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Young Researchers Seminar 2007 Brno, Czech Republic, 27 to 30 May 2007. artificial PErception under Adverse CONditions: The Case of the Visibility Range LCPC in cooperation with INRETS , France. Nicolas Hautière. Overview. ADAS and adverse visibility conditions

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artificial PErception under Adverse CONditions: The Case of the Visibility Range

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Artificial perception under adverse conditions the case of the visibility range

Young Researchers Seminar 2007

Brno, Czech Republic, 27 to 30 May 2007

artificial PErception under Adverse CONditions:

The Case of the Visibility Range

LCPC in cooperation with INRETS, France

Nicolas Hautière


Overview

Overview

  • ADAS and adverse visibility conditions

  • Visibility range under daytime fog

    • Modelling

    • Measurement methods

    • Experimental validation

  • Under way applications

  • Discussion and future works

PErception under Adverse CONditions

Nicolas Hautière, LCPC


Adas and adverse visibility conditions

ADAS and Adverse Visibility Conditions

To detect the visibility conditions allows:

Human aspects

  • To switch or to adapt the operation of vehicle lights (AFS)

  • To adapt the speed according to the weather conditions (ISA)

Sensor aspects

  • To improve the operation range of exteroceptive sensors

  • To qualify / adapt / stop the other driving assistances

Improve the safety !

Let’s talk about daytime fog

[Hautière and Aubert, 2005a] Hautière, N. and Aubert, D. (2005). Onboard evaluation of the atmospheric visibility for driving assistance systems, Recherche Transports Sécurité, 87:89-108.

PErception under Adverse CONditions

Nicolas Hautière, LCPC


Visibility range under daytime fog

Koschmieder’s Law:

Let express the contrast of an object against the sky:

 Contrast attenuation

Daylight

Visibility Range under Daytime Fog

Atmospheric veil

Scattering

Direct transmission

  • For a black object (C0=1) and a visibility contrast threshold of 5%:

“the greatest distance at which a black object of suitable dimensions can be recognized by aday against the horizon sky” (CIE, 1987)

PErception under Adverse CONditions

Nicolas Hautière, LCPC


Exploitation of the atmospheric veil

u

C

x

vh

f

z

v

y

Image

plane

H

q

Measurement and derivation of intensity curveExtraction of the inflection point

B&W camera

S

Road plane

X

M

Z

d

Y

Estimation of the meteorological visibility distance

Extraction of a region of interestFitting of a measurement bandwidth

Vmet = 50m

Exploitation of the Atmospheric Veil

Method: Instanciation of Koschmieder’s Law

Assuming a flat road:

[Hautière et al., 2006a] Hautière, N., Tarel, J.-P, Lavenant, J. and Aubert, D. (2006). Automatic Fog Detection and Measurement of the visibility Distance through use of an Onboard Camera. Machine Vision Applications Journal, 17(1):8-20

vh horizon line,  camera parameters

PErception under Adverse CONditions

Nicolas Hautière


Exploitation of contrast attenuation

Local contrast measurement based on a binarisation method:

where

f(x)

  • Evaluated contrast on F(s) is equal to 2C(s0)

F(s)

s

Cx,x1(s)

f(x1)

x

x1

Exploitation of Contrast Attenuation

Method: computation of the range to the most distant visible object

  • Estimation of the so-called mobilized visibility distance

  • Range map obtained by “v-disparity” stereovision approach

  • Visible objects are those having a local contrast above 5%

[Hautière et al., 2006b] Hautière, N., Labayrade, R. and Aubert, D. (2005). Real-Time Disparity Contrast Combination for Onboard Estimation of the Visibility Distance. IEEE Transactions on Intelligent Transportation Systems, 7(2):201-212.

