Keywords

1 Introduction

Recent studies have focused on accelerating the convergence of IT technology and automobile technology. As a result, automobile industry now shift forward self-driving cars which control themselves. So, vehicles are not just regarded as transportation. Various technologies are needed to materialize the self-driving. In particular, location determination technology is a core technology in the field of the self-driving cars [1–4].

Almost all of vehicle location determination system is based on Global Navigation Satellite or GNSS which provide absolute location information [5–11]. However, areas which are not able to receive satellite signals and dense urban areas limit the use of the system.

High precision location determination technology for the self-driving is made possible for interconnecting the navigation system of the vehicles with Intelligent Transportation System or ITS based on V2X which means a connected network for traffic information exchange of vehicles to vehicles, vehicles to facilities and vehicles to people [12–14].

Recently, Light Emitting Diode or LED is remarkably advanced. Therefore, the usage of LED is now increasing in the field of infrastructure industries. In this sense, infra-based lightings such as streetlamps are replaced with LED. Among them, streetlamps for lighting can consist of various LED which has different color temperatures which picture different optical spectrum distributions. As a result, vehicle location determination is possible if the vehicles receive the light of LED streetlamps with chroma-meter and analyze the distributions and the streetlamps analyze road lanes which have different color temperatures [12, 15].

This paper analyzed the optical spectrum distributions of load lanes by examining the LED streetlamps in the tunnels which are GNSS-receiving area for the location determination of the load lanes and took account for the economic advantages of the study and the possibility of commercialization.

2 System Design

The location determination technology of the self-driving cars based on V2I can read the spectral sensitivity of the street lanes emitted by the LED street lighting and determine the location of the vehicles. There are many of technologies used by the road infrastructure. However, the paper takes account for the environment using the optical wavelength sensitivity of the road lanes on the tunnel lighting in tunnels which are the GNSS-receiving area or the area which are not able to receive the satellite signals as the following Fig. 1 [12].

Fig. 1.
figure 1

GNSS receiving area

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2.1 Color Specification

The color stimulus which makes color sense is normally shown as color equation consisting of the mixture of reference color stimuli, and the mixture of the representative reference color stimuli is in the following X, Y, Z. the value is as the formula [15].

$$ {\text{X}} = {\text{k}}\int_{vis} {\varPsi (\lambda )} \cdot \bar{x}\left( \lambda \right)d\lambda $$
(1)
$$ {\text{Y}} = {\text{k}}\int_{vis} {\varPsi (\lambda )} \cdot \bar{y}\left( \lambda \right)d\lambda $$
(2)
$$ {\text{Z}} = {\text{k}}\int_{vis} {\varPsi (\lambda )} \cdot \bar{z}\left( \lambda \right)d\lambda $$
(3)

In this formula, integral calculus \( \int_{vis} {} \) is calculated in a visible ray area, and \( \bar{x},\bar{y},\bar{z} \) is a color matching function. In addition, the color stimulus of an object color \( \uppsi(\uplambda) \) is \( \uppsi\left(\uplambda \right) = {\text{R}}(\uplambda) \cdot {\text{P}}(\uplambda) \) in the case of the reflection body, and it is \( \uppsi\left(\uplambda \right) = {\text{T}}(\uplambda) \cdot {\text{P}}(\uplambda) \) in the case of a transmission object. \( {\text{P}}(\uplambda) \) is a spectral distribution, \( {\text{R}}(\uplambda) \) is a luminous reflectance and, \( {\text{P}}\left(\uplambda \right) \) is a luminous transmittance. The whole number \( k \) is calculated as the follows [15].

$$ k = \frac{100}{{\int_{vis} {P(\lambda ) \cdot \bar{y}\left( \lambda \right)d\lambda } }} $$
(4)

The mixture of the three color stimuli is indicated in a color space which is a three dimension color area. However, the color space is difficult to indicate it. So, XYZ color space defines X + Y + Z = 1 and the intersection of color vector (X, Y, Z) as the follows. It indicates two dimension (\( {\mathcal{X}},{\mathcal{Y}}) \) [15].

$$ {\mathcal{X}} = \frac{X}{X + Y + Z} $$
(5)
$$ {\mathcal{Y}} = \frac{Y}{X + Y + Z} $$
(6)

This becomes the chromaticity coordinates of a random color, and the chromaticity coordinates is indicated in the following Fig. 2, chromaticity diagram. In this regard, the random color should be calculated for chromaticity coordinates to indicate chromaticity point in chromaticity diagram. Further, the chromaticity coordinates of monochromatic light is connected in wavelength order, and spectrum locus is indicated in the following Fig. 2 [15].

