无人驾驶汽车中的车道线检测研究
|
摘要
随着人工智能的发展,无人驾驶将成为一种新的交通趋势。无人驾驶汽车是通过车载传感系统感知道路环境,自动规划行车路线并控制车辆到达预定目标的智能汽车。无人驾驶技术集自动控制、体系结构、人工智能、视觉计算等众多技术于一体,是计算机科学、模式识别和智能控制技术高度发展的产物。有效的获取车道线信息,对无人车的决策有至关重要的作用。本文对车道线图片进行预处理,包括中值平滑去噪、形态学变换、OSTU阈值分割,最后采用Sobel算法进行车道线轮廓提取,MATLAB仿真结果表明,该方法能快速准确的检测出车道线。
With the development of artificial intelligence,driverless driving will become a new traffic trend. A driverless car is a smart car that senses the road environment through an in-vehicle sensing system, automatically plans driving routes, and controls the vehicle to reach a predetermined target. The unmanned technology integrates many technologies such as automatic control, architecture, artificial intelligence, and visual computing. It is a product of computer science, pattern recognition and intelligent control technology. Effective access to lane line information is critical to the decision-making of unmanned vehicles. In this paper, the lane line image is preprocessed, including median smoothing denoising, morphological transformation, OSTU threshold segmentation.Finally, the sobel algorithm is used to extract the lane line contour. The MATLAB simulation results show that the method can detect the lane line quickly and accurately.
With the development of artificial intelligence,driverless driving will become a new traffic trend. A driverless car is a smart car that senses the road environment through an in-vehicle sensing system, automatically plans driving routes, and controls the vehicle to reach a predetermined target. The unmanned technology integrates many technologies such as automatic control, architecture, artificial intelligence, and visual computing. It is a product of computer science, pattern recognition and intelligent control technology. Effective access to lane line information is critical to the decision-making of unmanned vehicles. In this paper, the lane line image is preprocessed, including median smoothing denoising, morphological transformation, OSTU threshold segmentation.Finally, the sobel algorithm is used to extract the lane line contour. The MATLAB simulation results show that the method can detect the lane line quickly and accurately.
引文
[1]贾瑞清,孙稚媛,张尚生.关于无人驾驶汽车存在问题的拟解决方案[J].测控技术,2018, 37(8):1-4.JIA Rui-qing, SUN Zhi-yuan, ZHANG Shang-sheng. Proposed solution to the problem of driverless cars[J]. Measurement and Control Technology, 2018, 37(8):1-4.
[2]郭应时,蒋拯民,白艳,等.无人驾驶汽车路径跟踪控制方法拟人程度研究[J].中国公路学报,2018, 31(8):189-196.GUO Ying-shi, JIANG Zheng-min, BAI Yan, Tang Jie, et al. A Study on the Anthropomorphic Degree of Path Tracking Control Method for Unmanned Vehicles[J]. China Journal of Highway and Transport, 2018, 31(8):189-196.
[3]彭博,蔡晓禹,张有节,等.基于对称帧差和分块背景建模的无人机视频车辆自动检测[J].东南大学学报(自然科学版),2017, 47(4):685-690.PENG Bo, CAI Xiao-yu, ZHANG You-jie, et al. Automatic detection of UAV video vehicles based on symmetric frame difference and block background modeling[J]. Journal of Southeast University(Natural Science Edition), 2017, 47(4):685-690.
[4] LIU C, WANG Z Q. The Research on Advertising Model of Self-Driving Car Platform[C]//2017 IEEE 3rd Information Technology and Mechatronics Engineering Conference(ITOEC2017), 2017.
[5] BAILEY J D, WALLIS J, CODLING E A. Navigational efficiency in a biased and correlated random walk model of individual animal movement[J]. Ecology, 2017.
[6] PARK J H, WHANGBO T K, KIM K J. A Novel Image Identifier Generation Method Using Luminance and Location[J].Wireless Personal Communications, 2016, 94(1):1-17.
