Combined Trajectory Planning and Tracking for Autonomous Vehicle Considering Driving Styles - Haoran Li; Chaozhong Wu; Duanfeng Chu; Liping Lu; Ken Cheng (IEEE Access, 2021)
Autonomous driving is one of the promising technologies to tackle traffic accident and congestion problems nowadays. Even though an autonomous vehicle is operated without humans, it is necessary to reflect the driving characteristics of a human driver. This can increase user acceptance to autonomous driving system, which in turn will improve driving safety because of human occupants' trust in it. In this paper, a combined trajectory planning and tracking algorithm is proposed for the vehicle control. Firstly, traffic environments and driving styles are modeled with the Artificial Potential Field (APF) approach. Secondly, those APF values are integrated into the Model Predictive Control (MPC) design process, which can optimize the trajectories and control outputs. In this way, we add people's driving habits and styles into the controller, so that the controlled vehicle can move under the effects of the traffic environments and human's driving styles. At last, autonomous driving, which reflects two types of human drivers' driving styles (a cautious driving style and an aggressive one), is tested by the simulation experiments in two scenarios (car-following and lane-changing). Furthermore, the result demonstrates that the proposed algorithm can reflect driving styles. Accordingly, this novel controller can be utilized in the autonomous vehicle control field.