Design and Vehicle Implementation of Autonomous Lane Change Algorithm based on Probabilistic Prediction

This paper describes design, vehicle implementation and validation of a motion planning and control algorithm of autonomous driving vehicle for lane change. Autonomous lane change is necessary for high-level autonomous driving. A vehicle equipped with diverse devices like sensors and computer is introduced for implementation and validation of autonomous driving. The autonomous driving system consists of three parts: perception, motion planning and control. In a perception part, surrounding vehicles' states and lane information are estimated. In motion planning part, using these information and chassis information, probabilistic prediction is conducted for ego vehicle and surrounding vehicle separately. And then, driving mode are decided among three modes: lane keeping, lane change and traffic pressure. Driving mode is determined based on a safety distance by predicting states of surrounding vehicles and ego vehicle. If the ego vehicle cannot perform lane change when the lane change is required, the most proper space is selected considering the probabilistic prediction information and the safety distance. Target states are defined based on driving mode and information of surrounding vehicles behaviors. In control part, the distributed control architecture for real time implementation to the vehicle. A linear quadratic regulator (LQR) optimal control and a model predictive control (MPC) are used to obtain the longitudinal acceleration and the desired steering angle. The proposed automated driving algorithm has been evaluated via vehicle test, which has used one autonomous vehicle and two normal vehicles.