[7]苏士斌,刘英策,林洪山,等.无人驾驶运输船发展现状与关键技术[J].船海工程,2018,47(5):56-59.SU Shi-bin, LIU Ying-ce, LIN Hong-shan, et al. Development Status and Key Technologies of Unmanned Transport Ships[J].Ship&Ocean Engineering, 2018, 47(5):56-59.
[8]余洪山,张文豪,杨振耕,等.一种改进超像素融合的图像分割方法[J].湖南大学学报(自然科学版),2018, 45(10):121-129.YU Hong-shan, Zhang Wen-hao, Yang Zhen-geng, et al. An Image Segmentation Method Based on Improved Superpixel Fusion[J]. Journal of Hunan University(Natural Science), 2018,45(10):121-129.
[9]黄秀常.基于多模糊阀值分割技术图像边沿提取方法研究[J].菏泽学院学报,2018, 40(5):22-26.HUANG Xiu-chang. Research on Image Edge Extraction Method Based on Multi-fuzzy Threshold Segmentation Technology[J]. Journal of Heze University, 2018, 40(5):22-26.
[10]裴晓飞,陈祯福,武冬梅,等.无人驾驶汽车串联式制动系统控制研究[J].汽车技术,2018(9):12-16.PEI Xiao-fei CHEN Zhen-fu, WU Dong-mei. Research on Series Braking System and Control for Unmanned Car[J].Automobile Technology, 2018, 37(8):1-4.
[11]谈敏,邵志勇.模糊图像去雾处理技术在安防领域中的应用[J].新型工业化,2016,6(12):25-30.TAN Min, SHAO Zhi-yong. Application of Fuzzy Image Dehazing Technology in Security Field[J]. The Journal of New Industrialization, 2016, 6(12):25-30.
[12]肖晖,韩轩,孙智权,等.基于Matlab的砝码自动装卸机械手图像定位方法的设计与研究[J].新型工业化,2014, 4(12):25-30.XIAO Hui, HAN Xuan, SUN Zhi-quan, et al. Design and Research of Image Positioning Method Based on Matlab for Automatic Loading and Unloading Manipulator[J]. The Journal of New Industrialization, 2014, 4(12):25-30.
[13]杜恩宇,张宁,李艳荻.基于Gabor滤波器的车道线快速检测方法[J].红外与激光工程,2018, 47(8):314-321.DU En-yu, ZHANG Ning, LI Yan-wei. Fast detection method of lane line based on Gabor filter[J]. Infrared and Laser Engineering, 2018, 47(8):314-321.
[14]蔡英凤,高力,孙晓强,等.一种基于形态学特征的车道线识别方法[J].中国机械工程,2018, 29(15):1863-1868.CAI Ying-feng, GAO Li, SUN Xiao-qiang, et al. A Lane Line Recognition Method Based on Morphological Features[J].China Mechanical Engineering, 2018, 29(15):1863-1868.
[15]钱基德,陈斌,钱基业,等.基于感兴趣区域模型的车道线快速检测算法[J].电子科技大学学报,2018, 47(3):356-361.QIAN Ji-de, CHEN Bin, QIAN Ji-ye, et al. Fast detection algorithm for lane lines based on region of interest model[J]. Journal of University of Electronic Science and Technology of China, 2018, 47(3):356-361.
[2]郭应时,蒋拯民,白艳,等.无人驾驶汽车路径跟踪控制方法拟人程度研究[J].中国公路学报,2018, 31(8):189-196.GUO Ying-shi, JIANG Zheng-min, BAI Yan, Tang Jie, et al. A Study on the Anthropomorphic Degree of Path Tracking Control Method for Unmanned Vehicles[J]. China Journal of Highway and Transport, 2018, 31(8):189-196.
[3]彭博,蔡晓禹,张有节,等.基于对称帧差和分块背景建模的无人机视频车辆自动检测[J].东南大学学报(自然科学版),2017, 47(4):685-690.PENG Bo, CAI Xiao-yu, ZHANG You-jie, et al. Automatic detection of UAV video vehicles based on symmetric frame difference and block background modeling[J]. Journal of Southeast University(Natural Science Edition), 2017, 47(4):685-690.
