Decision making and trajectory planning of a novel vehicle lane-changing control method inspired by automatic parallel parking 2020-01-0134
With the development of automation technology in automobiles, lane-changing systems have been developed and applied to improve environmental adaptability of advanced driver assistant system (ADAS) as well as driver comfort. Lane-changing control consists of three steps: decision making, trajectory planning and trajectory tracking. Decision making and trajectory planning are usually integrated in recent studies, where decision making is related to potential trajectories so that environmental adaptability is improved. However, current methods are not perfect due to weaknesses like high computation cost, low robustness to uncertainties, etc. In this paper, a novel lane changing control method is proposed, where lane-changing behavior is analogized to parallel parking behavior. The focus of this study lies on decision making and trajectory planning. In the perspective of host vehicle with lane-changing intention, the space between vehicles in the target adjacent lane can be regarded as dynamic parking space. A decision making and trajectory planning algorithm of parallel parking is adapted to deal with lane-changing condition. The adopted algorithm is based on rules which checks lane-changing feasibility and generates desired path according to obstacle avoidance condition. Compared to algorithm for static parking space, the uncertainty of the space between moving vehicles and host vehicle dynamics at higher speed propose stricter requirements for algorithms. Work has been conducted to improve the capacity of original algorithm to deal with dynamically changing scenarios. PreScan-Simulink-CarSim simulation platform is established to verify efficacy of the proposed method, where different simulation software is employed to ensure high fidelity of road condition and vehicle dynamics property. Experiments will be conducted if possible.
Liangyao Yu, Ze Ru, Zhenghong Lu, Guanqun Liang, Cenbo Xiong, Abi Lanie, Ruyue Wang