The autonomous vehicle can recognize and understand environment, self-control, and achieve the driving level of the human driver. To create this kind of system, the following work was carried out: (a) a real time lane detection system is proposed, based on vision system functions, using webcam camera; (b) In order to detect curve lanes, deep learning is applied for lane detection, based on fully Convolutional Neural Network (CNN); (c) The cubic spline interpolation method is used for path generation, based on Global Positioning System (GPS) data, where distance between two adjacent path generation points is same. Compared with the connection method of the cubic polynomial fitting algorithm, the curve fitted path by the cubic spline interpolation method is smoother and more satisfied with the vehicle motion pattern; (d) Based on frenet coordinate frame, optimize trajectory planning method is used to plan safe, easy and comfort trajectory. Compared to the Cartesian coordinate frame, frenet coordinate frame simplifies the solution of road curve fitting problems, especially in the case of complex road environment; (e) Fuzzy sliding mode control method is proposed for vehicle steering control. Finally, the simulation and experiment results are used to prove the effectiveness of the proposed methods.

自动驾驶汽车可以识别和理解环境，进行自我控制，并达到驾驶员的驾驶水平。为了创建这种系统，进行了以下工作：（a）基于网络摄像头的视觉系统功能，提出了一种实时车道检测系统；（b）为了检测弯道，基于完全卷积神经网络（CNN）将深度学习应用于车道检测；（c）三次样条插值方法用于基于全球定位系统（GPS）数据的路径生成，其中两个相邻路径生成点之间的距离相同。与三次多项式拟合算法的连接方法相比，三次样条插值法的曲线拟合路径更加平滑，对车辆的运动方式更加满意。（d）基于微结构坐标系，采用优化轨迹规划方法对安全，舒适，舒适的轨迹进行规划。与笛卡尔坐标系相比，frenet坐标系简化了道路曲线拟合问题的求解，尤其是在复杂的道路环境中。（e）提出了用于车辆转向控制的模糊滑模控制方法。最后，通过仿真和实验结果证明了所提方法的有效性。