Against this background, the present work examines the visual perception of foveal and extrafoveal visual signs both under laboratory conditions and in a field study under real conditions. Thus, both homogeneous and temporally altered background luminances are analysed. The ability to detect and localize visual stimuli have great significance for practical information presentation. Basic questions are “is a signal present?” and “where is it with regard to your own position?” Therefore, the first 20
1.1. MOTIVATION 21 task is the determination of the stimulus presence. Along with the detection the eye also computes the stimulus position automatically. This becomes clear in a subse-quent precise movement of the fixation.
This work will have three essential parts. An overview of the structure including investigated parameters, methods and research objectives is illustrated in Figure 1.1.
All three parts have their own research questions and pursue the goal to investigate the effect of different luminous intensity distributions in front of the vehicle pro-vided with LED headlamps using a target detection paradigm and contrast-based modelling.
Figure 1.1: Thesis structure. Investigated parameters, methods and research objectives are illustrated.
As can be seen from Figure 1.1, both homogeneous and inhomogeneous, and also temporally altering background luminance distributions will be analysed. Funda-mentally, it is essential to know the limits of the required visual characteristics, in lighting technology this is ensured as a function of luminance and contrast. This thesis presents the task of measuring binocular properties in the threshold and supra-threshold range from simple to difficult requirements:
• Determine the presence of a visual sign in front of a homogeneous, uniformly illuminated background (laboratory conditions). This will be investigated in Chapter 5.
• Detection in a real environment, the contrast with the environment is tempo-rally variable and inhomogeneous (field study). This will be investigated in Chapter 6.
• Determination of the visually relevant contrast value from the luminous in-tensity distribution measured in front of the test vehicle provided by its LED headlamps. This will be described in Chapter 7.
The latter case corresponds to the most common situation in vehicle lighting prac-tice. In this case, for appropriate modelling, a visually relevant contrast value is to be determined from more or less noisy spatially resolved luminance images of the object and its background.
The first part of the experiment deals with the detection of visual targets at luminance levels as on night-time road lighting (0.1mcd2to 1.0mcd2) under laboratory conditions. The results of the experiments show a probability profile over the off-axis angle for each contrast.
The attention of the human being to visual stimuli is concentrated in a more or less narrow area around the visual axis. Objects can be perceived in this area deliberately and make it possible to adapt actions accordingly. In the road traffic, danger mainly occurs to road users when obstacles appear suddenly from the side.
Since the distance to the obstacles is very low, short reaction times are necessary, that are actuated due to a certain reflex to avoid a collision. In order to be able to react in a timely and controlled manner, a large visibility field is fundamentally important. This means that the conspicuousness of the visual object must be large, so that it can also be perceived under a large eccentricity apart from the visual axis. Uncontrolled and thus endangering reactions of a car driver can occur even if objects are difficult or too late to be perceived.
Still the task of a front lighting system is to transfer light onto the road and also the roadside areas to ensure safe viewing conditions for the driver. With the partial high beam function, it is possible to illuminate the drivers own lane even if there is some oncoming traffic. These problems will be analysed in the second part of this thesis. A field study setup with real detection objects is provided. In this case the object is integrated into an environment with constantly changing parameters, meaning, the subjects have to connect direction information across time and space to observe the object’s mean direction and speed.
If the luminance contrast of the detection object on and alongside the road is high enough in order to achieve 99.0% detection probability for a sufficient distance, a safe visibility condition is highly guaranteed. This distance needs to be longer than the stopping distance (at a given vehicle speed) in order to avoid an accident.
To enhance the lighting conditions for safe detection, headlamp systems have been improved (glare free high beam) by adopting new technologies.
The third part is concerned with the contrast perception in night-time traffic sit-uations. A particularly important point for the design of the luminous intensity distribution related to the detection distance is the influence of the area ahead of the vehicle (3.0 to 12.0 m). According to classical stray light theory, a bright front area would have a negative effect on the threshold contrast. In addition, the position of the object has an influence on the threshold contrast. In order to design luminous intensity in angular direction of relevant objects, on-board luminance images are analysed. The necessary amount of luminous intensity in direction of the object can be calculated from the level of object luminance contrast on a certain background luminance distribution. In this work factors influencing this contrast value for safe object detection will be examined.
The models calculating the detection distance, that were determined in earlier in-vestigations, are based exclusively on foveal observation. However, this is rarely the case for applications in real road traffic. Furthermore, the equations for the calcu-lation of the contrast threshold towards foveal vision were obtained under photopic
1.1. MOTIVATION 23 conditions. This includes answering the question of the impact of the background luminance on the predicted relationship, which has not been taken into account yet.
Different already existing methods for determining the contrast based on luminance images are introduced and then compared with the own results.
With the introduction of the white light emitting diodes (LED) at the begin-ning of the 21st century as light source for headlamp systems, new concepts for intelligent and adaptive lighting systems were implemented. The state of the art in front lighting systems are ADB (adaptive driving beam) modules using LED pixels instead of mechanical actuators [42]. For the development of new ADB systems different technologies (e.g. LED-pixel, LCD with LED illumination, MEMS with both laser and LED illumination) are used to increase the number of pixels up to 2 million [43]. At the points where an oncoming or preceding vehicle is detected by the camera, the light intensities are dimmed or switched off for every angle segment dynamically in relation to a level below the glare limit.
The main light distribution is designed by high-resolution LED pixel light sources. Actual headlamp systems consist of 84 discretely constructed LEDs, all of which can be controlled individually and achieve an angular resolution of 1.0◦ [42].
With three rows and a maximum of thirty columns, an LED precision raster module was developed for the first time [44]. With this increased resolution, it is possible to adapt the system to the particular traffic situation and thus to fade out required areas. With a resolution increased by a factor of 3.5 compared to the previous model, the faded out regions can be minimized, that is, the driver receives a safety gain through an increased use of the high beam, which results in a larger illuminated area.
The increased number of pixels in each headlight increases the illuminated area and can be fully configured without the use of a mechanical actuator. Since the sensors within the vehicle have increased in number and quality, a new control concept has been created with the software program Matlab Simulink [45]. The control units receive their information via the camera and navigation system of the vehicle.
Within these control units, the individual desired light distributions are calculated a hundred times per second for both the left and the right [44]. One advantage of the new system is, on the one hand, the easy integration into existing vehicle electron-ics and, on the other hand, the implementation of new optimized light functions [42].
In order to compare and evaluate these technologies, criteria such as illuminance and resolution need to be analysed. The first important parameter is the achievable contrast ratio of the module, which is limited by the contrast ratio of the light source matrix and its optics. Secondly, the angular range (for example only partial high beam or full bending light) is another key parameter for the design. Subsequent, the contrast (luminance level) needs to exceed the legal requirements and must be related to an eccentricity range (definition of the vertical and horizontal cut-off line).
If the pixel light source has a contrast ratio of less than 200:1 it is challenging to build a useful ADB-module [43].
These technological improvements are currently enhanced by social changes. In
the past 20 years, urbanisation has been taken place on all continents, as there is a constant growth of metropolises with more than 10 million inhabitants in continents like North and South America, Asia and parts of Europe. Associated with those effects is a strong traffic compression, so that visual improvement for night drive situations in road traffic becomes more important. At the same time, there is a demographic change resulting in an aging society, which can especially be seen from industrial nations. This leads to the question of how the deficit of vision faculty should be registered related to age and how it could have an impact on the development of future light systems.