孙怀江, 杨静宇. 一种改进的神经网络道路跟踪方法[J]. 机器人, 2001, 23(3): 197-200.
引用本文: 孙怀江, 杨静宇. 一种改进的神经网络道路跟踪方法[J]. 机器人, 2001, 23(3): 197-200.
SUN Huai-jiang, YANG Jing-yu. AN IMPROVED ROAD FOLLOWER BASED ON NEURAL NETWORK[J]. ROBOT, 2001, 23(3): 197-200.
Citation: SUN Huai-jiang, YANG Jing-yu. AN IMPROVED ROAD FOLLOWER BASED ON NEURAL NETWORK[J]. ROBOT, 2001, 23(3): 197-200.

一种改进的神经网络道路跟踪方法

AN IMPROVED ROAD FOLLOWER BASED ON NEURAL NETWORK

  • 摘要: ALVINN是目前世界上性能最好的基于神经网络的智能车道路跟踪系统,但由于其道路跟踪摄像机是固定不变的,导致在转弯时可能丢失道路信息,从而使其性能下降,甚至不能完成这一任务.本文提出一种道路跟踪方法,使得在学习过程中和自主道路跟踪状态下,都能有效地控制道路跟踪摄像机的方位角,以保证道路尽可能处于摄像机采集的图像中央,在客观上为改进ALVINN的道路跟踪性能提供了可能,计算机仿真结果验证了这种方法的有效性.

     

    Abstract: ALVINN(Autonomous Land Vehicle in a Neural Network) is the best intelligent vehicle road follower based on neural network in the world. Road following camera is fixed on ALVINN,so information about road may be missed during steering and following performance may be decreased,even the task of road following cannot be completed. In this paper,an improved based on road follower always is proposed,in which the azimuth of road following camera is efficiently controlled so that the road is always in the center of image. This opens possibilities for improving road following performance. Performance improvement is verified by computer simulation results.

     

/

返回文章
返回