PErception under Adverse CONditions

Nicolas Hautière, LCPC


Video samples

Video samples

Daytime fog

Twilight fog

PErception under Adverse CONditions

Nicolas Hautière, LCPC


Experimental validation

Experimental Validation

Development of a validation site

  • Objectives :

    • To estimate Vmet thanks to the targets:

    • To obtain a ground truth,

    • To compare it with the in-vehicle methods

  • Triangle based pattern

  • Constant solid angle

  • 5 fixed targets:

  • d=65m 1mx1m

  • d=98m 1.5mx1.5m

  • d=131m 2mx2m

  • d=162m 2.5mx2.5m

  • d=195m 3mx3m

  • 1 mobile targe: 0.5mx0.5m

PErception under Adverse CONditions

Nicolas Hautière, LCPC


Experimental validation1

Content

Experimental Validation

Sample images of the validation site

Sunny weather Vmet= 5000 m

Light rain Vmet= 3400 m

Haze Vmet= 2130 m

Fog Vmet= 255 m

Thick fog Vmet= 61 m

Snow fall Vmet= 1000 m

PErception under Adverse CONditions

Nicolas Hautière, LCPC


Experimental validation2

Experimental Validation

Quantitative results

Meteorological visibility estimation

Mobilized visibility distance estimation

[Hautière et al., 2006c] Hautière, N., Aubert, D., Dumont, E. and Tarel, J.-P. (2008).Experimental Validation of Dedicated Methods to In-Vehicle Estimation of Atmospheric Visibility. IEEE Transactions on Instrumentation and Measurement, 57(10), 2218-2225.

PErception under Adverse CONditions

Nicolas Hautière, LCPC


Under way applications

Under Way Applications

Improved Road Departure Prevention

  • Principle: reversal of Koschmieder’s law

  • Assuming a flat world, we have:

d1=28m

d2=62m

Enhancement of road markings extraction under adverse visibility conditions

[Hautière and Aubert, 2005b] Hautière, N. and Aubert, D. (2005). Contrast Restoration of Foggy Images through use of an Onboard Camera, IEEE Conference on Intelligent Transportation Systems (ITSC’05), Vienna, Austria

PErception under Adverse CONditions

Nicolas Hautière, LCPC


Under way applications1

Contrast enhancement of the road scene

Iterative contrast restoration

Under Way Applications

Improved obstacle detection

[Hautière et al., 2007] Hautière, N., Tarel, J.-P., Aubert, D. (2007).Towards Fog-Free In-Vehicle Vision Systems through Contrast Restoration. IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’07), Minneapolis, USA

PErception under Adverse CONditions

Nicolas Hautière, LCPC


Artificial perception under adverse conditions the case of the visibility range

Under Way Applications

ISA and “risk mitigation” or “safety margin”

  • Objective: to compute an adequate speed according to the weather conditions based solely on a digital map and a camera

  • One of the requirements: a mapping function between the driver and the sensor visions:

[Hautière and Aubert, 2006d] Hautière, N. and Aubert, D. (2006). Visible Edges Thresholding: a HVS based Approach, International Conference on Pattern Recognition (ICPR’06), Hong-Kong, China

PErception under Adverse CONditions

Nicolas Hautière, LCPC


Discussion current and future works

We have presented two methods, issued from the ARCOS French project, to estimate the visibility range and some applications,

Methods are being extended in the REACT project by the Mines de Paris to develop probe vehicles,

Currently, we are adapting the methods to the use of fixed CCTV cameras in the SAFESPOT IP,

In the future, we would like to apply our scientific processes to other adverse visibility conditions, like:

Rain

Glare

Nocturnal Fog

Discussion, Current and Future works

 This is the heart of the FP7 PEACON proposal lead by LCPC/INRETS !

PErception under Adverse CONditions

Nicolas Hautière, LCPC


Acknowledgments

I would like to acknowledge the contributions of my colleagues Didier Aubert, Jean-Philippe Tarel, Raphaël Labayrade, Benoit Lusetti, Eric Dumont from LCPC and INRETS, of Michel Jourlin from the University of Saint-Etienne, and of Clément Boussard from the Mines Paris.

Acknowledgments

PErception under Adverse CONditions

Nicolas Hautière, LCPC


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