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.

Previous Work:

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.

Approach:

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 system.

Current Status and Future Work:

Early experimental results were obtained as shown in Figures 4 and 5, and some improvements are under way.