In order to improve the comfort performance and reduce the planning algorithm complexity in autonomous vehicle, an intelligent longitudinal velocity planning method based on fuzzy neural network (FNN) is proposed. With the manual driving experience, fuzzy planning model is established. By utilizing the self-learning function of neural network, fuzzy planning model is modified, which is attempted to establish FNN planning model. The planning method is applied to velocity planning. Three kinds of driving scenes are analyzed, and velocity planning models based on FNN are established accordingly. The simulation and experiment results indicate that acceleration generated by FNN planning model has good smooth property, and it is easy to be tracked by the subsequent control module. Compared with traditional method, the proposed method has certain anti-disturbance ability and self-adaptability. Also, the proposed method is convenient for engineering application, which ensures both the real-time performance and stability of the algorithm.

Model predictive control (MPC) is often used for controlling the autonomous vehicle tracking the target path. But to apply MPC schemes, the nonlinear model of vehicle kinematics needs to be approximately converted to a linear format, and the path tracking problem has to be converted into certain formats in order to implement the solver of convex quadratic programming. To solve these issues, a control strategy combining MPC and genetic algorithm (GA) is put forward. The nonlinear predictive model is adopted to predict the future movement of a controlled vehicle. The objective function is established according to the future movement and target path. Instead of using a convex quadratic programming solver, GA is applied to solve the optimization problem. The proposed MPC-GA method can handle the arbitrary nonlinear problem and make the objective function more comprehensible and flexible. This method is applied in solving the path tracking problem of an autonomous vehicle. Both simulations and on-field tests are conducted. The results validate the efficiency of the proposed MPC-GA path tracking method in comparison with traditional methods. With the MPC-GA controller, the automatic driving on the park road is basically realized. The control strategy can be considered as an alternative method to solve the path tracking problem for an autonomous vehicle.