Perception, Planning and Control for Self-Driving System Based on On-board Sensors

Abstract

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.