The Problem and Goal:
Our goal is to develop an integrated a system architecture which carries out
various intelligent vehicle functions with the single versatile vision system
described on page 15. Examples of the functions are intelligent cruise
control, obstacle detection, and reliable lane sensing.
Conventionally, three-dimensional vision systems for intelligent cruise
control and obstacle detection were too slow or too expensive to achieve the
required distance resolution. Conventional two-dimensional lane sensing
algorithms have the problem of tracing the lane and continuing the trace up
the side of the vehicle.
For intelligent cruise control and obstacle detection, laser radar and
millimeter-wave radar were investigated intensively. The laser radar is
sensitive to the reflection characteristics of object surfaces, and the
outputs of the millimeter wave radar depend on the material of object.
Various models were proposed to increase the reliability of lane sensing
which is based on two-dimensional image processing. Most previous work
considered intelligent cruise control and lane sensing separately.
Our approach is to use a three-dimensional vision system for both intelligent
cruise control and lane sensing functions. This integrated approach is
expected to be more effective than addressing those two functions separately.
In the first step, the distance to each vertical edge is calculated by using
real-time three-dimensional vision system described on page 15. In the
second step, a histogram is calculated as shown in Figure 1.
The vehicles have a large concentration of edges at a particular distance and the lanes
have a small number of edges at the majority of the distances. Therefore
the vehicles correspond to the peaks in the histogram and the lane markings
correspond to the lower region of the histogram. Thresholding is used to
distinguish the lanes from the vehicles indicated by the dotted line in
Figure~1. Figure~2 is a diagram of how the system distinguishes between
the lanes and vehicles. Figure~3 shows the center original image, edge
image and depth map. The colors correspond to the various distances. This
information along with lane sensing creates an intelligent cruise control
Current Status and Future Work:
Early experimental results were obtained as shown in Figures 4 and 5, and
some improvements are under way.