基于动态ROI识别的智能车辆车道线检测

Intelligent vehicle lane detection based on dynamic ROI recognition

车道线检测技术是实现汽车无人驾驶的关键技术之一,能够帮助无人驾驶更快地实时处理从相机捕获的图像。近年来,研究人员在车道线检测的精度上取得了很大的进展,但在实际驾驶中,车辆可能会因有限的控制器算力而对车道线检测的速度和准确度产生影响。提出了一种动态识别感兴趣区域的方法,以Donkey car自动避障小车为主体进行实车试验,通过实时减少摄像头采集的图像中干扰信息,更加准确地识别车道线,在采集的图像数量不变的情况下,提高了卷积神经网络(convolutional neural networks,CNN)模型训练的速度和小车自动驾驶时车道线识别的准确度,进而减少了因车道线误判而存在的压线和驶离路面的情况。优化后的模型比原模型在训练时间方面缩短了约38.71%,在小车自动驾驶时间上缩短了约21.67%,同时小车在转弯处行驶速度更加均匀,左右摇摆幅度减小。结果表明,所提方法具有良好的检测效果和准确率。

Lane detection technology is a crucial technology for achieving autonomous driving of vehicles,facilitating the real-time processing of images captured from cameras for autonomous driving.In recent years,researchers have made significant progress in the accuracy of lane detection.However,in practical driving scenarios,the limited computing power of the vehicle's controller may influence the speed and accuracy of lane detection.This paper presents a method of dynamically identifying regions of interest,using the Donkey car obstacle avoidance vehicle for real vehicle experiments.By reducing interference information in the images captured by the camera in real-time,the lane lines are identified more accurately.With a constant number of collected images,the proposed method improves the speed of convolutional neural network(CNN)model training and the accuracy of lane detection during autonomous driving,thereby reducing the occurrences of crossing the lane or leaving the road due to misjudgment of lane lines.The optimized model is about 38.71%shorter than the original model in terms of training time and about 21.67%shorter in terms of trolley autopilot time,while the trolley travels more uniformly at turns and has less sway from side to side.The experimental results demonstrate that the proposed method exhibits excellent detection performance and accuracy.