2.
Fig. 2.

(a) Description of calibration method used for the spectrometer. (b) Measuring procedure explaining measuring angles of illumination and detection.

Fig. 3.
Fig. 3.

Polarization-resolved measurements with the angular separation α. (a) P-polarized illumination and P-polarized detection (PP). (b) S-polarized illumination and S-polarized detection (SS). (c) P-polarized illumination and S-polarized detection (PS). (d) S-polarized illumination and P-polarized detection (SP).

Fig. 4.
Fig. 4.

Distributions for SZB and SZF: (a), (e) dry, (b), (f) water, (c), (g) ice, (d), (h) snow.

Fig. 5.
Fig. 5.

Distributions for ΩSS: (a) dry, (b) water, (c) ice, (d) snow.

Fig. 6.
Fig. 6.

Distributions for θ: (a) dry, (b) water, (c) ice, (d) snow.

Fig. 7.
Fig. 7.

Distributions for θS and θP (a), (e) dry, (b), (f) water, (c), (g) ice, (d), (h) snow.

Fig. 8.
Fig. 8.

Distributions for θSS: (a) dry, (b) water, (c) ice, (d) snow.

Fig. 9.
Fig. 9.

Distributions for ΩSS (λ2): (a) dry, (b) water, (c) ice, (d) snow.

Fig. 10.
Fig. 10.

Classification based on the two ratios θSS and ΩSS (λ2).

Tables (1)

Tables Icon

Table 1. Backward Scatter POWC Calculations for Ratio Measurements between λ1/λ2 for Different Illumination and Analyzer Configurations, for All Road Condition Distributions, and Water/Ice Only

Equations (13)

Equations on this page are rendered with MathJax. Learn more.

Rzij(α)=λ=λminλmaxRzij(λ,α)λmaxλmin,
Rzij(180)=max(Rzij(170),Rzij(180),Rzij(190)).
RZSS(α)=Rz1SS(α)Rz2SS(α)RzlSS(α),
SZS=RZSS(180)RZPS(180)RZPP(180)RZSP(180),SZB(α)=RZSS(α)RZPS(α)RZPP(α)RZSP(α),forα=10,0,10
ΩZSS=RZSS(α)RZSS(180),forα=10,0,10,
POWCij=n=1nmaxP(Lnij|Unij)+P(Unij|Lnij)fornmax=2(p1),
fij[BUijL]=n=IminijBUijLΩUij[n]+n=BUijLImaxijΩLij[n]forBIJ=Iminij:Imaxij,
θzij=Rzij(λ1,α)Rzij(λ2,α),forα=10,0,10,
θZij=θz1ijθz2ijθzkij,
θZ=θZSSθZSPθZPSθZPP,θZP=θZPSθZPP,θZS=θZSSθZSP.
ΩZSS(λ1)=RZSS(λ1,α)RZSS(λ1,180),forα=10,0,10,ΩZSS(λ2)=RZSS(λ2,α)RZSS(λ2,180),forα=10,0,10,
[Ωθ]=[cosφsinφsinφcosφ][Ωθ]forφ=0,1,,90deg.
fij[BUijL,φ]=n=IminijBUijLΛUij[n,φ]+n=BUijLImaxijΛLij[n,φ]forBIJ=Iminij:Imaxij,φ=0:90°,

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