The next promising key issue of the automobile development is a self-driving technique. One of the challenges for intelligent self-driving includes a lane-detecting and lane-keeping capability for advanced driver assistance systems. This paper introduces an efficient and lane detection method designed based on top view image transformation that converts an image from a front view to a top view space. After the top view image transformation, a Hough transformation technique is integrated by using a parabolic model of a curved lane in order to estimate a parametric model of the lane in the top view space. The parameters of the parabolic model are estimated by utilizing a least-square approach. The experimental results show that the newly proposed lane detection method with the top view transformation is very effective in estimating a sharp and curved lane leading to a precise self-driving capability.

In recent years, the researches regarding a self-driving capability for an advanced driver assistant systems (ADAS) have received great attentions [

In general, the road lanes can be divided into two types of trajectories, that is, a curved lane and a straight line [

Top view image from a front view camera.

In this paper, an effective lane detection algorithm is proposed with an improved curved lane detection performance based on a top view image transform approach [

The flow diagram of the lane detection algorithms using the top view transformation and least-square based lane model estimation.

The remainder of the paper is described as follows. In Section

Top view image transformation is a very effective method as an advanced image processing. Some researchers used the top view transformation approach to detect obstacles and even to measure distances to objects. An object’s shape on the road is infracted in the top view transformed image where a lane and a sign of the road are almost the same as the real lane and sign (Figure

Figure

Schematic illustration of the top view image transformation.

Top view image transformation.

(a) Road image. (b) TVI transformed image.

Figure

In the near view image, a straight line detection algorithm is formulated by using a standard Hough transformation. The Hough transform method searches for lines using the equation as can be seen in Figure

Hough transform.

It is necessary to choose the longest straight line from the lines detected from the Hough transformation. The applied Hough transformation returns the coordinate of a starting point (

(a) Binary image of top view. (b) Hough transform results.

Now, the equation of a straight line model equation is defined and the parameters of the linear road model are calculated by using the starting and ending coordinates from each boundary condition of near section image. Equation (

In the far view image, a curved line detection is necessary, and the previous parameters of the straight line model are used again. Since a curved line is modeled as a continuous one starting right after the straight line, it has a common boundary condition

Road Line models for the near section and the far section.

On the same boundary points, the functional value of the straight line equation is equal to the value of the parabolic curved line equation as

White points of far section.

Then, the coordinates of all the white points are used to define parameter

Sequence of finding white points.

Each

Result of curve lane detection based on parabolic model.

In the previous section, the parameters in the parabolic model are computed by using the white points in the curved line section. In this section, in order to increase the accuracy of the computation of the parameters of the curved line, an effective least-square estimation technique which uses all the given data

Result of curve lane detection based on least-square method.

It is noted that each method of the parabolic approach and the least-square method has its own advantages and disadvantages in the curved line detection step. The previous ideas obtained in the curved line detection lead us to invent a new curved line detection methodology by integrating two methods as for an effective and precise curved line detection technique. For a new curved line detection technique, the parabolic detection approach and the least-square methods are integrated together by calculating the parameters used in the curved line model as

Curved line detection results: integrated curved line detection (green).

In this section, realistic road experiments are carried out. In the experiments, 10 images, which contain straight line and curved line, are used. Example results are shown in Figure

Road image.

See Figures

Top view transformed image.

(a) Binary image of top view. (b) Hough transform results.

Result of curve lane detection based on parabolic model.

Result of curve lane detection based on least-square method.

Curved line detection results: integrated curved line detection (green).

Error graphic of first line.

Error graphic of second line.

See Figures

Road image.

Top view transformed image.

(a) Binary image of top view. (b) Hough transform results.

Result of curve lane detection based on parabolic model.

Result of curve lane detection based on least-square method.

Curved line detection results: integrated curved line detection (green).

Error graphic of experiment number 2.

Relation of

The newly proposed detection algorithm requires 0.5–2 sec for the one-time detection; the required computational time depends on the adopted image size, tilt angle, and height of camera. 80% of this process time is due to the usage of the top view image transformation. If either a GPU or a FPGA processor is utilized for top view image transformation, the expected processing time for the line detection could be reduced more. In the near future work, we will use GPU and FPGA processor for the top view transformation.

The most important advantage of the newly proposed curved line detection algorithm lies in the fact that the parameter values used in the line detection could be computed precisely, which result in a more robust ADAS performance. In specific, if the parameter value of

In this paper, an effective lane detection method is proposed by using the top view image transformation approach. In order to detect a precise line of the entire lane in the transformed image, the top view image is divided into two sections, near image and far image. In the near image section, a straight line detection is performed by using the Hough transformation, while, in the far image section, an effective curved line detection method is proposed by integrating an analytic parabolic model approach and the least-square estimation method in order to precisely compute the parameters used in the curved line model. For the verification of the newly proposed hybrid detection method, experiments are carried out. From the results it is shown that a curved line shape of the white lines after the top view image transformation almost perfectly matches the real road’s white lines. The effectiveness of the proposed integrated lane detection method can be applied to not only the self-driving car systems but also the advanced driver assistant systems in smart car systems.

The authors declare that there is no conflict of interests regarding the publication of this paper.

This work was supported by the National Research Foundation of Korea (NRF) (no. 2014-063396) and also was supported by the Human Resource Training Program for Regional Innovation and Creativity through the Ministry of Education and National Research Foundation of Korea (no. 2014-066733).