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Adjacent Lane Detection and Lateral Vehicle Distance Measurement Using Vision-Based Neuro-Fuzzy Approaches

机译:使用基于视觉的神经模糊方法进行相邻车道检测和横向车辆距离测量

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The aim of this article attempts to propose an advanced design of driver assistance system which can provide the driver advisable information about the adjacent lanes and approaching lateral vehicles. The experimental vehicle has a camera mounted at the left side rear view mirror which captures the images of adjacent lane. The detection of lane lines is implemented with methods based on image processing techniques. The candidates for lateral vehicle are explored with lane-based transformation, and each one is verified with the characteristics of its length, width, time duration, and height. Finally, the distances of lateral vehicles are estimated with the well-trained recurrent functional neuro-fuzzy network. The system is tested with nine video sequences captured when the vehicle is driving on Taiwan’s highway, and the experimental results show it works well for different road conditions and for multiple vehicles.
机译:本文的目的是尝试提出一种驾驶员辅助系统的高级设计,该系统可以为驾驶员提供有关相邻车道和接近侧向车辆的建议信息。实验车辆在左侧后视镜上安装了一个摄像头,该摄像头捕获相邻车道的图像。车道线的检测通过基于图像处理技术的方法来实现。通过基于车道的变换来探索横向车辆的候选者,并通过长度,宽度,持续时间和高度的特征对每个候选者进行验证。最后,通过训练有素的递归功能神经模糊网络估计横向车辆的距离。该系统通过在台湾公路上行驶时捕获的九个视频序列进行了测试,实验结果表明该系统适用于不同的路况和多种车辆。

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