首页|Vehicle-to-vehicle distance estimation using artificial neural network and a toe-in-style stereo camera
Vehicle-to-vehicle distance estimation using artificial neural network and a toe-in-style stereo camera
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NSTL
Elsevier
? 2022 Elsevier LtdIn road traffic, adjusting the tracking distance between your vehicle and the one in front of your vehicle is necessary for safe driving. The systems developed for this purpose must detect object distance with high reliability. Accordingly, this article presents a stereo-vision-based method for estimating the distance between a given vehicle and the vehicle in front of it. In the proposed method, cameras are positioned such that their optical axes are equally tilted toward and intersect each other (i.e., the toe-in style). The tilt of the optical axes toward each other makes the different horizontal positions of the front vehicle important in the proposed distance estimation. Images of different horizontal deviation values at specified distances from the front vehicle are used to develop an artificial neural network (ANN) model. The results obtained from the test data show that, the proposed methodology is effective for determining the distance between vehicles.
NSTL
Elsevier
