1. 毕业设计（论文）的内容和要求
主要内容： 本课题设计一个自动驾驶小车，通过视觉传感器动态实时监控和识别路上的路标、红绿灯和道路交通标线，达到自动驾驶的目的。
该系统主要由三个模块构成，一个是对路边路标的识别，让小车能够按指定的要求在道路上行驶；另一个是对地面的道路交通标线进行识别，可以让小车运行时不会出现压线或者违反交通规则的情况，从而实现自动驾驶，减轻驾驶员的疲劳程度；还有一个是通过对识别出来的信息对小车进行控制。
本设计要求完成对小车主体部件的连接，对识别路标的神经网络的训练并对该模型的运用，对不同的道路交通标线的识别，以及根据识别出的信息对小车发出正确的指令，实现小车的自动驾驶。
2. 参考文献
[1]陈林. 基于深度学习的路标识别系统研究[D].华东师范大学,2019[2]陈璐媛. 无人驾驶的路标识别算法研究[D].浙江科技学院,2019[3]李泽滨,裴崇利.基于SSD模型的道路交通标志识别方法研究[J].客车技术与研究,2019,41(06):44-47[4]Sunila Gollapudi. OpenCV with Python[M].Apress:2019-04-27. [5]V. Swaminathan, S. Arora, R. Bansal and R. Rajalakshmi, "Autonomous Driving System with Road Sign Recognition using Convolutional Neural Networks," 2019 International Conference on Computational Intelligence in Data Science (ICCIDS), Chennai, India, 2019, pp. 1-4.[6]H. Luo, Y. Yang, B. Tong, F. Wu and B. Fan, "Traffic Sign Recognition Using a Multi-Task Convolutional Neural Network," in IEEE Transactions on Intelligent Transportation Systems, vol. 19, no. 4, pp. 1100-1111, April 2018.[7]Z. Huang, Y. Yu, J. Gu and H. Liu, "An Efficient Method for Traffic Sign Recognition Based on Extreme Learning Machine," in IEEE Transactions on Cybernetics, vol. 47, no. 4, pp. 920-933, April 2017.[8]孟琭,孙霄宇,赵滨.基于卷积神经网络的铁轨路牌识别方法[J/OL].自动化学报:1-13[2020-01-08].https://doi.org/10.16383/j.aas.c190182.[9]韩习习. 数字图像的交通标志检测与识别方法研究[D].中国矿业大学,2019.[10]杨刚. 复杂情况下的道路边缘检测算法研究[D].北京交通大学,2019.[11]J. Duan, L. Shi, K. Zheng and D. Liu, "Road and obstacle detection research based on four-line ladar," 2014 IEEE International Conference on Mechatronics and Automation, Tianjin, 2014, pp. 1728-1733.[12]A. F. Cela, L. M. Bergasa, F. L. Snchez and M. A. Herrera, "Lanes Detection Based on Unsupervised and Adaptive Classifier," 2013 Fifth International Conference on Computational Intelligence, Communication Systems and Networks, Madrid, 2013, pp. 228-233.[13]T. Harasthy, J. Turn and L. Ovsenk, "Road line detection based on Optical Correlator," 2013 36th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), Opatija, 2013, pp. 298-300.[14]J. M. Alvarez, T. Gevers and A. M. Lopez, "Evaluating Color Representations for On-Line Road Detection," 2013 IEEE International Conference on Computer Vision Workshops, Sydney, NSW, 2013, pp. 594-599.[15]钟皇平,王丽君,俞超.基于卷积神经网络的自动行驶小车研究与设计[J].杭州电子科技大学学报(自然科学版),2018,38(06):46-50 98.
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