The Ego-Lane Detection under rainy condition

雨天条件下的自我车道检测

基本信息

批准号：
09650485
负责人：
OZAWA Shinji
金额：
$ 1.6万
依托单位：
Keio University
依托单位国家：
日本
项目类别：
Grant-in-Aid for Scientific Research (C)
财政年份：
1997
资助国家：
日本
起止时间：
1997 至 1998
项目状态：
已结题

来源：
https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-09650485/
关键词：
Rainy Weather Lane-Marker Template Matching Moving Image Processing モデル

项目摘要

Generation of Lane-marker candidate-imageThe Lane-Marker of the road in both fine and rainy weather proved to have the feature that the intensity of the image became peak around the Lane-Marker area by examining the profile of road surface and the area. By using the feature, we tried to extract the Lane-Marker's candidate-regions which have high probability to be white lines, where input sequential images taken from the camera installed on the ruuning car.Then, by speeding up our system with the image processor, we could also adopt the information of previous frames. As a result, our system could generate more hopefull Lane-Marker candidate-image. Robust Recognition of Ego-LaneWe considered a road boundary as an arc model, and we extracted the road boundary by our template matching process between the Lane-Marker candidate-image. The measurement of road's radius of curvature and the position and moving direction of the car could be obtained, that is, the Ego-Lane could be recognized, where the direction of the car was parallel with the road's line.Then, we improved the above method by adding a line model to road model, etc. At first, the straight line model were used as a template since we assumed the Lane-Maker was approaching to a straight line near the car, and the position and moving direction of the car could be obtained. Next, by using these information and applying the arc model, the road's radius of curvature can be obtained.As the necessary model parameters were increased, the reseaching space of the template matching process became large. But we could reduce the space by using our method in order which was expalined in the previous statements, and ccould get a better capability to recognize the Ego-Lane.Meanwhile, the motion of wiper in rainy weather is also considered. Since the motion disturbs a good scene for detection of Lane-Marker, we proposed a method to eliminate the scene with wiper so as not to reduce the detection capability.

事实证明，在晴朗和多雨的天气中，这条道路上的道路上的象征候选人赛车界的生成，表明图像的强度通过检查道路表面和该区域的轮廓，在车道标记区域周围达到了峰值。通过使用该功能，我们试图提取具有很高可能是白线的候选区域的候选区域，其中从安装在Ruuning Car上的相机中拍摄的输入顺序图像。然后，通过使用图像处理器加速我们的系统，我们还可以采用先前帧的信息。结果，我们的系统可以产生更多希望的车道候选图像。对自我的识别的强烈识别将道路边界视为弧形模型，我们通过在车道标记候选图像之间的模板匹配过程提取了道路边界。可以获得可以识别的道路曲率半径以及汽车的位置和移动方向的测量，即可以识别自我车道，在汽车的方向与道路的方向与道路线平行的地方，然后，我们通过将线模型添加到路线模型中，从而改善了上述方法。首先，将直线模型添加到路线上。获得。接下来，通过使用这些信息并应用ARC模型，可以获得道路的曲率半径。随着必要的模型参数增加，模板匹配过程的解决空间变得很大。但是，我们可以通过使用我们的方法来减少空间，以便在先前的陈述中实现，并且可以更好地识别自我车道。同样，还考虑了雨刮器在多雨的天气中的运动。由于该运动打扰了检测车道标志物的好场景，因此我们提出了一种用刮水器消除场景的方法，以免降低检测能力。