Fig. 2.
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\( x,\,y \) Chromaticity diagram in XYZ color system

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The color indication method that the paper explained is available for any color stimulus. However, color stimulus is in the case of blackbody radiation, and the method can be more simplified. That is the absolute temperature of blackbody or the wavelength of blackbody radiation to color temperatures is shown as the following Fig. 3 [15].

Fig. 3.
figure 3

The color temperature of blackbody to blackbody energy distribution (560 [nm] is normalized as 1)

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In the case of the color temperature that light fixtures make, chromaticity point can be indicated in chromaticity diagram. For the color temperature, the chromaticity point of blackbody radiation can be defined as blackbody locus, which can be indicated in the following Fig. 4 [15]. The paper measures different colors according to x.y value of the chromaticity diagram and confirms that the value goes up when the color temperature shrinks as the following Fig. 4.

Fig. 4.
figure 4

Black body locus in chromaticity diagram

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2.2 Color Measurement

The paper explained how to indicate color or light in the previous chapter. This chapter now focuses on explaining how to measure color or light that light fixtures make. There are color stimulus values and spectral sensitivity measurement to measure color specification values which is used for color indication. This paper uses photoelectric colorimeter which can immediately read illuminant from the output of photo electricity optical receiver which is satisfied with Luther condition. This colorimeter can immediately read the value of the color that the light fixtures can make [15] (Fig. 5).

Fig. 5.
figure 5

Chroma meter

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2.3 LED Tunnel Lighting Design

This paper has the three LED groups which have three color temperatures in LED tunnels to distinguish three-lane loads, and every group should be controlled in order to equally shine the three roads.

Therefore, the angle of LED, the luminance of LED should be controlled. Further, the color temperature distribution of LED in the tunnels should be properly chosen to differentiate the spectrum distribution to every lane. The paper distributes the three types-LED light fixtures of 2 W which have every different color as in the following Fig. 6, and the proper luminance of the light fixtures is illuminated on three lanes.

Fig. 6.
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LED tunnel lighting design

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3 Experiment and Result Consideration

3.1 Chromaticity Coordinates Measurement to Every Lanes in LED Tunnels

This paper measures chromaticity points which have each 5 cm-distances from first lane to third lane as photo-electricity colorimeter. When the LED light fixtures are shined on the imagine road, which is an experimental condition. When three lights including 3000 k, 4500 k and 6000 k are shined on a street, the first line, the second line and the third line have every different color temperature. For example, when the light, 3000 k shines the first line, and the light 4500 k shines the second line, the lights are mixed in the two lines as the following Fig. 7. As a result, the paper measures the data of the mixed lights and records Table 1.

Fig. 7.
figure 7

Road condition shined by various LED streetlamp having different color temperature.

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Table 1. Measured luminance and color coordinate to every point having the width of W
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3.2 Chromaticity Coordinates and Functional Relation of Road Lane Position

The evidence of the Table 1 has the three real variable functions which can find the direction of the lanes. Therefore, there are 2 position functions: one is the position function which has a luminance variable and the other is the position function which has a color coordinate variable. In addition, the luminance variable has a large noise factor around the light fixtures, and it position function determination on the variable includes a non-linear factor. Therefore, the other position function is chosen for self-driving car location determination because it is relatively indicated by liner function. Cubic position function formula from Table 1 data is as follows.

$$ {\text{Position}} = 2226500y^{3} - 2579600y^{2} + 997490y - 128690 $$
(7)

With the above formula, location estimations and real-measured position value are arranged in Table 2. The result of this experiment is based on the analysis of the streetlamps to make sure the location determination of the road lanes, which it would be essential for expanding to the application of the location determination of self-driving cars.

Table 2. Road location and real-measured location determined by color coordinate function (RM: Ream measurement, DV: Determination value)
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4 Conclusion

Automobiles are not just regarded as transportation but they are shifting forward the step of self-driving cars. Further, the location determination technology is a key to realize the self-driving cars.

The location determination system is usually based on GPS. However, the areas which are not able to receive the satellite signals and the high density areas have the large disadvantages of the use of it. In this context, recent studies focus on the location determination technology based on the connection of ITS using V2X and vehicle navigation system.

This paper aims at the use of LED streetlamps while using the technology of the location determination. In this regard, this paper arranges various LED streetlamps which have different color temperatures and shines them on each road lanes. The experiment recognized that the experimental cars facilitated the location of them and the functional relations of different color coordinates of the LED streetlamps and analyzed where they are with the color coordinates of the LED.

Advanced studies are expected that the technology can analyze the real time color coordinates to make sure the location determination of self-driving cars and it can develop more advanced self-driving cars in the following years.