[4] LIU C, WANG Z Q. The Research on Advertising Model of Self-Driving Car Platform[C]//2017 IEEE 3rd Information Technology and Mechatronics Engineering Conference(ITOEC2017), 2017.
[5] BAILEY J D, WALLIS J, CODLING E A. Navigational efficiency in a biased and correlated random walk model of individual animal movement[J]. Ecology, 2017.
[6] PARK J H, WHANGBO T K, KIM K J. A Novel Image Identifier Generation Method Using Luminance and Location[J].Wireless Personal Communications, 2016, 94(1):1-17.
[7]苏士斌,刘英策,林洪山,等.无人驾驶运输船发展现状与关键技术[J].船海工程,2018,47(5):56-59.SU Shi-bin, LIU Ying-ce, LIN Hong-shan, et al. Development Status and Key Technologies of Unmanned Transport Ships[J].Ship&Ocean Engineering, 2018, 47(5):56-59.
[8]余洪山,张文豪,杨振耕,等.一种改进超像素融合的图像分割方法[J].湖南大学学报(自然科学版),2018, 45(10):121-129.YU Hong-shan, Zhang Wen-hao, Yang Zhen-geng, et al. An Image Segmentation Method Based on Improved Superpixel Fusion[J]. Journal of Hunan University(Natural Science), 2018,45(10):121-129.
[9]黄秀常.基于多模糊阀值分割技术图像边沿提取方法研究[J].菏泽学院学报,2018, 40(5):22-26.HUANG Xiu-chang. Research on Image Edge Extraction Method Based on Multi-fuzzy Threshold Segmentation Technology[J]. Journal of Heze University, 2018, 40(5):22-26.
[10]裴晓飞,陈祯福,武冬梅,等.无人驾驶汽车串联式制动系统控制研究[J].汽车技术,2018(9):12-16.PEI Xiao-fei CHEN Zhen-fu, WU Dong-mei. Research on Series Braking System and Control for Unmanned Car[J].Automobile Technology, 2018, 37(8):1-4.
[11]谈敏,邵志勇.模糊图像去雾处理技术在安防领域中的应用[J].新型工业化,2016,6(12):25-30.TAN Min, SHAO Zhi-yong. Application of Fuzzy Image Dehazing Technology in Security Field[J]. The Journal of New Industrialization, 2016, 6(12):25-30.
[12]肖晖,韩轩,孙智权,等.基于Matlab的砝码自动装卸机械手图像定位方法的设计与研究[J].新型工业化,2014, 4(12):25-30.XIAO Hui, HAN Xuan, SUN Zhi-quan, et al. Design and Research of Image Positioning Method Based on Matlab for Automatic Loading and Unloading Manipulator[J]. The Journal of New Industrialization, 2014, 4(12):25-30.
[13]杜恩宇,张宁,李艳荻.基于Gabor滤波器的车道线快速检测方法[J].红外与激光工程,2018, 47(8):314-321.DU En-yu, ZHANG Ning, LI Yan-wei. Fast detection method of lane line based on Gabor filter[J]. Infrared and Laser Engineering, 2018, 47(8):314-321.
[14]蔡英凤,高力,孙晓强,等.一种基于形态学特征的车道线识别方法[J].中国机械工程,2018, 29(15):1863-1868.CAI Ying-feng, GAO Li, SUN Xiao-qiang, et al. A Lane Line Recognition Method Based on Morphological Features[J].China Mechanical Engineering, 2018, 29(15):1863-1868.
[15]钱基德,陈斌,钱基业,等.基于感兴趣区域模型的车道线快速检测算法[J].电子科技大学学报,2018, 47(3):356-361.QIAN Ji-de, CHEN Bin, QIAN Ji-ye, et al. Fast detection algorithm for lane lines based on region of interest model[J]. Journal of University of Electronic Science and Technology of China, 2018, 47(3):356-361.